Nature Genetics

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Telomere length CRISPR–Cas9 screening reveals that dT nucleotide metabolism genes are required for telomere length control a, Diagram of the flow-FISH telomere content-screening strategy. Cas9-expressing K562 cells are transduced with an sgRNA library, cultured for weeks at split ratios sufficient to maintain library representation and sorted by flow-FISH to isolate cells with the highest and lowest 5% of telomere fluorescence. b, Results of CRISPR–Cas9 screening, performed in duplicate, of 53 nucleotide metabolism genes with ten sgRNAs per gene and 200 nontargeting control sgRNAs. sgRNA enrichment was analyzed using the MAGeCK RRA. The RRA gene enrichment score is plotted against the log2[fold change] of sgRNA abundance in high versus low fifth percentile TelC-A647 fluorescence populations. The black dots represent genes significantly enriched in long or short populations with a false discovery rate of <0.05, as calculated via the Benjamini–Hochberg procedure. sgRNAs targeting genes labeled in blue and pink were enriched in the in the short and long telomere populations, respectively. c, Diagram of dT nucleotide metabolism. Products of genes identified in recent telomere length GWASs9,34 are highlighted. The gene encoding TYMS (boxed) was recently associated with dyskeratosis congenita³⁵. ADP, adenosine diphosphate; CDP, cytidine diphosphate; dADP, deoxyadenosine diphosphate; dATP, deoxyadenosine triphosphate; dC, deoxycytidine; dCDP, deoxycytidine diphosphate; dCMP, deoxycytidine monophosphate; dCTP, deoxycytidine triphosphate; dGDP, deoxyguanosine diphosphate; dGTP, deoxyguanosine triphosphate; dTDP, thymidine diphosphate; dUDP, deoxyuridine diphosphate; dUTP, deoxyuridine triphosphate; GDP, guanosine diphosphate; NDPK, nucleoside diphosphate kinase; RNR, ribonucleotide reductase; UDP, uridine diphosphate.
DT treatment drives telomerase-dependent telomere lengthening in human cells a, TRF Southern blot probed for the telomere repeat of K562, 293T or TERC-null (TERC−/−) 293T cells treated with or without dNs (100 µM each of dA, dC, dG and dT) for 8 d. b, TRF of 293T cells treated with 100 µM of the indicated dNs for 9 d. Untreated lanes are technical replicates. c, TRF of K562 cells treated with 100 µM of the indicated dNs for 8 d. d, Quantification of b and c (see Methods). Numbers of samples were as follows: n = 8 for 293T cells with or without dT and n = 5 for K562 cells with or without dT. Statistical significance was determined by one-way analysis of variance (ANOVA) with Bonferroni correction for multiple comparisons for each cell line. The data are presented as means ± s.d. NS, not significant (P > 0.05). e, TRF of 293T or 293T TERC−/− cells treated with or without 100 µM dT for 7 d. f, TRF of 293T cells treated with the indicated dose of dT for 10 d. g, TRF of K562 cells treated with the indicated dose of dT for 8 d. h, Quantification of f and g, as in d. i, 293T cells treated with or without 100 µM dT for the indicated period of time. On day 15, dT-treated cells were split and continued to be cultured either with or without 100 µM dT for the indicated number of days. Day 33 untreated lanes are technical replicates. j, Quantification of i. The results presented in this figure represent single experiments.
DT nucleotide metabolism genes control dT-mediated telomere lengthening a, Diagram of dT salvage. b, Representative TRF of 293T cells targeted with Cas9 and the indicated sgRNA, followed by treatment with the indicated dose of dT for 14 d. c, Quantification of b (n = 3 biological replicates). Statistical significance was determined by one-way repeated measures ANOVA with Geisser–Greenhouse correction and Dunnett’s multiple comparison test. d, Representative TRF of K562 cells targeted with Cas9 and the indicated sgRNA, followed by treatment with the indicated dose of dT for 10 d. e, Quantification of d, as in c (n = 3 biological replicates). f, Diagram of dT and dU salvage. g, Representative TRF of 293T cells targeted with Cas9 and the indicated sgRNA, followed by treatment with the indicated doses of dT and dU for 10 d. h, Quantification of the median telomere lengths shown in g, as in c (n = 3 biological replicates). i, Representative TRF of K562 cells targeted with Cas9 and the indicated sgRNA, followed by treatment with the indicated doses of dT and dU for 10 d. j, Quantification of the median telomere lengths shown in g and i. Statistical significance was determined by one-way repeated measures ANOVA with Geisser–Greenhouse correction and Bonferroni’s multiple comparison test (n = 3 biological replicates for each cell line). The data in c, e, h and j are presented as means ± s.d.
SAMHD1 restricts human telomere length and limits dT-mediated telomere elongation a, Diagram of SAMHD1 dNTPase activity. b, TRF of 293T or K562 cells cultured for 27 d following Cas9-sgRNA targeting with the indicated sgRNA. The results represent a single experiment. c, Representative TRF of K562 cells cultured for 33 d following infection with an shRNA expression construct targeting luciferase (control) or SAMHD1. d, Quantification of the telomere lengths shown in c. Statistical significance was determined by one-way repeated measures ANOVA with Geisser–Greenhouse correction and Dunnett’s multiple comparison test (n = 2 biological replicates). e, Representative TRF of 293T or 293T TERC−/− cells cultured for 14 d following Cas9-sgRNA targeting with the indicated sgRNA. f, Quantification of e. Statistical significance was determined by paired two-sided t-test (n = 3 biological replicates). KO, knockout. g, Representative TRF of 293T cells transduced with the indicated shRNA construct, then supplemented with the indicated dose of dT for 8 d. h, Quantification of g, as in f (n = 2 biological replicates). i, Representative TRF of 293T cells overexpressing eGFP or SAMHD1 of the indicated genotype and supplemented with the indicated dose of dT for 10 d. H215A represents substitution of His with Ala at amino acid residue 215 and K312A represents substitution of Lys with Ala at amino acid residue 312. WT, wild type. j, Quantification and statistical analysis of i, as in d (n = 3 biological replicates). k, Representative TRF of K562 cells manipulated as in i. l, Quantification and statistical analysis of k, as in d (n = 3 biological replicates). The data in d, f, h, j and l are presented as means and error bars indicate s.d.
Telomere lengthening from dT treatment occurs without inhibiting cell growth a, Growth curves of 293T cells treated with the indicated doses of dT. b, Growth curves of 293T cells treated with the indicated doses of dT (n = 4 biological replicates). Inset, graph showing population doublings after 27 d. Statistical significance was determined by one-way repeated measures ANOVA with Geisser–Greenhouse correction and Dunnett’s multiple comparison test. c, Representative TRF of the 293T cells from b after 34 d of culture in media containing the indicated dose of dT. d, Quantification of c (n = 4 biological replicates). Statistical significance was determined as in b. e, Growth curves of K562 cells treated with the indicated dose of dT (n = 4 biological replicates). Inset, graph showing population doublings after 27 d. Quantification and statistical analysis were as in b. f, Representative TRF of the K562 cells from e after 34 d of culture in media containing the indicated dose of dT. g, Quantification of f (n = 4 biological replicates). Statistical significance was determined as in b. The data in b, d, e and g are presented as means and error bars indicate s.d.
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Thymidine nucleotide metabolism controls human telomere length
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Telomere length in humans is associated with lifespan and severe diseases, yet the genetic determinants of telomere length remain incompletely defined. Here we performed genome-wide CRISPR–Cas9 functional telomere length screening and identified thymidine (dT) nucleotide metabolism as a limiting factor in human telomere maintenance. Targeted genetic disruption using CRISPR–Cas9 revealed multiple telomere length control points across the thymidine nucleotide metabolism pathway: decreasing dT nucleotide salvage via deletion of the gene encoding nuclear thymidine kinase (TK1) or de novo production by knockout of the thymidylate synthase gene (TYMS) decreased telomere length, whereas inactivation of the deoxynucleoside triphosphohydrolase-encoding gene SAMHD1 lengthened telomeres. Remarkably, supplementation with dT alone drove robust telomere elongation by telomerase in cells, and thymidine triphosphate stimulated telomerase activity in a substrate-independent manner in vitro. In induced pluripotent stem cells derived from patients with genetic telomere biology disorders, dT supplementation or inhibition of SAMHD1 promoted telomere restoration. Our results demonstrate a critical role of thymidine metabolism in controlling human telomerase and telomere length, which may be therapeutically actionable in patients with fatal degenerative diseases.
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Distribution of endocardium-derived fibroblasts in the adult heart a, Schematic showing the genetic tracing of endocardium-derived fibroblasts. b, Schematic showing the experimental strategy. c, Immunostaining for PDGFRα and GFP on whole adult heart sections. d, Cartoon image showing the distribution pattern of endocardium-derived fibroblasts in the adult heart. e–h, Immunostaining for PDGFRα and GFP on adult heart sections. The boxed regions are magnified in the panels on the right with split channels. The arrowheads indicate GFP⁺PDGFRα⁺ endocardium-derived fibroblasts. Images in e,f,g,h are regions of valve, VS, RV, LV, respectively. i, Flow cytometry analysis of the percentage of PDGFRα⁺ endocardium-derived fibroblasts expressing GFP. j, Quantification of the percentage of PDGFRα⁺ cells expressing GFP by FACS. k, Quantification of the percentage of PDGFRα⁺ cells expressing GFP in heart valves and the myocardium. l, Quantification of the percentage of PDGFRα⁺ endocardium-derived fibroblasts expressing GFP in different regions of the myocardium. OMW, outer myocardium wall; IMW, inner myocardium wall. All quantification data are represented as the mean ± s.e.m.; n = 5 mice. Scale bars, yellow, 1 mm; white, 100 µm. Each image is a representative of five individual mouse samples.
Endocardium-derived fibroblasts expand regionally during cardiac fibrosis after TAC a, Schematic showing simultaneous tracing of general and endocardium-derived fibroblasts. b, Schematic showing the experimental design. c, Whole-mount fluorescence views of hearts collected from Nfatc1-Dre;Col1a2-CreER;R26-RL-GFP;R26-LSL-tdTomato mice under sham treatment or at 4 days or 7 days after TAC. The inserts show brightfield images. d, Sirius Red staining of heart sections. e, Immunostaining for tdTomato and GFP on whole heart sections from mice at 4 days or 7 days after TAC. f, Cartoon images showing the distribution of endocardium-derived fibroblasts in hearts. g, Flow cytometry analysis of the percentage of endocardium-derived fibroblasts expressing GFP in the hearts of sham- or TAC-treated mice. h, Immunostaining for tdTomato, GFP and EdU on different heart sections collected from mice at 4 days or 7 days after TAC. The arrowheads point to EdU⁺ endocardium-derived fibroblasts. i, Quantification of the percentage of endocardium-derived fibroblasts expressing GFP in different parts of the heart. Data are the mean ± s.e.m.; n = 5 mice. j, Quantification of the percentage of GFP⁺tdTomato⁺ fibroblasts (endocardium-derived fibroblasts) or GFP⁻tdTomato⁺ fibroblasts (non-endocardium-derived fibroblasts) incorporating EdU in the hearts collected from mice at 4 days or 7 days after TAC. Data are the mean ± s.e.m.; 4 d after TAC graph, n = 6 mice; VS¹: endocardium-derived fibroblasts versus non-endocardium-derived fibroblasts, *P < 0.0001; LV¹: endocardium-derived fibroblasts versus non-endocardium-derived fibroblasts, *P = 0.0425; 7 days after TAC graph, n = 5 mice; VS¹: endocardium-derived fibroblasts versus non-endocardium-derived fibroblasts, *P < 0.0001; NS, not significant. Statistical analysis was performed using two-way analysis of variance (ANOVA) with Šidák’s multiple comparisons test. Scale bars, yellow, 1 mm; white, 100 µm. Each image is representative of five individual mouse samples.
Contribution of epicardium-derived fibroblasts to cardiac fibrosis after TAC a, Schematic showing the lineage-tracing strategy of embryonic epicardium-derived cells. b, Schematic showing the experimental design. c, Immunostaining for tdTomato and DAPI on whole heart sections from Wt1-CreER;R26-LSL-tdTomato mice 4 days or 7 days after TAC compared with the hearts of sham-treated mice. d, Immunostaining for PDGFRα and tdTomato on hearts of sham-treated mice. e, Quantification of PDGFRα⁺ fibroblasts expressing tdTomato on the VS and LV zones of the heart sections. f, Immunostaining for PDGFRα, tdTomato and EdU on different parts of the heart collected from mice at 4 days or 7 days after TAC. The arrowheads indicate tdTomato⁺PDGFRα⁺EdU⁺ fibroblasts. g, Quantification of tdTomato⁺PDGFRα⁺ fibroblasts incorporating EdU in different parts of the heart sections collected from mice 4 days or 7 days after TAC. h, Cartoon images showing the distribution of epicardium-derived fibroblasts in the hearts of sham- or TAC-treated mice. All quantification data are presented as the mean ± s.e.m.; n = 5 mice. Scale bars, yellow, 1 mm; white, 100 µm. Each image is representative of five individual mouse samples.
Fibroblast-specific proliferation tracing reveals regional fibroblast proliferation after TAC a, Schematic showing the fibroblast-specific proliferation tracing strategy. Col1a2-CreER induced the Ki67-LSL-2A-Dre allele, which primes the system to continuously record Ki67⁺ fibroblasts from time point 1 (T1) to time point n (Tn). b, Schematic showing the experimental design. c, Whole-mount and epifluorescence images of hearts collected from mice 3 weeks after TAM injection. d, Immunostaining for PDGFRα and GFP on heart sections. The arrowheads indicate PDGFRα⁺GFP⁺ fibroblasts. e, Schematic showing the experimental design. f, Whole-mount and epifluorescence images of hearts collected from mice 4 days or 7 days after TAC. g, Sirius Red staining of TAC heart sections. h, Immunostaining for PDGFRα and GFP on heart sections collected from mice at 4 days or 7 days after TAC. The arrowheads indicate PDGFRα⁺GFP⁺ fibroblasts. i, Quantification of the percentage of PDGFRα⁺ fibroblasts expressing GFP in different parts of the hearts from sham- or TAC-treated mice. Data are the mean ± s.e.m. n = 5 mice; in each comparison group, *P < 0.0001. Statistical analysis was performed using two-way ANOVA with Tukey’s multiple comparisons test. j, Cartoon images showing regional distribution of proliferated fibroblasts in hearts after TAC. Scale bars, yellow, 1 mm; white, 100 µm. Each image is representative of five individual mouse samples.
Genetic ablation of endocardium-derived fibroblasts alleviates cardiac fibrosis and reduces the decline of heart function after TAC a, Schematic showing specific endocardium-derived fibroblast ablation by dual recombinase-mediated genetic approach using Nfatc1-Dre;Col1a2-CreER;R26-LSL-RSR-tdT-DTR mice. b, Schematic showing the experimental design. c, Whole-mount brightfield and fluorescence views of hearts collected from mice after PBS or DT injection 28 days after TAC. d, Sirius Red staining of heart sections from PBS- or DT-treated TAC mice. e, Quantification of fibrotic areas in different areas of TAC hearts after PBS or DT treatment. Data are the mean ± s.e.m. n = 5 mice; VS¹: PBS versus DT, *P < 0.0001; LV¹: PBS versus DT, *P < 0.0001. f, Immunostaining for PDGFRα and tdTomato on heart sections collected from PBS- or DT-treated mice. The arrowheads indicate PDGFRα⁺tdTomato⁺ endocardium-derived fibroblasts. g, Quantification of PDGFRα⁺ fibroblasts expressing tdTomato in different areas of hearts after PBS or DT treatment. Data are the mean ± s.e.m. n = 5 mice; VS¹: PBS versus DT, *P < 0.0001; VS²: PBS versus DT, *P = 0.0001; LV¹: PBS versus DT, *P < 0.0001; LV²: PBS versus DT, *P < 0.0001. h,i, Quantification of EF and FS of mice treated with PBS or DT at different time points after TAC. In h, data are the mean ± s.e.m. n = 5 mice; TAC 14 days, PBS versus DT, *P = 0.0147; TAC 28 days, PBS versus DT, *P < 0.0001. In i, data are the mean ± s.e.m. n = 5 mice; TAC 28 days, PBS versus DT, *P = 0.0016. j, H&E staining of sham-treated or TAC heart sections collected from mice treated with PBS or DT. k, Quantification of the relative cross-sectional area of hearts. Data are the mean ± s.e.m. n = 5 mice; sham-wild type (WT) versus TAC-PBS, *P = 0.0001; TAC-PBS versus TAC-DT, *P = 0.0217. l, Quantification of heart-to-body weight ratio in mice after Sham or TAC. m, Immunostaining for WGA on sham or TAC hearts. Data are the mean ± s.e.m.; n = 5 mice; sham-WT versus TAC-PBS and TAC-PBS versus TAC-DT, *P < 0.0001. n, Quantification of the cardiomyocyte cross-sectional area of hearts. Data are the mean ± s.e.m. n = 5 mice; sham-WT versus TAC-PBS and TAC-PBS versus TAC-DT, *P < 0.0001. Scale bars, yellow, 1 mm; white, 100 µm; magenta, 50 µm. Each image is representative of five individual mouse samples. In e and g–i, statistical analysis was performed by two-way ANOVA with Šidák’s multiple comparisons test; in k, l and n, one-way ANOVA with Tukey’s multiple comparisons test was used.
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Dual genetic tracing reveals a unique fibroblast subpopulation modulating cardiac fibrosis
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After severe heart injury, fibroblasts are activated and proliferate excessively to form scarring, leading to decreased cardiac function and eventually heart failure. It is unknown, however, whether cardiac fibroblasts are heterogeneous with respect to their degree of activation, proliferation and function during cardiac fibrosis. Here, using dual recombinase-mediated genetic lineage tracing, we find that endocardium-derived fibroblasts preferentially proliferate and expand in response to pressure overload. Fibroblast-specific proliferation tracing revealed highly regional expansion of activated fibroblasts after injury, whose pattern mirrors that of endocardium-derived fibroblast distribution in the heart. Specific ablation of endocardium-derived fibroblasts alleviates cardiac fibrosis and reduces the decline of heart function after pressure overload injury. Mechanistically, Wnt signaling promotes activation and expansion of endocardium-derived fibroblasts during cardiac remodeling. Our study identifies endocardium-derived fibroblasts as a key fibroblast subpopulation accounting for severe cardiac fibrosis after pressure overload injury and as a potential therapeutic target against cardiac fibrosis.
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Analysis flowchart For common variant analyses across 65 quantitative traits, we performed GWAS among UK Biobank samples who were unrelated to individuals with WES data (‘out-sample’), GWAS among UK Biobank samples with WES data (‘in-sample’) and GWAS among all UK Biobank samples (‘total’). From each GWAS, we constructed PGSs using clumping-and-thresholding methods and using PRS-CS¹¹. We then performed exome-wide testing of rare variants within the WES samples, using models without PGSs and adjusting for various PGSs. LOF and missense variants were used to assess rare variant yields, while synonymous variants were used to assess inflation and false-positive rates. In the flowchart, blue boxes describe steps revolving around common variant analyses and PGS construction, while the red boxes highlight steps involving rare variant analyses. λGC, inflation factor computed as observed χ² at the median over the expected under the null hypothesis.
PGS adjustment improves discovery yield in analysis of rare deleterious variants a, Bar charts for the improvement in deleterious RVAT yield after PGS adjustment at different α levels, expressed in percentage relative to the no-PGS (reference) model. b, Violin plots for the difference (δ) in significance of tests from deleterious RVATs, comparing models with PGS versus models without PGS. Here, the δ in P values (on the −log10 scale) is displayed for tests reaching P < 2.6 × 10⁻⁶ (Methods). The P values and distributions are derived from two-sided paired Wilcoxon signed-rank tests (where n gene–trait pairs equals 263, 270, 258, 260, 265 and 278 from left to right), while the d̅ values plotted above the violins are derived from two-sided paired t-tests (after removing outliers). The left plot shows all results, while the right plot is capped at y = 10 for clarity. Boxplots: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. c, Quantile–quantile plots for PGS-adjusted RVAT of the phenotype height. The left plot shows expected versus observed P values for the model with no PGS adjustment, while the second and third plots show results for PGSlead-SNP (out-sample) and PGSCS (out-sample), respectively. Exome-wide significant genes are annotated with gene names; genes highlighted in bold were only identified after PGS adjustment. d̅, estimated paired group difference; δ, difference; α, significance cutoff. Ref, reference model (that is, the baseline model without inclusion of PGS).
PGS adjustment does not increase false-positive rates or genomic inflation in the analysis of rare synonymous variants a, Boxplots for per-trait association rate from synonymous RVATs at different α levels across the 65 traits. A median of 18,060 genes were analyzed per trait. b, Violin plots for genomic inflation factors for exome-wide RVATs of synonymous variants across the 65 traits. c, Violin plots for difference (δ) in significance of tests from synonymous variant RVATs, comparing models with PGSs versus models without PGSs. Here, the δ in P values (on the −log10 scale) is displayed for tests reaching P < 0.05 (Methods), with the contributing n gene–trait pairs equaling 75,044, 77,524, 75,838, 89,792, 77,784 and 85,187 (from left to right). Boxplots: center line, median; box limits, upper and lower quartiles; whiskers, 1.5 × interquartile range; points, outliers.
Adjusting for common variant polygenic scores improves yield in rare variant association analyses
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With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α = 2.6 × 10⁻⁶), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery.
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Concepts of estimating similarity in the causal effects across local ancestries a, For a given trait, with phased genotype (paternal haplotype at the top and maternal haplotype at the bottom) and inferred local ancestry (denoted by color), we investigate whether βs,afr≈βs,eur\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{s,{{{\mathrm{afr}}}}} \approx \beta _{s,{{{\mathrm{eur}}}}}$$\end{document} across each causal SNP s. b, We focus on estimating the genome-wide correlation of genetic effects across ancestries radmix=Cor[βafr,βeur]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{{{{\mathrm{admix}}}}} = {{{\mathrm{Cor}}}}[\beta _{{{{\mathrm{afr}}}}},\beta _{{{{\mathrm{eur}}}}}]$$\end{document}, which is the regression slope (orange line) of ancestry-specific causal effects. For reference, the gray dashed line corresponds βafr=βeur\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{{{{\mathrm{afr}}}}} = \beta _{{{{\mathrm{eur}}}}}$$\end{document}.
Results of genetic correlation radmix estimation in genome-wide simulations Simulations were based on 17,299 PAGE individuals and 6.9 million genome-wide imputed variants with MAF > 0.5% in both ancestries. We fixed the proportion of causal variants Pcausal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{{{{\mathrm{causal}}}}}$$\end{document} as 0.1% and varied genetic correlation radmix = 0.90, 0.95 and 1.0. a, Impact of using HapMap3 or imputed variants in estimation. We varied simulated genome-wide heritability hg2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_g^2$$\end{document} = 0.1, 0.25 and 0.5. b, Impact of selecting common variants at different MAF thresholds in estimation. hg2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_g^2$$\end{document} was fixed to 0.25, and imputed variants at different MAF thresholds were used in estimation. c, Impact of prior assumption in estimation. hg2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_g^2$$\end{document} was fixed to 0.25, and imputed variants were used in estimation. For each simulated genetic architecture, we plot the mode and 95% credible interval based on the meta-analysis across 100 simulations (Methods). Numerical results are reported in Supplementary Tables 1–4 (including results for other Pcausal,radmix\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{{{{\mathrm{causal}}}}},r_{{{{\mathrm{admix}}}}}$$\end{document}).
Similarity of causal effects and marginal effects across local ancestries meta-analyzed across PAGE, UKBB and AoU a, We plot the trait-specific estimated radmix for 16 traits. For each trait, dots denote the estimation modes; bold lines and thin lines denote 50%/95% highest density credible intervals, respectively. Traits are ordered according to total number of individuals included in the estimation (shown in parentheses). These traits are selected to be displayed either because they have the largest total sample sizes, or because the associated SNPs of these traits exhibit heterogeneity in marginal effects (see the panel on the right). We also display the meta-analysis results across 60 study–trait pairs (38 unique traits). Numerical results are provided in Table 1. b, Comparison of radmix (n = 38 traits) to meta-analysis results from transcontinental genetic correlation of African versus European (n = 26 traits) and East Asian versus European (n = 31 traits). Point estimates and 95% confidence intervals are denoted using triangles and lines. c, We plot the ancestry-specific marginal effects for 217 GWAS significant clumped trait–SNP pairs across 60 study–trait pairs. Trait–SNP pairs with significant heterogeneity in marginal effects by ancestry (pHET < 0.05/217 via HET test) are denoted in color (non-significant trait–SNP pairs denoted as black dots; some black dots with large differences across ancestries were not significant because of the large standard errors in estimated effects). Numerical results are reported in Supplementary Table 11. Point estimates and 95% confidence intervals for Deming regression slopes of βs,eur(m)̂~βs,afr(m)̂\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat {\beta _{s,{{{\mathrm{eur}}}}}^{(m)}} \sim \widehat {\beta _{s,{{{\mathrm{afr}}}}}^{(m)}}$$\end{document} are provided either for all 217 SNPs (red), or for 193 SNPs after excluding 24 MCH-associated SNPs (blue). RBC, red blood cell; CRP, C-reactive protein; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; TC, total cholesterol; BMI, body mass index; WHR, waist-to-hip ratio.
Induced heterogeneities in marginal effects across local ancestries a, Illustrations that different LD patterns across local ancestries can induce differential tagging between a causal SNP and a tag SNP in b or another causal SNP in c. LD strengths between the two SNPs are indicated both in the thickness of arrows and in the color shades of ‘*’ elements in LD matrices. b, Example of single causal SNP with no heterogeneity. Causal effects are the same across local ancestries, and the estimated marginal effects at causal SNP will be also very similar with sufficient sample size. However, because of differential tagging across local ancestries, the estimated marginal effects evaluated at the tag SNP are different. c, Example of multiple causal SNPs with no heterogeneity. Causal effects for both SNPs are the same across local ancestries. In this example, the correlation between the two causal variants is higher for genotypes in African local ancestries than those in European local ancestries. Therefore, African ancestry-specific genotypes tag more effects, creating different ancestry-specific marginal effects at each causal SNP.
Pitfalls of including local ancestry in estimating heterogeneity In each simulation, we selected a single causal variant and simulated quantitative phenotypes where these causal variants explain heritability hg2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_g^2$$\end{document} = 0.6%; we also varied ratios of effects across ancestries βeur:βafr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{{{{\mathrm{eur}}}}}:\beta _{{{{\mathrm{afr}}}}}$$\end{document}. a, False positive rate in null simulation βeur:βafr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{{{{\mathrm{eur}}}}}:\beta _{{{{\mathrm{afr}}}}}$$\end{document} = 1.0. b, Power to detect βeur≠βafr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{{{{\mathrm{eur}}}}} \ne \beta _{{{{\mathrm{afr}}}}}$$\end{document} in power simulations with βeur:βafr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{{{{\mathrm{eur}}}}}:\beta _{{{{\mathrm{afr}}}}}$$\end{document} > 1. We did not include ‘lanc regressed’ because it is not well-calibrated in null simulations. We plot the mean and 95% confidence intervals, calculated via 100 random subsamplings with each sample consisting of 500 SNPs (Methods). Numerical results are reported in Supplementary Table 12.
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Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals
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Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93–0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.
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MOFA of whole genomes, transcriptomes and methylomes of the MESOMICS cohort (n = 120) a, Proportion of interpatient variance within a molecular layer explained by WHO-defined histopathological type (left) and MOFA latent factors 1–4 (right). For example, 7% of variation present in RNA expression can be explained by mesothelioma types, in contrast with 20% explained by integrative MOFA. CN, segmental copy number; DNA alt, rearrangements and mutations; MethBod, DNA methylation level at body regions; MethEnh, DNA methylation level at enhancer regions; MethPro, DNA methylation level at promoter regions; RNA, gene expression level. b, Network of the correlations between latent factors, tumor histopathology and previously published molecular scores. The arc colors, widths and transparency correspond to Pearson correlation coefficients. Features uncorrelated with any other features are highlighted in bold. AI, artificial intelligence; C/V ratio, log2 ratio of CLDN15 to VIM gene expression; S score, sarcomatoid gene expression score; E score, epithelioid gene expression score. c, Interpretation of MOFA latent factors. Plus signs indicate positive correlations and minus signs indicate negative correlations. d, Correlation between the ploidy factor (LF1) and ploidy. e, Correlation between the CIMP factor (LF4) and CIMP index. The samples are colored by histological type. f, Forest plot of the hazard ratios of MOFA latent factors for overall survival. The squares correspond to estimated hazard ratios and the segments correspond to their 95% confidence intervals. In b–e, P values, q values and r coefficients were determined by two-sided Pearson correlation tests. In d and e, the gray bands represent 95% confidence intervals. Source data
Cancer task inference from the morphology and adaptive response factors (n = 120) a, Sample positions along the morphological (LF2) and adaptive response factors (LF3) are contained within a triangle formed by three phenotypic archetypes (colored vertices). The P value corresponds to a one-sided test from the Pareto fit. b, Ternary plot representing the sample’s distance from the three specialized profiles. The bar plots represent the association between archetypes and histopathological types. c, Summary table of the main phenotypes, features and overexpressed pathways (columns) identified in each profile (rows). Left, arrows indicate the focal profile of each row. Middle left, ternary plots with a color-filled background representing key features for each profile. NRC, normalized read count. Middle right, lollipop plots presenting the correlation between RNA-seq-estimated immune cell infiltration and the proportion of archetypes. Right, expression heatmaps of cancer tasks inferred from each phenotype. The rows represent enriched pathways and the columns represent the samples, ordered by increasing phenotype proportion. The heatmap color scale corresponds to the averaged z score of each gene set. The colored tiles on the right annotate the gene sets that belong to the hyper-pathways inferred from each phenotype. Source data
Genomic characterization of MPM from the MESOMICS cohort a, Recurrent large genomic events. Top, clinical, epidemiological, morphological and technical features per sample. T only represents samples with WGS on the tumor sample only. Bottom left, oncoplot describing the genomic events per sample. amp, amplification; del, deletion; ND, HRD type not determined. Bottom middle, barplot of the frequency of each event within the cohort. Bottom right, comparison of the gene expression of cancer-relevant genes belonging to frequent deletions detected by GISTIC, with regards to their copy number (CN) status. Wild-type (WT) cases correspond to samples without copy number, structural variant or single-nucleotide variant events detected. The box plots represent the median and interquantile range and the whiskers the maximum and minimum values, excluding outliers. The n number above represents the number of biologically independent samples for each test. *0.01 < q value ≤ 0.05; **0.001 < q value ≤ 0.01; ***q value ≤ 0.001. NRC, normalized read count. b, Cohort-level copy number profile (top), with significantly altered regions identified by GISTIC in focal peaks (middle) and at the chromosome (chr.) arm level (bottom). cnLOH, copy-neutral LOH. c, Data from a patient with oncogene amplification due to a chromothripsis event (MESO_019). Left, chromosomes involved in the chromothripsis event (outer circle, shattered regions; intermediate circle, copy number gain and loss; inner circle, structural variants (SVs)). Middle, reconstructed ecDNA structure. Right, gene expression in MESO_019 relative to the expression in other tumors of the cohort (quantile). Oncogenes found within the ecDNA region are represented in red. The P value was determined by two-sided Wilcoxon rank-sum test. kb, kilobases. Source data
MPM driver genes in the MESOMICS cohort Top, tumor mutational burden (TMB), number of segments or copy number burden (CNB) and structural variant burden (SVB) of each sample. Main, oncoplot describing genomic alterations in IntOGen and structural variant MPM driver genes per sample. These genomic events can co-occur with copy number changes. Large indels and translocations refer to structural variant events detected by structural variant callers while fusion transcripts are detected at the transcriptomic level. Each gene is also annotated as belonging to one focal or arm-level GISTIC event, as well as for being regulated by DNA methylation (right bars). Right, frequency of alterations within the cohort. For each gene, the dark green dot represents the frequency of structural variants. In the legend, ERG indicates whether the sample has one or more alteration in an ERG. Key clinical, epidemiological, morphological and technical features are given for each sample. PCAWG, Pan-Cancer Analysis of Whole Genomes; SNV, single-nucleotide variant; SV, structural variant. Source data
Impact of genomic events on MPM molecular profiles a, Association between genomic events and MOFA factors. For each event, the ALT (altered) versus wild-type difference corresponds to the difference between the mean factor value of wild-type samples and that of altered samples. The q values correspond to an adjusted analysis of variance P value; the dashed horizontal line represents the q value threshold of 0.05. AMP, amplification; Decr., decrease; DEL, deletion. b, Association between CIMP index, EZH2 expression (n = 109 samples) and PRC2 target gene methylation (n = 119 samples). Left, heatmap of EZH2 gene expression (NRC) and CpG island methylation (z score) of PRC2 target genes whose methylation level was significantly positively correlated with CIMP index (q < 0.05), for samples ordered by CIMP index. Right: correlation between WT1 expression and CIMP index. The q value was determined by Pearson correlation test and the gray band corresponds to the 95% confidence interval. c, Effect vector of key alterations affecting specialization in the tumor tasks from Fig. 2. The effect vector corresponds to the difference in position on the Pareto front between the centroid of altered samples and the centroid of wild-type samples. d, Comparison of the timing of large-scale amplifications in the MESOMICS and PCAWG cohorts. The points represent estimates of the timing of genomic events. The empirical P values (red data points) were determined by one-sided outlier tests. Source data
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Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity
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Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that—in the case of the interdependent tumor cell morphology and adapted immune response—reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.
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Circular Manhattan plots for genome-wide association analysis for overall endometriosis, rASRM stage 3/4 disease, rASRM stage 1/2 disease and infertility Circular Manhattan plots for genome-wide association analysis for overall endometriosis (blue), rASRM stage 3/4 disease (green), rASRM stage 1/2 disease (orange) and endometriosis-associated infertility (yellow). Genome-wide significant signals are marked in red and their chromosomal location is denoted with dotted gray lines. The six loci with substantially larger effect sizes in rASRM stage 3/4 versus rASRM stage 1/2 analysis are annotated in green.
GDAP1/8q21.11 regional association results and SMR evidence based on expression (eQTL) and methylation (mQTL) quantitative trait loci data in blood a, Illustration of the regional association plot for the GDAP1/8q21.11 locus. eSMR blood single-nucleotide polymorphism (SNP)—SMR SNP identified as causal for endometriosis utilizing the eQTL data from blood tissue. mSMR blood SNP—SMR SNP identified as causal for endometriosis utilizing the mQTL data from blood tissue. b, SMR significant blood-based eQTL for GDAP1 and mQTL of probe cg23779890 located in GDAP1 gene are shown. c, The genic content and locations for the region are illustrated.
SRP14-AS1/15q15.1 regional association results and SMR evidence based on eQTL and mQTL data in endometrium and blood a, Illustration of the regional association plot for the SRP14-AS1/15q15.1 locus. eSMR endometrium SNP—SMR SNP identified as causal for endometriosis utilizing the eQTL data from endometrium; mSMR blood SNP—SMR SNP identified as causal for endometriosis utilizing the mQTL data from blood tissue. eSMR blood SNP—SMR SNP identified as causal for endometriosis utilizing the eQTL data from blood tissue. SMR SNPs not passing the HEIDI-heterogeneity test are annotated in black. b, SMR significant eQTLs and mQTLs in the SRP14-AS1/15q15.1 locus along with SRP14 promoter-associated chromatin loops with anchor points containing the lead SNP and the SMR SNPs. SMR significant SNPs associated with expression of SRP14 in endometrium and blood (eQTL), methylation at cg27155939 in blood (mQTL) and endometriosis are shown in black, and the associated SRP14/CpG target is shown in red. Valid promoter-associated chromatin loops were generated from H3K27Ac HiChIP libraries from a normal immortalized endometrial cell line (E6E7hTERT) and three endometrial cancer cell lines (ARK1, Ishikawa and JHUEM-14)⁶⁸.
SYNE1/6q25.1 regional association results with five distinct signals and SMR evidence-based eQTL and mQTL data in endometrium and blood a, Illustration of the regional association plot for the SYNE1/6q25.1 locus that includes five distinct signals from approximate conditional analysis. On the plot are also the SMR significant eQTL and mQTLs from endometrium and blood tissue along with promoter-associated chromatin loops with anchor points containing the lead SNP and the SMR SNPs. Multiple distinct signals substantially associated with endometriosis were identified in this region and r² values displayed are relative to the closest lead SNP. eSMR endometrium SNP—SMR SNP identified as causal for endometriosis utilizing the eQTL data from endometrium; mSMR endometrium SNP—SMR SNP identified as causal for endometriosis utilizing the mQTL data from endometrium; mSMR blood SNP—SMR SNP identified as causal for endometriosis utilizing the mQTL data from blood tissue. eSMR blood SNP—SMR SNP identified as causal for endometriosis utilizing the eQTL data from blood tissue. SMR SNPs not passing the HEIDI-heterogeneity test are annotated in black. b, SMR significant SNPs associated with expression of ESR1 in blood (eQTL), methylation at cg24075332 (mQTL1), cg14416726 (mQTL2), cg18745416 (mQTL3) and cg01066157 (mQTL4) in blood, and endometriosis are shown in black and the associated ESR1/CpG targets are shown in red. Ovarian enhancers were available from the Roadmap Epigenomics Project. Valid promoter-associated chromatin loops were generated from H3K27Ac HiChIP libraries from a normal immortalized endometrial cell line (E6E7hTERT) and three endometrial cancer cell lines (ARK1, Ishikawa and JHUEM-14)⁶⁸.
Correlation of the effect sizes of lead SNPs between rASRM disease stage, surgical subtypes and extended pelvic pain a–e, Correlation between the effect sizes of 42 endometriosis-associated lead SNPs comparing rASRM stage 3/4 disease versus surgical subtypes (endometrioma, deep lesions and superficial lesions (a–c), and rASRM stage 1/2, rASRM stage 3/4 and deep lesions versus risk of reporting more than two types of pelvic pain (d,e). Loci nominally associated (P < 0.05) with individual subphenotypes are annotated with locus names and larger circles. Circle size represents the significance level of correlation—larger circles have smaller P values and smaller circles have larger P values. The solid black line represents the linear regression line, the dotted black line is the x = y reference slope and the dotted gray lines x = 1 and y = 1 are references to the odds ratio (OR). The gray error band represents 95% confidence interval. Test statistics including P values for all the associations are provided in Supplementary Table 21.
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The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions
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Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and East Asian descent, identified 42 genome-wide significant loci comprising 49 distinct association signals. Effect sizes were largest for stage 3/4 disease, driven by ovarian endometriosis. Identified signals explained up to 5.01% of disease variance and regulated expression or methylation of genes in endometrium and blood, many of which were associated with pain perception/maintenance (SRP14/BMF, GDAP1, MLLT10, BSN and NGF). We observed significant genetic correlations between endometriosis and 11 pain conditions, including migraine, back and multisite chronic pain (MCP), as well as inflammatory conditions, including asthma and osteoarthritis. Multitrait genetic analyses identified substantial sharing of variants associated with endometriosis and MCP/migraine. Targeted investigations of genetically regulated mechanisms shared between endometriosis and other pain conditions are needed to aid the development of new treatments and facilitate early symptomatic intervention.
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Identification of an LPC state in mouse and human livers a, Schematic showing experimental strategy. b, UMAP visualization of the EPCAM⁺ epithelial cell clusters—BECs, LPCs and proliferating BECs. c, UMAP plots show expression of indicated genes and cell cycle score. Proportion of proliferating cells in BECs and LPCs is shown in the right panel. d, Integrated UMAP showing BECs from Fah-LSL/LSL mice and BECs from mice fed with DDC diet. DDC data are retrieved from previous studies—DDC-1 (ref. ³²) and DDC-2 (ref. ³³). Proportion of LPCs in three groups is shown in the right panel. e, Immunostaining for HNF4α, CK19, EPCAM, A6 and OPN on Fah-LSL/LSL liver sections. White arrows indicate LPCs. f, Immunostaining for CK19 and HNF4α liver sections from mice fed with DDC for 3 weeks. g, Heatmap showing the differentially expressed genes between BECs and LPCs. Each column represents a cell and each row represents a gene. h, Selected GO terms enriched in LPCs cluster. The gene enrichment analysis was done by Metascape, which uses the well-adopted hypergeometric test and Benjamini-Hochberg P value correction algorithm to identify all enriched ontology terms. i, Schematic showing experimental strategy. j, Immunostaining for p21, CK19 and HNF4α in the indicated human liver biopsies. k,l, Quantification of the percentage of hepatocytes with nuclear p21 staining (senescence, k) or CK19⁺ cells expressing HNF4α (LPCs, l). Data represent mean ± s.d. n = patients. In k, unpaired two-tailed t tests were used (versus healthy, n = 6): NASH (n = 6), hepatitis (Hep)B (n = 6), HepC (n = 6), acute liver failure (ALF; n = 8), PSC (n = 5), PBC; (n = 5), AIH (n = 5), ASH cirrhosis (n = 5), NASH cirrhosis (n = 6), HepB cirrhosis (n = 6), HepC cirrhosis (n = 5). In l, unpaired two-tailed t tests were used (versus healthy, n = 6): NASH (n = 6), HepB (n = 5), HepC (n = 6), ALF (n = 9), PSC (n = 5), PBC (n = 5), AIH (n = 5), ASH cirrhosis (n = 5), NASH cirrhosis (n = 6), HepB cirrhosis (n = 6), HepC cirrhosis (n = 5). m, Correlation plot of the LPC number and hepatocyte senescence percentage. Pearson correlation was performed for statistical analysis. Scale bars in all immunostaining images, 100 µm. Source data
LPCs are a transitional cellular state between BECs and hepatocytes during transdifferentiation a, UMAP embedding of RNA velocity of EPCAM⁺ cells collected from Fah-LSL/LSL mice at day 25 after initial NTBC removal indicates the transition from BECs to LPCs. b, Schematic showing experimental strategy. c, Immunostaining for tdT, HNF4α, CK19, EPCAM, A6 or OPN on liver sections collected at days 25 and 33 after NTBC removal from CK19-CreER;Fah-LSL/LSL;R26-tdT mice. Yellow arrows indicate tdT⁺ LPCs and white arrows indicate tdT⁺ hepatocytes. Scale bars, 100 µm. d, Immunostaining for tdT, HNF4α, CK19 and FAH on liver sections collected at days 25 and 33 after NTBC removal. Scale bars, 50 µm. Yellow arrows indicate LPCs and white arrows indicate tdT⁺ hepatocytes. e, Immunostaining of tdT, HNF4α, CK19 and Ki67 on liver sections collected at days 25 and 33 after NTBC removal. Yellow arrows indicate LPCs and white arrows indicate tdT⁺ hepatocytes. Percentage of Ki67⁺ TLPCs and Ki67⁺tdT⁺ hepatocytes in tdT⁺ hepatocytes is shown on the right panel. Data represent mean ± s.d.; n = 5 mice. Scale bars, 100 µm. f, Immunostaining for tdT, HNF4α and CK19 on liver sections collected at different time points. Scale bars, 10 µm. g, Cell area quantification in TLPCs (CK19⁺HNF4α⁺ and CK19lowHNF4α⁺) and hepatocytes (tdT⁺). Data represent mean ± s.d.; CK19⁺HNF4α⁺: n = 6 mice, CK19lowHNF4α⁺ and Heps: n = 5 mice; CK19lowHNF4α⁺ versus CK19⁺HNF4α⁺: *P = 0.02; Heps versus CK19lowHNF4α⁺: *P < 0.0001. Statistical analysis was performed by ANOVA followed by Tukey’s method for multiple comparisons, and adjustments were made for multiple comparisons. h, Quantification of the number of TLPCs (CK19⁺HNF4α⁺ and CK19lowHNF4α⁺) and hepatocytes (tdT⁺) per portal region ×60 field at different time points. Data represent mean ± s.d.; n = 5 mice. i, Schematic showing that TLPCs originate from BECs and differentiate into hepatocytes. Hep, hepatocyte; w/o, without.
Bipotent TLPCs generate hepatocytes or adopt BEC fate during liver repair a, Schematic showing the strategy for TLPC lineage tracing. b, Schematic showing experimental strategy. c, Immunostaining for tdT, HNF4α and CK19 on liver sections collected at day 25 from CK19-CreER;HNF4α-DreER;Fah-LSL/LSL;R26-RL-tdT mice. Scale bars, 50 µm. Right panel shows the percentage of BECs, TLPCs and hepatocytes in tdT⁺ cells. Data represent mean ± s.d.; n = 5 mice; total 289 tdT⁺ cells were counted (BECs: 25; TLPCs: 258; hepatocyte: 6). d, Percentage of Ki67⁺ cells in tdT⁺ TLPCs. Data represent mean ± s.d.; n = 5 mice. e, Immunostaining for tdT, HNF4α and CK19 on liver sections collected at day 25 from CK19-CreER; Fah-LSL/LSL;R26-RL-tdT or HNF4α-DreER;Fah-LSL/LSL;R26-RL-tdT mice. Scale bars, 50 µm. f, Whole-mount tdT fluorescent liver images from indicated mice at 7 weeks after the first NTBC removal. Bright field images are shown as inserts. Arrows indicate tdT⁺ clones. Scale bars, 1 mm. g, Immunostaining for tdT, HNF4α and CK19 on serial sections (20 µm) of livers collected at week 7 from CK19-CreER;HNF4α-DreER;Fah-LSL/LSL;R26-RL-tdT mice. Scale bars, 50 µm. h, Immunostaining of tdT, and CK19 on serial liver sections (20 µm) collected at week 7 from CK19-CreER;HNF4α-DreER;Fah-LSL/LSL;R26-RL-tdT mice. Scale bars, 50 µm. White arrows indicate tdT⁺ BECs. i, Immunostaining for tdT, HNF4α and CK19 on liver sections collected from CK19-CreER;Fah-LSL/LSL;R26-RL-tdT or HNF4α-DreER;Fah-LSL/LSL;R26-RL-tdT mice. Scale bars, 1 mm. j, Percentage of tdT⁺ clones containing BECs, TLPCs and hepatocytes. Schematic on the right panel showing that TLPCs could give rise to hepatocytes and revert back to BECs. Data represent mean ± s.d.; n = 5 mice; total 480 tdT⁺ clones were counted. k, Percentage of hybrid clone that consists of BECs and hepatocytes. Schematic showing that TLPCs could not give rise to hepatocytes and BEC simultaneously. Data represent mean ± s.d.; n = 5 mice. l–n, Immunostaining for tdT, FAH, p21 and Ki67 on tissue sections. Right panels are quantifications for percentage of hepatocytes expressing these markers. Scale bars, 100 µm. Data represent mean ± s.d.; n = 5 mice; m, *P < 0.0001; n, *P < 0.0001. Statistical analysis was performed by two-tailed unpaired Student’s t test in m and n. w, weeks.
Inhibition of Notch signaling promotes the activation of TLPCs a, Schematic figure showing experimental strategy for Rbpj gene knockout in BECs. b, Representative gating for FACS sorting of EPCAM⁺ BECs used for qRT-PCR. Relative expression levels of Rbpj mRNA in BECs from indicated mice. Data represent mean ± s.d.; n = 5 mice; *P = 0.0016. c, Immunostaining for HNF4α and CK19 on liver sections collected at day 25 after NTBC removal from indicated mice. Scale bars, 50 µm. Quantification of the number of TLPCs per portal region ×60 field is shown on the right panel. Data represent mean ± s.d.; n = 5 mice; *P = 0.0002. Total count of TLPCs in control: 717; in mutant: 1,159. White arrowheads indicate TLPCs. d, Schematic showing that inhibition of Notch signaling promotes BEC-to-TLPC transition. e, Whole-mount GFP fluorescent liver images collected at week 7 after NTBC removal. Scale bars, 2 mm. f, Immunostaining for GFP, HNF4α and CK19 on the liver sections collected at week 7 after NTBC removal. Scale bars, 500 µm. g, Percentage of GFP⁺ hepatocytes and GFP⁺ BECs. Data represent mean ± s.d.; n = 5 mice; GFP⁺ Heps (%): *P = 0.0016; GFP⁺ BECs (%): *P = 0.7691. h, Immunostaining for GFP, HNF4α and Ki67 on liver sections collected at week 7 after NTBC removal. Quantification of proliferation (Ki67) in GFP⁺ hepatocytes is shown on the right panel. Data represent mean ± s.d.; n = 5 mice. Scale bars, 100 µm. Statistical analysis was performed by two-tailed unpaired Student’s t test in b, c, g and h. NS, not significant; w, weeks.
Notch activation inhibits BEC-to-TLPC activation a, Schematic showing experimental strategy. Control, CK19-2A-CreER;Fah-LSL/LSL;R26-tdT. NICD-OE, CK19-2A-CreER;Fah-LSL/LSL;R26-NICD-GFP. b, Immunostaining for HNF4α and CK19 on liver sections collected at day 25 after NTBC removal. Scale bars, 50 µm. c, Quantification of the number of TLPCs and GFP⁺ TLPCs per portal region ×60 field from the indicated mice. Data represent mean ± s.d.; n = 5 mice; *P = 0.0004; Total count of TLPCs in control: 789, in NICD group: 92. d, Immunostaining for tdT or GFP, Ki67 and CK19 on liver sections collected at day 25 after NTBC removal. Percentage of Ki67⁺ BECs is shown on the right panel. Data represent mean ± s.d.; n = 5 mice; *P < 0.0001. Scale bars, 50 µm. e, UMAP visualization of EPCAM⁺ cells collected from control and NICD-OE mice at day 25 after NTBC removal. The percentage of TLPCs and proliferating BECs is shown on the right. f, Upper panel is scRNA-seq heatmap for EPCAM⁺ cells from control and NICD-OE mice showing expression of selected genes per cell. Middle panel shows violin plot of cell cycle score of genes related to G2M and S phases. Lower panel shows UMAP plot of gene set score of S, G2M and cell cycle. g, Immunostaining for tdT or GFP, HNF4α, and CK19 on liver sections collected at week 7 after NTBC removal. Quantification of the ductular reaction per ×10 field and the percentage of tdT⁺ or GFP⁺ hepatocytes is shown on the right panel. Data represent mean ± s.d.; n = 5 mice; CK19 density (10×): *P < 0.0001; Reporter⁺ Heps: *P < 0.0001. Scale bars, 100 µm. h, Immunostaining for tdT or GFP, Ki67 and CK19 on liver sections collected at week 7 after NTBC removal. Percentage of Ki67⁺ BECs is shown on the right panel. Data represent mean ± s.d.; n = 5 mice; *P < 0.0001. Scale bars, 100 µm. i, Schematic showing that Notch signaling inhibited BEC-to-TLPCS activation. Statistical analysis was performed by two-tailed unpaired Student’s t test in c, d, g and h. w, weeks.
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Bipotent transitional liver progenitor cells contribute to liver regeneration
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Following severe liver injury, when hepatocyte-mediated regeneration is impaired, biliary epithelial cells (BECs) can transdifferentiate into functional hepatocytes. However, the subset of BECs with such facultative tissue stem cell potential, as well as the mechanisms enabling transdifferentiation, remains elusive. Here we identify a transitional liver progenitor cell (TLPC), which originates from BECs and differentiates into hepatocytes during regeneration from severe liver injury. By applying a dual genetic lineage tracing approach, we specifically labeled TLPCs and found that they are bipotent, as they either differentiate into hepatocytes or re-adopt BEC fate. Mechanistically, Notch and Wnt/β-catenin signaling orchestrate BEC-to-TLPC and TLPC-to-hepatocyte conversions, respectively. Together, our study provides functional and mechanistic insights into transdifferentiation-assisted liver regeneration.
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Genomic landscape of gastric cancer a, Landscape of copy number and mutation. Each column represents an individual tumor. b, Landscape of SMGs in diffuse- and intestinal-type non-hypermutated tumors. c, Landscape of SMGs in hypermutated tumors. The panel shows altered cases in each SMG. SMGs with a q value < 0.1 are shown. Color coding indicates the alteration types.
Landscape of SMGs in non-hypermutated gastric cancer a, Oncogenic signaling pathways of non-hypermutated gastric cancer. b, Intranuclear oncogenic pathways of non-hypermutated gastric cancer. Representative SMGs were classified into 13 cell signaling pathways. Light-blue shaded cornering and the orange arc indicate the cell and nuclear membranes, respectively. Oncogenic and tumor-suppressive pathways are indicated by red and blue dotted lines, respectively. Oncogenic and tumor-suppressive SMGs are denoted in red and blue, respectively. Alteration frequencies (percentage of altered cases) in each subtype are indicated in boxes, and the box is colored if the difference was statistically significant (P < 0.05, Fisher’s exact test). Alteration frequencies include mutations, amplifications (copy-number ratio ≥ 4), and homozygous deletions (Supplementary Tables 3–5). c–e, Significant differences in SMGs of non-hypermutated gastric cancer between diffuse- and intestinal types (c), between EBV-positive and EBV-negative cases (d) and between East Asian and European ancestry cases (e). Planar dimensions of circles indicate the −log10 of the P value (two-sided Fisher’s exact test). Red (OR > 1) and blue (OR < 1) circles show significant differences (P < 0.05) in the number of altered cases between the two groups. f, Significantly co-altered SMGs in non-hypermutated cases (≥7 cases). The −log10 of the q value (x-axis) and OR (y-axis) calculated by Fisher’s exact test are shown. Red circles show significantly co-altered SMGs with OR > 8 or P < 1.0 × 10⁻⁶.
Distribution of PIGR, SOX9, TRIM49 and ARHGAP5 mutations in non-hypermutated gastric cancer a, Distribution of PIGR and SOX9 mutations. b, Hotspot mutations in TRIM49/49B/49 C. Distribution of mutated cases in TRIM49/49B/49 C is shown on the right. c, Distribution of ARHGAP5 mutations. EGFR copy-number ratio in ARHGAP5-mutated cases is indicated below. Hotspot mutation cases are highlighted in red.
Aberrant expressions of PD-L1, PD-L2 and FGFR2 a, RNA-seq read depth of typical cases with aberrant 3′ UTRs of PD-L1, PD-L2 and FGFR2. Yellow and gray shading indicate aberrant and nonaberrant cases, respectively. Normalized read depths of the 3′ UTR of aberrant cases (red dotted circles) were fully or partially lower than those of non-aberrant cases. Decreased 3′ UTR expression was also observed in cases with gene fusions. RNA-seq read depths were normalized by the maximum depth in coding exons for each case. b, Comparison of coding expression between 3′ UTR aberration positive and negative cases. Cases with gene fusions were excluded. The P value was calculated using a two-sided Wilcoxon rank-sum test. Boxplots represent the median and 25th and 75th percentiles of the data. Whiskers represent the highest and lowest values within 1.5 times the interquartile range. c, Comparison of the number of PD-L1 3′ UTR altered cases between hypermutated and non-hypermutated cases. d, Comparison of the number of FGFR2 3′ UTR altered cases between European and East Asian ancestry cases. The vertical axis indicates the percentage of cases. P values were calculated using two-sided Fisher’s exact tests. ex, exon.
CDH1 mutations in gastric cancer a, Schematic diagram representing the structural domains of CDH1 and the distribution of CDH1 mutations. Top: somatic CDH1 mutations in 1,155 gastric cancers (this study). Middle: germline CDH1 mutations in hereditary diffuse gastric cancer⁴⁶. Bottom: somatic CDH1 mutations in breast cancer (COSMIC V.91.). Sixteen exons and their boundaries are shown as orchid-colored numbers and dashed lines. b, Distribution of non-protein truncating mutations in human CDH1 protein modeled using the mouse CDH1 (PDB3q2v) (ref. ⁵¹) as a template. EC1-EC5 (D155-G690) of the CDH1 protein structural model is shown as a ribbon model. Deleted portions are colored in blue (caused by splicing alteration) and light blue (caused by genomic deletion). Insertion positions are in red (caused by splicing alteration) and hot pink (caused by genomic insertion). If two or more indels overlapped, the indel-ID of the smaller one is shown in parentheses. Somatic substitutions are shown as ball-and-stick models. Yellow indicates a hydrophobic core, orange contributes Ca²⁺ ion binding, and green indicates a dimerization interface. See Extended Data Fig. 6a, Supplementary Tables 17 and 25 and Supplementary information for the interaction details. Trans- and cis-dimerization interfaces52,53 are enlarged in the side panels. The dimerization partner is denoted using pale green. c, Summary diagram for the number of non-protein truncating mutations detected in each functional region. Four functional regions, where the mutations were significantly concentrated, are shown with the number of mutations in bold font. The definition of each functional region is described in the legend to Extended Data Fig. 6a.
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Multiancestry genomic and transcriptomic analysis of gastric cancer
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Gastric cancer is among the most common malignancies worldwide, characterized by geographical, epidemiological and histological heterogeneity. Here, we report an extensive, multiancestral landscape of driver events in gastric cancer, involving 1,335 cases. Seventy-seven significantly mutated genes (SMGs) were identified, including ARHGAP5 and TRIM49C. We also identified subtype-specific drivers, including PIGR and SOX9, which were enriched in the diffuse subtype of the disease. SMGs also varied according to Epstein–Barr virus infection status and ancestry. Non-protein-truncating CDH1 mutations, which are characterized by in-frame splicing alterations, targeted localized extracellular domains and uniquely occurred in sporadic diffuse-type cases. In patients with gastric cancer with East Asian ancestry, our data suggested a link between alcohol consumption or metabolism and the development of RHOA mutations. Moreover, mutations with potential roles in immune evasion were identified. Overall, these data provide comprehensive insights into the molecular landscape of gastric cancer across various subtypes and ancestries.
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Single-cell transcriptomics analysis of human breast tissues from BRCA1+/mut and noncarriers a, Schematic depiction of single-cell analysis workflow using human breast tissue samples that are mechanically and enzymatically dissociated into single-cell suspensions that are subjected to FACS to isolate stromal (EpCAM⁻) and epithelial (EpCAM⁺) cells for scRNA-seq analysis. b, Integrated clustering analysis of n = 11 noncarrier and n = 11 BRCA1+/mut scRNA-seq dataset in UMAP projection showing the main identified cell types. c, Top ten marker gene heatmap for each cell type identified by scRNA-seq analysis (rows = genes, columns = cells). The corresponding cell types are indicated with letter abbreviations as follows: basal epithelial cells (B), luminal 1 (L1) and luminal 2 (L2) epithelial cells, fibroblasts (F), pericytes (P), endothelial cells (E), lymphatic cells (L) and immune cells (I). Source data
Increased proliferation and accumulation of a luminal epithelial progenitor subset with altered differentiation in BRCA1+/mut breast tissues a, Unbiased clustering using UMAP projection of all patient epithelial cells. Cells are labeled by mammary epithelial cell state classification as indicated. b, UMAP feature plots displaying single-cell energy (scEnergy) in faceted plots for noncarrier (upper plot) and BRCA1+/mut cells (lower plot). c, scEnergy distributions are plotted as mean scEnergy values from an individual patient (expressed as mean ± s.e.m.) across basal (noncarrier basal = 0.5009 ± 0.01214, BRCA1+/mut basal = 0.5513 ± 0.01677), luminal 1 (noncarrier luminal 1 = 0.3910 ± 0.01150, BRCA1+/mut luminal 1 = 0.4420 ± 0.01590) and luminal 2 (noncarrier luminal 2 = 0.4150 ± 0.009314, BRCA1+/mut luminal 2 = 0.723 ± 0.01549) cell types from noncarrier and BRCA1+/mut samples. P values were determined by Welch’s two-sample t-test. d, Volcano plot displaying genes differentially expressed between noncarrier and BRCA1+/mut luminal 1 epithelial cells; genes greater than log2FC > 0.25 are colored. The Wilcoxon rank sum test (two sided) is used to determine differentially expressed genes; adjusted P values are determined using the Bonferroni method for multiple testing correction. e, Gene signature scoring of luminal 1 cells from noncarrier and BRCA1+/mut epithelial cells plotted as mean signature score values from individual patients (expressed as mean ± s.e.m.) for basal (noncarrier luminal 1 = 0.2685 ± 0.01195, BRCA1+/mut luminal 1 = 0.3396 ± 0.02406), myoepithelial (noncarrier luminal 1 = 0.2250 ± 0.01616, BRCA1+/mut luminal 1 = 0.3396 ± 0.02406), luminal 2-AREG (noncarrier luminal 1 = 0.3481 ± 0.01287, BRCA1+/mut luminal 1 = 0.3848 ± 0.01801) and luminal 2-MUCL1 (noncarrier luminal 1 = 0.3299 ± 0.01016, BRCA1+/mut luminal 1 = 0.3535 ± 0.01473) marker gene signatures. P values were determined by Welch’s two-sample t-test. f, In situ IF analysis of KRT14/KRT19-double positive cells of lobular and ductal regions in noncarrier and BRCA1+/mut tissues with representative images shown. Scale bar = 50 µm. Bar chart (bottom left) indicates the percentage of KRT14/KRT19-double positive cells in noncarrier (n = 6) and BRCA1+/mut (n = 6). Values are expressed as mean ± s.d. quantified from at least five random fields per patient sample. P value was determined using an unpaired two-tailed t-test. g, Single-cell Western blot (ScWB)-based quantification of KRT23-positive luminal epithelial cells isolated by FACS from noncarrier (n = 3) and BRCA1+/mut (n = 3). Images are representative regions of scWB chips post electrophoresis and antibody probing. Bar chart values are represented as mean ± s.d. from at least 1,000 cells per individual; n = 3 noncarrier, and n = 3 BRCA1+/mut. P value was determined using an unpaired two-tailed t-test. h, Bar chart shows the percentage (expressed as mean ± s.e.m.) of each patient’s noncarrier (0.3346 ± 0.01990, n = 11) and BRCA1+/mut (0.4245 ± 0.2323, n = 11) epithelial cells in S phase as identified by Seurat cell-cycle scoring analysis. P value was calculated by an unpaired two-tailed t-test. i, Representative images from IF analysis of pan-cytokeratin (PanCK, green) and PCNA (red) expression in ductal and lobular regions of noncarrier and BRCA1+/mut breast tissues. Scale bar = 50 µm. j, Bar graphs showing the average percentage (expressed as mean ± s.e.m.) of PCNA + cells in five regions each from noncarrier (n = 3) and BRCA1+/mut (n = 3) patients by in situ IF analysis of lobular (noncarrier = 25.89 ± 1.957, BRCA1+/mut = 62.11 ± 7.286) and ductal (noncarrier = 29.40 ± 2.812, BRCA1+/mut = 54.23 ± 4.366) areas. P values were determined by unpaired two-tailed t-test.
Receptor–ligand interaction analysis reveals increased stromal cell-induced epithelial proliferation in BRCA1+/mut breast tissue a, Circos plots showing ligand–receptor interactions enhanced in BRCA1+/mut tissues with ligands expressed by pericytes (left), fibroblasts (center) and endothelial cells (right) and receptors expressed by the three epithelial cell types basal, luminal 1 and luminal 2. b, Enrichment scores of GO terms (GO-Biological Processes 2018) of BRCA1+/mut pericyte (left), fibroblast (center) and endothelial (right) ligands and epithelial receptors are shown. c, Representative FACS plots showing gating for NGFR⁺ basal (top: gated on EpCAM⁺, CD49f-high) and luminal cells (bottom: gated on EpCAM-high, CD49f⁻). d, Mammosphere assay using FACS-isolated primary human NGFR⁺ basal epithelial cells grown in the presence of recombinant NGF (100 ng ml⁻¹) compared to untreated Ctrl. Representative images are shown on the left, and bar charts showing the number (left) and size (right) of mammospheres in each condition. Data are presented as the mean ± s.d.; each point represents one matrigel mammosphere culture (n = 3 Ctrl, n = 3 NGF). P values were determined by unpaired two-tailed t-tests. Scale bars = 50 μm. e, Mammosphere assay using FACS-isolated primary human luminal epithelial cells grown in the presence of recombinant NGF (100 ng ml⁻¹) compared to untreated Ctrl. Representative images are shown on the left, and bar charts showing the number (left) and size (right) of mammospheres in each condition. Data are presented as the mean ± s.d.; each point represents one matrigel mammosphere culture (n = 3 Ctrl, n = 3 NGF). P values were determined by unpaired two-tailed t-tests. Scale bars = 50 μm. Ctrl, control cells.
Expansion of CAF-like, MMP3-expressing fibroblasts in premalignant BRCA1+/mut breast tissues a, UMAP projection of cell density in noncarrier and BRCA1+/mut fibroblasts. b, Volcano plot with all differentially expressed genes between noncarrier and BRCA1+/mut fibroblasts. The Wilcoxon rank sum test is used to determine differentially expressed genes. Adjusted P values are determined using the Bonferroni method for multiple testing correction. Top 50 BRCA1+/mut genes were used to define pre-CAF signature. Top 50 noncarrier genes were used to define noncarrier fibroblast signature. c, Gene signature scoring of all noncarrier (n = 9 patients) and BRCA1+/mut (n = 9 patients) fibroblasts CAF and iCAF signatures. Each point represents the average score from one patient’s fibroblasts. Data are presented as the mean ± s.d. P values were determined by Welch’s two-sample t-tests. Patient scRNA-seq libraries with less than ~250 fibroblasts (<10% of mean number of fibroblasts) were excluded. d, Pre-CAF gene signature scoring in fibroblasts from individual patients. Patient scRNA-seq libraries with less than ~250 fibroblasts (<10% of mean number of fibroblasts) were excluded. Boxplots indicate median and 25 and 75% quantiles, respectively; minima and maxima represent the 10th and 90th percentile, respectively. P value was determined by Welch two-sample t-test comparing mean pre-CAF signature scores between noncarrier and BRCA1+/mut patients. e, Bar chart of the average MMP3 expression in noncarrier (n = 9 patients) and BRCA1+/mut (n = 9 patients) fibroblasts. Each point represents the average score from one patient’s fibroblasts, data are presented as the mean ± s.d. P values were determined by Welch’s two-sample t-test. f, Representative images of in situ IF analysis of MMP3 (red) and PanCK (green) expression in lobular and ductal regions of breast epithelium from noncarrier and BRCA1+/mut human tissue sections. Scale bar = 50 µm. DAPI signal is shown in blue. g, Percentages of MMP3-positive stromal cells in noncarrier (blue) and BRCA1+/mut (red) samples as manually quantified from IF images. PanCK-positive epithelial cells were excluded from counts. Values are expressed as mean ± s.d. quantified from at least five random fields per patient sample. P value was determined using an unpaired two-tailed t-test.
MMP3-expressing pre-CAFs promote breast epithelial proliferation and altered differentiation in primary human cocultures in vitro a, Schematic depicting experimental set-up for sphere assays of primary human 3D coculture using FACS-isolated epithelial cells and lentivirally transduced fibroblasts. b, Representative images depicting green fluorescent protein (GFP) expression in transduced fibroblasts in close proximity to epithelial organoids (arrow). Scale bar = 100 μm. c, Cocultures (5 d) of 4,000 breast epithelial cells seeded alone (no fibroblasts) or with 1 × 10⁵ noncarrier fibroblasts transduced with lentivirus (+GFP) or transduced to express MMP3 and GFP (+MMP3). Western blots show increased expression of MMP3 in cells and cultured supernatant of +MMP3 fibroblasts. Representative merged bright-field and GFP images of cocultures (scale bar = 400 μm) with arrows indicating epithelial mammospheres (GFP-negative). Bar charts (right) represent the number of mammospheres per well; values expressed as mean ± s.d. from three separate experiments with three triplicate wells from each. P values were determined using unpaired two-tailed t-tests. No fibroblasts versus +GFP, P = 0.0004; no fibroblasts versus +MMP3, P = 0.0005. d, Cocultures (5 d) of 4,000 breast epithelial cells seeded alone (No fibroblasts) or with 1 × 10⁵BRCA1+/mut fibroblasts transduced with lentivirus to express CRISPR–Cas9 and MMP3 gRNA (−MMP3) and GFP or GFP only (+GFP) vectors. Western blots show decreased expression of MMP3 in cells and medium from MMP3-deficient fibroblast cultures. Representative overlay bright-field and GFP images of cocultures (scale bar = 400 μm) with arrows indicating mammospheres (GFP-negative). Bar charts (right) represent the number of mammospheres per well; values expressed as mean ± s.d. from triplicates of three independent experiments. P values were determined using unpaired two-tailed t-tests. e, 1 × 10⁵ FACS-isolated epithelial cells from patient sample ‘noncarrier 36’ were seeded in Matrigel and treated with 0.5 µg ml⁻¹ or 1 μg ml⁻¹ recombinant MMP3 and spheres were counted after 5 d and 10 d. Representative bright-field images of mammospheres after 10 d of culture (scale bar = 400 μm). Bar chart depicts mean ± s.d. from triplicates of three independent experiments. P values were determined using unpaired two-tailed t-tests. f, 1 × 10⁴ primary breast epithelial cells were seeded and cultured in Matrigel for 10 d with or without human recombinant MMP3. After 10 d, mammospheres were collected and subjected to IF staining for basal (KRT14; green) and luminal (KRT19; red) markers. Representative fluorescence images of mammospheres are shown. Scale bar = 50 µm. Bar chart shows the percentage of KRT14/KRT19-double positive cells. Data are presented as mean ± s.d. from four different patient epithelial cell donors per group (n = 4), with five random fields quantified per sample. P values were determined by unpaired two-tailed t-tests. g, IF staining for Cyclin D1 (red) of organoids with and without exogenous MMP3 after 10 d of mammosphere culture. DAPI staining is shown in blue. Representative fluorescence images of mammospheres are shown. Scale bar = 50 µm. Bar chart shows the percentage of Cyclin D1-positive cells. Data are presented as mean ± s.d. from four different patient epithelial cell donors per group (n = 4), with five random fields quantified per sample. P values were determined by unpaired two-tailed t-tests. h, IF staining for c-Myc (red) of organoids with and without exogenous MMP3 after 10 d of mammosphere culture. DAPI staining is shown in blue. Representative fluorescence images of mammospheres are shown. Scale bar = 50 µm. Bar chart shows the percentage of c-Myc-positive cells. Data are presented as mean ± s.d. from four different patient epithelial cell donors per group (n = 5), with five random fields quantified per sample. P values were determined by unpaired two-tailed t-tests.
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Preneoplastic stromal cells promote BRCA1-mediated breast tumorigenesis
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Women with germline BRCA1 mutations (BRCA1+/mut) have increased risk for hereditary breast cancer. Cancer initiation in BRCA1+/mut is associated with premalignant changes in breast epithelium; however, the role of the epithelium-associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here we show that the premalignant stromal niche promotes epithelial proliferation and mutant BRCA1-driven tumorigenesis in trans. Using single-cell RNA sequencing analysis of human preneoplastic BRCA1+/mut and noncarrier breast tissues, we show distinct changes in epithelial homeostasis including increased proliferation and expansion of basal-luminal intermediate progenitor cells. Additionally, BRCA1+/mut stromal cells show increased expression of pro-proliferative paracrine signals. In particular, we identify pre-cancer-associated fibroblasts (pre-CAFs) that produce protumorigenic factors including matrix metalloproteinase 3 (MMP3), which promotes BRCA1-driven tumorigenesis in vivo. Together, our findings demonstrate that precancerous stroma in BRCA1+/mut may elevate breast cancer risk through the promotion of epithelial proliferation and an accumulation of luminal progenitor cells with altered differentiation.
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Identifying individuals at extreme risk of venous thromboembolism using polygenic risk scores
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Current risk assessment and treatment strategies for venous thromboembolism (VTE) consider genetic factors only in a limited way. New work shows a more pervasive role of common variants in VTE risk, inspiring genetic predictors that surpass and complement individual clinical risk factors and monogenic thrombophilia testing.
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ATPase activity-competent mSWI/SNF complexes are essential for SARS-CoV-2 infection a, Schematic of the three mSWI/SNF family complexes, cBAF, PBAF and ncBAF, with subunits colored according to scores in the Vero E6 SARS-CoV-2 CRISPR–Cas9 screen. The average proviral z-scores for each complex are shown. Complex-specific scores represent the sum of two complex-specific subunits, one core subunit and one reader subunit. b, Bar graph depicting the percentage of mNeonGreen-expressing Vero E6 cells (control cells or those with polyclonal CRISPR-mediated knockout of shared or unique mSWI/SNF subunits or ACE2) after infection by icSARS-CoV-2-mNG at an MOI of 1. c, Immunoblot performed in SMARCA4 knockout Vero E6 cells reconstituted with empty vector, WT SMARCA4 or SMARCA4 ATPase-dead mutant (K785R). d, SMARCA4 knockout-complemented and WT Vero E6 cells were infected with icSARS-CoV-2-mNG at an MOI of 1. Infected cells were imaged via fluorescence microscopy (left); mNeonGreen-expressing cell frequency was measured 2 d after infection (right). e, SMARCA4 knockout-complemented and WT Vero E6 cells were infected with SARS-CoV-2 at an MOI of 0.1. Virus titer was measured by plaque assay. PFU, plaque-forming unit. f, SMARCA4 knockout-complemented and WT Vero E6 cells were infected with SARS-CoV-2 (left), HKU5-SARS-CoV-1-S (middle) and MERS-CoV (right) at an MOI of 0.2. Cell viability relative to a mock-infected control was measured 3 d after infection with CellTiter-Glo (CTG). RLUs, relative light units. g, SMARCA4 knockout-complemented and WT Vero E6 cells were infected with VSV pseudovirus (VSVpp): VSVpp-VSV-G; VSVpp-SARS-CoV-2-S (left), VSVpp-SARS-CoV-1-S (middle) and VSVpp-MERS-CoV-S (right). Luciferase relative to the VSVpp-VSV-G control was measured 1 d after infection. Data in b and d–g were analyzed by one-way ANOVA with Tukey’s multiple comparison test. The mean ± s.e.m. are shown. **P < 0.01, ***P < 0.001, NS, not significant. n = 3 biological replicates. Data in c are representative one of three independent experiments. Source data
Top-ranked sites of ATPase-active BAF complex occupancy and DNA accessibility include ACE2 a, Immunoblot performed on nuclear extract (input) and anti-V5 immunoprecipitates from SMARCA4 knockout Vero E6 cells expressing either empty vector, WT V5-SMARCA4 or V5-SMARCA4 K785R. b, Heatmap and clustering analysis performed on the merged BAF complex (SMARCA4, SMARCC1 and ARID1A), H3K27ac occupancies (n = 1) and ATAC–seq (n = 2 biological replicates) peaks performed in Vero E6 cells rescued with the conditions shown in a, grouped into three clusters. c, Metaplots of SMARCA4 occupancy (C&T) and ATAC–seq peaks at WT SMARCA4-dependent sites (cluster 3). d, Distance to TSS distribution of C&T and ATAC–seq merged peaks for all conditions across clusters 1–3 from b. e, Cumulative distribution function plot reflecting genes nearest to SMARCC1 gained sites in SMARCA4 knockout cells rescued with WT SMARCA4 versus empty vector in cluster 3 from b; the top one-tenth fraction reflects genes associated with the top changed sites; sites highlighted in red indicate genes that scored as proviral determinants in the CRISPR screen. f, ATAC–seq and C&T tracks at the ACE2 locus in SMARCA4 knockout Vero E6 cells rescued with empty vector, WT SMARCA4 or SMARCA4 K785R. g, Reads per kilobase of transcript per million reads mapped (RPKM) levels for ACE2 in Vero E6 cells across the conditions shown (n = 2 biological replicates). The P values shown were calculated in edgeR using a quasi-likelihood negative binomial test. h, Volcano plots reflecting gene expression changes (RNA-seq) (n = 2 biological replicates) between the conditions shown. i, Overexpression of hACE2 in SMARCA4 knockout Vero E6 cells. j, VSVpp-based pseudovirus entry assay and plaque assays in WT Vero E6 cells and SMARCA4 knockout cells rescued with human ACE2. Data in a and i are representative of one of three independent experiments. Data in j were analyzed using a one-way ANOVA with Tukey’s multiple comparisons test. The mean ± s.e.m. are shown. **P < 0.01, ***P < 0.001, n = 3 biological replicates. The dashed line indicates limit of detection. Source data
HNF1A–BAF complex binding cooperates with high motif density at the ACE2 locus to regulate ACE2 expression a, Transcription factor motif enrichment analysis at BAF-gained sites (cluster 3). b, Transcription factor motif enrichment at BAF-occupied gained sites after rescue of Vero E6 SMARCA4 knockout cells with WT SMARCA4 plotted against log2 fold change of the transcript levels of the transcription factors (empty vector versus WT SMARCA4 conditions). HNF1A and HNF1B are circled in red. c, Immunoblot of HNF1A/B across WT Vero E6 and SMARCA4 knockout cells rescued with empty vector, WT SMARCA4 or SMARCA4 K785R. d, ACE2 expression in HNF1A and HNF1B knockout Vero E6 cells measured by RT–qPCR (left) and immunoblot (right). e, WT and HNF1A/B knockout Vero E6 cells were infected with icSARS-CoV-2-mNG at an MOI of 1. The frequency of infected cells was measured using mNeonGreen expression 2 d after infection. f, Vero E6 cells were infected with SARS-CoV-2 at an MOI of 0.1. Virus production was measured by plaque assays. g, HNF1A and HNF1B knockout Vero E6 cells were infected with SARS-CoV-2 pseudovirus. Luciferase relative to VSVpp-VSV-G control was measured 1 d after infection. h, Coimmunoprecipitation of endogenous SMARCA4 and HNF1A in nuclear extracts isolated from Vero E6 cells. i, HNF1 dimerization and association studies in WT and SMARCA2/4 double-knockout HEK 293T cells. j, Heatmap of SMARCA4 and SMARCC1 merged C&T (n = 1) and ATAC–seq (n = 2) peaks in control and HNF1A knockout Vero E6 cells, divided into three clusters. k, Bar graph depicting the fraction of sites with an HNF1 motif near cluster A (lost sites), cluster B (gained sites) and cluster C (unchanged sites) from j. l, Normalized gene rank of genes closest to cluster A sites plotted against the number of HNF1 motifs per gene at cluster A sites; selected genes within the top 10% of sites regulated by HNF1A are shown. m, C&T and ATAC–seq tracks at the ACE2 locus in control and HNF1A knockout Vero E6 cells. The data in c, d, h and i are representative of one of three independent experiments. The data in d–g were analyzed using a one-way ANOVA with Tukey’s multiple comparisons test. The mean ± s.e.m. are shown. ***P < 0.001, n = 3 biological replicates. Source data
Small-molecule inhibition of the SMARCA4 ATPase of mSWI/SNF complexes downregulates ACE2 expression and blocks SARS-CoV-2 infection a, Top: chemical structures of the mSWI/SNF SMARCA4/2 ATPase inhibitors, Comp12 and Comp14. Bottom: three-dimensional structure highlighting Comp12 docked in the ATPase site of the SMARCA2/4 ATPase subunit (Protein Data Bank ID: 6EG2). b, Vero E6 cells were treated with 1.25 μM Comp12 for the indicated times. ACE2 mRNA and protein levels were measured using RT–qPCR and immunoblot, respectively. c, Vero E6 cells were pretreated with Comp12 inhibitors for 2 d and then infected with SARS-CoV-2 at an MOI of 0.2. Cell viability was measured 3 d after infection. d, Vero E6 cells were pretreated with 1.25 and 2.5 μM Comp12 for 2 d and then infected with SARS-CoV-2 pseudovirus. The ACE2 antibody was preincubated with cells for 1 h before infection as a positive control. Luciferase relative to VSVpp-VSV-G control was measured 1 d after infection. e, SARS-CoV-2 production in Comp12 pretreated Vero E6 cells with the indicated concentrations of Comp12 for 2 d. f, ACE2 transcript and protein levels in Comp12-treated Vero E6, Huh7.5 and Calu-3 cells for 2 d at 1.25 and 2.5 μM. g, ACE2 transcript and protein levels in inhibitor and degrader-treated Vero and Huh7.5 cells for 2 d at 1.25 and 2.5 μM. h, Vero E6 and Huh7.5 cells were pretreated with the indicated inhibitors and/or degraders at 2.5 μM for 2 d and then infected with icSARS-CoV-2-mNG. The frequency of infected cells was measured by mNeonGreen expression. i, Vero E6 cells were pretreated with 2.5 μΜ Comp12 for 2 d and then infected with the indicated SARS-CoV-2 variants at an MOI of 0.2. Cell viability was measured 3 d after infection. j, Vero E6 or Calu-3 cells were pretreated with 2.5 μΜ of Comp12 for 2 d and then infected with the SARS-CoV-2 WA1 and E802D viruses. Virus production was measured by plaque assays 1 d after infection. Data in b–j were analyzed using a one-way ANOVA with Tukey’s multiple comparisons test. The mean ± s.e.m. are shown. *P < 0.05, **P < 0.01, ***P < 0.001, n = 3 biological replicates. Source data
SMARCA4 is required for ACE2 expression and sarbecovirus susceptibility in primary human cells a, Schematic of SMARCA4/2 ATPase inhibitor treatment and virus infection in primary HBECs. b–e, HBECs were pretreated with 2.5 and 5 μΜ Comp12 for 4 d and then infected with SARS-CoV-2 (b), HKU5-SARS1-S (c), MERS-CoV (d) and IAV (e). Virus replication was measured by plaque assay and/or RT–qPCR. f, HBECs were pretreated with 2.5 μΜ Comp12 for 4 d and then infected with SARS-CoV-2 WA1 or E802D virus at an MOI of 0.5. The virus titer was measured using plaque assay. Remdesivir was added right after infection. g, ACE2 expression was measured using RT–qPCR; SARS-CoV-2 replication was measured using a plaque assay after virus infection in HBECs pretreated with 2.5 μΜ of the indicated compounds for 4 d. h, hPSC-derived pneumocyte-like cells were pretreated with Comp12 for 2 d and then infected with SARS-CoV-2 at an MOI of 0.1. i,j, Infectivity was measured by the accumulation of viral nucleoprotein in the nucleus of the cells 2 d after infection. ACE2 expression and SARS-CoV-2 infection were measured in HIEs (i) and MIEs (j) pretreated with 2.5 μΜ of the indicated compounds for 3 d except remdesivir, which was added right after virus infection. k, Model depicting the mechanism of mSWI/SNF complex-mediated regulation of ACE2 expression and SARS-CoV-2 entry. In b, c, d, f, g and i, the dashed line indicates the limit of detection. Data in b–j were analyzed using a one-way ANOVA with Tukey’s multiple comparisons test. The mean ± s.e.m. are shown. **P < 0.01, ***P < 0.001, ****P < 0.0001, n = 3 biological replicates. Source data
Pharmacological disruption of mSWI/SNF complex activity restricts SARS-CoV-2 infection
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Identification of host determinants of coronavirus infection informs mechanisms of viral pathogenesis and can provide new drug targets. Here we demonstrate that mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) chromatin remodeling complexes, specifically canonical BRG1/BRM-associated factor (cBAF) complexes, promote severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and represent host-directed therapeutic targets. The catalytic activity of SMARCA4 is required for mSWI/SNF-driven chromatin accessibility at the ACE2 locus, ACE2 expression and virus susceptibility. The transcription factors HNF1A/B interact with and recruit mSWI/SNF complexes to ACE2 enhancers, which contain high HNF1A motif density. Notably, small-molecule mSWI/SNF ATPase inhibitors or degraders abrogate angiotensin-converting enzyme 2 (ACE2) expression and confer resistance to SARS-CoV-2 variants and a remdesivir-resistant virus in three cell lines and three primary human cell types, including airway epithelial cells, by up to 5 logs. These data highlight the role of mSWI/SNF complex activities in conferring SARS-CoV-2 susceptibility and identify a potential class of broad-acting antivirals to combat emerging coronaviruses and drug-resistant variants.
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Overview of immune selection calculation using SOPRANO a, Estimates can be performed at the cohort or at the single individual level. b, In each case, it is possible to estimate immune selection on a single HLA allele (that is, HLA-A0201), a generic combination of HLA alleles (proto-HLA) or the germline-specific HLA-immunopeptidome. c, Immune selection determines the evolutionary trajectories of clonal growth; fully immune-edited tumors with strong immune selection signals can transit towards fully immune-escaped tumors where signals are absent. d, Toy model of mixing immune-edited and immune-escaped tumors. It is possible to estimate immune dN/dS by aggregating all mutations and generate a single cohort estimate or to estimate a distribution of values per patient. In both cases, we hypothesize that mixing escaped with edited tumors leads to loss of immune selection signals reflected by immune dN/dS values closer to one (depicted as red dashed lines in the figure).
Immune dN/dS landscape across multiple tumor types a,b, Immune dN/dS (ON-dN/dS/OFF-dN/dS ratio) in multiple tumor types using either a curated HLA-A0201 based immunopeptidome (a) or a proto-HLA consisting of the most common HLA haplotypes in the population (b). Error bars indicate 95% confidence interval to the point estimate obtained with SOPRANO and number of samples for each tumor type is described in Supplementary Table 1. c, Proto-HLA immune dN/dS from SOPRANO versus proto-HLA normalized HBMR reported previously²⁸. d,e, Immune dN/dS on HLA-A0201 (d) and HBMR on the proto-HLA versus median CD8 T cell infiltration, including microsatellite-unstable (MSI)-rich tumors (e). f,g, Immune dN/dS on HLA-A0201 (f) and HBMR on the proto-HLA versus median CD8 T cell infiltration, excluding MSI-rich tumors (g). We assumed that MSI-rich tumors are also escape-rich tumors. P values and correlation coefficients were calculated using Pearson’s correlation (two-sided t-test). Gray shaded areas represent error bands indicating the 95% confidence interval. Red dashed lines indicate neutral dN/dS at one. h, log2 dN/dS versus -log10(P value) of selected escape genes (Supplementary Table 3) using missense and truncating mutations. i, Linear mixed model using dN/dS as the dependent variables and all immune metrics as independent variables. x axis shows the B coefficients. Model selection using the AIC revealed that immune dN/dS is strongly associated to the median abundance of CD8 T cells. No immune subpopulation was significantly associated to OFF-dN/dS. For ON-dN/dS, adjusted R² = 0.927, F-statistic = 18.8 on 10 and 4 degrees of freedom, P = 0.00617. For OFF-dN/dS, adjusted R² = 0.898, F-statistic = 13.3 on 10 and 4 degrees of freedom, P = 0.0117 (significance codes are described as ‘***’ for P < 0.001, ‘**’ P < 0.01, ‘*’ P < 0.05, and NS, for not significant P > 0.05). APC, antigen presenting cell; FDR, false discovery rate; pDC, plasma dendritic cell. R indicates Pearson r correlation coefficient.
Immune dN/dS analysis of a curated set of individuals from three tumor types Analysis of a curated set of individuals from three MSI-rich tumor types. Patient-specific analysis of primary untreated colorectal (CRC), stomach (STAD) and uterine/endometrial cancer (UCEC) (n = 879) with annotated escape mechanisms obtained from Lakatos et al.³². a,b, TMB (a) and immune dN/dS (b) for different subtypes of cancers, including MSS escaped− (MSS−, n = 130) and escaped+ (MSS+, n = 144), microsatellite-unstable escaped− (MSI−, n = 53) and escaped+ (MSI+, n = 125) and POLE mutants (n = 38). c, Immune dN/dS versus TMB for immune-escaped and immune-edited MSS groups using all mutations (MSS− n = 107, MSS + n = 133). d, Immune dN/dS comparison between escaped− and escaped+ tumors using clonal (MSS−, n = 93, MSS+, n = 94) or all mutations (MSS−, n = 130, MSS+, n = 144). e, Immune dN/dS comparison between clonal versus all mutations in escaped− and escaped+ MSS tumors. Reported P values from paired two-samples two-sided Wilcoxon signed rank test after multiple test correction using Holm method. f, Relationship between immune dN/dS and the reported CD8 T cell infiltration for escaped− and escaped+ in MSS and MSI tumors. Boxplots represent the median, 25th percentile and 75th percentile, and whiskers correspond to 1.5 times the interquartile range. CRC, circle; STAD, triangle; UCEC, square. For two-sample comparisons, P values were calculated using a nonparametric two-sided Mann–Whitney U test. For linear correlations, P values and coefficients were calculated using Pearson’s correlation (two-sided t-test). Red dashed lines indicate immune neutral dN/dS = 1.
Analysis of the Hartwig Medical Foundation metastatic cohort under immunotherapy a, A total of 308 patients with clinical response to immunotherapy based on RECIST1.1, including responders (R) and nonresponders (NR). Patients were primarily treated with ipilimumab (Ipi), nivolumab (Nivo), pembrolizumab (Pembro) or a combination of ipilimumab plus nivolumab (IPI/nivo). b, Cohort immune dN/dS for responders (n = 79) and non-responders (n = 229) using a common HLA-A0201 immunopeptidome reveal immune dN/dS lower than 1 for nonresponders consistent with a low overall tumor antigenicity. c, Comparison of individual immune dN/dS for responders (median = 1.05, n = 67) and nonresponders (median = 0.77, n = 154) using patient-specific HLA immunopeptidomes (P = 0.034). d, Proportion of escaped+ (NR n = 81, R n = 48) and escaped− (NR n = 148, R n = 31) tumors classified by clinical response. Responders were enriched in genetic escape mechanisms (χ²P = 8 × 10⁻⁵). e, TMB for escaped (NR+ n = 69, R+ n = 43) and escaped− tumors (NR− n = 85, R− n = 43) classified by response status show that escaped+ tumors have significantly more mutations than immune-edited tumors. f, Patient-specific immune dN/dS revealed a significant depletion of nonsynonymous mutations only in the immunopeptidome of escaped− nonresponders (NR−, median = 0.69, P = 0.0012). One-sample one-sided Wilcoxon signed rank test with mu = 1 (NR− n = 85, NR+ n = 69, R− n = 24, R+ n = 43). Boxplots represent the median, 25th percentile and 75th percentile, and whiskers correspond to 1.5 times the interquartile range. g, Cohort dN/dS for driver genes (196 genes from Martincorena et al.¹⁴), escape genes (Supplementary Table 3), all the exome (global dN/dS) and the immunopeptidome (immune dN/dS). All error bars include the point estimate plus the 95% confidence interval calculated using SOPRANO package. h, Kaplan–Meier curves of overall survival for responders and nonresponders grouped by escape status (log rank P = 1.4 × 10⁻⁶). i, Kaplan–Meier curves of overall survival for individuals classified based on immune dN/dS and escape status (−, escaped−; +, escaped+) (log rank P = 0.0025). Red dashed lines indicate immune-neutral dN/dS = 1.
Impact of immune dN/dS on outcome for ICI therapy in a metastatic melanoma cohort a, A clinically annotated cohort of 48 patients with sequencing data before (Pre) and after (On) receiving ICIs was obtained from Riaz et al.³⁹. b, dN/dS distributions for nonresponders (n = 36) and responders (n = 12) before and after therapy. We estimated OFF-dN/dS (left), ON-dN/dS (middle) and immune dN/dS (right) using each patient’s six HLA alleles. P values were calculated using two-sided Wilcoxon rank sum test and corrected using Benjamini-Hochberg. c, Mutations and their prevalence in genes classified as escaped in the Riaz cohort. Individuals were also classified according to: homozygosity status (Hom, homozygous; Het, heterozygous), somatic loss of HLA heterozygocity (N, no; Y, yes) and their immune dN/dS category (N, neutral; L, low; H, high). d, Pre-therapy immune dN/dS for escaped− and escaped+ cohorts classified as responders (R) and nonresponders (NR). Escaped− nonresponders were the only group with cohort immune dN/dS less than 1. All escaped+ tumors before therapy displayed immune dN/dS equals to one. e, Immune dN/dS distribution for randomized escaped− tumors. The point estimate for nonresponders escaped− patients was significantly lower than the mean of randomized immune dN/dS values (exact P = 0.019). f, Kaplan–Meier curves of overall survival between high-TMB and low-TMB patients. High-TMB patients had significantly longer overall survival (log-rank P = 0.031) than low-TMB individuals. g, Kaplan–Meier curves of overall survival between immune neutral (escape+ and neutral immune dN/dS) and immune edited (escape− and low-immune dN/dS). The association between overall survival and immune dN/dS was more significant than with TMB (log-rank P = 0.015). Red dashed lines indicate immune-neutral dN/dS = 1.
Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors
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In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment.
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MERVL RNA exhibits dynamic nuclear-cytoplasmic expression during early stages of mouse preimplantation development a, Schematic of mouse preimplantation development. Totipotency is restricted to early-stage development (that is, zygote and two-cell stages). Blastomeres gradually transition to a pluripotent state from the four-cell stage onward and develop into a blastocyst consisting of inner cell mass (ICM) and trophectoderm (TE) before implantation in the uterus at 4.5-days postcoitum (dpc). b,c, Violin plots showing the log2-transformed reads per kilobase of exon per million reads mapped (log2RPKM) values of MERVL-int (b) and its LTR promoter, MT2_Mm (c) during preimplantation development. Each plot encompasses box plot; central bars represent medians, box edges indicate 50% of data points and the whiskers show 90% of data points. d, Representative images of smFISH for MERVL RNA with 4,6-diamidino-2-phenylindole (DAPI) counterstain during preimplantation development, from four independent experiments. Scale bars, 20 µm. ♀, female pronucleus (PN); ♂, male PN. e, Representative images of immunofluorescence staining for MERVL-Gag protein with DAPI counterstain during preimplantation development, from six independent experiments. Scale bars, 20 µm. ♀, female PN; ♂, male PN. Data for panels in b and c are available as source data. Source data
MERVL plays a critical role in early lineage specification and maintaining genomic stability of preimplantation embryos a, Schematic of full-length MERVL, indicating the positions of ASOs. b, Representative images of smFISH for MERVL RNA (green) with DAPI counterstain (gray) in control and MERVL-KD embryos at early and late two-cell stages, from six independent experiments (top). Representative images of immunofluorescence staining for MERVL-Gag protein (pink) with DAPI counterstain (gray) in control and MERVL-KD embryos at late two-cell stage, from three independent experiments (bottom). Scale bars, 20 µm. c, Representative phase-contrast images of 4.5 dpc blastocysts in control and MERVL-KD, from four independent experiments. Scale bars, 100 µm. d, Percentage of embryos by stages of development in control (n = 84) and MERVL-KD (n = 104). NS, not significant; **P < 0.01; ***P < 0.001, chi-square test. e, Expressions of ICM- and TE-associated genes, as measured by qRT-PCR in control and MERVL-KD morula at 3.5 dpc. Bars show means with standard error of the mean (s.e.m.). Dots represent biological replicates (n = 6). *P < 0.05, **P < 0.01, ***P < 0.001, two-tailed unpaired t-tests. f, Representative images of immunofluorescence staining for OCT4 and CDX2 with DAPI counterstain in control and MERVL-KD morula at 3.5 dpc, from three independent experiments. Scale bars, 20 µm. g,h, Dot plots showing the total number of blastomere per embryo and relative intensity for OCT4 and CDX2 per nucleus in control and MERVL-KD morula at 3.5 dpc, normalized to DAPI signal. Central bars represent medians, and the top and bottom lines encompass 50% of the data points. ***P < 0.001, two-tailed unpaired t-tests. i, Representative images of immunofluorescence staining for E-cadherin (E-Cad, green) with DAPI counterstain (gray) in control and MERVL-KD morula at 3.5 dpc, from two independent experiments (left). Arrows and arrowheads indicate nuclear deformation and micronuclei. Scale bars, 20 µm. Bar chart showing the percentage of abnormal nuclear morphology at morula stage (right). *P < 0.05, chi-square test. j, Representative images of immunofluorescence staining for cleaved caspase-3 (cCasp3, green) with DAPI counterstain (gray) in control and MERVL-KD morula at 3.5 dpc, from three independent experiments (left). Scale bars, 20 µm. Bar chart showing the percentage of apoptotic cells per embryo at morula stage (right). ***P < 0.001, chi-square test. Data for panels in d, e and g–j are available as source data. Source data
Retroviral proteins and trans-acting MERVL RNA are dispensable for preimplantation development a, Schematic of the distinct mechanisms underlying ASO- versus siRNA-mediated RNA targeting. b Representative images of smFISH for MERVL RNA (green) with DAPI counterstain (gray) in control and MERVL-KD embryos at early and late two-cell stages, from four independent experiments (top). Representative images of immunofluorescence staining for MERVL-Gag protein (pink) with DAPI (gray) counterstain in control and MERVL-KD embryos at late two-cell stage, from three independent experiments (bottom). Scale bars, 20 µm. c, Representative phase-contrast images of 4.5 dpc blastocysts in control and MERVL-KD by siRNA, from three independent experiments. Scale bars, 100 µm. d, Percentage of embryos by stages of development in control (n = 73) and MERVL-KD (n = 59). *P < 0.05, chi-square test. e, Schematic of experimental procedure for in trans rescuing MERVL-KD by ASOs. f, Representative images of smFISH for MERVL RNA with DAPI counterstain in early and late two-cell stage embryos in each experimental condition, from four independent experiments. Scale bars, 20 µm. g, Representative phase-contrast images of 4.5 dpc blastocysts in each experimental condition, from two independent experiments. Scale bars, 100 µm. h, Percentage of embryos by stages of development in each experimental condition (n = 78 for control, n = 80 for MERVL-KD and n = 132 for in trans rescue). *P < 0.05; **P < 0.01; ***P < 0.001, chi-square test. i, Schematic of experimental procedure for CRISPRi targeting the MERVL sequence. j, Representative images of smFISH for MERVL RNA (green) with DAPI counterstain (gray) in GFPi control (CRISPRi targeting GFP) and MERVLi (CRISPRi targeting MERVL) embryos at early and late two-cell stages, from three independent experiments (top). Representative images of immunofluorescence staining for MERVL-Gag protein (pink) with DAPI counterstain (gray) in GFPi control and MERVLi embryos at late two-cell stage, from three independent experiments (bottom). Scale bars, 20 µm. k, Representative phase-contrast images of 4.5 dpc blastocysts upon GFPi control and MERVLi condition, from 3 independent experiments. Scale bars, 100 µm. l Percentage of embryos by stages of development upon GFPi control (n = 149) and MERVLi (n = 137). ***P < 0.001, chi-square test. Data for panels in d, h and l are available as source data. Source data
Ablation of MERVL impacts the expression of a subset of two-cell genes through preimplantation development a, Schematic of timing for minor and major ZGA (adapted from Abe et al.⁶). b, Representative images of EU incorporation assay (green) with Hoechst 33342 counterstain (gray) in control and MERVL-KD two-cell stage embryos, from three independent experiments. Scale bars, 20 µm. c, The relative intensity of EU per nucleus, normalized to Hoechst 33342 signal. NS, not significant by two-tailed unpaired t-tests. d, Bidimensional principal-component (PC) analysis of gene expression profiles in control and MERVL-KD embryos through preimplantation development. e, RNA-seq differential gene expression analysis: MERVL-KD versus control embryos obtained at two-cell, four-cell and eight-cell stages; 938, 745 and 277 genes evinced significant changes in expression in MERVL-KD embryos (blue circle, fold change ≥ |2|, P value adjusted (Padj) < 0.05, binomial test with Benjamini–Hochberg correction). f, Stacked bar chart shows the number of upregulated (up)- and downregulated (down)-DEGs. The populations of maternally inherited RNA (as defined in DBTMEE v2; refs. 32,33) and others (zygotically expressed genes) are highlighted in red and blue. g, Predicted factors that are upstream of all up-DEGs upon MERVL-KD, assessed by the ChEA database³⁴. ChIP-seq, chromatin immunoprecipitation with sequencing; HaCaT, cultured human keratinocyte; MEF, mouse embryonic fibroblast; MESC, mouse embryonic stem cell; LNCaP, lymph node carcinoma of the prostate. h, Bubble plot showing overlap between all DEGs in MERVL-KD embryos with the list of two-cell (2C) genes and DBTMEE v2 transcriptome categories. The bubble plot sizes show the –log10[P values] derived from a hypergeometric test. i, Track views show RNA-seq signals in control and MERVL-KD embryos, on two representative 2C gene loci (as defined in Macfarlan et al.¹²). The y-axis represents normalized tag counts for total RNA-seq in each sample. Refgene, refseq gene in NCBI and UCSC. j, Representative images of immunofluorescence staining for phosphorylated p53 on serine 15 (p-S15-p53, pink) with DAPI counterstain (gray) in control and ASO-mediated MERVL-KD embryos, from three independent experiments. Scale bars, 20 µm. k, Dot plots showing the relative intensity for p-S15-p53 per nucleus in control and MERVL-KD embryos, normalized to DAPI signal. Central bars represent medians, the top and bottom lines encompass 50% of the data points. *P < 0.05 and ***P < 0.001, two-tailed unpaired t-tests. Data for panels in c–h and k are available as source data. Source data
MERVL-KD embryos retain two-cell-like chromatin accessibility even at mid-preimplantation stage a, Genome-wide correlation of ATAC-seq signals by stages of development between control and MERVL-KD embryos. Enrichment levels per 10-kb bin are shown in log2RPKM values. The Pearson correlation coefficient values (R) indicate the similarity between control and MERVL-KD embryos. b, Average tag density plots of ATAC-seq enrichment around MERVL-int copies (±5 kb) by stages of development in control and MERVL-KD embryos. RPM, reads per million. **Padj < 0.01, Mann–Whitney U test with Bonferroni correction. c, Average tag density plots of ATAC-seq enrichment around TSS (±1 kb) of 2C genes by stages of development in control and MERVL-KD embryos. **Padj < 0.01, Mann–Whitney U test with Bonferroni correction. d, Track view shows ATAC-seq signals in control and MERVL-KD embryos at two-cell, four-cell and eight-cell stages, on representative 2C gene locus (as defined in Macfarlan et al.¹²). The y-axis represents normalized tag counts for ATAC-seq in each sample. The flanking region around TSS is highlighted in red. e, Heatmaps showing ATAC-seq signals across all peak regions ±1 kb in control embryos at two-cell, four-cell, and eight-cell stages. Each peak was ordered by k-means clustering of ATAC-seq signal and yielded five clusters. Cluster 1–2, increased accessibility during preimplantation development (defined as category A); cluster 3–5, were transiently accessible at two-cell stage and reduced accessibility during preimplantation development (defined as category B). f, Stacked bar chart shows ATAC-seq peak distributions across genomic entities (intergenic, intron, exon and promoter) in each cluster. g, GO analysis of genes adjacent to ATAC-seq peaks of cluster 1–2 (category A) and cluster 3–5 (category B). The pluripotent-related and 2C genes were obviously enriched in gene set adjacent to category A and category B, respectively. h, Average tag density plots of ATAC-seq enrichment around peak regions (±1 kb) in each cluster by stages of development in control and MERVL-KD embryos. *Padj < 0.05, **Padj < 0.01, ***Padj < 0.001, Mann–Whitney U test with Bonferroni correction. Data for panels in a, f and g are available as source data. Source data
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Transcription of MERVL retrotransposons is required for preimplantation embryo development
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Zygotic genome activation (ZGA) is a critical postfertilization step that promotes totipotency and allows different cell fates to emerge in the developing embryo. MERVL (murine endogenous retrovirus-L) is transiently upregulated at the two-cell stage during ZGA. Although MERVL expression is widely used as a marker of totipotency, the role of this retrotransposon in mouse embryogenesis remains elusive. Here, we show that full-length MERVL transcripts, but not encoded retroviral proteins, are essential for accurate regulation of the host transcriptome and chromatin state during preimplantation development. Both knockdown and CRISPRi-based repression of MERVL result in embryonic lethality due to defects in differentiation and genomic stability. Furthermore, transcriptome and epigenome analysis revealed that loss of MERVL transcripts led to retention of an accessible chromatin state at, and aberrant expression of, a subset of two-cell-specific genes. Taken together, our results suggest a model in which an endogenous retrovirus plays a key role in regulating host cell fate potential.
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Profiling the chromatin landscape in Arabidopsis sperm and vegetative nuclei a, Schematic view of the overall workflow, including intact Ms, Spm, Veg and Sdl nuclei separation and the sequencing techniques involved in this study. b, H3K27me3 and H3K4me3 profiles in Ms, Spm, Veg and Sdl. A ~500-kb window from chromosome 1 is shown. c, Venn diagram illustrating overlaps of the identified H3K27me3 and H3K4me3 peaks in different cell types. d,e, Boxplots showing mRNA levels of H3K4me3- (d) and H3K27me3-marked (e) genes in different cell types. Sample sizes used for assaying mRNA levels of H3K4me3-marked genes: n = 14,238 (Ms), n = 10,885 (Spm). n = 9,318 (Veg), n = 10,053 (Sdl); sample sizes used for assaying mRNA levels of H3K27me3-marked genes: n = 11,012 (Ms), n = 10,141 (Spm), n = 12,921 (Veg), n = 7,082(Sdl). Boxplots show the median, with the left and right edges of the box representing the 25th and 75th percentiles, respectively; whiskers represent the most extreme values within 1.5× interquartile range of the 25th and 75th percentiles; outliers outside the whiskers are shown as dots. Statistical significance was calculated using the two-sided Wilcoxon test in the R package ggpubr, and P values are shown. IP, immunoprecipitation; RT, reverse transcription; FANS, fluorescence assisted nuclei sorting.
Prevalent chromatin bivalency in sperm nucleus a, Venn diagram illustrating overlaps between the identified H3K27me3 and H3K4me3 peaks in Spm, Veg, Ms and Sdl. Overlap P values are calculated based on simulations. For detailed statistical tests used for Venn diagrams, see Methods. b, Pie chart showing that Spm bivalent peaks can be further divided into HBGs and CBGs based on their expression values. c, Typical examples of H3K27me3 and H3K4me3 profiles for selected genes in H3K27me3 monovalent, HBGs, CBGs and H3K4me3 monovalent groups. d, Heatmap representation of expression levels of genes in c. e, Boxplot demonstration of the expression levels of genes associated with different groups of histone modifications. Sample sizes (gene numbers) used for assaying mRNA levels of different gene groups: n = 2,929 (H3K4me3 monovalent), n = 2,600 (H3K27me3 monovalent), n = 3,033 (CBG), n = 4,597 (HBG). f, Scatter plot demonstrating positive correlation between H3K27me3 and H3K4me3 levels for both HBGs and CBGs. Sample size of HBG quartiles: n = 1,159; sample size of CBG quartiles: n = 766. Boxplot shows relationships between chromatin modification levels and RNA expression levels for HBGs and CBGs. Genes within each group are divided into four quartiles according to their level of H3K27me3 and H3K4me3, respectively; the first quartile harbors the highest modification levels. e,f, Boxplots show median with the top and bottom edges of the box representing the 75th and 25th percentiles, respectively; whiskers represent the most extreme values within 1.5× interquartile range of the 25th and 75th percentiles; outliers outside the whiskers are shown as dots; statistical significance was calculated using two-sided Wilcoxon test in the R package ggpubr, and P values are shown. g, Dimensionality reduction by UMAP of scRNA-seq data from 601 isolated Spm (blue) and Veg (red).
HBGs and CBGs are associated with different functional groups, and their chromatin landscapes are generated by different routes a–f, HBG and CBG associations. a,c, Metagene plots showing H3K27me3 and H3K4me3 patterns in HBGs (a) and CBGs (c) from sperm; the corresponding genes from Sdl and Msp are also shown. HBGs and CBGs were sorted according to H3K27me3 levels in sperm, from high to low. Note that HBGs are largely free of H3K27me3 in Msp while CBGs are often marked by H3K27me3 in Sdl and Ms. b,d, H3K27me3 and H3K4me3 patterns in typical examples of HBGs (b) and CBGs (d), indicating that they are established through different routes. e,f, Enriched GO terms in 4,597 HBGs (e) and 3,033 CBGs (f) in sperm. TES, transcription end sites.
Resetting of H3K27me3 in sperm a, Heatmap demonstration of H3K27me3 patterns in somatic H3K27me3-marked genes. All genes marked by H3K27me3 in seedlings are divided into five groups according to their chromatin states in sperm. The distribution patterns of histone marks at corresponding groups in seedlings, microspore and sperm are displayed. b, Histogram showing fold change of H3K27me3 signals between seedlings and sperm. Genes with H3K27me3 peaks from which FPKM > 20 in seedlings were selected. A fold change (seedling versus sperm) of at least four is used as the threshold for defining resetting genes. c, Heatmap demonstration of H3K27me3 pattern in the 714 resetting genes in seedlings, microspore and sperm. d, Strip plot demonstration of H3K27me3 levels at the 714 resetting genes in the indicated cell types. Statistical significance was calculated using the two-sided Wilcoxon test in the R package ggpubr, and P values are shown. e, Enriched GO terms in 714 resetting genes in sperm. f, Schematic view of the different development stages (floral organ initiation and embryo development) before germline specification or after fertilization. Gene examples that are expressed and function in different developmental stages are indicated below. g, Examples of resetting genes that are expressed and reset at stages before microspore formation. Top: genome browser tracks of H3K27me3 from Sdl, Ms, Spm and Veg; bottom: corresponding expression patterns of genes from different cell types and different developmental stages. Independent biological replicates used for mRNA-seq analysis: n = 3 (flower), n = 2 (male meiocyte), n = 3 (microspore), n = 3 (Veg), n = 3 (Spm), n = 3 (preglobular), n = 3 (globular), n = 6 (heart), n = 6 (torpedo), n = 3 (bent cotyledon), n = 3 (mature green), n = 2 (seedling). h, Examples of resetting genes expressed at stages post fertilization and reset post microspore formation. Top: genome browser tracks of H3K27me3 from Sdl, Ms, Spm and Veg; bottom: corresponding expression patterns of genes from different cell types and different developmental stages. The independent biological replicates used for mRNA-seq analysis are the same as in g. g,h, Boxplots show median with the top and bottom edges of the box representing the 75th and 25th percentiles, respectively; whiskers represent the most extreme values within 1.5× interquartile range of the 25th and 75th percentiles; outliers outside the whiskers are shown as dots. i, Heatmap showing expression levels of resetting genes in sperm across different developmental stages. Except for microspore, sperm and vegetative nucleus, RNA-seq data are derived from previous studies (Methods).
H3K27me3 patterns in sperm following loss of HTR10 a, H3K27me3 profiles in different cell types, including sperm of mutant htr10 (light blue). A 680-kb window from chromosome 3 is shown. b, Venn diagram showing overlap of H3K27me3 peaks between the sperm of Col-0 and htr10. htr10-specific peaks, hrt10-Col-0 overlapped peaks and Col-0 specific peaks are labeled as groups I, II and III, respectively. Overlap of groups I and III with other groups of H3K27me3 peaks is indicated at the bottom. Overlap P values were calculated based on simulations. For details of statistical testing used, see Methods. c, Heatmap showing H3K27me3 patterns of genes corresponding to groups 1, II and III (from top to bottom) in different cell types. d, Heatmap demonstration of H3K27me3 patterns of genes marked by H3K27me3 in seedlings (RPKM > 20) in sperm of htr10 and in other cell types. e, Example genes well marked by H3K27me3 in seedlings; note that their H3K27me3 is mainly unaffected by the deletion of HTR10. f,g, Example genes associated with group I (f) and group III (g).
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Distinct chromatin signatures in the Arabidopsis male gametophyte
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Epigenetic reprogramming in the germline contributes to the erasure of epigenetic inheritance across generations in mammals but remains poorly characterized in plants. Here we profiled histone modifications throughout Arabidopsis male germline development. We find that the sperm cell has widespread apparent chromatin bivalency, which is established by the acquisition of H3K27me3 or H3K4me3 at pre-existing H3K4me3 or H3K27me3 regions, respectively. These bivalent domains are associated with a distinct transcriptional status. Somatic H3K27me3 is generally reduced in sperm, while dramatic loss of H3K27me3 is observed at only ~700 developmental genes. The incorporation of the histone variant H3.10 facilitates the establishment of sperm chromatin identity without a strong impact on resetting of somatic H3K27me3. Vegetative nuclei harbor thousands of specific H3K27me3 domains at repressed genes, while pollination-related genes are highly expressed and marked by gene body H3K4me3. Our work highlights putative chromatin bivalency and restricted resetting of H3K27me3 at developmental regulators as key features in plant pluripotent sperm.
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Ten high-quality assembled genomes and pan-genome construction in pearl millet a, The pearl millet accessions are derived from geographically representative regions (PmiG: Tift 23D2B1-P1-P5). The geographical map was adapted from the one provided by the NASA Earth Observatory (https://visibleearth.nasa.gov/images/147190/explorer-base-map/147191w/). b, Synteny plot across the ten genomes. c, Core gene clusters and pan-genome of pearl millet. The histogram illustrates the core gene clusters (present in all genomes), dispensable gene clusters (present in 2–10 genomes) and private gene clusters (present in one genome). d, Composition of gene and nucleotide diversity (pi) in core, dispensable and private genes. The center line represents the median; the box limits represent the upper and lower quartiles; the whiskers represent 1.5 times the interquartile range (IQR). Significant differences were tested by two-tailed t-test (**P < 0.01, ***P < 0.0005).
Identification of SVs a, Composition of SVs in each genome. b, Comparisons of the proportions of private (present in only one accession) and non-private SVs (present in at least two accessions). c, Numbers of SVs in the repeat and non-repeat regions of each genome. d, Numbers of SVs overlapping with different genomic features in each genome.
Expansion of the RWP-RK transcription factor family contributes to heat tolerance a, Comparison of MDA levels between the control (CK) and heat treatment (H) groups of pearl millet (Tifleaf3). The error bars indicate the mean ± s.d.; n = 3 biological replicates. Significant differences were tested by two-tailed t-test (**P < 0.01, ***P < 0.0005; NS, not significant). b, Proportion of RWP-RK transcription factor family members among all the genes in the 11 pearl millet genomes and ten other genomes. Ath, Arabidopsis thaliana; Bdi, Brachypodium distachyon; Can, Capsicum annuum; Dex, Digitaria exilis; Dol, Dichanthelium oligosanthes; Eco, Eleusine coracana; Hvu, Hordeum vulgare; Mes, Manihot esculenta; Osa, O. sativa; Oth, Oropetium thomaeum; Pgl, P. glaucum (pearl millet); Pha, Panicum hallii; Pmi, Panicum miliaceum; Sbi, Sorghum bicolor; Sit, Setaria italica; Sof, Saccharum officinarum; Svi, Setaria viridis; Zma, Z. mays. c, Comparisons of RWP-RK gene numbers overlapping with intact LTR TEs among pearl millet, rice, sorghum and maize. Significant differences were tested by one-tailed binomial test (*P < 0.05, **P < 0.01, ***P < 0.0005). d, Estimated insertion times of LTR TEs encompassing the RWP-RK genes shown in c. The center line represents the median; the box limits represent the upper and lower quartiles; the whiskers represent 1.5 times the IQR; the dots represent the outliers. e, Phenotypes of two transgenic lines and a control (WT) after 72 h of heat treatment. f, POD and SOD activity after 12 h of heat treatment and MDA content after 72 h of heat treatment in transgenic lines and WT plants. The error bars represent the mean ± s.d.; n = 3 biological replicates. Significant differences were tested using a two-tailed t-test (*P < 0.05, **P < 0.01, ***P < 0.0005). g, Dual luciferase assays (firefly luciferase to Renilla luciferase ratio) were applied to verify that PMF0G00024.1 (RWP) could transactivate PMA2G00541.1 (541) and PMA6G02031.1 (2031). The error bars represent the mean ± s.d.; n = 3 biological replicates. Significant differences were tested using a two-tailed t-test and are shown as P values.
Transcriptome analyses reveal that pearl millet responds to heat stress via ER-related pathways a, Functional enrichment of DEGs coexisting in Tifleaf3 under high temperature stress (40 °C and 35 °C) at eight time points (1–144 h; dataset A in Supplementary Table 3) and in six accessions (PI521612, PI537069, PI526529, PI583800, PI587025 and Tifleaf3) under high temperature stress (45 °C under light and 40 °C in darkness) for 1 h and 24 h (dataset B in Supplementary Table 3). b, Comparison of the proportions of upregulated ER-related and HSF genes after 1 h and 24 h of heat treatment in pearl millet, maize and rice. Heat-resistant (HR) and heat-susceptible (HS) rice samples, respectively. c, Dual luciferase assays were applied to verify that PMF0G00024.1 (RWP) could transactivate the PMA2G00107.1 (107) and PMA4G03758.1 (3758) genes. The error bars represent the mean ± s.d.; n = 3 biological replicates. Significant differences were tested using a two-tailed t-test and are shown as P values. d, Proposed activation network of pearl millet in response to growth under heat stress. After 1 h of high-temperature stress in six pearl millet accessions, many misfolded proteins activated the expression of degradation-related genes in the ER, such as genes encoding recognition proteins, including calnexin (CNX) and calreticulin (CRT), and degradation-related proteins, including heat shock proteins (HSPs), thereby correcting or degrading misfolded proteins to maintain protein homeostasis in cells. In addition, the HSF and RWP-RK genes potentially participate in this process to coregulate HSP genes.
Impact of SVs on genes and their contributions to heat tolerance in pearl millet a, Enrichment of SVs near genes with altered expression in each accession relative to PI537069. The asterisk indicates significance (q < 0.05). DEL, deletion; Dup, duplication; INS, insertion. b, Proportions of DEGs among total genes overlapping with SVs (SV genes) and those not overlapping with SVs (nSV genes) in leaf tissue under heat treatment. c, Deletion near PMA5G04793.1 with expression changes in different accessions. TPM, transcripts per million. +SV and −SV: accessions with and without SVs, respectively. The error bars indicate the mean ± s.d.; n = 3 biological replicates. d,e, Transformation of the PMA5G04793.1 promoter in tobacco leaves. d, Glucuronidase (GUS) reporter gene expression observed by histochemical staining. e, Quantitative detection of the GUS enzyme in leaves inoculated with different recombinant vectors at different time points using a microplate plate-based GUS fluorescence assay. The error bars indicate the mean ± s.d.; n = 3 biological replicates. Significant differences were tested using a two-tailed t-test (***P < 0.0005). f, Phenotypic comparison of six accessions under heat treatment for 96 h. r1–r4: Tifleaf3, PI583800, PI526529 and PI587025 (HR group). s1 and s2: PI521612 and PI537069 (HS group). The HS plants were more wilted than the HR plants. g, Three examples showing the presence of fixed SVs in the HR groups (≥3 accessions) near three heat-related protein genes. Descriptive gene models are presented below the bar charts. The error bars indicate the mean ± s.d.; n = 3 biological replicates.
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Pangenomic analysis identifies structural variation associated with heat tolerance in pearl millet
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Pearl millet is an important cereal crop worldwide and shows superior heat tolerance. Here, we developed a graph-based pan-genome by assembling ten chromosomal genomes with one existing assembly adapted to different climates worldwide and captured 424,085 genomic structural variations (SVs). Comparative genomics and transcriptomics analyses revealed the expansion of the RWP-RK transcription factor family and the involvement of endoplasmic reticulum (ER)-related genes in heat tolerance. The overexpression of one RWP-RK gene led to enhanced plant heat tolerance and transactivated ER-related genes quickly, supporting the important roles of RWP-RK transcription factors and ER system in heat tolerance. Furthermore, we found that some SVs affected the gene expression associated with heat tolerance and SVs surrounding ER-related genes shaped adaptation to heat tolerance during domestication in the population. Our study provides a comprehensive genomic resource revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.
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| Study overview. a, Discovery meta-analysis. *For signals present in more than one trait, the signal is only counted once (for the most significant trait). b, Pathway analyses, GRS analyses and PheWAS studies.
Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk
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Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies. Multi-ancestry genome-wide association analyses and systematic variant-to-gene mapping strategies implicate new genes and pathways influencing lung function and chronic obstructive pulmonary disease risk.
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| Global enrichment in 80 panel genes under strong constraint (pLI > 0.9). a, Case-control enrichment of rare (minor allele count ≤ 5) proteintruncating, missense and synonymous variants in all ancestries combined. The PGC3SEQ results were derived from 11,580 individuals with SCZ and 10,555 controls and are shown in red/orange. We conducted the same analysis in the SCHEMA samples (shown in gray; 19,108 cases and 18,001 controls) that we had access to for comparison. b, Ancestry-stratified rare variant (MAF < 0.1%) enrichment in the meta-analysis of PGC3SEQ and SCHEMA (29,381 cases and
Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations
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Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study—and most other large-scale human genetics studies—was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10−6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations. Targeted sequencing finds a higher burden of rare protein-truncating variants in constrained genes among schizophrenia cases of diverse ancestries. Meta-analyses with existing datasets show that this excess burden is consistent across five ancestral populations.
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A medley of resistance in ovarian cancers
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Multi-omic profiling of lesions at autopsy reveals a plethora of resistance mechanisms present within individual patients with ovarian cancer. This highlights the extreme challenge faced in treating end-stage disease and underscores the need for new methods of early detection and intervention.
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End-stage HGSC cohort, samples and experimental modalities a, Summary of the clinical features of the cohort. s, somatic; g, germline; PDS, primary debulking surgery; Tx, treatment. b, Sankey plot showing the samples collected for each case at autopsy (Aut), by organ site. c, Aggregated primary (Pri) and recurrent (Rec) tumor samples utilized across all cases; colored circles indicate samples at each timepoint for cases indicated on the x axis. d, Summary of the number of tumor samples that underwent each experimental modality (excludes ctDNA). *For mcIF only, samples refer to the number of TMA cores which, in some cases, are from within the same metastatic deposit. For further detail see Extended Data Fig. 5a.
Clonal composition of end-stage HGSC a, Clonal reconstructions for 13 patients (two cases where WGS did not meet quality control parameters were excluded). Top: phylogenetic tree with each clone given a unique identifier; bottom: boxes indicate the CCF of each clone by tumor sample; the shape represents the type of clone (truncal, diamond; branch, square; private, circle). b, Number of clones per sample by organ site (P = 0.13, F-ratio = 1.92, GLMM). Sites: small intestine, n = 3; mesentery, n = 3; lymph node, n = 9; liver, n = 11; chest wall, n = 3; lung, n = 3; diaphragm, n = 3. Left and right whiskers terminate at the minimum and maximum values no further than 1.5× interquartile range; center line represents median (50th percentile); left and right boundaries of box represent the first (25th percentile) and third (75th percentile) quartiles, respectively; outlying values are plotted as individual points beyond whiskers. c, Proportion of truncal, branch and private mutations in each tumor site per patient. Each bar represents a sample for that patient (case ID indicated above grouped bars).
of HGSC genomic landscape at autopsy, as assessed by WGS Top, oncoprint of somatic SNVs, INDELs, SVs and CN variants in curated genes of interest recurrently or importantly altered in relation to resistance (14 patients, excludes case 65682, n = 54 autopsy samples); right-hand bar chart indicates their frequency in the autopsy cohort. Middle, the HR gene that is mutated, TMB; SV count (colored by type of SV); and proportion of autosomal genome with major CN ≥2 (>0.5 is the threshold for WGD). Bottom, relative abundance of mutational signatures across the cohort for ovarian-specific CN signatures (s), SBS, small insertion and deletion (ID) and DBS signatures.
Known resistance mechanisms in HR-deficient HGSC a, High-/moderate-confidence reversion mutations detected in seven cases; only sites with reversions are shown. If a reversion was called for that case (that is, in another sample) it is depicted at other sites regardless of frequency, as per reversion calling (Methods). The variant allele frequencies of reversion mutations at each tumor site are shown on the pie charts; reversions at ≤3% were rounded up to enable pragmatic visualization (colors indicate different reversions in the patient, with gray denoting the proportion of reads without a reversion mutation). ctDNA samples are indicated by blood tube, and ascites by drainage bag schematic. Total samples per patient (all timepoints): case 15292, n = 19; case 15295, n = 7 + 1 ctDNA; case 15306, n = 12; case 65914, n = 17 + 1 ctDNA; case 65629, n = 19; case 65913, n = 19 + 1 ctDNA; case 65938, n = 25 + 1 ctDNA. b, Left, TP53BP1 loss by two mechanisms (yellow SV in WGS, blue indel in TS) in case 66462; right, boxplots of TP53BP1 protein abundance across all cases. Points colored by timepoint; case 66462 highlighted in orange. Left and right whiskers terminate at minimum and maximum values no further than 1.5× interquartile range; center line represents median (50th percentile); left and right boundaries of box represent first (25th percentile) and third (75th percentile) quartiles, respectively; outlying values plotted as individual points beyond whiskers. n = 102 tumor samples (all timepoints) from 15 patients; number of samples per patient labeled on each respective boxplot.
Multisite and multitemporal immune profiling of HR-deficient HGSC a, Comparison of mcIF immune cell percentages in tumor epithelial compartment between primary (P) and autopsy (A) samples. The x axis is logarithmic, to aid visualization. n = 282 primary, n = 488 autopsy (T and B cells); n = 283 primary, n = 501 autopsy (PD1/MΦ) cores. Left and right whiskers terminate at the minimum and maximum values no further than 1.5× interquartile range; center line represents median (50th percentile); left and right boundaries of box represent the first (25th percentile) and third (75th percentile) quartiles; outlying values plotted as individual points beyond whiskers. P values calculated by GLMM and adjusted for multiple testing corrections (full results given in Supplementary Table 4). b, Top, representative mcIF images from T and B cell panels from each cluster. Bottom, representative mcIF images for two cores from one liver metastasis from case 65914, from the PD1 pathway/macrophage panel. Scale bars, 100 µm. PD1/MΦ, PD1 pathway/macrophage panel; T/B cell, T and B lymphocyte panel. c, Hierarchical clustering of T and B cell proportions in tumor epithelial and stromal regions. Epithelial/stromal infiltrated, n = 36 cores; stromal restricted, n = 185 cores; Imm-des, n = 571 cores. d, PCA plot incorporating CCL5 and CXCL9 expression, mcIF CD8 T cell percentage and M2 macrophage abundance. Left, colored and circled according to the presence of CCL5 hypermethylation and HLA LOH; right, colored according to mcIF immune cluster. RNA-seq and methylation samples, n = 19, corresponding to 42 mcIF cores, n = 1; ES-inf, n = 1; stromal restricted, n = 8; Imm-des, n = 33. Dim, PCA dimension. Source data
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Multiomic analysis of homologous recombination-deficient end-stage high-grade serous ovarian cancer
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High-grade serous ovarian cancer (HGSC) is frequently characterized by homologous recombination (HR) DNA repair deficiency and, while most such tumors are sensitive to initial treatment, acquired resistance is common. We undertook a multiomics approach to interrogate molecular diversity in end-stage disease, using multiple autopsy samples collected from 15 women with HR-deficient HGSC. Patients had polyclonal disease, and several resistance mechanisms were identified within most patients, including reversion mutations and HR restoration by other means. We also observed frequent whole-genome duplication and global changes in immune composition with evidence of immune escape. This analysis highlights diverse evolutionary changes within HGSC that evade therapy and ultimately overwhelm individual patients.
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Overview of the study We downloaded publicly available RNA-seq and genotype data from 14 different datasets consisting of 8,613 RNA-seq measurements from seven main brain regions and 6,518 genotype samples. We created six eQTL meta-analysis datasets and performed cis-, trans- and interaction-eQTL analyses, built a brain-specific gene co-regulation network and prioritized genes using MR, co-localization and the co-regulation network. Image of sagittal cut of brain created with BioRender.com. This figure summarizes values from Supplementary Tables 1, 3, 8, 12 and 25–30.
Overview of the datasets a, Number of samples per included cohort stratified according to the seven major brain regions. b, PCA dimensionality reduction plot of the normalized expression data after covariate correction. Each dot represents an RNA-seq sample and is colored according to the brain region. The figure shows that the samples cluster mainly on the brain region. c, Number of genotypes per cohort stratified according to ancestry. AMR, Admixed Americans; SAS, South Asian. d, Number of individuals per cohort, with each color representing an eQTL dataset. The number of individuals differ from the intersection between the number of RNA-seq samples and number of genotypes because not all samples with genotypes have RNA-seq samples and vice versa, and some individuals with genotypes have multiple RNA-seq measurements. Source data
Conditional cis-eQTLs a, Number of conditional cis-eQTLs per eQTL dataset. b, Comparison of characteristics between primary and non-primary eQTLs. For the mean expression and pLI score values, each row compares the eQTL genes for that rank with eQTL genes from the previous rank (for example, for tertiary eQTLs, the non-significant (grey) distribution is from genes that have secondary but lack tertiary eQTLs). P values were calculated using a Wilcoxon test between significant and non-significant genes. Differences in mean gene expression levels (left), the distance between the most significant SNP–gene combination and the TSS (middle), and pLI scores (right) are shown. For primary, secondary and quaternary eQTLs, non-significant eQTLs have higher pLI scores. Vertical dotted lines indicate median of the distribution for the current rank (coloured) versus the previous rank (black). c,d, Number of overlapping eQTLs along with the Rb and standard error (s.e.) values of primary cis-eQTLs between the cortex eQTLs of different ancestries (c) and the different brain regions for the EUR datasets (d). n, sample size of the dataset. e, Correlation of effect sizes and standard error of primary cis-eQTLs of Cortex-EUR (discovery, excluding GTEx) in all of the GTEx tissues (replication). Each dot is a different GTEx tissue; the x axis indicates the number of eQTLs that are significant in both discovery and replication. Source data
Cell-type ieQTLs a, Pearson’s correlations between the seven predicted cell-count proportions within cortex samples. b, Predicted cell-type proportions compared with cell-type proportions measured using IHC for 42 ROSMAP samples. Pearson’s correlation coefficients are provided. The cell-count predictions for most cell types closely approximate actual IHC cell counts. Shaded areas around regression lines indicate 95% confidence interval. c, Number of cell-type ieQTLs for Cortex-EUR deconvoluted cell types. The first 20 intersections with the highest overlap are shown. Oligodendrocytes have the most interactions, followed by astrocytes and other neurons. Notably, most interactions are unique for one cell type in 87.1% of the cases. d–f, Replication of cell-type ieQTLs for STMN4 (d), FAM221A (e) and CD38 (f), consisting of the scatterplot of the cell-type ieQTL in MetaBrain Cortex-EUR bulk RNA-seq (left) and a forest plot for the eQTL effect in the ROSMAP snRNA-seq data (right). Each dot in the scatterplots (left) represents a sample; colors indicate SNP genotype, with yellow being the minor allele; values under the genotypes are the Pearson’s correlation coefficients; interaction P values were determined using a one-sided F-test; eQTL P values were derived using the standard normal distribution from meta-analyzed z-scores. Forest plot (right): eQTL β values (dots) and standard error (error bars) with effect direction relative to the minor allele when replicating the eQTL effect in ROSMAP single-nucleus data (n = 38); each row denotes a cell type-specific dataset; cell types highlighted in bold reflect the equivalent to the cell type used in the ieQTL. Vertical dashed lines indicate an eQTL beta of 0. TMM, trimmed mean of M-values; AST, astrocytes; END, endothelial cells; EX, excitatory neurons; IN, inhibitory neurons; MIC, microglia; OPC, oligodendrocyte precursor cells; OLI, oligodendrocytes; NEU, other neuron; PER, pericytes. Source data
Co-localization and MR analysis of brain-related traits a, Number of significant Wald ratio effects (blue) and those with both Wald ratio and co-localization (coloc; red) evidence for 13 brain-related traits. b, Forest plots showing the SNP and effect allele (EA), eQTL β and GWAS odds ratio for 20 MS-associated genes that are both MR and co-localization significant as well as their Wald ratio P value. The dots indicate the eQTL β or odds ratio, the error bars indicate the 95% confidence interval. WR, Wald ratio; and OR, odds ratio. Grey dotted line indicates an OR of 1. c,d, Cell-type ieQTL for CYP24A1 (c) and CLECL1 (d) showing interactions with predicted excitatory neuron and microglia proportions, respectively. Each dot represents a sample. Colors indicate the SNP genotype, with yellow being the minor allele. Values under the alleles are the Pearson’s correlation coefficients. Interaction P values were derived using a one-sided F-test; eQTL P values were derived using the standard normal distribution from meta-analyzed z-scores. TMM, trimmed mean of M-values. Source data
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Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases
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Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.
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Implementation and benchmarking of network-based augmentation of GWAS a, Edge and node counts of the combined interactome and its components. OTAR is the Open Targets combined physical protein interaction network that is provided via a Neo4j Graph Database. b, Graphic representation of some L2G components: SNP-to-gene distance, data from QTLs and variant effect predictions. The integration of information into the L2G score has been described previously¹¹. c, Graphical representation of the network-based approach: network propagation of the initial input, clustering using a random walker to find gene communities and scoring of modules using the distribution of PageRank score. KS, Kolmogorov–Smirnov. d, Number of starting genes linked to traits, grouped in therapeutic areas. In the violin plot, the red dots represent the median, the limits of the thick line correspond to quartiles 1 and 3 (25% and 75% of the distribution) and the limits of the thin line are 1.5× the interquartile range. e, Benchmarking of the method, using as a starting signal genes from the Open Targets Genetics portal with a L2G score >0.5. AUC values are calculated using as positive hits the DISEASE database, with increasing cutoff values for its gene-to-trait score (Methods), as well as clinical trials data from the ChEMBL database (clinical phase II or higher). We also re-calculated the AUC values and determined Z-scores reflecting the deviation in AUCs relative to those observed after randomization of the list of true positives (TPs). In the boxplots, the middle lines represent the median, the limits of the box are quartiles 1 and 3 and the whiskers represent 1.5× the interquartile range.
Trait–trait genetic and functional similarities determined from network expansion of GWAS data a, Tree showing the Manhattan distance between all traits, using the full PPR score. Hierarchical clustering was performed using a cutoff of h = 1, leading to 54 clusters, colored depending on the predominant EFO ancestry term. The right-hand panel is a barplot showing the 54 clusters with the frequencies for the predominant EFO ancestry terms and a heatmap showing the counts for ChEMBL targets and drugs. The text label next to each cluster corresponds to the second most predominant EFO terms that, on average, label 35% of the traits within the clusters that have a text label. b, Examples of traits grouped using the Manhattan distance, extracted from the tree in a. CSF, colony-stimulating factor; Ig, immunolglobulin; LDL, low-density lipoprotein.
Multitrait gene module associations for studies of shared biological processes and drug-repurposing opportunities a, Heatmap showing the overlap between gene modules across traits. Traits were clustered using hierarchical clustering (Methods) and subgroups were defined by a cutoff of 0.6 average correlation coefficient. A module was considered the same across different traits when most genes are in common (Jaccard index > 0.7). Significant trait–module relations are marked in yellow or pink, with yellow indicating modules overrepresented in one of the subgroups of traits (one-sided Fisher’s exact test, adjusted P < 0.05) and pink otherwise. The heatmap in the right-hand panel shows the number of genes in modules from each subgroup of traits that are drug targets (phase III or higher, ChEMBL database), linked with clinical variants (ClinVar database) or with mouse KO phenotypes (International Mouse Phenotyping Consortium database). b, Barplot showing the number of traits linked with the top six most pleiotropic gene modules. The GOBP description is based on the results of a GOBP enrichment test (Methods). c, Simplified heatmap of the clusters in a concerning bone-related and fasciitis traits. The represented network includes genes from the modules indicated in blue letters and the represented interactions have been filtered for visualization (Methods). Blue nodes, relevant mouse KO phenotypes; green nodes, diseases with clinical variants enriched in this gene module; red nodes, drugs in clinical trials. Genes linked to blue, green or yellow nodes have the linked mouse phenotypes, clinical variants in the linked disease or are targets of the linked drug. Genes that are the targets of drugs in clinical trials have yellow nodes. GWAS-linked genes (L2G score > 0.5) have borders colored in an orange to red gradient (count of GWAS-linked traits). d, Simplified heatmap of one the clusters in a concerning allergic reactions (node and edge color code are the same as in c). In this case, two modules were merged to build the interaction network in the right-hand panel. mRNA, messenger RNA; SRP, signal recognition particle.
Gene module analysis of autoimmune diseases a, Heatmap showing the overlap between gene modules across traits (color-coded as in Fig. 3a,c,d). The GOBP description is based on the results of a GOBP enrichment test (one-sided Fisher’s exact test, BH adjustment, Methods). The heatmap in the right-hand panel shows the gene set enrichment analysis carried out on the expression data from different tissues extracted from Human Protein Atlas (HPA) for the gene modules in blue (two-sided Kolmogorov–Smirnov test, Methods). After BH adjustment for multiple testing, the P value of the test was log transformed and given a positive value if the median distribution for the foreground was higher than the background and a negative value if it was lower. b, Shared modules as a network, nodes are gene modules associated with different immune-related traits colored blue or red for the two trait subgroups; edges represent a high degree of overlap at the gene level (Jaccard index > 0.7). Gene modules linked to different traits are given in black circles. Gene modules are linked with the yellow node ‘ChEMBL-drugs’ when they contain targets for drugs in clinical trials (phases III and IV, ChEMBL); linked with green nodes when they are enriched in genes with clinical variants for a given disease; and linked with purple nodes when they are enriched for the corresponding KO phenotypes (one-sided Fisher’s exact test, adjusted P < 0.05). c, Network corresponding to genes found in gene modules enriched for Type I interferon (INF) signaling, phospholipase C-activating GPCR signaling, neutrophil activation (integrins) and protein kinase A (PKA) activity. Edge filtering, node and edge colors are the same as in Fig. 3c,d.
An IBD-specific network is enriched for likely causal genes a, Curated IBD seed genes (N = 37) tend to have a higher network propagation score (PPR percentile) than other genes within 200 kb at the same loci. b, Genes selected by high Open Targets L2G score also tend to have high PRR percentile, highlighting network evidence as complementary to typical locus features. In the boxplots, the middle lines represents the median, the limits of the box are quartiles 1 and 3 and the whiskers represents 1.5× the interquartile range. c, Genome-wide, genes with low P-value SNPs within 10 kb are enriched for high PPR percentile (one-sided Fisher’s exact test). Data are presented as the mean ± s.d.
Network expansion of genetic associations defines a pleiotropy map of human cell biology
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Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support.
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The drought-resistant phenotypes and genome assembly of CIMBL55 a, Seedling survival of B73, CIMBL55 and Mo17. Photographs were taken of the seedlings before or after the drought treatment when the differences were most evident. Scale bar, 5 cm. b, The morphology of representative, 1-month-old plants grown under normal, well-watered conditions in a greenhouse. Scale bar, 10 cm. c, Two-month-old plants of CIMBL55 and B73 subjected to water deficit conditions in the field. Scale bar, 20 cm. d, Ears of B73, CIMBL55 and Mo17 collected under well-watered and drought conditions (see Methods). Scale bar, 10 cm. e, The pipeline used for the CIMBL55 genome assembly. f, Large SVs in CIMBL55, relative to B73, Mo17 and SK genomes. The x axis in each graph represents the CIMBL55 genome sequence of the indicated regions.
Gene synteny analysis in CIMBL55 and B73 a, Circular graph indicating the percentage of the five classes of genes in regard to their syntenic status. The center circle represents the percentage of syntenic and nonsyntenic genes in CIMBL55, relative to B73. The middle circle indicates that the syntenic genes comprised of Classes 1, 2 and 3 genes, while the nonsyntenic genes comprised Classes 4 and 5 genes. The small pie chart indicates that Class 5 genes were further divided into two categories based on whether a homologous DNA sequence could be identified in B73 (see Methods). The outer ring indicates if the syntenic genes were present as a one-one synteny or multi-one/multi synteny arrangement. b, Synteny map between the B73 and CIMBL55 genomes. Upper panel, entire chromosome 8 of B73 and CIMBL55. Lower panel, zoomed view of the synteny blocks on B73 Chr8: 171.42–172.37 Mb and CIMBL55 Chr8: 173.94–174.66 Mb, which also share homology with the synteny block on B73 Chr3: 179.45–177.61 Mb and CIMBL55 Chr3: 185.75–183.78 Mb. The syntenic genes were connected by gray lines. c, The ratio of Class 1–3 genes within the different-sized synteny blocks. N, the number of the synteny blocks of the indicated size; n, the number of syntenic genes in the synteny block. d, The expression level of Class 1–5 genes in seedlings, without the inclusion of nonexpressed genes. n: the number of expressed genes (FPMK > 1) in each class. For the box plots, boxes span the first and third quartiles, center lines represent medians and whiskers represent the smallest and largest sample values. Statistical significance was determined by a two-sided t-test. In all of the panels, the five classes of genes are presented in different colors as indicated in a. Source data
Identification of genetic variants and their associations with drought resistance a, Statistics for SNP, deletion (DEL), insertion (INS), duplication (DUP), inversion (INV), translocation (TRA) and copy number variation (CNV) computed by four kinds of programs between B73 and other 30 high-quality maize assembles. b, Average drought-survival rate of the germplasms harboring either the CIMBL55 or non-CIMBL55 genotype of 108 candidate drought-resistant genes based on the previously identified lead SNPs. The loci are arrayed in descending order of the CIMBL55 allelic effect. c, Average drought-survival rate of the germplasms harboring either the CIMBL55 or non-CIMBL55 haplotype of significant SVs. Three genes analyzed in the present research are indicated in red, and three previously reported genes16,18,50 are indicated in black. The blue and yellow colors represent the CIMBL55 and non-CIMBL55 genotypes. In b and c, ‘Zm’ for Zea mays. was omitted in the gene names. d, Violin plots of ZmABF4 expression levels and survival rate (e) of the germplasms with different genotypes at the S1479 and S3205 loci of ZmABF4. For the violin plots (in this and all following figures), dashed lines represent the first and third quartiles, center lines represent medians, and whiskers represent the smallest and largest sample values. The yellow and blue colors represent the B73 and CIMBL55 genotypes. ‘+’, with the insertion; ‘−’, without the insertion. The numbers in the parentheses denote the sample size used in the two-sided t-test. f, Drought survival assay of two ZmABF4-overexpression (ZmABF4-OE) lines. Scale bar, 5 cm. g, Percentage survival rate. Eighteen plants of each genotype were tested in each experiment, and at least three replicated experiments were conducted. Data represent the mean ± s.d. h, Rate of leaf water loss of WT and ZmABF4-OE lines. Four detached leaves of each genotype were left to dehydrate on a clean bench and weighed over a period of 90 min. The experiment was repeated three times for WT and OE 1 (mean ± s.d.) and twice for OE 5 (mean value). Statistical significance between WT and all the OE plants was determined, by a two-sided t-test: *P < 0.05, **P < 0.01 (in g and h). Source data
SV-related alterations in DNA methylation a, Diagram illustrating SV-related hyper- or hypo-DNA methylation. The insertional sequences, identified in CIMBL55 referenced to B73 and Mo17. The small lines represent the bisulfite sequencing (BS-seq) reads mapped to CIMBL55. The orange box denotes the DNA hypermethylation, and the blue-green box denotes the DNA hypo-methylation in the two insertional regions in CIMBL55. b, Heatmap of the level of mCG, mCHG and mCHH within the insertion start sites and their 0.5-kb-flanking regions. SV-length, the length of the insertion in CIMBL55. SV-TSS, distance of the insertional site to the transcriptional start site of the closest gene. TE-type, insertion sequences are characterized as TE. The abbreviations for the types of TE are listed in Supplementary Table 2. MIX, multiple types of DNA-TE and RNA-TE. c, Profiles of DNA methylation level in the 0.5-kb-flanking region of the insertional fragments of five clusters. d, ZmNAC075-based association mapping of gene expression under adequate water conditions. The association analysis is based on a mix linear model²⁸, in this and all the following figures. The position of the lead SNP (8_21515682) and the two insertions are indicated by lines, and their LD levels are indicated by red asterisks in the pairwise LD heatmap of all the variants (P < 10⁻³). e,f, Violin plots of ZmNAC075 expression levels (e) and survival rate (f) of the germplasms with different alleles at the S-9014 and S-1425 sites. The numbers in the parentheses denote the sample size used in the two-sided t-test. g, Upper panel, sequence comparison and DNA methylation status of the ZmNAC075 locus in B73 and CIMBL55. Hypermethylation of two insertions, S-9014 (182 bp) and S-1425 (5123 bp), upstream of ZmNAC075B73 indicated by the light-blue boxes. Lower panel, The DNA methylation status of the two insertional fragments in WT and two zmdrd1-KO lines. h–k, DNA methylation levels at S-9041 and S-1425 between WT and two zmdrd1-KO lines. Mean value of two biological replicates was shown. l, Relative expression of ZmNAC075B73 in WT and two zmdrd1-KO lines. The statistical significance is determined by a two-sided t-test from four biological replicates. Source data
ZmRtn16 positively contributes to drought resistance in maize a–c, Manhattan plot of the GWAS identifying the genetic loci associated with ZmRtn16 expression levels under well-watered (WW), moderate drought (WS1) and severe drought (WS2) conditions. SNPs located within the ZmRtn16 and its flanking 10-kb region are indicated by red dots. d, Upper panel, ZmRtn16-based association mapping for survival rate after exposure to drought treatment. The 132.5–134.0 Mb region of Chr9 is shown. Lower panel, Zoom view of the ZmRtn16 gene region and pairwise LD heatmap for the variants. Significant variants (P < 10⁻⁷) are connected to the pairwise LD heatmap by lines. Red asterisks indicate a strong LD between the variants. e, f, Violin plots of the expression level of ZmRtn16 under WS2 condition and the survival rate of germplasms carrying the different alleles of ZmRtn16 at S2290, with (+) or without (−) the 28-bp insertion. Yellow and blue colors represented the germplasms carrying the alleles of ZmRtn16B73 and ZmRtn16CIMBL55, respectively. The numbers in the parentheses denote the sample size in a two-sided t-test. g, Left panel, diagram indicating the genotypes of the downstream sequence of ZmRtn16B73 and ZmRtn16CIMBL55. Right panel, mRNA abundance of ZmRtn16 when it was fused with different 3' -UTR sequences. Different letters indicate a significant difference (P < 0.05) based on the one-way ANOVA (Tukey’s test). h,i, Drought resistance assays and the rate of leaf water loss. Eighteen plants of each genotype were tested in each experiment, and at least three replicates were conducted. Four detached leaves of each genotype were dehydrated on a clean bench and weights were measured over the course of 6 h. Data represent the mean ± s.d., based on three replicated experiments. Statistical significance was determined by a two-sided t-test: *P < 0.05, **P < 0.01. j, Grain yield of ZmRtn16-OE and WT plants under well-watered and drought conditions in field. Representative photographs were taken of the ears of WT and two ZmRtn16-OE plants. The data were obtained from at least 72 plants for each genotype. Statistical significance was determined by a two-sided t-test in h–j. Scale bar, 5 cm. Source data
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Genome assembly and genetic dissection of a prominent drought-resistant maize germplasm
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In the context of climate change, drought is one of the most limiting factors that influence crop production. Maize, as a major crop, is highly vulnerable to water deficit, which causes significant yield loss. Thus, identification and utilization of drought-resistant germplasm are crucial for the genetic improvement of the trait. Here we report on a high-quality genome assembly of a prominent drought-resistant genotype, CIMBL55. Genomic and genetic variation analyses revealed that 65 favorable alleles of 108 previously identified drought-resistant candidate genes were found in CIMBL55, which may constitute the genetic basis for its excellent drought resistance. Notably, ZmRtn16, encoding a reticulon-like protein, was found to contribute to drought resistance by facilitating the vacuole H⁺-ATPase activity, which highlights the role of vacuole proton pumps in maize drought resistance. The assembled CIMBL55 genome provided a basis for genetic dissection and improvement of plant drought resistance, in support of global food security.
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Characteristics of obesity and WHRadjBMI genetic risk a, LDSC-SEG for WHRadjBMI (left) and obesity characteristics (right), measuring the enrichment of traits near genes with tissue-specific expression. The P values are based on standard error and represent the contribution of tissue-specific expression to trait heritability after correction for baseline annotations. The bars are colored by tissue category. CR, cardiorespiratory; MS, musculoskeletal; SAT, subcutaneous adipose tissue. b, xCell-derived cell type enrichment predictions, normalized to generate proportions and averaged across adipose cell types (preadipocyte and adipocyte). The bars are colored by tissue category. c, Genetic correlation and heritability for sex-specific GWASs conducted for obesity and WHRadjBMI (for obesity, n = ~681,275 individuals; for WHRadjBMI, n = 694,649 individuals). The bars are colored by trait. The data are represent correlation or heritability estimates ± s.e. The genetic correlation is high between the sexes for obesity and lower for WHRadjBMI. The sexual dimorphism of WHRadjBMI is partially driven by increased heritability of WHRadjBMI in women. Welch’s two-sided t-test was used to evaluate within-trait heritability between sexes (WHR P = 3.3 × 10⁻⁴; obesity P = 0.16). F, female; M, male.
Genes identified by TWAS a, Overlap between TWAS-identified gene sets (pie chart; top) and the top 10 results for each TWAS. Results show some known and some novel genes associated with obesity and WHR. Significance based on permutation distribution with subsequent Bonferroni correction. Data are colored by sex and trait. b, Sexual dimorphism of WHRadjBMI TWAS results. Every gene that was tested in both male and female TWASs is displayed. The data are colored by sex. c, rs1534696 eQTL effect on SNX10 in women and men. The data are colored by rs1534696 genotype. The P values for the significance of the differences in of log2[transcripts per million (tpm)] values between sexes at each rs1534696 genotype were obtained via Student’s two-sided t-test (for females, there were 60 A/A participants, 91 C/A participants and 42 A/A participants; for males, there were 54 A/A participants, 96 C/A participants and 43 C/C participants). The box plot whiskers represent the minimum (1st percentile) and maximum (99th percentile) points of the data, the box bounds represent the first quartile (25th percentile and 75th percentiles) and the center line represents the median of the data (50th percentile). FC, fold change. d, Sexual dimorphism of eQTLs (left) and GWASs (right) for female WHR. The data are colored by data type (eQTL versus GWAS). Each dot represents the top eQTL or GWAS for a female WHR TWAS gene. The GWAS betas are normalized by sex-specific trait heritability (h²). The left panel represents P values of each variant as a sex-specific eQTL; the right represents the P value of each variant in a WHRadjBMI sex-specific GWAS. The variants show sexual dimorphism in GWASs, but not as eQTLs. e, GWAS locus near SNX10 in women (left) and men (right). rs1534696 is sexually dimorphic for WHRadjBMI.
Regulatory network of female WHRadjBMI a, MPRA results. The outside ring shows female WHRadjBMI TWAS genes (n = 91). The blue ring shows variants that had significant enhancer activity (n = 426). The red ring shows variants that modulated the activity of significant enhancers (n = 58). There were n = 1,455 nonsignificant variants. Significance was determined by one-sided Mann–Whitney U-test and subsequent FDR correction. b, Transcription factor (TF) motifs enriched in enhancer-modulating variants relative to nonsignificant enhancer sequences. Transcription factors contributing to common adipose motifs are highlighted in green. The P values represent the probability of finding a degree of enrichment by chance, based on HOMER significance testing based on a null binomial distribution of motifs in the genome and FDR multiple comparisons correction. c, Conservation of MPRA sequences by significance status and the presence of Alu repeat elements in MPRA sequences. The data are colored by MPRA significance status. Conservation significance was determined by Student’s two-sided t-test between average conservation rates across MPRA sequences. The data represent mean conservation rates ± s.e. (for the enhancer-modulating variant versus enhancer, P = 1.81 × 10⁻¹⁷; for the enhancer versus nonsignificant variant, P = 2.22 × 10⁻²). The Alu proportion significance was calculated via chi-squared test comparing observed proportions of MPRA variants in Alu elements with those expected by chance in MPRA variants (the enhancer-modulating variant overlap was 44/58 sequences, the enhancer overlap was 148/368 sequences and the nonsignificant overlap was 287/1433 sequences). d, Average enhancer activities of MPRA sequences with and without Alu elements. The data are colored by MPRA significance status. Significance was determined by t-test comparing average normalized activities between variants within or not within Alu elements per MPRA significance level. e, Density plot reflecting the presence of enriched adipogenesis motifs within the first monomer of Alu elements in MPRA sequences.
Effects of the rs1534696 allele on human primary adipocytes a, ABC-predicted interactions between the SNX10 promoter and gene body loci in differentiating primary adipocytes. b, LipocyteProfiler method. c, LipocyteProfiler features across primary human adipocyte differentiation. The data are colored by feature class. Larger dots represent features that differ significantly between AA and CC/AA genotype groups at each time point for adipocytes from subcutaneous and visceral depots. Significance is defined as P < 0.05 and FDR q < 0.05. The y axes represent the P value of significance of each feature and the x axes represent the effect size, represented as the t statistic, of each feature. The dashed lines represent the P value significance threshold. d, LipocyteProfiler features significant in day 14 subcutaneous adipocytes. The data are colored by feature class. The x axis shows significant features grouped by cellular organelle staining target. Organelle targets are visualized and consist of: (1) mitochondria; (2) intracellular lipids; (3) DNA; and (4) actin cytoskeleton and Golgi membrane. The features shown are collapsed across redundant features (for example, different measures of cell shape). For subcutaneous adipocytes, n = 20 CC/CA and n = 11 AA. For visceral adipocytes, n = 23 CC/CA and n = 12 AA.
SNX10-mediated inhibition of adipogenesis a, Oil Red O staining (left) and reverse transcription quantitative real-time PCR quantification (right) representing lipid accumulation after adipocyte differentiation in hMSCs upon SNX10 shRNA introduction. The Oil Red O images are representative of samples from that condition. The data represent mean expression ± s.d. (n = 2 technical replicates). The data are colored by shRNA experiment. mRNA, messenger RNA. b, DEXA scans from female and male control mice (top) and female and male mmΔSnx10Adipoq mice. c,d, Body weight (c) and body fat mass (d) of control and mmΔSnx10Adipoq mice after HFD administration (n = 6 female control mice (CF), n = 5 female knockout mice (KOF), n = 3 male control mice (CM) and n = 6 male knockout mice (KOM); pairwise t-test, P < 0.01). The asterisks represent the results of two-sided Student’s t-tests. The box plot whiskers represent the minimum (1st percentile) and maximum (99th percentile) points of data, the box bounds represent the first quartile (25th percentile and 75th percentile) and the center lines represent medians of the data (50th percentile). The data are colored by sex and SNX10 genotype. NS, not significant.
Genetics of sexually dimorphic adipose distribution in humans
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Obesity-associated morbidity is exacerbated by abdominal obesity, which can be measured as the waist-to-hip ratio adjusted for the body mass index (WHRadjBMI). Here we identify genes associated with obesity and WHRadjBMI and characterize allele-sensitive enhancers that are predicted to regulate WHRadjBMI genes in women. We found that several waist-to-hip ratio-associated variants map within primate-specific Alu retrotransposons harboring a DNA motif associated with adipocyte differentiation. This suggests that a genetic component of adipose distribution in humans may involve co-option of retrotransposons as adipose enhancers. We evaluated the role of the strongest female WHRadjBMI-associated gene, SNX10, in adipose biology. We determined that it is required for human adipocyte differentiation and function and participates in diet-induced adipose expansion in female mice, but not males. Our data identify genes and regulatory mechanisms that underlie female-specific adipose distribution and mediate metabolic dysfunction in women.
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Variant cohort details a,b, Phenotypes associated with 88 experimentally verified clinical splicing variants (a) and position of the 76/88 variants that are SNVs relative to the ESs (b). c, Nature of 148 unannotated splicing events (mis-splicing) induced by the 88 variants. IR = intron retention. Source data
Unannotated splicing events seen in 300K-RNA a, Exon-skipping events are evidenced by split-reads spanning nonconsecutive exons within the transcript. Splice sites (GT/AG motifs) shown in bold and black are those for which events are being ranked. b, Cryptic activation events are evidenced by split-reads spanning: (i) an annotated acceptor and an unannotated donor or (ii) an annotated donor and an unannotated acceptor. c, Example showing the Top-4* events for NM_130786 (A1BG) exon 2 donor (g.58353291). Exon/intron lengths are not drawn to scale. Arc thickness corresponds to event rank. d, One hundred percent (119/119) of exon-skipping and cryptic activation events detected across 88 variants are present in 300K-RNA, and 92% are in the Top-4* events for their respective splice site. e, Percent of the 119 true-positive events detected within random subsets of the 335,663 source specimens in 300K-RNA. Gray dots show proportion across 20 random samples; blue line shows mean proportions with LOESS smoothing. f, Top-1* and Top-2* events around the splice sites affected by our 88 variants typically occur in mutually exclusive specimens—with both events seen, on average, in only 5% of total samples where either event was detected. Internal lines of boxplot denote the median value, and the lower and upper limits of the boxes represent 25th and 75th percentiles. Whiskers extend to the largest and smallest values at most 1.5IQR. An asterisk indicates our filter for events involving skipping one or two exons and cryptic activation within 600 nt of the annotated splice site. Source data
300K-RNA event rankings across tissues and data sources a, Heatmap showing the proportion of mis-splicing events* with the same event rank in each GTEx tissue subtype, as compared to all GTEx tissue subtypes combined—for the 88 splice sites affected by our cohort of variants. Only tissues with ≥100 GTEx samples are shown. The Top-1* event in individual tissues is concordant with the Top-1* event in ‘all GTEx tissues’ for ≥80% of splice sites. b, Concordance of top-ranked mis-splicing events* in GTEx versus SRA. The Top-1* event in GTEx is the Top-1* event in SRA for 80/88 (91%) splice sites in our cohort. c, Spearman correlation of all mis-splicing events* across 98,810 annotated splice sites in clinically relevant Mendelian disease genes (Methods) in each GTEx tissue subtype versus GTEx overall. Only tissues with ≥100 GTEx samples are shown. Black, clinically accessible tissues. d, Upset plot²⁶ showing 300K-RNA Top-4* across clinically relevant Mendelian disease genes (four events per splice site, n = 383,677 in total) versus Top-4* specific to four clinically accessible tissues in GTEx. Twenty percent (77,461/383,677) of all 300K-RNA Top-4* across clinically relevant Mendelian disease genes are captured as Top-4* events among all four clinically accessible tissues (blood, fibroblasts, EBV-LCL and muscle). An asterisk indicates filtering to skipping one or two exons and cryptic activation within 600 nt of the annotated splice site. Source data
Comparison of 300K-RNA Top-4* with SpliceAI a–c, Custom interpretive rules applied to SpliceAI Δ-scores to predict the nature of mis-splicing. Δ-scores below 0.001 were excluded as our applied threshold for no predicted impact on splicing (threshold shown with gray dashed lines). Heights of red lines denote example Δ-scores that predict mis-splicing events according to our rules. a, Single-exon skipping is predicted if both splice sites flanking the exon have donor and acceptor loss Δ-scores above threshold, and double-exon skipping was inferred if the splice site of the upstream or downstream intron also had donor loss or acceptor loss Δ-score above threshold. b, Intron retention was predicted if both splice sites flanking an intron had donor loss and acceptor loss Δ-score above threshold. c, Cryptic activation was predicted by donor gain or acceptor gain Δ-scores above threshold for any unannotated donor or acceptor. d, Example showing SpliceAI predictions of exon skipping and cryptic activation in case number 6. e, Sensitivity and PPV of 300K-RNA and SpliceAI for exon-skipping and cryptic-activation prediction at different thresholds, for the 86/88 variants that can be scored by SpliceAI. Points on the 300K-RNA curve (blue) show metrics when using Top-1*, Top-2*, Top-3*, Top-4*, etc. events as predictions of the nature of mis-splicing. Points on the SpliceAI curve (red) show metrics at Δ-scores that predict the same number of exon skipping and cryptic activation as 300K-RNA Top-1*, Top-2*, Top-3*, Top-4* and so on. f, 300K-RNA and SpliceAI predictions of exon skipping (seen/not seen in RNA studies across 86 cases). g, 300K-RNA and SpliceAI predictions of cryptic splice-site activation (seen/not seen in RNA studies across 86 cases). Dashed lines indicate the threshold of Top-4* and SpliceAI Δ-score ≥ 0.011 identified in e. Black dots, mis-splicing events seen in RNA studies but not meeting the Δ-score threshold of 0.001. An asterisk indicates that filtering to skipping one or two exons and cryptic activation within 600 nt of the annotated splice site. TP, true positives, FN, false negatives, FP, false positives. Source data
SpliceVault predicts the precise nature of variant-associated mis-splicing
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Even for essential splice-site variants that are almost guaranteed to alter mRNA splicing, no current method can reliably predict whether exon-skipping, cryptic activation or multiple events will result, greatly complicating clinical interpretation of pathogenicity. Strikingly, ranking the four most common unannotated splicing events across 335,663 reference RNA-sequencing (RNA-seq) samples (300K-RNA Top-4) predicts the nature of variant-associated mis-splicing with 92% sensitivity. The 300K-RNA Top-4 events correctly identify 96% of exon-skipping events and 86% of cryptic splice sites for 140 clinical cases subject to RNA testing, showing higher sensitivity and positive predictive value than SpliceAI. Notably, RNA re-analyses showed we had missed 300K-RNA Top-4 events for several clinical cases tested before the development of this empirical predictive method. Simply, mis-splicing events that happen around a splice site in RNA-seq data are those most likely to be activated by a splice-site variant. The SpliceVault web portal allows users easy access to 300K-RNA for informed splice-site variant interpretation and classification.
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SCR is required for Sox2 expression in epiblast cells a, CHi-C 1D interaction frequency heatmap in WT mES cells (top). Black arrowhead points to the center of the Sox2–SCR interaction and this corner signal overlaps with CTCF binding suggesting the formation of a CTCF-mediated loop. Publicly available ChIP-seq of RAD21, CTCF and NIPBL as well as CUT&RUN of H3K27ac in mES cells are shown at the bottom. CTCF motif orientation (red and blue arrowheads) is shown for significant CTCF motifs (Q < 0.05) as detected by FIMO. Shaded box shows deleted region in SCR∆ mice. b, qPCR analysis of Sox2 expression in blastocysts at E3.5 was done using the ∆∆CT method and Gapdh as a reference. Sox2 expression was calculated by comparing it to the median of all analyzed WT embryos. Each dot represents a single blastocyst. The number of biologically independent blastocysts (n) analyzed of each genotype is shown in the legend. Boxplots show minimum, maximum, median, first and third quartiles. A Wilcoxon two-sided test was performed to assess statistical significance. Het, heterozygous; Hom, homozygous. c, E6.5–E7.5 embryos were stained for GATA4, T and SOX2. Eight of nine SCR∆ homozygotes showed arrested development shortly after implantation and failed to initiate gastrulation as shown by T expression. Eight of eight WT and heterozygotes displayed correct pattern of T expression. Scale bars, 80 µm.
SCR activates Sox2 expression independently of CTCF a, CHi-C 1D interaction frequency heatmaps in homozygotic CTCF∆(C2–C4) and CTCF∆(C5) mES cells, compared to WT. Rectangles represent the Sox2–SCR interaction. Insulation scores for 5-kb windows in this region are shown below publicly available CTCF and H3K27ac enrichment tracks. Lower scores represent higher insulation. b, Differential CHi-C interaction frequency heatmap. Red signal represents interactions occurring at higher frequency in mutant cell lines compared to control and the blue shows interactions of lower frequency. Dashed lines represent the Sox2–SCR domain as detected in WT control cells. c, Virtual 4C plots using the Sox2 and SCR viewpoints. Region surrounding viewpoint was removed from the analysis. Dashed lines highlight SCR in the Sox2 viewpoint (top) and Sox2 in the SCR viewpoint (bottom). Virtual 4C signal is shown as the average of the two replicates in 5-kb overlapping windows. Colored dots represent regions of statistically significant difference compared to WT (adj. P < 0.01). Black arrowhead indicates region of the highest intensity of the Sox2–SCR interaction, which overlaps with the SCR–CTCF motif. d, qPCR analysis of Sox2 expression in blastocysts at E3.5 was done using the ∆∆CT method and Gapdh as a reference. Sox2 expression was calculated by comparing it to the median of all analyzed WT embryos. Each dot represents a single blastocyst. The number of biologically independent blastocysts (n) analyzed of each genotype is shown below the plot. Boxplots show minimum, maximum, median, first and third quartiles. A Wilcoxon two-sided test was performed to assess statistical significance.
SCR can activate Sox2 across CTCF-mediated insulation a, CHi-C 1D interaction frequency heatmaps in homozygotic mES cells of the CTCFi3×⁻, CTCFi3×⁺, CTCFi18×⁺ and CTCFi3×⁻;3×⁺ strains compared to WT. Rectangles show the Sox2–SCR interaction. Inserted CTCF motif orientation and position in each mutant are shown below the plots. Insulation scores for 5-kb windows are shown below publicly available CTCF and H3K27ac tracks. Dashed lines below heatmap show CTCF insertion sites. b, Virtual 4C plots using the Sox2 and SCR viewpoints. Dashed lines highlight SCR in the Sox2 viewpoint (top) and Sox2 in the SCR viewpoint (bottom). Region surrounding viewpoint was removed from analysis. Virtual 4C signal is shown as the average of the two replicates in 5-kb overlapping windows. Colored dots represent regions of statistically significant difference compared to WT using a Wald test and after correction for multiple comparisons (Q < 0.01). c, qPCR analysis of Sox2 expression in blastocysts at E3.5 was done using the ∆∆CT method and Gapdh as a reference. Sox2 expression was compared to the median of all WT embryos. Each dot represents a blastocyst and a Wilcoxon two-sided test assessed statistical significance. The number of biologically independent blastocysts (n) analyzed of each genotype is shown below the plot. Boxplots show minimum, maximum, median, first and third quartiles. d, IF of blastocysts with antibodies targeting GATA6, NANOG and SOX2. Each dot in the quantification plots represents the signal intensity of a single cell normalized by the cell with the highest intensity in heterozygotes. The number of biologically independent blastocysts (n) analyzed of each genotype is shown below the plot. Boxplots show minimum, maximum, median, first and third quartiles. A Wilcoxon two-sided test was performed to assess statistical significance. Scale bars, 10 µm.
DNE also induces Sox2 across CTCF-mediated insulation a, CHi-C 1D interaction frequency heatmap in E11.5 heads. Data of WT ES cells are shown for comparison (bottom). Insets on the right show 2D interaction heatmaps highlighting interactions between regions surrounding Sox2 and DNE. CTCF ChIP-seq publicly available data were obtained from in vitro differentiated neural progenitor cells. RAD21 ChIP-seq was performed on E11.5 heads isolated as for CHi-C. 2D insets show the same tracks on the x and y axes but at different locations. Rectangles represent the Sox2–DNE interaction, arrowheads represent loops with CTCF downstream of DNE established by transgene insertions, black arrow represents loops with CTCF upstream of Sox2 established by transgene insertions, white arrow represents loops in ES cells between CTCF upstream of Sox2 and CTCF downstream of DNE, white brackets in the insets highlight the region between DNE and CTCF. Dashed triangle in head CHi-C represents the Sox2–SCR interaction domain detected in ES cells. b, Virtual 4C plots using Sox2 and DNE viewpoints using 5-kb overlapping windows and signal are shown as an average of the two replicates of each genotype. Region surrounding viewpoint was removed from the analysis. Insets show signal at DpnII fragments. c, qPCR analysis of Sox2 expression in NPCs was done using the standard curve dilution method and Eef2 as a reference. Each dot represents a technical replicate and three individual mutant cell lines (n) were analyzed. A Wilcoxon two-sided test assessed the statistical significance by comparing WT to all mutant clones combined. d, qPCR analysis of Sox2 expression in E11.5 midbrains was done using the ∆∆CT method and Gapdh as a reference. Sox2 expression was compared to the median of all WT embryos. Each dot represents one embryo and a Wilcoxon two-sided test assessed statistical significance. The number of biologically independent embryos (n) analyzed for each genotype is shown in the legend. Boxplots show minimum, maximum, median, first and third quartiles. e, IF of E9.5–10.5 embryos stained with an antibody targeting SOX2. Three homozygotes of each line were stained and imaged together with two WT littermates. Scale bars, 500 µm.
CTCF loops can completely insulate Sox2 from its AFG-specific enhancers a, IF of E9.5–10.5 embryos stained with an antibody targeting SOX2. Bracket highlights the AFG. First four images were taken with a dissection microscope and the two on the right with a confocal microscope. Three homozygotes of each line were stained and imaged together with two WT littermates. Scale bars, 150 µm. b, IF with antibodies targeting SOX2 and NKX2.1 using dissected E13.5 AFG-derived tissues. Tr, trachea; Es, esophagus; Lu, lungs; St, stomach. Six embryos of each genotype were stained and imaged. Scale bars, 500 µm. c, qPCR analysis of Sox2 expression in stomach at E13.5 was done using the ∆∆CT method and Gapdh as a reference. Sox2 expression was calculated by comparing it to the median of all analyzed WT embryos. Each dot represents a single embryo. The number of embryos (n) analyzed for each genotype is shown below the plot. Boxplots show minimum, maximum, median, first and third quartiles. A Wilcoxon two-sided test was performed to assess statistical significance. d, GFP expression in AFG-derived organs dissected from E15.5 fetuses originating from crosses between CTCFi3×⁻;3×⁺ and Sox2GFP heterozygous mice. Three embryos of each genotype were imaged. Scale bars, 500 µm. e, CHi-C 1D interaction frequency heatmaps in WT E11.5 heads (top) and GFP⁺ cells from E11.5–12.5 AFG-derived tissues dissected from Sox2GFP heterozygotes (bottom). Publicly available CTCF, ATAC-seq and H3K27ac enrichment data from different tissues are shown below the heatmaps. Green arrowheads indicate putative regulatory elements with tissue-specific activity in AFG derivatives. Insets show a 2D interaction heatmap where y axis shows region centered around Sox2 and x axis shows region around a CTCF motif downstream of DNE. For the E11.5 head inset, CTCF data from NPCs are shown on both axes. For AFG-derived tissues, CTCF from stomach is shown on x axis and from lungs on the y axis. White bracket highlights the DNE and CTCF regions. In the AFG, the signal is restricted to the CTCF motifs and not to DNE.
Enhancer–promoter interactions can bypass CTCF-mediated boundaries and contribute to phenotypic robustness
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How enhancers activate their distal target promoters remains incompletely understood. Here we dissect how CTCF-mediated loops facilitate and restrict such regulatory interactions. Using an allelic series of mouse mutants, we show that CTCF is neither required for the interaction of the Sox2 gene with distal enhancers, nor for its expression. Insertion of various combinations of CTCF motifs, between Sox2 and its distal enhancers, generated boundaries with varying degrees of insulation that directly correlated with reduced transcriptional output. However, in both epiblast and neural tissues, enhancer contacts and transcriptional induction could not be fully abolished, and insertions failed to disrupt implantation and neurogenesis. In contrast, Sox2 expression was undetectable in the anterior foregut of mutants carrying the strongest boundaries, and these animals fully phenocopied loss of SOX2 in this tissue. We propose that enhancer clusters with a high density of regulatory activity can better overcome physical barriers to maintain faithful gene expression and phenotypic robustness. Genetic manipulation of the Sox2 locus in mice shows that gene activation by distal enhancers does not require CTCF-mediated loops and can occur across ectopic CTCF-mediated boundaries. The ability to bypass CTCF boundaries varies with their insulation strength and the tissue-specific enhancers responsible for activation.
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Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains
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Attention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a major genetic component. Here, we present a genome-wide association study meta-analysis of ADHD comprising 38,691 individuals with ADHD and 186,843 controls. We identified 27 genome-wide significant loci, highlighting 76 potential risk genes enriched among genes expressed particularly in early brain development. Overall, ADHD genetic risk was associated with several brain-specific neuronal subtypes and midbrain dopaminergic neurons. In exome-sequencing data from 17,896 individuals, we identified an increased load of rare protein-truncating variants in ADHD for a set of risk genes enriched with probable causal common variants, potentially implicating SORCS3 in ADHD by both common and rare variants. Bivariate Gaussian mixture modeling estimated that 84–98% of ADHD-influencing variants are shared with other psychiatric disorders. In addition, common-variant ADHD risk was associated with impaired complex cognition such as verbal reasoning and a range of executive functions, including attention. Genome-wide analyses identify 27 loci associated with attention-deficit hyperactivity disorder and provide insights into its genetic architecture in relation to other psychiatric disorders and cognitive traits.
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Schematic description of the TESLA method TESLA uses meta-regression to model phenotypic effect estimates as functions of the PCs of genome-wide allele frequencies from each cohort. For a given gene expression prediction model generated from an eQTL dataset, we use TESLA to more accurately estimate phenotypic effects, then use them to perform TWASs and attain optimal power. We also performed fine mapping and enrichment analysis using the TESLA results (which we call eTESLA).
Manhattan plot for multi-tissue TESLA results using GTEx for CigDay phenotype For each chromosome, we labeled the fine-mapped genes with posterior inclusion probability (PIP) > 0.9 (with P < 2.5 × 10⁻⁶). If more than ten genes were significant for a chromosome, only the top ten genes were labeled. The Manhattan plot for other traits can be found in Supplementary Fig. 2. All P values are two sided. We have now labeled the fine-mapped genes with PIP > 0.9 in the Manhattan plot. For smoking initiation trait, there are a large number of fine-mapped signals, so we labeled only ten genes per chromosome with the largest PIP values.
Key addiction-related pathways are ubiquitously enriched with TESLA hits in multiple brain tissues We displayed TESLA enrichment P values (two sided) across 13 GTEx brain tissues using radar plots. a,b, The enrichment of TESLA hits for cigarettes per day for the dopaminergic synapse pathways (a) and the behavioral response to nicotine pathways (b). Gridlines in the radar plots indicate different levels of statistical significance. Each spoke represents a brain tissue and the length of the spoke represents the −log10(P) of enrichment. Brain tissues with significant enrichment P values after multiple testing corrections are shown in red. CC, cellular component; BP, biological process.
Different brain tissues are enriched with distinct pathways We used REVIGO to reduce redundant GO terms and facilitate the visualization of enrichment results. We highlighted three brain regions (that is, cortex, substantia nigra and cerebellum) with distinct patterns of enrichment. For brain cortex, one GO term (relaxation of smooth muscle) accounts for 98.3% of the pathways enriched with TWAS hits, whereas, for substantia nigra and cerebellum, a diverse set of GO terms was enriched with TWAS hits. The brain figures are generated by R package ggseg⁴³.
TESLA identified substantially more loci and new loci than FE-TWASs, RE-TWASs and EURO-TWASs using GTEx data and PrediXcan weights
Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing
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Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction. A multi-ancestry transcriptome-wide association study using an optimal linear combination of association statistics provides insights into tobacco use biology and suggests opportunities for drug repurposing.
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Burdens and mutational signatures in normal human small intestinal crypts a, SBS burden versus age, showing regression lines for the three different sectors of the small intestine. Regression lines were estimated using linear mixed-effects models. Error bands represent 95% confidence interval for the fixed effect of age. Colors indicate biopsy regions, with orange, green and blue representing duodenum, ileum and jejunum, respectively. Shapes indicate whether the donor has a celiac history or not. Crosses indicate donors with a celiac history, and dots indicate donors without a celiac history. b, ID burden versus age, showing regression lines for the three different sectors of the small intestine. c, The proportion of mutations in each crypt attributed to each SBS mutational signature (arranged by ascending age). Signatures are color coded as indicated on the right.
APOBEC mutagenesis on phylogenetic trees Phylogenies of small intestine crypts with mutational signature annotation. Branch lengths correspond to SBS burdens. Signatures exposures are color coded below the trees. a, Phylogeny of PD41851, an individual with frequent APOBEC mutagenesis exhibiting SBS1, SBS5 and SBS18 with SBS2/SBS13 in 8 of 11 crypts. b, Phylogeny of PD41853, exhibiting SBS1, SBS5 and SBS18 with SBS2/SBS13 in 1 of 11 crypts. c, Phylogeny of PD43953, a 4-year-old child exhibiting SBS1, SBS5, SBS18 and SBS2/SBS13. d, Phylogeny of PD41852 with APOBEC mutagenesis detected before a crypt fission event at ~25 years of age. e, Phylogeny of PD43401 with APOBEC mutagenesis detected after a crypt fission event at ~30 years of age. f, Phylogeny of PD43403 with APOBEC mutagenesis detected after a crypt fission event at ~30 years of age.
Spatial distribution of APOBEC-positive crypts APOBEC-positive crypts and their surrounding crypts before microdissection with their SBS mutational spectrums. Signatures exposures are color coded on top left. Red dots, APOBEC-positive crypts. Blue dots, APOBEC-negative crypts that have been sequenced. Gray rectangles on the mutational spectra circle characteristic peaks of SBS2/SBS13. a, PD43401, this individual has one APOBEC-positive crypt but all the remaining crypts in the neighborhood are negative. b, PD52487, with an APOBEC-negative crypt (PD52487b_lo0005) between APOBEC-positive crypts. c, PD45778, an APOBEC-negative crypt (PD45778b_lo0002) between APOBEC-positive crypts.
Local mutation clusters (kataegis) in the normal small intestine a,b, Two examples of crypts with kataegis. Types of SBSs are indicated by the color code below. Red arrowheads show the location of kataegis.a, Rainfall plot of a crypt from PD28690 showing a mutation cluster on chromosome 16. b, Rainfall plot of a crypt from PD28690 showing mutation clusters on chromosomes 7, 14 and 22.
APOBEC1/APOBEC3A/APOBEC3B expression across normal tissues Bulk tissue gene APOBEC expression from The HPA project consensus dataset³⁸. Image credit: HPA (v21.proteinatlas.org). a, APOBEC1 bulk tissue gene expression (https://v21.proteinatlas.org/ENSG00000111701-APOBEC1/tissue). Duodenum nTPM = 29, small intestine nTPM = 26 and colon nTPM = 1.1. b, APOBEC3A bulk tissue gene expression (https://v21.proteinatlas.org/ENSG00000128383-APOBEC3A/tissue). Duodenum nTPM = 1.4, small intestine nTPM = 0.8 and colon nTPM = 1.5. c, APOBEC3B (https://v21.proteinatlas.org/ENSG00000179750-APOBEC3B/tissue) bulk tissue gene expression. Duodenum nTPM = 7.3, small intestine nTPM = 4.3 and colon nTPM = 12.1.
APOBEC mutagenesis is a common process in normal human small intestine
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APOBEC mutational signatures SBS2 and SBS13 are common in many human cancer types. However, there is an incomplete understanding of its stimulus, when it occurs in the progression from normal to cancer cell and the APOBEC enzymes responsible. Here we whole-genome sequenced 342 microdissected normal epithelial crypts from the small intestines of 39 individuals and found that SBS2/SBS13 mutations were present in 17% of crypts, more frequent than most other normal tissues. Crypts with SBS2/SBS13 often had immediate crypt neighbors without SBS2/SBS13, suggesting that the underlying cause of SBS2/SBS13 is cell-intrinsic. APOBEC mutagenesis occurred in an episodic manner throughout the human lifespan, including in young children. APOBEC1 mRNA levels were very high in the small intestine epithelium, but low in the large intestine epithelium and other tissues. The results suggest that the high levels of SBS2/SBS13 in the small intestine are collateral damage from APOBEC1 fulfilling its physiological function of editing APOB mRNA. Whole-genome sequencing of healthy human epithelial crypts from the small intestines of 39 individuals highlights APOBEC enzymes as a common contributor to the overall mutational burden in this tissue.
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The Singapore National Precision Medicine Strategy
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Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic–phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world’s population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption. This Perspective article discusses Singapore’s efforts to implement a National Precision Medicine Strategy through the integration of genomic, clinical and lifestyle data of up to one million Singaporean individuals.
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Effect size distribution and pleiotropic associations a, Relationship between VTE risk alleles and risk allele frequency. OR values for VTE of the 93 lead variants in the VTE GWAS meta-analysis (81,190 cases and 1,419,671 controls) plotted against risk allele frequency. Variants with OR > 1.3 are annotated. The color of each point indicates whether the locus is known (gray) or novel (red). Note that variants SERPINC1 and P2RX3 are represented by a single point, due to similar risk allele frequency and effect size. b, Effect sizes for DVT (x axis) and PE (y axis) for the 93 lead VTE variants. Points represent effect estimates (log(OR), measure of center) and error bars represent 95% CI, estimated using GWAS meta-analysis data from deCODE, UKB, FinnGen and the CHB-CVDC and DBDS. Summary data from the four cohorts were combined using inverse-variance-weighted fixed-effects meta-analysis. c, Heatmap showing nominally significant associations (P < 0.05) between lead variants at the 93 VTE risk loci and 24 selected blood traits. Blood cell traits (n ≤ 870,000) were derived from GWAS summary statistics while data on coagulation factors (n ~36,000) were derived from proteomics data. Bonferroni significant associations, after correcting for testing of 24 traits and 93 lead variants (P < 2.2 × 10⁻⁵), are marked by an asterisk (*). P values (two-sided) were derived from linear regression models. Coloring represents effect estimate (beta) for each respective trait, all oriented according to the VTE risk allele. Red and blue indicate an increase or decrease, respectively, in the trait. APTT, activated partial thromboplastin time; PAI-1, plasminogen activator inhibitor-1.
Gene prioritization Displayed are genome-wide significant loci mapped to candidate genes based on at least two lines of evidence, namely: PoPS, effect on gene expression (eQTL), plasma protein levels (pQTL) and predicted effects on protein function (coding). The first column shows whether the gene was associated with PoPS z > 1; the second column shows whether there was evidence for colocalization (posterior probability > 0.75) between lead variant and eQTL variant; the third column shows whether the lead variant or variant in high LD (r² > 0.8) was associated with plasma protein levels; the fourth column shows whether the lead variant or variant in high LD (r² > 0.8) was protein coding.
Phenome-wide associations between PRSVTE and selected cardiometabolic traits, autoimmune disorders, malignancies and biomarkers in the UKB a, Association between PRSVTE and binary traits. b, Association between PRSVTE and quantitative traits. Data are presented as beta (points, measure of center) and 95% CI (error bars) per 1 s.d. increase in PRSVTE. Beta estimates, CI and two-sided P values were calculated using either logistic (a) or linear regression (b). Significant associations are highlighted in red (P < 0.001 (0.05 / 49 traits)). Point size corresponds to the level of significance. Abbreviations, effect estimates, P values and sample sizes are shown in Supplementary Table 17. COPD, chronic pulmonary obstructive disease; AAA, abdominal aortic aneurysm; ALAT, alanine aminotransferase.
PRSVTE and risk of VTE in the UKB a, Phenotypic variance (R²) explained by four different PRSs: 5-SNP PRS by de Haan et al.³⁸, 297-SNP PRS by Klarin et al.¹², 93-SNP PRS (this study) and ~1.1 million-SNP PRS (this study). b, OR of VTE per 1 s.d. increase in PRS according to the four PRSs. Points refer to OR (measure of center) and error bars represent 95% CI. The model included 23,723 cases of VTE (prevalent and incident) and 412,717 controls. Logistic regression models were used, adjusted for age, sex and four PCs. c, Risk of VTE according to polygenic and monogenic carrier status. OR, 95% CIs and two-sided P values were obtained using logistic regression models adjusted for age, sex and first four PCs. For PRS analyses, each percentile was compared with the rest of the population. For monogenic risk assessment, noncarriers were set as the reference group. The second column shows the number of carriers and noncarriers for each comparison, while the third column shows the number of VTE events among carriers and noncarriers. Black squares indicate adjusted OR and horizontal lines represent 95% CI. d, Plot showing predictive performance of PRS in relation to known demographic and clinical risk factors. Points represent AUC (measure of center), the triangle the benchmark model (age, sex and PCs) and error bars 95% CI.
Ten-year risk of VTE according to PRS and clinical and genetic risk factors a, Ten-year cumulative risk curves showing interplay between PRS and F2 (rs1799963) carrier status in individuals without previous VTE. b, 10-year cumulative risk curves showing interplay between PRS and F5 (rs6025) carrier status in individuals without previous VTE. a,b, Population average was defined as individuals with a PRS between the 10th and 90th percentile. c, Ten-year risk of incident VTE according to combinations of age group, sex (male/female), obesity (yes/no), regular exercise (yes/no), smoking (yes/no) and PRS.
Genome-wide meta-analysis identifies 93 risk loci and enables risk prediction equivalent to monogenic forms of venous thromboembolism
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We report a genome-wide association study of venous thromboembolism (VTE) incorporating 81,190 cases and 1,419,671 controls sampled from six cohorts. We identify 93 risk loci, of which 62 are previously unreported. Many of the identified risk loci are at genes encoding proteins with functions converging on the coagulation cascade or platelet function. A VTE polygenic risk score (PRS) enabled effective identification of both high- and low-risk individuals. Individuals within the top 0.1% of PRS distribution had a VTE risk similar to homozygous or compound heterozygous carriers of the variants G20210A (c.*97 G > A) in F2 and p.R534Q in F5. We also document that F2 and F5 mutation carriers in the bottom 10% of the PRS distribution had a risk similar to that of the general population. We further show that PRS improved individual risk prediction beyond that of genetic and clinical risk factors. We investigated the extent to which venous and arterial thrombosis share clinical risk factors using Mendelian randomization, finding that some risk factors for arterial thrombosis were directionally concordant with VTE risk (for example, body mass index and smoking) whereas others were discordant (for example, systolic blood pressure and triglyceride levels).
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