Théo Dupuis’s research while affiliated with University of Dundee and other places

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Publications (4)


Impact of sample size and tissue relevance on T2D gene identification
  • Preprint
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November 2024

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17 Reads

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Theo Dupuis

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Andrew Brown

Identification of genes and proteins mediating the activity of GWAS variants requires molecular data from disease relevant tissues, but these may be difficult to collect. Using multiple gene expression reference datasets and GWAS summary statistics for T2D we identified 1,818 unique genes associated with T2D. Comparing the performance of different reference datasets, we found that sample size, and not the relevance of the tissue to the disease, was the critical factor in identifying relevant genes. Genes implicated using a well powered expression dataset were also more likely to have multiple lines of genetic evidence. A targeted proteomics reference dataset from plasma samples showed similar power to identify T2D related proteins as gene expression with the same sample size. Accounting for BMI reduces power across all tissues and phenotypes by ~30%, suggesting that many GWAS links to T2D are mediated by BMI, potentially implicating insulin resistance related effects. Finally, using data from smaller GWAS studies with precisely defined T2D subtypes uncovers genes directly relevant to that subtype, such as LST1, an immune response gene for Severe Autoimmune Diabetes and TRMT2A, involved in beta-cell apoptosis, for Severe Insulin Deficient Diabetes. Our work demonstrates the benefits of well powered reference datasets in accessible tissues and well-defined disease subtypes when studying complex diseases involving multiple tissues.

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Fig. 2 | Abundant pleiotropy identified across molecular phenotypes. A, B Distribution of the P values for SNP in significant (FDR < 0.05) cis-eQTL (A) as pQTLs and for SNPs in significant (FDR < 0.05) cis-pQTLs (B) as eQTLs. Most pairs showed consistent direction of effect. Data shown are the -log10 P values of the linear regressions between gene expression or protein abundances and SNPs. C Local network of QTLs for rs34097845, a SNP significantly associated with both the expression of MPO (P value = 1.7e-10, blue) and its protein (MPO, P value = 2.08e-14, orange) with a consistent direction of effect (ß expression = −0.87, ß protein = −0.40). D We identified 101 trios of expression-SNP-proteins, of which 48 involved a protein and its coding gene, while 53 involved the expression of a nearby gene different that the coding gene for the protein.
Fig. 3 | Tissue specific genetic regulation partially explains the lack of shared associations between gene expression and proteins. A Using n = 3027 biologically independent samples, we detected a cis-pQTL for CCL16 in whole blood (P value = 9.5e-243, n = 3029). The GTEx consortium reported a cis-eQTL, with the same SNP (rs10445391) affecting the expression of the gene in liver (n = 208). Violin plots show the median and first and last quartiles as defined by ggplot geom_violin function. Partially created with BioRender.com B Between 91.2% (pancreatic islets) and 71.6% (esophagus mucosa) of cis-eQTLs discovered by GTEx v8 were also active in whole blood DIRECT datasets (n = 3029) as shown by the π 1 values (y-axis). The number of P values per tissue used to calculate the π 1 estimates ranged from 334 in kidney to 14,920 in thyroid. C Comparison of the effect size of cis-eQTLs from pancreatic islets (InsPIRE) and whole blood (DIRECT). A total of 486 eQTLs were not significant in blood (P value > 0.035, orange color) but significant in pancreatic islets (n = 420) and 294 had opposite direction of effect (N = 2691). Data shown are the ß values (effect) resulting from the linear regressions between gene expression and SNPs identifying eQTLs in both studies. D Comparison of the π 1 enrichment analysis between an earlier version of GTEx (v6p) and a larger later version (v8). eQTLs from DIRECT blood detected in GTEx v8 decreased compared to v6p independently of the change in sample size across versions (Supplementary Fig. 5H). E Degree of sharing of pQTLs detected as eQTLs in GTEx v8 tissues. Up to 66.6% of plasma cis-pQTLs were also active as DIRECT whole blood cis-eQTLs. The number of overlapping QTLs across tissues oscillates between 13 (kidney) and 311 (Thyroid). F Degree of sharing of metabo-QTLs acting as cis-eQTLs in GTEx v8. Up to 16.88% (testis) of the metabo-QTLs detected in blood were active eQTLs in other tissues, with many tissues sharing no associations with metabolites-QTLs. The number of P values used to calculate π 1 values per tissues ranged from 4298 in whole blood to 6575 in testis.
Fig. 5 | QTL integration identifies regulatory networks associated to GWAS variants. A Of the GWAS signal overlapping SNPs in the full network (Supplementary Fig. 9), the largest number were cis-eSNPs followed by trans-eSNPs (Number). However, when considering the number of significant QTLs evaluated (Percentage), we observed that more metabo-SNPs were also reported GWAS followed by trans-eSNPs. The barplots show numbers and percentages of SNPs involved in QTLs that were also reported as lead GWAS by the GWAS catalogue (Supplementary Data 13). B Network of associations for the resistin gene (RETN). The RETN gene and its protein (orange node) have been associated with low density lipoproteins (LDL) levels. The regulatory network associated with the gene included GWAS variants (purple nodes) associated to RETN abundance (rs1477341); cardiovascular diseases and cholesterol levels (rs13284665); platelet counts
Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

August 2023

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291 Reads

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10 Citations

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.


Figure 4. Forest plots showing random effects of XBP1 eQTL variant on HbA1c (mmol/mol).
Allelefrequencies across ancestries and effects of lead eQTL variant on XBP1 expression in pancreatic beta-cells, T2DM risk.
XBP1 expression in pancreatic islet cells is associated with poor glycaemic control across ancestries especially in young non-obese onset diabetes

May 2023

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67 Reads

Objective Certain ethnicities such as South Asians and East Asians have higher rates of type 2 diabetes mellitus, in part, driven by insulin deficiency. Insulin deficiency can be due to beta-cell insufficiency, low beta-cell mass, or early cell death. Transcription factor XBP1 maintains beta-cell function and prevents early cell death by mitigating cellular endoplasmic reticulum stress. We examine the role of XBP1 expression in maintaining glucose homeostasis, glycaemic control, and response to diabetes therapeutics. Research Design and Methods Colocalisation analyses were used to determine if expression of XBP1 in pancreatic islets and type 2 diabetes shared common causal genetic variants. We identify a lead eQTL variant associated exclusively with XBP1 expression and examine its association HOMA-B and stimulated glucose in cohorts of newly diagnosed Asian Indians from Dr. Mohans Diabetes Specialities Centre, India (DMDSC) and the Telemedicine Project for Screening diabetes and complications in rural Tamil Nadu (TREND). We then examine longer term glycaemic control using HbA1c in Asian Indian cohorts, the Tayside Diabetes Study (TDS) of white European ancestry in Scoltand, and the Genes & Health (G&H) study of British South Asian Bangladeshi and Pakistani ancestry. Finally, we assess the effect of eQTL variant on drugs designed to improve insulin secretion (sulphonylureas and GLP1-RA). Results Variants affecting XBP1 expression in the pancreatic islets colocalised with variants associated with T2DM risk in East Asians but not in white Europeans. Lower expression of XBP1 was associated with higher risk of T2DM. rs7287124 was the lead eQTL variant and had a higher risk allele frequency in East (65%) and South Asians (50%) compared to white Europeans (25%). In 470 South Asian Indians, the variant was associated with lower beta-cell function and higher stimulated glucose (Beta log HOMAB =-0.14, P=5x10-3). Trans-ancestry meta-analysed effect of the variant in 179,668 individuals was 4.32 mmol/mol (95%CI:2.60,6.04, P=8x10-7) per allele. In 477 individuals with young onset diabetes with non-obese BMI, the per allele effect was 6.41 mmol/mol (95%CI:3.04, 9.79, P =2x10-4). Variant carriers showed impaired response to sulphonylureas. Conclusion XBP1 expression is a novel target for T2DM with particular value for individuals of under-researched ancestries who have greater risk of young, non-obese onset diabetes. The effect of XBP1 eQTL variant was found to be comparable with or greater that the effect of novel glucose-lowering therapies.


Genetic analysis of blood molecular phenotypes reveals regulatory networks affecting complex traits: a DIRECT study

March 2021

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266 Reads

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2 Citations

Genetic variants identified by genome-wide association studies can contribute to disease risk by altering the production and abundance of mRNA, proteins and other molecules. However, the interplay between molecular intermediaries that define the pathway from genetic variation to disease is not well understood. Here, we evaluated the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3,029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant was associated with multiple molecular phenotypes over multiple genomic regions. We find varying proportions of shared genetic regulation across phenotypes, highest between expression and proteins (66.6%). We were able to recapitulate a substantial proportion of gene expression genetic regulation in a diverse set of 44 tissues, with a median of 88% shared associations for blood expression and 22.3% for plasma proteins. Finally, the genetic and molecular associations were represented in networks including 2,828 known GWAS variants. One sub-network shows the trans relationship between rs149007767 and RTEN, and identifies GRB10 and IKZF1 as candidates mediating genes. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants across different molecular phenotypes.

Citations (2)


... After stringent quality control (see ESM Methods), we identified 132 (ESM Table 1) and 779 (ESM Table 2) metabolites from targeted and untargeted metabolomics measurements, respectively, that were profiled for 3000 samples (ESM Table 3) [28]. Baseline characteristics (Table 1) revealed that there were significant differences in BMI, fasting variables and health status observed between NGR, IGR and type 2 diabetes groups. ...

Reference:

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study
Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

... Many of these relevant tissues are difficult to collect pre-mortem, with consequences for our ability to discover the genetic and molecular processes involved in the development of T2D. On the other hand, gene expression studies with thousands of samples have shown that the expression of most genes is affected by multiple eQTLs and many of these effects are shared across multiple tissues 12,13 . Therefore, it is possible that larger molecular studies using samples from tissues that are easy to collect, such as blood, could identify disease relevant loci through these shared signals. ...

Genetic analysis of blood molecular phenotypes reveals regulatory networks affecting complex traits: a DIRECT study