Jamie Robinson’s research while affiliated with University of Bristol and other places

Background The brain’s default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. However, the genetic basis linking sociability with DMN function remains underexplored. This study aimed to elucidate the shared genetics and causal relationship between sociability and DMN-related resting-state functional MRI (rs-fMRI) traits. Methods We conducted a comprehensive genomic analysis using large-scale genome-wide association study (GWAS) summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits ( N = 34,691–342,461). We performed global and local genetic correlations analyses and bi-directional Mendelian randomization (MR) to assess shared and causal effects. We prioritized genes influencing both sociability and rs-fMRI traits by combining expression quantitative trait loci MR analyses, the CELLECT framework – integrating single-nucleus RNA sequencing (snRNA-seq) data with GWAS – and network propagation within a protein–protein interaction network. Results Significant local genetic correlations were identified between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the cingulate and angular/temporal cortices. MR analyses suggested potential causal effects of sociability on 12 rs-fMRI traits. Seventeen genes were highly prioritized, with LINGO1 , ELAVL2 , and CTNND1 emerging as top candidates. Among these, DRD2 was also identified, serving as a robust internal validation of our approach. Conclusions By combining genomic and transcriptomic data, our gene prioritization strategy may serve as a blueprint for future studies. Our findings can guide further research into the biological mechanisms underlying sociability and its role in the development, prognosis, and treatment of neuropsychiatric disorders.

Immune dysfunction is implicated in the aetiology of psychiatric, neurodevelopmental, and neurodegenerative conditions, but the issue of causality remains unclear impeding attempts to develop new interventions. Using genomic data on protein and gene expression across blood and brain, we assessed evidence of a potential causal role for 736 immune response-related biomarkers on 7 neuropsychiatric conditions by applying Mendelian randomization (MR) and genetic colocalisation analyses. A systematic three-tier approach, grouping biomarkers based on increasingly stringent criteria, was used to appraise evidence of causality (passing MR sensitivity analyses, colocalisation, False Discovery Rate and Bonferroni thresholds). We provide evidence for a potential causal role of 29 biomarkers for 7 conditions. The identified biomarkers suggest a role of both brain specific and systemic immune response in the aetiology of schizophrenia, Alzheimer’s disease, depression, and bipolar disorder. Of the identified biomarkers, 20 are therapeutically tractable, including ACE, TNFRSF17, SERPING1, AGER and CD40, with drugs currently approved or in advanced clinical trials. Based on the largest available selection of plasma immune-response related biomarkers, our study provides insight into possible influential biomarkers for the aetiology of neuropsychiatric conditions. These genetically prioritised biomarkers now require examination to further evaluate causality, their role in the aetiological mechanisms underlying the conditions, and therapeutic potential.

Background Genetic variants associated with molecular traits that are also associated with liability to glioma can provide causal evidence for the identification and prioritisation of drug targets. Methods We performed comprehensive two-sample Mendelian randomisation (Wald ratio and/or IVW) and colocalisation analyses of molecular traits on glioma. Instrumentable traits (QTLs P < 5 × 10−8) were identified amongst 11 985 gene expression measures, 13 285 splicing isoforms and 10 198 protein abundance measures, derived from 15 brain regions. Glioma summary-level data was extracted from a genome-wide association meta-analysis of 12 496 cases and 18 190 controls. Results We found evidence for causal effect of 22 molecular traits (across 18 genes/proteins) on glioma risk. Thirteen molecular traits have been previously linked with glioma risk and five were novel; HBEGF (5q31.3) expression and all glioma [OR 1.36 (95%CI 1.19–1.55); P = 4.41 × 10−6]; a CEP192 (18p11.21) splice isoform and glioblastoma [OR 4.40 (95%CI 2.28–8.48); P = 9.78 × 10−4]; a FAIM (3q22.3) splice isoform and all glioma [OR 2.72–3.43; P = 1.03 × 10−5 to 1.09 × 10−5]; a SLC8A1 (2p22.1) splice isoform and all glioma [OR 0.37 (95%CI 0.24–0.56; P = 5.72 × 10−6]; D2HGDH (2q37.3) protein and all glioma [OR 0.86 (95%CI 0.80–0.92); P = 5.94 × 10−6)]. Conclusions We provide robust causal evidence for prioritising genes and their protein products in glioma research. Our results highlight the importance of alternative splicing as a mechanism in gliomagenesis and as an avenue for exploration of drug targets.

We evaluated the effect of sodium-glucose cotransporter 2 (SGLT2) inhibition on prostate cancer by evidence triangulation. Using Mendelian randomization, we found that genetically proxied SGLT2 inhibition reduced the risk of overall (odds ratio = 0.56, 95% confidence interval [CI] = 0.38 to 0.82; 79,148 prostate cancer cases and 61,106 controls), advanced, and early-onset prostate cancer. Using electronic healthcare data (nSGLT2i = 24,155; nDPP4i = 24,155), we found that the use of SGLT2 inhibitors was associated with a 23% reduced risk of prostate cancer (hazard ratio = 0.77, 95% CI = 0.61 to 0.99) in men with diabetes. Using data from two prospective cohorts (n4C = 57,779; nUK_Biobank = 165,430), we found little evidence to support the association of HbA1c with prostate cancer, implying a non-glycemic effect of SGLT2 inhibition on prostate cancer. In summary, this study provides multiple layers of evidence to support the beneficial effect of SGLT2 inhibition on reducing prostate cancer risk. Future trials are warranted to investigate whether SGLT2 inhibitors can be recommended for prostate cancer prevention.

The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. In the present study, we examined the genetic relationship between sociability and DMN-related resting-state functional magnetic resonance imaging (rs-fMRI) traits. To this end, we used genome-wide association summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N=34,691-342,461). First, we examined global and local genetic correlations between sociability and the rs-fMRI traits. Second, to assess putatively causal relationships between the traits, we conducted bi-directional Mendelian randomisation (MR) analyses. Finally, we prioritised genes influencing both sociability and rs-fMRI traits by combining three methods: gene-expression eQTL MR analyses, the CELLECT framework using single-nucleus RNA-seq data, and network propagation in the context of a protein-protein interaction network. Significant local genetic correlations were found between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the frontal/cingulate and angular/temporal cortices. Sociability affected 12 rs-fMRI traits when allowing for weakly correlated genetic instruments. Combing all three methods for gene prioritisation, we defined 17 highly prioritised genes, with DRD2 and LINGO1 showing the most robust evidence across all analyses. By integrating genetic and transcriptomics data, our gene prioritisation strategy may serve as a blueprint for future studies. The prioritised genes could be explored as potential biomarkers for social dysfunction in the context of neuropsychiatric disorders and as drug target genes.

Background/Objectives Glioma represents the largest entity of primary brain tumours in adults, with an overall survival of less than 20% over 5 years. Glioblastoma is the most frequent and aggressive glioma subtype. At present, there are few well-established pre-clinical predictors for glioma incidence. Due to the availability and size of prognostic studies in glioma, we utilised a Mendelian randomization framework to identify non-causal protein biomarkers which are associated with early-onset of glioma in the European population. Methods We generated polygenic risk scores (PRS) for glioma (n=12,496), glioblastoma (n=6,191), and non-glioblastoma (n=5,819) cases. We used reverse Mendelian randomization (MR) to examine the relationship between the genetic liability of glioma and 1,463 and 90 proteins were measured using an Olink panel (UKBB, n=35,571 and SCALLOP, n=21,758), additionally 4,907 and 2,994 aptamers were assayed using SOMAscan assays (deCODE n=35,559 and INTERVAL, n=3,301). We further performed a forward cis-MR and colocalization analysis leveraging the circulating protein markers in risk of glioma, glioblastoma and non-glioblastoma. Results Reverse MR identified 161 unique proteins associated with the PRS of glioma, 79 proteins associated with the PRS of glioblastoma, and 11 proteins associated with the PRS of non-glioblastoma. Enrichment analyses identified a proportion of plasma proteins to be associated with the PRS of glioma to be correlated with response to external stimulus. A group of plasma proteins linked to the PRS of glioma and glioblastoma were related to the immune system process. Forward MR of the putative relationships were found to have little or no evidence of association on the causal pathway. Candidate markers ETFA, RIR1 and BT3A1 are evidenced in glioma risk. Conclusion Our findings identify a high genetic liability to glioma is associated with the immune system processes. Non-causal plasma biomarkers identified through PRS associations could indicate novel non-causal biomarkers of early glioma development.

Immune dysfunction is implicated in the aetiology of psychiatric, neurodevelopmental, and neurodegenerative conditions, but the issue of causality remains unclear impeding attempts to develop new interventions. We have tested evidence for causality for 735 immune response-related biomarkers on 7 neuropsychiatric conditions, using cutting-edge genomic causal inference methods (Mendelian randomization and genetic colocalization) applied to genomic data on protein and gene expression across blood and brain. We provide robust evidence of causality for 21 biomarkers, including two previously unreported (LATS1, and FCN1), confirming a role of both brain specific and systemic immune response in the pathogenesis of several neuropsychiatric conditions especially schizophrenia, Alzheimers disease, depression, and bipolar disorder. Furthermore, 18 of the identified biomarkers are therapeutically tractable, including ACE, TNFRSF17, and CD40, with drugs approved or in advanced clinical trials, offering a potential opportunity for drug repurposing.

Objectives Previous studies have suggested that fibrates and glitazones may have a role in brain tumour prevention. We examined if there is support for these observations using primary care records from the UK Clinical Practice Research Datalink (CPRD). Design We conducted two nested case–control studies using primary and secondary brain tumours identified within CPRD between 2000 and 2016. We selected cases and controls among the population of individuals who had been treated with any anti-diabetic or anti-hyperlipidaemic medication to reduce confounding by indication. Setting Adults older than 18 years registered with a general practitioner in the UK contributing data to CPRD. Results We identified 7496 individuals with any brain tumour (4471 primary; 3025 secondary) in total. After restricting cases and controls to those prescribed any anti-diabetic or anti-hyperlipidaemic medication, there were 1950 cases and 7791 controls in the fibrate and 480 cases with 1920 controls in the glitazone analyses. Longer use of glitazones compared with all other anti-diabetic medications was associated with a reduced risk of primary (adjusted OR (aOR) 0.89 per year, 95% CI 0.80 to 0.98), secondary (aOR 0.87 per year, 95% CI 0.77 to 0.99) or combined brain tumours (aOR 0.88 per year, 95% CI 0.81 to 0.95). There was little evidence that fibrate exposure was associated with risk of either primary or secondary brain tumours. Conclusions Longer exposure to glitazones was associated with reduced primary and secondary brain tumour risk. Further basic science and population-based research should explore this finding in greater detail, in terms of replication and mechanistic studies.

Background Tumour-promoting inflammation is a “hallmark” of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear. Methods We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 × 10⁻⁸) cis-acting SNPs (i.e., in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r² < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P-value (“q-value”) <0.05 was used as a threshold to define “strong evidence” to support associations and 0.05 ≤ q-value < 0.20 to define “suggestive evidence”. A colocalisation posterior probability (PPH4) >70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis. Findings We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR: 1.19, 95% CI: 1.10–1.29, q-value = 0.033, PPH4 = 84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR: 1.42, 95% CI: 1.20–1.69, q-value = 0.055, PPH4 = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53–0.81, q-value = 0.067, PPH4 = 81.8%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR: 0.92, 95% CI: 0.88–0.97, q-value = 0.15, PPH4 = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48–4.10, q-value = 0.072, PPH4 = 76.1%), this finding was not replicated when pooled with the FinnGen study. For 22 of 30 cancer outcomes examined, there was little evidence (q-value ≥0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk. Interpretation Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated. Funding 10.13039/501100000289Cancer Research UK (C68933/A28534, C18281/A29019, PPRCPJT∖100005), 10.13039/501100000321World Cancer Research Fund (IIG_FULL_2020_022), 10.13039/501100000272National Institute for Health Research (NIHR202411, BRC-1215-20011), 10.13039/501100000265Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4), 10.13039/501100002341Academy of Finland Project 326291, European Union's 10.13039/501100007601Horizon 2020 grant agreement no. 848158 (EarlyCause), 10.13039/501100006364French National Cancer Institute (INCa SHSESP20, 2020-076), 10.13039/501100012041Versus Arthritis (21173, 21754, 21755), 10.13039/100000002National Institutes of Health (U19 CA203654), 10.13039/100000054National Cancer Institute (U19CA203654).