Verma, Shefali S.

Background Irritable bowel syndrome (IBS) is a complex disorder of gut-brain interaction, with heterogeneous symptoms, no available biomarkers and limited pathogenetic insight. Objective To identify genetic risk factors and actionable mechanisms for future clinical translation in IBS. Design We conducted a genome-wide association study (GWAS) meta-analysis of IBS in 2 775 539 individuals from 22 biobanks. IBS genetics was studied across multiple ancestries, different case definitions and symptom-related subtypes. Heritability and genetic correlations with other traits were estimated, and Mendelian randomisation was used to test causal relationships. GWAS data were functionally annotated and fine-mapped to prioritise tissues, cell types, pathways, candidate genes, specific mechanisms and druggable targets. Results Significant heritability was only detected in individuals of European ancestry, with near-identical genetic architecture across case definitions. Genetic correlations with GI, psychiatric and cardiometabolic traits were observed, including causal relationships with triglyceride (TG) levels. Functional annotation of IBS risk loci highlighted cell types and pathways relevant to brain, enteric neuro-glial and cardiometabolic domains, as well as actionable targets like GCKR, a regulator of TG metabolism. Druggability analyses converged on cardiometabolic mechanisms, including TG modulation. IBS polygenic risk scores were derived and showed a significant association with case status in an independent case-control dataset, supporting further evaluation in external population-based and clinically ascertained cohorts. Conclusions This study provides the most comprehensive assessment of IBS genetics to date, demonstrating reproducible polygenic inheritance. We link IBS risk to convergent neurogastrointestinal and novel cardiometabolic mechanisms, highlight specific biological pathways and actionable mechanisms and outline translational opportunities emerging from integrated computational analyses.

Background: Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality; however, its genetic determinants remain incompletely defined. In total, 10 previously identified risk loci explain a small fraction of AAA heritability. Methods: We performed a genome-wide association study in the Million Veteran Program testing ≈18 million DNA sequence variants with AAA (7642 cases and 172 172 controls) in veterans of European ancestry with independent replication in up to 4972 cases and 99 858 controls. We then used mendelian randomization to examine the causal effects of blood pressure on AAA. We examined the association of AAA risk variants with aneurysms in the lower extremity, cerebral, and iliac arterial beds, and derived a genome-wide polygenic risk score (PRS) to identify a subset of the population at greater risk for disease. Results: Through a genome-wide association study, we identified 14 novel loci, bringing the total number of known significant AAA loci to 24. In our mendelian randomization analysis, we demonstrate that a genetic increase of 10 mm Hg in diastolic blood pressure (odds ratio, 1.43 [95% CI, 1.24–1.66]; P =1.6×10 −6 ), as opposed to systolic blood pressure (odds ratio, 1.06 [95% CI, 0.97–1.15]; P =0.2), likely has a causal relationship with AAA development. We observed that 19 of 24 AAA risk variants associate with aneurysms in at least 1 other vascular territory. A 29-variant PRS was strongly associated with AAA (odds ratio PRS , 1.26 [95% CI, 1.18–1.36]; P PRS =2.7×10 −11 per SD increase in PRS), independent of family history and smoking risk factors (odds ratio PRS+family history+smoking , 1.24 [95% CI, 1.14–1.35]; P PRS =1.27×10 −6 ). Using this PRS, we identified a subset of the population with AAA prevalence greater than that observed in screening trials informing current guidelines. Conclusions: We identify novel AAA genetic associations with therapeutic implications and identify a subset of the population at significantly increased genetic risk of AAA independent of family history. Our data suggest that extending current screening guidelines to include testing to identify those with high polygenic AAA risk, once the cost of genotyping becomes comparable with that of screening ultrasound, would significantly increase the yield of current screening at reasonable cost.

Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a “virtual proteomic” approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08–1.58; P =0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66–0.94; P =0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

ABSTRACTBioinformatics approaches to examine gene‐gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge‐driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty‐three SNP‐SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell‐to‐cell adhesion signaling, cell‐cell junction organization, and cell‐cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP‐SNP models, which included signal transduction and PI3K‐Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology‐driven method, applicable for any genome‐wide association study dataset.