👤 Emily R Holzinger

Integration of human genomics and other omics across different ancestries provides novel, affordable, and systematic approach for target identification. We used Mendelian randomization approaches to unravel causal associations between 2,940 circulating proteins and 19 CVD. We found 218 proteins that impacted risk of one or more CVDs through forward MR (106 and 182 using cis-pQTLs only and cis- + trans-pQTLs, respectively), among which 107 were previously reported as associated with CVD or CVD-related traits. There were 102 proteins replicated (FDR < 5%, 53 with cis-pQTLs only and 88 with cis- + trans-pQTLs) using the FinnGen Olink data. BTN3A2 was highlighted as a novel candidate gene for ischemic stroke, suggesting a crosstalk between immune modulation and stroke pathogenesis. Single cell integration prioritized PAM for stable angina pectoris and ventricular arrhythmia and LPL for peripheral artery disease, whose transcriptional expressions were enriched in cardiomyocytes. Forward and reverse MR found largely non-overlapping proteins (only 2 overlapped: LGALS4 and MMP12), suggesting distinct proteomic causes and consequences of CVD. Our study provides human genetics-based evidence of novel candidate genes, a foundational step towards full-scale causal human biology-based drug discovery for CVD. Show less

Despite heritability estimates of 40-70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics. Show less