👤 L Rasmussen-Torvik

Elevated triglycerides (TG) are associated with, and may be causal for, cardiovascular disease (CVD), and co-morbidities such as type II diabetes and metabolic syndrome. Pathogenic variants in APOA5 and APOC3 as well as risk SNVs in other genes [APOE (rs429358, rs7412), APOA1/C3/A4/A5 gene cluster (rs964184), INSR (rs7248104), CETP (rs7205804), GCKR (rs1260326)] have been shown to affect TG levels. Knowledge of genetic causes for elevated TG may lead to early intervention and targeted treatment for CVD. We previously identified linkage and association of a rare, highly conserved missense variant in SLC25A40, rs762174003, with hypertriglyceridemia (HTG) in a single large family, and replicated this association with rare, highly conserved missense variants in a European American and African American sample. Here, we analyzed a longitudinal mixed-ancestry cohort (European, African and Asian ancestry, N = 8966) from the Electronic Medical Record and Genomics (eMERGE) Network. We tested associations between median TG and the genes of interest, using linear regression, adjusting for sex, median age, median BMI, and the first two principal components of ancestry. We replicated the association between TG and APOC3, APOA5, and risk variation at APOE, APOA1/C3/A4/A5 gene cluster, and GCKR. We failed to replicate the association between rare, highly conserved variation at SLC25A40 and TG, as well as for risk variation at INSR and CETP. Analysis using data from electronic health records presents challenges that need to be overcome. Although large amounts of genotype data is becoming increasingly accessible, usable phenotype data can be challenging to obtain. We were able to replicate known, strong associations, but were unable to replicate moderate associations due to the limited sample size and missing drug information. Show less

Background Genome sequencing coupled with electronic heath record data can uncover medically important genetic variation. Interpretation of rare genetic variation and its role in mediating cardiovascular phenotypes is confounded by variants of uncertain significance. Methods and Results We analyzed the whole genome sequence of 900 racially and ethnically diverse biobank participants selected from a single US center. Participants were equally divided among European, African, Hispanic, and mixed races/ethnicities. We evaluated the American College of Medical Genetics and Genomics medically actionable list of 59 genes, focusing on the cardiac genes. Variation was interpreted using the most recent reports in ClinVar, a database of medically relevant human variation. We identified 19 individuals with pathogenic or likely pathogenic variants in cardiac actionable genes (2%) and found evidence of related clinical correlates in the electronic health record. Participants of African ancestry, compared with those of European ancestry, had more variants of uncertain significance in the medically actionable genes including the 30 cardiac actionable genes, even when normalized to total variant count per person. Longitudinal measures of left ventricle size from ≈400 biobank participants (1723 patient-years) were correlated with genetic findings. The presence of ≥1 uncertain variant in the actionable cardiac genes and a cardiomyopathy diagnosis correlated with increased left ventricular internal diameter in diastole and in systole. In particular, 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

Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation. Show less

Hyperglycaemia disproportionately affects African-Americans (AfAs). We tested the transferability of 18 single-nucleotide polymorphisms (SNPs) associated with glycaemic traits identified in European ancestry (EuA) populations in 5,984 non-diabetic AfAs. We meta-analysed SNP associations with fasting glucose (FG) or insulin (FI) in AfAs from five cohorts in the Candidate Gene Association Resource. We: (1) calculated allele frequency differences, variations in linkage disequilibrium (LD), fixation indices (F(st)s) and integrated haplotype scores (iHSs); (2) tested EuA SNPs in AfAs; and (3) interrogated within ± 250 kb around each EuA SNP in AfAs. Allele frequency differences ranged from 0.6% to 54%. F(st) exceeded 0.15 at 6/16 loci, indicating modest population differentiation. All iHSs were <2, suggesting no recent positive selection. For 18 SNPs, all directions of effect were the same and 95% CIs of association overlapped when comparing EuA with AfA. For 17 of 18 loci, at least one SNP was nominally associated with FG in AfAs. Four loci were significantly associated with FG (GCK, p = 5.8 × 10(-8); MTNR1B, p = 8.5 × 10(-9); and FADS1, p = 2.2 × 10(-4)) or FI (GCKR, p = 5.9 × 10(-4)). At GCK and MTNR1B the EuA and AfA SNPs represented the same signal, while at FADS1, and GCKR, the EuA and best AfA SNPs were weakly correlated (r(2) <0.2), suggesting allelic heterogeneity for association with FG at these loci. Few glycaemic SNPs showed strict evidence of transferability from EuA to AfAs. Four loci were significantly associated in both AfAs and those with EuA after accounting for varying LD across ancestral groups, with new signals emerging to aid fine-mapping. Show less