Biology subjects

Kooperberg, C.

Publications and source records attributed to Kooperberg, C..

21 records · Page 2Linked to original sources

bioRxiv2019

Role of Rare and Low Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

BackgroundAlcohol intake influences plasma lipid levels and such effects may be modulated by genetic variants. ObjectiveWe aimed to characterize the role of aggregated rare and low-frequency variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels. DesignIn the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, fasting plasma triglycerides (TG), and high- and low-density lipoprotein cholesterol (HDL-c and LDL-c) were measured in 34,153 European Americans from five discovery studies and 32,275 individuals from six replication studies. Rare and low-frequency protein coding variants (minor allele frequency [≤] 5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction (GxE) test and a joint test of genetic main and GxE interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least two drinks per week, were considered. ResultsWe discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (PCSK9, LPA, LPL, LIPG, ANGPTL4, APOB, APOC3 and CD300LG) remained significant after conditioning on the common index single nucleotide polymorphism (SNP) identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on TG was discovered at a Bonferroni corrected significance level (p-value <5x10-5) and replicated (p-value <0.013 for the interaction test) in SMC5. ConclusionsIn conclusion, this study applied new gene-based statistical approaches to uncover the role of rare and low-frequency variants in gene-alcohol consumption interactions on lipid levels.

epidemiology↗

bioRxiv2018

Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole genome sequencing studies

With advances in Whole Genome Sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and Sequence Kernel Association Test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally-efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-Set Mixed Model Association Tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institutes Trans-Omics for Precision Medicine (TOPMed) program. SMMAT tests share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be only fit once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMAT tests correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

genetics↗

bioRxiv2018

GWAS of QRS Duration Identifies New Loci Specific to Hispanic/Latino Populations

BackgroundThe electrocardiographically quantified QRS duration measures ventricular depolarization and conduction. QRS prolongation has been associated with poor heart failure prognosis and cardiovascular mortality, including sudden death. While previous genome-wide association studies (GWAS) have identified 32 QRS SNPs across 26 loci among European, African, and Asian-descent populations, the genetics of QRS among Hispanics/Latinos has not been previously explored.\n\nMethodsWe performed a GWAS of QRS duration among Hispanic/Latino ancestry populations (n=15,124) from four studies using 1000 Genomes imputed genotype data (adjusted for age, sex, global ancestry, clinical and study-specific covariates). Study-specific results were combined using fixed-effects, inverse variance-weighted meta-analysis.\n\nResultsWe identified six loci associated with QRS (P<5x10-8), including two novel loci: MYOCD, a nuclear protein expressed in the heart, and SYT1, an integral membrane protein. The top association in the MYOCD locus, intronic SNP rs16946539, was found in Hispanics/Latinos with a minor allele frequency (MAF) of 0.04, but is monomorphic in European and African descent populations. The most significant QRS duration association was for intronic SNP rs3922344 (P= 8.56x10-26) in SCN5A/SCN10A. Three additional previously identified loci, CDKN1A, VTI1A, and HAND1, also exceeded the GWAS significance threshold among Hispanics/Latinos. A total of 27 of 32 previously identified QRS duration SNPs were shown to generalize in Hispanics/Latinos.\n\nConclusionsOur QRS duration GWAS, the first in Hispanic/Latino populations, identified two new loci, underscoring the utility of extending large scale genomic studies to currently under-examined populations.

genetics↗