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Updated: May 10, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in
Marc A Coram1, Qing Duan, Thomas J Hoffmann
1Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.
Abstract:
Blood lipid concentrations are heritable risk factors associated with atherosclerosis and cardiovascular diseases. Lipid traits exhibit considerable variation among populations of distinct ancestral origin as well as between individuals within a population. We performed association analyses to identify genetic loci influencing lipid concentrations in African American and Hispanic American women in the Women's Health Initiative SNP Health Association Resource. We validated one African-specific high-density lipoprotein cholesterol locus at CD36 as well as 14 known lipid loci that have been previously implicated in studies of European populations. Moreover, we demonstrate striking similarities in genetic architecture (loci influencing the trait, direction and magnitude of genetic effects, and proportions of phenotypic variation explained) of lipid traits across populations. In particular, we found that a disproportionate fraction of lipid variation in African Americans and Hispanic Americans can be attributed to genomic loci exhibiting statistical evidence of association in Europeans, even though the precise genes and variants remain unknown. At the same time, we found substantial allelic heterogeneity within shared loci, characterized both by population-specific rare variants and variants shared among multiple populations that occur at disparate frequencies. The allelic heterogeneity emphasizes the importance of including diverse populations in future genetic association studies of complex traits such as lipids; furthermore, the overlap in lipid loci across populations of diverse ancestral origin argues that additional knowledge can be gleaned from multiple populations.
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