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Biological knowledge-driven analysis of epistasis in human GWAS with application to lipid traits.

TitleBiological knowledge-driven analysis of epistasis in human GWAS with application to lipid traits.
Publication TypeJournal Article
Year of Publication2015
AuthorsMa L, Keinan A
Secondary AuthorsClark AG
JournalMethods Mol Biol
Date Published2015
KeywordsAged, Aged, 80 and over, Cholesterol, HDL, Cohort Studies, Epistasis, Genetic, Genetic Loci, Genome-Wide Association Study, Humans, Knowledge, Lipids, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Reproducibility of Results

While the importance of epistasis is well established, specific gene-gene interactions have rarely been identified in human genome-wide association studies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to detect interactions, we only tested pairs of SNPs filtered by prior biological knowledge, including GWAS results, protein-protein interactions (PPIs), and pathway information. Using published GWAS and 9,713 European Americans (EA) from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and LIPC affecting high-density lipoprotein cholesterol (HDL-C) levels. We then validated this interaction in additional multiethnic cohorts from ARIC, the Framingham Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Both HMGCR and LIPC are involved in the metabolism of lipids and lipoproteins, and LIPC itself has been marginally associated with HDL-C. Furthermore, no significant interaction was detected using PPI and pathway information, mainly due to the stringent significance level required after correcting for the large number of tests conducted. These results suggest the potential of biological knowledge-driven approaches to detect epistatic interactions in human GWAS, which may hold the key to exploring the role gene-gene interactions play in connecting genotypes and complex phenotypes in future GWAS.

Alternate JournalMethods Mol Biol
PubMed ID25403526
PubMed Central IDPMC4930274
Grant ListR01 GM108805 / GM / NIGMS NIH HHS / United States
R01 HG003229 / HG / NHGRI NIH HHS / United States