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APOEModulates the Correlation Between Triglycerides, Cholesterol, and CHD Through Pleiotropy, and Gene-by-Gene Interactions
Author(s) -
Taylor J. Maxwell,
Christie M. Ballantyne,
James M. Cheverud,
Cameron Guild,
Chiadi E. Ndumele,
Eric Boerwinkle
Publication year - 2013
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1534/genetics.113.157719
Subject(s) - apolipoprotein e , pleiotropy , allele , biology , genetics , genotype , gene , correlation , quantitative trait locus , medicine , disease , phenotype , geometry , mathematics
Relationship loci (rQTL) exist when the correlation between multiple traits varies by genotype. rQTL often occur due to gene-by-gene (G × G) or gene-by-environmental interactions, making them a powerful tool for detecting G × G. Here we present an empirical analysis of apolipoprotein E (APOE) with respect to lipid traits and incident CHD leading to the discovery of loci that interact with APOE to affect these traits. We found that the relationship between total cholesterol (TC) and triglycerides (ln TG) varies by APOE isoform genotype in African-American (AA) and European-American (EA) populations. The e2 allele is associated with strong correlation between ln TG and TC while the e4 allele leads to little or no correlation. This led to a priori hypotheses that APOE genotypes affect the relationship of TC and/or ln TG with incident CHD. We found that APOE*TC was significant (P = 0.016) for AA but not EA while APOE*ln TG was significant for EA (P = 0.027) but not AA. In both cases, e2e2 and e2e3 had strong relationships between TC and ln TG with CHD while e2e4 and e4e4 results in little or no relationship between TC and ln TG with CHD. Using ARIC GWAS data, scans for loci that significantly interact with APOE produced four loci for African Americans (one CHD, one TC, and two HDL). These interactions contribute to the rQTL pattern. rQTL are a powerful tool to identify loci that modify the relationship between risk factors and disease and substantially increase statistical power for detecting G × G.

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