Identifying gene–gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts

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• 作者：Rishika De ; Shefali S. Verma ; Emily Holzinger ; Molly Hall ; Amber Burt…
• 刊名：Human Genetics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：136
• 期：2
• 页码：165-178
• 全文大小：
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Human Genetics; Molecular Medicine; Gene Function; Metabolic Diseases;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1203
• 卷排序：136

文摘

Genetic loci explain only 25–30 % of the heritability observed in plasma lipid traits. Epistasis, or gene–gene interactions may contribute to a portion of this missing heritability. Using the genetic data from five NHLBI cohorts of 24,837 individuals, we combined the use of the quantitative multifactor dimensionality reduction (QMDR) algorithm with two SNP-filtering methods to exhaustively search for SNP–SNP interactions that are associated with HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), total cholesterol (TC) and triglycerides (TG). SNPs were filtered either on the strength of their independent effects (main effect filter) or the prior knowledge supporting a given interaction (Biofilter). After the main effect filter, QMDR identified 20 SNP–SNP models associated with HDL-C, 6 associated with LDL-C, 3 associated with TC, and 10 associated with TG (permutation P value <0.05). With the use of Biofilter, we identified 2 SNP–SNP models associated with HDL-C, 3 associated with LDL-C, 1 associated with TC and 8 associated with TG (permutation P value <0.05). In an independent dataset of 7502 individuals from the eMERGE network, we replicated 14 of the interactions identified after main effect filtering: 11 for HDL-C, 1 for LDL-C and 2 for TG. We also replicated 23 of the interactions found to be associated with TG after applying Biofilter. Prior knowledge supports the possible role of these interactions in the genetic etiology of lipid traits. This study also presents a computationally efficient pipeline for analyzing data from large genotyping arrays and detecting SNP–SNP interactions that are not primarily driven by strong main effects.