Abstract
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.
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Acknowledgments
CARe acknowledges the support of the National Heart, Lung and Blood Institute and the contributions of the research institutions, study investigators, field staff, and study participants in creating this resource for biomedical research (NHLBI contract number HHSN268200960009C). The IBC array data (also known as ‘Cardiochip’ or ‘CVDSNP55v1_A’) from the National Heart, Lung and Blood Institute’s (NHLBI) Candidate Gene Association Resource (CARe) was downloaded with appropriate permissions from the database of Genotypes and Phenotypes (dbGaP) (http://www.ncbi.nlm.gov/gap). The imputed genotype data for eMERGE-I and eMERGE-II can be downloaded from the database of Genotypes and Phenotypes (dbGaP) (http://www.ncbi.nlm.gov/gap).
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This work was supported by National Institutes of Health grants: NLM R01 grants (LM0l0098, LM011360, LM009012), GMS P20 grants (GM103506, GM103534 and GM104416), and F31 HG008588. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
eMERGE Network (Phase II—Year 1) Acknowledgement
The eMERGE Network was initiated and funded by the National Human Genome Research Institute (NHGRI) through the following grants: U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); and U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers.
eMERGE Network (Phase I) Acknowledgement
The eMERGE Network was initiated and funded by the National Human Genome Research Institute (NHGRI), in conjunction with additional funding from the National Institute of General Medical Sciences (NIGMS) through the following grants: U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and Vanderbilt University Medical Center); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-04603 (Vanderbilt University Medical Center, also serving as the Administrative Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers.
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De, R., Verma, S.S., Holzinger, E. et al. Identifying gene–gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts. Hum Genet 136, 165–178 (2017). https://doi.org/10.1007/s00439-016-1738-7
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DOI: https://doi.org/10.1007/s00439-016-1738-7