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Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

Holzinger, ER and Verma, SS and Moore, CB and Hall, M and De, R and Gilbert-Diamond, D and Lanktree, MB and Pankratz, N and Amuzu, A and Burt, A and Dale, C and Dudek, S and Furlong, CE and Gaunt, TR and Kim, DS and Riess, H and Sivapalaratnam, S and Tragante, V and Van Iperen, EPA and Brautbar, A and Carrell, DS and Crosslin, DR and Jarvik, GP and Kuivaniemi, H and Kullo, IJ and Larson, EB and Rasmussen-Torvik, LJ and Tromp, G and Baumert, J and Cruickshanks, KJ and Farrall, M and Hingorani, AD and Hovingh, GK and Kleber, ME and Klein, BE and Klein, R and Koenig, W and Lange, LA and MOrz, W and North, KE and Charlotte Onland-Moret, N and Reiner, AP and Talmud, PJ and Van Der Schouw, YT and Wilson, JG and Kivimaki, M and Kumari, M and Moore, JH and Drenos, F and Asselbergs, FW and Keating, BJ and Ritchie, MD (2017) 'Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.' BioData Mining, 10 (1). ISSN 1756-0381

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Abstract

© 2017 The Author(s). Background: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.

Item Type: Article
Subjects: H Social Sciences > H Social Sciences (General)
R Medicine > R Medicine (General)
Divisions: Faculty of Social Sciences > Institute for Social and Economic Research
Depositing User: Jim Jamieson
Date Deposited: 04 Aug 2017 11:01
Last Modified: 17 Aug 2017 17:15
URI: http://repository.essex.ac.uk/id/eprint/20173

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