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

Emily R. Holzinger, Shefali S. Verma, Carrie B. Moore, Molly Hall, Rishika De, Diane Gilbert-Diamond, Matthew B. Lanktree, Nathan Pankratz, Antoinette Amuzu, Amber Burt, Caroline Dale, Scott Dudek, Clement E. Furlong, Tom R. Gaunt, Daniel Seung Kim, Helene Riess, Suthesh Sivapalaratnam, Vinicius Tragante, Erik P.A. Van Iperen, Ariel BrautbarDavid S. Carrell, David R. Crosslin, Gail P. Jarvik, Helena Kuivaniemi, Iftikhar J. Kullo, Eric B. Larson, Laura J. Rasmussen-Torvik, Gerard Tromp, Jens Baumert, Karen J. Cruickshanks, Martin Farrall, Aroon D. Hingorani, G. K. Hovingh, Marcus E. Kleber, Barbara E. Klein, Ronald Klein, Wolfgang Koenig, Leslie A. Lange, Winfried MOrz, Kari E. North, N. Charlotte Onland-Moret, Alex P. Reiner, Philippa J. Talmud, Yvonne T. Van Der Schouw, James G. Wilson, Mika Kivimaki, Meena Kumari, Jason H. Moore, Fotios Drenos, Folkert W. Asselbergs, Brendan J. Keating, Marylyn D. Ritchie

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number25
JournalBioData Mining
Volume10
Issue number1
DOIs
StatePublished - Jul 24 2017

Bibliographical note

Funding Information:
This study was supported in part by grant R01-HL095603 and R01-HL59367. The following parent studies have contributed parent study data, ancillary study data, and DNA samples through the Broad Institute of Harvard University and the Massachusetts Institute of Technology (N01-HC-65226) to create the CARe data base for this project: ARIC: N01-HC-55015, N01-HC-55016, N01-HC-55021, N01-HC-55019, N01-HC-55020, N01-HC-55017, and N01-HC-55018; BDES: U10EY06594; BOSS: R01AG021917; CARDIA: HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, HHSN268200900041C, and AG032136; CHS: N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, HL080295, AG-023269, AG-15928, AG-20098, AG-027058, HL-075366, and P30-AG-024827; EHLS: R37AG11099; FHS: N01-HC-25195; MESA: N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169. Jason Moore: NIH grants LM009012 and LM010098. The Whitehall II has been supported by the Medical Research Council (K013351); the British Heart Foundation; the Economic and Social Research Council; the National Heart Lung and Blood Institute (NHLBI: HL36310); and the National Institute on Aging (AG13196), US, NIH. The British Women’s Heart and Health Study (BWHHS) has been supported by funding from the British Heart Foundation (BHF) and the UK Department of Health Policy Research Programme, with HumanCVD genotyping funded by the BHF (PG/07/131/24254). Genotyping for the EPIC-NL study was funded by IOP Genomics grant IGE05012 from Netherlands. CLEAR was funded by RO1 HL67406 Enterprise Agency (RVO). Folkert W. Asselbergs is supported by a Dekker scholarship-Junior Staff Member 2014 T001 – Netherlands Heart Foundation and UCL Hospitals NIHR Biomedical Research Centre. eMERGE Network (Phase I): The eMERGE Network was initiated and funded by NHGRI, in conjunction with additional funding from 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. eMERGE Network (Phase II – Year 1): The eMERGE Network was initiated and funded by 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. Emily Rose Holzinger is supported by the Postdoctoral Research Associate Training program of the National Institute for General Medical Sciences (NIH) and the following Protocols of the Intramural Program of the National Human Genome Research Institute (NIH): Z01 HG00153–08-IDRB and Z01 HG-200327-02 SG.

Funding Information:
The IBC array data (also known as ‘Cardiochip’ or ‘CVDSNP55v1_A’ from the National Heart, Lung and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe) was downloaded with appropriate permissions from the database of Genotypes and Phenotypes (dbGaP) (https://www.ncbi.nlm.nih.gov/gap). We wish to thanks the CARe parent sites, investigators and patients who participated in the creation of this dataset. This work was supported in part by the Intramural Research Programs of the National Human Genome Research Institute, a part of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Publisher Copyright:
© 2017 The Author(s).

Keywords

  • Computational genetics
  • Genetic epidemiology
  • Genetics
  • Interactions
  • Lipids

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