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Human Genetics

, Volume 136, Issue 2, pp 165–178 | Cite as

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

  • Rishika De
  • Shefali S. Verma
  • Emily Holzinger
  • Molly Hall
  • Amber Burt
  • David S. Carrell
  • David R. Crosslin
  • Gail P. Jarvik
  • Helena Kuivaniemi
  • Iftikhar J. Kullo
  • Leslie A. Lange
  • Matthew B. Lanktree
  • Eric B. Larson
  • Kari E. North
  • Alex P. Reiner
  • Vinicius Tragante
  • Gerard Tromp
  • James G. Wilson
  • Folkert W. Asselbergs
  • Fotios Drenos
  • Jason H. Moore
  • Marylyn D. Ritchie
  • Brendan Keating
  • Diane Gilbert-Diamond
Original Investigation

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.

Keywords

Total Cholesterol Lipid Trait eMERGE Network Discovery Dataset Hepatic Insulin Signaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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).

Compliance with ethical standards

Funding statement

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.

Conflict of interests

The authors declare that no competing interests exist.

Supplementary material

439_2016_1738_MOESM1_ESM.pdf (518 kb)
Supplementary material 1 (PDF 518 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Rishika De
    • 1
  • Shefali S. Verma
    • 2
  • Emily Holzinger
    • 2
  • Molly Hall
    • 2
  • Amber Burt
    • 3
  • David S. Carrell
    • 4
  • David R. Crosslin
    • 5
  • Gail P. Jarvik
    • 3
    • 5
  • Helena Kuivaniemi
    • 6
  • Iftikhar J. Kullo
    • 7
  • Leslie A. Lange
    • 8
  • Matthew B. Lanktree
    • 9
  • Eric B. Larson
    • 4
  • Kari E. North
    • 10
  • Alex P. Reiner
    • 11
  • Vinicius Tragante
    • 12
    • 13
  • Gerard Tromp
    • 6
  • James G. Wilson
    • 14
  • Folkert W. Asselbergs
    • 12
    • 15
    • 16
  • Fotios Drenos
    • 17
    • 18
  • Jason H. Moore
    • 19
  • Marylyn D. Ritchie
    • 24
  • Brendan Keating
    • 20
    • 21
  • Diane Gilbert-Diamond
    • 22
    • 23
  1. 1.Department of GeneticsGeisel School of Medicine at DartmouthHanoverUSA
  2. 2.The Center for Systems GenomicsThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.Division of Medical Genetics, Department of MedicineUniversity of WashingtonSeattleUSA
  4. 4.Group Health Research InstituteSeattleUSA
  5. 5.Department of Genome SciencesUniversity of WashingtonSeattleUSA
  6. 6.Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health SciencesStellenbosch UniversityTygerbergSouth Africa
  7. 7.Division of Cardiovascular DiseasesMayo ClinicRochesterUSA
  8. 8.Department of GeneticsUniversity of North Carolina School of Medicine at Chapel HillChapel HillUSA
  9. 9.Departments of Medicine and Biochemistry, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonCanada
  10. 10.Department of Epidemiology, School of Public HealthUniversity of North Carolina at Chapel HillChapel HillUSA
  11. 11.Division of Public Health SciencesFred Hutchinson Cancer Research CenterSeattleUSA
  12. 12.Department of Cardiology, Division Heart and LungsUniversity Medical Center UtrechtUtrechtThe Netherlands
  13. 13.Department of Medical Genetics, Biomedical GeneticsUniversity Medical CenterUtrechtThe Netherlands
  14. 14.Department of Physiology and BiophysicsUniversity of Mississippi Medical CenterJacksonUSA
  15. 15.Institute of Cardiovascular ScienceUniversity College LondonLondonUK
  16. 16.Durrer Center for Cardiogenetic ResearchICIN-Netherlands Heart InstituteUtrechtThe Netherlands
  17. 17.MRC Integrative Epidemiology Unit, School of Social and Community MedicineUniversity of BristolBristolUK
  18. 18.Centre for Cardiovascular Genetics, Institute of Cardiovascular ScienceUniversity College LondonLondonUK
  19. 19.Institute for Biomedical Informatics, The Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  20. 20.Penn Transplant Institute, University of PennsylvaniaPhiladelphiaUSA
  21. 21.The Children’s Hospital of PhiladelphiaPhiladelphiaUSA
  22. 22.Institute for Quantitative Biomedical Sciences at DartmouthHanoverUSA
  23. 23.Department of EpidemiologyGeisel School of Medicine at DartmouthHanoverUSA
  24. 24.Department of Biomedical and Translational InformaticsGeisinger Health SystemDanvilleUSA

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