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 Brautbar; David 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

doi:10.1186/s13040-017-0145-5

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

Cardiovascular Medicine

Research output: Contribution to journal › Article › peer-review

5 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 language	English (US)
Article number	25
Journal	BioData Mining
Volume	10
Issue number	1
DOIs	https://doi.org/10.1186/s13040-017-0145-5
State	Published - Jul 24 2017

Keywords

Computational genetics
Genetic epidemiology
Genetics
Interactions
Lipids

ASJC Scopus subject areas

Computational Mathematics
Genetics
Molecular Biology
Biochemistry
Computer Science Applications
Computational Theory and Mathematics

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10.1186/s13040-017-0145-5

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Holzinger, E. R., Verma, S. S., Moore, C. B., Hall, M., De, R., Gilbert-Diamond, D., Lanktree, M. B., Pankratz, N., Amuzu, A., Burt, A., Dale, C., Dudek, S., Furlong, C. E., Gaunt, T. R., Kim, D. S., Riess, H., Sivapalaratnam, S., Tragante, V., Van Iperen, E. P. A., ... Ritchie, M. D. (2017). Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Mining, 10(1), Article 25. https://doi.org/10.1186/s13040-017-0145-5

Holzinger, ER, Verma, SS, Moore, CB, Hall, M, De, R, Gilbert-Diamond, D, Lanktree, MB, Pankratz, N, Amuzu, A, Burt, A, Dale, C, Dudek, S, Furlong, CE, Gaunt, TR, Kim, DS, Riess, H, Sivapalaratnam, S, Tragante, V, Van Iperen, EPA, Brautbar, A, Carrell, DS, Crosslin, DR, Jarvik, GP, Kuivaniemi, H, Kullo, IJ, Larson, EB, Rasmussen-Torvik, LJ, Tromp, G, Baumert, J, Cruickshanks, KJ, Farrall, M, Hingorani, AD, Hovingh, GK, Kleber, ME, Klein, BE, Klein, R, Koenig, W, Lange, LA, MOrz, W, North, KE, Charlotte Onland-Moret, N, Reiner, AP, Talmud, PJ, Van Der Schouw, YT, Wilson, JG, Kivimaki, M, Kumari, M, Moore, JH, Drenos, F, Asselbergs, FW, Keating, BJ & Ritchie, MD 2017, 'Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals', BioData Mining, vol. 10, no. 1, 25. https://doi.org/10.1186/s13040-017-0145-5

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

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.",

keywords = "Computational genetics, Genetic epidemiology, Genetics, Interactions, Lipids",

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

note = "Publisher Copyright: {\textcopyright} 2017 The Author(s).",

year = "2017",

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doi = "10.1186/s13040-017-0145-5",

language = "English (US)",

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T1 - Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

AU - Holzinger, Emily R.

AU - Verma, Shefali S.

AU - Moore, Carrie B.

AU - Hall, Molly

AU - De, Rishika

AU - Gilbert-Diamond, Diane

AU - Lanktree, Matthew B.

AU - Pankratz, Nathan

AU - Amuzu, Antoinette

AU - Burt, Amber

AU - Dale, Caroline

AU - Dudek, Scott

AU - Furlong, Clement E.

AU - Gaunt, Tom R.

AU - Kim, Daniel Seung

AU - Riess, Helene

AU - Sivapalaratnam, Suthesh

AU - Tragante, Vinicius

AU - Van Iperen, Erik P.A.

AU - Brautbar, Ariel

AU - Carrell, David S.

AU - Crosslin, David R.

AU - Jarvik, Gail P.

AU - Kuivaniemi, Helena

AU - Kullo, Iftikhar J.

AU - Larson, Eric B.

AU - Rasmussen-Torvik, Laura J.

AU - Tromp, Gerard

AU - Baumert, Jens

AU - Cruickshanks, Karen J.

AU - Farrall, Martin

AU - Hingorani, Aroon D.

AU - Hovingh, G. K.

AU - Kleber, Marcus E.

AU - Klein, Barbara E.

AU - Klein, Ronald

AU - Koenig, Wolfgang

AU - Lange, Leslie A.

AU - MOrz, Winfried

AU - North, Kari E.

AU - Charlotte Onland-Moret, N.

AU - Reiner, Alex P.

AU - Talmud, Philippa J.

AU - Van Der Schouw, Yvonne T.

AU - Wilson, James G.

AU - Kivimaki, Mika

AU - Kumari, Meena

AU - Moore, Jason H.

AU - Drenos, Fotios

AU - Asselbergs, Folkert W.

AU - Keating, Brendan J.

AU - Ritchie, Marylyn D.

PY - 2017/7/24

Y1 - 2017/7/24

N2 - 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.

AB - 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.

KW - Computational genetics

KW - Genetic epidemiology

KW - Genetics

KW - Interactions

KW - Lipids

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DO - 10.1186/s13040-017-0145-5

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AN - SCOPUS:85025619193

SN - 1756-0381

VL - 10

JO - BioData Mining

JF - BioData Mining

IS - 1

M1 - 25

ER -

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

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ASJC Scopus subject areas

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