Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency.

dc.contributor.author

Kulminski, Alexander M

dc.contributor.author

Loika, Yury

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Culminskaya, Irina

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Arbeev, Konstantin G

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Ukraintseva, Svetlana V

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Stallard, Eric

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Yashin, Anatoliy I

dc.coverage.spatial

England

dc.date.accessioned

2017-06-02T15:16:49Z

dc.date.accessioned

2017-06-02T17:09:12Z

dc.date.available

2017-06-02T17:09:12Z

dc.date.issued

2016-10-14

dc.description.abstract

Common strategy of genome-wide association studies (GWAS) relying on large samples faces difficulties, which raise concerns that GWAS have exhausted their potential, particularly for complex traits. Here, we examine the efficiency of the traditional sample-size-centered strategy in GWAS of these traits, and its potential for improvement. The paper focuses on the results of the four largest GWAS meta-analyses of body mass index (BMI) and lipids. We show that just increasing sample size may not make p-values of genetic effects in large (N > 100,000) samples smaller but can make them larger. The efficiency of these GWAS, defined as ratio of the log-transformed p-value to the sample size, in larger samples was larger than in smaller samples for a small fraction of loci. These results emphasize the important role of heterogeneity in genetic associations with complex traits such as BMI and lipids. They highlight the substantial potential for improving GWAS by explicating this role (affecting 11-79% of loci in the selected GWAS), especially the effects of biodemographic processes, which are heavily underexplored in current GWAS and which are important sources of heterogeneity in the various study populations. Further progress in this direction is crucial for efficient use of genetic discoveries in health care.

dc.identifier

https://www.ncbi.nlm.nih.gov/pubmed/27739495

dc.identifier

srep35390

dc.identifier.eissn

2045-2322

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https://hdl.handle.net/10161/14753

dc.language

eng

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Springer Science and Business Media LLC

dc.relation.ispartof

Sci Rep

dc.relation.isversionof

10.1038/srep35390

dc.relation.replaces

http://hdl.handle.net/10161/14746

dc.relation.replaces

10161/14746

dc.title

Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency.

dc.type

Journal article

duke.contributor.orcid

Arbeev, Konstantin G|0000-0002-4195-7832

pubs.author-url

https://www.ncbi.nlm.nih.gov/pubmed/27739495

pubs.begin-page

35390

pubs.organisational-group

Center for Population Health & Aging

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Duke

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Duke Population Research Institute

pubs.organisational-group

Institutes and Provost's Academic Units

pubs.organisational-group

Sanford School of Public Policy

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Social Science Research Institute

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Staff

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University Institutes and Centers

pubs.publication-status

Published online

pubs.volume

6

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