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

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Date
2016-10-14
Authors
Kulminski, Alexander M
Loika, Yury
Culminskaya, Irina
Arbeev, Konstantin G
Ukraintseva, Svetlana V
Stallard, Eric
Yashin, Anatoliy I
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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.
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Journal article
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https://hdl.handle.net/10161/14753
Published Version (Please cite this version)
10.1038/srep35390
Publication Info
Kulminski, Alexander M; Loika, Yury; Culminskaya, Irina; Arbeev, Konstantin G; Ukraintseva, Svetlana V; Stallard, Eric; & Yashin, Anatoliy I (2016). Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency. Sci Rep, 6. pp. 35390. 10.1038/srep35390. Retrieved from https://hdl.handle.net/10161/14753.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Arbeev

Konstantin Arbeev

Associate Research Professor in the Social Science Research Institute
Konstantin G. Arbeev received the M.S. degree in Applied Mathematics from Moscow State University (branch in Ulyanovsk, Russia) in 1995 and the Ph.D. degree in Mathematics and Physics (specialization in Theoretical Foundations of Mathematical Modeling, Numerical Methods and Programming) from Ulyanovsk State University (Russia) in 1999. He was a post-doctoral fellow in Max Planck Institute for Demographic Research in Rostock (Germany) before moving to Duke University in 2004 to work as a Resea
Kulminskaya

Irina Kulminskaya

Research Scientist, Senior
Kulminski

Alexander Kulminski

Research Professor in the Social Science Research Institute
Loika

Yury Loika

Research Scientist, Senior
Ukraintseva

Svetlana Ukraintseva

Associate Research Professor in the Social Science Research Institute
Dr. Ukraintseva studies causes of human aging and related decline in resilience, to identify genetic and other factors responsible for the increase in mortality risk with age eventually limiting longevity. She explores complex relationships, including trade-offs, between physiological aging-changes and risks of major diseases (with emphasis on Alzheimer’s and cancer), as well as survival, to find new genetic and other targets for anti-aging interventions and disease prevention. S
Yashin

Anatoli I. Yashin

Research Professor in the Social Science Research Institute
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