Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency.
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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https://hdl.handle.net/10161/14753Published Version (Please cite this version)
10.1038/srep35390Publication 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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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
Irina Kulminskaya
Research Scientist, Senior
Alexander Kulminski
Research Professor in the Social Science Research Institute
Yury Loika
Research Scientist, Senior
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
Anatoli I. Yashin
Research Professor in the Social Science Research Institute
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