Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency.
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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.
Published Version (Please cite this version)
Kulminski, Alexander M, Yury Loika, Irina Culminskaya, Konstantin G Arbeev, Svetlana V Ukraintseva, Eric Stallard and Anatoliy I Yashin (2016). Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency. Sci Rep, 6. p. 35390. 10.1038/srep35390 Retrieved from https://hdl.handle.net/10161/14753.
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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 Research Scientist and a Senior Research Scientist in the Department of Sociology and the Social Science Research Institute (SSRI). He is currently an Associate Research Professor in SSRI. Dr. Arbeev's major research interests are related to three interconnected fields of biodemography, biostatistics and genetic epidemiology as pertains to research on aging. The focus of his research is on discovering genetic and non-genetic factors that can affect the process of aging and determine longevity and healthy lifespan. He is interested in both methodological advances in this research area as well as their practical applications to analyses of large-scale longitudinal studies with phenotypic, genetic and, recently, genomic information. Dr. Arbeev authored and co-authored more than 150 peer-reviewed publications in these areas.
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. She also investigates possibilities of repurposing of existing vaccines and treatments for AD prevention and interventions into the aging. For this, Dr. Ukraintseva and her team use data from several large human studies containing rich genetic and phenotypic information (including longitudinal measurements) on thousands of individuals. Dr. Ukraintseva is a PI and Key Investigator on several NIH funded grants, and has more than 130 peer-reviewed publications, including in major journals such as Nature Reviews, Stroke, European Journal of Human Genetics, and some other.
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