Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency.
Published
Journal Article
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.
Full Text
Duke Authors
- Arbeev, Konstantin
- Kulminskaya, Irina
- Kulminski, Alexander
- Loika, Yury
- Stallard, Patrick J. Eric
- Ukraintseva, Svetlana
- Yashin, Anatoli I.
Cited Authors
- Kulminski, AM; Loika, Y; Culminskaya, I; Arbeev, KG; Ukraintseva, SV; Stallard, E; Yashin, AI
Published Date
- October 14, 2016
Published In
Volume / Issue
- 6 /
Start / End Page
- 35390 -
PubMed ID
- 27739495
Pubmed Central ID
- 27739495
Electronic International Standard Serial Number (EISSN)
- 2045-2322
International Standard Serial Number (ISSN)
- 2045-2322
Digital Object Identifier (DOI)
- 10.1038/srep35390
Language
- eng