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.
Highlights
Common strategy of genome-wide association studies (GWAS) relying on large samples faces difficulties, which raise concerns that Genome-wide association studies (GWAS) have exhausted their potential, for complex traits
We examine the potential for improving the traditional GWAS strategy, which relies on increasing the sample size, in the case of inherently heterogeneous traits
It is argued that the traditional GWAS strategy merely relying on increasing the sample size is problematic because of the inherent heterogeneity of complex traits[7]
Summary
Common strategy of genome-wide association studies (GWAS) relying on large samples faces difficulties, which raise concerns that GWAS have exhausted their potential, for complex traits. 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. The analyses of genetic predisposition to heterogeneous traits (as opposite to homogeneous traits defined above) relying on increasing sample size become problematic because “increasing the size of human disease cohorts is likely only to scale the heterogeneity in parallel”[7]
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