Leveraging existing GWAS summary data of genetically correlated and uncorrelated traits to improve power for a new GWAS.

ABSTRACT: In spite of the tremendous success of genome-wide association studies (GWAS) in identifying genetic variants associated with complex traits and common diseases, many more are yet to be discovered. Hence, it is always desirable to improve the statistical power of GWAS. Paralleling with the intensive efforts of integrating GWAS with functional annotations or other omic data, we propose leveraging other published GWAS summary data to boost statistical power for a new/focus GWAS; the traits of the published GWAS may or may not be genetically correlated with the target trait of the new GWAS. Building on weighted hypothesis testing with a solid theoretical foundation, we develop a novel and effective method to construct single-nucleotide polymorphism (SNP)-specific weights based on 22 published

SUBMITTER: Xue H 

PROVIDER: S-EPMC7722071 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature