AbstractPower of many genome-wide association studies (GWAS) remains low despite of increasing sample size, because the genetic effects for complex traits are small, the case sample size may not be large, and the variants analyzed may be rare. One direction is to integrate available functional annotation meta-score such as CADD and Eigen to increase power of a GWAS. Here we examine four data-integration methods, including meta-analysis, Fisher’s method, weighted p-value, and stratified FDR control, all based on summary statistics only. We focus on robustness study, considering settings where the functional meta-score mayor may not be informative, or possibly be misleading. In addition to extensive simulation studies, we also apply the four methods to 945 binary outcomes in the UK Biobank data, including all 633 traits with ICD-10 codes, 28 self-reported cancers and 284 self-reported non-cancer diseases, integrating publicly available GWAS summary statistics (http://www.nealelab.is/uk-biobank/) with CADD or Eigen scores. While the trade-off between power and robustness observation is expected, our application shows some but limited utility of current functional meta-score in terms of leading to new genome-wide significant association findings.
Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes
