Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores.

ABSTRACT: Publicly available genome-wide association studies (GWAS) summary statistics exhibit uneven quality, which can impact the validity of follow-up analyses. First, we present an overview of possible misspecifications that come with GWAS summary statistics. Then, in both simulations and real-data analyses, we show that additional information such as imputation INFO scores, allele frequencies, and per-variant sample sizes in GWAS summary statistics can be used to detect possible issues and correct for misspecifications in the GWAS summary statistics. One important motivation for us is to improve the predictive performance of polygenic scores built from these summary statistics. Unfortunately, owing to the lack of reporting standards for GWAS summary statistics, this additional information is no

SUBMITTER: Prive F 

PROVIDER: S-EPMC9465343 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature