CCAFE: Estimating case and control allele frequencies from GWAS summary statistics

Genetic summary statistics can be used in a variety of analyses, such as causal inference, genetic correlation, and risk scores, to provide insights into the genetic architecture of conditions and traits. However, complete statistics are often not reported, limiting the utility of these data. Indeed, many post hoc analyses of diseases require case and control allele frequencies (AFs), which are not always published. Here, we present methods and software to derive case and control AFs from genome-wide association study (GWAS) summary statistics using the odds ratio, case and control sample sizes, and either the total (case and control aggregated) AF or the standard error (SE). In simulations and real data, derivations of case and control AFs using total AFs are highly accurate, whereas using SE underestimates AFs when covariates were included in the GWAS. While estimating case and control AFs using the total AF is preferred due to its high accuracy, the SE is more commonly available. Thus, we developed a bias adjustment using gnomAD AFs as a proxy for true AFs, reducing bias when using the SE. The methods and software provided here expand the utility of publicly available genetic summary statistics and promote the reusability of genomic data. The R package Case-Control Allele Frequency Estimation (CCAFE) is freely available on Bioconductor and GitHub.