Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.