Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank

Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNPxsex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNPxsex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation. When genetic variant effects on complex traits differ between females and males, sex-stratified analysis is often applied, offering easy-to-interpret, sex-specific effect estimates. However, from the viewpoint of maximizing the power and robustness of association testing, sex-stratified analysis may not be the best analytical strategy. As sex-specific genetic effects imply an SNPxsex interaction effect, jointly testing SNP main effects and SNPxsex interactions could be more powerful than sex-stratified analysis or the standard main-effect testing. Furthermore, this joint test is applicable even when individual-level data are not available, by leveraging sex-specific summary statistics through an omnibus meta-analysis. In this study, we performed such an omnibus meta-analysis using the UK Biobank data. Across 290 phenotypes, our results showed that this method is generally comparable to traditional analyses and excels by identifying new loci for traits such as testosterone, which standard tests do not detect. Our findings suggest that leveraging genetic heterogeneity enhances the detection of genetic associations, with significant implications for the future analysis of diverse data.