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

• Published in: PLoS genetics Vol. 20; no. 4; p. e1011221
• Main Authors: Lin, Boxi, Paterson, Andrew D., Sun, Lei
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.04.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Biobanks; Biology and Life Sciences; Females; Gender differences; Genome-wide association studies; Genomes; Internet resources; Meta-analysis; Methods; Multifactorial traits; Physical Sciences; Research and Analysis Methods; Sex hormones; Sexes; Single-nucleotide polymorphism; Statistical analysis; Testosterone; United Kingdom; United Kingdom > UK
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1011221

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 SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP’s main and SNP×sex 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.