Sex-stratified linear mixed models: Clinical binary traits (Item 1/3)

Sex-stratified GWAS can help shed light on sexual differences in genetic architecture. In Bernabeu et al (2021) we fit sex-stratified linear mixed models (using DISSECT) across a total of 530 phenotypes to assess the effects of sex on genetic effect estimates, and compared estimates between males and females in a search for genetic variants that presented significant differences in association to the traits considered. Here, the summary statistics of said efforts, pertaining to clinical binary traits, are included (note: does not include UK Biobank cancer traits – these are found in DataShare item pertaining to non-clinical binary traits). Each file contains the results for a single clinical binary trait, as stated in the file name, using its corresponding UK Biobank trait code. Trait descriptions, including their respective UK Biobank codes, are stated in the “trait_description.tsv” file. For each trait (each .gz file), GWAS summary statistics obtained for over 4 million genetic variants across the genome (both autosomal, and X chromosome, MAF 10% filtered) and circa 450K individuals, as well as the results of the t-test comparing genetic effect estimates between the sexes, are included.