Sex-stratified linear mixed models: Non-clinical binary traits (Item 2/3)

Dataset

Abstract

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 non-clinical binary traits, are included (note: includes UK Biobank cancer traits). Each file contains the results for a single non-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.

Data Citation

Bernabeu, Elena; Canela-Xandri, Oriol; Rawlik, Konrad; Tenesa, Albert. (2021). Sex-stratified linear mixed models: Non-clinical binary traits (Item 2/3), [dataset]. University of Edinburgh. The Roslin Institute. https://doi.org/10.7488/ds/3047.
Date made available25 May 2021
PublisherEdinburgh DataShare

Cite this