The snpnet polygenic risk score coefficients for Testosterone levels described in 'Sex-specific genetic effects across biomarkers'

This dataset contains the coefficients of the polygenic risk scores for Testosterone levels described in the following publication:<br><br> E. Flynn, Y. Tanigawa, F. Rodriguez, R. B. Altman, N. Sinnott-Armstrong, M. A. Rivas, Sex-specific genetic effects across biomarkers. European Journal of Human Genetics, 1-10 (2020). https://doi.org/10.1038/s41431-020-00712-w<br>We provide 3 files corresponding to the polygenic risk score models training on the following set of individuals:<br><br>- "snpnet.BETAs.Testosterone.combined.tsv.gz": A PRS model trained on both male and female individuals<br>- "snpnet.BETAs.Testosterone.female-specific.tsv.gz": A PRS model trained on female individuals<br>- "snpnet.BETAs.Testosterone.male-specific.tsv.gz": A PRS model trained on male individuals<br><br>Those files are compressed tab-delimited table files, each of which contains the coefficients (weights) of the polygenic risk score and have the following columns:<br><br>- CHROM: the chromosome. The pseudoautosomal region in X chromosome is coded as XY.<br>- POS: the position<br>- ID: the variant identifier<br>- REF: the reference allele<br>- ALT: the alternate allele<br>- BETA: the coefficients (weights) of the PRS<br><br>Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele.<br><br>We used the BASIL algorithm implemented in R snpnet package, which is described in another preprint:<br><br>J. Qian, et al, A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems. bioRxiv, 630079 (2019). doi:10.1101/630079<br><br>