In this pre-print, we describe a new method to fit a sparse Cox Model for multiple time-to-event phenotypes from large-scale (> 1 million features) genetic dataset.
This study built on other techniques that we’ve been developping in the Rivas lab with a collaboration with the Hastie and the Tibshirani labs.
- BASIL algorithm and
snpnet, J. Qian et al, 2019
- This manuscript describes a method to obtain the exact solution for large-scale penalized regression model (Lasso). We show several examples using quantitative traits and disease outcomes in UK Biobank.
- Single-response Cox model, Li et al, 2020
- This manuscript describes an extension of BASIL algorithm to Cox model. We show examples using time-to-event phenotypes (onset of diseases) in UK Biobank.
- Sparse reduced rank regression, J. Qian et al, 2020
- This manuscript describes a method to fit large-scale penalized regression for multiple response.
Citation: R. Li, Y. Tanigawa, J. M. Justesen, J. Taylor, T. Hastie, R. Tibshirani, M. A. Rivas, Fast Sparse-Group Lasso Method for Multi-response Cox Model with Applications to UK Biobank. bioRxiv, 2020.06.21.163675 (2020). https://doi.org/10.1101/2020.06.21.163675