A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank

Published in PLOS Genetics, 2020

snpnet striking image

In this project led by Junyang Qian, we developed BASIL, a novel algorithm to fit large-scale L1 penalized (Lasso) regression model using an iterative procedure, and implemented R snpnet package specially designed for genetic data. We demonstrate the ability of this approach in an application to UK Biobank dataset.

snpnet figure 1

How to use the snpnet package

If you’re interested in the software, please check out our GitHub repo for the snpnet package. It has some sample data and vignette document that describes the usage of the package.

Citation: J. Qian, Y. Tanigawa, W. Du, M. Aguirre, C. Chang, R. Tibshirani, M. A. Rivas, T. Hastie, A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLOS Genetics. 16, e1009141 (2020). https://doi.org/10.1371/journal.pgen.1009141