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

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