RT Journal Article
SR Electronic
T1 A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 630079
DO 10.1101/630079
A1 Qian, Junyang
A1 Du, Wenfei
A1 Tanigawa, Yosuke
A1 Aguirre, Matthew
A1 Tibshirani, Robert
A1 Rivas, Manuel A.
A1 Hastie, Trevor
YR 2019
UL http://biorxiv.org/content/early/2019/05/07/630079.abstract
AB Since its first proposal in statistics (Tibshirani, 1996), the lasso has been an effective method for simultaneous variable selection and estimation. A number of packages have been developed to solve the lasso efficiently. However as large datasets become more prevalent, many algorithms are constrained by efficiency or memory bounds. In this paper, we propose a meta algorithm batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and build a scalable lasso solution for large datasets. We also introduce snpnet, an R package that implements the proposed algorithm on top of glmnet (Friedman et al., 2010a) for large-scale single nucleotide polymorphism (SNP) datasets that are widely studied in genetics. We demonstrate results on a large genotype-phenotype dataset from the UK Biobank, where we achieve state-of-the-art heritability estimation on quantitative and qualitative traits including height, body mass index, asthma and high cholesterol.