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

TitleA fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank.
Publication TypeJournal Article
Year of Publication2020
AuthorsQian, J, Tanigawa, Y, Du, W, Aguirre, M, Chang, C, Tibshirani, R, Rivas, MA, Hastie, T
JournalPLoS Genet
Date Published2020 10
KeywordsAlgorithms, Asthma, Biological Specimen Banks, Body Height, Body Mass Index, Cholesterol, Cohort Studies, Genetics, Population, Genome-Wide Association Study, Genotype, Humans, Logistic Models, Phenotype, Polymorphism, Single Nucleotide, Proportional Hazards Models, United Kingdom

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods.

Alternate JournalPLoS Genet
PubMed ID33095761
PubMed Central IDPMC7641476
Grant ListR01 EB001988 / EB / NIBIB NIH HHS / United States
MC_QA137853 / MR / Medical Research Council / United Kingdom
R01 GM134483 / GM / NIGMS NIH HHS / United States
U01 HG009080 / HG / NHGRI NIH HHS / United States
MC_PC_17228 / MR / Medical Research Council / United Kingdom
T15 LM007033 / LM / NLM NIH HHS / United States
R01 HG010140 / HG / NHGRI NIH HHS / United States