A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank
Autoři:
Junyang Qian aff001; Yosuke Tanigawa aff002; Wenfei Du aff001; Matthew Aguirre aff002; Chris Chang aff003; Robert Tibshirani aff001; Manuel A. Rivas aff002; Trevor Hastie aff001
Působiště autorů:
Department of Statistics, Stanford University, Stanford, CA, United States of America
aff001; Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
aff002; Grail, Inc., Menlo Park, CA, United States of America
aff003
Vyšlo v časopise:
A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet 16(10): e32767. doi:10.1371/journal.pgen.1009141
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009141
Souhrn
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.
Klíčová slova:
Algorithms – Asthma – Body Mass Index – Genetics – Genome-wide association studies – Heredity – Hypercholesterolemia – Single nucleotide polymorphisms
Zdroje
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