Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies

Haohan Wang, Bryon Aragam, Eric P. Xing

Research output: Contribution to journalArticlepeer-review

Abstract

A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method.

Original languageEnglish (US)
Pages (from-to)2-9
Number of pages8
JournalMethods
Volume145
DOIs
StatePublished - Aug 1 2018
Externally publishedYes

Keywords

  • Confounding correction
  • Genome-wide association study
  • Heterogeneity
  • Mixed model
  • Variable selection

ASJC Scopus subject areas

  • Molecular Biology
  • General Biochemistry, Genetics and Molecular Biology

Fingerprint

Dive into the research topics of 'Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies'. Together they form a unique fingerprint.

Cite this