Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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
doi: https://doi.org/10.1101/228106
Haohan Wang
Language Technologies Institute, School of Computer Science Carnegie Mellon University, Pittsburgh, PA, USA Email: haohanw@cs.cmu.edu
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: haohanw@cs.cmu.edu
Bryon Aragam
Machine Learning Department, School of Computer Science Carnegie Mellon University, Pittsburgh, PA, USA Email: naragam@cs.cmu.edu, epxing@cs.cmu.edu
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: naragam@cs.cmu.edu epxing@cs.cmu.edu
Eric P. Xing
Machine Learning Department, School of Computer Science Carnegie Mellon University, Pittsburgh, PA, USA Email: naragam@cs.cmu.edu, epxing@cs.cmu.edu
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: naragam@cs.cmu.edu epxing@cs.cmu.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted December 03, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
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
bioRxiv 228106; doi: https://doi.org/10.1101/228106
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
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
bioRxiv 228106; doi: https://doi.org/10.1101/228106

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (1524)
  • Biochemistry (2479)
  • Bioengineering (1731)
  • Bioinformatics (9670)
  • Biophysics (3896)
  • Cancer Biology (2968)
  • Cell Biology (4189)
  • Clinical Trials (135)
  • Developmental Biology (2624)
  • Ecology (4098)
  • Epidemiology (2031)
  • Evolutionary Biology (6894)
  • Genetics (5204)
  • Genomics (6496)
  • Immunology (2183)
  • Microbiology (6937)
  • Molecular Biology (2751)
  • Neuroscience (17261)
  • Paleontology (126)
  • Pathology (425)
  • Pharmacology and Toxicology (705)
  • Physiology (1056)
  • Plant Biology (2488)
  • Scientific Communication and Education (643)
  • Synthetic Biology (831)
  • Systems Biology (2687)
  • Zoology (429)