Sequential parallel LASSO models for eQTL analysis

Anhong He, Benika Hall, Jia Wen, Yingbin Liang, Xinghua Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The availability of large-scale genomic and transcriptomic data on populations makes it necessary to perform computationally intensive expression quantitative trait locus (eQTL) analysis. Modeling in a sparse learning framework, LASSO based tools are powerful for eQTL analysis. However, classical LASSO becomes limited for big genomic data. We thus propose two novel methods, namely sequential LASSO and parallel LASSO, to conduct eQTL analysis for datasets of ultra-high dimension. We theoretically prove the consistency of our methods under mild conditions and perform extensive simulations on synthetic data to validate our methods. We also apply our methods to a real human genomics database demonstrate the application of our method. Copyright is held by the author/owner(s).

Original languageEnglish (US)
Title of host publicationBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages533-534
Number of pages2
ISBN (Print)9781450338530
DOIs
StatePublished - Sep 9 2015
Event6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States
Duration: Sep 9 2015Sep 12 2015

Other

Other6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015
CountryUnited States
CityAtlanta
Period9/9/159/12/15

Keywords

  • eQTL analysis
  • LASSO models

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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  • Cite this

    He, A., Hall, B., Wen, J., Liang, Y., & Shi, X. (2015). Sequential parallel LASSO models for eQTL analysis. In BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 533-534). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808719.2811449