Sequential parallel LASSO models for eQTL analysis

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

doi:10.1145/2808719.2811449

Sequential parallel LASSO models for eQTL analysis

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

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference 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 language	English (US)
Title of host publication	BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
Publisher	Association for Computing Machinery, Inc
Pages	533-534
Number of pages	2
ISBN (Print)	9781450338530
DOIs	https://doi.org/10.1145/2808719.2811449
State	Published - Sep 9 2015
Event	6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States Duration: Sep 9 2015 → Sep 12 2015

Other

Other	6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015
Country/Territory	United States
City	Atlanta
Period	9/9/15 → 9/12/15

Keywords

eQTL analysis
LASSO models

ASJC Scopus subject areas

Software
Health Informatics
Computer Science Applications
Biomedical Engineering

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10.1145/2808719.2811449

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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. Association for Computing Machinery, Inc, pp. 533-534, 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015, Atlanta, United States, 9/9/15. https://doi.org/10.1145/2808719.2811449

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title = "Sequential parallel LASSO models for eQTL analysis",

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).",

keywords = "eQTL analysis, LASSO models",

author = "Anhong He and Benika Hall and Jia Wen and Yingbin Liang and Xinghua Shi",

year = "2015",

month = sep,

day = "9",

doi = "10.1145/2808719.2811449",

language = "English (US)",

isbn = "9781450338530",

pages = "533--534",

booktitle = "BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics",

publisher = "Association for Computing Machinery, Inc",

note = "6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 ; Conference date: 09-09-2015 Through 12-09-2015",

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TY - GEN

T1 - Sequential parallel LASSO models for eQTL analysis

AU - He, Anhong

AU - Hall, Benika

AU - Wen, Jia

AU - Liang, Yingbin

AU - Shi, Xinghua

PY - 2015/9/9

Y1 - 2015/9/9

N2 - 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).

AB - 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).

KW - eQTL analysis

KW - LASSO models

UR - http://www.scopus.com/inward/record.url?scp=84963576846&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963576846&partnerID=8YFLogxK

U2 - 10.1145/2808719.2811449

DO - 10.1145/2808719.2811449

M3 - Conference contribution

AN - SCOPUS:84963576846

SN - 9781450338530

SP - 533

EP - 534

BT - BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

PB - Association for Computing Machinery, Inc

T2 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015

Y2 - 9 September 2015 through 12 September 2015

ER -

Sequential parallel LASSO models for eQTL analysis

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Keywords

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