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
N1 - Publisher Copyright:
Copyright is held by the author/owner(s).
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 - LASSO models
KW - eQTL analysis
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
T3 - BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
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 -