Fast proximal gradient optimization of the empirical Bayesian Lasso for multiple quantitative trait locus mapping

Indika Priyantha Kurappu Appuhamilage, Anhui Huang, Xiaodong Cai

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

Abstract

Complex quantitative traits are influenced by many factors including the main effects of many quantitative trait loci (QTLs), the epistatic effects involving more than one QTLs, environmental effects and the effects of gene-environment interactions. We recently developed an empirical Bayesian Lasso (EBlasso) method that employs a high-dimensional sparse regression model to infer the QTL effects from a large set of possible effects. Although EBlasso outperformed other state-of-the-art algorithms in terms of power of detection (PD) and false discovery rate (FDR), it was optimized by a greedy coordinate ascent algorithm that limited its capability and efficiency in handling a relatively large number of possible QTLs. In this paper, we developed a fast proximal gradient optimization algorithm for the EBlasso method. The new algorithm inherits the accuracy of our previously developed coordinate ascent algorithm, and achieves much faster computational speed. Simulation results demonstrated that the proximal gradient algorithm provided better PD with the same FDR as the coordinate ascent algorithm, and computational time was reduced by more than 30%. The proximal gradient algorithm enhanced EBlasso will be a useful tool for multiple QTL mappings especially when there are a large number of possible effects. A C/C++ software implementing the proximal gradient algorithm is freely available upon request.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1348-1351
Number of pages4
ISBN (Print)9781479970889
DOIs
StatePublished - Feb 5 2014
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Other

Other2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems

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    Appuhamilage, I. P. K., Huang, A., & Cai, X. (2014). Fast proximal gradient optimization of the empirical Bayesian Lasso for multiple quantitative trait locus mapping. In 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 (pp. 1348-1351). [7032344] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2014.7032344