title: Nonconvex Penalty Based Low-Rank Representation and Sparse Regression for eQTL Mapping.
abstract: This paper addresses the problem of accounting for confounding factors and expression quantitative trait loci (eQTL) mapping in the study of SNP-gene associations. The existing convex penalty based algorithm has limited capacity to keep main information of matrix in the process of reducing matrix rank. We present an algorithm, which use nonconvex penalty based low-rank representation to account for confounding factors and make use of sparse regression for eQTL mapping (NCLRS). The efficiency of the presented algorithm is evaluated by comparing the results of 18 synthetic datasets given by NCLRS and presented algorithm, respectively. The experimental results or biological dataset show that our approach is an effective tool to account for non-genetic effects than currently existing methods.
authors.forename: Lin
authors.initials: L
authors.lastname: Yuan
authors.forename: Lin
authors.initials: L
authors.lastname: Zhu
authors.forename: Wei-Li
authors.initials: WL
authors.lastname: Guo
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
authors.forename: Youhua
authors.initials: Y
authors.lastname: Zhang
authors.forename: Zhenhua
authors.initials: Z
authors.lastname: Huang
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015233:Models, Statistical; D040641:Quantitative Trait Loci / Q000235:genetics*; D015003:Yeasts / Q000235:genetics