Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping | Zendy

Can Yang | Zendy; Lin Wang | Zendy; Shuqin Zhang | Zendy; Hongyu Zhao | Zendy

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Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping

Author(s) -

Can Yang,

Lin Wang,

Shuqin Zhang,

Hongyu Zhao

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt075

Subject(s) - spurious relationship , expression quantitative trait loci , covariate , computer science , regression , representation (politics) , consistency (knowledge bases) , data mining , rank (graph theory) , confounding , software , single nucleotide polymorphism , computational biology , machine learning , artificial intelligence , biology , statistics , mathematics , genetics , gene , genotype , combinatorics , politics , political science , law , programming language

Expression quantitative trait loci (eQTL) studies investigate how gene expression levels are affected by DNA variants. A major challenge in inferring eQTL is that a number of factors, such as unobserved covariates, experimental artifacts and unknown environmental perturbations, may confound the observed expression levels. This may both mask real associations and lead to spurious association findings.

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