Bayesian model selection for characterizing genomic imprinting effects and patterns | Zendy

Runqing Yang | Zendy; Xin Wang | Zendy; Zeyuan Wu | Zendy; Daniel R. Prows | Zendy; Min Lin | Zendy

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Bayesian model selection for characterizing genomic imprinting effects and patterns

Author(s) -

Runqing Yang,

Xin Wang,

Zeyuan Wu,

Daniel R. Prows,

Min Lin

Publication year - 2009

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/btp620

Subject(s) - imprinting (psychology) , quantitative trait locus , genomic imprinting , bayesian probability , bayes' theorem , trait , biology , bayesian inference , computer science , genetics , artificial intelligence , gene , gene expression , dna methylation , programming language

Although imprinted genes have been ubiquitously observed in nature, statistical methodology still has not been systematically developed for jointly characterizing genomic imprinting effects and patterns. To detect imprinting genes influencing quantitative traits, the least square and maximum likelihood approaches for fitting a single quantitative trait loci (QTL) and Bayesian method for simultaneously modeling multiple QTLs have been adopted in various studies.

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