A General Statistical Framework for Mapping Quantitative Trait Loci in Nonmodel Systems: Issue for Characterizing Linkage Phases | Zendy

Min Lin | Zendy; XiangYang Lou | Zendy; Myron Chang | Zendy; Rongling Wu | Zendy

Open Access

A General Statistical Framework for Mapping Quantitative Trait Loci in Nonmodel Systems: Issue for Characterizing Linkage Phases

Author(s) -

Min Lin,

XiangYang Lou,

Myron Chang,

Rongling Wu

Publication year - 2003

Publication title -

genetics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.792

H-Index - 246

eISSN - 1943-2631

pISSN - 0016-6731

DOI - 10.1093/genetics/165.2.901

Subject(s) - quantitative trait locus , linkage (software) , family based qtl mapping , inclusive composite interval mapping , biology , genetic linkage , trait , outcrossing , statistical model , genetics , computational biology , tree (set theory) , gene mapping , statistics , computer science , mathematics , gene , chromosome , ecology , pollen , programming language , mathematical analysis

Because of uncertainty about linkage phases of founders, linkage mapping in nonmodel, outcrossing systems using molecular markers presents one of the major statistical challenges in genetic research. In this article, we devise a statistical method for mapping QTL affecting a complex trait by incorporating all possible QTL-marker linkage phases within a mapping framework. The advantage of this model is the simultaneous estimation of linkage phases and QTL location and effect parameters. These estimates are obtained through maximum-likelihood methods implemented with the EM algorithm. Extensive simulation studies are performed to investigate the statistical properties of our model. In a case study from a forest tree, this model has successfully identified a significant QTL affecting wood density. Also, the probability of the linkage phase between this QTL and its flanking markers is estimated. The implications of our model and its extension to more general circumstances are discussed.

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