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

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

Min Lin^a, Xiang-Yang Lou^a,b, Myron Chang^a, and Rongling Wu^a,c
^a Department of Statistics, University of Florida, Gainesville, Florida 32611,
^b Department of Agronomy, Zhejiang University, Hangzhou, Zhejiang 310029, People's Republic of China
^c Laboratory of Statistical Genetics, Zhejiang Forestry College, Lin'An, Zhejiang 311300, People's Republic of China

Corresponding author: Rongling Wu, 533 McCarty Hall C, University of Florida, Gainesville, FL 32611., rwu{at}stat.ufl.edu (E-mail)

Communicating editor: S. TAVARÉ

Because of uncertainty about linkage phases of founders, linkagemapping in nonmodel, outcrossing systems using molecular markerspresents one of the major statistical challenges in geneticresearch. In this article, we devise a statistical method formapping QTL affecting a complex trait by incorporating all possibleQTL-marker linkage phases within a mapping framework. The advantageof this model is the simultaneous estimation of linkage phasesand QTL location and effect parameters. These estimates areobtained through maximum-likelihood methods implemented withthe EM algorithm. Extensive simulation studies are performedto investigate the statistical properties of our model. In acase study from a forest tree, this model has successfully identifieda significant QTL affecting wood density. Also, the probabilityof the linkage phase between this QTL and its flanking markersis estimated. The implications of our model and its extensionto more general circumstances are discussed.

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