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

Min Lin, Xiang Yang Lou, Myron Chang, Rongling Wu

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)901-913
Number of pages13
JournalGenetics
Volume165
Issue number2
StatePublished - Oct 1 2003

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Quantitative Trait Loci
Genetic Research
Chromosome Mapping
Uncertainty
Forests

All Science Journal Classification (ASJC) codes

  • Genetics

Cite this

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abstract = "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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A General Statistical Framework for Mapping Quantitative Trait Loci in Nonmodel Systems : Issue for Characterizing Linkage Phases. / Lin, Min; Lou, Xiang Yang; Chang, Myron; Wu, Rongling.

In: Genetics, Vol. 165, No. 2, 01.10.2003, p. 901-913.

Research output: Contribution to journalArticle

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