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Haplotyping a Quantitative Trait with a High-Density Map in Experimental Crosses
Author(s) -
Wei Hou,
John Stephen F. Yap,
Song Wu,
Tian Liu,
James M. Cheverud,
Rongling Wu
Publication year - 2007
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0000732
Subject(s) - quantitative trait locus , haplotype , genetics , biology , family based qtl mapping , context (archaeology) , genetic linkage , positional cloning , population , single nucleotide polymorphism , linkage (software) , computational biology , inclusive composite interval mapping , trait , candidate gene , allele , gene mapping , gene , phenotype , genotype , computer science , chromosome , paleontology , demography , sociology , programming language
Background The ultimate goal of genetic mapping of quantitative trait loci (QTL) is the positional cloning of genes involved in any agriculturally or medically important phenotype. However, only a small portion (≤ 1%) of the QTL detected have been characterized at the molecular level, despite the report of hundreds of thousands of QTL for different traits and populations. Methods/Results We develop a statistical model for detecting and characterizing the nucleotide structure and organization of haplotypes that underlie QTL responsible for a quantitative trait in an F 2 pedigree. The discovery of such haplotypes by the new model will facilitate the molecular cloning of a QTL. Our model is founded on population genetic properties of genes that are segregating in a pedigree, constructed with the mixture-based maximum likelihood context and implemented with the EM algorithm. The closed forms have been derived to estimate the linkage and linkage disequilibria among different molecular markers, such as single nucleotide polymorphisms, and quantitative genetic effects of haplotypes constructed by non-alleles of these markers. Results from the analysis of a real example in mouse have validated the usefulness and utilization of the model proposed. Conclusion The model is flexible to be extended to model a complex network of genetic regulation that includes the interactions between different haplotypes and between haplotypes and environments.

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