haplotyping a quantitative trait with a high-density map in experimental crosses单体型与高密度地图量化特征实验十字架.pdfVIP

haplotyping a quantitative trait with a high-density map in experimental crosses单体型与高密度地图量化特征实验十字架.pdf

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haplotyping a quantitative trait with a high-density map in experimental crosses单体型与高密度地图量化特征实验十字架

Haplotyping a Quantitative Trait with a High-Density Map in Experimental Crosses 1 2 2 2 3 2 Wei Hou , John Stephen F. Yap , Song Wu , Tian Liu , James M. Cheverud , Rongling Wu * 1 Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida, United States of America, 2 Department of Statistics, University of Florida, Gainesville, Florida, United States of America, 3 Department of Anatomy and Neurobiology, Washington University Medical School, St. Louis, Missouri, United States of America 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 F2 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 net

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