Linear models for joint association and linkage QTL mapping

被引:27
作者
Legarra, Andres [1 ]
Fernando, Rohan L. [2 ,3 ]
机构
[1] INRA, UR631, F-31326 Castanet Tolosan, France
[2] Iowa State Univ, Dept Anim Sci, Ames, IA USA
[3] Iowa State Univ, Ctr Integrated Anim Genom, Ames, IA USA
关键词
QUANTITATIVE TRAIT LOCI; MAXIMUM-LIKELIHOOD; ALTERNATIVE MODELS; DAIRY-CATTLE; MIXED-MODEL; DISEQUILIBRIUM; PREDICTION; PEDIGREES; IDENTITY; INFORMATION;
D O I
10.1186/1297-9686-41-43
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Background: Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure. Results: We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission) average of the substitution effects of founders' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component) model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided. Conclusion: The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward.
引用
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页数:17
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