Genetic mapping of multiple pleiotropic quantitative trait loci in livestock exploiting a multiplicative mixed model

Document Type : Research Article (Regular Paper)

Authors

Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.

Abstract

A multiple marker analysis approach in the framework of the mixed-effects model was developed, allowing all markers of the entire genome to be included simultaneously in the analysis. The approach was extended to multitrait situations. The proposed method is a one-stage process, which simultaneously models the residuals and genetic effects. In addition, it can easily accommodate co-variates, extra sources of variation, fixed or random including polygenic effects and it can easily be generalized to experimental and crossing designs commonly used. The developed approach considered an unstructured co-variance model for the traits residuals and fitted a multiplicative model for the trait by marker effects. The particular multiplicative model considered herein was the factor analytic model. This provided a parsimonious model specification to limit the numberof parameters to be estimated. It was shown through the simulation study that modelling multiple phenotypes in a single linkage analysis simultaneously could markedly increase the power, compared with modelling of each phenotype separately. Correlations among phenotypes can arise from several different causal processes, which may have different implications for the power and performance of the multivariate linkage analysis. Obviously, further studies using the approach suggested herein for multitrait quantitative trait loci (QTL) mapping that specifically consider different situations, should be undertaken. Furthermore, the efficiency of the model to distinguish between a pleiotropic QTL and closely linked QTL affecting different traits is another area that needs more investigation.

Keywords

Main Subjects

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Journal of Livestock Science and Technologies
Volume 8, Issue 2
December 2020
Pages 21-35

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