Single linkage group per chromosome genetic linkage map for the horse, based on two three-generation, full-sibling, crossbred horse reference families

A genetic linkage map of the horse consisting of 742 markers, which comprises a single linkage group for each of the autosomes and the X chromosome, is presented. The map has been generated from two three-generation full-sibling reference families, sired by the same stallion, in which there are 61 individuals in the F2 generation. Each linkage group has been assigned to a chromosome and oriented with reference to markers mapped by fluorescence in situ hybridization. The average interval between markers is 3.7 cM and the linkage groups collectively span 2772 cM. The 742 markers comprise 734 microsatellite and 8 gene-based markers. The utility of the microsatellite markers for comparative mapping has been significantly enhanced by comparing their flanking sequences with the human genome sequence; this enabled conserved segments between human and horse to be identified. The new map provides a valuable resource for genetically mapping traits of interest in the horse.

Power and Robustness of Linkage Tests for Quantitative Traits in General Pedigrees

There are numerous statistical methods for quantitative trait linkage analysis in human studies. An ideal such method would have high power to detect genetic loci contributing to the trait, would be robust to non-normality in the phenotype distribution, would be appropriate for general pedigrees, would allow the incorporation of environmental covariates, and would be appropriate in the presence of selective sampling. We recently described a general framework for quantitative trait linkage analysis, based on generalized estimating equations, for which many current methods are special cases. This procedure is appropriate for general pedigrees and easily accommodates environmental covariates. In this paper, we use computer simulations to investigate the power robustness of a variety of linkage test statistics built upon our general framework. We also propose two novel test statistics that take account of higher moments of the phenotype distribution, in order to accommodate non-normality. These new linkage tests are shown to have high power and to be robust to non-normality. While we have not yet examined the performance of our procedures in the context of selective sampling via computer simulations, the proposed tests satisfy all of the other qualities of an ideal quantitative trait linkage analysis method.