Mapping viability loci using molecular markers.
In genetic mapping experiments, some molecular markers often show distorted segregation ratios. We hypothesize that these markers are linked to some viability loci that cause the observed segregation ratios to deviate from Mendelian expectations. Although statistical methods for mapping viability loci have been developed for line-crossing experiments, methods for viability mapping in outbred populations have not been developed yet. In this study, we develop a method for mapping viability loci in outbred populations using a full-sib family as an example. We develop a maximum likelihood (ML) method that uses the observed marker genotypes as data and the proportions of the genotypes of the viability locus as parameters. The ML solutions are obtained via the expectation-maximization algorithm. Application and efficiencies of the method are demonstrated and tested using a set of simulated data. We conclude that mapping viability loci can be accomplished using similar statistical techniques used in quantitative trait locus mapping for quantitative traits.
Luo
L
L
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.
Xu
S
S
D000465:Algorithms; D000818:Animals; D021461:Animals, Outbred Strains; D002874:Chromosome Mapping; D005328:Fetal Viability / Q000235:genetics*; D005819:Genetic Markers; D016013:Likelihood Functions; D008957:Models, Genetic; D009010:Monte Carlo Method
D005819:Genetic Markers