Efficient computation of genotype probabilities for loci with many alleles: II. Iterative method for large, complex pedigrees.

Abstract

An algorithm for computing genotype probabilities for marker loci with many alleles in large, complex pedigrees with missing marker data is presented. The algorithm can also be used to calculate grandparental origin probabilities, which summarize the segregation pattern and are useful for mapping quantitative trait loci. The algorithm is iterative and is based on peeling on alleles instead of the traditional peeling on genotypes. This makes the algorithm more computationally efficient for loci with many alleles. The algorithm is approximate in pedigrees that contain loops, including loops generated by full sibs. The algorithm has no restrictions on pedigree structure or missing marker phenotypes, although together those factors affect the degree of approximation. In livestock pedigrees with dense marker data, the degree of approximation may be minimal. The algorithm can be used with an incomplete penetrance model for marker loci. Thus, it takes into account the possibility of marker scoring errors and helps to identify them. The algorithm provides a computationally feasible method to analyze genetic marker data in large, complex livestock pedigrees.