A Bayesian approach to the inference of diploid genotypes using haploid genotypes.

Morris and Spieth (1978) described a method of calculating unbiased estimates of diploid genotype frequencies given information on the genotypes of haploid cells derived from diploid individuals. They concluded that three haploids per diploid would minimize sampling variance of genotype frequencies, given a fixed total number of haploids examined. If the identity of individual diploid genotypes is needed, Morris and Spieth (1978) stated that more haploids should be collected per diploid. We extend this work by showing from a Bayesian perspective that the probability of misclassification of individuals depends not only on the number of haploids sampled, but also on the genetic structure of the population since misclassification error will increase as the frequency of heterozygotes increases. Since information on the genetic structure (allele frequencies, inbreeding coefficient) of a population is rarely known prior to the initiation of an empirical study, the usefulness of our Bayesian approach is in experimental design, by revealing the magnitude of possible misclassification errors given a particular choice of number of haploids.