Resumen
- The aim of this paper was to develop statistical models to estimate individual breed composition based on the previously proposed idea of regressing discrete random variables corresponding to counts of reference alleles of biallelic molecular markers located across the genome on the allele frequencies of each marker in the pure (base) breeds. Some of the existing regression‐based methods do not guarantee that estimators of breed composition will lie in the appropriate parameter space, and none of them account for uncertainty about allele frequencies in the pure breeds, that is, uncertainty about the design matrix. To overcome these limitations, we proposed two Bayesian generalized linear models. For each individual, both models assume that the counts of the reference allele at each marker locus follow independent Binomial distributions, use the logit link and pose a Dirichlet prior over the vector of regression coefficients (which corresponds to breed composition).