Estimation of across-breed metafounder parameters in all-breed US dairy cattle
Abstract
Metafounders relationships Gamma are useful to model missing pedigrees and different breeds. They are estimated from markers and pedigree, yet their estimation is difficult. Here we explore the behavior of two algorithms: General Least Squares (GLS) and Pseudo-Expectation Maximization (Pseudo-EM) to estimate Gamma. We use a reduced set of US genotypes and pedigree, consisting in six breeds: Ayrshire, Brown Swiss, Guernsey, Holstein and Jersey, with number of genotypes (69 200 SNP markers) equal to 350, 681, 237, 13492 y 3541, with 370 307 animals in the overall pedigree. Gamma estimates were also transformed to Fst differentiation indexes for ease of interpretation. Algorithms were fast. GLS resulted in biased estimates, with too high diagonal values of Gamma for small breeds, and too high differentiation indexes across breeds. Algorithm Pseudo-EM resulted in more sensible estimates. Further, estimates of allele frequencies by Pseudo-EM agreed well with upstream estimates of base allele frequencies based on imputation. We recommend use of Pseudo-EM.
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