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Accuracy of Prediction of Gene Content in Large Animal Populations and its Use for Candidate Gene Detection and Genetic Evaluation

N. Gengler^*,,1, S. Abras^*,, C. Verkenne^*, S. Vanderick^*, M. Szydlowski^*, and R. Renaville

^* Animal Science Unit, Gembloux Agricultural University, B-5030 Gembloux, Belgium
National Fund for Scientific Research, B-1000 Brussels, Belgium
Animal and Microbial Biology Unit, Gembloux Agricultural University, B-5030 Gembloux, Belgium
Department of Genetics and Animal Breeding, August Cieszkowski Agricultural University of Poznan, Poland

¹ Corresponding author: gengler.n{at}fsagx.ac.be

To estimate and to use the effects of single genes on quantitative traits, genotypes need to be known. However, in large animal populations, the majority of animals are not genotyped. These missing genotypes have to be estimated. However, currently used methods are impractical for large pedigrees. An alternative method to estimate missing gene content, defined as the number of copies of a particular allele, was recently developed. In this study, the proposed method was tested by assessing its accuracy in estimation and use of gene content in large animal populations. This was done for the bovine transmembrane growth hormone receptor and its effects on first-lactation milk, fat, and protein test-day yields and somatic cell score in Holstein cows. Estimated gene substitution effects of replacing a copy of the phenylalanine-coding allele with a copy of the tyrosine-coding allele were 295 g/d for milk, –8.14 g/d for fat, –1.83 g/d for protein, and –0.022/d for somatic cell score. However, only the gene substitution effect for milk was found to be significant. The accuracy of the estimated effects was evaluated by simulations and permutations. To validate the use of predicted gene content in a mixed inheritance model, a cross-validation study was done. The model with an additional regression of milk, fat, and protein yields and SCS on predicted gene content showed a better capacity to predict breeding values for milk, fat, and protein. Given these results, the estimation and use of allelic effects using this method proved functional and accurate.

Key Words: regression on gene content • single gene effect • test-day model