Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment

Abstract

BackgroundToday, genomic evaluations are an essential feature of dairy cattle breeding. Initially, genomic evaluation targeted young bulls but recently, a rapidly increasing number of females (both heifers and cows) are being genotyped. A rising issue is whether and how own performance of genotyped cows should be included in genomic evaluations. The purpose of this study was to assess the impact of including yield deviations, i.e. own performance of cows, in genomic evaluations.MethodsTwo different genomic evaluations were performed: one including only reliable daughter yield deviations of proven bulls based on their non-genotyped daughters, and one including both daughter yield deviations for males and own yield deviations for genotyped females. Milk yield, the trait most prone to preferential treatment, and somatic cell count, for which such a bias is very unlikely, were studied. Data consisted of two groups of animals from the three main dairy breeds in France: 11 884 elite females genotyped by breeding companies and 7032 cows genotyped for a research project (and considered as randomly selected from the commercial population).ResultsFor several measures that could be related to preferential treatment bias, the elite group presented a different pattern of estimated breeding values for milk yield compared to the other combinations of trait and group: for instance, for milk yield, the average difference between estimated breeding values with or without own yield deviations was significantly different from 0 for this group. Correlations between estimated breeding values with or without yield deviations were lower for elite females than for randomly selected cows for milk yield but were very similar for somatic cell count.ConclusionsThis study demonstrated that including own milk performance of elite females leads to biased (over-estimated) genomic evaluations. Thus, milk production records of elite cows require specific treatment in genomic evaluation.

Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey

“…Some previous studies have detected bias of genomic prediction when including cows in reference population (Wiggans et al, 2010 andDassonneville et al, 2012a). However, in the current study, inclusion of cows in reference population actually slightly reduced bias of GEBV.…”

Effect of cow reference group on validation reliability of genomic evaluation

“…When b 1 is <1, the predictions are inflated and the differences in estimated genetic merit of young individuals are exaggerated compared with their future performance. Wiggans et al (2011) and Dassonneville et al (2012) found that, the inclusion of cow genotypes can result in a decrease in the reliability of bull genomic evaluations. The reason for this was assumed to be in pre-selection of cows, because selection of cows for genotyping is based on high EBVs or potential for a high genetic evaluation.…”

“…These populations, therefore, have low reliabilities of genomic-enhanced breeding values (GEBVs) (Thomasen et al, 2012). By including genotyped cows in the reference population, the size of the reference group can be easily increased (Bapst et al, 2013;Dassonneville et al, 2012). For example, in the United States, cow genotypes have been included in the US genomic evaluations since their beginning (Wiggans et al, 2011).…”

“…This allows including the information of genotyped females into evaluation even if some production data are from the same years as omitted records by daughters of validation bulls. Wiggans et al (2011) and Dassonneville et al (2012) found that the inclusion of cow genotypes can even result in a decrease in the reliability of bull genomic evaluations. The reason for this decrease was assumed to be bias due to preselection of cows, where cows have been selected for genotyping based on their genetic merit or expectation for a high genetic evaluation.…”

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

“…Elite cows will be selected when genotyping costs are comparably high. However, preferential treatment of bull dams can result in over-estimated genomic predictions; hence, a specific treatment of these cow records is advisable (Dassonneville et al, 2012). Even though the results published by Lourenco et al (2014a) negate the adverse effects on evaluation accuracy when including elite cows, the selection strategy for female candidates should be to maximize the phenotypic variance that can be captured by these cows.…”