Including different groups of genotyped females for genomic prediction in a Nordic Jersey population

Abstract

Including genotyped females in a reference population (RP) is an obvious way to increase the RP in genomic selection, especially for dairy breeds of limited population size. However, the incorporation of these females must be conducted cautiously because of the potential preferential treatment of the genotyped cows and lower reliabilities of phenotypes compared with the proven pseudo-phenotypes of bulls. Breeding organizations in Denmark, Finland, and Sweden have implemented a female-genotyping project with the possibility of genotyping entire herds using the low-density (LD) chip. In the present study, 5 scenarios for building an RP were investigated in the Nordic Jersey population: (1) bulls only, (2) bulls with females from the LD project, (3) bulls with females from the LD project plus non-LD project females genotyped before their first calving, (4) bulls with females from the LD project plus non-LD project females genotyped after their first calving, and (5) bulls with all genotyped females. The genomically enhanced breeding value (GEBV) was predicted for 8 traits in the Nordic total merit index through a genomic BLUP model using deregressed proof (DRP) as the response variable in all scenarios. In addition, (daughter) yield deviation and raw phenotypic data were studied as response variables for comparison with the DRP, using stature as a model trait. The validation population was formed using a cut-off birth year of 2005 based on the genotyped Nordic Jersey bulls with DRP. The average increment in reliability of the GEBV across the 8 traits investigated was 1.9 to 4.5 percentage points compared with using only bulls in the RP (scenario 1). The addition of all the genotyped females to the RP resulted in the highest gain in reliability (scenario 5), followed by scenario 3, scenario 2, and scenario 4. All scenarios led to inflated GEBV because the regression coefficients are less than 1. However, scenario 2 and scenario 3 led to less bias of genomic predictions than scenario 5, with regression coefficients showing less deviation from scenario 1. For the study on stature, the daughter yield deviation/daughter yield deviation performed slightly better than the DRP as the response variable in the genomic BLUP (GBLUP) model. Therefore, adding unselected females in the RP could significantly improve the reliabilities and tended to reduce the prediction bias compared with adding selectively genotyped females. Although the DRP has performed robustly so far, the use of raw data is recommended with a single-step model as an optimal solution for future genomic evaluations.

Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle

“…The inclusion of cows in the training population yielded a slight increase in the theoretical reliabilities of GEBV (Table 2), which may be related to the increase in the amount of information available for the analyses. These findings are in agreement with previous studies that also showed that adding cows in the training population may significantly improve the theoretical reliabilities (Gao et al, 2015;Su et al, 2016). Although several studies have evaluated the effect of including cows in the training population of different dairy cattle breeds (e.g., Gao et al, 2015;Koivula et al, 2016;Su et al, 2016), to our best knowledge no studies have evaluated the effect of including cows in the validation population, especially under an RRM approach.…”

“…Nowadays, official genomic evaluations for the Holstein breed in Canada only include bulls in the training and validation populations (Interbull, 2017). In addition, various studies have included only bulls in the validation population (e.g., Gao et al, 2015;Koivula et al, 2015;Baba et al, 2017).…”

Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies

“…Pryce et al () suggested that the best strategy for including female genotypes in GS is to select them randomly, because females with the best phenotypes represent a biased sample of the whole population. Similarly, Gao et al () found that adding unselected females to a reference population improved GEBV reliabilities for Nordic Jersey cattle and reduced prediction bias compared to adding genotypes for just the best animals. Although their results relate to the quality of genomic predictions, quality and unbiasedness of variance components in this study followed the same trend.…”

National single-step genomic method that integrates multi-national genomic information

“…The multi-step methods assume 477 independent pseudo-records for sires tested on a large number of progeny with precisely estimated contemporary groups in large breeding programs, leading to little effect of double counting contributions from relatives. However, training populations increasingly include genotyped animals with lower reliabilities; for example, cows or sires tested on a smaller number of progeny (e.g., Gao et al, 2015). The (modified) TSA algorithm could be used in such a setting for computation of independent pseudo-records required by the multi-step methods, similarly to Calus et al (2016).…”