A robust method for simultaneous estimation of single gene and polygenic effects in dairy cows using externally estimated breeding values as prior information

Abstract

The aim of this study was to develop a robust method to estimate single gene and random polygenic animal effects simultaneously in a small field dataset with limited pedigree information. The new method was based on a Bayesian approach using additional prior information on the distribution of externally estimated breeding values. The field dataset consisted of 40,269 test-day records for milk performance traits for 1455 genotyped dairy cows for the 11 bp-deletion in the coding sequence of the myostatin gene. For all traits, estimated additive effects of the favoured wild-type allele ('+' allele) were smaller when applying the new method in comparison with the application of a conventional mixed inheritance test-day model. Dominance effects of the myostatin gene showed the same behaviour but were generally lower than additive effects. Robustness of methods was tested using a data-splitting technique, based on the correlation of estimated breeding values from two samples, with one-half of the data eliminated randomly from the first sample and the remaining data eliminated from the second sample. Results for 100 replicates showed that the correlation between split datasets when prior information included was higher than the conventional method. The new method led to more robust estimations for genetic effects and therefore has potential for use when only a small number of genotyped animals with field data and limited pedigree information are available.

Short communication: Influence of the muscle hypertrophy mutation of the myostatin gene on milk production traits and milk fatty acid composition in dual-purpose Belgian Blue dairy cattle

“…Concerning additive effects, one copy of the "+" allele led to increases on milk, protein, and fat yields, the latter being significant (Table 4). These results are in good agreement with earlier studies (Buske et al, 2010). In the current study, important dominance effects were also observed for milk, fat, and protein yields, which were significant for fat and protein yields.…”

“…Breeders of DP-BB make their selection decisions on the basis of the muscle hypertrophy mutation of the myostatin (MSTN) gene. Several recent studies (e.g., Buske et al, 2010 showed that the wild-type "+" allele of this gene is responsible for moderately higher milk, protein, and fat yields in DP-BB cows. It is also known that meat composition for fatty acids of Belgian Blue animals seems to be affected by muscle hypertrophy genotypes (Raes et al, 2001).…”

Estimating myostatin gene effect on milk performance traits using estimated gene content for a large number of non-genotyped cows

“…Allele substitution effects for one copy of the mh allele through all lactations were 276.06, 23.62 and 22.84 kg/lactation period (305 days) for milk, fat and protein yield, respectively. These values were lower than those found by Buske et al (2010) in a previous study using a Bayesian approach for only 1455 genotyped BBB cows. This difference might be explained by assumptions of this study, which included that allele substitution effects were the same across several breeds, which may not be accurate.…”

“…However, generally only few animals of a large population are genotyped for single genes of interest, making it difficult to estimate a single gene effect reliably (Gengler et al, 2008). This difficulty is more pronounced when data of genotyped animals are limited, unconnected or when the pedigree is incomplete (Buske et al, 2010). Genotyping more animals would be one solution, but this is costly or even unfeasible (e.g.…”

Comparison and improvements of different Bayesian procedures to integrate external information into genetic evaluations

“…Furthermore, as an anonymous reviewer reported, the matrix G* −1 is very similar to the inverse of the matrix H used in the single-step genomic evaluations and included both pedigree-based relationships and differences between pedigree-based and genomic-based relationships (Aguilar et al, 2010;Christensen and Lund, 2010). In a different setting and after taking precautions to avoid double counting because of the use of the same data, regular genetic evaluation results from a larger population could also be used as external priors in gene effect discovery studies (e.g., Buske et al, 2010) or any other studies requiring accurate estimation of a polygenic effect jointly with marker, SNP, or gene effects.…”