Genomic selection improves the possibility of applying multiple breeding programs in different environments

Abstract

One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (r g ). The break-even correlation (r b ) refers to the specific r g where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if r g is higher than r b , whereas 2 environment-specific BP have higher genetic gain if r g is lower than r b . Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find r b when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on r b and genetic gain; and (4) to find r b from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The r b was 0.65 and not different from r b with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on r b was not large. A different size in 2 envi-ronments decreased r b by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high r g . When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when r g between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP.

Genomic Breeding Programs Realize Larger Benefits by Cooperation in the Presence of Genotype × Environment Interaction Than Conventional Breeding Programs

“…To check the contribution of joint genomic evaluation, we simulated AG-WS in which the cooperation included only joint genomic evaluation, but selection was within environment. Results showed that even such a "single-layer" cooperation strategy was also beneficial for genetic gain and rate of inbreeding, as reported by Slagboom et al (2019). Subsequently when introducing the second level of cooperation, i.e.…”

Review: Genetic and genomic selection as a methane mitigation strategy in dairy cattle

“…However, under genomic selection schemes, this threshold of 0.80 between two traits for them to be appropriate to use one trait as an indicator for the other tends to be far higher under certain conditions (Slagboom et al, 2019). Yet, measurements using both methods on 10 3 to 10 4 related individuals are required to estimate genetic correlations with meaningful SEs (Visscher, 1998).…”

Possibilities for a specific breeding program for organic dairy production

“…The correlation between the organic and the conventional BG was strong for both the Holstein and the Jersey breed and thus expected to be above the previously mentioned break-even correlation. This break-even correlation indicates the specific genetic correlation between BG for different environments above which it is better for genetic gain to have 1 common breeding program with 1 BG for the 2 environments instead of 2 breeding programs with an environment-specific BG for each environment (Mulder et al, 2006;Slagboom et al, 2019). The break-even correlation was estimated to be 0.61 by Mulder et al (2006) for a breeding program with progeny testing and 0.65 by Slagboom et al (2019) for a breeding program with genomic selection.…”

“…Mulder (2007) suggested that the break-even correlation will be higher with genomic selection due to a possible higher intensity of selection. Slagboom et al (2019) showed by stochastic simulation that the break-even correlation is higher with genomic selection if the selection intensity is higher, but with the same selection intensity, the break-even correlation is the same. Differences in BG weights and G×E need to be combined to estimate the correlation between BG for organic and conventional dairy production.…”