Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit

Abstract

The objective of this study was to evaluate a genomic breeding scheme in a small dairy cattle population that was intermediate in terms of using both young bulls (YB) and progeny-tested bulls (PB). This scheme was compared with a conventional progeny testing program without use of genomic information and, as the extreme case, a juvenile scheme with genomic information, where all bulls were used before progeny information was available. The population structure, cost, and breeding plan parameters were chosen to reflect the Danish Jersey cattle population, being representative for a small dairy cattle population. The population consisted of 68,000 registered cows. Annually, 1,500 bull dams were screened to produce the 500 genotyped bull calves from which 60 YB were selected to be progeny tested. Two unfavorably correlated traits were included in the breeding goal, a production trait (h(2)=0.30) and a functional trait (h(2)=0.04). An increase in reliability of 5 percentage points for each trait was used in the default genomic scenario. A deterministic approach was used to model the different breeding programs, where the primary evaluation criterion was annual monetary genetic gain (AMGG). Discounted profit was used as an indicator of the economic outcome. We investigated the effect of varying the following parameters: (1) increase in reliability due to genomic information, (2) number of genotyped bull calves, (3) proportion of bull dam sires that are young bulls, and (4) proportion of cow sires that are young bulls. The genomic breeding scheme was both genetically and economically superior to the conventional breeding scheme, even in a small dairy cattle population where genomic information causes a relatively low increase in reliability of breeding values. Assuming low reliabilities of genomic predictions, the optimal breeding scheme according to AMGG was characterized by mixed use of YB and PB as bull sires. Exclusive use of YB for production cows increased AMGG up to 3 percentage points. The results from this study supported our hypothesis that strong interaction effects exist. The strongest interaction effects were obtained between increased reliabilities of genomic estimated breeding values and more intensive use of YB. The juvenile scheme was genetically inferior when the increase in reliability was low (5 percentage points), but became genetically superior at higher reliabilities of genomic estimated breeding values. The juvenile scheme was always superior according to discounted profit because of the shorter generation interval and minimizing costs for housing and feeding waiting bulls.

Review: How to improve genomic predictions in small dairy cattle populations

“…This poses a challenge for their future genetic gain relative to breeds with large reference populations. Thomasen et al (2014) showed that low reliabilities of genomic prediction is the single most important factor that limits the genetic gain in smaller populations with more intensive use of young bulls without a progeny test. The aim of this paper is to review theory and practical results on strategies and methods to increase the accuracies of GEBV in numerically small dairy populations.…”

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

“…There are also other conflicting goals such as genetic diversity, genetic uniqueness or specific local conditions. However, even in small dairy cattle populations, genomic breeding schemes are genetically (Kariuki et al, 2014;Thomasen et al, 2014a), as well as economically superior to conventional breeding schemes (Thomasen et al 2014a). This superiority is lower than estimated for larger populations, as the major limitation for small populations is the comparatively low accuracy of genomic predictions (Kariuki et al, 2014;Thomasen et al, 2014a).…”

“…However, even in small dairy cattle populations, genomic breeding schemes are genetically (Kariuki et al, 2014;Thomasen et al, 2014a), as well as economically superior to conventional breeding schemes (Thomasen et al 2014a). This superiority is lower than estimated for larger populations, as the major limitation for small populations is the comparatively low accuracy of genomic predictions (Kariuki et al, 2014;Thomasen et al, 2014a). Consequently, it is difficult to implement more efficient and at the same time more cost-effective breeding schemes (Thomasen et al, 2014a), although, in terms of genetic gain, reduced accuracies are partially compensated by shortened generation intervals (Kariuki et al, 2014).…”

Effect of increasing accuracy of genomic evaluations on economic efficiency of dairy cattle breeding programmes

“…They concluded that raising the accuracy of GEBV would result in further increase of profit. In another study, Thomasen et al (2014) reported 13% higher discounted profit in response to 40% improvement in the accuracy of a three-trait selection index in a small population. In these studies, the additional costs associated more individuals being genotyped was not considered; therefore, their estimated discounted profit could be overestimated.…”

“…Zhou et al 2013;Su et al 2014). K€ onig et al (2009) and Thomasen et al (2014) evaluated the potential effects of increasing genotyping cost and accuracies on discounted profit of genomic selection schemes without considering the costs related to increase in the number of individuals in the training set and recording their daughters. B€ orner and Reinsch (2012) assessed optimisation of genomic breeding programme under a cost limitation strategy, without incorporating the cost of performance recording of bulls' daughters in training set.…”