Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Genetic evaluation is the process of combing individual animal performance and ancestry data along with information on contributing non-genetic effects to estimate the genetic merit of individuals. This is a routine process in all developed countries with the outcome being estimates of genetic merit for individual animals. Genetic evaluations are undertaken either by breed associations (e.g., US, Canada), nationally (e.g., Ireland) or internationally (e.g., BreedPlan, INTERBULL, some breed associations). Seedstock breeders are those who produce germplasm for general use in the commercial population; it is the seedstock breeders therefore who have a vested interest in the recording of performance data and, in many countries, it is only data from these breeders that are used in the genetic evaluations. Data, however, also exists from commercial herds – it is these herds who are the actual users of the seedstock germplasm. Leveraging these data has many benefits as evidenced in some countries; capturing the performance and ancestry data, as well as information on contributing non-genetic effects in these commercial herds is, however, not trivial.

Technical Abstract: Genetic evaluations are predicated on routine access to large quantities of data on a range of performance traits from individual animals, their genetic relationships, as well as data on the factors other than additive genetic merit that influence phenotypic performance. Based on the well-established breeding pyramid, far more commercial animals generally exist relative to seedstock animals. Despite this, performance data from commercial animals is not always used in genetic evaluations. These data are not utilized for many reasons such as 1) no individual animal data actually exists or is recorded in a useful format from commercial animals, 2) no ancestry is recorded, 3) systematic environmental effects are not recorded, 4) infrastructure is not in place to collate such data, and 5) issues relating to data ownership, governance, and use. Given the end customer of elite germplasm is the commercial producer, systems that only consider seedstock data in the genetic evaluations are sub-optimal for several reasons: 1) assumes a genetic correlation of one between performance in seedstock herds and performance in commercial settings, 2) fails to benefit from additional (commercial) data to increase the accuracy of selection, 3) omits data for traits that are profit drivers for commercial enterprises, and 4) misses an opportunity to provide commercial producers with genetic-based management tools. Two contrasting case studies relating to beef genetic evaluations are explored: 1) US where generally only data from seedstock animals are used and many different genetic evaluations and breeding objectives exist for the multitude of breeds, and 2) Ireland which has a national database of all bovines and uses data from both seedstock and commercial producers to generate multi-breed genetic evaluations which are then applied to and disseminated to all bovines in the country both as breeding and management support indexes.