Guy, S Z Y

Brown, D J

2021

Paper presented by S Z Y Guy

<p>Genetic gain can be maximised when selection is based on the most accurate breeding values and selection indices. To more explicitly take into account aspects pertaining to the quality of information used to estimate breeding values, metrics to characterise the quantity and quality of genetic evaluation data were previously proposed. This paper examines the relationships between these data quantity and quality metrics and genetic gains for Merino flocks. Stepwise regression analysis was used to analyse 3 metrics describing genetic gains: index accuracy, average index value and index trend. Index accuracy had the most number of significant predictors, with 4 quantity and 3 quality predictors explaining 85% of the observed variation. The most important metrics explaining index accuracy were level of genetic linkage for wool traits, average proportion of pedigree known in the last 3 years, and the level of wool and reproduction trait recording (<i>p</i> < 0.0005). Data characteristic metrics were also associated with average index and index trend, although to a lesser level (~24% variation explained). This study demonstrates that both data quantity and quality are associated with index accuracy and genetic gains in Merino flocks. This decomposition provides a basis for informing ram breeders on improvements in their data recording. Used in conjunction with optimum selection decisions, this will enable higher rates of genetic progress.</p>

Proceedings of the Association for the Advancement of Animal Breeding and Genetics, v.24, p. 143-146

1328-3227

https://hdl.handle.net/1959.11/37721

en

Association for the Advancement of Animal Breeding and Genetics (AAABG)

Maximising genetic gains with data quantity and quality in Merino flocks

Conference Publication

Publication