Region-specific analyses of beef cattle growth and implementation of single-step genomic blup with many genotyped animals
Bradford, Heather Lynn
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Beef cattle are raised in a wide range of environmental conditions in the United States. Producers use calving seasons to help match the cows’ nutritional needs throughout the year with forage quantity and quality in their production environment. As a result, some regions have large differences in calf weight depending on the calving season. One potential factor contributing to regional differences in beef production is heat stress. Heat stress causes economic losses for beef producers, and one way to mitigate the effects of heat stress is through genetic selection. Weaning and yearling weights were associated with the owner’s zip code and the weigh date to determine retrospectively the weather conditions preceding the weigh date. Heat tolerance, the ability of an animal to grow despite heat stress, was heritable and could be incorporated into national cattle evaluations to enable producers to identify animals that best match their environment. Another issue for the livestock industries is the use of single-step genomic BLUP (ssGBLUP) when the number of genotyped animals makes implementation computationally prohibitive. The Algorithm for Proven and Young (APY) enables the use of ssGBLUP in such a scenario. Genotyped animals need to be partitioned into core and noncore subsets. Accuracy and bias for young animals were the same no matter which animals were selected as the core animals. Livestock populations have limited effective population size, and this algorithm exploits the limited dimensionality of genomic information resulting in similar accuracies. Accuracy did not decrease as the relationship between the core and noncore animals decreased even though this result commonly occurs in multi-step methods. Randomly selecting core animals was consistently one of the most accurate and unbiased scenarios and is recommended for implementation of APY in ssGBLUP.