Implementation of genomic selection using a cow training population is of interest for countries with no or limited proven bull data. However, preferential treatment of elite cows may cause bias in genomic predictions. The objective of this study was to investigate the impact of using preferentially treated cows in the training population on accuracy and bias of genomic predictions. A population undergoing a four-pathway selection strategy similar to that in dairy cattle was simulated. Two traits with low (0.05) and moderate (0.3) heritability were considered. Training population consisted of between 2,500 and 20,000 randomly selected cows. Preferential treatment (PT) was simulated and introduced to 5, 10 and 20% of elite cows. Preferential treatment of elite cows resulted in lower accuracy of predictions compared to the control scenario without PT. The accuracy of genomic predictions in the control scenario ranged from 0.72 to 0.83 and from 0.75 to 0.86 for traits with h2 of 0.05 and 0.3, respectively. When training population included 20% preferentially treated cows the corresponding accuracies decreased to 0.68 to 0.80 and 0.64 to 0.77, respectively. Training cows with PT has resulted in upward bias (linear regression coefficient of 0.79 and 0.45 for traits with h2 of 0.05 and 0.3, respectively). Generally, using cow data in the training population is an attractive way to implement genomic selection for countries with no or limited proven bull data. However, further investigation is needed to adjust for or remove bias due to potentially existent preferential treatment. Keywords: accuracy, bias, genomic selection, preferential treatment
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Electronic Poster Session - Genetic Gain - Breeding Strategies 2, , 793, 2018
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