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Symposium review: Building a better cow—The Australian experience and future perspectives1
- Pryce, J.E., Nguyen, T.T.T., Axford, M., Nieuwhof, G., Shaffer, M.
- Journal of dairy science 2018 v.101 no.4 pp. 3702-3713
- animal health, breeding value, cows, farm management, farmers' attitudes, farms, feed conversion, gene expression, genetic trend, genomics, genotyping, heat tolerance, marker-assisted selection, phenotype, prediction, profitability, spectral analysis, Australia
- Genomic selection has led to opportunities for developing new breeding values that rely on phenotypes in dedicated reference populations of genotyped cows. In Australia, it has been applied to 2 novel traits: feed efficiency, which was released in 2015 as feed saved breeding values, and heat tolerance genomic breeding values, released for the first time in 2017. Feed saved is already included in the national breeding objective, which is focused on profitability and designed to be in line with farmer preferences. Our future focus is on traits associated with animal health, either directly or in combination with predictor traits, such as mid-infrared spectral data and, into the future, automated data capture. Although it is common for many evaluated traits to have genomic reliabilities ranging between 60 and 75%, many new, genomic information–only traits are likely to have reliabilities of less than 50%. Pooling of phenotype data internationally and investing in maintenance of reference populations is one option to increase the reliability of these traits; the other is to apply improved genomic prediction methods. For example, advances in the use of sequence data, in addition to gene expression studies, can lead to improved persistence of genomic breeding values across breeds and generations and potentially lead to greater reliabilities. Lower genomic reliabilities of novel traits could reduce the overall index reliability. However, provided these traits contribute to the overall breeding objective (e.g., profit), they are worth including. Bull selection tools and personalized genetic trends are already available, but increased access to economic and automatic capture farm data may see even better use of data to improve farm management and selection decisions.