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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #319317
Title: Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey

Author
item SU, GUOSHENG - University Of Aarhus
item MA, PEIPEI - University Of Aarhus
item NIELSEN, ULRIK SANDER - Collaborator
item AAMAND, GERT PEDERSEN - Collaborator
item Wiggans, George
item GULDBRANDTSEN, BERNT - University Of Aarhus
item LUND, MOGENS - University Of Aarhus

Submitted to: Animal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/4/2015
Publication Date: 6/1/2016
Citation: Su, G., Ma, P., Nielsen, U., Aamand, G., Wiggans, G.R., Guldbrandtsen, B., Lund, M.S. 2016. Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey. Animal. 10(6):1067-1075.

Interpretive Summary: Genomic selection has been widely implemented in dairy cattle breeding, but its success depends on accurate genomic predictions. A key factor affecting accuracy of genomic prediction is the amount of information from a reference population, such as progeny-tested bulls. However, the number of progeny-tested bulls is limited for numerically small dairy cattle populations. Two approaches to improve genomic prediction by increasing the size of the reference population were investigated for Danish Jerseys. Both sharing reference data from the United States and including cows in the reference population greatly increased the accuracy of genomic predictions. The accuracy gain from the two approaches was more than 10 percentage points. The results indicate that sharing reference data and including cows in the reference population are efficient approaches to increase accuracy of genomic evaluations and thus increase genetic gain, especially for populations where the number of progeny-tested bulls is small. By efficiently using information resources, genomic prediction for numerically small breeds is a promising technique for improving traits of economic interest.

Technical Abstract: Small reference populations limit the accuracy of genomic prediction in numerically small breeds, such as the Danish Jersey. The objective of this study was to investigate two approaches to improve genomic prediction by increasing the size of the reference population for Danish Jerseys. The first approach was to include North American Jersey bulls in the Danish Jersey reference population. The second was to genotype cows and use them as reference animals. The validation of genomic prediction was carried out on bulls and cows, respectively. In validation on bulls, about 300 Danish bulls (depending on traits) born in 2005 and later were used as validation data, and the reference populations were (1) about 1,050 Danish bulls and (2) about 1,050 Danish bulls and about 1,150 U.S. bulls. In validation on cows, about 3,000 Danish cows from 87 young half-sib families were used as validation data, and the reference populations were (1) about 1,250 Danish bulls, (2) about 1,250 Danish bulls, and about 1,150 U.S. bulls, (3) about 1,250 Danish bulls and about 4,800 cows, and (4) about 1,250 Danish bulls, 1,150 US bulls, and 4,800 Danish cows. A genomic best linear unbiased prediction model was used to predict breeding values. De-regressed proofs were used as response variables. In the validation on bulls for eight traits, the joint Danish-U.S. bull reference population led to higher reliability of genomic prediction than did the Danish bull reference population for six traits but not for fertility and longevity. Averaged over the eight traits, the gain was 3 percentage points. In the validation on cows for six traits (fertility and longevity were not available), the gain from inclusion of U.S. bulls in reference population was 6.6 percentage points averaged over the six traits, and the gain from inclusion of cows was 8.2 percentage points. However, the gains from cows and U.S. bulls were not accumulative. The total gain of including both U.S. bulls and Danish cows was 10.5 percentage points. The results indicate that sharing reference data and including cows in the reference population are efficient approaches to increase reliability of genomic prediction. Therefore, genomic selection is promising for a numerically small population.