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Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits

Overview of attention for article published in Genetics Selection Evolution, February 2016
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Title
Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits
Published in
Genetics Selection Evolution, February 2016
DOI 10.1186/s12711-016-0186-0
Pubmed ID
Authors

Hassan Aliloo, Jennie E. Pryce, Oscar González-Recio, Benjamin G. Cocks, Ben J. Hayes

Abstract

Dominance effects may contribute to genetic variation of complex traits in dairy cattle, especially for traits closely related to fitness such as fertility. However, traditional genetic evaluations generally ignore dominance effects and consider additive genetic effects only. Availability of dense single nucleotide polymorphisms (SNPs) panels provides the opportunity to investigate the role of dominance in quantitative variation of complex traits at both the SNP and animal levels. Including dominance effects in the genomic evaluation of animals could also help to increase the accuracy of prediction of future phenotypes. In this study, we estimated additive and dominance variance components for fertility and milk production traits of genotyped Holstein and Jersey cows in Australia. The predictive abilities of a model that accounts for additive effects only (additive), and a model that accounts for both additive and dominance effects (additive + dominance) were compared in a fivefold cross-validation. Estimates of the proportion of dominance variation relative to phenotypic variation that is captured by SNPs, for production traits, were up to 3.8 and 7.1 % in Holstein and Jersey cows, respectively, whereas, for fertility, they were equal to 1.2 % in Holstein and very close to zero in Jersey cows. We found that including dominance in the model was not consistently advantageous. Based on maximum likelihood ratio tests, the additive + dominance model fitted the data better than the additive model, for milk, fat and protein yields in both breeds. However, regarding the prediction of phenotypes assessed with fivefold cross-validation, including dominance effects in the model improved accuracy only for fat yield in Holstein cows. Regression coefficients of phenotypes on genetic values and mean squared errors of predictions showed that the predictive ability of the additive + dominance model was superior to that of the additive model for some of the traits. In both breeds, dominance effects were significant (P < 0.01) for all milk production traits but not for fertility. Accuracy of prediction of phenotypes was slightly increased by including dominance effects in the genomic evaluation model. Thus, it can help to better identify highly performing individuals and be useful for culling decisions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
France 1 1%
Unknown 83 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 21%
Researcher 12 14%
Student > Master 8 9%
Student > Doctoral Student 6 7%
Student > Bachelor 6 7%
Other 13 15%
Unknown 23 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 51%
Veterinary Science and Veterinary Medicine 4 5%
Biochemistry, Genetics and Molecular Biology 3 3%
Mathematics 2 2%
Business, Management and Accounting 2 2%
Other 4 5%
Unknown 27 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 February 2016.
All research outputs
#14,784,639
of 25,374,917 outputs
Outputs from Genetics Selection Evolution
#424
of 822 outputs
Outputs of similar age
#199,185
of 406,429 outputs
Outputs of similar age from Genetics Selection Evolution
#6
of 19 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 406,429 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.