Invited Review Genomic Selection In Dairy Cattle Progress And Challenges Pdf

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invited review genomic selection in dairy cattle progress and challenges pdf

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Invited review: A perspective on the future of genomic selection in dairy cattle.

Metrics details. In France, implementation of genomic evaluations in dairy cattle breeds started in and this has modified the breeding schemes drastically. We compared annual genetic gains, inbreeding rates based on runs of homozygosity ROH and pedigree data, and mean ROH length within breeds, before and after the implementation of genomic selection. The mean ROH length was longer for bulls from the Holstein breed than for those from the other two breeds. With the implementation of genomic selection, the annual genetic gain increased for bulls from the three major French dairy cattle breeds. The increase in mean ROH length in Holstein may reflect the occurrence of recent inbreeding.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Hayes and P. Bowman and A.

Genomic Selection GS has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. Meanwhile, state-of-the-art statistical methods were developed to make the best use of high marker density genotype data. Results indicate that in the GBLUP method, higher marker density leads to a higher prediction accuracy. BayesR outperforms GBLUP in predicting high or medium heritability trait that affected by one or several genes with large effects, while GBLUP performs similarly or slightly better than BayesR in predicting low heritability trait that controlled by a large amount of genes with minor effects. Prediction accuracy of trait with complex genetic architecture can be improved by increasing the marker density. Interestingly, for simple traits that controlled by one or several genes with large effects, higher marker density can cause a lower prediction accuracy if the QTN is included, but leads to a higher prediction accuracy if the QTN is excluded.

Beef cattle breeding in Australia with genomics: opportunities and needs

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A new technology called genomic selection is revolutionizing dairy cattle breeding. In this paper, we first review the progress of genomic selection, including results from dairy cattle breeding the effect of genomic selection on long-term genetic gain and other challenges. Download: Download Acrobat PDF file (58KB).


Invited review: Genomic selection in dairy cattle: progress and challenges.

Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload this page. Read article at publisher's site DOI : J Dairy Sci, 8

B Corresponding author. Email: djohnsto une. Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull.

Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes.

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