The use of whole sequence data for genomic selection in dairy cattle

Publikation: Bog/antologi/afhandling/rapportPh.d.-afhandling

  • Irene van den Berg, Danmark
This thesis investigated the use of Whole sequence data for genomic selection in dairy cattle. From a simulation study it was concluded that sequence data can result in increases in reliability by using prediction markers clse to the causative mutation rather than by using the full sequence. A large part of the thesis focused therefore on the detection of such variants. Two different approaches a concordance analysis and a multi breed GWAS detected variants Associated with several quantitative traits. Using these variants for genomic prediction resulted in substantial increases in reliability, especially for multi breed prediction
Antal sider236
ISBN (Trykt)978-87-93176-96-6
Rekvirerende organGraduate School of Science and Technology
StatusUdgivet - 7 sep. 2015

Note vedr. afhandling

During her PhD studies, Irene van den Berg researched the use of sequence data for genomic prediction in dairy cattle. Sequence data could improve the accuracy of genomic selection, mainly because it contains variants that directly influence quantitative traits and variants strongly linked to them. This could be especially beneficial in situations where the accuracy of genomic selection is currently limited, such as across breed and multi breed prediction. Multi breed prediction would allow breeds with smaller datasets to use information from breeds with larger datasets and thereby improve prediction accuracy.

In her dissertation, Irene van den Berg investigated how sequence variants can improve genomic prediction of dairy cattle. Largest improvements were expected when only variants very close to the causative mutations were used, and therefore, a large part of the thesis focused on the identification of such variants. Finally, the detected variants were used for genomic prediction, resulting in up to 10% increases in prediction reliability. The results show that sequence variants have the potential to improve genomic prediction, especially in a multi breed setting, but that it is important to use only variants close to the causative mutations.

The PhD degree was completed at the GABI unit at AgroParisTech and INRA, and the Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Science and Technology, Aarhus University.

This résumé is prepared by the PhD student.

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