Genomic prediction in families of perennial ryegrass based on genotyping-by-sequencing

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

  • Bilal Ashraf, Danmark
In this thesis we investigate the potential for genomic prediction in perennial ryegrass using genotyping-by-sequencing (GBS) data. Association method based on family-based breeding systems was developed, genomic heritabilities, genomic prediction accurancies and effects of some key factors wer explored. Results show that low sequencing depth caused underestimation of allele substitution effects in GWAS and overestimation of genomic heritability in prediction studies. Other factors susch as SNP marker density, population structure and size of training population influenced accuracy of genomic prediction. Overall, GBS allows for genomic prediction in breeding families of perennial ryegrass and holds good potential to expedite genetic gain and encourage the application of genomic prediction
OriginalsprogEngelsk
UdgivelsesstedAU-Foulum
ForlagAarhus University, Faculty of Science and Technology
Antal sider108
ISBN (Trykt)ISBN 978-87-93176-63-8
Rekvirerende organGraduate School of Science and Technology
StatusUdgivet - 27 mar. 2015

Note vedr. afhandling

During his studies, Bilal investigate the potential for genomic prediction in perennial ryegrass using genotyping-by-sequencing (GBS) data. Association method based on family-based breeding systems was developed, genomic heritabilities, genomic prediction accuracies and effects of some key factors were explored.
Using GBS for genomic prediction in breeding families of perennial ryegrass holds good potential to expedite genetic gain and encourage the application of genomic prediction.
It is expected that, genomic breeding will be one of the most promising ways for continued and faster development of forage species.
The PhD degree was completed at Centre for Quantitative Genetics and Genomics (QGG), Science and Technology, Aarhus University.

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