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ACROSS BREED QTL DETECTION AND GENOMIC PREDICTION IN FRENCH AND DANISH DAIRY CATTLE BREEDS

Monday, August 18, 2014
Posters (The Westin Bayshore)
Irene van den Berg , AgroParisTech, UMR1313 GABI, Paris, France
Bernt Guldbrandtsen , Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
Chris Hoze , INRA, UMR1313 GABI, Jouy-en-Josas, France
Rasmus F Brøndum , Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
Didier Boichard , INRA, UMR1313 GABI, Jouy-en-Josas, France
Mogens Sandø Lund , Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
Abstract Text:

Our objective was to investigate the potential benefits of using sequence data to improve across breed genomic prediction, using data from five French and Danish dairy cattle breeds. First, QTL for protein yield were detected using high density genotypes. Part of the QTL detected within breed was shared across breed. Second, sequence data was used to quantify the loss in prediction reliabilities that results from using genomic markers rather than the causal variants. 50, 100 or 250 causative mutations were simulated and different sets of prediction markers were used to predict genomic relationships at causative mutations. Prediction of genomic relationships at causative mutations was most accurate when predicted by a selective number of markers within 1 Kb of the causative mutations. Whole-genome sequence data can help to get closer to the causative mutations and therefore improve genomic prediction across breed.

Keywords:

dairy cattle

across breed

genomic prediction

QTL

See more of: Posters: Prediction using molecular information (Group 3)
See more of: Genetic Improvement Programs: Selection using molecular information
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