Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study.
Sequence data are expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across-breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared to knowing the causative variants. Our objective was to use sequence data to investigate the potential benefits of sequence data for the prediction of genomic relationships, and consequently reliability of genomic breeding values. We used sequence data from five dairy cattle breeds, and a larger number of imputed sequences for two of the five breeds. We focused on the influence of linkage disequilibrium between markers and causative mutations, and assumed that a fraction of the causative mutations was shared across breeds and had the same effect across breeds. By comparing the loss in reliability of different scenarios, varying the distance between markers and causative mutations, using either all genome wide markers from commercial SNP chips, or only the markers closest to the causative mutations, we demonstrate the importance of using only variants very close to the causative mutations, especially for across-breed prediction. Rare variants improved prediction only if they were very close to rare causative mutations, and all causative mutations were rare. Our results show that sequence data can potentially improve genomic prediction, but careful selection of markers is essential.
Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, DK-8830 Tjele, Denmark Génétique Animale et Biologie Intégrative (GABI), French National Institute for Agricultural Research (INRA), AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France irene.vandenberg@unimelb.edu.au.
Irene
I
van den Berg
Génétique Animale et Biologie Intégrative (GABI), French National Institute for Agricultural Research (INRA), AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France.
Didier
D
Boichard
Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, DK-8830 Tjele, Denmark.
Bernt
B
Guldbrandtsen
Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, DK-8830 Tjele, Denmark.
Mogens S
MS
Lund
D000818:Animals; D001947:Breeding / Q000379:methods; D002417:Cattle / Q000235:genetics*; D003198:Computer Simulation; D030541:Databases, Genetic; D005787:Gene Frequency; D005819:Genetic Markers; D014644:Genetic Variation; D016678:Genome; D015810:Linkage Disequilibrium; D008297:Male; D008957:Models, Genetic; D009154:Mutation; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results
D005819:Genetic Markers
GenPred; across-breed prediction; genomic relationships; genomic selection; linkage disequilibrium; sequence data; shared data resource