title: Using genomics to enhance selection of novel traits in North American dairy cattle.
abstract: The objectives of this paper were to briefly review progress in the genetic evaluation of novel traits in Canada and the United States, assess methods to predict selection accuracy based on cow reference populations, and illustrate how the use of indicator traits could increase genomic selection accuracy. Traits reviewed are grouped into the following categories: udder health, hoof health, other health traits, feed efficiency and methane emissions, and other novel traits. The status of activities expected to lead to national genetic evaluations is indicated for each group of traits. For traits that are more difficult to measure or expensive to collect, such as individual feed intake or immune response, the development of a cow reference population is the most effective approach. Several deterministic methods can be used to predict the reliability of genomic evaluations based on cow reference population size, trait heritability, and other population parameters. To provide an empirical validation of those methods, predicted accuracies were compared with observed accuracies for several cow reference populations and traits. Reference populations of 2,000 to 20,000 cows were created through random sampling of genotyped Holstein cows in Canada and the United States. The effects of single nucleotide polymorphisms (SNP) were estimated from those cow records, after excluding the dams of validation bulls. Bulls that were first progeny tested in 2013 and 2014 were then used to carry out a validation and estimate the observed accuracy of genomic selection based on those SNP effects. Over the various cow population sizes and traits considered in the study, even the best prediction methods were found, on average, to either under-evaluate observed accuracy by 0.20 or over-evaluate it by 0.22, depending on the approach used to estimate the number of independently segregating chromosome segments. In some instances, differences between observed and predicted accuracies were as large as 0.47. Indicator traits can be very useful for the selection of novel traits. To illustrate this, protein yield, body weight, and mid-infrared data were used as indicator traits for feed efficiency. Using those traits in conjunction with 5,000 cow records for dry matter intake increased the reliability of genomic predictions for young animals from 0.20 to 0.50.
authors.affiliation: The Semex Alliance, Guelph, ON N1G 3Z2, Canada. Electronic address: jpchesnais@rogers.com.
authors.forename: J P
authors.initials: JP
authors.lastname: Chesnais
authors.affiliation: Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350.
authors.forename: T A
authors.initials: TA
authors.lastname: Cooper
authors.affiliation: Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350.
authors.forename: G R
authors.initials: GR
authors.lastname: Wiggans
authors.affiliation: The Semex Alliance, Guelph, ON N1G 3Z2, Canada; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Sargolzaei
authors.affiliation: Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria 3083, Australia.
authors.forename: J E
authors.initials: JE
authors.lastname: Pryce
authors.affiliation: Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Canadian Dairy Network, Guelph, ON N1K 1E5, Canada.
authors.forename: F
authors.initials: F
authors.lastname: Miglior
mesh_terms: D000818:Animals; D001947:Breeding; D002170:Canada; D002417:Cattle / Q000235:genetics*; D003612:Dairying; D005260:Female; D023281:Genomics / Q000379:methods*; D009656:North America; D010641:Phenotype; D015203:Reproducibility of Results; D012641:Selection, Genetic
keywords: genetic evaluation; genomics; novel trait; prediction accuracy; selection