title: Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.
abstract: BACKGROUND
  Dominance effects may contribute to genetic variation of complex traits in dairy cattle, especially for traits closely related to fitness such as fertility. However, traditional genetic evaluations generally ignore dominance effects and consider additive genetic effects only. Availability of dense single nucleotide polymorphisms (SNPs) panels provides the opportunity to investigate the role of dominance in quantitative variation of complex traits at both the SNP and animal levels. Including dominance effects in the genomic evaluation of animals could also help to increase the accuracy of prediction of future phenotypes. In this study, we estimated additive and dominance variance components for fertility and milk production traits of genotyped Holstein and Jersey cows in Australia. The predictive abilities of a model that accounts for additive effects only (additive), and a model that accounts for both additive and dominance effects (additive + dominance) were compared in a fivefold cross-validation.
  
  
  RESULTS
  Estimates of the proportion of dominance variation relative to phenotypic variation that is captured by SNPs, for production traits, were up to 3.8 and 7.1 % in Holstein and Jersey cows, respectively, whereas, for fertility, they were equal to 1.2 % in Holstein and very close to zero in Jersey cows. We found that including dominance in the model was not consistently advantageous. Based on maximum likelihood ratio tests, the additive + dominance model fitted the data better than the additive model, for milk, fat and protein yields in both breeds. However, regarding the prediction of phenotypes assessed with fivefold cross-validation, including dominance effects in the model improved accuracy only for fat yield in Holstein cows. Regression coefficients of phenotypes on genetic values and mean squared errors of predictions showed that the predictive ability of the additive + dominance model was superior to that of the additive model for some of the traits.
  
  
  CONCLUSIONS
  In both breeds, dominance effects were significant (P < 0.01) for all milk production traits but not for fertility. Accuracy of prediction of phenotypes was slightly increased by including dominance effects in the genomic evaluation model. Thus, it can help to better identify highly performing individuals and be useful for culling decisions.
authors.affiliation: Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. hassan.aliloo@ecodev.vic.gov.au.|School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia. hassan.aliloo@ecodev.vic.gov.au.|Dairy Futures Cooperative Research Centre (CRC), AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. hassan.aliloo@ecodev.vic.gov.au.
authors.forename: Hassan
authors.initials: H
authors.lastname: Aliloo
authors.affiliation: Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. jennie.pryce@ecodev.vic.gov.au.|School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia. jennie.pryce@ecodev.vic.gov.au.|Dairy Futures Cooperative Research Centre (CRC), AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. jennie.pryce@ecodev.vic.gov.au.
authors.forename: Jennie E
authors.initials: JE
authors.lastname: Pryce
authors.affiliation: Dairy Futures Cooperative Research Centre (CRC), AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. gonzalez.oscar@inia.es.|Department of Animal Breeding, INIA, Ctra La Coruña, km 7.5, 28040, Madrid, Spain. gonzalez.oscar@inia.es.
authors.forename: Oscar
authors.initials: O
authors.lastname: González-Recio
authors.affiliation: Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. ben.cocks@ecodev.vic.gov.au.|School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia. ben.cocks@ecodev.vic.gov.au.|Dairy Futures Cooperative Research Centre (CRC), AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. ben.cocks@ecodev.vic.gov.au.
authors.forename: Benjamin G
authors.initials: BG
authors.lastname: Cocks
authors.affiliation: Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. ben.hayes@ecodev.vic.gov.au.|School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia. ben.hayes@ecodev.vic.gov.au.|Dairy Futures Cooperative Research Centre (CRC), AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia. ben.hayes@ecodev.vic.gov.au.
authors.forename: Ben J
authors.initials: BJ
authors.lastname: Hayes
mesh_terms: D000818:Animals; D001315:Australia; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D003612:Dairying; D005260:Female; D005298:Fertility / Q000235:genetics*; D005799:Genes, Dominant; D023281:Genomics; D005838:Genotype; D007774:Lactation / Q000235:genetics*; D016013:Likelihood Functions; D008055:Lipids / Q000032:analysis; D008297:Male; D008892:Milk / Q000737:chemistry; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011247:Pregnancy; D019655:Quantitative Trait, Heritable; D012641:Selection, Genetic
chemical_list: D008055:Lipids
references.citation: Genet Sel Evol. 2014;46:40
references.pmid: 24962065
references.citation: Nat Rev Genet. 2014 Nov;15(11):722-33
references.pmid: 25200660
references.citation: Genet Sel Evol. 2014;46:71
references.pmid: 25407532
references.citation: J Anim Breed Genet. 2015 Feb;132(1):3-8
references.pmid: 24974900
references.citation: Genet Sel Evol. 2015;47:26
references.pmid: 25880217
references.citation: PLoS One. 2014;9(8):e103934
references.pmid: 25084281
references.citation: J Anim Sci. 2014 Aug;92(8):3270-83
references.pmid: 25074450
references.citation: PLoS One. 2014;9(1):e87666
references.pmid: 24498162
references.citation: PLoS One. 2014;9(1):e85792
references.pmid: 24416447
references.citation: Genetics. 2013 Dec;195(4):1223-30
references.pmid: 24121775
references.citation: J Dairy Sci. 2009 Jan;92(1):16-24
references.pmid: 19109259
references.citation: PLoS Genet. 2008 Oct;4(10):e1000231
references.pmid: 18949033
references.citation: J Dairy Sci. 2008 Nov;91(11):4414-23
references.pmid: 18946147
references.citation: J Dairy Sci. 1995 May;78(5):1174-80
references.pmid: 7622728
references.citation: J Dairy Sci. 2013;96(12):8014-23
references.pmid: 24119810
references.citation: J Dairy Sci. 1991 Feb;74(2):557-69
references.pmid: 2045562
references.citation: J Dairy Sci. 2000 Aug;83(8):1873-7
references.pmid: 10984165
references.citation: J Dairy Sci. 2013 Jan;96(1):655-67
references.pmid: 23127912
references.citation: J Dairy Sci. 2012 Jul;95(7):4114-29
references.pmid: 22720968
references.citation: Genet Sel Evol. 2010;42:33
references.pmid: 20699012
references.citation: Nat Genet. 2010 Jul;42(7):565-9
references.pmid: 20562875
references.citation: J Dairy Sci. 2009 Jun;92(6):2931-46
references.pmid: 19448026
references.citation: Am J Hum Genet. 2009 Feb;84(2):210-23
references.pmid: 19200528
references.citation: J Dairy Sci. 2009 Feb;92(2):433-43
references.pmid: 19164653
references.citation: J Dairy Sci. 2016 Jan;99(1):493-504
references.pmid: 26547639
references.citation: J Dairy Sci. 1991 May;74(5):1743-52
references.pmid: 1880275
references.citation: J Dairy Sci. 1994 Apr;77(4):1114-25
references.pmid: 8201046