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Predictive ability of selected subsets of single nucleotide polymorphisms (SNPs) in a moderately sized dairy cattle population

Published online by Cambridge University Press:  17 January 2014

J. I. Weller ,
G. Glick ,
A. Shirak ,
E. Ezra ,
E. Seroussi ,
M. Shemesh ,
Y. Zeron  and
M. Ron
J. I. Weller*
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, ARO, the Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
G. Glick
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, ARO, the Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel Genetics and Breeding, The Robert H. Smith Faculty of Agriculture, The Hebrew University of Jerusalem, Rehovot 76100, Israel
A. Shirak
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, ARO, the Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
E. Ezra
Affiliation:
Israel Cattle Breeders Association, Caesaria Industrial Park, Caesaria 38900, Israel
E. Seroussi
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, ARO, the Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
M. Shemesh
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, ARO, the Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
Y. Zeron
Affiliation:
Sion, AI Institute, Shikmim 79800, Israel
M. Ron
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, ARO, the Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
*
† E-mail: [email protected]
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Abstract

Several studies have shown that computation of genomic estimated breeding values (GEBV) with accuracies significantly greater than parent average (PA) estimated breeding values (EBVs) requires genotyping of at least several thousand progeny-tested bulls. For all published analyses, GEBV computed from the selected samples of markers have lower or equal accuracy than GEBV derived on the basis of all valid single nucleotide polymorphisms (SNPs). In the current study, we report on four new methods for selection of markers. Milk, fat, protein, somatic cell score, fertility, persistency, herd life and the Israeli selection index were analyzed. The 972 Israeli Holstein bulls genotyped with EBV for milk production traits computed from daughter records in 2012 were assigned into a training set of 844 bulls with progeny test EBV in 2008, and a validation set of 128 young bulls. Numbers of bulls in the two sets varied slightly among the nonproduction traits. In EFF12, SNPs were first selected for each trait based on the effects of each marker on the bulls’ 2012 EBV corrected for effective relationships, as determined by the SNP matrix. EFF08 was the same as EFF12, except that the SNPs were selected on the basis of the 2008 EBV. In DIFmax, the SNPs with the greatest differences in allelic frequency between the bulls in the training and validation sets were selected, whereas in DIFmin the SNPs with the smallest differences were selected. For all methods, the numbers of SNPs retained varied over the range of 300 to 6000. For each trait, except fertility, an optimum number of markers between 800 and 5000 was obtained for EFF12, based on the correlation between the GEBV and current EBV of the validation bulls. For all traits, the difference between the correlation of GEBV and current EBV and the correlation of the PA and current EBV was >0.25. EFF08 was inferior to EFF12, and was generally no better than PA EBV. DIFmax always outperformed DIFmin and generally outperformed EFF08 and PA. Furthermore, GEBV based on DIFmax were generally less biased than PA. It is likely that other methods of SNP selection could improve upon these results.

Keywords

genomic selectionSNPdairy cattlegenetic evaluationsubsets of SNPs
Type
Full Paper
Information
animal , Volume 8 , Issue 2 , February 2014 , pp. 208 - 216
Copyright
© The Animal Consortium 2014 

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References

Calus, MPL 2010. Genomic breeding value prediction: methods and procedures. Animal 4, 157–164.CrossRefGoogle ScholarPubMed
Cohen-Zinder, M, Seroussi, E, Larkin, DM, Loor, JJ, Everts-van der Wind, A, Lee, JH, Drackley, JK, Band, MR, Hernandez, AG, Shani, M, Lewin, HA, Weller, JI and Ron, M 2005. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Research 15, 936–944.CrossRefGoogle Scholar
Daetwyler, HD, Villanueva, B and Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. Plos One 3, e3395.CrossRefGoogle ScholarPubMed
Glick, G, Shirak, A, Uliel, S, Zeron, Y, Ezra, E, Seroussi, E, Ron, M and Weller, JI 2012. Signatures of contemporary selection in the Israeli Holstein dairy cattle. Animal Genetics 43 (Suppl. 1), 45–55.CrossRefGoogle ScholarPubMed
Habier, D, Fernando, RL and Dekkers, JCM 2009. Genomic selection using low-density marker panels. Genetics 182, 343–353.CrossRefGoogle ScholarPubMed
Kang, HM, Sul, JJ, Service, SK, Zaitlen, NA, Kong, SY, Freimer, NB, Sabatti, C and Eskin, E 2010. Variance component model to account for sample structure in genome-wide association studies. Nature Genetics 42, 348–354.CrossRefGoogle ScholarPubMed
Lynch, M and Walsh, B 1998. Genetics and analysis of quantitative traits. Sinauer Associates Inc., Sunderland, MA.Google Scholar
Misztal, I and Wiggans, GR 1988. Approximation of prediction error variance in large-scale animal models. Journal of Dairy Science 71 (Suppl. 2), 27–32.CrossRefGoogle Scholar
Misztal, I, Lawlor, TJ, Short, TH and Wiggans, GR 1991. Continuous genetic evaluation of Holstein for type. Journal of Dairy Science 74, 2001–2009.CrossRefGoogle Scholar
Moser, G, Khatkar, MS, Hayes, BJ and Raadsma, HW 2010. Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genetics Selection Evolution 42, 37.CrossRefGoogle ScholarPubMed
Moser, G, Tier, B, Crump, RE, Khatkar, MS and Raadsma, HW 2009. A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genetics Selection Evolution 41, 56.CrossRefGoogle ScholarPubMed
Pintus, MA, Gaspa, G, Nicolazzi, EL, Vicario, D, Rossoni, A, Ajmone-Marsan, P, Nardone, A, Dimauro, C and Macciotta, NPP 2012. Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach. Journal of Dairy Science 95, 3390–3400.CrossRefGoogle Scholar
Settar, P and Weller, JI 1999. Genetic analysis of cow survival in the Israeli dairy cattle population. Journal of Dairy Science 82, 2170–2177.CrossRefGoogle ScholarPubMed
Van Grevenhof, EM, Van Arendonk, JAM and Bijma, P 2012. Response to genomic selection: the Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting. Genetics Selection Evolution 44, 26.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
VanRaden, PM and Wiggans, GR 1991. Derivation, calculation and use of national animal model information. Journal of Dairy Science 74, 2737–2746.CrossRefGoogle ScholarPubMed
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF and Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
Vazquez, AI, Rosa, GJM, Weigel, KA, de los Campos, G, Gianola, D and Allison, DB 2010. Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins. Journal of Dairy Science 93, 5942–5949.CrossRefGoogle ScholarPubMed
Weigel, KA, de los Campos, G, Gonzalez-Recio, O, Naya, H, Wu, XL, Long, N, Rosa, GJ and Gianola, D 2009. Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science 92, 5248–5257.CrossRefGoogle ScholarPubMed
Weller, JI and Ezra, E 1997. Genetic analysis of somatic cell concentration and female fertility of Israeli Holsteins by the individual animal model. Journal of Dairy Science 80, 586–593.CrossRefGoogle Scholar
Weller, JI and Ezra, E 2004. Genetic analysis of the Israeli Holstein dairy cattle population for production and nonproduction traits with a multitrait animal model. Journal of Dairy Science 87, 1519–1527.CrossRefGoogle ScholarPubMed
Weller, JI, Ezra, E and Leitner, G 2006. Genetic analysis of persistency in the Israeli Holstein population by the multitrait animal model. Journal of Dairy Science 89, 2738–2746.CrossRefGoogle ScholarPubMed
Weller, JI, Ron, M, Glick, G, Shirak, A, Zeron, Y, Misztal, I and Ezra, E 2011. A simple algorithm for genomic selection for moderately sized dairy cattle populations. Animal 6, 193–202.CrossRefGoogle Scholar
Wiggans, GR, VanRaden, PM and Cooper, TA 2011. The genomic evaluation system in the United States: past, present, future. Journal of Dairy Science 94, 3202–3211.CrossRefGoogle ScholarPubMed
Zhang, Z, Ding, X, Liu, J, Zhang, Q and de Koning, D-J 2011. Accuracy of genomic prediction using low-density marker panels. Journal of Dairy Science 94, 3642–3650.CrossRefGoogle ScholarPubMed