Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-05T16:34:10.705Z Has data issue: false hasContentIssue false
  • English
  • Français

A simple method for genomic selection of moderately sized dairy cattle populations

Published online by Cambridge University Press:  26 September 2011

J. I. Weller ,
M. Ron ,
G. Glick ,
A. Shirak ,
Y. Zeron  and
E. Ezra
J. I. Weller*
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel
M. Ron
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel
G. Glick
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel Department of Ruminant Science, The Robert H. Smith Faculty of Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
A. Shirak
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel
Y. Zeron
Affiliation:
Department of Ruminant Science, Sion – Israeli Company for Artificial Insemination & Breeding Ltd, M. P. Shikmim 79800, Israel
E. Ezra
Affiliation:
Department of Ruminant Science, Israel Cattle Breeders Association, Caesarea Industrial Park 38900, Israel
*
†E-mail: [email protected]
Get access

Abstract

An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a ‘cow model,’ estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.

Keywords

genomic selectionquantitative trait locisingle nucleotide polymorphismsdairy cattle breeding
Type
Full Paper
Information
animal , Volume 6 , Issue 2 , 03 January 2012 , pp. 193 - 202
Copyright
Copyright © The Animal Consortium 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aguilar, I, Misztal, I, Johnson, DL, Legarra, A, Tsuruta, S, Lawlor, TJ 2010. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743–752.CrossRefGoogle Scholar
Baruch, E, Weller, JI 2008. Incorporation of discrete genotype effects for multiple genes into animal model evaluations when only a small fraction of the population has been genotyped. Journal of Dairy Science 91, 4365–4371.CrossRefGoogle Scholar
Baruch, E, Weller, JI 2009. Incorporation of genotype effects into animal model evaluations when only a small fraction of the population has been genotyped. Animal 3, 16–23.CrossRefGoogle Scholar
Cohen-Zinder, M, Seroussi, E, Larkin, DM, Loor, JJ, Wind, AE-vd, Lee, J-H, Drackley, JK, Band, MR, Hernandez, AG, Shani, M, Lewin, HA, Weller, JI, 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
Cromie, AR, Berry, DP, Wickham, B, Kearney, JF, Pena, J, van Kaam, JBCH, Gengler, N, Szyda, J, Schnyder, U, Coffey, M, Moster, B, Hagiya, K, Weller, JI, Abernethy, D, Spelman, R 2010. International genomic co-operation; who, what, when, where, why and how? Proceedings of the Interbull Meeting, Riga, Latvia 42, 72–78.Google Scholar
Ducrocq, V, Fritz, S, Guillaume, F, Boichard, D 2009. French report on the use of genomic evaluation. Proceedings of the Interbull Meeting, Uppsala, Sweden, pp. 17–22.Google Scholar
Ezra, E, Weller, JI, Drori, D 1987. Estimation of environmental effect of milk protein content. Heker Umas 9, 31–35 (in Hebrew).Google Scholar
Gianola, D, de los Campos, G, Hill, WG, Manfredi, E, Fernando, R 2009. Additive genetic variability and the Bayesian alphabet. Genetics 183, 347–363.CrossRefGoogle ScholarPubMed
Goddard, ME, Hayes, BJ 2007. Genomic selection. Journal of Animal breeding and Genetics 124, 323–330.CrossRefGoogle ScholarPubMed
Guillaume, F, Fritz, S, Boichard, D, Druet, T 2008. Short communication: correlations of marker-assisted breeding values with progeny-test breeding values for eight hundred ninety-nine French Holstein bulls. Journal of Dairy Science 91, 2520–2522.CrossRefGoogle ScholarPubMed
Habier, D, Fernando, RL, Dekkers, JCM 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.CrossRefGoogle ScholarPubMed
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.CrossRefGoogle ScholarPubMed
Israel, C, Weller, JI 1998. Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 81, 1653–1662.CrossRefGoogle ScholarPubMed
Kerr, RJ, Kinghorn, BP 1996. An efficient algorithm for segregation analysis in large populations. Journal of Animal breeding and Genetics 113, 457–469.CrossRefGoogle Scholar
Kuhn, MT, Freeman, AE, Fernando, RL 1999. Approaches investigated to correct for preferential treatment. Journal of Dairy Science 82, 181–190.CrossRefGoogle Scholar
Legarra, A, Aguilar, I, Misztal, I 2009. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663.CrossRefGoogle ScholarPubMed
Liu, Z, Seefried, F, Reinhardt, F, Reents, R 2009. A simple method for correcting the bias caused by genomic pre-selection in conventional genetic evaluation. Proceedings of the Interbull Meeting, Barcelona, Spain, pp. 185–188.Google Scholar
Loberg, A, Durr, JW 2009. Interbull survey on the use of genomic information. Proceedings of the Interbull International Workshop on Genomic Information in Genetic Evaluations, Uppsala, Sweden, pp. 3–14.Google Scholar
Maher, B 2008. Personal genomes: the case of the missing heritability. Nature 45, 18–21.CrossRefGoogle Scholar
Mäntysaari, E, Liu, Z, VanRaden, P 2010. Interbull validation test for genomic evaluations. Proceedings of the Interbull International Workshop on Genomic Information in Genetic Evaluations, Paris, France.Google Scholar
Matukumalli, LK, Lawley, CT, Schnabel, RD, Taylor, JF, Allan, MF, Heaton, MP, OConnell, J, Moore, SS, Smith, TPL, Sonstegard, TS, Van Tassell, CP 2009. Development and characterization of a high density SNP genotyping assay for cattle. PLoS ONE 4, e5350.CrossRefGoogle ScholarPubMed
Misztal, I, Legarra, A, Aguilar, I 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science 92, 4648–4655.CrossRefGoogle ScholarPubMed
Party, C, Ducrocq, V 2009. Bias due to genomic selection. Proceedings of the Interbull Meeting 39, Uppsala, Sweden.Google Scholar
Settar, P, Weller, JI 1999. Genetic analysis of cow survival in the Israeli dairy cattle population. Journal of Dairy Science 82, 2170–2177.CrossRefGoogle ScholarPubMed
Tsuruta, S, Aguilar, I, Misztal, I, Legarra, A, Lawlor, T 2010. Multiple trait genetic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 0489_PP2-15, Leipzig, Germany.Google Scholar
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
VanRaden, PM, 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, Schenkel, FS 2009a. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
VanRaden, PM, Wiggans, GR,, Sonstegard, TS, Schenkel, F 2 009 b. Benefits from cooperation in genomics. Proceedings of the Interbull International Workshop on Genomic Information in Genetic Evaluations, Uppsala, Sweden, pp. 67–72.Google Scholar
Weigel, KA, de los Campos, G, González-Recio, O, Naya, H, Wu, XL, Long, N, Rosa, GJM, 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, Ezra, E 2004. Genetic analysis of the Israeli Holstein dairy cattle population for production and non-production traits with a multitrait animal model. Journal of Dairy Science 87, 1519–1527.CrossRefGoogle Scholar
Weller, JI, Ezra, E, 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, Song, JZ, Heyen, DW, Lewin, HA, Ron, M 1998. A new approach to the problem of multiple comparisons in the genetic dissection of complex traits. Genetics 150, 1699–1706.CrossRefGoogle Scholar
Weller, JI, Golik, M, Seroussi, E, Ezra, E, Ron, M 2003. Population-wide analysis of a QTL affecting milk-fat production in the Israeli Holstein population. Journal of Dairy Science 86, 2219–2227.CrossRefGoogle ScholarPubMed
Weller, JI, Glick, G, Ezra, E, Zeron, Y, Seroussi, E, Ron, M 2010. Paternity validation and estimation of genotyping error rate for the BovineSNP50 BeadChip. Animal Genetics 41, 551–553.CrossRefGoogle ScholarPubMed
Wiggans, GR, VanRaden, PM, Cooper, TA 2011. The genomic evaluation system in the United States: past, present, future. Journal of Dairy Science 94, 3202–3211.CrossRefGoogle ScholarPubMed