Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-20T01:02:22.642Z Has data issue: false hasContentIssue false
  • English
  • Français

Genotyping strategies for genomic selection in small dairy cattle populations

Published online by Cambridge University Press:  24 February 2012

J. A. Jiménez-Montero ,
O. González-Recio  and
R. Alenda
J. A. Jiménez-Montero*
Affiliation:
Departamento de Producción Animal, E.T.S.I. Agrónomos-Universidad Politécnica de Madrid, 28040 Madrid, Spain
O. González-Recio
Affiliation:
Departamento de Mejora Genética Animal, INIA, 28040 Madrid, Spain
R. Alenda
Affiliation:
Departamento de Producción Animal, E.T.S.I. Agrónomos-Universidad Politécnica de Madrid, 28040 Madrid, Spain
*
†E-mail: joseantonio.jimenez.montero@upm.es
Get access

Abstract

This study evaluated different female-selective genotyping strategies to increase the predictive accuracy of genomic breeding values (GBVs) in populations that have a limited number of sires with a large number of progeny. A simulated dairy population was utilized to address the aims of the study. The following selection strategies were used: random selection, two-tailed selection by yield deviations, two-tailed selection by breeding value, top yield deviation selection and top breeding value selection. For comparison, two other strategies, genotyping of sires and pedigree indexes from traditional genetic evaluation, were included in the analysis. Two scenarios were simulated, low heritability (h2 = 0.10) and medium heritability (h2 = 0.30). GBVs were estimated using the Bayesian Lasso. The accuracy of predicted GBVs using the two-tailed strategies was better than the accuracy obtained using other strategies (0.50 and 0.63 for the two-tailed selection by yield deviations strategy and 0.48 and 0.63 for the two-tailed selection by breeding values strategy in low- and medium-heritability scenarios, respectively, using 1000 genotyped cows). When 996 genotyped bulls were used as the training population, the sire’ strategy led to accuracies of 0.48 and 0.55 for low- and medium-heritability traits, respectively. The Random strategies required larger training populations to outperform the accuracies of the pedigree index; however, selecting females from the top of the yield deviations or breeding values of the population did not improve accuracy relative to that of the pedigree index. Bias was found for all genotyping strategies considered, although the Top strategies produced the most biased predictions. Strategies that involve genotyping cows can be implemented in breeding programs that have a limited number of sires with a reliable progeny test. The results of this study showed that females that exhibited upper and lower extreme values within the distribution of yield deviations may be included as training population to increase reliability in small reference populations. The strategies that selected only the females that had high estimated breeding values or yield deviations produced suboptimal results.

Keywords

genomicsgenotypefemaletwo-tailedtraining population
Type
Full Paper
Information
animal , Volume 6 , Issue 8 , August 2012 , pp. 1216 - 1224
Copyright
Copyright © The Animal Consortium 2012

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

Amer, PR, Banos, G 2010. Implications of avoiding overlap between training and testing data sets when evaluating genomic predictions of genetic merit. Journal of Dairy Science 93, 3320–3330.CrossRefGoogle ScholarPubMed
Banos, G, Coffey, MP 2010. Short communication: characterization of the genome-wide linkage disequilibrium in 2 divergent selection lines of dairy cows. Journal of Dairy Science 93, 2775–2778.CrossRefGoogle ScholarPubMed
Blonk, RJW, Komen, J, van Arendonk, JAM 2010. Minimizing genotyping in breeding programs with natural mating. World Congress on Genetic Applied to Livestock Production Abstract no. 195, 2–7 August 2010, Leipzig, Germany.Google Scholar
Calus, MPL 2009. Genomic breeding value prediction: methods and procedures. Animal 4, 157–164.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? Interbull Meeting, 31 May–4 June 2010, Riga, Latvia.Google Scholar
De los Campos, G, Naya, H, Gianola, D, Crossa, J, Legarra, A, Manfredi, E, Weigel, K, Cotes, JM 2009. Posterior predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182, 375–385.CrossRefGoogle Scholar
De Roos, APW, Hayes, BJ, Spelman, RJ, Goddard, ME 2008. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 179, 1503–1512.CrossRefGoogle ScholarPubMed
De Roos, AP, Hayes, BJ, Goddard, ME 2009. Reliability of genomic predictions across multiple populations. Genetics 183, 1545–1553.CrossRefGoogle ScholarPubMed
Ducrocq, V, Santus, E 2011. Moving away from progeny test schemes: consequences on conventional (inter)national evaluations. Interbull Bulletin 43 (http://www.interbull.org/images/stories/Ducrocq_copy.pdf).Google Scholar
Ehsani, A, Janss, L, Christensen, OF 2010. Effects of selective genotyping on genomic prediction. World Congress on Genetic Applied to Livestock Production Abstract no. 444, 2–7 August 2010, Leipzig, Germany.Google Scholar
Goddard, ME, Hayes, BJ 2007. Genomic selection. Journal of Animal Breeding and Genetics 124, 323–330.CrossRefGoogle ScholarPubMed
Goddard, ME, Hayes, BJ 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics 10, 381–391.CrossRefGoogle ScholarPubMed
González-Recio, O, Alenda, R 2005. Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle. Journal of Dairy Science 88, 3282–3289.CrossRefGoogle Scholar
González-Recio, O, Ugarte, C, Alenda, R 2005. Genetic analysis of an artificial insemination progeny test program. Journal of Dairy Science 88, 783–789.CrossRefGoogle ScholarPubMed
González-Recio, O, López de Maturana, E, Gutiérrez, JP 2007. Inbreeding depression on female fertility and calving ease in Spanish dairy cattle. Journal of Dairy Science 90, 5744–5752.CrossRefGoogle ScholarPubMed
González-Recio, O, Alenda, R, Chang, YM, Weigel, K, Gianola, D 2006. Selection for female fertility using censored fertility traits and investigation of the relationship with milk production. Journal of Dairy Science 89, 4438–4444.CrossRefGoogle ScholarPubMed
Habier, D, Dekkers, JCM, Fernando, RL 2009. Genomic selection using low-density marker panels. Genetics 182, 343–353.CrossRefGoogle ScholarPubMed
Habier, D, Tetens, J, Seefried, FR, Lichtner, P, Thaller, G 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution 42, 5.CrossRefGoogle ScholarPubMed
Hayes, BJ, Goddard, ME 2001. The distribution of effects of genes affecting quantitative traits in livestock. Genetics Selection Evolution 33, 209–229.CrossRefGoogle ScholarPubMed
Hayes, BJ, Visscher, PM, Mcpartlan, HC, Goddard, ME 2003. Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Research 13, 635–643.CrossRefGoogle ScholarPubMed
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009. Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.CrossRefGoogle ScholarPubMed
Hill, WG, Robertson, A 1968. Linkage disequilibrium in finite populations. Theoretical and Applied Genetics 38, 226–231.CrossRefGoogle ScholarPubMed
Kearney, JF, Wall, E, Villanueva, B, Coffey, MP 2004. Inbreeding trends and application of optimized selection in the UK Holstein population. Journal of Dairy Science 87, 3503–3509.CrossRefGoogle ScholarPubMed
Loberg, A, Dürr, JW 2009. Interbull survey on the use of genomic information. In Proceedings of the Interbull technical workshop 39, 3–14.Google Scholar
Long, N, Gianola, D, Rosa, GJM, Weigel, KA, Avendaño, S 2009. Comparison of classification methods for detecting associations between SNPs and chick mortality. Genetics Selection Evolution 41, 18.CrossRefGoogle ScholarPubMed
Luan, T, Woolliams, JA, Lien, S, Kent, M, Svendsen, M, Meuwissen, THE 2009. The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183, 1119–1126.CrossRefGoogle ScholarPubMed
Lund, MS, de Roos, APW, de Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, M, Su, G 2010. Improving genomic prediction by EuroGenomics collaboration. World Congress on Genetic Applied to Livestock Production Abstract no. 880, 2–7 August 2010, Leipzig, Germany.Google Scholar
Meuwissen, THE, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
Olson, KM, VanRaden, PM, Tooker, ME, Cooper, TA 2011. Differences among methods to validate genomic evaluations for dairy cattle. Journal of Dairy Science 94, 2613–2620.CrossRefGoogle ScholarPubMed
Patry, C, Ducrocq, V 2009. Evidence of a bias in genetic evaluation due to genomic selection. Interbull Bulletin 40, 167–171.Google Scholar
Sargolzaei, M, Schenkel, FS, Jansen, GB, Schaeffer, LR 2008. Extent of linkage disequilibrium in Holstein cattle in North America. Journal of Dairy Science 91, 2106–2117.CrossRefGoogle ScholarPubMed
Sargolzaei, M, Schenkel, FS 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics 25, 680–681.CrossRefGoogle ScholarPubMed
Sen, S, Johannes, F, Broman, KW 2009. Selective genotyping and phenotyping strategies in a complex trait context. Genetics 181, 1613–1626.CrossRefGoogle Scholar
Sørensen, AC, Sørensen, MK, Berg, P 2005. Inbreeding in Danish dairy cattle breeds. Journal of Dairy Science 88, 1865–1872.CrossRefGoogle ScholarPubMed
Spangler, ML, Sapp, RL, Bertrand, JK, Mac Neil, MD, Rekaya, R 2008. Different methods of selecting animals for genotyping to maximize the amount of genetic information known in the population. Journal of Animal Science 86, 2471–2479.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, Sontegard, TSG, Schnabel, RD, Taylor, JF, Schenkel, FS 2009a. Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
VanRaden, PM, Wiggans, GR, Van Tassell, CP, Sontegard, TSG, Schenkel, FS 2009b. Benefits from collaboration in genomics. Interbull Bulletin 40, 67–72.Google Scholar
Weigel, KA, Van Tassell, CP, O′Connel, JR, VanRaden, PM, Wiggans, GR 2010. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science 93, 2229–2238.CrossRefGoogle ScholarPubMed
Wiggans, GR, Sonstegard, TS, VanRaden, PM, Matukumalli, LK, Schnabel, RD, Taylor, JF, Chesnais, JP, Schenkel, F, Van Tassell, CP 2008. Genomic evaluations in the United States and Canada: a collaboration. In Procedings of International Commitee of Animal Recording, 16–20 June 2008, Niagara Falls, NY, 6pp.Google Scholar
Zhao, H, Nettleton, D, Soller, M, Dekkers, JCM 2005. Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL. Genetical Research 86, 77–87.CrossRefGoogle ScholarPubMed