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Genomic breeding value prediction: methods and procedures*

Published online by Cambridge University Press:  06 November 2009

M. P. L. Calus
M. P. L. Calus*
Affiliation:
Animal Sciences Group, Animal Breeding and Genomics Centre, Wageningen University and Research Centre, 8200 AB Lelystad, The Netherlands
*
†E-mail: [email protected]
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Abstract

Animal breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.

Keywords

genomic selection, SNP, methods
Type
Full Paper
Information
animal , Volume 4 , Issue 2 , February 2010 , pp. 157 - 164
Copyright
Copyright © The Animal Consortium 2009

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Footnotes

*

This paper has been presented at the session ‘Genomics Selection and Bioinformatics’ of the 59th Annual meeting of the European Association for Animal Production held in Vilnius (Lithuania), 24 to 27 August 2008. Dr A. Maki-Tanila acted as Guest Editor.

References

Bennewitz, J, Meuwissen, THE 2008. Genomic breeding value estimation using kernel regression and additive models. In 12th Quantitative Trait Locus and Marker Assisted Selection Workshop, Uppsala, Sweden, p. 34.CrossRefGoogle Scholar
Berry, DP, Kearney, F, Harris, BL 2009. Genomic Selection in Ireland. Proceedings of the Interbull International Workshop – Genomic Information in Genetic Evaluations, Uppsala, Sweden, Bulletin no. 39.Google Scholar
Calus, MPL, Veerkamp, RF 2007. Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. Journal Of Animal Breeding And Genetics 124, 362–368.CrossRefGoogle ScholarPubMed
Calus, MPL, Meuwissen, T, De Roos, APW, Veerkamp, RF 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics 178, 553–561.CrossRefGoogle ScholarPubMed
Calus, MPL, Meuwissen, T, Windig, JJ, Knol, EF, Schrooten, C, Vereijken, ALJ, Veerkamp, RF 2009. Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values. Genetics Selection Evolution 41, 11.CrossRefGoogle ScholarPubMed
de los Campos, G, Naya, H, Gianola, D, Crossa, J, Legarra, A, Manfredi, E, Weigel, K, Cotes, JM 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182, 375–385.CrossRefGoogle ScholarPubMed
De Roos, APW, Hayes, BJ, Goddard, ME 2008a. Reliability of genomic breeding values across multiple populations. In 12th QTL-MAS Workshop, Uppsala, Sweden, p. 33.Google Scholar
De Roos, APW, Hayes, BJ, Spelman, RJ, Goddard, ME 2008b. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 179, 1503–1512.CrossRefGoogle ScholarPubMed
De Roos, APW, Schrooten, C, Mullaart, E, Calus, MPL, Veerkamp, RF 2007. Breeding value estimation for fat percentage using dense marker maps on Bos taurus autosome 14. Journal of Dairy Science 90, 4821–4829.CrossRefGoogle ScholarPubMed
De Roos, APW, Schrooten, C, Mullaart, E, Van der Beek, S, De Jong, G, Voskamp, W 2009. Genomic Selection at CRV. Proceedings of the Interbull International Workshop – Genomic Information in Genetic Evaluations, Uppsala, Sweden, Bulletin no. 39.Google Scholar
Dekkers, JCM, Hospital, F 2002. The use of molecular genetics in the improvement of agricultural populations. Nature Reviews Genetics 3, 22–32.CrossRefGoogle ScholarPubMed
Fernando, RL 1998. Genetic evaluation and selection using genotypic, phenotypic and pedigree information. Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, Armidale, Australia.Google Scholar
Fikse, WF, Banos, G 2001. Weighting factors of sire daughter information in international genetic evaluations. Journal of Dairy Science 84, 1759–1767.CrossRefGoogle ScholarPubMed
Gianola, D, Fernando, RL, Stella, A 2006. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173, 1761–1776.CrossRefGoogle ScholarPubMed
Gianola, D, van Kaam, JBCHM 2008. Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178, 2289–2303.CrossRefGoogle ScholarPubMed
Goddard, M 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.CrossRefGoogle ScholarPubMed
González-Recio, O, Gianola, D, Long, N, Weigel, KA, Rosa, GJM, Avendaño, S 2008. Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers. Genetics 178, 2305–2313.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
Harris, BL, Johnson, DL, Spelman, RJ 2008. Genomic selection in New Zealand and the implications for national genetic evaluation. In Proceedings of the 36th ICAR Biennial Session Held in Niagara Falls, USA, pp. 325–330.Google Scholar
Harris, BL, Montgomerie, WA 2009. Current status of the use of genomic information in the national genetic evaluation in New Zealand. Proceedings of the Interbull International Workshop – Genomic Information in Genetic Evaluations, Uppsala, Sweden, Bulletin no. 39.Google Scholar
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
Hayes, B, Goddard, ME 2001. The distribution of the effects of genes affecting quantitative traits in livestock. Genetics Selection Evolution 33, 209–229.CrossRefGoogle ScholarPubMed
Hayes, BJ, Goddard, ME 2008. Technical note: prediction of breeding values using marker-derived relationship matrices. Journal of Animal Science 86, 2089–2092.CrossRefGoogle ScholarPubMed
Henderson, CR 1975. Rapid method for computing inverse of a relationship matrix. Journal of Dairy Science 58, 1727–1730.CrossRefGoogle Scholar
Hill, WG, Goddard, ME, Visscher, PM 2008. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genetics 4, 2.CrossRefGoogle ScholarPubMed
Jansen, RC 1993. Interval mapping of multiple quantitative trait loci. Genetics 135, 205–211.CrossRefGoogle ScholarPubMed
Janss, L, Gregersen, V, Bendixen, C, Lund, M 2008. Validation of genomic predictions in pigs using medium-dense marker coverage. In Book of Abstracts of the 59th Annual meeting of the EAAP, Vilnius, Lithuania.Google Scholar
Ke, XY, Cardon, LR 2003. Efficient selective screening of haplotype tag SNPs. Bioinformatics 19, 287–288.CrossRefGoogle ScholarPubMed
Khatkar, MS, Nicholas, FW, Collins, AR, Zenger, KR, Al Cavanagh, J, Barris, W, Schnabel, RD, Taylor, JF, Raadsma, HW 2008. Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel. BMC Genomics 9, 187.CrossRefGoogle ScholarPubMed
Legarra, A, Misztal, I 2008. Computing strategies in genome-wide selection. Journal of Dairy Science 91, 360–366.CrossRefGoogle ScholarPubMed
Legarra, A, Robert-Granié, C, Manfredi, E, Elsen, J-M 2008. Performance of genomic selection in mice. Genetics 180, 611–618.CrossRefGoogle ScholarPubMed
Long, N, Gianola, D, Rosa, GJM, Weigel, KA, Avendaño, S 2007. Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. Journal Of Animal Breeding And Genetics 124, 377–389.CrossRefGoogle ScholarPubMed
Lund, M, Su, G 2009. Genomic selection in the Nordic countries. Proceedings of the Interbull International Workshop – Genomic Information in Genetic Evaluations, Uppsala, Sweden, Bulletin no. 39.Google Scholar
Lynch, M, Walsh, B 1998. Genetics and analysis of quantitative traits. Sinauer Associates, Sunderland.Google Scholar
Meuwissen, T 2007. Genomic selection : marker assisted selection on a genome wide scale. Journal of Animal Breeding and Genetics 124, 321–322.CrossRefGoogle ScholarPubMed
Meuwissen, T, Goddard, ME 2001. Prediction of identity by descent probabilities from marker-haplotypes. Genetics Selection Evolution 33, 605–634.CrossRefGoogle ScholarPubMed
Meuwissen, T, Goddard, ME 2004. Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data. Genetics Selection Evolution 36, 261–279.CrossRefGoogle ScholarPubMed
Meuwissen, T, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
Muir, WM 2007. Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics 124, 342–355.CrossRefGoogle ScholarPubMed
Raadsma, HW, Moser, G, Crump, R, Khatkar, MS, Zenger, KR, Cavanagh, JAL, Hawken, RJ, Sölkner, J, Tier, B 2008. Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens. In Conference of the International Society for Animal Genetics, Amsterdam, The Netherlands.CrossRefGoogle Scholar
Schenkel, FS, Sargolzaei, M, Kistemaker, G, Jansen, GB, Sullivan, P, Van Doormaal, BJ, VanRaden, PM, Wiggans, GR 2009. Reliability of genomic evaluation of Holstein cattle in Canada. Proceedings of the Interbull International Workshop – Genomic Information in Genetic Evaluations, Uppsala, Sweden, Bulletin no. 39.Google Scholar
Solberg, TR 2008. Methods for prediction of genome-wide breeding values using dense marker genotyping. PhD, Norwegian University of Life Sciences.CrossRefGoogle Scholar
Solberg, TR, Sonesson, AK, Woolliams, JA, Meuwissen, THE 2008. Genomic selection using different marker types and densities. Journal of Animal Science 86, 2447–2454.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF, Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
Van Tassell, CP, Smith, TPL, Matukumalli, LK, Taylor, JF, Schnabel, RD, Lawley, CT, Haudenschild, CD, Moore, SS, Warren, WC, Sonstegard, TS 2008. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature Methods 5, 247–252.CrossRefGoogle ScholarPubMed
Villumsen, TM, Janss, L 2008. Genomic selection focusing on haplotype length and prior settings. In 12th Quantitative Trait Locus and Marker Assisted Selection Workshop, Uppsala, Sweden, p. 41.Google Scholar
Villumsen, TM, Janss, L, Lund, MS 2009. The importance of haplotype length and heritability using genomic selection in dairy cattle. Journal of Animal Breeding and Genetics 126, 3–13.CrossRefGoogle ScholarPubMed
Weller, JI, Kashi, Y, Soller, M 1990. Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy-cattle. Journal of Dairy Science 73, 2525–2537.CrossRefGoogle ScholarPubMed
Xu, SZ 2003a. Theoretical basis of the Beavis effect. Genetics 165, 2259–2268.CrossRefGoogle ScholarPubMed
Xu, SZ 2003b. Estimating polygenic effects using markers of the entire genome. Genetics 163, 789–801.CrossRefGoogle ScholarPubMed
Xu, SZ, Jia, ZY 2007. Genomewide analysis of epistatic effects for quantitative traits in barley. Genetics 175, 1955–1963.CrossRefGoogle ScholarPubMed
Yu, K, Xu, J, Rao, DC, Province, M 2005. Using tree-based recursive partitioning methods to group haplotypes for increased power in association studies. Annals of Human Genetics 69, 577–589.CrossRefGoogle ScholarPubMed
Zeng, ZB 1994. Precision mapping of quantitative trait loci. Genetics 136, 1457–1468.CrossRefGoogle ScholarPubMed
Zhong, S, Dekkers, JCM, Fernando, RL, Jannink, J-L 2009. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study. Genetics 182, 355–364.CrossRefGoogle Scholar