Genomic prediction when some animals are not genotyped
Tóm tắt
The use of genomic selection in breeding programs may increase the rate of genetic improvement, reduce the generation time, and provide higher accuracy of estimated breeding values (EBVs). A number of different methods have been developed for genomic prediction of breeding values, but many of them assume that all animals have been genotyped. In practice, not all animals are genotyped, and the methods have to be adapted to this situation. In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals. The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect, where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix. The extension of the model to non-genotyped animals is made by using the pedigree to derive an extension of the genomic relationship matrix to non-genotyped animals. As a result, in the extended model the estimated breeding values are obtained by blending the information used to compute traditional EBVs and the information used to compute purely genomic EBVs. Parameters in the model are estimated using average information REML and estimated breeding values are best linear unbiased predictions (BLUPs). The method is illustrated using a simulated data set. The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic, pedigree and phenotype information into a one-step procedure for genomic prediction. Such a one-step procedure results in more accurate estimated breeding values and has the potential to become the standard tool for genomic prediction of breeding values in future practical evaluations in pig and cattle breeding.
Tài liệu tham khảo
Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157: 1819-1829.
VanRaden PM: Efficient methods to compute genomic predictions. Interbull Bull. 2007, 37: 111-114.
VanRaden PM: Efficient methods to compute genomic predictions. J Dairy Sci. 2008, 91: 4414-4423. 10.3168/jds.2007-0980.
Gianola D, van Kamm BCHM: Reproducing kernel Hilbert spaces regression methods for genomic prediction of quantitative traits. Genetics. 2008, 178: 2289-2303. 10.1534/genetics.107.084285.
Legarra A, Aguilar I, Misztal I: A relationship matrix including full pedigree and genomic information. J Dairy Sci. 2009, 92: 4656-4663. 10.3168/jds.2009-2061.
Calus MPL, Veerkamp RF: 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. J Anim Breed Genet. 2007, 124: 362-368.
Gianola D, Fernando RL, Stella A: Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics. 2006, 173: 1761-1776. 10.1534/genetics.105.049510.
Baruch E, Weller JI: Incorporation of genotype effects into animal model evaluations when only a small fraction of the population has been genotyped. Animal. 2009, 3: 16-23. 10.1017/S1751731108003339.
Gengler N, Mayeres P, Szydlowski M: A simple method to approximate gene content in large pedigree populations: application to the myostation gene in dual-purpose Belgian Blue cattle. Animal. 2007, 1: 21-28. 10.1017/S1751731107392628.
Gilmour AR, Thompson R, Cullis BR: Average information REML: an efficient algorithm for parameter estimation in linear mixed models. Biometrics. 1995, 51: 1440-1450. 10.2307/2533274.
Johnson DL, Thompson R: Restricted maximum likelihood estimation of variance components for univariate animal models using sparse matrix techniques and average information. J Dairy Sci. 1995, 78: 449-456.
Lee SH, Werf van der JHJ: An efficient variance component approach implementing an average REML suitable for combined LD and linkage mapping with a general pedigree. Genet Sel Evol. 1995, 38: 25-43. 10.1186/1297-9686-38-1-25.
Madsen P, Jensen J: A users guide to DMU, version 6, release 4.7. Manual, Faculty of agricultural science, University of Aarhus. 2008
VanRaden PM, Van Tassel CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS: Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci. 2009, 92: 16-24. 10.3168/jds.2008-1514.
Su G, Guldbrandtsen B, Gregersen VR, Lund MS: Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. J Dairy Sci. 2010,
Misztal I, Legarra A, Aguilar I: Computing procedures for genetic evaluation including phenotypic, full pedigree and genomic information. Proceedings of the annual meeting EAAP: 24-27 August 2009; Barcelona, Spain. 2009