DOI: 10.3724/SP.J.1005.2011.01308

Hereditas (Beijing) (遗传) 2011/33:12 PP.1308-1316

Genomic selection and its application

Selective breeding is very important in agricultural production and breeding value estimation is the core of selective breeding. With the development of genetic markers, especially high throughput genotyping technology, it becomes available to estimate breeding value at genome level, i.e. genomic selection (GS). In this review, the methods of GS was categorized into two groups: one is to predict genomic estimated breeding value (GEBV) based on the allele effect, such as least squares, random regression - best linear unbiased prediction (RR-BLUP), Bayes and principle component analysis, etc; the other is to predict GEBV with genetic relationship matrix, which constructs genetic relationship matrix via high throughput genetic markers and then predicts GEBV through linear mixed model, i.e. GBLUP. The basic principles of these methods were also introduced according to the above two classifications. Factors affecting GS accuracy include markers of type and density, length of haplotype, the size of reference population, the extent between marker-QTL and so on. Among the methods of GS, Bayes and GBLUP are usually more accurate than the others and least squares is the worst. GBLUP is time-efficient and can combine pedigree with genotypic information, hence it is superior to other methods. Although progress was made in GS, there are still some challenges, for examples, united breeding, long-term genetic gain with GS, and disentangling markers with and without contribution to the traits. GS has been applied in animal and plant breeding practice and also has the potential to predict genetic predisposition in humans and study evolutionary dynamics. GS, which is more precise than the traditional method, is a breakthrough at measuring genetic relationship. Therefore, GS will be a revolutionary event in the history of animal and plant breeding.

Key words:genomic selection,high throughput genetic marker,breeding value estimation

ReleaseDate:2014-07-21 15:58:57

[1] Hazel LN. The genetic basis for constructing selection in-dexes. Genetics, 1943, 28(6): 476-490.

[2] Hendersen CR. Best linear unbiased estimation and pre-diction under a selection model. Biometrics, 1975, 31(2): 423-447.

[3] Fernando RL, Grossman M. Marker assisted selection us-ing best linear unbiased prediction. Genet Sel Evol, 1989, 21(4): 467-477.

[4] Lander ES, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics, 1989, 121(1): 185-199.

[5] Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157(4): 1819-1829.

[6] Schaeffer LR. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet, 2006, 123(4): 218-223.

[7] Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: Genomic selection in dairy cattle: progress and challenges. J Dairy Sci, 2009, 92(2): 433-443.

[8] Sonesson AK, Meuwissen TH. Testing strategies for genomic selection in aquaculture breeding programs. Genet Sel Evol, 2009, 41: 37.

[9] Jannink JL. Dynamics of long-term genomic selection. Genet Sel Evol, 2010, 42(1): 35.

[10] Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE. Reducing dimensionality for prediction of genome-wide breeding values. Genet Sel Evol, 2009, 41(1): 29.

[11] Crossa J, de los Campos G, Pérez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan JB, Arief V, Banziger M, Braun HJ. Prediction of genetic values of quantitative traits in plant breeding using pedi-gree and molecular markers. Genetics, 2010, 186(2): 713-724.

[12] Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE. Genomic selection using different marker types and densities. J Anim Sci, 2008, 86(10): 2447-2454.

[13] Meuwissen THE, Solberg TR, Shepherd R, Woolliams JA. A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value. Genet Sel Evol, 2009, 41: 2.

[14] Villumsen TM, Janss L, Lund MS. The importance of haplotype length and heritability using genomic selection in dairy cattle. J Anim Breed Genet, 2009, 126(1): 3-13.

[15] Wang WYS, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet, 2005, 6(2): 109-118.

[16] Long N, Gianola D, Rosa GJM, Weigel KA. Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins. J Anim Breed Genet, 2011, 128(4): 247-257.

[17] VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci, 2008, 91(11): 4414-4423.

[18] Legarra A, Aguilar I, Misztal I. A relationship matrix in-cluding full pedigree and genomic information. J Dairy Sci, 2009, 92(9): 4656-4663.

[19] Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci, 2009, 92(9): 4648-4655.

[20] Lund MS, Sahana G, de Koning DJ, Su G S, Carlborg Ö. Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection. BMC Proc, 2009, 3(Suppl. 1): S1.

[21] Calus MPL, Meuwissen THE, de Roos APW, Veerkamp RF. Accuracy of genomic selection using different meth-ods to define haplotypes. Genetics, 2008, 178(1): 553-561.

[22] Pszczola MJ, Mulder HA, Calus MPL. The Accuracy of genomic selection using(un)genotyped animals to enlarge the reference population. paper presented at 9th world congress on genetics applied to livestock production. Leipzig, Germany, 2010 August.

[23] Habier D, Fernando RL, Dekkers JCM. The impact of genetic relationship information on genome-assisted breed-ing values. Genetics, 2007, 177(4): 2389-2397.

[24] Zhong SQ, Dekkers JCM, Fernando RL, Jannink JL. Factors affecting accuracy from genomic selection in popula-tions derived from multiple inbred lines: a barley case study. Genetics, 2009, 182(1): 355-364.

[25] Su G, Guldbrandtsen B, Gregersen VR, Lund MS. Pre-liminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. J Dairy Sci, 2010, 93(3): 1175-1183.

[26] Chen CY, Misztal I, Aguilar I, Tsuruta S, Meuwissen THE, Aggrey SE, Wing T, Muir WM. Genome-wide marker-assisted selection combining all pedigree pheno-typic information with genotypic data in one step: an ex-ample using broiler chickens. J Anim Sci, 2011, 89(1): 23-28.

[27] Long N, Gianola D, Rosa GJM, Weigel KA, Avendaño S. Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet, 2007, 124(6): 377-389.

[28] de los Campos G, Gianola D, Allison DB. Predicting genetic predisposition in humans: the promise of whole-genome markers. Nat Rev Genet, 11(12): 880-886.

[29] Maher B. Personal genomes: The case of the missing heritability. Nature, 2008, 456(7218): 18-21.

[30] Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM. Finding the missing heritability of complex diseases. Nature, 2009, 461(7265): 747-753.

[31] Silventoinen K, Sammalisto S, Perola M, Boomsma DI, Cornes BK, Davis C, Dunkel L, De Lange M, Harris JR, Hjelmborg JVB, Luciano M, Martin NG, Mortensen J, Nisticò L, Pedersen NL, Skytthe A, Spector TD, Stazi MA, Willemsen G, Kaprio J. Heritability of adult body height: a comparative study of twin cohorts in eight countries. Twin Res, 2003, 6(5): 399-408.

[32] Macgregor S, Cornes BK, Martin NG, Visscher PM. Bias, precision and heritability of self-reported and clinically measured height in Australian twins. Hum Genet, 2006, 120(4): 571-580.

[33] Gudbjartsson DF, Walters GB, Thorleifsson G, Stefansson H, Halldorsson BV, Zusmanovich P, Sulem P, Thorlacius S, Gylfason A, Steinberg S, Helgadottir A, Ingason A, Stein-thorsdottir V, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Pedersen O, Aben KK, Witjes JA, Swinkels DW, den Hei-jer M, Franke B, Verbeek AL, Becker DM, Yanek LR, Becker LC, Tryggvadottir L, Rafnar T, Gulcher J, Kieme-ney LA, Kong A, Thorsteinsdottir U, Stefansson K. Many sequence variants affecting diversity of adult human height. Nat Genet, 2008, 40(5): 609-615.

[34] Lettre G, Jackson AU, Gieger C, Schumacher FR, Berndt SI, Sanna S, Eyheramendy S, Voight BF, Butler JL, Guiducci C, Illig T, Hackett R, Heid IM, Jacobs KB, Lyssenko V, Uda M, Diabetes Genetics Initiative; FUSION; KORA; Prostate, Lung Colorectal and Ovarian Cancer Screening Trial; Nurses' Health Study; SardiNIA, Boehnke M, Chanock SJ, Groop LC, Hu FB, Isomaa B, Kraft P, Peltonen L, Salomaa V, Schlessinger D, Hunter DJ, Hayes RB, Abecasis GR, Wichmann HE, Mohlke KL, Hirschhorn JN. Identification of ten loci associated with height highlights new biological pathways in human growth. Nat Genet, 2008, 40(5): 584-591.

[35] Melzer D, Perry JR, Hernandez D, Corsi AM, Stevens K, Rafferty I, Lauretani F, Murray A, Gibbs JR, Paolisso G, Rafiq S, Simon-Sanchez J, Lango H, Scholz S, Weedon MN, Arepalli S, Rice N, Washecka N, Hurst A, Britton A, Henley W, van de Leemput J, Li R, Newman AB, Tranah G, Harris T, Panicker V, Dayan C, Bennett A, McCarthy MI, Ruokonen A, Jarvelin MR, Guralnik J, Bandinelli S, Fray-ling TM, Singleton A, Ferrucci L. A genome-wide asso-ciation study identifies protein quantitative trait loci (pQTLs). PLoS Genet, 2008, 4(5): e1000072.

[36] Visscher PM. Sizing up human height variation. Nat Genet, 2008, 40(5): 489-490.

[37] Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Mont-gomery GW, Goddard ME, Visscher PM. Common SNPs explain a large proportion of the heritability for human height. Nat Genet, 42(7): 565-569.

[38] Lee SH, Wray NR, Goddard ME, Visscher PM. Estimating missing heritability for disease from genome-wide asso-ciation studies. Am J Hum Genet, 2011, 88(3): 294-305.

[39] Shaw FH, Shaw RG, Wilkinson GS, Turelli M. Changes in genetic variances and covariances: G whiz! Evolution, 1995, 49(6): 1260-1267.

[40] Paulsen SM. Quantitative genetics of the wing color pattern in the buckeye butterfly(Precis coenia and Precis evarete): evidence against the constancy of G. Evolution, 1996, 50(4): 1585-1597.

[41] Steppan SJ, Phillips PC, Houle D. Comparative quantitative genetics: evolution of the G matrix. Trends Ecol Evol, 2002, 17(7): 320-327.

[42] Kruuk LEB. Estimating genetic parameters in natural populations using the "animal model". Philos Trans R Soc Lond B Biol Sci, 2004, 359(1446): 873-890.

[43] Ellegren H, Sheldon BC. Genetic basis of fitness differences in natural populations. Nature, 2008, 452(7184): 169-175.

[44] Kruuk LEB, Slate J, Wilson AJ. New answers for old questions: the evolutionary quantitative genetics of wild animal populations. Annu Rev Ecol Evol Syat, 2008, 39(1): 525-548.

[45] Wilson AJ, Réale D, Clements MN, Morrissey MM, Postma E, Walling CA, Kruuk LEB, Nussey DH. An ecologist's guide to the animal model. J Anim Ecol, 79(1): 13-26.

[46] Goddard ME, Hayes BJ. Genomic selection. J Anim Breed Genet, 2007, 124(6): 323-330.

[47] Muir WM. Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J Anim Breed Genet, 2007, 124(6): 342-355.

[48] Goddard M. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica, 2009, 136(2): 245-257.

[49] De Roos APW, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Hol-stein-Friesian, Jersey and Angus cattle. Genetics, 2008, 179(3): 1503-1512.

[50] Zhang Z, Liu J, Ding X, Bijma P, de Koning DJ, Zhang Q. Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS One, 2010, 5(9): e12648.

[51] Harris BL, Johnson DL, Spelman RJ. Genomic selection in New Zealand and the implications for national genetic evaluation. paper presented at Proc Interbull meeting. Ni-agara falls, Canada, 2008.

[52] Cheverud JM, Routman EJ. Epistasis and its contribution to genetic variance components. Genetics, 1995, 139(3): 1455-1461.

[53] Hu Z, Li Y, Song X, Han Y, Cai X, Xu S, Li W. Genomic value prediction for quantitative traits under the epistatic model. BMC Genetics, 2011, 12(1): 15.

[54] Gianola D, Fernando RL, Stella A. Genomic-assisted pre-diction of genetic value with semiparametric procedures. Genetics, 2006, 173(3): 1761-1776.

[55] Habier D, Fernando RL, Dekkers JCM. Genomic selection using low-density marker panels. Genetics, 2009, 182(1): 343-353.

[56] Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SF, Hakonarson H, Bucan M. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res, 2007, 17(11): 1665-1674.