Genomic selection in forage breeding: Accuracy and methods

Crop Science

Volumn 54, Issue 1, 2014, Pages 143-156

  Resende, Rosangela Maria Simeão ^a   Casler, Michael D ^b   de Resende, Marcos Deon Vilela ^c  

^a EMBRAPA CERRADOS   (Brazil)

^b U S DAIRY FORAGE RESEARCH CENTER   (United States)

^c FEDERAL UNIVERSITY OF VIÇOSA   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84890407418     PISSN: 0011183X     EISSN: 14350653     Source Type: Journal    
DOI: 10.2135/cropsci2013.05.0353     Document Type: Article

References (73)

1. Arumuganathan, K., S.P. Tallury, M.L. Fraser, A.H. Bruneau, and R. Qu. 1999. Nuclear DNA content of thirteen turfgrass species by flow cytometry. Crop Sci. 39:1518-1521. doi:10.2135/ cropsci1999.3951518x
2. Asoro, F.G., M.A. Newell, W.D. Beavis, M.P. Scott, and J.-L. Jannink. 2011. Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Gen. 4:132-144. doi:10.3835/plantgenome2011.02.0007
3. Auzanneau, J., C. Huyghe, B. Julier, and P. Barre. 2007. Linkage disequilibrium in synthetic varieties of perennial ryegrass. Theor. Appl. Genet. 115:837-847. doi:10.1007/s00122-007-0612-3
4. Bennett, M.D., and I.J. Leitch. 2005. Nuclear DNA amounts in angiosperms: Progress, problems and prospects. Ann. Bot. (London) 95:45-90. doi:10.1093/aob/mci003
5. Bernardo, R., and J.M. Yu. 2007. Prospects for genomewide selection for quantitative traits in maize. Crop Sci. 47:1082-1090. doi:10.2135/cropsci2006.11.0690
6. Bhandari, H.S., M.C. Saha, V.A. Fasoula, and J.H. Bouton. 2011. Estimation of genetic parameters for biomass yield in lowland switchgrass (Panicum virgatum L.). Crop Sci. 51:1525-1533. doi:10.2135/ cropsci2010.10.0588
7. Bhandari, H.S., M.C. Saha, P.N. Mascia, V.A. Fasoula, and J.H. Bouton. 2010. Variation among half-sib families and heritability for biomass yield and other traits in Lowland switchgrass (Panicum virgatum L.). Crop Sci. 50:2355-2363. doi:10.2135/cropsci2010.02.0109
8. Boller, B., and S.L. Greene. 2009. Genetic resources. In: B. Boller, U.K. Posselt, and F. Veronesi, editors, Fodder crops and amenity grasses. Springer, New York, NY. p. 13-37.
9. Burton, G.W., and B.G. Mullinix. 1998. Yield distributions of spaced plants within Pensacola bahiagrass populations developed by recurrent restricted phenotypic selection. Crop Sci. 38:333-336. doi:10.2135/cropsci1998.0011183X003800020008x
10. Carpenter, J.A., and M.D. Casler. 1990. Divergent phenotypic selection response in smooth bromegrass for forage yield and nutritive value. Crop Sci. 30:17-22. doi:10.2135/cropsci1990.0011183X003 000010004x
11. Casler, M.D. 1990. Cultivar and cultivar × environment effects on relative feed value of temperate perennial grasses. Crop Sci. 30:722-728. doi:10.2135/cropsci1990.0011183X003000030050x
12. Casler, M.D. 2008. Among-and-within-family selection in eight forage grass populations. Crop Sci. 48:434-442. doi:10.2135/cropsci2007.05.0267
13. Casler, M.D. 2010. Changes in mean and genetic variance during two cycles of within-family selection in switchgrass. Bioenergy Res. 3:47-54. doi:10.1007/s12155-009-9071-9
14. Casler, M.D., and E.C. Brummer. 2008. Theoretical expected genetic gains for among-and-within-family selection methods in perennial forage crops. Crop Sci. 48:890-902. doi:10.2135/cropsci2007.09.0499
15. Conaghan, P., and M.D. Casler. 2011. A theoretical and practical analysis of the optimum breeding system for perennial ryegrass. Ir. J. Agric. Food Res. 50:47-63.
16. Cowling, W.A. 2013. Sustainable plant breeding. Plant Breed. 132:1-9. doi:10.1111/pbr.12026
17. Culvenor, R.A., and M.D. Casler. 1999. Response to divergent selection for ease of particle size reduction of dried leaves of smooth bromegrass (Bromus inermis Leyss) and correlated effects on nutritive value indicators and plant fitness. Euphytica 107:61-70. doi:10.1023/A:1003583832552
18. Daetwyler, H.D., M.P.L. Calus, R. Pong-Wong, G. de los Campos, and J.M. Hickey. 2013. Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics 193:347-365. doi:10.1534/genetics.112.147983
19. Daetwyler, H.D., B. Villanueva, and J.A. Woolliams. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3:e3395. doi:10.1371/journal.pone.0003395
20. Dekkers, J.C.M., and J.A. Van Arendonk. 1998. Optimizing selection for quantitative traits with information on an identified locus in outbred populations. Genet. Res. 71:257-275. doi:10.1017/ S0016672398003267
21. Espinoza, L.C.L., and B. Julier. 2013. QTL detection for forage quality and stem histology in four connected mapping populations of the model legume Medicago truncatula. Theor. Appl. Genet. 126:497-509. doi:10.1007/s00122-012-1996-2
22. Fiil, A., I. Lenk, K. Petersen, C.S. Jensen, K.K. Nielsen, B. Schejbel, J.R. Andersen, and T. Lübberstedt. 2011. Nucleotide diversity and linkage disequilibrium of nine genes with putative effects on flowering time in perennial ryegrass (Lolium perenne L.). Plant Sci. 180:228-237. doi:10.1016/j.plantsci.2010.08.015
23. Flint-Garcia, S.A., J.M. Thornsberry, and E.S. Buckler, IV. 2003. Structure of linkage disequilibrium in plants. Annu. Rev. Plant Biol. 54:357-374. doi:10.1146/annurev.arplant.54.031902.134907
24. Gianola, D., and R.L. Fernando. 1986. Bayesian methods in animal breeding theory. J. Anim. Sci. 63:217-244.
25. Goddard, M.E. 2009. Genomic selection: Prediction of accuracy and maximization of long term response. Genetica (The Hague) 136:245-257. doi:10.1007/s10709-008-9308-0
26. Goddard, M.E., B.J. Hayes, and T.H.E. Meuwissen. 2011. Using the genomic relationship matrix to predict the accuracy of genomic selection. J. Anim. Breed. Genet. 128:409-421. doi:10.1111/ j.1439-0388.2011.00964.x
27. Grattapaglia, D., and M.D.V. Resende. 2010. Genomic selection in forest tree breeding. Tree Genet. Genomes 7:241-255. doi:10.1007/ s11295-010-0328-4
28. Habier, D., R.L. Fernando, and J.C.M. Dekkers. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389-2397. doi:10.1534/genetics.107.081190
29. Han, L.X., and M.D. Casler. 1999. Theoretical inbreeding at selectively neutral loci uniparental mass selection and recurrent selection with polycrossing of selected plants. Crop Sci. 39:1009-1015. doi:10.2135/ cropsci1999.0011183X003900040008x
30. Hayes, B.J., P.J. Bowman, A.C. Chamberlain, and M.E. Goddard. 2009a. Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92:433-443. doi:10.3168/jds.2008-1646
31. Hayes, B.J., P.J. Bowman, A.C. Chamberlain, K. Verbyla, and M.E. Goddard. 2009b. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 41:51. doi:10.1186/1297-9686-41-51
32. Hayes, B.J., N.O.I. Cogan, L.W. Pembleton, M.E. Goddard, J. Wang, G.C. Spangenberg, and J.W. Forster. 2013. Prospects for genomic selection in forage plant species. Plant Breed. 132:133-143. doi:10.1111/pbr.12037
33. Hayes, B.J., I.M. Macleod, and M. Baranski. 2009c. Sampling strategies for whole genome association studies in aquaculture and outcrossing plant species. Genet. Res. 91:367-371. doi:10.1017/ S0016672309990310
34. Hayes, B.J., P.M. Visscher, and M.E. Goddard. 2009d. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. 91:47-60. doi:10.1017/S0016672308009981
35. Hill, W.G. 1981. Estimation of effective population size from data on linkage disequilibrium. Genet. Res. 38:209-216. doi:10.1017/ S0016672300020553
36. Hopkins, A.A., K.P. Vogel, and K.J. Moore. 1993. Predicted and realized gains from selection for in vitro dry matter digestibility and forage yield in switchgrass. Crop Sci. 33:253-258. doi:10.2135/cro psci1993.0011183X003300020007x
37. Iwata, H., and J.-L. Jannink. 2011. Accuracy of genomic selection prediction in barley breeding programs: A simulation study based on the real single nucleotide polymorphism data of barley breeding lines. Crop Sci. 51:1915-1927. doi:10.2135/cropsci2010.12.0732
38. Jannink, J.-L. 2010. Dynamics of long-term genomic selection. Genet. Sel. Evol. 42:35. doi:10.1186/1297-9686-42-35
39. Jannink, J.-L., A.J. Lorenz, and H. Iwata. 2010. Genomic selection in plant breeding: From theory to practice. Briefings Funct. Genomics 9:166-177. doi:10.1093/bfgp/elq001
40. Klimenko, I., N. Razgulayeva, M. Gau, K. Okumura, A. Nakay, S. Tabata, N.N. Kozlov, and S. Isobe. 2010. Mapping candidate QTLs related to plant persistency in red clover. Theor. Appl. Genet. 120:1253-1263. doi:10.1007/s00122-009-1253-5
41. Li, X., and E.C. Brummer. 2012. Applied genetics and genomics in alfalfa breeding. Agronomy 2:40-61. doi:10.3390/agronomy2010040
42. Li, X., Y. Wei, K.J. Moore, R. Michaud, D.R. Viands, J.L. Hansen, A. Acharya, and E.C. Brummer. 2011. Association mapping of biomass yield and stem composition in a tetraploid alfalfa breeding population. Plant Genome 4:24-35. doi:10.3835/plantgenome2010.09.0022
43. Long, N., D. Gianola, G.J.M. Rosa, and K.A. Weigel. 2011. Longterm impacts of genome-enabled selection. J. Appl. Genet. 52:467-480. doi:10.1007/s13353-011-0053-1
44. Lu, K., S.W. Kaeppler, K.P. Vogel, K. Arumuganathan, and D.J. Lee. 1998. Nuclear DNA content and chromosome numbers in switchgrass. Great Plains Res. 8:269-280.
45. Luo, N., X. Yu, J. Liu, and Y. Jiang. 2012. Nucleotide diversity and linkage disequilibrium in antioxidant genes of Brachypodium distachyon. Plant Sci. 197:122-129. doi:10.1016/j.plantsci.2012.09.010
46. Lynch, M., and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer, Sunderland, MA.
47. Meuwissen, T.H.E. 2009. Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping. Genet. Sel. Evol. 41:35. doi:10.1186/1297-9686-41-35
48. Moser, G., M.S. Khatkar, B.J. Hayes, and H.W. Raadsma. 2010. Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genet. Sel. Evol. 42:37. doi:10.1186/1297-9686-42-37
49. Moser, G., B. Tier, R.E. Crump, M.S. Khatkar, and H.W. Raadsma. 2009. A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet. Sel. Evol. 41:56. doi:10.1186/1297-9686-41-56
50. Mrode, R.A., and R. Thompson. 2005. Linear models for the prediction of animal breeding values. Cromwell Press, Trowbridge, UK.
51. Muir, W.M. 2007. Comparison of genomic and traditional BLUPestimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim. Breed. Genet. 124:342-355. doi:10.1111/j.1439-0388.2007.00700.x
52. Newell, M.A., D. Cook, N.A. Tinker, and J.-L. Jannink. 2011. Population structure and linkage disequilibrium in oat (Avena sativa L.): Implications for genome-wide association studies. Theor. Appl. Genet. 122:623-632. doi:10.1007/s00122-010-1474-7
53. Ponting, R.C., M.C. Drayton, N.O.I. Cogan, M.P. Dobrowolski, G.C. Spangenberg, K.F. Smith, and J.W. Forster. 2007. SNP discovery, validation, haplotype structure and linkage disequilibrium in full-length herbage nutritive quality genes of perennial ryegrass (Lolium perenne L.). Mol. Genet. Genomics 278:585-597. doi:10.1007/s00438-007-0275-4
54. Posselt, U.K. 2010. Alternative breeding strategies to exploit heterosis in forage crops. Biotechnol. Anim. Husb. 26(Spec. issue):49-66.
55. Resende, M.D.V. 2008. Genômica quantitativa e seleção no melhoramento de plantas perenes e animais. (Quantitative genomics and selection in animal and perennial plant breeding.) (In Portuguese.) Embrapa Florestas, Colombo, Brazil.
56. Resende, M.D.V., P.S. Lopes, R.L. Silva, and I.E. Pires. 2008. Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. (Genome wide selection (GWS) and maximization of the genetic improvement efficiency.) (In Portuguese, with English Abstract.) Pesquisa Florestal Brasileira 56:63-77.
57. Resende, M.D.V., M.F.R. Resende Jr., C.P. Sansaloni, C.D. Petroli, A.A. Missiaggia, A.M. Aguiar, J.M. Abad, E.K. Takahashi, A.M. Rosado, D.A. Faria, G.J. Pappas Jr., A. Kilian, and D. Grattapaglia. 2012a. Genomic selection for growth and wood quality in Eucalyptus: Capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 194:116-128. doi:10.1111/j.1469-8137.2011.04038.x
58. Resende, M.F.R., Jr., P. Munõz, J.J. Acosta, G.F. Peter, J.M. Davis, D. Grattapaglia, M.D.V. Resende, and M. Kirst. 2012b. Accelerating the domestication of trees using genomic selection: Accuracy of prediction models across ages and environments. New Phytol. 193:617-624. doi:10.1111/j.1469-8137.2011.03895.x
59. Resende, R.M.S., M.D. Casler, and M.D.V. Resende. 2013. Selection methods in forage breeding: A quantitative appraisal. Crop Sci. 53:1925-1936. doi:10.2135/cropsci2013.03.0143
60. Rizza, M.D., D. Real, R. Reyno, V. Porro, J. Burgueño, E. Errico, and K.H. Quesenberry. 2007. Genetic diversity and DNA content of three South American and three Eurasiatic Trifolium species. Genet. Mol. Biol. 30:1118-1124. doi:10.1590/S1415-47572007000600015
61. Rose, L.W., IV, M.K. Das, and C.M. Taliaferro. 2008. Estimation of genetic variability and heritability for biofuel feedstock yield in several populations of switchgrass. Ann. Appl. Biol. 152:11-17. doi:10.1111/j.1744-7348.2007.00186.x
62. Sakiroglu, M., and M.M. Kaya. 2012. Estimating genome size and confirming ploidy levels of wild tetraploid alfalfa accessions (Medicago sativa subsp. × varia) using flow cytometry. Turk. J. Field Crop 17:151-156. doi:10.1007/s00122-012-1854-2
63. Saski, C.A., Z. Li, F.A. Feltus, and H. Luo. 2011. New genomic resources for switchgrass: A BAC library and comparative analysis of homoeologous genomic regions harboring bioenergy traits. BMC Genomics 12:369. doi:10.1186/1471-2164-12-369
64. Schaeffer, L.R. 2006. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123:218-223. doi:10.1111/ j.1439-0388.2006.00595.x
65. Solberg, T.R., A.K. Sonesson, J.A. Woolliams, and T.H.E. Meuwissen. 2008. Genomic selection using different marker types and densities. J. Anim. Sci. 86:2447-2454. doi:10.2527/jas.2007-0010
66. Sonesson, A.K., and T.H.E. Meuwissen. 2009. Testing strategies for genomic selection in aquaculture breeding programs. Genet. Sel. Evol. 41:37. doi:10.1186/1297-9686-41-37
67. Sved, J.A. 1971. Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theor. Popul. Biol. 2:125-141. doi:10.1016/0040-5809(71)90011-6
68. Taliaferro, C.M., A.A. Hopkins, J.C. Henthorn, C.D. Murphy, and R.M. Edwards. 1997. Use of flow cytometry to estimate ploidy level in Cynodon species. Int. Turfgrass Soc. Res. J. 8:385-392.
69. Vogel, K.P., and J.F. Pedersen. 1993. Breeding systems for cross-pollinated perennial grasses. Plant Breed. Rev. 11:251-274.
70. Wang, J., M.C. Drayton, J. George, N.O.I. Cogan, R.C. Baillie, M.L. Hand, G.A. Kearney, S. Erb, T. Wilkinson, N.R. Bannan, J.W. Forster, and K.F. Smith. 2010. Identification of genetic factors influencing salt stress tolerance in white clover (Trifolium repens L.) by QTL analysis. Theor. Appl. Genet. 120:607-619. doi:10.1007/ s00122-009-1179-y
71. Wilkins, P.W., and M.O. Humphreys. 2003. Progress in breeding perennial forage grasses for temperate agriculture. J. Agric. Sci. 140:129-150. doi:10.1017/S0021859603003058
72. Zhao, Y., M. Gowda, W. Liu, T. Würschum, H.P. Maurer, F.H. Longin, N. Ranc, and J.C. Reif. 2012. Accuracy of genomic selection in European maize elite breeding populations. Theor. Appl. Genet. 124:769-776. doi:10.1007/s00122-011-1745-y
73. Zhong, S., J.C.M. Dekkers, R.L. Fernando, and J.-L. Jannink. 2009. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study. Genetics 182:355-364. doi:10.1534/genetics.108.098277