Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end-use quality. With the need to accelerate the development of improved varieties, genomics-assisted breeding is becoming an important tool in breeding programs. With marker-assisted selection, there has been success in breeding for disease resistance; however, much of this work and research has focused on identifying, mapping, and selecting for major resistance genes that tend to be highly effective but vulnerable to breakdown with rapid changes in pathogen races. In contrast, breeding for minor-gene quantitative resistance tends to produce more durable varieties but is a more challenging breeding objective. As the genetic architecture of resistance shifts from single major R genes to a diffused architecture of many minor genes, the best approach for molecular breeding will shift from marker-assisted selection to genomic selection. Genomics-assisted breeding for quantitative resistance will therefore necessitate whole-genome prediction models and selection methodology as implemented for classical complex traits such as yield. Here, we examine multiple case studies testing whole-genome prediction models and genomic selection for disease resistance. In general, whole-genome models for disease resistance can produce prediction accuracy suitable for application in breeding. These models also largely outperform multiple linear regression as would be applied in marker-assisted selection. With the implementation of genomic selection for yield and other agronomic traits, whole-genome marker profiles will be available for the entire set of breeding lines, enabling genomic selection for disease at no additional direct cost. In this context, the scope of implementing genomics selection for disease resistance, and specifically for quantitative resistance and quarantined pathogens, becomes a tractable and powerful approach in breeding programs.


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Literature Cited

  1. Arruda MP, Brown PJ, Lipka AE, Krill AM, Thurber C, Kolb FL. 1.  2015. Genomic selection for predicting head blight resistance in a wheat breeding program. Plant Genome doi:10.3835/plantgenome2015.01.0003
  2. Bai G-H, Shaner G, Ohm H. 2.  2000. Inheritance of resistance to Fusarium graminearum in wheat. Theor. Appl. Genet. 100:1–8 [Google Scholar]
  3. Ballvora A, Ercolano MR, Weiß J, Meksem K, Bormann CA. 3.  et al. 2002. The R1 gene for potato resistance to late blight (Phytophthora infestans) belongs to the leucine zipper/NBL/LRR class of plant resistance genes. Plant J. 30:361–71 [Google Scholar]
  4. Bao Y, Vuong T, Meinhardt C, Tiffin P, Denny R. 4.  et al. 2014. Potential of association mapping and genomic selection to explore PI 88788 derived soybean cyst nematode resistance. Plant Genome doi: 10.3835/plantgenome2013.11.0039
  5. Belcher AR, Zwonitzer JC, Santa Cruz J, Krakowsky MD, Chung C-L. 5.  et al. 2012. Analysis of quantitative disease resistance to southern leaf blight and of multiple disease resistance in maize, using near-isogenic lines. Theor. Appl. Genet. 124:433–45 [Google Scholar]
  6. Bernardo R. 6.  2014. Genomewide selection when major genes are known. Crop Sci. 54:68–75 [Google Scholar]
  7. Bonnett D, Rebetzke G, Spielmeyer W. 7.  2005. Strategies for efficient implementation of molecular markers in wheat breeding. Mol. Breed. 15:75–85 [Google Scholar]
  8. Boyd LA, Ridout C, O’Sullivan DM, Leach JE, Leung H. 8.  2013. Plant-pathogen interactions: disease resistance in modern agriculture. Trends Genet. 29:233–40 [Google Scholar]
  9. Breiman L. 9.  2001. Random forests. Mach. Learn. 45:5–32 [Google Scholar]
  10. Bryan GT, Wu K-S, Farrall L, Jia Y, Hershey HP. 10.  et al. 2000. A single amino acid difference distinguishes resistant and susceptible alleles of the rice blast resistance gene Pi-ta. Plant Cell Online 12:2033–45 [Google Scholar]
  11. Bulmer M. 11.  1971. The effect of selection on genetic variability. Am. Nat. 105:201–11 [Google Scholar]
  12. Burgueno J, de los Campos G, Weigel K, Crossa J. 12.  2012. Genomic prediction of breeding values when modeling genotype×environment interaction using pedigree and dense molecular markers. Crop Sci. 52:707–19 [Google Scholar]
  13. Carson ML, Stuber CW, Senior ML. 13.  2004. Identification and mapping of quantitative trait loci conditioning resistance to Southern leaf blight of maize caused by Cochliobolus heterostrophus race O. Phytopathology 94:862–67 [Google Scholar]
  14. Collins NC, Webb CA, Seah S, Ellis JG, Hulbert SH, Pryor A. 14.  1998. The isolation and mapping of disease resistance gene analogs in maize. Mol. Plant-Microbe Interact. 11:968–78 [Google Scholar]
  15. Cregan PB, Mudge J, Fickus EW, Danesh D, Denny R, Young ND. 15.  1999. Two simple sequence repeat markers to select for soybean cyst nematode resistance coditioned by the rhg1 locus. Theor. Appl. Genet. 99:811–18 [Google Scholar]
  16. Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. 16.  2010. The impact of genetic architecture on genome-wide evaluation methods. Genetics 185:1021–31 [Google Scholar]
  17. Dexter J, Clear R, Preston K. 17.  1996. Fusarium head blight: effect on the milling and baking of some Canadian wheats. Cereal Chem. 73:695–701 [Google Scholar]
  18. Falconer DS, Mackay TFC. 18.  1996. Introduction to Quantitative Genetics Upper Saddle River, NJ: Pearson Educ, 4th ed..
  19. Forster BP, Thomas WT. 19.  2005. Doubled haploids in genetics and plant breeding. Plant Breed. Rev. 25:57–88 [Google Scholar]
  20. Frisch M, Melchinger AE. 20.  2005. Selection theory for marker-assisted backcrossing. Genetics 170:909–17 [Google Scholar]
  21. Gianola D, Van Kaam JB. 21.  2008. Reproducing Kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–303 [Google Scholar]
  22. Habier D, Fernando RL, Dekkers JCM. 22.  2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–97 [Google Scholar]
  23. Habier D, Fernando RL, Kizilkaya K, Garrick DJ. 23.  2011. Extension of the Bayesian alphabet for genomic selection. BMC Bioinform. 12:186 [Google Scholar]
  24. Hayes B, Bowman P, Chamberlain A, Verbyla K, Goddard M. 24.  2009. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 41:51 [Google Scholar]
  25. Hazel LN. 25.  1943. The genetic basis for constructing selection indexes. Genetics 28:476–90 [Google Scholar]
  26. Heffner EL, Jannink J-L, Iwata H, Souza E, Sorrells ME. 26.  2011. Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci. 51:2597–606 [Google Scholar]
  27. Heffner EL, Jannink J-L, Sorrells ME. 27.  2011. Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4:65–75 [Google Scholar]
  28. Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME. 28.  2010. Plant breeding with genomic selection: gain per unit time and cost. Crop Sci. 50:1681–90 [Google Scholar]
  29. Heffner EL, Sorrells ME, Jannink J-L. 29.  2009. Genomic selection for crop improvement. Crop Sci. 49:1–12 [Google Scholar]
  30. Henderson CR. 30.  1963. Selection index and expected genetic advance. Stat. Genet. Plant Breed. 982:141–63 [Google Scholar]
  31. Henderson CR. 31.  1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31423–47
  32. Herrera-Foessel SA, Lagudah ES, Huerta-Espino J, Hayden MJ, Bariana HS. 32.  et al. 2011. New slow-rusting leaf rust and stripe rust resistance genes Lr67 and Yr46 in wheat are pleiotropic or closely linked. Theor. Appl. Genet. 122:239–49 [Google Scholar]
  33. Hillel J, Schaap T, Haberfeld A, Jeffreys A, Plotzky Y. 33.  et al. 1990. DNA fingerprints applied to gene introgression in breeding programs. Genetics 124:783–89 [Google Scholar]
  34. Huang N, Angeles E, Domingo J, Magpantay G, Singh S. 34.  et al. 1997. Pyramiding of bacterial blight resistance genes in rice: marker-assisted selection using RFLP and pcr. Theor. Appl. Genet. 95:313–20 [Google Scholar]
  35. Jannink J-L, Lorenz AJ, Iwata H. 35.  2010. Genomic selection in plant breeding: from theory to practice. Brief. Funct. Genom. 9:166–77 [Google Scholar]
  36. Jenkins MT, Robert AL. 36.  1952. Inheritance of resistance to the leaf blight of corn caused by Helminthosporium turcicum. Agron. J. 44:136–40 [Google Scholar]
  37. Jiang Y, Zhao Y, Rodemann B, Plieske J, Kollers S. 37.  et al. 2015. Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.). Heredity 114:318–26 [Google Scholar]
  38. Johnson R. 38.  1992. Past, present and future opportunities in breeding for disease resistance, with examples from wheat. Euphytica 63:3–22 [Google Scholar]
  39. Jones DA, Thomas CM, Hammond-Kosack KE, Balint-Kurti PJ, Jones JDG. 39.  1994. Isolation of the tomato cf-9 gene for resistance to Cladosporium fulvum by transposon tagging. Science 266:789–93 [Google Scholar]
  40. Juliana P, Rutkoski JE, Poland JA, Singh RP, Murugasamy S. 40.  et al. 2015. Genome-wide association mapping for leaf tip necrosis and pseudo-black chaff in relation to durable rust resistance in wheat. Plant Genome 8:1–12 [Google Scholar]
  41. Kelly AM, Smith AB, Eccleston JA, Cullis BR. 41.  2007. The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials. Crop Sci. 47:1063–70 [Google Scholar]
  42. Krattinger SG, Lagudah ES, Spielmeyer W, Singh RP, Huerta-Espino J. 42.  et al. 2009. A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science 323:1360–63 [Google Scholar]
  43. Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR. 43.  et al. 2011. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat. Genet. 43:163–68 [Google Scholar]
  44. Lindhout P. 44.  2002. The perspectives of polygenic resistance in breeding for durable disease resistance. Euphytica 124:217–26 [Google Scholar]
  45. Liu S, Abate Z, McKendry A. 45.  2005. Inheritance of Fusarium head blight resistance in the soft red winter wheat Ernie. Theor. Appl. Genet. 110:454–61 [Google Scholar]
  46. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T. 46.  et al. 2011. Genomic selection in plant breeding: knowledge and prospects. Adv. Agron. 110:77 [Google Scholar]
  47. Lorenz AJ, Smith KP, Jannink J-L. 47.  2012. Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci. 52:1609–21 [Google Scholar]
  48. Ly D, Hamblin M, Rabbi I, Melaku G, Bakare M. 48.  et al. 2013. Relatedness and genotype×environment interaction affect prediction accuracies in genomic selection: a study in cassava. Crop Sci. 53:1312–25 [Google Scholar]
  49. Mackay TF. 49.  2001. The genetic architecture of quantitative traits. Annu. Rev. Genet. 35:303–39 [Google Scholar]
  50. Meuwissen THE, Hayes BJ, Goddard ME. 50.  2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–29 [Google Scholar]
  51. Miedaner T, Korzun V. 51.  2012. Marker-assisted selection for disease resistance in wheat and barley breeding. Phytopathology 102:560–66 [Google Scholar]
  52. Mirdita V, He S, Zhao Y, Korzun V, Bothe R. 52.  et al. 2015. Potential and limits of whole genome prediction of resistance to Fusarium head blight and Septoria tritici blotch in a vast central European elite winter wheat population. Theor. Appl. Genet. 128:2471–81 [Google Scholar]
  53. Olivera Firpo P, Newcomb M, Szabo L, Rouse MN, Johnson JL. 53.  et al. 2015. Phenotypic and genotypic characterization of race TKTTF of Puccinia graminis f. sp. Tritici that caused a wheat stem rust epidemic in Southern Ethiopia in 2013/14. Phytopathology 105:917–28 [Google Scholar]
  54. Ornella L, Singh S, Perez P, Burgueño J, Singh R. 54.  et al. 2012. Genomic prediction of genetic values for resistance to wheat rusts. Plant Genome 5:136–48 [Google Scholar]
  55. Park T, Casella G. 55.  2008. The Bayesian lasso. J. Am. Stat. Assoc. 103:681–86 [Google Scholar]
  56. Parlevliet JE. 56.  2002. Durability of resistance against fungal, bacterial and viral pathogens; present situation. Euphytica 124:147–56 [Google Scholar]
  57. Periyannan S, Moore J, Ayliffe M, Bansal U, Wang X. 57.  et al. 2013. The gene Sr33, an ortholog of barley mla genes, encodes resistance to wheat stem rust race Ug99. Science 341:786–88 [Google Scholar]
  58. Pestka JJ, Smolinski AT. 58.  2005. Deoxynivalenol: toxicology and potential effects on humans. J. Toxicol. Environ. Health Part B 8:39–69 [Google Scholar]
  59. Poland JA, Balint-Kurti PJ, Wisser RJ, Pratt RC, Nelson RJ. 59.  2009. Shades of gray: the world of quantitative disease resistance. Trends Plant Sci. 14:21–29 [Google Scholar]
  60. Poland JA, Bradbury PJ, Buckler ES, Nelson RJ. 60.  2011. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. PNAS 108:6893–98 [Google Scholar]
  61. Pradhan SK, Nayak DK, Mohanty S, Behera L, Barik SR. 61.  et al. 2015. Pyramiding of three bacterial blight resistance genes for broad-spectrum resistance in deepwater rice variety, Jalmagna. Rice 8:19 [Google Scholar]
  62. Pszczola M, Strabel T, Mulder H, Calus M. 62.  2012. Reliability of direct genomic values for animals with different relationships within and to the reference population. J. Dairy Sci. 95:389–400 [Google Scholar]
  63. Pszczola M, Veerkamp R, De Haas Y, Wall E, Strabel T, Calus M. 63.  2013. Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population. Animal 7:1759–68 [Google Scholar]
  64. Ramakrishna W, Emberton J, Ogden M, SanMiguel P, Bennetzen JL. 64.  2002. Structural analysis of the maize Rp1 complex reveals numerous sites and unexpected mechanisms of local rearrangement. Plant Cell 14:3213–23 [Google Scholar]
  65. Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink J-L, Melchinger AE. 65.  2013. Genomic predictability of interconnected biparental maize populations. Genetics 194:493–503 [Google Scholar]
  66. Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D. 66.  et al. 2012. Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics 192:715–28 [Google Scholar]
  67. Ruffel S, Dussault MH, Palloix A, Moury B, Bendahmane A. 67.  et al. 2002. A natural recessive resistance gene against Potato virus Y in pepper corresponds to the eukaryotic initiation factor 4e (eIF4e). Plant J. 32:1067–75 [Google Scholar]
  68. Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink J-L, Sorrells M. 68.  2012. Evaluation of genomic prediction methods for Fusarium head blight resistance in wheat. Plant Genome 5:51–61 [Google Scholar]
  69. Rutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J. 69.  et al. 2015. Efficient use of historical data for genomic selection: a case study of stem rust resistance in wheat. Plant Genome doi: 10.3835/plantgenome2014.09.0046
  70. Rutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J. 70.  et al. 2015. Genetic gain from phenotypic and genomic selection for quantitative resistance to stem rust of wheat. Plant Genome doi: 10.3835/plantgenome2014.10.0074
  71. Rutkoski JE, Heffner EL, Sorrells ME. 71.  2011. Genomic selection for durable stem rust resistance in wheat. Euphytica 179:161–73 [Google Scholar]
  72. Rutkoski JE, Poland JA, Singh RP, Huerta-Espino J, Bhavani S. 72.  et al. 2014. Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome doi: 10.3835/plantgenome2014.02.0006
  73. Saintenac C, Zhang W, Salcedo A, Rouse MN, Trick HN. 73.  et al. 2013. Identification of wheat gene Sr35 that confers resistance to Ug99 stem rust race group. Science 341:783–86 [Google Scholar]
  74. Sallam AH, Endelman JB, Jannink J-L, Smith KP. 74.  2015. Assessing genomic selection prediction accuracy in a dynamic barley breeding population. Plant Genome doi: 10.3835/plantgenome2014.05.0020
  75. Singh AK, Singh VK, Singh A, Ellur RK, Pandian R. 75.  et al. 2015. Introgression of multiple disease resistance into a maintainer of basmati rice CMS line by marker assisted backcross breeding. Euphytica 203:97–107 [Google Scholar]
  76. Singh RP, Hodson DP, Jin Y, Huerta-Espino J, Kinyua MG. 76.  et al. 2006. Current status, likely migration and strategies to mitigate the threat to wheat production from race Ug99 (TTKS) of stem rust pathogen. CAB Rev. Perspect. Agric. Vet. Sci. Nutr. Nat. Resour. 1:13 [Google Scholar]
  77. Singh RP, Hodson DP, Jin Y, Lagudah ES, Ayliffe MA. 77.  et al. 2015. Emergence and spread of new races of wheat stem rust fungus: continued threat to food security and prospects of genetic control. Phytopathology 105:872–84 [Google Scholar]
  78. Singh S, Sidhu JS, Huang N, Vikal Y, Li Z. 78.  et al. 2001. Pyramiding three bacterial blight resistance genes (xa5, xa13 and Xa21) using marker-assisted selection into indica rice cultivar PR106. Theor. Appl. Genet. 102:1011–15 [Google Scholar]
  79. Singh VK, Singh A, Singh S, Ellur RK, Choudhary V. 79.  et al. 2012. Incorporation of blast resistance into “PRR78”, an elite basmati rice restorer line, through marker assisted backcross breeding. Field Crops Res. 128:8–16 [Google Scholar]
  80. Snijders C. 80.  1990. Fusarium head blight and mycotoxin contamination of wheat: a review. Neth. J. Plant Pathol. 96:187–98 [Google Scholar]
  81. Song J, Bradeen JM, Naess SK, Raasch JA, Wielgus SM. 81.  et al. 2003. Gene RB cloned from Solanum bulbocastanum confers broad spectrum resistance to potato late blight. PNAS 100:9128–33 [Google Scholar]
  82. Song W-Y, Wang G-L, Chen L-L, Kim H-S, Pi L-Y. 82.  et al. 1995. A receptor kinase-like protein encoded by the rice disease resistance gene, Xa21. Science 270:1804–6 [Google Scholar]
  83. Technow F, Bürger A, Melchinger AE. 83.  2013. Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups. G3 3:197–203 [Google Scholar]
  84. Vanderplank JE. 84.  2012. Disease Resistance in Plants Philadelphia: Elsevier
  85. Van Ginkel M, Van Der Schaar W, Zhuping Y, Rajaram S. 85.  1996. Inheritance of resistance to scab in two wheat cultivars from Brazil and China. Plant Dis. 80:863–67 [Google Scholar]
  86. Wang GL, Mackill DJ, Bonman JM, McCouch SR, Champoux MC, Nelson RJ. 86.  1994. RFLP mapping of genes conferring complete and partial resistance to blast in a durably resistant rice cultivar. Genetics 136:1421–34 [Google Scholar]
  87. Webb CA, Richter TE, Collins NC, Nicolas M, Trick HN. 87.  et al. 2002. Genetic and molecular characterization of the maize Rp3 rust resistance locus. Genetics 162:381–94 [Google Scholar]
  88. Whittaker JC, Thompson R, Denham MC. 88.  2000. Marker-assisted selection using ridge regression. Genet. Res. 75:249–52 [Google Scholar]
  89. William M, Singh R, Huerta-Espino J, Islas SO, Hoisington D. 89.  2003. Molecular marker mapping of leaf rust resistance gene Lr46 and its association with stripe rust resistance gene Yr29 in wheat. Phytopathology 93:153–59 [Google Scholar]
  90. Wisser RJ, Balint-Kurti PJ, Nelson RJ. 90.  2006. The genetic architecture of disease resistance in maize: a synthesis of published studies. Phytopathology 96:120–29 [Google Scholar]
  91. Xu Y, Crouch JH. 91.  2008. Marker-assisted selection in plant breeding: from publications to practice. Crop Sci. 48:2 [Google Scholar]
  92. Zhou J, Loh Y-T, Bressan RA, Martin GB. 92.  1995. The tomato gene Pti1 encodes a serine/threonine kinase that is phosphorylated by Pto and is involved in the hypersensitive response. Cell 83:925–35 [Google Scholar]

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