#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Genomic Selection and Association Mapping in Rice (): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines


Genomic selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying genomic selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that genomic selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.


Vyšlo v časopise: Genomic Selection and Association Mapping in Rice (): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines. PLoS Genet 11(2): e32767. doi:10.1371/journal.pgen.1004982
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004982

Souhrn

Genomic selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying genomic selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that genomic selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.


Zdroje

1. Peng SBaGSK (2003) Four decades of breeding for varietal improvement of irrigated lowland rice in the international rice research institute. Plant Production Science 6: 157–164.

2. Khush GS, Virk P.S. (2005) IR varieties and their impact. Los Banos, Philippines: International Rice Research Institute. 163 p. 25275211

3. Mackill DJ, Coffman W.R., Garrity D.P. (1996) Rainfed Lowland Rice Improvement. Los Banos, Philippines: International Rice Research Institute. 25121335

4. Collard BC, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philosophical transactions of the Royal Society of London Series B, Biological sciences 363: 557–572. 17715053

5. Jena KK, Mackill DJ (2008) Molecular markers and their use in marker-assisted selection in rice. Crop Science 48: 1266–1276.

6. Gregorio GB IM, Vergara GV, Thirumeni S (2013) Recent advances in rice science to design salinity and other abiotic stress tolerant rice varieties. SABRAO Journal of Breeding and Genetics 45: 31–41.

7. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819–1829. 11290733

8. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, et al. (2011) Genomic Selection in Plant Breeding. 110: 77–123.

9. Bernardo R (2010) Genomewide Selection with Minimal Crossing in Self-Pollinated Crops. Crop Science 50: 624–627.

10. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic Selection for Crop Improvement. Crop Science 49: 1–12.

11. 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. doi: 10.3168/jds.2008-1646 19164653

12. Bernardo R (2009) Genomewide Selection for Rapid Introgression of Exotic Germplasm in Maize. Crop Science 49: 419–425.

13. Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant Breeding with Genomic Selection: Gain per Unit Time and Cost. Crop Science 50: 1681–1690. 21365924

14. Jongdee B, Pantuwan G, Fukai S, Fischer K (2006) Improving drought tolerance in rainfed lowland rice: An example from Thailand. Agricultural Water Management 80: 225–240.

15. Guo ZG, Tucker DM, Lu JW, Kishore V, Gay G (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theoretical and Applied Genetics 124: 261–275. doi: 10.1007/s00122-011-1702-9 21938474

16. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theoretical and Applied Genetics 120: 151–161. doi: 10.1007/s00122-009-1166-3 19841887

17. Massman JM, Jung HJG, Bernardo R (2013) Genomewide Selection versus Marker-assisted Recurrent Selection to Improve Grain Yield and Stover-quality Traits for Cellulosic Ethanol in Maize. Crop Science 53: 58–66.

18. Asoro FG, Newell MA, Beavis WD, Scott MP, Jannink J-L (2011) Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats. The Plant Genome Journal 4: 132.

19. Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, et al. (2013) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity (Edinb).

20. Heffner EL, Jannink JL, Sorrells ME (2011) Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program. Plant Genome 4: 65–75.

21. Lorenz AJ, Smith KP, Jannink JL (2012) Potential and Optimization of Genomic Selection for Fusarium Head Blight Resistance in Six-Row Barley. Crop Science 52: 1609–1621.

22. Ornella L, Singh S, Perez P, Burgue√±o J, Singh R, et al. (2012) Genomic Prediction of Genetic Values for Resistance to Wheat Rusts. Plant Gen 5: 136–148.

23. Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink JL, et al. (2012) Evaluation of Genomic Prediction Methods for Fusarium Head Blight Resistance in Wheat. Plant Genome 5: 51–61.

24. de Oliveira EJ, de Resende MDV, Santos VD, Ferreira CF, Oliveira GAF, et al. (2012) Genome-wide selection in cassava. Euphytica 187: 263–276.

25. Gouy M, Rousselle Y, Bastianelli D, Lecomte P, Bonnal L, et al. (2013) Experimental assessment of the accuracy of genomic selection in sugarcane. TAG Theoretical and applied genetics Theoretische und angewandte Genetik.

26. Ly D, Hamblin M, Rabbi I, Melaku G, Bakare M, et al. (2013) Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava. Crop Science 53: 1312.

27. Wurschum T, Reif JC, Kraft T, Janssen G, Zhao YS (2013) Genomic selection in sugar beet breeding populations. Bmc Genetics 14. doi: 10.1186/1471-2156-14-125 24378210

28. Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, et al. (2013) Genomic Predictability of Interconnected Biparental Maize Populations. Genetics 194: 493–+. doi: 10.1534/genetics.113.150227 23535384

29. Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink JL, et al. (2012) Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments. G3-Genes Genomes Genetics 2: 1427–1436. doi: 10.1534/g3.112.003699 23173094

30. Zhao K, Tung C-W, Eizenga GC, Wright MH, Ali ML, et al. (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2: 467. doi: 10.1038/ncomms1467 21915109

31. Zhao K, Wright M, Kimball J, Eizenga G, McClung A, et al. (2010) Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PloS one 5: e10780. doi: 10.1371/journal.pone.0010780 20520727

32. Jennings PR, Coffman WR, Kauffman HE (1979) Rice Improvement: International Rice Research Institute. 25121236

33. Guo Z, Tucker D, Basten C, Gandhi H, Ersoz E, et al. (2014) The impact of population structure on genomic prediction in stratified populations. Theoretical and Applied Genetics 127: 749–762. doi: 10.1007/s00122-013-2255-x 24452438

34. Briggs WH, McMullen MD, Gaut BS, Doebley J (2007) Linkage mapping of domestication loci in a large maize-teosinte backcross resource. Genetics 177: 1915–1928. 17947434

35. Edwards MD, Stuber CW, Wendel JF (1987) Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action. Genetics 116: 113–125. 3596228

36. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, et al. (2009) Genetic Properties of the Maize Nested Association Mapping Population. Science 325: 737–740. doi: 10.1126/science.1174320 19661427

37. Stuber CW, Williams WP, Moll RH (1973) Epistasis in Maize (Zea mays L.): III. Significance in Predictions of Hybrid Performances. Crop Sci 13: 195–200.

38. Famoso AN, Zhao K, Clark RT, Tung CW, Wright MH, et al. (2011) Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS genetics 7: e1002221. doi: 10.1371/journal.pgen.1002221 21829395

39. Venuprasad R, Bool ME, Quiatchon L, Cruz MTS, Amante M, et al. (2012) A large-effect QTL for rice grain yield under upland drought stress on chromosome 1. Molecular Breeding 30: 535–547.

40. Xu KN, Mackill DJ (1996) A major locus for submergence tolerance mapped on rice chromosome 9. Molecular Breeding 2: 219–224. 9160626

41. Li Z, Pinson Shannon R., Park William D., Paterson Andrew H., Stansel James W. (1997) Epistasis for three grain yield components in rice. Genetics 145: 453–465. 9071598

42. Ashikari M, Sasaki A, Ueguchi-Tanaka M, Itoh H, Nishimura A, et al. (2002) Loss-of-function of a rice gibberellin biosynthetic gene, GA20 oxidase (GA20ox-2), led to the rice 'green revolution'. Breeding Science 52: 143–150.

43. Chen JB, Li XY, Cheng C, Wang YH, Qin M, et al. (2014) Characterization of Epistatic Interaction of QTLs LH8 and EH3 Controlling Heading Date in Rice. Scientific Reports 4. doi: 10.1038/srep07387 25552271

44. Yano M, Katayose Y, Ashikari M, Yamanouchi U, Monna L, et al. (2000) Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the arabidopsis flowering time gene CONSTANS. Plant Cell 12: 2473–2483. 11148291

45. Begum H, Spindel, J., Lalusin, A.G., Borromeo, T.H., Gregorio, G.B., Hernandez, J.E., Virk, P.S., Collardy, B.C.Y., McCouch, S. (2014) Association Mapping and Genomic Selection in rice (Oryza sativa): Association mapping for yield and other agronomic traits in elite, tropical rice breeding lines.

46. Glaubitz JC CT, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES. (2014) TASSEL-GBS: A high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9: 9034.

47. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Meth 9: 357–359.

48. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, et al. (2013) Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol 14: R55. doi: 10.1186/gb-2013-14-6-r55 23759205

49. Heslot N, Yang H-P, Sorrells ME, Jannink J-L (2012) Genomic Selection in Plant Breeding: A Comparison of Models. Crop Science 52: 146.

50. Perez-Rodriguez P, Gianola D, Gonzalez-Camacho JM, Crossa J, Manes Y, et al. (2012) Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat. G3-Genes Genomes Genetics 2: 1595–1605. doi: 10.1534/g3.112.003665 23275882

51. Zhao Y, Mette MF, Gowda M, Longin CFH, Reif JC (2014) Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Heredity 112: 638–645. doi: 10.1038/hdy.2014.1 24518889

52. Crossa J, de los Campos G, Perez P, Gianola D, Burgueno J, et al. (2010) Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers. Genetics 186: 713–U406. doi: 10.1534/genetics.110.118521 20813882

53. Daetwyler HD, Villanueva B, Bijma P, Woolliams JA (2007) Inbreeding in genome-wide selection. J Anim Breed Genet 124: 369–376. 18076474

54. Rodin AS, Litvinenko A, Klos K, Morrison AC, Woodage T, et al. (2009) Use of Wrapper Algorithms Coupled with a Random Forests Classifier for Variable Selection in Large-Scale Genomic Association Studies. Journal of Computational Biology 16: 1705–1718. doi: 10.1089/cmb.2008.0037 20047492

55. Bernardo R (2014) Genomewide Selection when Major Genes Are Known. Crop Science 54: 68.

56. Thomson MJ (2014) High-Throughput SNP Genotyping to Accelerate Crop Improvement. Plant Breed Biotech 2: 195–212.

57. Dixit S, Singh A, Cruz MTS, Maturan PT, Amante M, et al. (2014) Multiple major QTL lead to stable yield performance of rice cultivars across varying drought intensities. Bmc Genetics 15. 25551672

58. Maas L, McClung A, McCouch S (2010) Dissection of a QTL reveals an adaptive, interacting gene complex associated with transgressive variation for flowering time in rice. Theoretical and Applied Genetics 120: 895–908. doi: 10.1007/s00122-009-1219-7 19949767

59. Thomson MJ, Edwards JD, Septiningsih EM, Harrington SE, McCouch SR (2006) Substitution Mapping of dth1.1, a Flowering-Time Quantitative Trait Locus (QTL) Associated With Transgressive Variation in Rice, Reveals Multiple Sub-QTL. Genetics 172: 2501–2514. 16452146

60. Xie X, Jin F, Song M-H, Suh J-P, Hwang H-G, et al. (2008) Fine mapping of a yield-enhancing QTL cluster associated with transgressive variation in an Oryza sativa¬†√ó¬†O. rufipogon cross. Theoretical and Applied Genetics 116: 613–622. 18092146

61. Endelman JB, Atlin GN, Beyene Y, Semagn K, Zhang X, et al. (2014) Optimal Design of Preliminary Yield Trials with Genome-Wide Markers. Crop Sci 54: 48–59.

62. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, et al. (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PloS one 6: e19379. doi: 10.1371/journal.pone.0019379 21573248

63. Spindel J, Wright M, Chen C, Cobb J, Gage J, et al. (2013) Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations. Theoretical and Applied Genetics: 1–18. doi: 10.1007/s13197-013-0993-z 25593984

64. Bradbury PJ, Zhang Zhiwu, Dallas E. Kroon, Casstevens Terry M., Ramdoss Yogesh, Buckler Edward S. (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23: 2633–2635. 17586829

65. Gianola D, van Kaam JB (2008) Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178: 2289–2303. doi: 10.1534/genetics.107.084285 18430950

66. Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183: 347–363. doi: 10.1534/genetics.109.103952 19620397

67. Breiman L (2001) Random forests. Machine Learning 45: 5–32.

68. Campos PPaGdl (2013) BGLR: A Stastical Package for Whole Genome Regression and Prediction. CRAN.

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2015 Číslo 2
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#