Cassava yield traits predicted by genomic selection methods

English version

Autoři: Luciano Rogério Braatz de Andrade ^aff001; Massaine Bandeira e Sousa ^aff002; Eder Jorge Oliveira ^aff003; Marcos Deon Vilela de Resende ^aff004; Camila Ferreira Azevedo ^aff006
Působiště autorů: Department of Plant Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil ^aff001; Center of Agrarian, Environmental and Biological Sciences, Universidade Federal do Recôncavo da Bahia, Cruz das Almas, Bahia, Brazil ^aff002; Embrapa Mandioca e Fruticultura, Cruz das Almas, Bahia, Brazil ^aff003; Department of Forestry Engineering, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil ^aff004; Embrapa Florestas, Colombo, Paraná, Brazil ^aff005; Department of Statistics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil ^aff006
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0224920

Souhrn

Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions’ BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 –RR-BLUP to 0.4756—RKHS) and dry root yield (0.4689 –G-BLUP to 0.4818—RKHS) in comparison with dry matter content (0.5655 –BLASSO to 0.5670 –RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99–1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.

Klíčová slova:

Genomics – Plant genomics – Heredity – Plant breeding – Structural genomics – Cassava

Zdroje

1. Oliveira EJ, Ferreira CF, Santos VS, Oliveira GA. Development of a cassava core collection based on single nucleotide polymorphism markers. Genet Mol Res, 2014; 13(3): 6472–6485. doi: 10.4238/2014.August.25.11 25158266

2. Cooper M, Podlich D, Jensen NM, Chapman SC, Hammer G. Modelling plant breeding programs. Trends in Agronomy, 1999; 2: 33–64.

3. Oliveira EJ, Resende MDV, Santos VS, Ferreira CF, Oliveira GAF, Silva MS, et al. Genome-wide selection in cassava. Euphytica, 2012; 187(2): 263–276.

4. Wolfe MD, Del Carpio DP, Alabi O, Ezenwaka LC, Ikeogu UM, Kayondo IS, et al. Prospects for genomic selection in cassava breeding. Plant Genome, 2017; 10(3): 1–19.

5. Ceballos H, Kawuri RS, Gracen VE, Yencho GC, Hershey CH. Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava. Theor Appl Genet, 2015; 128(9): 1647–1667. doi: 10.1007/s00122-015-2555-4 26093610

6. Ceballos H, Iglesias CA, Pérez JC, Dixon AG. Cassava breeding: opportunities and challenges. Plant Mol Biol, 2004; 56(4): 503–516. doi: 10.1007/s11103-004-5010-5 15630615

7. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001; 157(4): 1819–1829. 11290733

8. Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME. Plant breeding with Genomic selection: Gain per unit time and cost. Crop Sci, 2010; 50(5): 1681–1690.

9. Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, et al. Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity (Edinb). 2013; 112: 48–60.

10. Resende MDV, Silva FFE, Azevedo CF. Estatística Matemática, Biométrica e Computacional: Modelos Mistos, Multivariados, Categóricos e Generalizados (REML/BLUP), Inferência Bayesiana, Regressão Aleatória, Seleção Genômica, QTL-GWAS, Estatística Espacial e Temporal, Competição, Sobrevivência. 1. ed. Visconde do Rio Branco: Suprema; 2014. Portuguese.

11. Isidro J, Jannink JL, Akdemir D, Poland J, Heslot N, Sorrells ME. Training set optimization under population structure in genomic selection. Theor Appl Genet, 2015; 128: 145–158. doi: 10.1007/s00122-014-2418-4 25367380

12. Marchini J, Cardon LR, Phillips MS, Donnelly P. The effects of human population structure on large genetic association studies. Nature Genetics, 2004; 36(5):512–517. doi: 10.1038/ng1337 15052271

13. Pereira HD, Viana JMS, Andrade ACB, Silva FF, Paes GP. Relevance of genetic relationship in GWAS and genomic prediction. J Appl Genet, 2018; 59(1): 1–8. doi: 10.1007/s13353-017-0417-2 29190011

14. Azevedo CF, Resende MDV, Silva FF, Viana JMS, Valente MSF, Resende MFR Jr, et al. Ridge, Lasso and Bayesian additive-dominance genomic models. BMC Genet, 2015; 16(105): 1–13.

15. Resende MDV, Lopes PS, Silva RL, Pires IE. Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesqui Florest Bras, 2008; 56(56): 63–78. Portuguese.

16. Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics, 2011; 12(186): 1–12.

17. Legarra A, Robert-Granié C, Croiseau P, Guillaume F, Fritz S. Improved Lasso for genomic selection. Genet Res (Camb), 2011; 93(1): 77–87.

18. Park T, Casella G. The Bayesian Lasso. J Am Stat Assoc, 2008; 103(482): 681–686.

19. Heslot N, Yang HP, Sorrells ME, Jannink JL. Genomic selection in plant breeding: a comparison of models. Crop Sci, 2012; 52(1): 146–160.

20. Bernardo R. Bandwagons I, too, have known. Theor Appl Genet, 2016; 129(12): 323–232.

21. Covarrubias-Pazaran G. Genome-assisted prediction of quantitative traits using the r package sommer. PLoS ONE, 2016; 11(6): 1–15.

22. De Los Campos G, Pérez P, Vazquez AI, Crossa J. Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package. Methods Mol Biol, 2013; 1019: 299–320. doi: 10.1007/978-1-62703-447-0_12 23756896

23. Endelman JB. Ridge Regression and other kernels for genomic selection with R package rrBLUP. Plant Genome, 2011; 4(3): 250–255.

24. Perez P, De Los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics, 2014; 198(2): 483–495. doi: 10.1534/genetics.114.164442 25009151

25. Ly D, Hamblin MT, Rabbi IY, Gedil M, Moshood BO, Gauch HG Jr, et al. Relatedness and genotype × environment interaction affects prediction accuracies in genomic selection: a study in cassava. Crop Sci, 2013; 53(4): 1312–1325.

26. Wolfe MD, Kulakow P, Rabbi IY, Jannink JL. Marker-Based Estimates Reveal Significant Non-additive effects in clonally propagated cassava (manihot esculenta): implications for the prediction of total genetic value and the selection of varieties. G3 (Bethesda), 2016; 6(11): 3497–3506.

27. Wolfe MD, Rabbi IY, Egesi C, Hamblin M, Kawuki R, Kulakow P, et al. Genome-wide association and prediction reveals genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement. Plant Genome, 2016; 9(2): 1–13.

28. Kawano K, Fukuda WMG, Cenpukdee U. Genetic and environmental effects on dry matter content of cassava root. Crop Sci, 1987; 27(1): 69–74.

29. Garrick DJ, Taylor JF, Fernando RL. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet. Sel. Evol, 2009; 41:55. doi: 10.1186/1297-9686-41-55 20043827

30. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 2015; 67(1): 1–48.

31. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2017. https://www.R-project.org/

32. Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin, 1987; 19: 11–15.

33. Hamblin MT, Rabbi IY. The Effects of restriction-enzyme choice on properties of genotyping-by-sequencing libraries: a study in cassava. Crop Sci, 2014; 54(6): 2603–2608.

34. Bredeson JV, Lyons JB, Prochnik SE, Wu GA, Ha CM, Edsinger-Gonzales E, Grimwood J, Schmutz J, Rabbi IY, Egesi C, Nauluvula P, Lebot V, Ndunguru J, Mkamilo G, Bart RS, Setter TL, Gleadow RM, Kulakow P, Ferguson ME, Rounsley S, Rokhsar DS. Sequencing wild and cultivated cassava and related species reveals extensive interspecific hybridization and genetic diversity. Nat Biotecnol, 2016; 34(5): 562–570.

35. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009; 25(14): 1754–1760. doi: 10.1093/bioinformatics/btp324 19451168

36. Glaubitz JC, Casstevens TM, Lu Fei, Harriman J, Elshire RJ, Sun Q, Buckler ES. TASSEL-GBS: A high capacity genotyping by sequencing analysis pipeline. PLoS ONE, 2014; 9(2): e90346. doi: 10.1371/journal.pone.0090346 24587335

37. Browning BL, Browning SR. Genotype imputation with millions of reference samples. Am J Hum Genet, 2016; 98(1): 116–126. doi: 10.1016/j.ajhg.2015.11.020 26748515

38. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet, 2010; 11(94): 1–15.

39. Vanraden PM. Genomic Measures of Relationship and Inbreeding. Interbull Annual Meeting Proceedings, 2007; 37: 33–36.

40. Jombart T, Ahmed I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics, 2011; 27(21): 3070–3071. doi: 10.1093/bioinformatics/btr521 21926124

41. Gianola D, Fernando RL, Stella A. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics, 2006; 173(3): 1761–1776. doi: 10.1534/genetics.105.049510 16648593

42. Crossa J, De Los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, et al. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics, 2010; 186(2): 713–724. doi: 10.1534/genetics.110.118521 20813882

43. Raftery AE, Lewis SM. [Practical Markov Chain Monte Carlo]: Comment: one long run with diagnostics: implementation strategies for Markov Chain Monte Carlo. Statist Sci, 1992; 7(4): 493–497.

44. Plummer M, Best N, Cowles K, Vines K. CODA: convergence diagnosis and output analysis for MCMC. R News, 2006; 6(1): 7–11.

45. Russell L. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.2.3. 2018. https://CRAN.R-project.org/package=emmeans

46. Cohen JA. Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, 1960; 20(1): 37–46.

47. Calle F, Perez JC, Gaitán W, Morante N, Ceballos H, Llano G, et al. Diallel inheritance of relevant traits in cassava (Manihot esculenta Crantz) adapted to acid-soil savannas. Euphytica, 2005; 144: 177–186.

48. Zacarias AM, Labuschagne MT. Diallel analysis of cassava brown streak disease, yield and yield related characteristics in Mozambique. Euphytica, 2010; 176(3): 309–320.

49. Tumuhimbise R, Melis R, Shanahan P. Diallel analysis of early storage root yield and disease resistance traits in cassava (Manihot esculenta Crantz). Field Crops Res, 2014; 167: 86–93.

50. Kayondo SI, Del Carpio DP, Lozano R, Ozimati A, Wolfe M, Baguma Y, et al. Genome-wide association mapping and genomic prediction for CBSD resistance in Manihot esculenta. Scientific Reports, 2018; 8(1549): 1–11.

51. Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, et al. Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome, 2012; 5(3): 103–113.

52. Gianola D, Van Kaam JBCHM. Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics, 2008; 178(4): 2289–2303. doi: 10.1534/genetics.107.084285 18430950

53. Morota G, Gianola D. Kernel-based whole-genome prediction of complex traits: a review. Front Genet, 2014; 5(363): 1–13.

54. Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G. The impact of genetic relationship information on genomic breeding values in German Hostein cattle. Genet Sel Evol, 2010; 42(5): 1–12.

55. Grattapaglia D, Resende MDV. Genomic selection in forest tree breeding. Tree Genet. Genomes, 2011; 7(2): 241–255.

56. Wientjes YCJ, Veerkamp RF, Calus MPL. The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction. Genetics, 2013; 193(2): 621–631. doi: 10.1534/genetics.112.146290 23267052

57. Resende RMS, Casler MD, Resende MDV. Genomic selection in forage breeding: accuracy and methods. Crop Sci, 2013; 54(1): 143–156.

58. Slatkin M. Linkage disequilibrium—understanding the evolutionary past and mapping the medical future. Nat Rev Genet, 2008; 9(6): 477–485. doi: 10.1038/nrg2361 18427557

59. Sorkheh K, Malysheva-Otto LV, Wirthensohn MG, Tarkesh-Esfahani S, Martínez-Gómez P. Linkage disequilibrium, genetic association mapping and gene localization in crop plants. Genet Mol Biol, 2008; 31(4): 805–814.

60. Akdemir D, Sanchez JI, Jannink JL. Optimization of genomic selection training populations with a genetic algorithm. Genet Sel Evol, 2015; 47(38): 1–10.

61. Freitas JPX, Santos VS, Oliveira EJ. Inbreeding depression in cassava for productive traits. Euphytica, 2016; 209: 137–145.

62. Edriss V, Gao Y, Zhang X, Jumbo MB, Makumbi D, Olsen MS, et al. Genomic prediction in a large African maize population. Crop Sci, 2017; 57(5): 2361–2371.

63. Parkes EY, Fregene M, Dixon A, Boakye-Peprah B, Labuschagne MT. Combining ability of cassava genotypes for cassava mosaic disease and cassava bacterial blight, yield and its related components in two ecological zones in Ghana. Euphytica, 2013; 194(1): 13–24.

Cassava yield traits predicted by genomic selection methods

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