Skip to main content

Genomic selection for grain yield and quality traits in durum wheat

Abstract

The prediction accuracies of genomic selection depend on several factors, including the genetic architecture of target traits, the number of traits considered at a given time, and the statistical models. Here, we assessed the potential of single-trait (ST) and multi-trait (MT) genomic prediction models for durum wheat on yield and quality traits using a breeding panel (BP) of 170 varieties and advanced breeding lines, and a doubled-haploid (DH) population of 154 lines. The two populations were genotyped with the Infinium iSelect 90K SNP assay and phenotyped for various traits. Six ST-GS models (RR-BLUP, G-BLUP, BayesA, BayesB, Bayesian LASSO, and RKHS) and three MT prediction approaches (MT-BayesA, MT-Matrix, and MT-SI approaches which use economic selection index as a trait value) were applied for predicting yield, protein content, gluten index, and alveograph measures. The ST prediction accuracies ranged from 0.5 to 0.8 for the various traits and models and revealed comparable prediction accuracies for most of the traits in both populations, except BayesA and BayesB, which better predicted gluten index, tenacity, and strength in the DH population. The MT-GS models were more accurate than the ST-GS models only for grain yield in the BP. Using BP as a training set to predict the DH population resulted in poor predictions. Overall, all the six ST-GS models appear to be applicable for GS of yield and gluten strength traits in durum wheat, but we recommend the simple computational models RR-BLUP or G-BLUP for predicating single trait and MT-SI for predicting yield and protein simultaneously.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Abbreviations

BL:

Bayesian Least Absolute Shrinkage, and Selection Operator

BLUP:

Best linear unbiased prediction

BP:

Breeding panel

CIMMYT:

International Maize and Wheat Improvement Center

CV:

Cross-validation

DAPC:

Discriminant analysis of principal components

DH:

Doubled-haploid

G-BLUP:

Genomic best linear unbiased prediction

G × E:

Genotype by environment interaction

GEBV:

Genomic estimated breeding value

GI:

Gluten index

GPC:

Grain protein content

GS:

Genomic selection

GYLD:

Grain yield

L:

Average abscissas at rupture (extensibility)

LD:

Linkage disequilibrium

MAF:

Minor allele frequency

MT:

Multi-traits

NIR:

Near infra-red

P:

Dough overpressure (tenacity)

QTL:

Quantitative trait loci

RKHS:

Reproducing kernel Hilbert space

RR-BLUP:

Ridge regression best linear unbiased prediction

SI:

Selection index

SNP:

Single-nucleotide polymorphism

ST:

Single trait

ST-GS:

Single-trait genomic selection

TP:

Training population

VP:

Validation population

W:

Deformation energy (strength)

References

  1. AACC (2000) Approved methods of American Association of Cereal Chemists, 10th edn. The Association, St. Paul, USA

    Google Scholar 

  2. Akdemir D, Godfrey O (2015) EMMREML: fitting mixed models with known covariance structures. R package version, vol 3, p 01

    Google Scholar 

  3. Akdemir D, Sanchez JI, Jannink J-L (2015) Optimization of genomic selection training populations with a genetic algorithm. Genet Sel Evol 47(1):38. https://doi.org/10.1186/s12711-015-0116-6

    Article  PubMed  PubMed Central  Google Scholar 

  4. Annicchiarico P, Nazzicari N, Li X, Wei Y, Pecetti L, Brummer EC (2015) Accuracy of genomic selection for alfalfa biomass yield in different reference populations. BMC Genomics 16:1020. https://doi.org/10.1186/s12864-015-2212-y

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. 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. Plant Genome 4(2):132–144. https://doi.org/10.3835/plantgenome2011.02.0007

    Article  Google Scholar 

  6. Bao Y, Kurle JE, Anderson G, Young ND (2015) Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm. Mol Breed 35(6):128. https://doi.org/10.1007/s11032-015-0324-3

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J (2016) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci 242(Supplement C):23–36. https://doi.org/10.1016/j.plantsci.2015.08.021

    Article  PubMed  CAS  Google Scholar 

  8. Battenfield SD, Guzman C, Gaynor RC, Singh RP, Pena RJ, Dreisigacker S, Fritz AK, Poland JA (2016) Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program. Plant Genome 9(2). https://doi.org/10.3835/plantgenome2016.01.0005

  9. Blanco A, Bellomo M, Lotti C, Pasqualone A (1998) Mapping of quantitative trait loci for grain quality using recombinant inbred lines of durum wheat. EWAC Newsletter:106–110

  10. Calus MP, Veerkamp RF (2011) Accuracy of multi-trait genomic selection using different methods. Genet Sel Evol 43(1):26. https://doi.org/10.1186/1297-9686-43-26

    Article  PubMed  PubMed Central  Google Scholar 

  11. Charmet G, Storlie E, Oury FX, Laurent V, Beghin D, Chevarin L, Lapierre A, Perretant MR, Rolland B, Heumez E, Duchalais L, Goudemand E, Bordes J, Robert O (2014) Genome-wide prediction of three important traits in bread wheat. Mol Breed 34(4):1843–1852. https://doi.org/10.1007/s11032-014-0143-y

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Clark SA, Hickey JM, Daetwyler HD, van der Werf JH (2012) The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet Sel Evol 44:4. https://doi.org/10.1186/1297-9686-44-4

    Article  PubMed  PubMed Central  Google Scholar 

  13. Clarke FR, Clarke JM, Ames NA, Knox RE, Ross RJ (2010a) Gluten index compared with SDS-sedimentation volume for early generation selection for gluten strength in durum wheat. Can J Plant Sci 90(1):1–11. https://doi.org/10.4141/CJPS09035

    Article  CAS  Google Scholar 

  14. Clarke JM, Clarke FR, Pozniak CJ (2010b) Forty-six years of genetic improvement in Canadian durum wheat cultivars. Can J Plant Sci 90(6):791–809. https://doi.org/10.4141/cjps10091

    Article  Google Scholar 

  15. Clarke FR, Clarke JM, Pozniak CJ, Knox RE, McCaig TN (2009) Protein concentration inheritance and selection in durum wheat. Can J Plant Sci 89(4):601–612. https://doi.org/10.4141/CJPS08191

    Article  CAS  Google Scholar 

  16. Clarke JM, McCaig TN, DePauw RM, Knox RE, Ames NP, Clarke FR, Fernandez MR, Marchylo BA, Dexter JE (2005b) Commander durum wheat. Can J Plant Sci 85(4):901–904. https://doi.org/10.4141/P04-189

    Article  Google Scholar 

  17. Clarke JM, McCaig TN, DePauw RM, Knox RE, Clarke FR, Fernandez MR, Ames NP (2005a) Strongfield durum wheat. Can J Plant Sci 85(3):651–654. https://doi.org/10.4141/P04-119

    Article  Google Scholar 

  18. Clarke JM, McLeod JG, DePauw RM, Marchylo BA, McCaig TN, Knox RE, Fernandez MR, Ames N (2000) AC navigator durum wheat. Can J Plant Sci 80(2):343–345. https://doi.org/10.4141/P99-108

    Article  Google Scholar 

  19. Clarke JM, Marchylo BA, Kovacs MIP, Noll JS, McCaig TN, Howes NK (1998) Breeding durum wheat for pasta quality in Canada. Euphytica 100:163–170. https://doi.org/10.1023/A:1018313603344

    Article  Google Scholar 

  20. Conti V, Roncallo PF, Beaufort V, Cervigni GL, Miranda R, Jensen CA, Echenique VC (2011) Mapping of main and epistatic effect QTLs associated to grain protein and gluten strength using a RIL population of durum wheat. J Appl Genet 52(3):287–298. https://doi.org/10.1007/s13353-011-0045-1

    Article  PubMed  CAS  Google Scholar 

  21. Crossa J, Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun H-J (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186(2):713–724. https://doi.org/10.1534/genetics.110.118521

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Crossa J, Jarquin D, Franco J, Perez-Rodriguez P, Burgueno J, Saint-Pierre C, Vikram P, Sansaloni C, Petroli C, Akdemir D, Sneller C, Reynolds M, Tattaris M, Payne T, Guzman C, Pena RJ, Wenzl P, Singh S (2016) Genomic prediction of Gene Bank wheat landraces. G3 (Bethesda) 6(7):1819–1834. https://doi.org/10.1534/g3.116.029637

    Article  CAS  Google Scholar 

  23. Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975. https://doi.org/10.1016/j.tplants.2017.08.011

    Article  PubMed  CAS  Google Scholar 

  24. D’Ovidio R, Masci S (2004) The low-molecular-weight glutenin subunits of wheat gluten. Journal of Cereal Science 39(3):321–339. https://doi.org/10.1016/j.jcs.2003.12.002

    Article  CAS  Google Scholar 

  25. Daetwyler HD, Bansal UK, Bariana HS, Hayden MJ, Hayes BJ (2014) Genomic prediction for rust resistance in diverse wheat landraces. Theor Appl Genet 127(8):1795–1803. https://doi.org/10.1007/s00122-014-2341-8

    Article  PubMed  CAS  Google Scholar 

  26. Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185(3):1021–1031. https://doi.org/10.1534/genetics.110.116855

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. de los Campos G, Pérez-Rodríguez P (2014) Bayesian generalized linear regression. R package version 1.0.4.

  28. Dexter J, Matsuo R, Kruger J (1990) The spaghetti-making quality of commercial durum wheat samples with variable alpha-amylase activity. Cereal Chem 67(5):405–412

    CAS  Google Scholar 

  29. Dexter JE, Matsuo RR (1977) Influence of protein content on some durum wheat quality parameters. Can J Plant Sci 57(3):717–727. https://doi.org/10.4141/cjps77-105

    Article  CAS  Google Scholar 

  30. Distelfeld A, Uauy C, Fahima T, Dubcovsky J (2006) Physical map of the wheat high-grain protein content gene Gpc-B1 and development of a high-throughput molecular marker. New Phytol 169(4):753–763. https://doi.org/10.1111/j.1469-8137.2005.01627.x

    Article  PubMed  CAS  Google Scholar 

  31. Elouafi I, Nachit MM, Elsaleh A, Asbati A, Mather DE (2000) QTL-mapping of genomic regions controlling gluten strength in durum (Triticum turgidum L. var. durum). Options Méditerranéennes Série A, Séminaires Méditerranéens (No. 40):505–509

  32. Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4. doi:https://doi.org/10.3835/plantgenome2011.08.0024

  33. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Longman Group, Essex

    Google Scholar 

  34. Feillet P, Dexter J (1996) Quality requirements of durum wheat for semolina milling and pasta production. Monograph on pasta and noodle. Technology:95–131

  35. Fiedler JD, Salsman E, Liu Y, Michalak de Jiménez M, Hegstad JB, Chen B, Manthey FA, Chao S, Xu S, Elias EM, Li X (2017) Genome-wide association and prediction of grain and semolina quality traits in durum wheat breeding populations. Plant Genome 10. https://doi.org/10.3835/plantgenome2017.05.0038

  36. Gaines CS, Reid JF, Vander Kant C, Morris CF (2006) Comparison of methods for gluten strength assessment. Cereal Chem J 83(3):284–286. https://doi.org/10.1094/CC-83-0284

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 10(6):381–391. https://doi.org/10.1038/nrg2575

    Article  PubMed  CAS  Google Scholar 

  39. Gouy M, Rousselle Y, Bastianelli D, Lecomte P, Bonnal L, Roques D, Efile JC, Rocher S, Daugrois J, Toubi L, Nabeneza S, Hervouet C, Telismart H, Denis M, Thong-Chane A, Glaszmann JC, Hoarau JY, Nibouche S, Costet L (2013) Experimental assessment of the accuracy of genomic selection in sugarcane. Theor Appl Genet 126(10):2575–2586. https://doi.org/10.1007/s00122-013-2156-z

    Article  PubMed  CAS  Google Scholar 

  40. Guo G, Zhao F, Wang Y, Zhang Y, Du L, Su G (2014) Comparison of single-trait and multiple-trait genomic prediction models. BMC Genet 15(1):30. https://doi.org/10.1186/1471-2156-15-30

    Article  PubMed  PubMed Central  Google Scholar 

  41. Guzman C, Peña RJ, Singh R, Autrique E, Dreisigacker S, Crossa J, Rutkoski J, Poland J, Battenfield S (2016) Wheat quality improvement at CIMMYT and the use of genomic selection on it. Appl Transl Genomics 11:3–8. https://doi.org/10.1016/j.atg.2016.10.004

    Article  Google Scholar 

  42. Habier D, Fernando R, Dekkers J (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177

  43. Hayashi T, Iwata H (2013) A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinformatics 14:34–34. https://doi.org/10.1186/1471-2105-14-34

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME (2009b) Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet Sel Evol 41(1):51. https://doi.org/10.1186/1297-9686-41-51

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009a) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443. https://doi.org/10.3168/jds.2008-1646

    Article  PubMed  CAS  Google Scholar 

  46. Hazel LN (1943) The genetic basis for constructing selection indexes. Genetics 28(6):476–490

    PubMed  PubMed Central  CAS  Google Scholar 

  47. Heffner E, Jannink J-L, Sorrells M (2011b) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4:65–75

    Article  Google Scholar 

  48. Heffner EL, Jannink J-L, Iwata H, Souza E, Sorrells ME (2011a) Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 51:2597–2606. https://doi.org/10.2135/cropsci2011.05.0253

    Article  Google Scholar 

  49. Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690. https://doi.org/10.2135/cropsci2009.11.0662

    Article  Google Scholar 

  50. Heslot N, Jannink J-L, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55:1–12. https://doi.org/10.2135/cropsci2014.03.0249

    Article  Google Scholar 

  51. Heslot N, Yang HP, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52. doi:https://doi.org/10.2135/cropsci2011.06.0297

  52. Hickey JM, Dreisigacker S, Crossa J, Hearne S, Babu R, Prasanna BM, Grondona M, Zambelli A, Windhausen VS, Mathews K, Gorjanc G (2014) Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci 54(4):1476–1488. https://doi.org/10.2135/cropsci2013.03.0195

    Article  Google Scholar 

  53. Hofheinz N, Borchardt D, Weissleder K, Frisch M (2012) Genome-based prediction of test cross performance in two subsequent breeding cycles. Theor Appl Genet 125(8):1639–1645. https://doi.org/10.1007/s00122-012-1940-5

    Article  PubMed  Google Scholar 

  54. ICC (2001) Standard methods of the International Association for Cereal Science and Technology. Standard No.121 and 158

  55. Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177. https://doi.org/10.1093/bfgp/elq001

    Article  PubMed  CAS  Google Scholar 

  56. Jia Y, Jannink JL (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192(4):1513–1522. https://doi.org/10.1534/genetics.112.144246

    Article  PubMed  PubMed Central  Google Scholar 

  57. Jiang J, Zhang Q, Ma L, Li J, Wang Z, Liu JF (2015) Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model. Heredity (Edinb) 115(1):29–36. https://doi.org/10.1038/hdy.2015.9

    Article  CAS  Google Scholar 

  58. Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11(1):94. https://doi.org/10.1186/1471-2156-11-94

    Article  PubMed  PubMed Central  Google Scholar 

  59. Knox R, Clarke J, DePauw R (2000) Dicamba and growth condition effects on doubled haploid production in durum wheat crossed with maize. Plant Breed 119:289–298. https://doi.org/10.1046/j.1439-0523.2000.00498.x

    Article  Google Scholar 

  60. Knox RE, Houshmand S, Clarke FR, Clarke JM, Ames NA (2004) QTL association with measures of gluten strength across environments in durum wheat. In: The gluten proteins. The Royal Society of Chemistry, pp 148–151. doi:https://doi.org/10.1039/9781847552099-00148

  61. Krchov L-M, Gordillo GA, Bernardo R (2015) Multienvironment validation of the effectiveness of phenotypic and genomewide selection within biparental maize populations. Crop Sci 55:1068–1075. https://doi.org/10.2135/cropsci2014.09.0608

    Article  Google Scholar 

  62. Kumar A, Elias EM, Ghavami F, Xu X, Jain S, Manthey FA, Mergoum M, Alamri MS, Kianian PMA, Kianian SF (2013) A major QTL for gluten strength in durum wheat (Triticum turgidum L. var. durum). J Cereal Sci 57(1):21–29. https://doi.org/10.1016/j.jcs.2012.09.006

    Article  CAS  Google Scholar 

  63. Lado B, Matus I, Rodríguez A, Inostroza L, Poland J, Belzile F, del Pozo A, Quincke M, Castro M, von Zitzewitz J (2013) Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. G3: Genes|Genomes|Genetics 3(12):2105–2114. https://doi.org/10.1534/g3.113.007807

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Leisle D (1972) Hercules, a new durum wheat. Can J Plant Sci 50:118–119 1970

    Article  Google Scholar 

  65. Littell R, Milliken G, Stroup W, Wolfinger R (1996) SAS®system for mixed models. .633

  66. Lorenz AJ, Smith KP, Jannink JL (2012) Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci 52:1609–1621. https://doi.org/10.2135/cropsci2011.09.0503

    Article  Google Scholar 

  67. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120(1):151–161. https://doi.org/10.1007/s00122-009-1166-3

    Article  PubMed  Google Scholar 

  68. Maccaferri M, Ricci A, Salvi S, Milner SG, Noli E, Martelli PL, Casadio R, Akhunov E, Scalabrin S, Vendramin V, Ammar K, Blanco A, Desiderio F, Distelfeld A, Dubcovsky J, Fahima T, Faris J, Korol A, Massi A, Mastrangelo AM, Morgante M, Pozniak C, N’Diaye A, Xu S, Tuberosa R (2015) A high-density, SNP-based consensus map of tetraploid wheat as a bridge to integrate durum and bread wheat genomics and breeding. Plant Biotechnol J 13:648–663. https://doi.org/10.1111/pbi.12288

    Article  PubMed  CAS  Google Scholar 

  69. Mackay I, Ober E, Hickey J (2015) GplusE: beyond genomic selection. Food and Energy Secur 4(1):25–35. https://doi.org/10.1002/fes3.52

    Article  Google Scholar 

  70. Marone D, Laido G, Gadaleta A, Colasuonno P, Ficco DB, Giancaspro A, Giove S, Panio G, Russo MA, De Vita P, Cattivelli L, Papa R, Blanco A, Mastrangelo AM (2012) A high-density consensus map of A and B wheat genomes. Theor App Gen 125:1619–1638. https://doi.org/10.1007/s00122-012-1939-y

    Article  Google Scholar 

  71. McCaig TN, DePauw RM, Williams PC (1993) Assessing seed-coat color in a wheat breeding program with a NIR/VIS instrument. Can J Plant Sci 73(2):535–539. https://doi.org/10.4141/cjps93-073

    Article  Google Scholar 

  72. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157

  73. N’Diaye A, Haile JK, Cory AT, Clarke FR, Clarke JM, Knox RE, Pozniak CJ (2017) Single marker and haplotype-based association analysis of semolina and pasta colour in elite durum wheat breeding lines using a high-density consensus map. PLoS One 12(1):e0170941. https://doi.org/10.1371/journal.pone.0170941

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103(482):681–686. https://doi.org/10.1198/016214508000000337

    Article  CAS  Google Scholar 

  75. Patil RM, Oak MD, Tamhankar SA, Rao VS (2009) Molecular mapping of QTLs for gluten strength as measured by sedimentation volume and mixograph in durum wheat (Triticum turgidum L. ssp durum). J Cereal Sci 49(3):378–386. https://doi.org/10.1016/j.jcs.2009.01.001

    Article  CAS  Google Scholar 

  76. Pérez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–495. https://doi.org/10.1534/genetics.114.164442

    Article  PubMed  PubMed Central  Google Scholar 

  77. Pérez P, de los Campos G, Crossa J, Gianola D (2010) Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome 3(2):106–116. https://doi.org/10.3835/plantgenome2010.04.0005

    Article  PubMed  PubMed Central  Google Scholar 

  78. Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Sci 49. doi:https://doi.org/10.2135/cropsci2008.10.0595

  79. Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink J-L (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5:103–113. https://doi.org/10.3835/plantgenome2012.06.0006

    Article  CAS  Google Scholar 

  80. Pozniak CJ, Clarke JM, Clarke FR (2012) Potential for detection of marker–trait associations in durum wheat using unbalanced, historical phenotypic datasets. Mol Breed 30(4):1537–1550. https://doi.org/10.1007/s11032-012-9737-4

    Article  Google Scholar 

  81. R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  82. Resende MD, Resende MF Jr, Sansaloni CP, Petroli CD, Missiaggia AA, Aguiar AM, Abad JM, Takahashi EK, Rosado AM, Faria DA, Pappas GJ Jr, Kilian A, Grattapaglia D (2012) 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(1):116–128. https://doi.org/10.1111/j.1469-8137.2011.04038.x

    Article  PubMed  Google Scholar 

  83. Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194(2):493–503. https://doi.org/10.1534/genetics.113.150227

    Article  PubMed  PubMed Central  Google Scholar 

  84. Riedelsheimer C, Technow F, Melchinger AE (2012) Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines. BMC Genomics 13(1):452. https://doi.org/10.1186/1471-2164-13-452

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Rincent R, Charcosset A, Moreau L (2017) Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations. Theor Appl Genet 130(11):2231–2247. https://doi.org/10.1007/s00122-017-2956-7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink J-L, Sorrells M (2012) Evaluation of genomic prediction methods for Fusarium head blight resistance in wheat. Plant Genome 5:51–61. https://doi.org/10.3835/plantgenome2012.02.0001

    Article  CAS  Google Scholar 

  87. Rutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J, Jannink JL, Sorrells ME (2015) Efficient use of historical data for genomic selection: a case study of stem rust resistance in wheat. Plant Genome 8. https://doi.org/10.3835/plantgenome2014.09.0046

  88. Sallam AH, Endelman JB, Jannink JL, Smith KP (2015) Assessing genomic selection prediction accuracy in a dynamic barley breeding population. Plant Genome 8. https://doi.org/10.3835/plantgenome2014.05.0020

  89. Schmidt M, Kollers S, Maasberg-Prelle A, Großer J, Schinkel B, Tomerius A, Graner A, Korzun V (2016) Prediction of malting quality traits in barley based on genome-wide marker data to assess the potential of genomic selection. Theor Appl Genet 129(2):203–213. https://doi.org/10.1007/s00122-015-2639-1

    Article  PubMed  CAS  Google Scholar 

  90. Schulthess AW, Wang Y, Miedaner T, Wilde P, Reif JC, Zhao Y (2016) Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor Appl Genet 129(2):273–287. https://doi.org/10.1007/s00122-015-2626-6

    Article  PubMed  CAS  Google Scholar 

  91. Shikha M, Kanika A, Rao AR, Mallikarjuna MG, Gupta HS, Nepolean T (2017) Genomic selection for drought tolerance using genome-wide SNPs in maize. Front Plant Sci 8:550. https://doi.org/10.3389/fpls.2017.00550

    Article  PubMed  PubMed Central  Google Scholar 

  92. Smith H (1936) A discriminant function for plant selection. Ann Eugenics 7:240–250

    Article  Google Scholar 

  93. Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink J-L, McCouch SR (2015) Genomic selection and association mapping in rice (Oryza sativa): 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):e1004982. https://doi.org/10.1371/journal.pgen.1004982

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Storlie E, Charmet G (2013) Genomic selection accuracy using historical data generated in a wheat breeding program. Plant Genome 6(1). https://doi.org/10.3835/plantgenome2013.01.0001

  95. Su G, Guldbrandtsen B, Gregersen VR, Lund MS (2010) Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. J Dairy Sci 93(3):1175–1183. https://doi.org/10.3168/jds.2009-2192

    Article  PubMed  CAS  Google Scholar 

  96. Subira J, Pena R, Álvaro F, Ammar K, Ramdani A, Royo C (2014) Breeding progress in the pasta-making quality of durum wheat cultivars released in Italy and Spain during the 20th century. Crop and Pasture Sci 65(1):16–26

    CAS  Google Scholar 

  97. Tayeh N, Aubert G, Pilet-Nayel M-L, Lejeune-Hénaut I, Warkentin TD, Burstin J (2015) Genomic tools in pea breeding programs: status and perspectives. Front Plant Sci 6:1037. https://doi.org/10.3389/fpls.2015.01037

    PubMed  PubMed Central  Article  Google Scholar 

  98. Thavamanikumar S, Dolferus R, Thumma BR (2015) Comparison of genomic selection models to predict flowering time and spike grain number in two hexaploid wheat doubled haploid populations. G3: Genes|Genomes|Genetics 5(10):1991–1998. https://doi.org/10.1534/g3.115.019745

    Article  PubMed  PubMed Central  Google Scholar 

  99. Townley-Smith TF, Patterson LA, Depauw RM, Lendrum CWB, McCrystal GE (1987) Kyle durum wheat. Can J Plant Sci 67(1):225–227. https://doi.org/10.4141/cjps87-026

    Article  Google Scholar 

  100. Tuberosa R, Pozniak C (2014) Durum wheat genomics comes of age. Mol Breed 34(4):1527–1530. https://doi.org/10.1007/s11032-014-0188-y

    Article  Google Scholar 

  101. VanRaden P (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423. https://doi.org/10.3168/jds.2007-0980

    Article  PubMed  CAS  Google Scholar 

  102. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92(1):16–24. https://doi.org/10.3168/jds.2008-1514

    Article  PubMed  CAS  Google Scholar 

  103. Viana JMS, Sobreira FM, De Resende MDV, Faria VR (2010) Multi-trait BLUP in half-sib selection of annual crops. Plant Breed 129(6):599–604. https://doi.org/10.1111/j.1439-0523.2009.01745.x

    Article  Google Scholar 

  104. Wang Y, Mette MF, Miedaner T, Gottwald M, Wilde P, Reif JC, Zhao Y (2014) The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years. BMC Genomics 15:556. https://doi.org/10.1186/1471-2164-15-556

    Article  PubMed  PubMed Central  Google Scholar 

  105. Whittaker JC, Thompson R, Denham MC (2000) Marker-assisted selection using ridge regression. Genet Res 75(2):249–252

    Article  PubMed  CAS  Google Scholar 

  106. Würschum T, Reif JC, Kraft T, Janssen G, Zhao Y (2013) Genomic selection in sugar beet breeding populations. BMC Genet 14(1):85. https://doi.org/10.1186/1471-2156-14-85

    Article  PubMed  PubMed Central  Google Scholar 

  107. Zhao Y, Gowda M, Liu W, Wurschum T, Maurer HP, Longin FH, Ranc N, Reif JC (2012) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124(4):769–776. https://doi.org/10.1007/s00122-011-1745-y

    Article  PubMed  Google Scholar 

  108. Zhong S, Dekkers JCM, Fernando RL, Jannink J-L (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182(1):355–364. https://doi.org/10.1534/genetics.108.098277

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge funding of this research by the Genome Canada and Genome Prairie as part of the Canadian Triticum Applied Genomics (CTAG) project. Additional funding provided by the Saskatchewan Ministry of Agriculture, Western Grains Research Foundation, Agriculture and Agri-Food Canada, and the Natural Sciences and Engineering Research Council of Canada is also gratefully acknowledged. We also acknowledge the technical assistance of Shawn Yates at the Agriculture and Agri-Food Canada Research and Development Centre, Swift Current, for curation of the database from which the breeding panel data were drawn. The data from these trials were conducted under the auspices of the Prairie Recommending Committee for Wheat, Rye, and Triticale, and we acknowledge them for permission to use the data presented for the breeding panel, and the many people who conducted the field trials. We acknowledge the coordinators of these trials, including Dr. D. Leisle, Agriculture and Agri-Food Canada, Winnipeg (retired), from 1961 to 1994, Dr. J. M. Clarke, from 1995 to 2007, Dr. A.K. Singh from 2008 to 2012, and Dr. R.M. DePauw in 2013, all from Agriculture and Agri-Food Canada. We also acknowledge those responsible for end-use quality analysis of these trials from the Grain Research Laboratory of the Canadian Grain Commission. Lastly, we acknowledge Krysta Wiebe, Jennifer Ens, and Justin Coulson for support in generation of the molecular data used in this project.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Jemanesh K. Haile or Curtis J. Pozniak.

Additional information

Fran Clarke is retired.

Electronic supplementary material

ESM 1

(DOCX 27.8 kb)

ESM 2

(DOCX 41.2 kb)

ESM 3

(DOCX 26.7 kb)

ESM 4

(DOCX 35.7 kb)

ESM 5

(DOCX 42.3 kb)

ESM 6

(DOCX 39.8 kb)

ESM 7

(DOCX 18.9 kb)

ESM 8

(DOCX 19.7 kb)

ESM 9

(DOCX 19.6 kb)

ESM 10

(DOCX 20.1 kb)

ESM 11

(DOCX 19.7 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Haile, J.K., N’Diaye, A., Clarke, F. et al. Genomic selection for grain yield and quality traits in durum wheat. Mol Breeding 38, 75 (2018). https://doi.org/10.1007/s11032-018-0818-x

Download citation

Keywords

  • Genomic selection
  • Quality traits
  • GS models
  • Multi-trait
  • Selection index
  • Triticum turgidum L. var. durum