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Genomic selection for wheat traits and trait stability

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Based on the estimates of accuracy, genomic selection would be useful for selecting for improved trait values and trait stability for agronomic and quality traits in wheat. Trait values and trait stability estimated by two methods were generally independent indicating a breeder could select for both simultaneously.

Abstract

Genomic selection (GS) is a new marker-assisted selection tool for breeders to achieve higher genetic gain faster and cheaper. Breeders face challenges posed by genotype by environment interaction (GEI) pattern and selecting for trait stability. Obtaining trait stability is costly, as it requires data from multiple environments. There are few studies that evaluate the efficacy of GS for predicting trait stability. A soft winter wheat population of 273 lines was genotyped with 90 K single nucleotide polymorphism markers and phenotyped for four agronomic and seven quality traits. Additive main effect and multiplicative interaction (AMMI) model and  Eberhart and Russell regression (ERR) were used to estimate trait stability. Significant GEI variation was observed and stable lines were identified for all traits in this study. The accuracy of GS ranged from 0.33 to 0.67 for most traits and trait stability. Accuracy of trait stability was greater than trait itself for yield (0.44 using AMMI versus 0.33) and heading date (0.65 using ERR versus 0.56). The opposite trend was observed for the other traits. GS did not predict the stability of the quality traits except for flour protein, lactic acid and softness equivalent. Significant GS accuracy for some trait stability indicated that stability was under genetic control for these traits. The magnitude of GS accuracies for all the traits and most of the trait stability index suggests the possibility of rapid selection for these trait and trait stability in wheat breeding.

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References

  • Akcura M, Kaya Y, Taner S, Ayranci R (2006) Parametric stability analyses for grain yield of durum wheat. Plant Soil Environ 52:254

    Google Scholar 

  • Aliyu OM, Adeigbe OO, Lawal OO (2014) Phenotypic stability analysis of yield components in cashew (Anacardium occidentale L.) using additive main effect and multiplicative interaction (AMMI) and GGE biplot analyses. Plant Breed Biotechnol 2:354–369

    Article  Google Scholar 

  • Amiri E, Farshadfar E, Jowkar MM (2013) AMMI analysis of wheat substitution lines for detecting genes controlling adaptability. Int J Adv Biol Biomed Res 1:1112–1123

    Google Scholar 

  • Asoro FG, Newell MA, Beavis WD, Scott MP, Jannink JL (2011) Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Genome 4:132–144

    Article  Google Scholar 

  • Ayers KL, Cordell HJ (2010) SNP Selection in genome-wide and candidate gene studies via penalized logistic regression. Genet Epidemiol 34:879–891

    Article  PubMed  PubMed Central  Google Scholar 

  • Bao Y, Vuong T, Meinhardt C, Tiffin P, Denny R, Chen S, Nguyen HT, Orf JH, Young ND (2014) Potential of association mapping and genomic selection to explore PI 88788 derived soybean cyst nematode resistance. Plant Genome 7(3). doi:10.3835/plantgenome2013.11.0039 

  • Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48

    Article  Google Scholar 

  • Berke TG, Baenziger PS, Morris R (1992) Chromosomal location of wheat quantitative trait loci affecting stability of six traits, using reciprocal chromosome substitutions. Crop Sci 32:628–633

    Article  Google Scholar 

  • Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090

    Article  Google Scholar 

  • Costa J, Bollero G (2001) Stability analysis of grain yield in barley (Hordeum vulgare) in the US mid-Atlantic region. Ann Appl Biol 139:137–143

    Article  Google Scholar 

  • Crossa J, de los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Crossa J, de los Campos G, Maccaferri M, Tuberosa R, Burgueño J, Pérez P (2015) Extending the marker × environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat. Crop Sci. doi:10.2135/cropsci2015.04.0260

    Google Scholar 

  • de los Campos G, Pérez P (2014) BGLR: bayesian generalized linear regression. R package version 1.0.4. http://CRAN.R-project.org/package=BGLR

  • de Mendiburu F (2015) Agricolae: statistical procedures for agricultural research. R package version 1.2-3. http://CRAN.R-project.org/package=agricolae

  • Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601

    Article  CAS  PubMed  Google Scholar 

  • Eberhart St, Russell W (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40

    Article  Google Scholar 

  • Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255

    Article  Google Scholar 

  • Fehr WR (1987) Heritability. In: Principles of cultivar development, vol 1, pp 95–105

  • Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Crop Pasture Sci 14:742–754

    Article  Google Scholar 

  • Forkman J, Piepho HP (2014) Parametric bootstrap methods for testing multiplicative terms in GGE and AMMI models. Biometrics 70:639–647

    Article  PubMed  Google Scholar 

  • Frashadfar E, Safari H, Jamshidi B (2012) GGE biplot analysis of adaptation in wheat substitution lines. Int J Agric Crop Sci 4:877–881

    Google Scholar 

  • Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22

    Article  PubMed  PubMed Central  Google Scholar 

  • Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44(3):705–715

    Article  Google Scholar 

  • Gauch HG, Piepho H-P, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Sci 48:866–889

    Article  Google Scholar 

  • Goddard ME, Hayes B (2007) Genomic selection. J Anim Breed Genet 124:323–330

    Article  CAS  PubMed  Google Scholar 

  • Grattapaglia D, Resende MD (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7:241–255

    Article  Google Scholar 

  • Hanson W (1970) Genotypic stability. Theor Appl Genet 40:226–231

    Article  CAS  PubMed  Google Scholar 

  • Hayes B, Bowman P, Chamberlain A, Goddard M (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443

    Article  CAS  PubMed  Google Scholar 

  • Hayes B, Lewin H, Goddard M (2013) The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet 29:206–214

    Article  CAS  PubMed  Google Scholar 

  • He S, Schulthess AW, Mirdita V, Zhao Y, Korzun V, Bothe R, Ebmeyer E, Reif JC, Jiang Y (2016) Genomic selection in a commercial winter wheat population. Theor Appl Genet 129:641–651

    Article  CAS  PubMed  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Heslot N, Yang H-P, Sorrells ME, Jannink J-L (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160

    Article  Google Scholar 

  • Heslot N, Jannink J-L, Sorrells ME (2013) Using genomic prediction to characterize environments and optimize prediction accuracy in applied breeding data. Crop Sci 53:921–933

    Article  Google Scholar 

  • Heslot N, Akdemir D, Sorrells ME, Jannink J-L (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127(2):463–480

    Article  PubMed  Google Scholar 

  • Holland JB, Nyquist WE, Cervantes-Martinez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed 22:9–112

    Google Scholar 

  • Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177

    Article  CAS  PubMed  Google Scholar 

  • Kraakman AT, Niks RE, Van den Berg PM, Stam P, Van Eeuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168:435–446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lado B, Barrios PG, Quincke M, Silva P, Gutiérrez L (2015) Modeling genotype by environment interaction for genomic selection with unbalanced data from a wheat (Triticum aestivum L.) breeding program. Crop Sci. doi:10.2135/cropsci2015.04.0207

    Google Scholar 

  • Legarra A, Robert-Granié C, Manfredi E, Elsen J-M (2008) Performance of genomic selection in mice. Genetics 180:611–618

    Article  PubMed  PubMed Central  Google Scholar 

  • Lin Z, Hayes B, Daetwyler H (2014) Genomic selection in crops, trees and forages: a review. Crop Pasture Sci 65:1177–1191

    Article  Google Scholar 

  • Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink J-L, Singh RP, Autrique E and de los Campos G (2015) Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model. G3 (Bethesda) 5(4):569–582

  • Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161

    Article  PubMed  Google Scholar 

  • Massman JM, Jung H-JG, Bernardo R (2013) Genomewide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53:58–66

    Article  CAS  Google Scholar 

  • Meuwissen T, Hayes B, Goddard M (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mohamed NE, Said AA, Amein KA (2013) Additive main effects and multiplicative interaction (AMMI) and GGE-biplot analysis of genotype × environment interactions for grain yield in bread wheat (Triticum aestivum L.). Afr J Agric 8:5197–5203

    Google Scholar 

  • Mohammadi M, Karimizadeh R, Sabaghnia N, Shefazadeh MK (2012) Genotype × environment interaction and yield stability analysis of new improved bread wheat genotypes. Turk J Field Crops 17:67–73

    Google Scholar 

  • Mukherjee A, Mohapatra N, Bose L, Jambhulkar N, Nayak P (2013) Additive main effects and multiplicative interaction (AMMI) analysis of G × E interactions in rice-blast pathosystem to identify stable resistant genotypes. Afr J Agric 8:5492–5507

    Google Scholar 

  • Namorato H, Miranda GV, Souza L, Oliveira LR, DeLima RO, Mantovani EE (2009) Comparing biplot multivariate analysis with Eberhart and Russell’method for genotype × environment interaction. Crop Breed Appl Biot 9:299–307

    Article  Google Scholar 

  • Ogutu JO, Schulz-Streeck T, Piepho H-P (2012) Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. In: BMC proceedings. BioMed Central Ltd, p S10

  • Pérez P, de los Campos G (2014) Genome-wide regression & prediction with the BGLR statistical package. Genetics 198:483–495

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 3:106–116

    Article  PubMed  PubMed Central  Google Scholar 

  • Perkins JM, Jinks J (1968) Environmental and genotype-environmental components of variability. 3. Multiple lines and crosses. Heredity 23:339–356

    Article  CAS  PubMed  Google Scholar 

  • Piepho HP (1998) Methods for comparing the yield stability of cropping systems. J Agr Crop Sci 180:193–213

    Article  Google Scholar 

  • Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888

    Article  PubMed  PubMed Central  Google Scholar 

  • Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1–2):209–228

    Article  Google Scholar 

  • Piepho HP, Möhring J, Schulz-Streeck T, Ogutu JO (2012) A stage-wise approach for the analysis of multi-environment trials. Biom J 54:844–860

    Article  PubMed  Google Scholar 

  • Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5:103–113

    Article  CAS  Google Scholar 

  • R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/

  • Rao PS, Reddy PS, Rathore A, Reddy BV, Panwar S (2011) Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype × environment interaction and seasonal adaptation. Indian J Agric Sci 81:438–444

    Google Scholar 

  • Resende MF, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190:1503–1510

    Article  PubMed  PubMed Central  Google Scholar 

  • Reynolds M, Bonnett D, Chapman SC, Furbank RT, Manès Y, Mather DE, Parry MA (2011) Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies. J Exp Bot 62:439–452

    Article  CAS  PubMed  Google Scholar 

  • Rezene Y, Bekele A, Goa Y (2014) GGE and ammi biplot analysis for field PEA yield stability in SNNPR State, Ethiopia. Int J Sust Agric Res 1:28–38

    Google Scholar 

  • Rharrabti Y, Villegas D, Royo C, Martos-Núñez V, Del Moral LG (2003) Durum wheat quality in Mediterranean environments: II. Influence of climatic variables and relationships between quality parameters. Field Crops Res 80:133–140

    Article  Google Scholar 

  • Rinaldo A, Bacanu SA, Devlin B, Sonpar V, Wasserman L, Roeder K (2005) Characterization of multilocus linkage disequilibrium. Genet Epidemiol 28:193–206

    Article  PubMed  Google Scholar 

  • Rodrigues PC, Malosetti M, Gauch HG, van Eeuwijk FA (2014) A weighted AMMI algorithm to study genotype-by-environment interaction and QTL-by-environment interaction. Crop Sci 54:1555–1570

    Article  Google Scholar 

  • Rondanini DP, Gomez NV, Agosti MB, Miralles DJ (2012) Global trends of rapeseed grain yield stability and rapeseed-to-wheat yield ratio in the last four decades. Eur J Agron 37:56–65

    Article  Google Scholar 

  • Rutkoski JE, Heffner EL, Sorrells ME (2011) Genomic selection for durable stem rust resistance in wheat. Euphytica 179:161–173

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Rutkoski JE, Poland JA, Singh RP, Huerta-Espino J, Bhavani S, Barbier H, Rouse MN, Jannink J-L, Sorrells ME (2014) Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome. doi:10.3835/plantgenome2014.02.0006

    Google Scholar 

  • SAS Institute Inc. (2008) SAS/STAT User's Guide, Version 9.2. SAS Institute Inc, Cary, NC

  • Sabaghnia N, Sabaghpour S, Dehghani H (2008) The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. J Agric Sci 146:571–581

    Article  Google Scholar 

  • Sabaghnia N, Mohammadi M, Karimizadeh R (2012) The evaluation of genotype × environment interactions of durum wheat’s yield using of the Ammi model. Agric For 55:5–21

    Google Scholar 

  • Sabaghpour SH, Razavi F, Fatemeh Danyali S, Tobe D, Ebadi A (2012) Additive main effect and multiplicative interaction analysis for grain yield of chickpea (Cicer arietinum L.) in Iran. ISRN Agron 2012:1–6

    Article  Google Scholar 

  • Sadeghi S, Samizadeh H, Amiri E, Ashouri M (2013) Additive main effects and multiplicative interactions (AMMI) analysis of dry leaf yield in tobacco hybrids across environments. Afr J Biotechnol 10:4358–4364

    Google Scholar 

  • Saleem N, Ahmad M, Vashnavi R, Bukhari A, Dar ZA (2015) Stability analysis in Wheat: an application of additive main effects and multiplicative interaction. Afr J Agric Res 10:295–300

    Article  Google Scholar 

  • Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78:629–644

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schulz-Streeck T, Ogutu JO, Gordillo A, Karaman Z, Knaak C, Piepho H-P (2013) Genomic selection allowing for marker-by-environment interactions. Plant Breed. 132:532–538

    Article  Google Scholar 

  • Shukla G (1972) Some statistical aspects of partitioning genotype environmental components of variability. Heredity 29:237–245

    Article  CAS  PubMed  Google Scholar 

  • Smith N, Guttieri M, Souza E, Shoots J, Sorrells M, Sneller C (2011) Identification and validation of QTL for grain quality traits in a cross of soft wheat cultivars Pioneer Brand 25R26 and Foster. Crop Sci 51:1424–1436

    Article  Google Scholar 

  • Sneller C, Kilgore-Norquest L, Dombek D (1997) Repeatability of yield stability statistics in soybean. Crop Sci 37:383–390

    Article  Google Scholar 

  • 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:e1004982

    Article  PubMed  Google Scholar 

  • Tai GC (1971) Genotypic stability analysis and its application to potato regional trials. Crop Sci 11:184–190

    Article  Google Scholar 

  • Tarakanovas P, Ruzgas V (2006) Additive main effect and multiplicative interaction analysis of grain yield of wheat varieties in Lithuania. Agron Res 4:91–98

    Google Scholar 

  • Tollenaar M, Lee E (2002) Yield potential, yield stability and stress tolerance in maize. Field Crops Res 75:161–169

    Article  Google Scholar 

  • Tribout T, Larzul C, Phocas F (2012) Efficiency of genomic selection in a purebred pig male line. J Anim Sci 90:4164–4176

    Article  CAS  PubMed  Google Scholar 

  • Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L (2014) Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol J 12:787–796

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang Y, Mette MF, Miedaner T, Wilde P, Reif JC, Zhao Y (2015) First insights into the genotype–phenotype map of phenotypic stability in rye. J Exp Bot 66:3275–3284

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zali H, Farshadfar E, Sabaghpour SH, Karimizadeh R (2012) Evaluation of genotype × environment interaction in chickpea using measures of stability from AMMI model. Ann Biol Res 3:3126–3136

    Google Scholar 

  • Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP, Longin FH, Ranc N, Reif JC (2012) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776

    Article  PubMed  Google Scholar 

  • Zhong S, Dekkers JC, Fernando RL, Jannink J-L (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J 80:388–393

    Article  Google Scholar 

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Acknowledgments

We thank Dr. C. Sneller’s lab members for helping with the field data collection. This project was supported by Triticeae Coordinated Agricultural Project (2011-68002-30029) of the USDA National Institute of Food and Agriculture.

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Correspondence to Clay Sneller.

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Huang, M., Cabrera, A., Hoffstetter, A. et al. Genomic selection for wheat traits and trait stability. Theor Appl Genet 129, 1697–1710 (2016). https://doi.org/10.1007/s00122-016-2733-z

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