Leveraging probability concepts for cultivar recommendation in multi-environment trials.
Journal
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
ISSN: 1432-2242
Titre abrégé: Theor Appl Genet
Pays: Germany
ID NLM: 0145600
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
22
04
2021
accepted:
07
01
2022
pubmed:
23
2
2022
medline:
27
4
2022
entrez:
22
2
2022
Statut:
ppublish
Résumé
We propose using probability concepts from Bayesian models to leverage a more informed decision-making process toward cultivar recommendation in multi-environment trials. Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials.
Identifiants
pubmed: 35192008
doi: 10.1007/s00122-022-04041-y
pii: 10.1007/s00122-022-04041-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1385-1399Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Annicchiarico P (1992) Cultivar adaptation and recommendation from alfalfa trials in Northern Italy. J Genet Breed 46:269–269
Annicchiarico P, Bellah F, Chiari T (2006) Repeatable genotype × location interaction and its exploitation by conventional and GIS-based cultivar recommendation for durum wheat in Algeria. Eur J Agron 24:70–78
doi: 10.1016/j.eja.2005.05.003
Buntaran H, Piepho HP, Hagman J, Forkman J (2019) A Cross-validation of statistical models for zoned-based prediction in cultivar testing. Crop Sci 59:1544–1553
doi: 10.2135/cropsci2018.10.0642
Buntaran H, Forkman JP, HP (2021) Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision. Theor Appl Genet 134:1531–1530
doi: 10.1007/s00122-021-03786-2
Burgueño J, de los Campos G, Weigel K, Crossa J, (2012) Genomic prediction of breeding values when modeling genotype x environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719
doi: 10.2135/cropsci2011.06.0299
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, Brubaker M, Guo J, Li P, Riddell A (2017) Stan: A probabilistic programming language. J Stat Softw 76(1):1–32. https://doi.org/10.18637/jss.v076.i01
doi: 10.18637/jss.v076.i01
Cerón-Rojas JJ, Castillo-González F, Sahagún-Castellanos J, Santacruz-Varela A, Benítez-Riquelme I, Crossa J (2008) A molecular selection index method based on eigenanalysis. Genetics 180:547–557
pubmed: 18716338
pmcid: 2535704
doi: 10.1534/genetics.108.087387
Cerón-Rojas JJ, Crossa J, Toledo FH, Sahagún-Castellanos J (2016) A predetermined proportional gains eigen selection index method. Crop Sci 56:2436–2447
doi: 10.2135/cropsci2015.11.0718
Costa-Neto GMF, Moraes-Junior OP, Heinemann AB, Castro AP, Duarte JB (2020) A novel GIS-based tool to reveal spatial trends in reaction norm: upland rice case study. Euphytica 216:1–16
doi: 10.1007/s10681-020-2573-4
Cotes JM, Crossa J, Sanches A, Cornelius PL (2006) A Bayesian approach for assessing the stability of genotype. Crop Sci 46:2654–2665
doi: 10.2135/cropsci2006.04.0227
Crossa J (1990) Statistical analyses of multilocation trials. Adv Agron 44:55–85
doi: 10.1016/S0065-2113(08)60818-4
Crossa J (2012) From genotype x environment interaction to gene x environment interaction. Curr Genomics 13:225–244
pubmed: 23115524
pmcid: 3382277
doi: 10.2174/138920212800543066
Crossa J, Cornelius PL (1997) Sites regression and shifted multiplicative models clustering of cultivar trials sites under heterogeneity of error variance. Crop Sci 37:406–415
doi: 10.2135/cropsci1997.0011183X003700020017x
Crossa J et al (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724
pubmed: 20813882
pmcid: 2954475
doi: 10.1534/genetics.110.118521
Crossa J, Perez-Elizalde S, Jarquin D, Cotes JM, Viele K, Liu G, Cornelius PL (2011) Bayesian estimation of the additive main effects and multiplicative interaction model. Crop Sci 51:1458–1469
doi: 10.2135/cropsci2010.06.0343
da Silva CP, de Oliveira L, Nuvunga JJ, Pamplona AKA, Balestre M (2015) A Bayesian shrinkage approach for AMMI models. PLoS ONE 10:1–27
de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MP (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327-345
de Oliveira AL, da Silva CP, Nuvunga JJ, da Silva AQ, Balestre M (2015) Credible intervals for genotypic and environmental scores in the AMMI model with random effects for genotype. Crop Sci 55:465–476
doi: 10.2135/cropsci2014.05.0369
Dias KOG et al (2018) Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. Heredity 121:24–37
pubmed: 29472694
pmcid: 5997769
doi: 10.1038/s41437-018-0053-6
Dias KOG et al (2020) Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. Theor Appl Genet 133:443–455
pubmed: 31758202
doi: 10.1007/s00122-019-03475-1
dos Santos JPR, Vasconcellos RC, Pires LPM, Balestre M, Von Pinho RG (2016) Inclusion of dominance effects in the multivariate GBLUP model. PLoS ONE 11:1–21
dos Santos JRP, Fernandes SB, Lozano R, Brown PJ, Buckler E, Garcia AAF, Gore M (2020) Novel bayesian networks for genomic prediction of developmental traits in biomass sorghum. G3: genes. Genomes, Genetics 10:769–781
Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40
doi: 10.2135/cropsci1966.0011183X000600010011x
Edwards JW, Jannink JL (2006) Bayesian modeling of heterogeneous error and genotype × environment interaction variances. Crop Sci 46:820–833
doi: 10.2135/cropsci2005.0164
Eskridge KM (1990) Selection of stable cultivars using a safety-first rule. Crop Sci 30:369–374
doi: 10.2135/cropsci1990.0011183X003000020025x
Eskridge KM, Mumm RF (1992) Choosing plant cultivars based on the probability of outperforming a check. Theor Appl Genet 84:494–500
pubmed: 24203213
doi: 10.1007/BF00229512
Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Longmans Green, Harlow, Essex, UK
Fernandes SB, Dias KOG, Ferreira DF, Brown PJ (2018) Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum. Theor Appl Genet 131:747–755
pubmed: 29218378
doi: 10.1007/s00122-017-3033-y
Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742–754
doi: 10.1071/AR9630742
Gabriel KR (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika 58:453–467
doi: 10.1093/biomet/58.3.453
Gage JL et al (2017) The effect of artificial selection on phenotypic plasticity in maize. Nat Commun 8:1–11
doi: 10.1038/s41467-017-01450-2
Gauch HG, Zobel RW (1988) Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet 76:1–10
pubmed: 24231975
doi: 10.1007/BF00288824
Gelman A (2006) Prior distributions for variance parameters in hierarchical models. Bayesian Anal 1:515–534
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis. Chapman and Hall/CRC
doi: 10.1201/b16018
Goodfellow I, Bengio Y, Courville A (2016) Machine learning basics (Chapter 5) (2016). In Deep Learning (MIT Press, 95–151).
Hoffman MD, Gelman A (2014) The No-U-Turn sampler: adaptively setting path lengths in hamiltonian monte carlo. J Mach Learn Res 15:1593–1623
Jarquín D et al (2014) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 127:595–607
pubmed: 24337101
doi: 10.1007/s00122-013-2243-1
Josse J, Van Eeuwijk F, Piepho HP, Denis JB (2014) Another look at Bayesian analysis of AMMI models for genotype-environment data. J Agric Biol Environ Stat 19:240–257
Kang MS (1988) A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res Commun 16:113–115
Krause MD et al (2020) Boosting predictive ability of tropical maize hybrids via genotype by environment interaction under multivariate GBLUP models. Crop Sci 60:3049–3065
doi: 10.1002/csc2.20253
Li X, Guo T, Mu Q, Li X, Yu J (2018) Genomic and environmental determinants and their interplay underlying phenotypic plasticity. Proc Natl Acad Sci 115:6679–6684
pubmed: 29891664
pmcid: 6042117
doi: 10.1073/pnas.1718326115
Lin CS, Binns MR (1988) A superiority measure of cultivar performance for cultivar× location data. Can J Plant Sci 68:193–198
doi: 10.4141/cjps88-018
Luce RD (1977) The choice axiom after twenty years. J Math Psychol 15:215–233
doi: 10.1016/0022-2496(77)90032-3
Mackay I, Piepho HP, Garcia AAF (2019) Statistical Methods for Plant Breeding (Chapter 17). In Handbook of Statistical Genomics, 4th Edition. Balding, D. Moltke, I. Marioni, J.
Malosetti M, Ribaut JM, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front Physiol 4:1–16
doi: 10.3389/fphys.2013.00044
Mead R, Riley J, Dear K, Singh SP (1986) Stability comparison of intercropping and monocropping systems. Biometrics 42:253–266
doi: 10.2307/2531048
Millet EJ et al (2019) Genomic prediction of maize yield across European environmental conditions. Nat Genet 51:952–956
pubmed: 31110353
doi: 10.1038/s41588-019-0414-y
Montesinos-López OA, Montesinos-López A, Crossa J, Toledo FH, Pérez-Hernández O, Eskridge KM, Rutkoski J (2016) A genomic Bayesian multi-trait and multi-environment model. G3: Genes. Genomes, Genetics 6:2725–2744
Pastina MM et al (2012) A mixed model QTL analysis for sugarcane multiple-harvest-location trial data. Theor Appl Genet 124:835–849
pubmed: 22159754
doi: 10.1007/s00122-011-1748-8
Piepho HP (1996) A simplified procedure for comparing the stability of cropping systems. Biometrics 52:315–320
doi: 10.2307/2533168
Piepho HP (1997) Analyzing genotype-environment data by mixed models with multiplicative terms. Biometrics 53:761–766
doi: 10.2307/2533976
Piepho HP (2000) Exact confidence limits for covariate-dependent risk in cultivar trials. J Agric Biol Environ Stat 5:202–213
doi: 10.2307/1400531
Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209–228
doi: 10.1007/s10681-007-9449-8
Plackett RL (1975) The analysis of permutations. Appl Statist 24:193–202
doi: 10.2307/2346567
Resende RT, Piepho HP, Silva-Junior OB, Silva FF, Resende MDV, Grattapaglia D (2021) Enviromics in breeding: applications and perspectives on envirotypic-assisted selection. Theor Appl Genet 134:95–112
pubmed: 32964262
doi: 10.1007/s00122-020-03684-z
R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Shukla GK (1972) Some statistical aspects of partitioning genotype environmental components of variability. Heredity 29:237–245
pubmed: 4507945
doi: 10.1038/hdy.1972.87
Smith AB, Cullis BR (2018) Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data. Euphytica 214:1–19
doi: 10.1007/s10681-018-2220-5
Smith AB, Cullis BR, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147
pubmed: 11764254
doi: 10.1111/j.0006-341X.2001.01138.x
Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143:449–462
doi: 10.1017/S0021859605005587
Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2, http://mc-stan.org/ .
Tabery J (2008) RA Fisher, Lancelot Hogben, and the origin (s) of genotype-environment interaction. J Hist Biol 41:717–761
pubmed: 19244846
doi: 10.1007/s10739-008-9155-y
van Eeuwijk F, Denis JB, Kang MS (1996) Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. In Genotype-by-Environment Interaction, eds M. S. Kang and H. G. Gauch (Boca Raton, FL: CRC Press Inc.), 15–50.
van Etten J et al (2019) Crop variety management for climate adaptation supported by citizen science. Proc Natl Acad Sci 116:4194–4199
pubmed: 30782795
pmcid: 6410884
doi: 10.1073/pnas.1813720116
Wallace JG, Rodgers-Melnick E, Buckler ES (2018) On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu Rev Genet 52:42–444
doi: 10.1146/annurev-genet-120116-024846
Wricke G (1965) Zur Berechnung der Ökovalenz bei Sommerweizen und Hafer. Z Pflanzenzüchtung 52:127–138
Yan W (2016) Analysis and handling of GxE in a practical breeding program. Crop Sci 56:2106–2118
doi: 10.2135/cropsci2015.06.0336
Yan W, Hunt L, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40:597–605
doi: 10.2135/cropsci2000.403597x
Yates F, Cochran WG (1938) The analysis of groups of experiments. J Agric Sci 28:556–580
doi: 10.1017/S0021859600050978