Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions

Theoretical and Applied Genetics

Volumn 127, Issue 2, 2014, Pages 463-480

  Heslot, Nicolas ^a,b   Akdemir, Deniz ^a   Sorrells, Mark E ^a   Jannink, Jean Luc ^a  

^a Department of Obstetrics Gynecology   (United States)

^b CS3911 ^*  (France)

Author keywords

[No Author keywords available]

Indexed keywords

CROPS; FORECASTING; GENES; KNOWLEDGE MANAGEMENT; METEOROLOGY; PLANTS (BOTANY); REGRESSION ANALYSIS; SOFTWARE TESTING;

GENETIC ARCHITECTURE; GENOTYPE-BY-ENVIRONMENT INTERACTION; NON-LINEAR RESPONSE; PERFORMANCE BASED; PREDICTION ACCURACY; QUANTITATIVE TRAIT LOCUS; SELECTION FRAMEWORK; SELECTION METHODS;

LARGE DATASET;

TRITICUM AESTIVUM;

ARTICLE; BIOLOGICAL MODEL; CROP; GENETICS; GENOTYPE; GENOTYPE ENVIRONMENT INTERACTION;

CROPS, AGRICULTURAL; GENE-ENVIRONMENT INTERACTION; GENOTYPE; MODELS, GENETIC;

EID: 84906911933     PISSN: 00405752     EISSN: 14322242     Source Type: Journal    
DOI: 10.1007/s00122-013-2231-5     Document Type: Article

References (80)

1. Akdemir D, Heslot N (2012) Soft rule ensembles for statistical learning. Arxiv Prepr Arxiv 1205:4476
2. Boer MP, Wright D, Feng L et al (2007) A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize. Genetics. doi:10.1534/genetics.107.071068
3. Brancourt-Hulmel M, Lecomte C, Meynard JM (1999) A diagnosis of yield-limiting factors on probe genotypes for characterizing environments in winter wheat trials. Crop Sci. doi:10.2135/cropsci1999.3961798x
4. Brancourt-Hulmel M, Denis JB, Lecomte C (2000) Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression. Theor Appl Genet. doi:10.1007/s001220050038
5. Breiman L (2001) Random forests. Mach Learn. doi:10.1023/A:1010933404324
6. Breiman L, Friedman J (1985) Estimating optimal transformations for multiple regression and correlation. J Am Stat Assoc 80:580-598
7. Bureau A, Dupuis J, Falls K et al (2005) Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol. doi:10.1002/gepi.20041
8. Burgueño J, Crossa J, Cornelius PL, Yang RC (2008) Using factor analytic models for joining environments and genotypes without crossover genotype × environment interaction. Crop Sci. doi:10.2135/cropsci2007.11.0632
9. Burgueño J, Crossa J, Cotes JM et al (2011) Prediction assessment of linear mixed models for multienvironment trials. Crop Sci. doi:10.2135/cropsci2010.07.0403
10. Burgueño J, De los Campos G, Weigel K, Crossa J (2012) Genomic Prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci. doi:10.2135/cropsci2011.06.0299
11. Chapman SC, Cooper M, Butler D, Henzell R (2000a) Genotype by environment interactions affecting grain sorghum I. Characteristics that confound interpretation of hybrid yield. Aust J Agric Res. doi:10.1071/AR99020
12. Chapman SC, Cooper M, Hammer G, Butler D (2000b) Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields. Aust J Agric Res 51:209-221
13. Chapman SC, Hammer G, Butler D, Cooper M (2000c) Genotype by environment interactions affecting grain sorghum III. Temporal sequences and spatial patterns in the target population of environments. Aust J Agric Res. doi:10.1071/AR99022
14. Chenu K, Chapman SC, Hammer G et al (2008) Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize. Plant Cell Environ. doi:10.1111/j.1365-3040.2007.01772.x
15. Chenu K, Deihimfard R, Chapman SC (2013) Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt--spatial and temporal trends. New Phytol. doi:10.1111/nph.12192
16. Chiquet J, Grandvalet Y, Charbonnier C (2012) Sparsity with signcoherent groups of variables via the cooperative-lasso. Ann Appl Stat. doi:10.1214/11-AOAS520
17. Comstock RE (1977) Quantitative genetics and the design of breeding programs. In: Pollak E, Kempthorne O, Bailey TB (eds) Proceedings of the international conference on quantitative genetics. Iowa State University Press, Ames, pp 705-718
18. Cooper M, DeLacy IH (1994) Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theor Appl Genet. doi:10.1007/BF01240919
19. Crossa J, Vargas M, Van Eeuwijk FA et al (1999) Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables. Theor Appl Genet. doi:10.1007/s001220051276
20. Cullis BR, Smith AB, Beeck CP, Cowling WA (2010) Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis. Genome. doi:10.1139/G10-080
21. DeLacy IH, Basford KE, Cooper M et al (1996) Analysis of multienvironment trials--an historical perspective. In: Cooper M, Hammer G (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 39-124
22. Demotes-Mainard S, Doussinault G, Meynard JM (1996) Abnormalities in the male developmental programme of winter wheat induced by climatic stress at meiosis. Agronomie. doi:10.1051/agro:19960804
23. Denis JB (1988) Two-way analysis using covariates. Statistics 19:123-132
24. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Pearson Prentice Hall, Harlow
25. Fischer RA (1985) Number of kernels in wheat crops and the influence of solar radiation and temperature. J Agri Sci. doi:10.1017/S0021859600056495
26. Friedman J, Popescu BE (2003) Importance sampled learning ensembles. J Mach Learn Res 94305:1-32
27. Friedman JH, Popescu BE (2008) Predictive learning via rule ensembles. Ann Appl Stat 2:916-954
28. Friedman JH, Hastie T, Tibshirani R (2010a) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1
29. Friedman JH, Hastie T, Tibshirani R (2010b) A note on the group lasso and a sparse group lasso. Arxiv Prepr Arxiv:10010736
30. Gallagher JN, Biscoe PV (1978) Radiation absorption, growth and yield of cereals. J Agri Sci. doi:10.1017/S0021859600056616
31. Gate P (1995) Ecophysiologie du blé. De la plante à la culture. Tec & Doc, Paris, p 430
32. Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci. doi:10.2135/cropsci2005.07-0193
33. Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedure. Genetics. doi:10.1534/genetics.105.049510
34. Gilmour AR, Gogel B, Cullis BR, et al (2009) ASREML user guide release 3.0. VSN International Ltd.
35. Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics. doi:10.1534/genetics.107.081190
36. Hammer G, Kropff MJ, Sinclair TR, Porter JR (2002) Future contributions of crop modelling—from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. Eur J Agron. doi:10.1016/S1161-0301(02)00093-X
37. He J, Le Gouis J, Stratonovitch P et al (2012) Simulation of environmental and genotypic variations of final leaf number and anthesis date for wheat. Eur J Agron. doi:10.1016/j.eja.2011.11.002
38. Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci. doi:10.2135/cropsci2009.11.0662
39. Heslot N, Jannink J-L, Sorrells ME (2013) Using genomic prediction to characterize environments and optimize prediction accuracy in applied breeding data. Crop Sci. doi:10.2135/cropsci2012.07.0420
40. Hunt LA (1991) Post anthesis temperature effects on duration and rate of grain filling in some winter and spring wheats. Can J Plant 617:609-617
41. Jamieson PD, Semenov MA, Brooking IR, Francis GS (1998) Sirius: a mechanistic model of wheat response to environmental variation. Eur J Agron. doi:10.1016/S1161-0301(98)00020-3
42. Jullien A, Mathieu A, Allirand JM et al (2011) Characterization of the interactions between architecture and source-sink relationships in winter oilseed rape (Brassica napus) using the GreenLab model. Ann Bot-Lond. doi:10.1093/aob/mcq205
43. Kelly AM, Cullis BR, Gilmour AR et al (2009) Estimation in a multiplicative mixed model involving a genetic relationship matrix. Genet Sel Evol. doi:10.1186/1297-9686-41-33
44. Landau S, Mitchell RA, Barnett V et al (1998) Testing winter wheat simulation models’ predictions against observed UK grain yields. Agric Forest Meteorol. doi:10.1016/S0168-1923(97)00069-5
45. Landau S, Mitchell RA, Barnett V et al (2000) A parsimonious, multiple-regression model of wheat yield response to environment. Agric Forest Meteorol. doi:10.1016/S0168-1923(99)00166-5
46. Lecomte C (2005) Experimental evaluation of varietal innovations. Proposition of genotype--environment analysis tools adapted to the diversity of needs and constraints of the professionals of the seeds industry. Diss AgroParisTech p 262
47. Levins R (1966) The strategy of model building in population biology. Am Sci 54:421-431
48. Löffler CM, Wei J, Fast T et al (2005) Classification of maize environments using crop simulation and geographic information systems. Crop Sci. doi:10.2135/cropsci2004.0370
49. Lorenz AJ, Chao S, Asoro FG et al (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron. doi:10.1016/B978-0-12-385531-2.00002-5
50. Ma CX, Casella G, Wu R (2002) Functional mapping of quantitative trait loci underlying the character process: a theoretical framework. Genetics 161:1751-1762
51. Malosetti M, Voltas J, Romagosa I et al (2004) Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica. doi:10.1023/B:EUPH.0000040511.46388.ef
52. Martre P, Jamieson PD, Semenov MA et al (2006) Modelling protein content and composition in relation to crop nitrogen dynamics for wheat. Eur J Agron. doi:10.1016/j.eja.2006.04.007
53. Messina C, Hammer G, Dong Z et al (2009) Modelling crop improvement in a GXEXM framework via gene-trail-phenotype relationships. In: Sadras VO, Calderini D (eds) Crop physiology: applications for genetic improvement and agronomy. Elsevier, Netherlands, pp 235-265
54. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829
55. Meynard JM, Sebillotte M (1994) L’élaboration du rendement du blé, base pour l’étude des autres céréales à paille. In: Picard D, Combe L (eds) Elaboration du rendement des principales cultures annuelles. INRA, Paris, pp 31-51
56. Monteith J (1972) Solar radiation and productivity in tropical ecosystems. J Appl Ecol 9:747-766
57. Ogutu JO, Piepho HP, Schulz-Streeck T (2011) A comparison of random forests, boosting and support vector machines for genomic selection. BMC Proc. doi:10.1186/1753-6561-5-S3-S11
58. Park T, Casella G (2008) The bayesian lasso. Am Stat Assoc. doi:10.1198/016214508000000337
59. 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 Gen. doi:10.3835/plantgenome2010.04.0005
60. Piepho HP (1998) Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures. Theor Appl Genet. doi:10.1007/s001220050885
61. Piepho HP, Möhring J (2006) Selection in cultivar trials—is it ignorable? Crop Sci. doi:10.2135/cropsci2005.04-0038
62. Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics. doi:10.1534/genetics.107.074229
63. Piepho HP, Denis JB, Van Eeuwijk FA (1998) Predicting cultivar differences using covariates. J Agric Biol Environ Stat. doi:10.2307/1400648
64. Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica. doi:10.1007/s10681-007-9449-8
65. Piepho HP, Ogutu JO, Schulz-Streeck T et al (2012) Efficient computation of ridge-regression best linear unbiased prediction in genomic selection in plant breeding. Crop Sci. doi:10.2135/cropsci2011.11.0592
66. Podlich DW, Cooper M, Basford KE (1999) Computer simulation of a selection strategy to accommodate genotype-environment interactions in a wheat recurrent selection programme. Plant Breed. doi:10.1046/j.1439-0523.1999.118001017.x
67. Quilot B, Génard M, Kervella J, Lescourret F (2004) Analysis of genotypic variation in fruit flesh total sugar content via an ecophysiological model applied to peach. Theor Appl Genet. doi:10.1007/s00122-004-1651-7
68. Reymond M, Muller B, Leonardi A et al (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol. doi:10.1104/pp.013839.soil
69. Reymond M, Muller B, Tardieu F (2004) Dealing with the genotype x environment interaction via a modelling approach: a comparison of QTLs of maize leaf length or width with QTLs of model parameters. J Exp Bot. doi:10.1093/jxb/erh200
70. 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. doi:10.1017/S0021859605005587
71. Sofield I, Evans L, Cook M, Wardlaw I (1977) Factors influencing the rate and duration of grain filling in wheat. Aust J Plant Physiol. doi:10.1071/PP9770785
72. Stone P, Nicolas M (1998) The effect of duration of heat stress during grain filling on two wheat varieties differing in heat tolerance: grain growth and fractional protein accumulation. Aust J Plant Physiol. doi:10.1071/PP96114
73. Tashiro T, Wardlaw I (1990) The response to high temperature shock and humidity changes prior to and during the early stages of grain development in wheat. Aust J Plant Physiol. doi:10.1071/PP9900551
74. Van der Goot E, Orlandi S (2003) Technical description of interpolation and processing of meteorological data in CGMS. Joint Research Centre of the European Commission, Ispra, Italy, p 23
75. Van Eeuwijk FA, Denis J-B, Kang MS (1996) Incorporating additional information on genotypes and environments in models for two-way genotype by environments tables. In: Kang MS, Gauch HG (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 15-50
76. Van Eeuwijk FA, Malosetti M, Yin X et al (2005) Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models. Aust J Agric Res. doi:10.1071/AR05153
77. White JW, Herndl M, Hunt LA et al (2008) Simulation-based analysis of effects of loci on flowering in wheat. Crop Sci. doi:10.2135/cropsci2007.06.0318
78. Windhausen VS, Wagener S, Magorokosho C et al (2012) Strategies to subdivide a target population of environments: results from the CIMMYT-led maize hybrid testing programs in Africa. Crop Sci. doi:10.2135/cropsci2012.02.0125
79. Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res. doi:10.1111/j.1365-3180.1974.tb01084.x
80. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. doi:10.1111/j.1467-9868.2005.00503.x