Abstract
Selection processes in plant breeding depend critically on the quality of phenotype predictions. The phenotype is classically predicted as a function of genotypic and environmental information. Models for phenotype prediction contain a mixture of statistical, genetic and physiological elements. In this chapter, we discuss prediction from linear mixed models (LMMs), with an emphasis on statistics, and prediction from crop growth models (CGMs), with an emphasis on physiology. Three modalities of prediction are distinguished: predictions for new genotypes under known environmental conditions, predictions for known genotypes under new environmental conditions, and predictions for new genotypes under new environmental conditions.
For LMMs, the genotypic input information includes molecular marker variation, while the environmental input can consist of meteorological, soil and management variables. However, integrated types of environmental characterizations obtained from CGMs can also serve as environmental covariable in LMMs. LMMs consist of a fixed part, corresponding to the mean for a particular genotype in a particular environment, and a random part defined by genotypic and environmental variances and correlations. For prediction via the fixed part, genotypic and/or environmental covariables are required as in classical regression. For predictions via the random part, correlations need to be estimated between observed and new genotypes, between observed and new environments, or both. These correlations can be based on similarities calculated from genotypic and environmental covariables. A simple type of covariable assigns genotypes to sub-populations and environments to regions. Such groupings can improve phenotype prediction.
For a second type of phenotype prediction, we consider CGMs. CGMs predict a target phenotype as a non-linear function of underlying intermediate phenotypes. The intermediate phenotypes are outcomes of functions defined on genotype dependent CGM parameters and classical environmental descriptors. While the intermediate phenotypes may still show some genotype by environment interaction, the genotype dependent CGM parameters should be consistent across environmental conditions. The CGM parameters are regressed on molecular marker information to allow phenotype prediction from molecular marker information and standard physiologically relevant environmental information.
Both LMMs and CGMs require extensive characterization of genotypes and environments. High-throughput technologies for genotyping and phenotyping provide new opportunities for upscaling phenotype prediction and increasing the response to selection in the breeding process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alam MM, Mace ES, van Oosterom EJ, Cruickshank A, Hunt CH, Hammer GL, Jordan DR (2014) QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering. TAG Theor App Genet (Theor angew Genet) 127(10):2253–2266. doi:10.1007/s00122-014-2377-9
Albrecht T, Auinger H-J, Wimmer V, Ogutu J, Knaak C, Ouzunova M, Piepho H-P, Schön C-C (2014) Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years. TAG Theor App Genet (Theor angew Genet) 127:1375–1386. doi:10.1007/s00122-014-2305-z
Alimi NA, Bink MCAM, Dieleman JA, Magán JJ, Wubs AM, Palloix A, van Eeuwijk FA (2013) Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper. Theor Appl Genet 126(10):2597–2625. doi:10.1007/s00122-013-2160-3
Araus JL, Slafer GA, Royo C, Serret MD (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27(6):377–412. doi:10.1080/07352680802467736
Atlin GN, Baker RJ, McRae KB, Lu X (2000) Selection response in subdivided target regions. Crop Sci 40(1):7–13. doi:10.2135/cropsci2000.4017
Atlin GN, Kleinknecht K, Singh GP, Piepho HP (2011) Managing genotype x environment interaction in plant breeding programs: a selection theory approach. J Indian Soc Agric Stat 65(2):237–247
Baker RJ (1988) Tests for crossover genotype-environmental interactions. Can J Plant Sci 68(2):405–410. doi:10.4141/cjps88-051
Bänziger M (2000) Breeding for drought and nitrogen stress tolerance in maize: from theory to practice. Cimmyt, Mexico
Basford KE, Cooper M (1998) Genotype x environment interactions and some considerations of their implications for wheat breeding in Australia. Aust J Agric Res 49(2):153–174. doi:10.1071/A97035
Bernardo R (2014) Genomewide selection when major genes are known. Crop Sci 54(1):68–75. doi:10.2135/cropsci2013.05.0315
Bertin N, Martre P, Génard M, Quilot B, Salon C (2010) Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case-study of fruit and grain quality traits. J Exp Bot 61(4):955–967. doi:10.1093/jxb/erp377
Boer MP, Wright D, Feng L, Podlich DW, Luo L, Cooper M, van Eeuwijk FA (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 177(3):1801–1813. doi:10.1534/genetics.107.071068
Bogard M, Ravel C, Paux E, Bordes J, Balfourier F, Chapman SC, Le Gouis J, Allard V (2014) Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. J Exp Bot 65(20):5849–5865. doi:10.1093/jxb/eru328
Bradshaw AD, Caspari EW, Thoday JM (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13:115–155
Bull JK, Cooper M, DeLacy IH, Basford KE, Woodruff DR (1992) Utility of repeated checks for hierarchical classification of data from plant breeding trials. Field Crops Res 30((1–2)):79–95. doi:10.1016/0378-4290(92)90058-H
Burgueño J, Crossa J, Cornelius PL, Yang R-C (2008) Using factor analytic models for joining environments and genotypes without crossover genotype × environment interaction. Crop Sci 48(4):1291–1305. doi:10.2135/cropsci2007.11.0632
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 52(2):707–719. doi:10.2135/cropsci2011.06.0299
Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret MD, Araus JL (2012) High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. J Integr Plant Biol 54(5):312–320. doi:10.1111/j.1744-7909.2012.01116.x
Calus M, Veerkamp R (2011) Accuracy of multi-trait genomic selection using different methods. Genet Sel Evol 43(1):26
Campbell BT, Baenziger PS, Eskridge KM, Budak H, Streck NA, Weiss A, Gill KS, Erayman M (2004) Using environmental covariates to explain genotype × environment and QTL × environment interactions for agronomic traits on chromosome 3A of wheat. Crop Sci 44(2):620–627. doi:10.2135/cropsci2004.6200
Chapman S (2008) Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. Euphytica 161(1–2):195–208. doi:10.1007/s10681-007-9623-z
Chapman SC, Cooper M, Hammer GL, Butler DG (2000a) 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(2):209–222. doi:10.1071/AR99021
Chapman SC, Hammer GL, Butler DG, Cooper M (2000b) Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments. Aust J Agric Res 51(2):223–234. doi:10.1071/AR99022
Chapman SC, Cooper M, Hammer GL (2002a) Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments. Aust J Agric Res 53(4):379–389. doi:10.1071/AR01070
Chapman SC, Hammer GL, Podlich DW, Cooper M (2002b) Linking bio-physical and genetic models to integrate physiology, molecular biology and plant breeding. In: Kang M (ed) Quantitative genetics, genomics, and plant breeding. CAB International, Wallingford, pp 167–187
Chapman S, Cooper M, Podlich D, Hammer G (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agron J 95(1):99–113. doi:10.2134/agronj2003.9900
Chenu K, Chapman SC, Hammer GL, McLean G, Salah HBH, Tardieu F (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 31(3):378–391. doi:10.1111/j.1365-3040.2007.01772.x
Chenu K, Chapman SC, Tardieu F, McLean G, Welcker C, Hammer GL (2009) Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a “gene-to-phenotype” modeling approach. Genetics 183(4):1507–1523. doi:10.1534/genetics.109.105429
Chenu K, Cooper M, Hammer GL, Mathews KL, Dreccer MF, Chapman SC (2011) Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia. J Exp Bot 62(6):1743–1755. doi:10.1093/jxb/erq459
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 198(3):801–820. doi:10.1111/nph.12192
Cobb JN, Declerck G, Greenberg A, Clark R, McCouch S (2013) Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126(4):867–887. doi:10.1007/s00122-013-2066-0
Cooper M (1999) Concepts and strategies for plant adaptation research in rainfed lowland rice. Field Crops Res 64(1–2):13–34. doi:10.1016/S0378-4290(99)00048-9
Cooper M, Hammer GL (1996) Plant adaptation and crop improvement. CAB International, Wallingford
Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G (2014) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci 65(4):311–336
Crossa J, Yang R-C, Cornelius P (2004) Studying crossover genotype × environment interaction using linear-bilinear models and mixed models. JABES 9(3):362–380. doi:10.1198/108571104x4423
Crossa J, Burgueño J, Cornelius PL, McLaren G, Trethowan R, Krishnamachari A (2006) Modeling genotype × environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop Sci 46(4):1722–1733. doi:10.2135/cropsci2005.11-0427
Crossa J, de los Campos G, Perez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan JB, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186(2):713-U406. doi:10.1534/genetics.110.118521
Crossa J, Beyene Y, Kassa S, Pérez P, Hickey JM, Chen C, de los Campos G, Burgueño J, Windhausen VS, Buckler E, Jannink J-L, Lopez Cruz MA, Babu R (2013) Genomic prediction in maize breeding populations with genotyping-by-sequencing. G3 Genes Genomes Genet 3(11):1903–1926. doi:10.1534/g3.113.008227
Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, Ceron-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K (2014) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112(1):48–60. doi:10.1038/hdy.2013.16
Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11(4):381–393. doi:10.1198/108571106x154443
Daetwyler HD, Kemper KE, van der Werf JHJ, Hayes BJ (2012) Components of the accuracy of genomic prediction in a multi-breed sheep population. J Anim Sci 90(10):3375–3384. doi:10.2527/jas.2011-4557
De la Vega AJ, Chapman SC (2010) Mega-environment differences affecting genetic progress for yield and relative value of component traits. Crop Sci 50(2):574–583. doi:10.2135/cropsci2009.04.0209
DeWitt TJ, Scheiner SM (2004) Phenotypic plasticity: functional and conceptual approaches. Oxford University Press, Oxford
Dobzhansky T, Spassky B (1963) Genetics of natural populations. XXXIV. Adaptive norm, genetic load and genetic elite in drosophila pseudoobscura. Genetics 48(11):1467–1485
Eberius M, Lima-Guerra J (2009) High-throughput plant phenotyping – data acquisition, transformation, and analysis. In: Edwards D, Stajich J, Hansen D (eds) Bioinformatics. pp 259–278. doi:10.1007/978-0-387-92738-1_13
Edmeades GO, McMaster GS, White JW, Campos H (2004) Genomics and the physiologist: bridging the gap between genes and crop response. Funct Plant Biol 90(1):5–18. doi:10.1016/j.fcr.2004.07.002
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6(5), e19379. doi:10.1371/journal.pone.0019379
Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. 4th edn. Harlow, GB: Longman
Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14(6):742–754. doi:10.1071/AR9630742
Gauch HG, Zobel RW (1997) Identifying mega-environments and targeting genotypes. Crop Sci 37(2):311–326. doi:10.2135/cropsci1997.0011183X003700020002x
Gu J, Yin X, Zhang C, Wang H, Struik PC (2014) Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress. Ann Bot 114(3):499–511. doi:10.1093/aob/mcu127
Guo Z, Tucker DM, Wang D, Basten CJ, Ersoz E, Briggs WH, Lu J, Li M, Gay G (2013) Accuracy of across-environment genome-wide prediction in maize nested association mapping populations. G3 Genes Genomes Genet 3(2):263–272. doi:10.1534/g3.112.005066
Guo Z, Tucker D, Basten C, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127(3):749–762. doi:10.1007/s00122-013-2255-x
Hammer GL, 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 18(1–2):15–31. doi:10.1016/S1161-0301(02)00093-X
Hammer G, Cooper M, Tardieu F, Welch S, Walsh B, van Eeuwijk F, Chapman S, Podlich D (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11(12):587–593. doi:10.1016/j.tplants.2006.10.006
Hammer GL, van Oosterom E, McLean G, Chapman SC, Broad I, Harland P, Muchow RC (2010) Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J Exp Bot 61(8):2185–2202. doi:10.1093/jxb/erq095
Harrison MT, Tardieu F, Dong Z, Messina CD, Hammer GL (2014) Characterizing drought stress and trait influence on maize yield under current and future conditions. Glob Chang Biol 20(3):867–878. doi:10.1111/gcb.12381
Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F (2011) HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinf 12(1):148
Heffner EL, Jannink J-L, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4(1):65–75. doi:10.3835/plantgenome2010.12.0029
Heslot N, Yang H-P, Sorrells ME, Jannink J-L (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52(1):146–160. doi:10.2135/cropsci2011.06.0297
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(3):921–933. doi:10.2135/cropsci2012.07.0420
Holzworth DP, Huth NI, deVoil PG, Zurcher EJ, Herrmann NI, McLean G, Chenu K, van Oosterom EJ, Snow V, Murphy C, Moore AD, Brown H, Whish JPM, Verrall S, Fainges J, Bell LW, Peake AS, Poulton PL, Hochman Z, Thorburn PJ, Gaydon DS, Dalgliesh NP, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li FY, Wang E, Hammer GL, Robertson MJ, Dimes JP, Whitbread AM, Hunt J, van Rees H, McClelland T, Carberry PS, Hargreaves JNG, MacLeod N, McDonald C, Harsdorf J, Wedgwood S, Keating BA (2014) APSIM – evolution towards a new generation of agricultural systems simulation. Environ Model Software. doi:10.1016/j.envsoft.2014.07.009
Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9(2):166–177. doi:10.1093/bfgp/elq001
Janss L, De los Campos G, Sheehan N, Sorensen D (2012) Inferences from genomic models in stratified populations. Genetics 192(2):693–704. doi:10.1534/genetics.112.141143
Jarquín D, Crossa J, Lacaze X, Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M, Burgueño J, Campos G (2013) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 3:1–13. doi:10.1007/s00122-013-2243-1
Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18(3–4):267–288. doi:10.1016/S1161-0301(02)00108-9
Kleinknecht K, Möhring J, Singh KP, Zaidi PH, Atlin GN, Piepho HP (2013) Comparison of the performance of best linear unbiased estimation and best linear unbiased prediction of genotype effects from zoned Indian maize data. Crop Sci 53(4):1384–1391. doi:10.2135/cropsci2013.02.0073
Laperche A, Brancourt-Hulmel M, Heumez E, Gardet O, Hanocq E, Devienne-Barret F, Le Gouis J (2007) Using genotype × nitrogen interaction variables to evaluate the QTL involved in wheat tolerance to nitrogen constraints. Theor Appl Genet 115(3):399–415. doi:10.1007/s00122-007-0575-4
Lin CS, Binns MR (1988) A method of analyzing cultivar x location x year experiments: a new stability parameter. Theor Appl Genet 76(3):425–430. doi:10.1007/bf00265344
Makumburage GB, Richbourg HL, LaTorre KD, Capps A, Chen C, Stapleton AE (2013) Genotype to phenotype maps: multiple input abiotic signals combine to produce growth effects via attenuating signaling interactions in maize. G3 Genes Genomes Genet 3(12):2195–2204. doi:10.1534/g3.113.008573
Malosetti M, Voltas J, Romagosa I, Ullrich SE, van Eeuwijk FA (2004) Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica 137(1):139–145. doi:10.1023/B:EUPH.0000040511.46388.ef
Malosetti M, Ribaut J-M, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front Physiol 4:1–17. doi:10.3389/fphys.2013.00044
Mathews K, Malosetti M, Chapman S, McIntyre L, Reynolds M, Shorter R, van Eeuwijk F (2008) Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117(7):1077–1091. doi:10.1007/s00122-008-0846-8
Messina CD, Podlich D, Dong Z, Samples M, Cooper M (2011) Yield–trait performance landscapes: from theory to application in breeding maize for drought tolerance. J Exp Bot 62(3):855–868. doi:10.1093/jxb/erq329
Metzker ML (2010) Sequencing technologies – the next generation. Nat Rev Genet 11(1):31–46
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829
Muir W, Nyquist WE, Xu S (1992) Alternative partitioning of the genotype-by-environment interaction. Theor Appl Genet 84(1–2):193–200. doi:10.1007/bf00224000
Passioura JB (2012) Phenotyping for drought tolerance in grain crops: when is it useful to breeders? Funct Plant Biol 39(11):851–859. doi:10.1071/FP12079
Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2(12), e190. doi:10.1371/journal.pgen.0020190
Piepho HP (1998) Methods for comparing the yield stability of cropping systems. J Agron Crop Sci 180(4):193–213. doi:10.1111/j.1439-037X.1998.tb00526.x
Piepho H-P (2000) A mixed-model approach to mapping quantitative trait loci in barley on the basis of multiple environment data. Genetics 156(4):2043–2050
Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Sci 49(4):1165–1176
Piepho HP, Möhring J (2005) Best linear unbiased prediction of cultivar effects for subdivided target regions. Crop Sci 45(3):1151–1159. doi:10.2135/cropsci2004.0398
Piepho H, Möhring J, Melchinger A, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1):209–228. doi:10.1007/s10681-007-9449-8
Podlich DW, Cooper M, Basford KE, Geiger HH (1999) Computer simulation of a selection strategy to accommodate genotype environment interactions in a wheat recurrent selection programme. Plant Breed 118(1):17–28. doi:10.1046/j.1439-0523.1999.118001017.x
Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5(3):92–102. doi:10.3835/plantgenome2012.05.0005
Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink J-L (2012a) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5(3):103–113. doi:10.3835/plantgenome2012.06.0006
Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012b) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One 7(2), e32253. doi:10.1371/journal.pone.0032253
Prasanna B, Araus J, Crossa J, Cairns J, Palacios N, Das B, Magorokosho C (2013) High-throughput and precision phenotyping for cereal breeding programs. In: Gupta PK, Varshney RK (eds) Cereal genomics II. pp 341–374. doi:10.1007/978-94-007-6401-9_13
Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (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 131(2):664–675. doi:10.1104/pp. 013839
Reymond M, Muller B, Tardieu F (2004) Dealing with the genotype×environment interaction via a modelling approach: a comparison of QTLs of maize leaf length or width with QTLs of model parameters. J Exp Bot 55(407):2461–2472. doi:10.1093/jxb/erh200
Reynolds M, Foulkes MJ, Slafer GA, Berry P, Parry MAJ, Snape JW, Angus WJ (2009a) Raising yield potential in wheat. J Exp Bot 60(7):1899–1918. doi:10.1093/jxb/erp016
Reynolds M, Manes Y, Izanloo A, Langridge P (2009b) Phenotyping approaches for physiological breeding and gene discovery in wheat. Ann App Biol 155(3):309–320. doi:10.1111/j.1744-7348.2009.00351.x
Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink J-L, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194(2):493–503. doi:10.1534/genetics.113.150227
Romagosa I, Fox P (1993) Genotype × environment interaction and adaptation. In: Hayward MD, Bosemark NO, Romagosa I, Cerezo M (eds) Plant breeding: principles and prospects. Springer, Dordrecht, pp 373–390
Romagosa I, Borràs-Gelonch G, Slafer G, Eeuwijk F (2013) Genotype by environment interaction and adaptation. In: Savin R, Costa-Pierce B, Misztal I, Whitelaw CB, Christou P (eds) Sustainable food production. Springer, New York, pp 846–870. doi:10.1007/978-1-4614-5797-8_199
Sadras VO, Lawson C (2011) Genetic gain in yield and associated changes in phenotype, trait plasticity and competitive ability of South Australian wheat varieties released between 1958 and 2007. Crop Pasture Sci 62(7):533–549. doi:10.1071/CP11060
Sadras VO, Rebetzke GJ, Edmeades GO (2013) The phenotype and the components of phenotypic variance of crop traits. Field Crop Res 154:255–259. doi:10.1016/j.fcr.2013.10.001
Sarkar S (1999) From the reaktionsnorm to the adaptive norm: the norm of reaction, 1909–1960. Biol Philos 14(2):235–252. doi:10.1023/a:1006690502648
Schulz-Streeck T, Ogutu JO, Karaman Z, Knaak C, Piepho HP (2012) Genomic selection using multiple populations. Crop Sci 52(6):2453–2461. doi:10.2135/cropsci2012.03.0160
Slafer GA (2003) Genetic basis of yield as viewed from a crop physiologist's perspective. Ann Appl Biol 142(2):117–128. doi:10.1111/j.1744-7348.2003.tb00237.x
Slafer G, Kernich G (1996) Have changes in yield (1900–1992) been accompanied by a decreased yield stability in Australian cereal production? Aust J Agric Res 47(3):323–334. doi:10.1071/AR9960323
Slafer G, Rawson H (1994) Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Funct Plant Biol 21(4):393–426. doi:10.1071/PP9940393
Smith A, Cullis B, Thompson R (2001a) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57(4):1138–1147. doi:10.1111/j.0006-341X.2001.01138.x
Smith AB, Cullis BR, Appels R, Campbell AW, Cornish GB, Martin D, Allen HM (2001b) The statistical analysis of quality traits in plant improvement programs with application to the mapping of milling yield in wheat. Aust J Agric Res 52(12):1207–1219. doi:10.1071/AR01058
Smith AB, Cullis BR, Thomson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143(06):449–462. doi:10.1017/S0021859605005587
Smith AB, Lim P, Cullis BR (2006) The design and analysis of multi-phase plant breeding experiments. J Agric Sci 144(05):393–409. doi:10.1017/S0021859606006319
Snape JW, Butterworth K, Whitechurch E, Worland AJ (2001) Waiting for fine times: genetics of flowering time in wheat. Euphytica 119(1–2):185–190. doi:10.1023/a:1017594422176
Spindel J, Wright M, Chen C, Cobb J, Gage J, Harrington S, Lorieux M, Ahmadi N, McCouch S (2013) Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations. Theor Appl Genet 126(11):2699–2716. doi:10.1007/s00122-013-2166-x
Stephens M (2013) A unified framework for association analysis with multiple related phenotypes. PLoS One 8(7), e65245. doi:10.1371/journal.pone.0065245
Tardieu F (2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends Plant Sci 8(1):9–14. doi:10.1016/S1360-1385(02)00008-0
Tardieu F, Reymond M, Muller B, Granier C, Simonneau T, Sadok W, Welcker C (2005) Linking physiological and genetic analyses of the control of leaf growth under changing environmental conditions. Aust J Agric Res 56(9):937–946. doi:10.1071/AR05156
Uitdewilligen JGAML, Wolters A-MA, D’hoop BB, Borm TJA, Visser RGF, van Eck HJ (2013) A next-generation sequencing method for genotyping-by-sequencing of highly heterozygous autotetraploid potato. PLoS One 8(5), e62355. doi:10.1371/journal.pone.0062355
Utz HF, Melchinger AE, Schön CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154(4):1839–1849
van der Heijden G, Song Y, Horgan G, Polder G, Dieleman A, Bink M, Palloix A, van Eeuwijk F, Glasbey C (2012) SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Funct Plant Biol 39(11):870–877. doi:10.1071/FP12019
van Eeuwijk F, Denis J, Kang M (1996) Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. In: Kang MS, Gauch HG Jr (eds) Genotype-by-environment interaction. Taylor & Francis Group, Boca Raton, Florida, pp 15–50
van Eeuwijk FA, Malosetti M, Yin X, Struik PC, Stam P (2005) Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models. Aust J Agric Res 56(9):883–894. doi:10.1071/AR05153
van Eeuwijk FA, Bink MCAM, Chenu K, Chapman SC (2010) Detection and use of QTL for complex traits in multiple environments. Curr Opin Plant Biol 13(2):193–205. doi:10.1016/j.pbi.2010.01.001
Varshney RK, Terauchi R, McCouch SR (2014) Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biol 12(6), e1001883. doi:10.1371/journal.pbio.1001883
Verbyla AP, Cullis BR (2012) Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments. Theor Appl Genet 125(5):933–953
Wang E, Robertson MJ, Hammer GL, Carberry PS, Holzworth D, Meinke H, Chapman SC, Hargreaves JNG, Huth NI, McLean G (2002) Development of a generic crop model template in the cropping system model APSIM. Eur J Agron 18(1–2):121–140. doi:10.1016/S1161-0301(02)00100-4
West BT, Welch KB, Galecki AT (2006) Linear mixed models: a practical guide using statistical software. Boca Raton, Florida
Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink J-L, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 Genes Genomes Genet 2(11):1427–1436. doi:10.1534/g3.112.003699
Woltereck R (1909) Weitere experimentelle Untersuchungen über Artveränderung, speziel über das Wesen quantitativer Artunterschiede bei Daphnien. Verhandlungen der deutschen zoologischen Gesellschaft 19:110–173
Wright S (1931) Evolution in mendelian populations. Genetics 16(2):97
Wright S (1932) The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Proceedings of the 6th international congress of genetics, Brooklin, NY, pp 356–366
Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40(3):597–605. doi:10.2135/cropsci2000.403597x
Yin X, Chasalow SD, Dourleijn CJ, Stam P, Kropff MJ (2000) Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley. Heredity 85(6):539–549. doi:10.1046/j.1365-2540.2000.00790.x
Yin X, Stam P, Kropff MJ, Schapendonk AHCM (2003) Crop modeling QTL mapping, and their complementary role in plant breeding. Agron J 95(1):90–98. doi:10.2134/agronj2003.9000
Yin X, Struik PC, Kropff MJ (2004) Role of crop physiology in predicting gene-to-phenotype relationships. Trends Plant Sci 9(9):426–432. doi:10.1016/j.tplants.2004.07.007
Zhao Y, Gowda M, Longin F, Würschum T, Ranc N, Reif J (2012) Impact of selective genotyping in the training population on accuracy and bias of genomic selection. Theor Appl Genet 125(4):707–713. doi:10.1007/s00122-012-1862-2
Zheng B, Chenu K, Fernanda Dreccer M, Chapman SC (2012) Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties? Glob Chang Biol 18(9):2899–2914. doi:10.1111/j.1365-2486.2012.02724.x
Zheng B, Biddulph B, Li D, Kuchel H, Chapman S (2013) Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments. J Exp Bot 64(12):3747–3761. doi:10.1093/jxb/ert209
Acknowledgements
Daniela Bustos-Korts thanks Becas Chile (CONICYT) for the financial support in form of a PhD scholarship. Marcos Malosetti and Fred van Eeuwijk worked on this chapter as part of a project financed by the Generation Challenge Program – Integrated Breeding Platform (https://www.integratedbreeding.net/).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Bustos-Korts, D., Malosetti, M., Chapman, S., van Eeuwijk, F. (2016). Modelling of Genotype by Environment Interaction and Prediction of Complex Traits across Multiple Environments as a Synthesis of Crop Growth Modelling, Genetics and Statistics. In: Yin, X., Struik, P. (eds) Crop Systems Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-20562-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-20562-5_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20561-8
Online ISBN: 978-3-319-20562-5
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)