Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems
Graeme L. Hammer A B E , Scott Chapman C , Erik van Oosterom A and Dean W. Podlich DA Agricultural Production Systems Research Unit, School of Land and Food Sciences, The University of Queensland, Brisbane, Qld 4072, Australia.
B Agricultural Production Systems Research Unit, Queensland Department of Primary Industries and Fisheries, Toowoomba, Qld 4350, Australia.
C CSIRO Plant Industry, Queensland Bioscience Precinct, 306 Carmody Rd, St Lucia, Qld 4067, Australia.
D 7250 NW 62nd Ave, PO Box 552, Pioneer Hi-Bred International Inc., Johnston, IA 50131, USA.
E Corresponding author. Email: g.hammer@uq.edu.au
Australian Journal of Agricultural Research 56(9) 947-960 https://doi.org/10.1071/AR05157
Submitted: 9 May 2005 Accepted: 20 June 2005 Published: 28 September 2005
Abstract
New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits—phenology, osmotic adjustment, transpiration efficiency, stay-green—and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location–season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.
Additional keywords: gene-to-phenotype modelling, complex traits, molecular breeding, virtual plants.
Acknowledgments
An earlier version of this paper was published in the Proceedings of the 4th International Crop Science Congress, held in Brisbane, 26 September–1 October 2004. We thank the Congress organisers for permission to publish this updated manuscript as part of this series, which was based on presentations made to a symposium forming part of that Congress. We also thank Mark Cooper for invaluable discussions on framing the ideas and analyses reported in this paper, and Jeremy Lecoeur for many useful suggestions on an earlier version of this manuscript.
Basnayake J,
Cooper M,
Ludlow MM,
Henzell RG, Snell PJ
(1995) Inheritance of osmotic adjustment in three grain sorghum crosses. Theoretical and Applied Genetics 90, 675–682.
| Crossref | GoogleScholarGoogle Scholar |
Blázquez M
(2000) Flower development pathways. Journal of Cell Science 113, 3547–3548.
| PubMed |
Borrell AK, Hammer GL
(2000) Nitrogen dynamics and the physiological basis of stay-green in sorghum. Crop Science 40, 1295–1307.
Borrell AK,
Hammer GL, Douglas ACL
(2000a) Does maintaining green leaf area in sorghum improve yield under drought? I. Leaf growth and senescence. Crop Science 40, 1026–1037.
Borrell AK,
Hammer GL, Henzell RG
(2000b) Does maintaining green leaf area in sorghum improve yield under drought? 2. Dry matter production and yield. Crop Science 40, 1037–1048.
Caddel JL, Weibel DE
(1971) Effect of photoperiod and temperature on the development of sorghum. Agronomy Journal 63, 799–803.
Cahill DJ, Schmidt DH
(2005) Use of marker assisted selection in a product development breeding program. ‘New directions for a diverse planet. Proceedings of the 4th International Crop Science Congress’, 26 Sept.–1 Oct. 2004, Brisbane, Australia. (Published on CD ROM. Web site::
)
www.cropscience.org.au)
Campos H,
Cooper M,
Habben JE,
Edmeades GO, Schussler JR
(2004) Improving drought tolerance in maize: a view from industry. Field Crops Research 90, 19–34.
| Crossref | GoogleScholarGoogle Scholar |
Chapman SC,
Cooper M, Hammer GL
(2002a) Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments. Australian Journal of Agricultural Research 53, 379–389.
| Crossref | GoogleScholarGoogle Scholar |
Chapman SC,
Cooper M,
Hammer GL, Butler DG
(2000) Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stresses are related to location effects on hybrid yields. Australian Journal of Agricultural Research 51, 209–221.
| Crossref | GoogleScholarGoogle Scholar |
Chapman SC,
Cooper M,
Podlich D, Hammer GL
(2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agronomy Journal 95, 99–113.
Chapman SC,
Hammer GL, Meinke HM
(1993) A sunflower simulation model: I. Model development. Agronomy Journal 85, 725–734.
Chapman SC, Hammer GL, Podlich DW, Cooper M
(2002) Linking bio-physical and genetic models to integrate physiology, molecular biology and plant breeding. In ‘Quantitative genetics, genomics, and plant breeding’. (Ed. M Kang)
pp. 167–187. (CAB International: Wallingford, UK)
Charles-Edwards, DA (1982).
Comstock RE, Moll RH
(1963) Genotype–environment interactions. In ‘Statistical genetics and plant breeding’. Publication 982 pp. 164–196. (National Academy of Sciences – National Research Council: Washington, DC)
Cooper M,
Chapman SC,
Podlich DW, Hammer GL
(2002) The GP problem: quantifying gene-to-phenotype relationships. In Silico Biology 2, 151–164.
| PubMed |
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. Theoretical and Applied Genetics 88, 561–572.
| Crossref | GoogleScholarGoogle Scholar |
Cooper M, Hammer GL
(2005) Complex traits and plant breeding: can we understand the complexities of gene-to-phenotype relationships and use such knowledge to enhance plant breeding outcomes? Australian Journal of Agricultural Research 56, 869–872.
Cooper M,
Podlich DW, Smith OS
(2005) Gene-to-phenotype models and complex trait genetics. Australian Journal of Agricultural Research 56, 895–918.
Craufurd PQ,
Mahalakshmi V,
Bidinger FR,
Mukuru SZ, Chantereau J , et al.
(1999) Adaptation of sorghum: characterisation of genotypic flowering responses to temperature and photoperiod. Theoretical and Applied Genetics 99, 900–911.
| Crossref | GoogleScholarGoogle Scholar |
Dingkuhn M
(1996) Modelling concepts for the phenotypic plasticity of dry matter and nitrogen partitioning in rice. Agricultural Systems 52, 383–397.
| Crossref | GoogleScholarGoogle Scholar |
Donatelli M,
Hammer GL, Vanderlip RL
(1992) Genotype and water limitation effects on phenology, growth, and transpiration efficiency in grain sorghum. Crop Science 32, 781–786.
Drouet J-L, Pages L
(2003) GRAAL: a model of Growth, Architecture, and carbon Allocation during the vegetative phase of the whole maize plant—model description and parameterisation. Ecological Modelling 165, 147–173.
| Crossref | GoogleScholarGoogle Scholar |
Edmeades GO,
McMaster GS,
White JW, Campos H
(2004) Genomics and the physiologist: bridging the gap between genes and crop response. Field Crops Research 90, 5–18.
| Crossref | GoogleScholarGoogle Scholar |
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. Australian Journal of Agricultural Research 56, 883–894.
Ellis RH,
Qi A,
Craufurd PQ,
Summerfield RJ, Roberts EH
(1997) Effects of photoperiod, temperature and asynchrony between thermoperiod and photoperiod on development to panicle initiation in sorghum. Annals of Botany 79, 169–178.
| Crossref | GoogleScholarGoogle Scholar |
Hammer GL
(1998) Crop modelling: current status and opportunities to advance. Acta Horticulturae 456, 27–36.
Hammer GL, Butler D, Muchow RC, Meinke H
(1996) Integrating physiological understanding and plant breeding via crop modelling and optimization. ‘Plant adaptation and crop improvement’. (Eds M Cooper, GL Hammer)
pp. 419–441. (CAB International, ICRISAT & IRRI: Wallingford, UK)
Hammer GL,
Carberry PS, Muchow RC
(1993) Modelling genotypic and environmental control of leaf area dynamics in grain sorghum. I. Whole plant level. Field Crops Research 33, 293–310.
| Crossref | GoogleScholarGoogle Scholar |
Hammer GL, Chapman SC, Snell P
(1999) Crop simulation modelling to improve selection efficiency in plant breeding programs. ‘Proceedings 9th Assembly Wheat Breeding Society of Australia, Toowoomba, 27 Sept.–1 Oct. 1999’. (Ed. P Williamson . )
pp. 79–85. (Wheat Breeding Society of Australia: Toowoomba)
Hammer GL,
Farquhar GD, Broad IJ
(1997) On the extent of genetic variation for transpiration efficiency in sorghum. Australian Journal of Agricultural Research 48, 649–655.
| Crossref | GoogleScholarGoogle Scholar |
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. European Journal of Agronomy 18, 15–31.
| Crossref | GoogleScholarGoogle Scholar |
Hammer GL, van Oosterom EJ, Chapman SC, McLean G
(2001) The economic theory of water and nitrogen dynamics and management in field crops. ‘Proceedings 4th Australian Sorghum Conference’. Kooralbyn, Qld, 5–8 Feb. 2001.
. (Ed. AK Borrell ,
RG Henzell )
(CD ROM Format. Range Media Pty Ltd. ISBN 0-7242-2163-8)
Hammer GL,
Sinclair TR,
Chapman S, van Oosterom E
(2004) On systems thinking, systems biology and the in silico plant. Plant Physiology 134, 909–911.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Hammer GL,
Vanderlip RL,
Gibson G,
Wade LJ,
Henzell RG,
Younger DR,
Warren J, Dale AB
(1989) Genotype by environment interaction in grain sorghum II. Effects of temperature and photoperiod on ontogeny. Crop Science 29, 376–384.
Hart GE,
Schertz KF,
Peng Y, Syed NH
(2001) Genetic mapping of Sorghum bicolor (L.) Moench QTLs that control variation in tillering and other morphological characters. Theoretical and Applied Genetics 103, 1232–1242.
| Crossref | GoogleScholarGoogle Scholar |
Henderson SA,
von Caemmerer S,
Farquhar GD,
Wade LJ, Hammer GL
(1996) Correlation between carbon isotope discrimination and transpiration efficiency in lines of the C4 species Sorghum bicolor in the glasshouse and the field. Australian Journal of Plant Physiology 25, 111–123.
Jeuffroy MH,
Ney B, Ourry A
(2002) Integrated physiological and agronomic modelling of N capture and use within the plant. Journal of Experimental Botany 53, 809–823.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Keating BA,
Carberry PS,
Hammer GL,
Probert ME, Robertson MJ , et al.
(2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.
| Crossref | GoogleScholarGoogle Scholar |
Li ZK,
Luo LJ,
Mei HW,
Wang DL, Shu QY , et al.
(2001) Overdominant epistatic loci are the primary genetic effects of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics 158, 1737–1753.
| PubMed |
Ludlow MM, Muchow RC
(1990) A critical evaluation of traits for improving crop yields in water-limited environments. Advances in Agronomy 47, 107–153.
Ludlow MM,
Santamaria JM, Fukai S
(1990) Contribution of osmotic adjustment to grain yield in Sorghum bicolor (L.) Moench under water-limited conditions. II. Water stress after anthesis. Australian Journal of Agricultural Research 41, 67–78.
| Crossref | GoogleScholarGoogle Scholar |
Luo LJ,
Li ZK,
Mei HW,
Shu QY,
Tabien R,
Zhong DB,
Ying CS,
Stansel JW,
Khush GS, Paterson AH
(2001) Overdominant epistatic loci are the primary genetic effects of inbreeding depression and heterosis in rice. II. Grain yield components. Genetics 158, 1755–1771.
| PubMed |
Major DJ,
Rood SB, Miller FR
(1990) Temperature and photoperiod effects mediated by the sorghum maturity genes. Crop Science 30, 305–310.
Miflin B
(2000) Crop improvement in the 21st Century. Journal of Experimental Botany 51, 1–8.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Minorsky PV
(2003) Achieving the in silico plant: systems biology and the future of plant biological research. Plant Physiology 132, 404–409.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Monteith JL
(1986) How do crops manipulate water-supply and demand? Transactions of the Royal Society of London A 316, 245–259.
Monteith JL
(1988) Does transpiration limit the growth of vegetation or vice-versa? Journal of Hydrology 100, 57–68.
| Crossref | GoogleScholarGoogle Scholar |
Morgan PW,
Finlayson SA,
Childs KL,
Mullet JE, Rooney WL
(2002) Opportunities to improve adaptability and yield in grasses: lessons from sorghum. Crop Science 42, 1791–1799.
Morgan PW,
Guy LW, Pao CI
(1987) Genetic regulation of development in Sorghum bicolor. III. Asynchrony of thermoperiods with photoperiods promotes floral initiation. Plant Physiology 83, 448–450.
Mortlock MY, Hammer GL
(2000) Genotype and water limitation effects on transpiration efficiency in sorghum. Journal of Crop Production 2, 265–286.
| Crossref | GoogleScholarGoogle Scholar |
Muchow RC, Carberry PS
(1990) Phenology and leaf area development in a tropical grain sorghum. Field Crops Research 23, 221–237.
| Crossref | GoogleScholarGoogle Scholar |
van Oosterom EJ,
Hammer GL,
Borrell AK,
Chapman SC, Broad IJ
(2005a) Functional dynamics of the nitrogen balance of sorghum. I. N-balance during pre-anthesis period. Field Crops Research In press ,
van Oosterom EJ,
Hammer GL,
Chapman SC,
Borrell AK, Broad IJ
(2005b) Functional dynamics of the nitrogen balance of sorghum. II. N-balance during grain filling. Field Crops Research In press ,
Passioura JB
(1983) Roots and drought resistance. Agricultural Water Managament 7, 265–280.
| Crossref | GoogleScholarGoogle Scholar |
Peccoud J,
Vander Velden K,
Podlich D,
Winkler C,
Arthur L, Cooper M
(2004) The selective values of alleles in a molecular network model are context dependent. Genetics 166, 1715–1725.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Podlich D, Cooper M
(1998) QU-GENE: a simulation platform for quantitative analysis of genetic models. Bioinformatics 14, 632–653.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Podlich DW,
Winkler CR, Cooper M
(2004) Mapping as you go: an effective approach for marker-assisted selection of complex traits. Crop Science 44, 1560–1571.
Reymond M,
Muller B,
Leonardi A,
Charcosset A, Tardieu F
(2003) Combining quantitative trait loci analysis and an ecophysiological model to analyse the genetic variability of the responses of leaf growth to temperature and water deficit. Plant Physiology 131, 664–675.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Serraj R, Sinclair TR
(2002) Osmolyte accumulation: can it really help increase crop yield under drought conditions? Plant, Cell and Environment 25, 333–341.
| Crossref | GoogleScholarGoogle Scholar |
Sinclair TR, Horie T
(1989) Leaf nitrogen, photosynthesis, and crop radiation use efficiency: a review. Crop Science 29, 90–98.
Sinclair TR, Seligman NG
(1996) Crop modelling: from infancy to maturity. Agronomy Journal 88, 698–704.
Snape J
(2004) Challenges of integrating conventional breeding and biotechnology personal view! In ‘New directions for a diverse planet. Proceedings of the 4th International Crop Science Congress, 26 Sept.–1 Oct. 2004, Brisbane, Australia’. (Published on CD ROM. Web site:.
www.cropscience.org.au)
Snell P
(2004) The contribution of osmotic adjustment to grain yield of sorghum in dryland production environments. PhD thesis, The University of Queensland, Brisbane, Australia.
Somerville C, Somerville S
(1999) Plant functional genomics. Science 285, 380–383.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Tanner CB, Sinclair TR
(1983) Efficient water use in crop production: research or re-search? In ‘Limitations to efficient water use in crop production’. (Eds HM Taylor, WR Jordan, TR Sinclair)
pp. 1–27. (American Society of Agronomy: Madison, WI)
Tao YZ,
Henzell RG,
Jordan DR,
Butler DG,
Kelly AM, McIntyre CL
(2000) Identification of genomic regions associated with staygreen in sorghum by testing RILs in multiple environments. Theoretical and Applied Genetics 100, 1225–1232.
| Crossref | GoogleScholarGoogle Scholar |
Tardieu F
(2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends in Plant Science 8, 9–14.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
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. Australian Journal of Agricultural Research 56, 937–946.
Thomas H, Howarth CJ
(2000) Five ways to stay green. Journal of Experimental Botany 51, 329–337.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
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. European Journal of Agronomy 18, 121–140.
| Crossref | GoogleScholarGoogle Scholar |
Welch SM,
Dong Z,
Roe JL, Das S
(2005) Flowering time control: gene network modelling and the link to quantitative genetics. Australian Journal of Agricultural Research 56, 919–936.
Welch SM,
Roe JL, Dong Z
(2003) A genetic neural network model for flowering time control in Arabidopsis thaliana. Agronomy Journal 95, 71–81.
White JW, Hoogenboom G
(1996) Simulating effects of genes for physiological traits in a process-oriented crop model. Agronomy Journal 88, 416–422.
de Wit CT, Penning de Vries FWT
(1983) Crop growth models without hormones. Netherlands Journal of Agricultural Science 31, 313–323.
Yin X,
Stam P,
Kropff MJ, Schapendonk Ad HCM
(2003) Crop modelling, QTL mapping, and their complimentary role in plant breeding. Agronomy Journal 95, 90–98.
Yin X,
Struik PC, Kropff MJ
(2004) Role of crop physiology in predicting gene-to-phenotype relationships. Trends in Plant Science 9, 426–432.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
