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Genomics-Assisted Crop Improvement pp 57–95Cite as

Modeling Qtl Effects and Mas in Plant Breeding

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Abstract

The empirical evidence accumulated to date indicates that the genetic architecture of the different traits of organisms, emphasizing here those relevant to plant breeding, should be viewed as a genetic complexity continuum. This concept is not new to plant breeders. What is new is that geneticists and plant breeders can now apply high throughput molecular technologies to identify and study the genes and alleles responsible for the standing genetic and phenotypic variation for traits in elite breeding populations. Plant breeders undertake research to develop robust breeding strategies that take advantage of this growing body of trait genetics knowledge and seek breeding methods that can be practically applied to improve multiple traits to achieve defined breeding objectives. While experimental and quantitative methods are developed to detect quantitative trait loci (QTL) and to implement marker-assisted selection (MAS) for the detected trait QTL as components of a comprehensive plant breeding strategy, simulation modeling methods can be applied to quantify the robustness of the chosen QTL analysis and MAS methods for the trait genetics complexity continuum. We review methods that can be applied to model the effects of QTL and outcomes from MAS in plant breeding as our view of the trait genetic complexity continuum unfolds. Some key lessons from this body of research are discussed.

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References

  • Axelrod R, Cohen MD (1999). Harnessing complexity: organizational implications of a scientific frontier. The Free Press, Sydney, Australia

    Google Scholar 

  • Barker T, Campos H, Cooper M, Dolan D, Edmeades G, Habben J, Schussler J, Wright D, Zinselmeier C (2005) Improving drought tolerance in maize. Plant Breed Rev 25: 173–253

    CAS  Google Scholar 

  • Bass TA (1999) The predictors. Henry Holt and Company, New York

    Google Scholar 

  • Basten CJ, Weir BS, Zeng ZB (1995) QTL cartographer: a reference manual and tutorial for QTL mapping. Center for Quantitative Genetics, NC St U

    Google Scholar 

  • Beavis WD (1998) QTL analyses: power, precision and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, Boca Raton, pp 145–162

    Google Scholar 

  • Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224

    Article  PubMed  CAS  Google Scholar 

  • Borlaug NE, Dowswell CR (2005) Feeding a world of ten billion people: a 21st century challenge. In: Tuberosa R, Phillips RL, Gale M (eds) In the wake of the double helix: from the green revolution to the gene revolution. Avenue media, Bologna, Italy, pp 1–23

    Google Scholar 

  • Bouchez A, Hospital F, Causse M, Gallais A, Charcosset A (2002) Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite lines. Genetics 162:1945–1959

    PubMed  CAS  Google Scholar 

  • Bower JM, Bolouri H (eds) (2001) Computational modeling of genetic and biochemical networks. A Bradford Book, The MIT Press, Cambridge, Massachusetts

    Google Scholar 

  • Bubeck, DM, Carlone MR, Fox RL, Hoffbeck MD, Segebart RL, Stucker DS (2006) Breeding progress measured in eight elite inbred families. Maydica 51:141–149

    Google Scholar 

  • Cahill DJ, Schmidt DH (2004) Use of marker assisted selection in a product development breeding program. In: Fischer T, Turner N, Angus J, McIntyre L, Robertson M, Borrell A, Lloyd D (eds) New directions for a diverse planet: proceedings of the 4th international crop science congress. (Brisbane, Australia, 26 September to 1 October, 2004, Online Proceedings www.cropscience.org.au)

    Google Scholar 

  • Campos H, Cooper M, Habben JH, Edmeades GO, Schussler JR (2004) Improving drought tolerance in maize: a view from industry. Field Crop Res 90:19–34

    Article  Google Scholar 

  • Carlborg Ö, Jacobsson L, Åhgren P, Siegel P, Andersson L (2006) Epistasis and the release of genetic variation during long-term selection. Nat Gene 38:418–420

    Article  CAS  Google Scholar 

  • Casti JL (1997) Would-be worlds: how simulation is changing the frontiers of science. John Wiley & Sons, Inc., Brisbane, Australia

    Google Scholar 

  • Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, Murigneux A, Charcosset A (2004) Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics 168:2169–2185

    Article  PubMed  CAS  Google Scholar 

  • Chapman SC, Crossa J, Edmeades GO (1997) Genotype by environment effects and selection for drought tolerance in tropical maize. I. Two mode pattern analysis of yield. Euphytica 95:1–9

    Article  Google Scholar 

  • Chapman SC, Cooper M, Butler DG, Henzell RG (2000a) Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield. Aus J Agr Sci 51:197–207

    Article  Google Scholar 

  • Chapman SC, Cooper M, Hammer GL, Butler DG (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. Aus J Agr Sci 51:209–221

    Google Scholar 

  • Chapman SC, Hammer GL, Butler DG, Cooper M (2000c) Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments. Aus J Agr Sci 51:223–233

    Article  Google Scholar 

  • Chapman S, Cooper M, Podlich D, Hammer G (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agrono J 95:99–113

    Article  Google Scholar 

  • Cheverud J, Routman E (1996) Epistasis as a source of increased additive genetic variance at population bottlenecks. Evolution 50:1042–1051

    Article  Google Scholar 

  • Clark AG (2000) Limits to prediction of phenotypes from knowledge of genotypes. Evolut Bio 32:205–224

    Google Scholar 

  • Comstock, RE (1996) Quantitative genetics with special reference to plant and animal breeding. Iowa State University Press Ames, Iowa

    Google Scholar 

  • Cooper M, Podlich DW (2002) The E(NK) model: extending the NK model to incorporate gene-by-environment interactions and epistasis for diploid genomes. Complexity 7:31–47

    Article  Google Scholar 

  • Cooper M, Podlich DW, Smith OS (2005) Gene-to-phenotype models and complex trait genetics. Aus J Agr Res 56:895–918

    Article  Google Scholar 

  • Cooper M, Chapman SC, Podlich DW, Hammer GL (2002) The GP problem: quantifying gene-to-phenotype relationships. In Silico Bio 2:151–164

    CAS  Google Scholar 

  • Cooper M, Smith OS, Merrill RE, Arthur L, Podlich DW, Löffler CM (2006) Integrating breeding tools to generate information for efficient breeding: past, present, and future. In: Lamkey KR, Lee M (eds) Plant breeding: The Arnel R. Hallauer International Symposium. Blackwell Publishing, Ames, Iowa, pp 1–154

    Google Scholar 

  • Crosbie TM, Eathington SR, Johnson GR, Edwards M, Reiter R, Stark S, Mohanty RG, Oyervides M, Buehler RE, Walker AK, Dobert R, Delannay X, Pershing JC, Hall MA, Lamkey KR (2006) Plant breeding: past, present and future. In: Lamkey KR, Lee M (eds) Plant breeding: The Arnel R. Hallauer International Symposium. Blackwell Publishing, Ames, Iowa, pp 1–50

    Google Scholar 

  • Crossa J, Vargas M, van Eeuwijk FA, Jiang C, Edmeades GO, Hoisington D (1999) Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables. Theor Appl Genet 99:611–625

    Article  Google Scholar 

  • Crow JF, Kimura M (1970) An introduction to population genetics theory. Burgess Publishing Company, Minneapolis, Minnesota

    Google Scholar 

  • Crutchfield JP, Schuster P (Eds) (2003) evolutionary dynamics: exploring the interplay of selection, accident, neutrality, and function. Oxford University Press, Oxford, UK

    Google Scholar 

  • Doebley J, Stec A, Gustus C (1995) Teosinte branched1 and the origin of maize: evidence for epistasis and the evolution of dominance. Genetics 141:333–346

    PubMed  CAS  Google Scholar 

  • Dorogovtsev SN, Mendes JFF (2003) Evolution of networks: from biological nets to the internet and WWW. Oxford University Press, Oxford

    Google Scholar 

  • Duvick, DN (1977). Genetic rates of gain in hybrid maize yields during the past 40 years. Maydica 22:187–196

    Google Scholar 

  • Duvick, DN (2006) Social and environmental benefits of plant breeding. In: Lamkey KR, Lee M (eds) Plant breeding: The Arnel R. Hallauer International Symposium. Blackwell Publishing, Ames, Iowa, pp 1–72

    Google Scholar 

  • Duvick DN, Smith JSC, Cooper M (2004) Long-term selection in a commercial hybrid maize breeding program. Plant Breed Rev 24(2):109–151

    Google Scholar 

  • Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, Burnt Mill, Harlow, Essex, England

    Google Scholar 

  • Fisher RA (1918) The correlation between relatives on the supposition of mendelian inheritance. T Roy Soc Edin 52:399–433

    Google Scholar 

  • Frary A, Nesbitt TC, Frary A, Grandillo S, van der Knaap E, Cong B, Liu J, Meller J, Elber R, Alpert KB, Tanksley SD (2000) fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science 289:85–88

    Article  PubMed  CAS  Google Scholar 

  • Fraser A, Burnell D (1970) Computer models in genetics. McGraw Hill Book Company, New York

    Google Scholar 

  • Hallauer AR, Miranda FO JB (1988). Quantitative genetics in maize breeding 2nd edn. Iowa State University Press, Ames

    Google Scholar 

  • Hallauer AR, Pandey S (2006) Defining and achieving plant-breeding goals. In: Lamkey KR, Lee M (eds) Plant breeding: the Arnel R. Hallauer International Symposium. Blackwell Publishing, Ames, Iowa, pp 1–89

    Google Scholar 

  • Hammer GL, Chapman S, van Oosterom E, Podlich DW (2005) Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Aus J Agr Res 56:947–960

    Article  Google Scholar 

  • 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 in Plant Sci 11:587–593

    Google Scholar 

  • Holland JB (2006) Theoretical and biological foundations of plant breeding. In: Lamkey KR, Lee M (eds) Plant breeding: the Arnel R. Hallauer International Symposium. Blackwell Publishing, Ames, Iowa, pp 1–140

    Google Scholar 

  • Janick J (ed) (2004a) Plant breeding reviews 24, Part 1: long-term selection: maize. John Wiley & Sons, Inc., Hoboken, New Jersey, USA

    Google Scholar 

  • Janick J (ed) (2004b) Plant breeding reviews 24, Part 2: long-term selection: crops, animals, and bacteria. John Wiley & Sons, Inc., Hoboken, New Jersey, USA

    Google Scholar 

  • Jansen RC, Jannink J-L, Beavis WD (2003) Mapping quantitative trait loci in plant breeding populations: use of parental haplotype sharing. Crop Sci 43:829–834

    Article  CAS  Google Scholar 

  • Kauffman, SA (1993) The origins of order: self-rganization and selection in evolution. Oxford University Press, New York

    Google Scholar 

  • Kearsey MJ, Farquhar GL (1998) QTL analysis in plants; where are we now? Heredity 80, 137–142

    Google Scholar 

  • Kempthorne O (1988) An overview of the field of quantitative genetics. In: Weir BS, Eisen EJ, Goodman MM, Namkoong G (eds) Proceedings of the second international conference on quantitative genetics, Sunderland, Massachusetts: Sinauer Associates, Inc., pp 1–56

    Google Scholar 

  • Khush GS (2005) Green revolution: challenges ahead. In: Tuberosa R, Phillips RL, Gale M (eds) In the wake of the double helix: from the green revolution to the gene revolution. Avenue media, Bologna, Italy, pp 1–51

    Google Scholar 

  • Lande R (1991) Marker-assisted selection in relation to traditional methods of plant breeding. In: Stalker HT, Murphy JP (eds) Plant breeding in the 1990s, CAB International, Wallingford, UK. pp 1–451

    Google Scholar 

  • Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756

    PubMed  CAS  Google Scholar 

  • Laurie CC, Chasalow SD, LeDeaux JR, McCarroll R, Bush D, Hauge B, Lai C, Clark D, Rocheford TR, Dudley JW (2004) The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics 168:2141–2155

    Article  PubMed  Google Scholar 

  • Li ZK, Arif M, Zhong DB, Fu BY, Xu JL, Domingo-Rey J, Ali J, Vijayakumar CHM, Yu SB, Khush GS (2006). Complex genetic networks underlying the defensive system of rice (Oryza sativa L.) to Xanthomonas oryzae pv. oryzae. Proc Nat Acad Sci 103:7994–7999

    Article  PubMed  CAS  Google Scholar 

  • Löffler CM, Wei J, Fast T, Gogerty J, Langton S, Bergman M, Merrill B, Cooper M (2005) Classification of maize environments using crop simulation and geographic information systems. Crop Sci 45:1708–1716

    Article  Google Scholar 

  • Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, Inc. Sunderland Massachusetts, USA

    Google Scholar 

  • Mackay TFC (2001) The genetic architecture of quantitative traits. Ann Rev Genet 35:303–339

    Article  PubMed  CAS  Google Scholar 

  • Mackay TFC (2004) The genetic architecture of quantitative traits: lessons from Drosophila. Curr Opin Genet Devel 14:1–5

    Article  CAS  Google Scholar 

  • McClearn GE (2006) Contextual genetics. Trends Genet 22:314–319

    Article  PubMed  CAS  Google Scholar 

  • Micallef KP, Cooper, M. and Podlich DW (2001) Using clusters of computers for large QU-GENE simulation experiments. Bioinformatics 17:194–195

    Article  PubMed  CAS  Google Scholar 

  • Moreau L, Charcosset A, Gallais A (2004a) Experimental evaluation of several cycles of marker-assisted selection in maize. Euphytica 137:111–118

    Article  CAS  Google Scholar 

  • Moreau L, Charcosset A, Gallais A (2004b) Use of trial clustering to study QTL × environment effects for grain yield and related traits in maize. Theor Appl Genet 110:92–105

    Article  CAS  Google Scholar 

  • Nguyen HT, Blum A (eds) (2004) Physiology and biotechnology integration for plant breeding. Marcel Dekker, Inc. New York

    Google Scholar 

  • Niebur WS, Rafalski JA, Smith OS, Cooper M (2004) Applications of genomics technologies to enhance rate of genetic progress for yield of maize within a commercial breeding program. In: Fischer T, Turner N, Angus J, McIntyre L, Robertson M, Borrell A, Lloyd D (eds) New directions for a diverse planet: proceedings of the 4th international crop science congress. Brisbane, Australia, 26 September to 1 October, 2004, Online Proceedings www.cropscience.org.au

    Google Scholar 

  • Newman M, Barabási A-L, Watts DJ (eds) (2006) The structure and dynamics of networks. Princeton University Press, Princeton

    Google Scholar 

  • Omholt SW, Plahte E, Øyehaug L, Xiang K (2000) Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics 155:969–980

    PubMed  CAS  Google Scholar 

  • Openshaw SJ, Frascaroli E (1997) QTL detection and marker-assisted selection for complex traits in maize. Proceedings of the 52nd annual corn and sorghum research conference. American Seed Trade Association, Washington DC, USA pp 1–53

    Google Scholar 

  • Peccoud J, Vander VK, Podlich DW, Winkler C, Arthur L, Cooper M (2004) The selective values of alleles in a molecular network model are context-dependent. Genetics 166:1715–1725

    Article  PubMed  CAS  Google Scholar 

  • Perkins JH (1979) Geopolitics and the green revolution: wheat, genes, and the cold war. Oxford University Press, Oxford

    Google Scholar 

  • Podlich DW, Cooper M (1998) QU-GENE: a platform for quantitative analysis of genetic models. Bioinformatics 14:632–653

    Article  PubMed  CAS  Google Scholar 

  • Podlich DW, Cooper M (1999) Modelling plant breeding programs as search strategies on a complex response surface. Lect Notes Comput Sci 1585:171–178

    Google Scholar 

  • Podlich DW, Winkler CR, Cooper M (2004) Mapping as you go: an effective approach for marker-assisted selection of complex traits. Crop Sci 44:1560–1571

    Google Scholar 

  • Rajaram S, van Ginkel M (2001) Mexico: 50 years of international wheat breeding. In: Bonjean AP, Angus WJ (eds) The world wheat book; A history of wheat breeding. Lavoisier Publishing, Paris, France, pp 1–608

    Google Scholar 

  • Rasmusson DC (1996) Germplasm is paramount. In: Reynolds MP, Rajaram S, McNab A (eds) Increasing yield potential in wheat: breaking the barriers. Mexico, DF, CIMMYT, pp 1–35

    Google Scholar 

  • Ribaut J-M, Hoisington DA, Deutsch JA, Jiang C, Gonzalez-de-Leon D (1996) Identification of quantitative trait loci under drought conditions in tropical maize. 1. Flowering parameters and the anthesis-silking interval. Theor Appl Genet 92:905–914

    Article  CAS  Google Scholar 

  • Ribaut J-M, Jiang C, Gonzalez-de-Leon D, Edmeades GO, Hoisington DA (1997) Identification of quantitative trait loci under drought conditions in tropical maize. 2. Yield components and marker-assisted selection strategies. Theor Appl Genet 94:887–896

    Article  Google Scholar 

  • Ribaut J-M, Hoisington D, Bänziger M, Setter TL, Edmeades GO (2004) Genetic dissection of drought tolerance in maize: a case study. In: Nguyen HT, Blum A (eds) Physiology and biotechnology integration for plant breeding. Marcel Dekker, Inc., New York, pp 1–609

    Google Scholar 

  • Salvi S, Tuberosa R (2005) To clone or not to clone plant QTLs : present and future challenges. Trends in Plant Sci 10:297–304

    Article  CAS  Google Scholar 

  • Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485–498

    Article  PubMed  Google Scholar 

  • Schrage M (2000) Serious play: how the world’s best companies simulate to innovate. Harvard Business School Press, Boston, Massachusetts

    Google Scholar 

  • Sing CF, Stengård JH, Kardia SLR (2003) Genes, environment, and cardiovascular disease. Arterioscler, Thromb, Vasc Biol 23:1190–1196

    Article  CAS  Google Scholar 

  • Smith A, Cullis B, Thompson R (2002) Exploring variety-environment data using random effects AMMI models with adjustments for spatial field trend: Part 1: Theory. In: Kang MS (ed) Quantitative genetics, genomics and plant breeding. CAB International, Wallingford, UK, pp 1–335

    Google Scholar 

  • Smith A, Cullis B, Luckett D, Hollamby G, Thompson R (2002) Exploring variety-environment data using random effects AMMI models with adjustments for spatial field trend: Part 2: applications. In: Kang MS (ed) Quantitative genetics, genomics and plant breeding. CAB International, Wallingford, UK, pp 1–351

    Google Scholar 

  • 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. Aus J Agr Res 56:937–946

    Article  Google Scholar 

  • 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 154:1839–1849

    PubMed  Google 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. Aus J Agr Res 56:883–894

    Article  Google Scholar 

  • Vargas M, van Eeuwijk FA, Crossa J, Ribaut J-M (2006) Mapping QTLs and QTL × environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods. Theor Appl Genet 112:1009–1023

    Article  PubMed  CAS  Google Scholar 

  • Wagner A (2005) Robustness and evolvability in living systems. Princeton University Press, Princeton.

    Google Scholar 

  • Walsh B (2005) The struggle to exploit non-additive variation. Aus J Agr Res 56:873–881

    Article  Google Scholar 

  • Wang J, van Ginkel M, Trethowan R, Ye G, DeLacy I, Podlich D, Cooper M (2004) Simulating the effects of dominance and epistasis on selection response in the CIMMYT wheat breeding program using QuCim. Crop Sci 44:2006–2018

    Article  Google Scholar 

  • Wang J, van Ginkel M, Podlich D, Ye G, Trethowan R, Pfeiffer W, DeLacy IH, Cooper M, Rajaram S (2003) Comparison of two breeding strategies by computer simulation. Crop Sci 43:1764–1773.

    Article  Google Scholar 

  • Welch SM, Dong Z, Roe JL, Das S (2005) Flowering time control: gene network modelling and the link to quantitative genetics. Aus J Agr Res 56:919–936

    Article  Google Scholar 

  • Williams GP (1997) Chaos theory tamed. Joseph Henry Press, Washington DC

    Google Scholar 

  • Winkler CR, Jensen NM, Cooper M, Podlich DW, Smith OS (2003) On the determination of recombination rates in intermated recombinant inbred population. Genetics 164:741–745

    PubMed  Google Scholar 

  • Wolfram S (2002) A new kind of science. Wolfram Media, Inc, Champaign, Illinois, USA

    Google Scholar 

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Authors and Affiliations

  1. Pioneer Hi-Bred International, 7250 NW 62nd Ave, Johnston, IA 50131-0552, USA

    Mark Cooper, Dean W. Podlich & Lang Luo

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  1. Mark Cooper
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  2. Dean W. Podlich
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  1. ICRISAT, Patancheru, India

    Rajeev K. Varshney

  2. University of Bologna, Italy

    Roberto Tuberosa

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Cooper, M., Podlich, D.W., Luo, L. (2007). Modeling Qtl Effects and Mas in Plant Breeding. In: Varshney, R.K., Tuberosa, R. (eds) Genomics-Assisted Crop Improvement. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6295-7_4

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