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Modeling QTL for complex traits: detection and context for plant breeding

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Cooper, Mark, van Eeuwijk, Fred A, Hammer, Graeme L, Podlich, Dean W, Messina, Carlos·2009·Current Opinion in Plant Biology

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Show all 13 references

Research works printed but not linked: 40

  1. Holland, 2007, Genetic architecture of complex traits in plants, Curr Opin Plant Biol, 10, 156, 10.1016/j.pbi.2007.01.003 · DOI · Paper page
  2. Paterson, 1988, Resolution of quantitative traits into Mendelian factors using a complete linkage map of restriction fragment length polymorphisms, Nature, 335, 721, 10.1038/335721a0 · DOI · Paper page
  3. Cheverud, 1995, Epistasis and its contribution to genetic variance components, Genetics, 139, 1455, 10.1093/genetics/139.3.1455 · DOI · Paper page
  4. Phillips, 2008, Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems, Nat Rev Genetics, 9, 855, 10.1038/nrg2452 · DOI · Paper page
  5. Duvick, 2004, Long-term selection in a commercial hybrid maize breeding program, Plant Breed Rev, 24, 109
  6. Jiang, 1997, Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines, Genetica, 101, 47, 10.1023/A:1018394410659 · DOI · Paper page
  7. Piepho, 2000, A mixed model approach to mapping quantitative trait loci in barley on the basis of multiple environment data, Genetics, 156, 2043, 10.1093/genetics/156.4.2043 · DOI
  8. Verbyla, 2003, The analysis of quantitative trait loci in multi-environment trials using a multiplicative mixed model, Aust J Agr Res, 54, 1395, 10.1071/AR02239 · DOI
  9. Piepho, 2004, Mixed modelling for QTL×environment interaction analysis, Euphytica, 137, 147, 10.1023/B:EUPH.0000040512.84025.16 · DOI
  10. Gilmour, 2007, Mixed model regression mapping for QTL detection in experimental crosses, Comput Stat Data Anal, 51, 3749, 10.1016/j.csda.2006.12.031 · doi:10.1016/j.csda.2006.12.031
  11. Ma, 2002, Functional mapping of quantitative trait loci underlying the character process: a theoretical framework, Genetics, 161, 1751, 10.1093/genetics/161.4.1751 · DOI · Paper page
  12. Malosetti, 2006, QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato, Theor Appl Genet, 113, 288, 10.1007/s00122-006-0294-2 · DOI
  13. Malosetti, 2008, A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.), Euphytica, 161, 241, 10.1007/s10681-007-9594-0 · DOI · Paper page
  14. Malosetti, 2007, A mixed model approach to association mapping using pedigree information with an illustration to resistance for Phytophthora infestans in potato, Genetics, 175, 879, 10.1534/genetics.105.054932 · DOI · Paper page
  15. Yu, 2005, A unified mixed-model method for association mapping that accounts for multiple levels of relatedness, Nat Genetics, 38, 203, 10.1038/ng1702 · DOI · Paper page
  16. Cavanagh, 2008, From mutations to MAGIC; resources for gene discovery, validation and delivery in crop plants, Curr Opin Plant Biol, 11, 215, 10.1016/j.pbi.2008.01.002 · DOI · Paper page
  17. Paulo, 2008, A mixed model QTL analysis for a complex cross population consisting of a half diallel of two-way hybrids in Arabidopsis thaliana: analysis of simulated data, Euphytica, 161, 107, 10.1007/s10681-008-9665-x · DOI
  18. Laurie, 2004, The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel, Genetics, 168, 2141, 10.1534/genetics.104.029686 · DOI · Paper page
  19. Dudley, 2008, Epistatic interactions in crosses of Illinois high oil×Illinois low oil and of Illinois high protein×Illinois low protein corn strains, Crop Sci, 48, 59, 10.2135/cropsci2007.04.0242 · doi:10.2135/cropsci2007.04.0242
  20. Salvi, 2005, To clone or not to clone plant QTLs: present and future challenges, Trends Plant Sci, 10, 297, 10.1016/j.tplants.2005.04.008 · DOI · Paper page
  21. Chapman, 2003, Evaluating plant breeding strategies by simulating gene action and dryland environment effects, Agron J, 95, 99, 10.2134/agronj2003.0099 · DOI · Paper page
  22. Hammer, 2005, Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems, Aust J Agric Res, 56, 947, 10.1071/AR05157 · DOI · Paper page
  23. Walsh, 2005, The struggle to exploit non-additive variation, Aust J Agric Res, 56, 873, 10.1071/AR05152 · DOI
  24. Tardieu, 2003, Virtual plants: modelling as a tool for the genomics of tolerance to water deficit, Trends Plant Sci, 8, 9, 10.1016/S1360-1385(02)00008-0 · DOI · Paper page
  25. Reymond, 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, 664, 10.1104/pp.013839 · DOI · Paper page
  26. Reymond, 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, 2461, 10.1093/jxb/erh200 · DOI
  27. Manschadi, 2006, The role of root architectural traits in adaptation of wheat to water-limited environments, Funct Plant Biol, 33, 823, 10.1071/FP06055 · DOI · Paper page
  28. Hammer, 2009, Can changes in canopy and/or root system architecture explain historical maize yield trends in the US corn belt?, Crop Sci., 49, 299, 10.2135/cropsci2008.03.0152 · DOI · Paper page
  29. Manschadi, 2008, Genotypic variation in seedling root architectural traits and implications for drought adaptation in wheat (Triticum aestivum L.), Plant Soil, 303, 115, 10.1007/s11104-007-9492-1 · DOI · Paper page
  30. Yuan, 2008, Plant systems biology comes of age, Trends Plant Sci, 13, 165, 10.1016/j.tplants.2008.02.003 · DOI · Paper page
  31. Komeda, 2004, Genetic regulation of time to flower in Arabidopsis thaliana, Annu Rev Plant Biol, 55, 521, 10.1146/annurev.arplant.55.031903.141644 · DOI · Paper page
  32. Lewis, 2008, From signals to patterns: space, time, and mathematics in developmental biology, Science, 322, 399, 10.1126/science.1166154 · DOI · Paper page
  33. Wagner, 2002, Estimating coarse gene network structure from large-scale gene perturbation data, Genome Res, 12, 309, 10.1101/gr.193902 · DOI
  34. Bornholdt, 2005, Less is more in modelling large genetic networks, Science, 310, 450, 10.1126/science.1119959 · DOI · Paper page
  35. Omholt, 2000, Gene regulatory networks generating the phenomena of additivity, dominance and epistasis, Genetics, 155, 969, 10.1093/genetics/155.2.969 · DOI · Paper page
  36. Welch, 2005, Flowering time control: gene network modelling and the link to quantitative genetics, Aust J Agric Res, 56, 919, 10.1071/AR05155 · DOI
  37. Gjuvsland, 2007, Statistical epistasis is a generic feature of gene regulatory networks, Genetics, 175, 411, 10.1534/genetics.106.058859 · DOI
  38. Cooper, 2002, The E(NK) model: extending the NK model to incorporate gene-by-environment interactions and epistasis for diploid genomes, Complexity, 7, 31, 10.1002/cplx.10044 · DOI
  39. Peccoud, 2004, The selective values of alleles in a molecular network model are context-dependent, Genetics, 166, 1715, 10.1534/genetics.166.4.1715 · DOI
  40. Podlich, 1998, QU-GENE: a platform for quantitative analysis of genetic models, Bioinformatics, 14, 632, 10.1093/bioinformatics/14.7.632 · DOI · Paper page

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Metadata

Authors
Cooper, M., van Eeuwijk, F. A., Hammer, G. L., Podlich, D. W., & Messina, C.
Venue
Current Opinion in Plant Biology
Publisher
Elsevier BV
Published
2009-04
Volume
12
Issue
2
Pages
231-240
ISSN
1369-5266 (ISSN-L)
OpenAlex
W2068519743
Cited by
202
References
13
FWCI
1.05
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: Modeling QTL for complex traits: detection and context for plant breeding. https://app.cochat.ai/papers/10.1016/j.pbi.2009.01.006
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Cooper, M., van Eeuwijk, F. A., Hammer, G. L., Podlich, D. W., & Messina, C. (2009). Modeling QTL for complex traits: detection and context for plant breeding. Current Opinion in Plant Biology. https://doi.org/10.1016/j.pbi.2009.01.006

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