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Theoretical and Applied Genetics

, Volume 132, Issue 12, pp 3309–3320 | Cite as

Genetic architecture of phenotypic means and plasticities of kernel size and weight in maize

  • Chunhui Li
  • Xun Wu
  • Yongxiang Li
  • Yunsu Shi
  • Yanchun Song
  • Dengfeng Zhang
  • Yu LiEmail author
  • Tianyu WangEmail author
Original Article
  • 172 Downloads

Abstract

Key message

Genetic relationships between the phenotypic means and plasticities of kernel size and weight revealed the common genetic control of these traits in maize.

Abstract

Kernel size and weight are crucial components of grain yield in maize, and phenotypic plasticity in these traits facilitates adaptations to changing environments. Elucidating the genetic architecture of the mean phenotypic values and plasticities of kernel size and weight may be essential for breeding climate-robust maize varieties. Here, a maize nested association mapping (CN-NAM) population and association panel were grown in different environments. A joint linkage analysis and genome-wide association mapping were performed for five kernel size and weight phenotypic traits and two phenotypic plasticity measures. The mean phenotypes and plasticities were significantly correlated. The overall results of quantitative trait locus (QTL) and candidate gene analyses indicated moderate and high levels of common genetic control for the two traits. Furthermore, the mean phenotypes or plasticities of the hundred-kernel weight and volume were commonly regulated to a high degree. One pleiotropic locus on chromosome 10 simultaneously controlled the mean phenotypic values and plasticities of kernel size and weight. Therefore, the plasticity of kernel size and weight might be indirectly selected during maize breeding; however, selecting for high or low plasticity in combination with high or low mean phenotypic values of kernel size and weight traits may be difficult.

Notes

Acknowledgments

We thank Aaron Kusmec of Iowa State University for providing the method for calculating plasticity values.

Author contribution statement

CHL analyzed the data and drafted the manuscript. YXL and XW participated in the data collection. YSS, YCS and DFZ provided phenotypic information. YL and TYW conceived the study, managed the project design and coordination, collected data, and helped to draft the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the MOST and MOA programs of China (2016YFD0100103), National Natural Science Foundation (31701433, 91335206), and the CARS-02 and CAAS Innovation Program.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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References

  1. Bonaparte EENA, Brawn RI (1975) The effect of intraspecific competition on the phenotypic plasticity of morphological and agronomic characters of four maize hybrids. Ann Bot 39:863–869.  https://doi.org/10.1093/oxfordjournals.aob.a085003 CrossRefGoogle Scholar
  2. Bradshaw AD (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13:115–155.  https://doi.org/10.1016/S0065-2660(08)60048-6 CrossRefGoogle Scholar
  3. Brown PJ, Upadyayula N, Mahone GS, Tian F, Bradbury PJ, Myles S, Holland JB, Flint-Garcia S, McMullen MD, Buckler ES, Rocheford TR (2011) Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genet 7:e1002383.  https://doi.org/10.1371/journal.pgen.1002383 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Buckler ES, Holland JB, Bradbury PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718.  https://doi.org/10.1126/science.1174276 CrossRefPubMedGoogle Scholar
  5. Chen J, Zhang L, Liu S et al (2016) The genetic basis of natural variation in kernel size and related traits using a four-way cross population in maize. PLoS ONE 11:e0153428.  https://doi.org/10.1371/journal.pone.0153428 CrossRefPubMedPubMedCentralGoogle Scholar
  6. D’Andrea KE, Otegui ME, Cirilo AG, Eyherabide GH (2013) Parent–progeny relationships between maize inbreds and hybrids: analysis of grain yield and its determinants for contrasting soil nitrogen conditions. Crop Sci 53:2147–2161.  https://doi.org/10.2135/cropsci2013.02.0111 CrossRefGoogle Scholar
  7. de Kroon H, Huber H, Stuefer JF, van Groenendael JM (2005) A modular concept of phenotypic plasticity in plants. New Phytol 166:73–82.  https://doi.org/10.1111/j.1469-8137.2004.01310.x CrossRefPubMedGoogle Scholar
  8. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620.  https://doi.org/10.1111/j.1365-294X.2005.02553.x CrossRefGoogle Scholar
  9. Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742–754.  https://doi.org/10.1071/AR9630742 CrossRefGoogle Scholar
  10. Gillespie JH, Turelli M (1989) Genotype-environment interactions and the maintenance of polygenic variation. Genetics 121:129–138PubMedPubMedCentralGoogle Scholar
  11. Hill WG, Weir BS (1994) Maximum-likelihood estimation of gene location by linkage disequilibrium. Am J Hum Genet 54:705–714PubMedPubMedCentralGoogle Scholar
  12. Jiang L, Ge M, Zhao H, Zhang T (2015) Analysis of heterosis and quantitative trait loci for kernel shape related traits using triple testcross population in maize. PLoS ONE 10:e0124779.  https://doi.org/10.1371/journal.pone.0124779 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Kusmec A, Srinivasan S, Nettleton D, Schnable PS (2017) Distinct genetic architectures for phenotype means and plasticities in Zea mays. Nat Plants 3:715–723.  https://doi.org/10.1038/s41477-017-0007-7 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Kusmec A, de Leon N, Schnable PS (2018) Harnessing phenotypic plasticity to improve maize yields. Front Plant Sci.  https://doi.org/10.3389/fpls.2018.01377 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Lacaze X, Hayes PM, Korol A (2009) Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare. Heredity (Edinb) 102:163–173.  https://doi.org/10.1038/hdy.2008.76 CrossRefGoogle Scholar
  16. Laitinen RAE, Nikoloski Z (2018) Genetic basis of plasticity in plants. J Exp Bot 70:739–745.  https://doi.org/10.1093/jxb/ery404 CrossRefGoogle Scholar
  17. Li Q, Li L, Yang X, Warburton ML, Bai G, Dai J, Li J, Yan J (2010a) Relationship, evolutionary fate and function of two maize co-orthologs of rice GW2 associated with kernel size and weight. BMC Plant Biol 10:143.  https://doi.org/10.1186/1471-2229-10-143 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Li Q, Yang X, Bai G, Warburton ML, Mahuku G, Gore M, Dai J, Li J, Yan J (2010b) Cloning and characterization of a putative GS3 ortholog involved in maize kernel development. Theor Appl Genet 120:753–763.  https://doi.org/10.1007/s00122-009-1196-x CrossRefPubMedGoogle Scholar
  19. Li C, Li Y, Sun B et al (2013) Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize. Euphytica 193:303–316.  https://doi.org/10.1007/s10681-013-0901-7 CrossRefGoogle Scholar
  20. Li C, Li Y, Bradbury PJ, Wu X, Shi Y, Song Y, Zhang D, Rodgers-Melnick E, Buckler ES, Zhang Z, Li Y, Wang T (2015) Construction of high-quality recombination maps with low-coverage genomic sequencing for joint linkage analysis in maize. BMC Biol 13:78.  https://doi.org/10.1186/s12915-015-0187-4 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Li D, Wang X, Zhang X et al (2016) The genetic architecture of leaf number and its genetic relationship to flowering time in maize. New Phytol 210:256–268.  https://doi.org/10.1111/nph.13765 CrossRefPubMedGoogle Scholar
  22. Lian L, de Los Campos G (2016) FW: an R package for Finlay-Wilkinson regression that incorporates genomic/pedigree information and covariance structures between environments. G3 (Bethesda) 6:589–597.  https://doi.org/10.1534/g3.115.026328 CrossRefGoogle Scholar
  23. Lin YR, Schertz KF, Paterson AH (1995) Comparative analysis of QTLs affecting plant height and maturity across the Poaceae, in reference to an interspecific sorghum population. Genetics 141:391–411PubMedPubMedCentralGoogle Scholar
  24. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399.  https://doi.org/10.1093/bioinformatics/bts444 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Liu Y, Wang L, Sun C, Zhang Z, Zheng Y, Qiu F (2014) Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Theor Appl Genet 127:1019–1037.  https://doi.org/10.1007/s00122-014-2276-0 CrossRefPubMedGoogle Scholar
  26. Liu J, Deng M, Guo H, Raihan S, Luo J, Xu Y, Dong X, Yan J (2015) Maize orthologs of rice GS5 and their trans-regulator are associated with kernel development. J Integr Plant Biol 57:943–953.  https://doi.org/10.1111/jipb.12421 CrossRefPubMedGoogle Scholar
  27. Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet 12:e1005767.  https://doi.org/10.1371/journal.pgen.1005767 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Liu J, Huang J, Guo H et al (2017) The conserved and unique genetic architecture of kernel size and weight in maize and rice. Plant Physiol 175:774–785.  https://doi.org/10.1104/pp.17.00708 CrossRefPubMedPubMedCentralGoogle Scholar
  29. McClintock B (1950) The origin and behavior of mutable loci in maize. Proc Natl Acad Sci U S A 36:344–355.  https://doi.org/10.1073/pnas.36.6.344 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, Poot P, Purugganan MD, Richards CL, Valladares F, van Kleunen M (2010) Plant phenotypic plasticity in a changing climate. Trends Plant Sci 15:684–692.  https://doi.org/10.1016/j.tplants.2010.09.008 CrossRefPubMedGoogle Scholar
  31. Peng B, Li Y, Wang Y et al (2011) QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet 122:1305–1320.  https://doi.org/10.1007/s00122-011-1532-9 CrossRefPubMedGoogle Scholar
  32. Prado SA, Sadras VO, Borras L (2014) Independent genetic control of maize (Zea mays L.) kernel weight determination and its phenotypic plasticity. J Exp Bot 65:4479–4487.  https://doi.org/10.1093/jxb/eru215 CrossRefGoogle Scholar
  33. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  34. Raihan MS, Liu J, Huang J, Guo H, Pan Q, Yan J (2016) Multi-environment QTL analysis of grain morphology traits and fine mapping of a kernel-width QTL in Zheng58×SK maize population. Theor Appl Genet 129:1465–1477.  https://doi.org/10.1007/s00122-016-2717-z CrossRefPubMedGoogle Scholar
  35. Sadras VO (2007) Evolutionary aspects of the trade-off between seed size and number in crops. Field Crops Res 100:125–138.  https://doi.org/10.1016/j.fcr.2006.07.004 CrossRefGoogle Scholar
  36. Sambatti JB, Caylor KK (2007) When is breeding for drought tolerance optimal if drought is random? New Phytol 175:70–80.  https://doi.org/10.1111/j.1469-8137.2007.02067.x CrossRefPubMedGoogle Scholar
  37. Scheiner SM (1993) Genetics and evolution of phenotypic plasticity. Annu Rev Ecol Syst 24:35–68.  https://doi.org/10.1146/annurev.es.24.110193.000343 CrossRefGoogle Scholar
  38. Scheiner SM, Lyman RF (1989) The genetics of phenotypic plasticity I. Heritability. J Evol Biol 2:95–107.  https://doi.org/10.1046/j.1420-9101.1989.2020095.x CrossRefGoogle Scholar
  39. Scheiner SM, Caplan RL, Lyman RF (1991) The genetics of phenotypic plasticity. III. Genetic correlations and fluctuating asymmetries. J Evol Biol 4:51–68.  https://doi.org/10.1046/j.1420-9101.1991.4010051.x CrossRefGoogle Scholar
  40. Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW (2013) Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet 14:483.  https://doi.org/10.1038/nrg3461 CrossRefPubMedPubMedCentralGoogle Scholar
  41. Stratton DA (1998) Reaction norm functions and QTL-environment interactions for flowering time in Arabidopsis thaliana. Heredity (Edinb) 81:144–155.  https://doi.org/10.1046/j.1365-2540.1998.00369.x CrossRefGoogle Scholar
  42. Su G, Madsen P, Lund MS, Sorensen D, Korsgaard IR, Jensen J (2006) Bayesian analysis of the linear reaction norm model with unknown covariates. J Anim Sci 84:1651–1657.  https://doi.org/10.2527/jas.2005-517 CrossRefPubMedGoogle Scholar
  43. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162.  https://doi.org/10.1038/ng.746 CrossRefPubMedGoogle Scholar
  44. Valdar W, Holmes CC, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182:1263–1277.  https://doi.org/10.1534/genetics.109.100727 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Valladares F, Sanchez-Gomez D, Zavala MA (2006) Quantitative estimation of phenotypic plasticity: bridging the gap between the evolutionary concept and its ecological applications. J Ecol 94:1103–1116.  https://doi.org/10.1111/j.1365-2745.2006.01176.x CrossRefGoogle Scholar
  46. Via S (1993) Adaptive phenotypic plasticity: target or by-product of selection in a variable environment? Am Nat 142:352–365.  https://doi.org/10.1086/285542 CrossRefPubMedGoogle Scholar
  47. Via S, Lande R (1985) Genotype-environment interaction and the evolution of phenotypic plasticity. Evolution 39:505–522.  https://doi.org/10.1111/j.1558-5646.1985.tb00391.x CrossRefPubMedGoogle Scholar
  48. Weber SL, Scheiner SM (1992) The genetics of phenotypic plasticity. IV. Chromosomal localization. J Evol Biol 5:109–120.  https://doi.org/10.1046/j.1420-9101.1992.5010109.x CrossRefGoogle Scholar
  49. Wu R (1998) The detection of plasticity genes in heterogeneous environments. Evolution 52:967–977.  https://doi.org/10.1111/j.1558-5646.1998.tb01826.x CrossRefPubMedGoogle Scholar
  50. Wu X, Li Y, Li X, Li C, Shi Y, Song Y, Zheng Z, Li Y, Wang T (2015) Analysis of genetic differentiation and genomic variation to reveal potential regions of importance during maize improvement. BMC Plant Biol 15:256.  https://doi.org/10.1186/s12870-015-0646-7 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Wu X, Li Y, Fu J, Li X, Li C, Zhang D, Shi Y, Song Y, Li Y, Wang T (2016) Exploring identity-by-descent segments and putative functions using different foundation parents in maize. PLoS ONE 11:e0168374.  https://doi.org/10.1371/journal.pone.0168374 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Zhang Z, Liu Z, Hu Y, Li W, Fu Z, Ding D, Li H, Qiao M, Tang J (2014) QTL analysis of kernel-related traits in maize using an immortalized F2 population. PLoS ONE 9:e89645.  https://doi.org/10.1371/journal.pone.0089645 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Crop SciencesChinese Academy of Agricultural SciencesBeijingChina

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