Advertisement

Genome-wide association study of maize plant architecture using F1 populations

  • Yang Zhao
  • Hengsheng Wang
  • Chen Bo
  • Wei Dai
  • Xingen Zhang
  • Ronghao Cai
  • Longjiang Gu
  • Qing Ma
  • Haiyang Jiang
  • Jun Zhu
  • Beijiu Cheng
Article
  • 92 Downloads

Abstract

Key message

Genome-wide association study of maize plant architecture using F1 populations can better dissect various genetic effects that can provide precise guidance for genetic improvement in maize breeding.

Abstract

Maize grain yield has increased at least eightfold during the past decades. Plant architecture, including plant height, leaf angle, leaf length, and leaf width, has been changed significantly to adapt to higher planting density. Although the genetic architecture of these traits has been dissected using different populations, the genetic basis remains unclear in the F1 population. In this work, we perform a genome-wide association study of the four traits using 573 F1 hybrids with a mixed linear model approach and QTXNetwork mapping software. A total of 36 highly significant associated quantitative trait SNPs were identified for these traits, which explained 51.86–79.92% of the phenotypic variation and were contributed mainly by additive, dominance, and environment-specific effects. Heritability as a result of environmental interaction was more important for leaf angle and leaf length, while major effects (a, aa, and d) were more important for leaf width and plant height. The potential breeding values of the superior lines and superior hybrids were also predicted, and these values can be applied in maize breeding by direct selection of superior genotypes for the associated quantitative trait SNPs. A total of 108 candidate genes were identified for the four traits, and further analysis was performed to screen the potential genes involved in the development of maize plant architecture. Our results provide new insights into the genetic architecture of the four traits, and will be helpful in marker-assisted breeding for maize plant architecture.

Keywords

Maize GWAS QTSs Plant architecture F1 population 

Abbreviations

PH

Plant height

LA

Leaf angle

LL

Leaf length

LW

Leaf width

GWAS

Genome-wide association study

QTSs

Quantitative trait SNPs

SNP

Single nucleotide polymorphism

RNA-Seq

RNA sequencing

GE

Genotype × environment interaction

a

Additive

d

Dominance

aa

Epistasis

Notes

Acknowledgements

We would like to thank Yunbi Xu and Chuanxiao Xie for their assistance in providing the genotype data of the 87 inbred lines.

Author contributions

JBC, JZ and YZ conceived and designed the experiments; HSW, CB, WD and RHC performed the experiments; JZ, YZ and LJG analyzed the data; QM and HYJ participated in the design of the study; YZ and JZ prepared the manuscript.

Funding

This research was supported by grants from the National Natural Science Foundation of China (91435110), the National Key Research and Development Program of China (2017YFD01012054) and the Science and Technology Major Project of Anhui Province (15czz03119).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

11103_2018_797_MOESM1_ESM.tif (978 kb)
Supplementary Figure S1 Heatmap of 108 candidate genes for the four traits at different developmental stages and in different tissues. (A), 24 candidate genes for PH; (B), 18 candidate genes for LA; (C), 31 candidate genes for LW; (D), 35 candidate genes for LL. The expression levels were transformed by log2 (FPKM+1), and are represented by the color scale at the right of the figure. (TIF 978 KB)
11103_2018_797_MOESM2_ESM.tif (1.9 mb)
Supplementary Figure S2 Gene Ontology (GO) classification of the genes for each trait. (A) GO classification of the genes for PH, (B) GO classification of the genes for LA, (C) GO classification of the genes for LL, and (D) GO classification of the genes for LW. (TIF 1907 KB)
11103_2018_797_MOESM3_ESM.tif (1.8 mb)
Supplementary Figure S3 KEGG pathway enrichment analysis of the genes for each trait. (A) Pathway enrichment analysis of the genes for PH, (B) Pathway enrichment analysis of the genes for LA, (C) Pathway enrichment analysis of the genes for LL, and (D) Pathway enrichment analysis of the genes for LW. (TIF 1888 KB)
11103_2018_797_MOESM4_ESM.docx (18 kb)
Supplementary Table S1 (DOCX 18 KB)
11103_2018_797_MOESM5_ESM.xlsx (20 kb)
Supplementary Table S2 (XLSX 19 KB)
11103_2018_797_MOESM6_ESM.xlsx (14 kb)
Supplementary Table S3 (XLSX 14 KB)
11103_2018_797_MOESM7_ESM.xls (43 kb)
Supplementary Table S4 (XLS 43 KB)

References

  1. Bauer P, Lubkowitz M, Tyers R, Nemoto K, Meeley RB, Goff SA, Freeling M (2004) Regulation and a conserved intron sequence of liguleless3/4 knox class-I homeobox genes in grasses. Planta 219:359–368CrossRefPubMedGoogle Scholar
  2. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC et al (2009) The genetic architecture of maize flowering time. Science 325:714–718CrossRefPubMedGoogle Scholar
  3. Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand WH, Samani NJ et al (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678CrossRefGoogle Scholar
  4. Caicedo AL, Stinchcombe JR, Olsen KM, Schmitt J, Purugganan MD (2004) Epistatic interaction between Arabidopsis FRI and FLC flowering time genes generates a latitudinal cline in a life history trait. Proc Natl Acad Sci USA 101:15670–15675CrossRefPubMedGoogle Scholar
  5. Cardwell VB (1982) Fifty years of Minnesota corn production: Sources of yield increase. Agron J 74:984–990CrossRefGoogle Scholar
  6. Carlborg Ö, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625CrossRefPubMedGoogle Scholar
  7. Doebley J (2004) The genetics of maize evolution. Annu Rev Genet 38:37–59CrossRefPubMedGoogle Scholar
  8. Duvick DN (2005) Genetic progress in yield of United States Maize (Zea mays L.). Maydica 50:193–202Google Scholar
  9. Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374CrossRefPubMedGoogle Scholar
  10. Holloway B, Li B (2010) Expression QTLs: applications for crop improvement. Mol Breed 26:381–391CrossRefGoogle Scholar
  11. Huang XH, Wei XH, Sang T, Zhao QA, Feng Q, Zhao Y, Li CY, Zhu CR, Lu TT, Zhang ZW et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–976CrossRefPubMedPubMedCentralGoogle Scholar
  12. Jia Y, Sun X, Sun J, Pan Z, Wang X, He S, Xiao S, Shi W, Zhou Z, Pang B et al (2014) Association mapping for epistasis and environmental interaction of yield traits in 323 cotton cultivars under 9 different environments. PLoS ONE 9:e95882CrossRefPubMedPubMedCentralGoogle Scholar
  13. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:480–484CrossRefGoogle Scholar
  14. Ku LX, Zhao WM, Zhang J, Wu LC, Wang CL, Wang PA, Zhang WQ, Chen YH (2010) Quantitative trait loci mapping of leaf angle and leaf orientation value in maize (Zea mays L.). Theor Appl Genet 121:951–959CrossRefPubMedGoogle Scholar
  15. Lambert R, Johnson R (1978) Leaf angle, tassel morphology, and the performance of maize hybrids. Crop Sci 18:499–502CrossRefGoogle Scholar
  16. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559CrossRefGoogle Scholar
  17. Li H, Peng ZY, Yang XH, Wang WD, Fu JJ, Wang JH, Han YJ, Chai YC, Guo TT, Yang N et al (2013) Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet 45:43–50CrossRefPubMedPubMedCentralGoogle Scholar
  18. Li CH, Li YX, Shi YS, Song YC, Zhang DF, Buckler ES, Zhang ZW, Wang TY, Li Y (2015) Genetic control of the leaf angle and leaf orientation value as revealed by ultra-high density maps in three connected maize populations. PLoS ONE 10:e0121624CrossRefPubMedPubMedCentralGoogle Scholar
  19. Liu SX, Wang XL, Wang HW, Xin HB, Yang XH, Yan JB, Li JS, Tran LSP, Shinozaki K, Yamaguchi-Shinozaki K et al (2013a) Genome-wide analysis of ZmDREB genes and their association with natural variation in drought tolerance at seedling stage of Zea mays L. PLoS Genet 9:e1003790CrossRefPubMedPubMedCentralGoogle Scholar
  20. Liu YJ, Xiu ZH, Meeley R, Tan BC (2013b) Empty pericarp5 encodes a pentatricopeptide repeat protein that is required for mitochondrial RNA editing and seed development in maize. Plant Cell 25:868–883CrossRefPubMedPubMedCentralGoogle Scholar
  21. Lu Y, Yan J, Guimarães CT, Taba S, Hao Z, Gao S, Chen S, Li J, Zhang S, Vivek BS et al (2009) Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms. Theor Appl Genet 120:93–115CrossRefPubMedGoogle Scholar
  22. Luo X, Ding Y, Zhang L, Yue Y, Snyder JH, Ma C, Zhu J (2017) Genomic prediction of genotypic effects with epistasis and environment interactions for yield-related traits of rapeseed (Brassica napus L.). Front Genet 8:15CrossRefPubMedPubMedCentralGoogle Scholar
  23. Mackay TF (2001) The genetic architecture of quantitative traits. Annu Rev Genet 35:303–339CrossRefPubMedGoogle Scholar
  24. Mackay TF, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10:565–577CrossRefPubMedGoogle Scholar
  25. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C et al (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740CrossRefPubMedGoogle Scholar
  26. Mei Y, Yu J, Xue A, Fan S, Song M, Pang C, Pei W, Yu S, Zhu J (2017) Dissecting genetic network of fruit branch traits in upland cotton by association mapping using SSR markers. PLoS ONE 12:e0162815CrossRefPubMedPubMedCentralGoogle Scholar
  27. Mickelson SM, Stuber CS, Senior L, Kaeppler SM (2002) Quantitative trait loci controlling leaf and tassel traits in a B73 × Mo17 population of maize. Crop Sci 42:1902–1909CrossRefGoogle Scholar
  28. Mohan M, Nair S, Bhagwat A, Krishna T, Yano M, Bhatia C, Sasaki T (1997) Genome mapping, molecular markers and marker-assisted selection in crop plants. Mol Breed 3:87–103CrossRefGoogle Scholar
  29. Multani DS, Briggs SP, Chamberlin MA, Blakeslee JJ, Murphy AS, Johal GS (2003) Loss of an MDR transporter in compact stalks of maize br2 and sorghum dw3 mutants. Science 302:81–84CrossRefPubMedGoogle Scholar
  30. Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang ZW, Costich DE, Buckler ES (2009) Association mapping: Critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202CrossRefPubMedPubMedCentralGoogle Scholar
  31. Peiffer JA, Romay MC, Gore MA, Flint-Garcia SA, Zhang Z, Millard MJ, Gardner CA, McMullen MD, Holland JB, Bradbury PJ et al (2014) The genetic architecture of maize height. Genetics 196:1337–1356CrossRefPubMedPubMedCentralGoogle Scholar
  32. Poland JA, Bradbury PJ, Buckler ES, Nelson RJ (2011) Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci USA 108:6893–6898CrossRefPubMedGoogle Scholar
  33. Sambandan D, Carbone MA, Anholt RRH, Mackay TEC (2008) Phenotypic plasticity and genotype by environment interaction for olfactory behavior in Drosophila melanogaster. Genetics 179:1079–1088CrossRefPubMedPubMedCentralGoogle Scholar
  34. Searle SR, Casella G, McCulloch CE (1992) Variance components. Wiley, New YorkCrossRefGoogle Scholar
  35. Sinclair TR, Sheehy JE (1999) Erect leaves and photosynthesis in rice. Science 283:1455CrossRefGoogle Scholar
  36. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432CrossRefPubMedGoogle Scholar
  37. Stelpflug SC, Sekhon RS, Vaillancourt B, Hirsch CN, Buell CR, de Leon N, Kaeppler SM (2016) An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development. Plant Genome.  https://doi.org/10.3835/plantgenome2015.04.0025 CrossRefPubMedGoogle Scholar
  38. 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 43:159–162CrossRefPubMedGoogle Scholar
  39. Uwatoko N, Onishi A, Ikeda Y, Kontani M, Sasaki A, Matsubara K, Itoh Y, Sano Y (2008) Epistasis among the three major flowering time genes in rice: coordinate changes of photoperiod sensitivity, basic vegetative growth and optimum photoperiod. Euphytica 163:167–175CrossRefGoogle Scholar
  40. Wang YH, Li JY (2008) Molecular basis of plant architecture. Annu Rev Plant Biol 59:253–279CrossRefPubMedGoogle Scholar
  41. Wang L, Wang Z, Xu YY, Joo SH, Kim SK, Xue Z, Xu ZH, Wang ZY, Chong K (2009) OsGSR1 is involved in crosstalk between gibberellins and brassinosteroids in rice. Plant J 57:498–510CrossRefPubMedGoogle Scholar
  42. Wen WW, Li D, Li X, Gao YQ, Li WQ, Li HH, Liu J, Liu HJ, Chen W, Luo J et al (2014) Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun 5:3438CrossRefPubMedPubMedCentralGoogle Scholar
  43. Wright SI, Bi IV, Schroeder SG, Yamasaki M, Doebley JF, McMullen MD, Gaut BS (2005) The effects of artificial selection on the maize genome. Science 308:1310–1314CrossRefPubMedGoogle Scholar
  44. Xiao YJ, Tong H, Yang XH, Xu SZ, Pan QC, Qiao F, Raihan MS, Luo Y, Liu HJ, Zhang XH et al (2016) Genome-wide dissection of the maize ear genetic architecture using multiple populations. New Phytol 210:1095–1106CrossRefPubMedPubMedCentralGoogle Scholar
  45. Xie C, Zhang S, Li M, Li X, Hao Z, Li B, Zhang D, Liang Y (2007) Inferring genome ancestry and estimating molecular relatedness among 187 Chinese Maize inbred lines. J Genet Genomics 34(8):738–748CrossRefPubMedGoogle Scholar
  46. Xie C, Weng J, Liu W, Zou C, Hao Z, Li W, Li M, Guo X, Zhang G, Xu Y (2013) Zea mays (L.) P1 locus for cob glume color identified as a post-domestication selection target with an effect on temperate maize genomes. Crop J 1:15–24CrossRefGoogle Scholar
  47. Xue YD, Warburton ML, Sawkins M, Zhang XH, Setter T, Xu YB, Grudloyma P, Gethi J, Ribaut JM, Li WC et al (2013) Genome-wide association analysis for nine agronomic traits in maize under well-watered and water-stressed conditions. Theor Appl Genet 126:2587–2596CrossRefPubMedGoogle Scholar
  48. Yamamuro C, Ihara Y, Wu X, Noguchi T, Fujioka S, Takatsuto S, Ashikari M, Kitano H, Matsuoka M (2000) Loss of function of a rice brassinosteroid insensitive1 homolog prevents internode elongation and bending of the lamina joint. Plant Cell 12:1591–1606CrossRefPubMedPubMedCentralGoogle Scholar
  49. Yang J, Zhu J (2005) Methods for predicting superior genotypes under multiple environments based on QTL effects. Theor Appl Genet 110:1268–1274CrossRefPubMedGoogle Scholar
  50. Yang J, Zhu J, Williams RW (2007) Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics 23:1527–1536CrossRefPubMedGoogle Scholar
  51. Yang XH, Gao SB, Xu ST, Zhang ZX, Prasanna BM, Li L, Li JS, Yan JB (2011) Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize. Mol Breed 28:511–526CrossRefGoogle Scholar
  52. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:1544–6115CrossRefGoogle Scholar
  53. Zhang FT, Zhu ZH, Tong XR, Zhu ZX, Qi T, Zhu J (2015a) Mixed linear model approaches of association mapping for complex traits based on omics variants. Sci Rep 5:10298CrossRefPubMedPubMedCentralGoogle Scholar
  54. Zhang SN, Wang SK, Xu YX, Yu CL, Shen CJ, Qian Q, Geisler M, Jiang DA, Qi YH (2015b) The auxin response factor, OsARF19, controls rice leaf angles through positively regulating OsGH3-5 and OsBRI1. Plant Cell Environ 38:638–654CrossRefPubMedGoogle Scholar
  55. Zhu ZX, Tong XR, Zhu ZH, Liang MM, Cui WY, Su KK, Li MD, Zhu J (2013) Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes. PLoS ONE 8:e61943CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.National Engineering Laboratory of Crop Stress Resistance Breeding, School of Life SciencesAnhui Agricultural UniversityHefeiChina
  2. 2.Key Laboratory of Crop Biology of Anhui Province, School of Life SciencesAnhui Agricultural UniversityHefeiChina
  3. 3.Institute of BioinformaticsZhejiang UniversityHangzhouChina

Personalised recommendations