Skip to main content
SpringerLink
Log in

Genome-wide association study of the husk number and weight in maize (Zea mays L.)

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

Maize husk number and weight are two vital traits, influencing the grain drying rate after physiological maturity, shattering and breakage rate in the progress of combine harvesting, in breeding varieties suitable for mechanized harvest. Unveiling the genetic basis of the husk number and weight would be useful for guiding maize genetic improvement of mechanical harvesting. The present study is the first to conduct a genome-wide association study of the husk number and weight. In this study, 253 maize inbred lines were evaluated in three environments to detect single nucleotide polymorphisms (SNPs) for the husk number and weight using the Maize SNP3 K Beadchip. Based on the mixed linear model, 24 associated SNPs for husk number and 29 associated SNPs for husk weight were detected with P < 0.001 in different environments as well as the best linear unbiased predictions over all environments. Eight and nine stable SNPs for husk number and weight were detected in all environments, respectively. Based on the phenotypic effects of the alleles of these stable SNPs, the favorable alleles were mined. Several typical accessions harboring favorable alleles with elite phenotypic performance of husk number and weight were identified, such as T53, BJT4, Zong3, A489, and BJT6. Five elite parental combinations were predicted for reducing maize husk number and weight. These results might serve as a basis for quantitative trait loci fine mapping and the genetic improvement of maize husk number and weight through molecular marker-assisted approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  PubMed  Google Scholar 

  • Brewbaker JL, Kim SW (1979) Inheritance of husk number and ear insect damage in maize. Crop Sci 19:32–36

    Article  Google Scholar 

  • Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia SA, Garcia A, Glaubitz JC, Goodman MM, Harjes C, Guill K, Kroon DE, Larsson S, Lepak NK, Li HH, Mitchell SE, Pressoir G, Peiffer JA, Rosas MO, Rocheford TR, Romay MC, Romero S, Salvo S, Villeda HS, Silva HS, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu JM, Zhang ZW, Kresovich S, McMullen MD (2009) The genetic architecture of maize flowering time. Science 325:714–718

    Article  CAS  PubMed  Google Scholar 

  • Cantell RG, Geadelmann JL (1981) Contribution of husk leaves to maize grain yield. Crop Sci 21:544–546

    Article  Google Scholar 

  • Ching A, Caldwell KS, Jung M, Dolan M, Smith OS, Tingey S, Morgante M, Rafslaski JA (2002) SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genet 3:19

    Article  PubMed  PubMed Central  Google Scholar 

  • Cross HZ (1985) A selection procedure for ear drying-rates in early maize. Euphytica 34:409–418

    Article  Google Scholar 

  • 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

    Article  CAS  PubMed  Google Scholar 

  • Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374

    Article  CAS  PubMed  Google Scholar 

  • Flint-Garcia SA, Thuillet AC, Yu JM, Pressoir G, Romero SM, Mitchell SE, Doebley J, Kresovich S, Goodman MM, Buckler ES (2005) Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J 44:1054–1064

    Article  CAS  PubMed  Google Scholar 

  • Fujita K, Sato H, Sawada O, Sendo S (1995) Husk leaves contribution on dry matter and grain production as well as N distribution in flint corn (Zea mays L.) genotypes differing in husk leaves area. Soil Sci Plant Nutr 41(3):587–596

    Article  Google Scholar 

  • Ganal MW, Durstewitz G, Polley A, Berard A, Buckler ES, Charcosset A, Clarke JD, Graner E-M, Hansen M, Joets J (2011) A larger maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with B73 reference genome. PLoS One 6:e28334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hallauer AR, Miranda JB (1998) Quantitative genetics in maize breeding, 2nd edn. Iowa State University Press, Ames

    Google Scholar 

  • Hao DR, Cao MN, Yin ZT, Yu DY (2012) Genome-wide association analysis detecting significant single nucleotide polymorphisms for chlorophyll and chlorophyll fluorescence parameters in soybean (Glycine max) landraces. Euphytica 186(3):919–931

    Article  CAS  Google Scholar 

  • Hao DR, Cheng YJ, Chen GQ, Lu HH, Shi ML, Zhang ZL, Huang XL, Mao YX, Xue L (2015) Identification of significant single nucleotide polymorphisms for resistance to maize rough dwarf disease in elite maize (Zea mays L.) inbred lines. Euphytica 203:109–120

    Article  CAS  Google Scholar 

  • Hardy O, Vekenmans X (2002) SPAGeDi: a versatile computer program to analysis spatial genetic structure at the individual or population levels. Mol Ecol Not 2:618–620

    Article  Google Scholar 

  • He D, Wang XQ, Liu CM, Yu XJ, Chen GM, Zhang CY, He Y (2001) Studies on relationship among husk agronomical traits and their inheritance in maize. J. Maize Sci 9:43–45

    Google Scholar 

  • Huo SP, Yan QJ, Xu ML, Zhang J (2000) Genetic analysis of ear bract characters in maize. Rain Fed Crops 20:8–12

    CAS  Google Scholar 

  • Johnson VE (2013) Revised standards for statistical evidence. Proc Natl Acad Sci USA 110:19313–19317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jung M, Ching A, Bhattramakki D, Dolan M, Tinger S, Morgante M, Rafalski A (2004) Linkage disequilibrium and sequence diversity in a germplasm. Theor Appl Genet 109:681–689

    Article  CAS  PubMed  Google Scholar 

  • Kang MS, Zuber MS, Colbert TR, Horroeks RD (1986) Effect of certain agronomic traits on and relationship between rates of grain moisture reduction and grain fill during the filling period in maize. Field crop research 14:339–346

    Article  Google Scholar 

  • Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeze-Rosas MA, Zwonitzer JC, Kresovich S, McMullen MD, Ware D, Balint-Kurti PJ, Holland JB (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163–168

    Article  CAS  PubMed  Google Scholar 

  • Li SF, Zhang CX, Lu M, Liu WG, Li XH (2014) Research development of kernel dehydration rate in maize. Mol. Plant Breed 12(4):825–829

    CAS  Google Scholar 

  • Liu K, Muse S (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128

    Article  CAS  PubMed  Google Scholar 

  • Liu CL, Weng JF, Zhang DG, Zhang XC, Yang XY, Shi LY, Meng QC, Yuan JH, Guo XP, Hao ZF, Xie CX, Li MS, Ci XK, Bai L, Li XH, Zhang SH (2014) Genome-wide association study of resistance to rough dwarf diease in maize. Eur J Plant Pathol 139:205–216

    Article  CAS  Google Scholar 

  • Lv GH, Chen JJ, Xu XH, Guo GJ (2015) Genetic analysis and assessment of corn-husk traits of fresh-eating waxy corn. Acta Agirc. Zhejiangensis 27(7):1122–1126

    Google Scholar 

  • Ma ZY, Dong YB, Qiao DH, Hu CH, Deng F, Li YL (2015) Analysis on bract traits of different maize hybrids. J Henan Agric Sci 44:15–18

    Google Scholar 

  • Malosetti M, Ribaut JM, Vargas M, Crossa J, Eeuwijk FA (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trails in maize (Zea mays L.). Euphytica 161:241–257

    Article  Google Scholar 

  • Messmer R, Fracheboud Y, Banziger M, Vargas M, Stamp P, Ribaut JM (2009) Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits. Theor Appl Genet 119:913–930

    Article  PubMed  Google Scholar 

  • Muarry M, Thompson W (1980) Rapid isolation of high molecular weight plant DNA. Nucl Acids Res 8:4321–4326

    Article  Google Scholar 

  • Nei M, Tajima F, Tateno Y (1983) Accuracy of estimated phylogenetic trees from molecular data. II: gene frequency data. J Mol Evol 19:153–170

    Article  CAS  PubMed  Google Scholar 

  • Pritchard JK, Stephens M, Donnelty P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rafalski JA (2010) Association genetics in crop improvement. Curr Opin Plant Biol 13:174–180

    Article  CAS  PubMed  Google Scholar 

  • SAS Institute Inc (2002) SAS guide for personal computer, 9th edn. SAS Institute, Cary

    Google Scholar 

  • Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: molecular evolutional genetics analysis (MEGA) software version 4.0. Mol Biol Evol 24:1596–1599

    Article  CAS  PubMed  Google Scholar 

  • Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES (2001) Dwarf8 polymorphisms associated with variation in flowering time. Nat Genet 28:286–289

    Article  CAS  PubMed  Google Scholar 

  • Troyer AF, Ambrose WB (1971) Plant characteristics affecting field drying rate of ear corn. Crop Sci 11:529–531

    Article  Google Scholar 

  • Xu YB (2010) Molecular plant breeding. CAB International, Wallingford

    Book  Google Scholar 

  • Yan JB, Warburton M, Crouch J (2011) Association mapping for enhancing maize genetic improvement. Crop Sci 51:1–7

    Article  Google Scholar 

  • Yang XH, Gao S, 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(4):511–526

    Article  Google Scholar 

  • Yang N, Lu YL, Yang XH, Huang J, Zhou Y, Ali F, Wen WW, Liu J, Li JS, Yan JB (2014) Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet 10:e1004573

    Article  PubMed  PubMed Central  Google Scholar 

  • Yu JM, Buckler ES (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotech 17:155–160

    Article  CAS  PubMed  Google Scholar 

  • Zhang LG, Wang ZH, Zhang L, Wang X, Liu XJ, Chen XC, Li B, Zhang Y (2007) Analysis of combining ability of kernel dehydration rate after physiological maturity in maize. Crop 3:21–22

    Google Scholar 

  • Zhu CS, Gore M, Buckler ES, Yu JM (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was funded by the Scientific and Technological Program of Jiangsu Province, China (BE2013434), the Fund for Independent Innovation of Agricultural Science and Technology of Jiangsu Province, China (CX(14)2006), the Sanxin Agricultural Project of Jiangsu Province, China (SXGC(2014)088), and the Natural Science Foundation of Jiangsu Province, China (BK20141241).

Author information

Authors and Affiliations

  1. Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong, 226541, China

    Guangfei Zhou, Derong Hao, Guoqing Chen, Huhua Lu, Mingliang Shi, Yuxiang Mao, Zhengliang Zhang, Xiaolan Huang & Lin Xue

  2. Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing, 210095, China

    Guoqing Chen & Lin Xue

Authors
  1. Guangfei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Derong Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoqing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huhua Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mingliang Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuxiang Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhengliang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaolan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lin Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Xue.

Electronic supplementary material

Below is the link to the electronic supplementary material.

10681_2016_1698_MOESM1_ESM.tif

Supplementary material 1 (TIFF 1077 kb). Fig. S1 Neighbor-joining tree of 253 maize inbred lines. Blue line represents common maize inbred lines; red represents waxy maize inbred lines

10681_2016_1698_MOESM2_ESM.tif

Supplementary material 2 (TIFF 3167 kb). Fig. S2 Neighbor-joining tree of common maize inbred lines. Green line represents BSSS heterotic group; red line represents LAN heterotic group; purple line represent PB heterotic group

10681_2016_1698_MOESM3_ESM.tif

Supplementary material 3 (TIFF 2685 kb). Fig. S3 Neighbor-joining tree of waxy maize inbred lines. Black line represents HB522 heterotic group; red line represents TX5 heterotic group; green line represent tropical germplasm

10681_2016_1698_MOESM4_ESM.tif

Supplementary material 4 (TIFF 2539 kb). Fig. S4 Quantile–quantile plots of estimated −log10 (P) from association analysis using four models. Blue dots represent observed P values using the GLM without Q and K; green dots represent the Q model with Q; purple dots represent the K model with K; red dots represent the MLM with Q and K. (a)–(d) represent the husk number in Taian (TA), Nantong (NT), Sanya (SY), and best linear unbiased predictions (BLUPs), respectively. (e)–(h) represent the husk weight in Taian (TA), Nantong (NT), Sanya (SY), and best linear unbiased predictions (BLUPs), respectively

Supplementary material 5 (DOCX 37 kb)

Supplementary material 6 (DOCX 16 kb)

Supplementary material 7 (DOCX 15 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, G., Hao, D., Chen, G. et al. Genome-wide association study of the husk number and weight in maize (Zea mays L.). Euphytica 210, 195–205 (2016). https://doi.org/10.1007/s10681-016-1698-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10681-016-1698-y

Keywords

Search

Navigation