Skip to main content
SpringerLink
Log in

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

  • Original Paper
  • Published:
Theoretical and Applied Genetics Aims and scope Submit manuscript

Abstract

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.

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

Similar content being viewed by others

Abbreviations

KL:

20-Kernel length

KW:

20-Kernel width

KT:

20-Kernel thickness

HKW:

100-Kernel weight

CIM:

Composite interval mapping

MCIM:

Mixed linear model-based composite interval mapping

QTL:

Quantitative trait loci

SSR:

Single sequence repeat

MAS:

Marker-assisted selection

QEI:

QTL × environment interaction

References

  • Arumuganathan K, Earle E (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9:208–218

    Article  CAS  Google Scholar 

  • Austin D, Lee M (1996) Comparative mapping in F2:3 and F6:7 generations of quantitative trait loci for grain yield and yield components in maize. Theor Appl Genet 92:817–826

    Article  PubMed  CAS  Google Scholar 

  • Austin DF, Lee M (1998) Detection of quantitative trait loci for grain yield and yield components in maize across generations in stress and nonstress environments. Crop Sci 38:1296–1308

    Article  CAS  Google Scholar 

  • Ayoub M, Symons S, Edney M, Mather D (2002) QTLs affecting kernel size and shape in a two-rowed by six-rowed barley cross. Theor Appl Genet 105:237–247

    Article  PubMed  CAS  Google Scholar 

  • Backes G, Graner A, Foroughi-Wehr B, Fischbeck G, Wenzel G, Jahoor A (1995) Localization of quantitative trait loci (QTL) for agronomic important characters by the use of a RFLP map in barley (Hordeum vulgare L.). Theor Appl Genet 90:294–302

    Article  PubMed  CAS  Google Scholar 

  • Beavis W, Smith O, Grant D, Fincher R (1994) Identification of quantitative trait loci using a small sample of topcrossed and F4 progeny from maize. Crop Sci 34:882–896

    Article  Google Scholar 

  • Borrás L, Otegui ME (2001) Maize kernel weight response to postflowering source–sink ratio. Crop Sci 41:1816–1822

    Article  Google Scholar 

  • Breseghello F, Sorrells ME (2007) QTL analysis of kernel size and shape in two hexaploid wheat mapping populations. Field Crop Res 101:172–179

    Article  Google Scholar 

  • Cai H, Morishima H (2002) QTL clusters reflect character associations in wild and cultivated rice. Theor Appl Genet 104:1217–1228

    Article  PubMed  CAS  Google Scholar 

  • Carlborg Ö, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625

    Article  PubMed  CAS  Google Scholar 

  • Cho YG, Kang HJ, Lee JS, Lee YT, Lim SJ, Gauch H, Eun MY, McCouch SR (2007) Identification of quantitative trait loci in rice for yield, yield components, and agronomic traits across years and locations. Crop Sci 47:2403–2417

    Article  Google Scholar 

  • Doebley JF, Gaut BS, Smith BD (2006) The molecular genetics of crop domestication. Cell 127:1309–1321

    Article  PubMed  CAS  Google Scholar 

  • Doerge RW, Churchill GA (1996) Permutation tests for multiple loci affecting a quantitative character. Genetics 142:285–294

    PubMed Central  PubMed  CAS  Google Scholar 

  • Fan C, Xing Y, Mao H, Lu T, Han B, Xu C, Li X, Zhang Q (2006) GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet 112:1164–1171

    Article  PubMed  CAS  Google Scholar 

  • Goldman I, Rocheford T, Dudley J (1993) Quantitative trait loci influencing protein and starch concentration in the Illinois long term selection maize strains. Theor Appl Genet 87:217–224

    Article  PubMed  CAS  Google Scholar 

  • Gupta PK, Rustgi S, Kumar N (2006) Genetic and molecular basis of grain size and grain number and its relevance to grain productivity in higher plants. Genome 49:565–571

    Article  PubMed  Google Scholar 

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

    Google Scholar 

  • Han Y, Li D, Zhu D, Li H, Li X, Teng W, Li W (2012) QTL analysis of soybean seed weight across multi-genetic backgrounds and environments. Theor Appl Genet 125:671–683

    Article  PubMed  CAS  Google Scholar 

  • Hittalmani S, Huang N, Courtois B, Venuprasad R, Shashidhar H, Zhuang J, Zheng K, Liu G, Wang G, Sidhu J (2003) Identification of QTL for growth-and grain yield-related traits in rice across nine locations of Asia. Theor Appl Genet 107:679–690

    Article  PubMed  Google Scholar 

  • Hosseini M, Houshmand S, Mohamadi S, Tarang A, Khodambashi M, Rahimsoroush H (2012) Detection of QTLs with main, epistatic and QTL × environment interaction effects for rice grain appearance quality traits using two populations of backcross inbred lines (BILs). Field Crop Res 135:97–106

    Article  Google Scholar 

  • Ishimaru K (2003) Identification of a locus increasing rice yield and physiological analysis of its function. Plant Physiol 133:1083–1090

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  • Kosambi D (1943) The estimation of map distances from recombination values. Ann Eugen 12:172–175

    Article  Google Scholar 

  • Kroymann J, Mitchell-Olds T (2005) Epistasis and balanced polymorphism influencing complex trait variation. Nature 435:95–98

    Article  PubMed  CAS  Google Scholar 

  • Lander E, Kruglyak L (1995) Genetic dissection of complex traits guidelines for interpreting and reporting linkage results. Nat Genet 11:241–247

    Article  PubMed  CAS  Google Scholar 

  • Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181

    Article  PubMed  CAS  Google Scholar 

  • Li Y, Niu S, Dong Y, Cui D, Wang Y, Liu Y, Wei M (2007) Identification of trait-improving quantitative trait loci for grain yield components from a dent corn inbred line in an advanced backcross BC2F2 population and comparison with its F2:3 population in popcorn. Theor Appl Genet 115:129–140

    Article  PubMed  CAS  Google Scholar 

  • Li Y, Wang Y, Shi Y, Song Y, Wand T, Li Y (2009) Correlation analysis and QTL mapping for traits of kernel structure and yield components in maize. Sci Agric Sin 42:408–418

    CAS  Google Scholar 

  • 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

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Li Y, Fan C, Xing Y, Jiang Y, Luo L, Sun L, Shao D, Xu C, Li X, Xiao J (2011) Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Nat Genet 43:1266–1269

    Article  PubMed  CAS  Google Scholar 

  • Li Y, Yang M, Dong Y, Wang Q, Zhou Y, Zhou Q, Shen B, Zhang F, Liang X (2012) Three main genetic regions for grain development revealed through QTL detection and meta-analysis in maize. Mol Breed 30:195–211

    Article  CAS  Google Scholar 

  • Li C, Li Y, Sun B, Peng B, Liu C, Liu Z, Yang Z, Li Q, Tan W, Zhang Y (2013) Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize. Euphytica 193:303–316

    Article  CAS  Google Scholar 

  • Lima MDA, de Souza CL, Bento DAV, de Souza AP, Carlini-Garcia LA (2006) Mapping QTL for grain yield and plant traits in a tropical maize population. Mol Breed 17:227–239

    Article  Google Scholar 

  • Lincoln S, Daly M, Lander E (1992) Constructing genetic maps with MAPMAKER/EXP 3.0. Whitehead Institute Technical Report

  • Liu Z, Ji H, Cui Z, Wu X, Duan L, Feng X, Tang J (2011) QTL detected for grain-filling rate in maize using a RIL population. Mol Breed 27:25–36

    Article  Google Scholar 

  • Lu GH, Tang JH, Yan JB, Ma XQ, Li JS, Chen SJ, Ma JC, Liu ZX, Zhang YR, Dai JR (2006) Quantitative trait loci mapping of maize yield and its components under different water treatments at flowering time. J Integr Plant Biol 48:1233–1243

    Article  CAS  Google Scholar 

  • Lu M, Xie CX, Li XH, Hao ZF, Li MS, Weng JF, Zhang DG, Bai L, Zhang SH (2011) Mapping of quantitative trait loci for kernel row number in maize across seven environments. Mol Breed 28:143–152

    Article  CAS  Google Scholar 

  • Ma X, Tang J, Teng W, Yan J, Meng Y, Li J (2007) Epistatic interaction is an important genetic basis of grain yield and its components in maize. Mol Breed 20:41–51

    Article  Google Scholar 

  • Mackay TF, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10:565–577

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Marathi B, Guleria S, Mohapatra T, Parsad R, Mariappan N, Kurungara VK, Atwal SS, Prabhu KV, Singh NK, Singh AK (2012) QTL analysis of novel genomic regions associated with yield and yield related traits in new plant type based recombinant inbred lines of rice (Oryza sativa L.). BMC Plant Biol 12:137

    Article  PubMed Central  PubMed  Google Scholar 

  • Martin A, Lee J, Kichey T, Gerentes D, Zivy M, Tatout C, Dubois F, Balliau T, Valot B, Davanture M (2006) Two cytosolic glutamine synthetase isoforms of maize are specifically involved in the control of grain production. Plant Cell 18:3252–3274

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  • McCouch SR, Cho YG, Yano M, Paul E, Blinstrub M, Morishima H, Kinoshita T (1997) Report on QTL nomenclature. Rice Genet Newlett 14:11–13

    Google Scholar 

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

    Article  Google Scholar 

  • Miedaner T, Würschum T, Maurer HP, Korzun V, Ebmeyer E, Reif JC (2011) Association mapping for Fusarium head blight resistance in European soft winter wheat. Mol Breed 28:647–655

    Article  Google Scholar 

  • Miura K, Ikeda M, Matsubara A, Song X-J, Ito M, Asano K, Matsuoka M, Kitano H, Ashikari M (2010) OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet 42:545–549

    Article  PubMed  CAS  Google Scholar 

  • Peng B, Li Y, Wang Y, Liu C, Liu Z, Tan W, Zhang Y, Wang D, Shi Y, Sun B (2011) QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet 122:1305–1320

    Article  PubMed  Google Scholar 

  • Phillips PC (2008) Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9:855–867

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  • Qiu X, Gong R, Tan Y, Yu S (2012) Mapping and characterization of the major quantitative trait locus qSS7 associated with increased length and decreased width of rice seeds. Theor Appl Genet 125:1717–1726

    Article  PubMed  Google Scholar 

  • Ramya P, Chaubal A, Kulkarni K, Gupta L, Kadoo N, Dhaliwal H, Chhuneja P, Lagu M, Gupt V (2010) QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.). J Appl Genet 51:421–429

    Article  PubMed  CAS  Google Scholar 

  • Reif JC, Maurer HP, Korzun V, Ebmeyer E, Miedaner T, Würschum T (2011) Mapping QTLs with main and epistatic effects underlying grain yield and heading time in soft winter wheat. Theor Appl Genet 123:283–292

    Article  PubMed  Google Scholar 

  • Revilla P, Butrón A, Malvar R, Ordás R (1999) Relationship among kernel weight, early vigor, and growth in maize. Crop Sci 39:654–658

    Article  Google Scholar 

  • Ribaut J-M, Jiang C, Gonzalez-de-Leon D, Edmeades G, Hoisington D (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 

  • Saghai-Maroof MA, Soliman KM, Jorgensen RA, Allard RW (1984) Ribosomal DNA spacer-length polymorphisms in barley: mendelian inheritance, chromosomal location, and population dynamics. Proc Natl Acad Sci 81:8014–8018

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  • Santos FR, Pena SD, Epplen JT (1993) Genetic and population study of a Y-linked tetranucleotide repeat DNA polymorphism with a simple non-isotopic technique. Hum Genet 90:655–656

    Article  PubMed  CAS  Google Scholar 

  • Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302

    Article  PubMed  CAS  Google Scholar 

  • Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115

    Article  PubMed  CAS  Google Scholar 

  • Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, Yano M (2008) Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet 40:1023–1028

    Article  PubMed  CAS  Google Scholar 

  • Song XJ, Huang W, Shi M, Zhu MZ, Lin HX (2007) A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet 39:623–630

    Article  PubMed  CAS  Google Scholar 

  • Steinhoff J, Liu W, Reif JC, Della Porta G, Ranc N, Würschum T (2012) Detection of QTL for flowering time in multiple families of elite maize. Theor Appl Genet 125:1539–1551

    Article  PubMed  Google Scholar 

  • Stuber CW, Edwards MD, Wendel JF (1987) Molecular marker-facilitated investigations of quantitative trait loci in maize. II. Factors influencing yield and its component traits. Crop Sci 27:639–648

    Article  Google Scholar 

  • Sun XY, Wu K, Zhao Y, Kong FM, Han GZ, Jiang HM, Huang XJ, Li RJ, Wang HG, Li SS (2009) QTL analysis of kernel shape and weight using recombinant inbred lines in wheat. Euphytica 165:615–624

    Article  CAS  Google Scholar 

  • Terao T, Nagata K, Morino K, Hirose T (2010) A gene controlling the number of primary rachis branches also controls the vascular bundle formation and hence is responsible to increase the harvest index and grain yield in rice. Theor Appl Genet 120:875–893

    Article  PubMed  CAS  Google Scholar 

  • Utz HF (1997) PLABSTAT: a computer program for statistical analysis of plant breeding experiments. Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, Germany. http://www.uni-hohenheim.de/~ipspwww/soft.html (version 3 Bwin of Feb 2010)

  • Veldboom LR, Lee M (1996) Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: I. Grain yield and yield components. Crop Sci 36:1310–1319

    Article  CAS  Google Scholar 

  • Wan XY, Wan JM, Jiang L, Wang JK, Zhai HQ, Weng JF, Wang HL, Lei CL, Wang JL, Zhang X (2006) QTL analysis for rice grain length and fine mapping of an identified QTL with stable and major effects. Theor Appl Genet 112:1258–1270

    Article  PubMed  CAS  Google Scholar 

  • Wan X, Weng J, Zhai H, Wang J, Lei C, Liu X, Guo T, Jiang L, Su N, Wan J (2008) Quantitative trait loci (QTL) analysis for rice grain width and fine mapping of an identified QTL allele gw-5 in a recombination hotspot region on chromosome 5. Genetics 179:2239–2252

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  • Wang S, Basten C, Zeng Z (2007) Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC

  • Wang P, Xing Y, Li Z, Yu S (2012a) Improving rice yield and quality by QTL pyramiding. Mol Breed 29:903–913

    Article  CAS  Google Scholar 

  • Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, Zeng R, Zhu H, Dong G, Qian Q (2012b) Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet 44:950–954

    Article  PubMed  CAS  Google Scholar 

  • Wassom JJ, Wong JC, Martinez E, King JJ, DeBaene J, Hotchkiss JR, Mikkilineni V, Bohn MO, Rocheford TR (2008) QTL associated with maize kernel oil, protein, and starch concentrations; kernel mass; and grain yield in Illinois high oil × B73 backcross-derived lines. Crop Sci 48:243–252

    Article  Google Scholar 

  • Würschum T (2012) Mapping QTL for agronomic traits in breeding populations. Theor Appl Genet 125:201–210

    Article  PubMed  Google Scholar 

  • Xing Y, Zhang Q (2010) Genetic and molecular bases of rice yield. Annu Rev Plant Biol 61:421–442

    Article  PubMed  CAS  Google Scholar 

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

    Book  Google Scholar 

  • Xu S, Jia Z (2007) Genomewide analysis of epistatic effects for quantitative traits in barley. Genetics 175:1955–1963

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  • Xu Y, Li HN, Li GJ, Wang X, Cheng LG, Zhang YM (2011) Mapping quantitative trait loci for seed size traits in soybean (Glycine max L. Merr.). Theor Appl Genet 122:581–594

    Article  PubMed  Google Scholar 

  • Yan JB, Tang H, Huang YQ, Zheng YL, Li JS (2004) Comparative analyses of QTL for important agronomic traits between maize and rice. Acta Genet Sin 31:1401–1407

    PubMed  CAS  Google Scholar 

  • Yang J, Zhu J, Williams RW (2007) Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics 23:1527–1536

    Article  PubMed  CAS  Google Scholar 

  • Yang X, Ma H, Zhang P, Yan J, Guo Y, Song T, Li J (2012) Characterization of QTL for oil content in maize kernel. Theor Appl Genet 125:1169–1179

    Article  PubMed  CAS  Google Scholar 

  • Zhang X, Wang J, Huang J, Lan H, Wang C, Yin C, Wu Y, Tang H, Qian Q, Li J (2012) Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proc Natl Acad Sci 109:21534–21539

    Article  PubMed Central  PubMed  Google Scholar 

  • Zhao F, Zhang G, Zeng R, Yang Z, Ling Y, Sang X, He G (2011) Analysis of epistatic and additive effects of QTLs for grain shape using single segment substitution lines in rice (Oryza sativa L.). Acta Agron Sin 37:469–476

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Basic Research Program of China (973 Program) (2014CB138203) and the National High Technology Research and Development Program of China (863 Program) (2012AA101104), the National Nature Science Foundation of China (No. 91335205) and the Fundamental Research Funds for the Central University (No. 2013PY027).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The experiments comply with the current laws of the country in which they were performed.

Author information

Authors and Affiliations

  1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China

    Ying Liu, Liwei Wang, Chuanlong Sun, Zuxin Zhang, Yonglian Zheng & Fazhan Qiu

Authors
  1. Ying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuanlong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zuxin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yonglian Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fazhan Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fazhan Qiu.

Additional information

Communicated by Natalia de Leon.

Electronic supplementary material

Below is the link to the electronic supplementary material.

122_2014_2276_MOESM1_ESM.pdf

Supplementary Fig. S1 Frequency distribution of F2:3 families for maize kernel size and weight in five environments in 2011 and 2012.WH11, HG11 and ES11 represent Wuhan, Huanggang and Enshi in 2011, respectively; HG12 and ES12 represent Huanggang and Enshi in 2012, respectively. KL (20-kernel length), KW (20-kernel width) and KT (20-kernel thickness) are measured in the unit of millimeter (mm); and the unit of HKW (100-kernel weight) is gram (g).Parental trait values are indicated by arrows. Supplementary Fig. S2 Comparison between genetic (G) and physical (P) map constructed by SSR markers of each chromosome.CHR was the acronym of chromosome. Genetic coordinates are based on linkage map in this study. The physical positions are from http://www.maizegdb.org. The positions of same marker in genetic linkage map (G) and physical map (P) were connected with the brown-dashed lines. Several markers with discrepant order and position in genetic and physical map are underlined. The positions in genetic linkage map were measured in the unit of centiMorgan (cM); while the unit of marker positions in physical map was million base pairs (Mb) (PDF 633 kb)

Supplementary material 2 (PDF 102 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, Y., Wang, L., Sun, C. et al. Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Theor Appl Genet 127, 1019–1037 (2014). https://doi.org/10.1007/s00122-014-2276-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00122-014-2276-0

Keywords

Search

Navigation