Skip to main content

Advertisement

SpringerLink
Log in

QTL mapping and phenotypic variation for root architectural traits in maize (Zea mays L.)

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

Abstract

Key message

QTL were identified for root architectural traits in maize.

Abstract

Root architectural traits, including the number, length, orientation, and branching of the principal root classes, influence plant function by determining the spatial and temporal domains of soil exploration. To characterize phenotypic patterns and their genetic control, three recombinant inbred populations of maize were grown for 28 days in solid media in a greenhouse and evaluated for 21 root architectural traits, including length, number, diameter, and branching of seminal, primary and nodal roots, dry weight of embryonic and nodal systems, and diameter of the nodal root system. Significant phenotypic variation was observed for all traits. Strong correlations were observed among traits in the same root class, particularly for the length of the main root axis and the length of lateral roots. In a principal component analysis, relationships among traits differed slightly for the three families, though vectors grouped together for traits within a given root class, indicating opportunities for more efficient phenotyping. Allometric analysis showed that trajectories of growth for specific traits differ in the three populations. In total, 15 quantitative trait loci (QTL) were identified. QTL are reported for length in multiple root classes, diameter and number of seminal roots, and dry weight of the embryonic and nodal root systems. Phenotypic variation explained by individual QTL ranged from 0.44 % (number of seminal roots, NyH population) to 13.5 % (shoot dry weight, OhW population). Identification of QTL for root architectural traits may be useful for developing genotypes that are better suited to specific soil environments.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Bailey PHJ, Currey JD, Fitter AH (2002) The role of root system architecture and root hairs in promoting anchorage against uprooting forces in Allium cepa and root mutants of Arabidopsis thaliana. J Exp Bot 53:333–340

    Article  PubMed  CAS  Google Scholar 

  • Beebe SE, Rojas-Pierce M, Yan XL, Blair MW, Pedraza F, Munoz F, Tohme J, Lynch JP (2006) Quantitative trait loci for root architecture traits correlated with phosphorus acquisition in common bean. Crop Sci 46:413–423

    Article  CAS  Google Scholar 

  • Bernier J, Atlin GN, Serraj R, Kumar A, Spaner D (2008) Breeding upland rice for drought resistance. J Sci Food Agr 88:927–939

    Article  CAS  Google Scholar 

  • Bonser AM, Lynch J, Snapp S (1996) Effect of phosphorus deficiency on growth angle of basal roots in Phaseolus vulgaris. New Phytol 132:281–288

    Article  PubMed  CAS  Google Scholar 

  • Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890

    Article  PubMed  CAS  Google Scholar 

  • Burton AL, Brown KM, Lynch JP (2013) Phenotypic diversity of root anatomical and architectural traits in Zea species. Crop Sci 53:1042–1055

    Article  Google Scholar 

  • Cai H, Chen F, Mi G et al (2012) Mapping QTLs for root system architecture of maize (Zea mays L.) in the field at different developmental stages. Theor Appl Genet 125:1313–1324. doi:10.1007/s00122-012-1915-6

    Article  PubMed  Google Scholar 

  • Cairns JE, Namuco OS, Torres R, Simborio FA, Courtois B, Aquino GA, Johnson DE (2009) Investigating early vigour in upland rice (Oryza sativa L.): part II. Identification of QTLs controlling early vigour under greenhouse and field conditions. Field Crop Res 113:207–217

    Article  Google Scholar 

  • Cattell RB (1966) The scree test for number of factors. Multivar Behav Res 1:245–276. doi:10.1207/s15327906mbr0102_10

    Article  Google Scholar 

  • Chen J-M, Yu X-P, Cheng J-A, Zheng X-S, Xu H-X, Lu Z-X, Zhang J-F, Chen L-Z (2005) Plant tolerance against insect pests and its mechanisms. Acta Ent Sin 48:262–272

    Google Scholar 

  • Clark LJ, Price AH, Steele KA, Whalley WR (2008) Evidence from near-isogenic lines that root penetration increases with root diameter and bending stiffness in rice. Fun Plant Biol 35:1163–1171

    Article  Google Scholar 

  • Collins NC, Tardieu F, Tuberosa R (2008) Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiol 147:469–486

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • de Dorlodot S, Forster B, Pages L, Price A, Tuberosa R, Draye X (2007) Root system architecture: opportunities and constraints for genetic improvement of crops. Trend Plant Sci 12:474–481

    Article  Google Scholar 

  • Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) Approach for high diversity species. PLoS One 6(5):e19379

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Gamuyao R, Chin JH, Pariasca-Tanaka J et al (2012) The protein kinase Pstoll from traditional rice confers tolerance of phosphorus deficiency. Nature 488:535–539. doi:10.1038/nature11346

    Article  PubMed  CAS  Google Scholar 

  • Giuliani S, Sanguineti MC, Tuberosa R, Bellotti M, Salvi S, Landi P (2005) Root-ABA1, a major constitutive QTL, affects maize root architecture and leaf ABA concentration at different water regimes. J Exp Bot 56:3061–3070

    Article  PubMed  CAS  Google Scholar 

  • Gowda VRP, Henry A, Yamauchi A, Shashidar HE, Serrak R (2011) Root biology and genetic improvement for drought avoidance in rice. Field Crop Res 122:1–13

    Article  Google Scholar 

  • Gregory PJ, Bengough AG, Grinev D, Schmidt S, Thomas WTB, Wojciechowski T, Young IM (2009) Root phenomics of crops: opportunities and challenges. Fun Plant Biol 36:922–929

    Article  Google Scholar 

  • Guingo E, Hebert Y, Charcosset A (1998) Genetic analysis of root traits in maize. Agronomie 18:225–235

    Article  Google Scholar 

  • Gutjahr C, Casieri L, Paszkowski U (2009) Glomus intraradices induces changes in root system architecture of rice independently of common symbiosis signaling. New Phytol 182:829–837

    Article  PubMed  Google Scholar 

  • Hebert Y, Guingo E, Argillier O, Barriere Y (1996) The variability of maize root architecture as a key to differences in root lodging. In: Proceedings of the XVIIth conference on genetics, biotechnology and breeding of maize and sorghum, Thessaloniki, Greece

  • Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Hund A, Fracheboud Y, Soldati A, Frascaroli E, Salvi S, Stamp P (2004) QTL controlling root and shoot traits of maize seedlings under cold stress. Theoret Appl Genet 109:618–629

    Article  CAS  Google Scholar 

  • Hund A, Reimer R, Messmer R (2011) A consensus map of QTLs controlling the root length of maize. Plant Soil 344:143–158

    Article  CAS  Google Scholar 

  • Jansen C, Van de Steeg HM, De Kroon H (2005) Investigating a trade-off in root morphological responses to a heterogeneous nutrient supply and to flooding. Fun Ecol 19:952–960

    Article  Google Scholar 

  • Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 20:141–151. doi:10.1177/001316446002000116

    Article  Google Scholar 

  • Kamoshita A, Babu RC, Boopathi NM, Fukai S (2008) Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments. Field Crop Res 109:1–23

    Article  Google Scholar 

  • Ku LX, Sun ZH, Wang CL, Zhang J, Zhao RF, Liu HY, Tai GQ, Chen YH (2012) QTL mapping and epistasis analysis of brace root traits in maize. Mol Breed 30:697–708

    Article  Google Scholar 

  • Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25

    Article  PubMed  PubMed Central  Google Scholar 

  • Lebreton C, Lazić-Jančić V, Steed A, Pekić S, Quarrie SA (1995) Identification of QTL for drought responses in maize and their use in testing causal relationships between traits. J Exp Bot 46:853–865

    Article  CAS  Google Scholar 

  • Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009a) The sequence alignment/map format and samtools. Bioinformatics 25(16):2078–2079

    Article  PubMed  PubMed Central  Google Scholar 

  • Li JZ, Xie Y, Dai AY, Liu LF, Li ZC (2009b) Root and shoot traits responses to phosphorus deficiency and QTL analysis at seedling stage using introgression lines of rice. J Genet Genomic 36:173–183

    Article  CAS  Google Scholar 

  • Liao H, Rubio G, Yan XL, Cao AQ, Brown KM, Lynch JP (2001) Effect of phosphorus availability on basal root shallowness in common bean. Plant Soil 232:69–79

    Article  PubMed  CAS  Google Scholar 

  • Liao H, Yan X, Rubio G, Pedraza F, Beebe S, Lynch JP (2004) Genetic mapping of basal root gravitropism and phosphorus acquisition efficiency in common bean. Fun Plant Biol 31:1–12

    Article  Google Scholar 

  • Liu JC, Li JS, Chen FJ, Zhang FS, Ren TH, Zhuang ZJ, Mi GH (2008a) Mapping QTLs for root traits under different nitrate levels at the seedling stage in maize (Zea mays L.). Plant Soil 305:253–265

    Article  CAS  Google Scholar 

  • Liu L, Liao H, Wang X-R, Yan X-L (2008b) Regulation effect of soil P availability on mycorrhizal infection in relation to root architecture and P efficiency of Glycine max. Yingyong Shengtai Xuebao (Chin J Appl Eco) 19:564–568

    CAS  Google Scholar 

  • Lynch J (2005) Nutrient acquisition by plants. an ecological perspective. In: BassiriRad H (ed) Root architecture and nutrient acquisition, vol 181. Springer, Heidelberg

    Google Scholar 

  • Lynch JP (2011) Root phenes for enhanced soil exploration and phosphorus acquisition: tools for future crops. Plant Physiol 156:1041–1049. doi:10.1104/pp.111.175414

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Lynch JP (2013) Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann Bot 112:347–357

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Lynch JP, Brown KM (2012) New roots for agriculture: exploiting the root phenome. Phil Trans R Soc B 367:1598–1604

    Article  PubMed  PubMed Central  Google Scholar 

  • Mano Y, Muraki M, Fujimori M, Takamizo T, Kindiger B (2005) Identification of QTL controlling adventitious root formation during flooding conditions in teosinte (Zea mays ssp. huehuetenangensis) seedlings. Euphytica 142:33–42

    Article  Google Scholar 

  • Mata GH, Sepulveda B, Richards A, Soriano E (2006) The architecture of Phaseolus vulgaris root is altered when a defense response is elicited by an oligogalacturointide. Braz J Plant Physiol 18:351–355

    Article  CAS  Google Scholar 

  • McCully M (1999) Roots in soil: unearthing the complexities of roots and their rhizospheres. Annu Rev Plant Physiol Plant Mol Biol 50:695–718

    Article  PubMed  CAS  Google Scholar 

  • Mickovski SB, Bengough AG, Bransby MF, Davies MCR, Hallett PD, Sonnenberg R (2007) Material stiffness, branching pattern and soil matric potential affect the pullout resistance of model root systems. Eur J Soil Sci 58:1471–1481

    Article  Google Scholar 

  • Miklas P, Kelly J, Beebe S, Blair M (2006) Common bean breeding for resistance against biotic and abiotic stresses: from classical to MAS breeding. Euphytica 147:105–131

    Article  CAS  Google Scholar 

  • Niklas K (1994) Plant allometry: the scaling of form and process. University of Chicago Press, Chicago, USA

    Google Scholar 

  • Ochoa IE, Blair MW, Lynch JP (2006) QTL analysis of adventitious root formation in common bean under contrasting phosphorus availability. Crop Sci 46:1609–1621

    Article  CAS  Google Scholar 

  • Postma JA, Dathe A, Lynch JP (2014) The optimal lateral root branching density for maize depends on nitrogen and phosphorus availability. Plant Physiol. doi:10.1104/pp.113.233916

  • Qu YY, Mu P, Zhang HL, Chen CY, Gao YM, Tian YX, Wen F, Li ZC (2008) Mapping QTLs of root morphological traits at different growth stages in rice. Genet 133:187–200

    Article  Google Scholar 

  • R Development Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org

  • Reimer R (2010) Responses of maize (Zea mays L.) seedlings to low and high temperature: association mapping of root growth and photosynthesis-related traits. In: AGRL Diss ETH No. 18807 Zurich

  • Ruta N, Liedgens M, Fracheboud Y, Stamp P, Hund A (2010) QTLs for the elongation of axile and lateral roots of maize in response to low water potential. Theoret Appl Genet 120:621–631

    Article  CAS  Google Scholar 

  • Saengwilai P, Tian X, Lynch JP (2014) Low crown root number enhances nitrogen acquisition from low nitrogen soils in maize (Zea mays L.). Plant Physiol. doi:10.1104/pp.113.232603

    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. P Natl Acad Sci USA 81(24):8014–8018

    Article  CAS  Google Scholar 

  • Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326(5956):1112–1115

    Article  PubMed  CAS  Google Scholar 

  • Semchenko M, Zobel K (2005) The effect of breeding on allometry and phenotypic plasticity in four varieties of oat (Avena sativa L.). Field Crops Res 93:151–168

    Article  Google Scholar 

  • Trachsel S, Messmer R, Stamp P, Hund A (2009) Mapping of QTLs for lateral and axile root growth of tropical maize. Theor Appl Genet 119:1413–1424

    Article  PubMed  CAS  Google Scholar 

  • Tuberosa R, Salvi S (2009) QTL for agronomic traits in maize production. In: Bennetzen JL, Hake SC (eds) Handbook of Maize: Its Biology. Springer Science and Business Media, LLC, pp 501–541

    Chapter  Google Scholar 

  • Tuberosa R, Sanguineti MC, Landi P, Giuliani MM, Salvi S, Conti S (2002) Identification of QTLs for root characteristics in maize grown in hydroponics and analysis of their overlap with QTLs for grain yield in the field at two water regimes. Plant Mol Biol 48:697–712

    Article  PubMed  CAS  Google Scholar 

  • Tuberosa R, Salvi S, Sanguineti MC, Maccaferri M, Giuliani S, Landi P (2003) Searching for quantitative trait loci controlling root traits in maize: a critical appraisal. Plant Soil 255:35–54

    Article  CAS  Google Scholar 

  • Uga Y, Okuno K, Yano M (2011) Dro1, a major QTL involved in deep rooting of rice under upland field conditions. J Exp Bot 62:2485–2494. doi:10.1093/jxb/erq429

    Article  PubMed  CAS  Google Scholar 

  • Uga Y, Yamamoto E, Kanno N et al (2013a) A major QTL controlling deep rooting on rice chromosome 4. Sci Rep 3:3040. doi:10.1038/srep03040

    Article  PubMed  PubMed Central  Google Scholar 

  • Uga Y, Sugimoto K, Ogawa S et al (2013b) Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat Gen 45(9):1097–1102. doi:10.1038/ng.2725

    Article  CAS  Google Scholar 

  • Zeng ZB (1994) Precision mapping of quantitative trait loci. Genet 136:1457–1468

    CAS  Google Scholar 

  • Zhao J, Fu J, Liao H et al (2004) Characterization of root architecture in an applied core collection for phosphorus efficiency of soybean germplasm. Chin Sci Bull 49:1611–1620

    Article  CAS  Google Scholar 

  • Zhao Q, Huang XH, Lin ZX, Han B (2010) SEG-Map: a novel software for genotype calling and genetic map construction from next-generation sequencing. Rice 3:98–102

    Article  Google Scholar 

  • Zhu J, Kaeppler S, Lynch J (2005a) Topsoil foraging and phosphorus acquisition efficiency in maize (Zea mays L.). Fun Plant Biol 32:749–762

    Article  CAS  Google Scholar 

  • Zhu J, Kaeppler SM, Lynch JP (2005b) Mapping of QTLs for lateral root branching and length in maize (Zea mays L.) under differential phosphorus supply. Theoret Appl Genet 111:688–695

    Article  CAS  Google Scholar 

  • Zhu JM, Mickelson SM, Kaeppler SM, Lynch JP (2006) Detection of quantitative trait loci for seminal root traits in maize (Zea mays L.) seedlings grown under differential phosphorus levels. Theoret Appl Genet 113:1–10

    Article  CAS  Google Scholar 

  • Zhu J, Ingram PA, Benfey PN, Elich T (2011) From lab to field, new approaches to phenotyping root system architecture. Curr Opin Plant Bio 14:310–317

    Article  Google Scholar 

Download references

Acknowledgments

We thank Lauren Gelesh, Johanna Mirenda, and Robert Snyder for technical assistance with plant cultivation and harvesting, and Anushree Sanyal for assistance with the QTL analysis. We thank Harini Rangarajan for creation of the image used in Fig. 1. This work was supported by United States Department of Agriculture National Research Initiative [grant # 207-35100-18365 to JPL and KMB].

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The research described in this paper complies with the current laws of the country in which they were performed.

Author information

Authors and Affiliations

  1. Department of Plant Science, The Pennsylvania State University, 102 Tyson Building, University Park, PA, 16801, USA

    Amy L. Burton, Meredith T. Hanlon, Kathleen M. Brown & Jonathan P. Lynch

  2. Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA

    James M. Johnson, Jillian M. Foerster & Shawn M. Kaeppler

  3. Department of Plant Biology, Michigan State University, 612 Wilson Road, East Lansing, MI, 48824, USA

    Candice N. Hirsch & C. R. Buell

  4. DOE Great Lakes Bioenergy Research Center, Michigan State University, 612 Wilson Road, East Lansing, MI, 48824, USA

    Candice N. Hirsch & C. R. Buell

Authors
  1. Amy L. Burton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James M. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jillian M. Foerster
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Candice N. Hirsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. C. R. Buell
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Meredith T. Hanlon
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shawn M. Kaeppler
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kathleen M. Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jonathan P. Lynch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonathan P. Lynch.

Additional information

Communicated by Frank Hochholdinger.

Electronic supplementary material

Below is the link to the electronic supplementary material.

122_2014_2353_MOESM1_ESM.xlsx

Map summaries IBM OhW NyH: marker number, map length, average and maximum spacing statistics of the genetic maps created for the IBM, NyH and OhW populations separated by chromosome (XLSX 13 kb)

122_2014_2353_MOESM2_ESM.xlsx

IBM bin map physicial positions: this file contains the chromosome, genetic position and physical position for each recombination bin marker in the IBM population. This information can be utilized to identify the physical location of QTL identified in mapping and narrow in on gene models that fall within the LOD support interval (XLSX 381 kb)

122_2014_2353_MOESM3_ESM.xlsx

IBM bin map rqtl: this file contains the genotypes for 8,224 recombination bins in the IBM population created using SEG-map and formatted for R/qtl. The chromosome and genetic position for each recombination bin is contained in row 2 and 3. Column 2 contains a dummy trait variable that would be replaced with trait data prior to being used in R/qtl. Additional traits may be added by inserting additional columns. B73 alleles are coded as “A” and Mo17 alleles are coded as “B” (XLSX 6310 kb)

122_2014_2353_MOESM4_ESM.tiff

IBM genetic map: B73 × Mo17 (IBM) mapping population genetic map constructed using SEG-Map with GBS marker data (TIFF 1330 kb)

122_2014_2353_MOESM5_ESM.pdf

IBM Marker pull directions: detailed instructions on how to use Marker_pull.pl for creating a subset of the raw data for a specified region of interest (PDF 26 kb)

122_2014_2353_MOESM6_ESM.pl

IBM Marker pull: this Perl script can be utilized to easily create a subset of the raw marker information that was used in the bin map construction when one is only interested in a specified region (PL 2 kb)

122_2014_2353_MOESM7_ESM.txt

IBM raw marker data: this file contains all of the raw marker information for 68,246 SNPs identified via genotyping-by-sequencing as well as 1,721 publically available SSR markers. The first six columns contain the marker name, chromosome, start position, end position, B73 consensus base and Mo17 consensus base respectively. This is followed by the marker information for each of the RILs. If the marker is a SNP, the start and end position are synonymous. Genotypes are coded A,C,G,T for homozygous, 2 bases for heterozygous and NN for missing (TXT 93684 kb)

122_2014_2353_MOESM8_ESM.tiff

IBM individual similarity plot: histogram of the proportion of markers with identical genotypes for each pair of individuals in the B73 × Mo17 (IBM) mapping population (TIFF 2930 kb)

122_2014_2353_MOESM9_ESM.tiff

IBM indivudal genotype frequency plot: genotype frequencies across all individuals in the B73 × Mo17 (IBM) mapping population (TIFF 2930 kb)

122_2014_2353_MOESM10_ESM.tiff

IBM RF plot: estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of bin markers in the B73 × Mo17 (IBM) mapping population. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction) (XLSX 4219 kb)

122_2014_2353_MOESM11_ESM.xlsx

NyH bin map physicial positions: this file contains the chromosome, genetic position and physical position for each recombination bin marker in the NyH population. This information can be utilized to identify the physical location of QTL identified in mapping and narrow in on gene models that fall within the LOD support interval (XLSX 209 kb)

122_2014_2353_MOESM12_ESM.xlsx

NyH bin map rqtl: this file contains the genotypes for 5,320 recombination bins in the NyH population created using SEG-map and formatted for R/qtl. The chromosome and genetic position for each recombination bin is contained in row 2 and 3. Column 2 contains a dummy trait variable that would be replaced with trait data prior to being used in R/qtl. Additional traits may be added by inserting additional columns. H99 alleles are coded as “A” and Ny821 alleles are coded as “B” (XLSX 3761 kb)

122_2014_2353_MOESM13_ESM.tiff

NyH genetic map: Ny821 × H99 (NyH) mapping population genetic map constructed using SEG-Map with GBS marker data (TIFF 2930 kb)

122_2014_2353_MOESM14_ESM.pdf

NyH Marker pull directions: detailed instructions on how to use Marker_pull.pl for creating a subset of the raw data for a specified region of interest (PDF 26 kb)

122_2014_2353_MOESM15_ESM.pl

NyH Marker pull: this Perl script can be utilized to easily create a subset of the raw marker information that was used in the bin map construction when one is only interested in a specified region (PL 2 kb)

122_2014_2353_MOESM16_ESM.txt

NyH raw marker data: This file contains all of the raw marker information for 19,970 SNPs identified via genotyping-by-sequencing. The first six columns contain the marker name, chromosome, start position, end position, H99 consensus base and Ny821 consensus base respectively. This is followed by the marker information for each of the RILs. If the marker is a SNP, the start and end position are synonymous. Genotypes are coded A,C,G,T for homozygous, 2 bases for heterozygous and NN for missing (TXT 23493 kb)

122_2014_2353_MOESM17_ESM.tiff

NyH individual similarity plot: histogram of the proportion of markers with identical genotypes for each pair of individuals in the Ny821 × H99 (NyH) mapping population (TIFF 2930 kb)

122_2014_2353_MOESM18_ESM.tiff

NyH indivudal genotype frequency_plot: Genotype frequencies across all individuals in the Ny821 × H99 (NyH) mapping population (TIFF 2930 kb)

122_2014_2353_MOESM19_ESM.tiff

NyH RF plot: estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of bin markers in the Ny821 x H99 (NyH) mapping population. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction) (TIFF 4219 kb)

122_2014_2353_MOESM20_ESM.xlsx

OhW bin map physicial positions: this file contains the chromosome, genetic position and physical position for each recombination bin marker in the OHW population. This information can be utilized to identify the physical location of QTL identified in mapping and narrow in on gene models that fall within the LOD support interval (XLSX 195 kb)

122_2014_2353_MOESM21_ESM.xlsx

OhW bin map rqtl: this file contains the genotypes for 5,683 recombination bins in the OhW population created using SEG-map and formatted for R/qtl. The chromosome and genetic position for each recombination bin is contained in row 2 and 3. Column 2 contains a dummy trait variable that would be replaced with trait data prior to being used in R/qtl. Additional traits may be added by inserting additional columns. Oh43 alleles are coded as “A” and W64a alleles are coded as “B” (XLSX 4383 kb)

122_2014_2353_MOESM22_ESM.tiff

OhW_genetic map: Oh43 × W64a (OhW) mapping population genetic map constructed using SEG-Map with GBS marker data (TIFF 2930 kb)

122_2014_2353_MOESM23_ESM.pdf

OhW Marker pull directions: detailed instructions on how to use Marker_pull.pl for creating a subset of the raw data for a specified region of interest (PDF 26 kb)

122_2014_2353_MOESM24_ESM.pl

OhW Marker pull: this Perl script can be utilized to easily create a subset of the raw marker information that was used in the bin map construction when one is only interested in a specified region (PL 2 kb)

122_2014_2353_MOESM25_ESM.txt

OhW raw marker data: This file contains all of the raw marker information for 40,959 SNPs identified via genotyping-by-sequencing. The first six columns contain the marker name, chromosome, start position, end position, Oh43 consensus base and W64a consensus base respectively. This is followed by the marker information for each of the RILs. If the marker is a SNP, the start and end position are synonymous. Genotypes are coded A,C,G,T for homozygous, 2 bases for heterozygous and NN for missing (TXT 52287 kb)

122_2014_2353_MOESM26_ESM.tiff

OhW individual similarity plot: histogram of the proportion of markers with identical genotypes for each pair of individuals in the Oh43 × W64a (OhW) mapping population (TIFF 2930 kb)

122_2014_2353_MOESM27_ESM.tiff

OhW indivudal genotype frequency plot: genotype frequencies across all individuals in the Oh43 × W64a (OhW) mapping population (TIFF 2930 kb)

122_2014_2353_MOESM28_ESM.tiff

OhW RF plot: estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of bin markers in the Oh43 × W64a (OhW) mapping population. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction) (TIFF 4219 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Burton, A.L., Johnson, J.M., Foerster, J.M. et al. QTL mapping and phenotypic variation for root architectural traits in maize (Zea mays L.). Theor Appl Genet 127, 2293–2311 (2014). https://doi.org/10.1007/s00122-014-2353-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00122-014-2353-4

Keywords

Search

Navigation