Skip to main content

Advertisement

SpringerLink
Log in

QTL for yield and associated traits in the Seri/Babax population grown across several environments in Mexico, in the West Asia, North Africa, and South Asia regions

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

An Erratum to this article was published on 16 April 2013

Abstract

Heat and drought adaptive quantitative trait loci (QTL) in a spring bread wheat population resulting from the Seri/Babax cross designed to minimize confounding agronomic traits have been identified previously in trials conducted in Mexico. The same population was grown across a wide range of environments where heat and drought stress are naturally experienced including environments in Mexico, West Asia, North Africa (WANA), and South Asia regions. A molecular genetic linkage map including 475 marker loci associated to 29 linkage groups was used for QTL analysis of yield, days to heading (DH) and to maturity (DM), grain number (GM2), thousand kernel weight (TKW), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTvg and CTgf), and early ground cover. A QTL for yield on chromosome 4A was confirmed across several environments, in subsets of lines with uniform allelic expression of a major phenology QTL, but not independently from PH. With terminal stress, TKW QTL was linked or pleiotropic to DH and DM. The link between phenology and TKW suggested that early maturity would favor the post—anthesis grain growth periods resulting in increased grain size and yields under terminal stress. GM2 and TKW were partially associated with markers at different positions suggesting different genetic regulation and room for improvement of both traits. Prediction accuracy of yield was improved by 5 % when using marker scores of component traits (GM2 and DH) together with yield in multiple regression. This procedure may provide accumulation of more favorable alleles during selection.

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

References

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Article  Google Scholar 

  • Blum A (2009) Effective use of water (EUA) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress. Field Crops Res 112:119–123

    Article  Google Scholar 

  • Botwright TL, Condon AG, Rebetzke AG, Richards RA (2002) Field evaluation of early vigour for genetic improvement of grain wheat. Aust J Agric Res 53:1137–1145

    Article  Google Scholar 

  • Breseghello F, Sorrels ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177

    Article  PubMed  Google Scholar 

  • Cui F, Li J, Zhao C, Wang L, Wang X, Li S, Bao Y, Li X, Feng D, Kong L, Wang H (2011) Conditional QTL mapping for plant height with respect to the length of the spike and internode in two mapping populations of wheat. Theor Appl Genet 122:1517–1536

    Article  PubMed  Google Scholar 

  • Ellis MH, Spielmeyer W, Gale KR, Rebetzke GJ, Richards RA (2002) “Perfect” markers for the Rht-B1b and Rht-D1b dwarfing genes in wheat. Theor Appl Gen 105:1038–1042

    Article  CAS  Google Scholar 

  • Flood RG, Halloran GM (1984) Basic development rate in spring wheat. Agron J 76:260–264

    Article  Google Scholar 

  • Hood GM (2010) PopTools version 3.2.5. Available on the internet. URL http://www.poptools.org

  • Jung C, Müller AE (2009) Flowering time control and applications in plant breeding. Trends Plant Sci 14:563–573

    Article  PubMed  CAS  Google Scholar 

  • Kirigwi FM, Van Ginkel M, Brown-Guedira G, Gill BS, Paulsen GM, Fritz AK (2007) Markers associated with a QTL for grain yield in wheat under drought. Mol Breed 20:401–413

    Article  CAS  Google Scholar 

  • Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95:221–227

    Article  PubMed  CAS  Google Scholar 

  • Liu H, Zou G, Liu G, Hu S, Li M, Yu X, Mei H, Luo L (2005) Correlation analysis and QTL identification for canopy temperature, leaf water potential and spikelet fertility in rice under contrasting moisture regimes. Chin Sci Bull 50:317–326

    CAS  Google Scholar 

  • Liu L, Wang L, Yao J, Zheng Y, Zhao C (2010) Association mapping of six agronomic traits on chromosome 5A of wheat (Triticum aestivum L.). Plant Breeding Vol.1 No.5 (doi:10.5376/mpb.2010.01.0005)

  • Lopes MS, Reynolds MP (2010a) Partitioning of assimilates to deeper roots is associated with cooler canopies and increased yield under drought in wheat. Funct Plant Biol 37:147–156

    Article  Google Scholar 

  • Lopes MS, Reynolds MP (2010b) Dissecting drought adaptation into its phenotypic and genetic components in wheat. Aspects Appl Biol 105:2010

    Google Scholar 

  • Lopes MS, Reynolds MP, Mannes Y, Singh R, Crossa J, Braun HJ (2012) Genetic yield gains and changes in associated traits of CIMMYT spring bread wheat in a “Historic” set representing 30 years of breeding. Crop Sci 52:1123–1131

    Google Scholar 

  • Ludlow MM, Muchow RC (1990) A critical evaluation of traits for improving crop yields in water limited environments. Adv Agron 43:107–153

    Article  Google Scholar 

  • Malosetti M, Voltas J, Romagosa I, Ullrich SE, van Eeuwijk FA (2004) Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica 137:139–145

    Article  CAS  Google Scholar 

  • Malosetti M, Boer MP, Bink MCAM, van Eeuwijk FA (2006) Multi-trait QTL analysis based on mixed models with parsimonious covariance matrices. In: Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, August 13–18, Belo Horizonte. MG, Brasil. Available: http://www.wcgalp8.org.br/wcgalp8/ Article 25–04

  • Mao SL, Wei YM, Cao WG, Lan XJ, Yu M, Chen ZM, Chen GY, Zheng YL (2010) Confirmation of the relationship between plant height and Fusarium head blight resistance in wheat (Triticum aestivum L) by QTL meta-analysis. Euphytica 174:343–356

    Article  Google Scholar 

  • Mathews KL, Malosetti M, Chapman S, McIntyre L, Reynolds M, Shorter R, van Eeuwijk F (2008) Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117:1077–1091

    Article  PubMed  Google Scholar 

  • Mayfield D, Chen ZF, Pires JC (2011) Epigenetic regulation of flowering time in polyploids. Curr Opin Plant Biol 14:174–178

    Article  PubMed  CAS  Google Scholar 

  • McIntyre LC, Mathews KL, Rattey A, Chapman SC, Drenth J, Ghaderi M, Reynolds M, Shorter R (2010) Molecular detection of genomic regions associated with grain yield evaluated under irrigated and rainfed conditions. Theor Appl Genet 120:527–541

    Article  PubMed  CAS  Google Scholar 

  • Mullan DJ, Reynolds MP (2010) Quantifying genetic effects of ground cover on soil water evaporation using digital imaging. Funct Plant Biol 37:703–712

    Article  Google Scholar 

  • Nezhad KZ, Weber WE, Röder MS, Sharma S, Lohwasser U, Meyer RC, Saal B, Börner A (2011) QTL analysis for thousand-grain weight under terminal drought stress in bread wheat (Triticum aestivum L). Euphytica (in press)

  • Olivares-Villegas JJ, Reynolds MP, McDonald GK (2007) Drought adaptive attributes in the Seri/Babax hexaploid wheat population. Funct Plant Biol 34:189–203

    Article  Google Scholar 

  • Peltonen-Sainio P, Kangas A, Salo Y, Jauhiainen L (2007) Grain number dominates grain weight intemperate cereal yield determination: evidence based on 30 years of multi-location trials. Field Crops Res 100:179–188

    Article  Google Scholar 

  • Peng JH, Ronin YF, Fahima T, Röder M, Li YC, Nevo E, Korol A (2003) Domestication quantitative trait loci in Triticum dicoccoides, the progenitor of wheat. Proc Natl Acad Sci USA 100:2489–2494

    Article  PubMed  CAS  Google Scholar 

  • Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivare-Villegas JJ, Chapman SC (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021

    Article  PubMed  Google Scholar 

  • Reynolds M, Tuberosa R (2008) Translational research impacting on crop productivity in drought-prone environments. Curr Opin Plant Biol 11:171–179

    Article  PubMed  Google Scholar 

  • Reynolds MP, Manes Y, Izanloo A, Langridge P (2009) Phenotyping for physiological breeding and gene discovery in wheat. Ann Appl Biol 155:309–320

    Article  Google Scholar 

  • Reynolds M, Manes Y, Rebetzke G (2012) Application of Physiology in breeding for heat and drought stress. In: Reynolds M, Pask A, Mullan D (eds) Physiological breeding I: interdisciplinary approaches to improve crop adaptation. CIMMYT, Mexico

    Google Scholar 

  • Samonte SOPB, Wilson LT, McClung AM, Medley JC (2005) Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analysis. Crop Sci 45:2414–2424

    Article  Google Scholar 

  • SAS (2004) SAS Institute Inc SAS Online Doc 913 SAS Institute Inc. Cary, NC

    Google Scholar 

  • Sayre KD, Rajaram S, Fischer RA (1997) Yield potential progress in short bread wheats in northwest Mexico. Crop Sci 37:36–42

    Article  Google Scholar 

  • Snape, JW, Semikhodskii A , Sarma RN, Korzun V, Fish L, Quarrie SA, Gill BS, Sasaki T, Galiba G, Sutka J (1998) Mapping vernalization loci in wheat and comparative mapping with other cereals In: Sliknard AE (ed), Proc 9th Intl Wheat Genet Symp, Vol 3, University Extension Press, University of Saskatchewan, p 156–158

  • Snape JW, Butterworth K, Whitechurcg E, Worland AJ (2001) Waiting for fine times: genetic of flowering time in wheat. Euphytica 119:185–190

    Article  CAS  Google Scholar 

  • Su Z, Hao C, Wang L, Dong Y, Zhang X (2011) Idenitification and development of a functional marker of TaGW2 associated with grain weight in bread wheat (Triticum aestivum L). Theor Appl Genet 122:211–223

    Article  PubMed  CAS  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 

  • Van Ooijen JW (2006) Software for the calculation of genetic linkage maps in experimental populations Kyazma BV. Wageningen, Netherlands

    Google Scholar 

  • VSN International (2011) GenStat for Windows 14th Edition. VSN International, Hemel Hempstead, UK. Web page: http://www.GenStat.co.uk

  • Wang Z, Wu X, Ren Q, Chang X, Li R, Jing R (2010) Qtl mapping for development behavior of plant height in wheat (Triticum aestivum L). Euphytica 174:447–458

    Article  Google Scholar 

  • Wiersma JJ, Busch RH, Fulcher GG, Hareland GA (2001) Recurrent selection for kernel weight in spring wheat. Crop Sci 41:999–1005

    Article  Google Scholar 

  • Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421

    Article  Google Scholar 

Download references

Acknowledgments

Authors would like to thank Mayra Barcelo, Araceli Torres and Eugenio Perez for assistance with data and trial management. Editing assistance was received from Emma Quilligan. The German Federal Ministry for Economy Cooperation and Development (BMZ) and the Australian Grains Research and Development Corporation (GRDC) are acknowledged for their financial support.

Author information

Authors and Affiliations

  1. CIMMYT Int. Apdo., Postal 6-641, DF Mexico, 06600, Mexico

    Marta S. Lopes, Matthew P. Reynolds & Ky L. Mathews

  2. CSIRO Plant Industry, Queensland Biosciences Precinct, 306 Carmody Rd, St Lucia, QLD, 4067, Australia

    C. Lynne McIntyre

  3. CIMMYT, c/o Seed and Plant Improvement Institute Campus (SPII), Mahdasht Avenue, P.O. Box 1119, Karaj, 31585, Iran

    M. R. Jalal Kamali

  4. Field Crops Research Institute, Agricultural Research Center, 9 El-Gamma Street, Giza, 12619, Egypt

    Moussa Mossad & Yousef Feltaous

  5. ARC, P.O. Box 126, Wad Medani, Sudan

    Izzat S. A. Tahir

  6. Directorate of Wheat Research (DWR), Karnal, 132 001, Haryana, India

    Ravish Chatrath

  7. ICARDA, P.O. Box 5466, Aleppo, Syria

    Izzat S. A. Tahir, Francis Ogbonnaya & Michael Baum

Authors
  1. Marta S. Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew P. Reynolds
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Lynne McIntyre
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ky L. Mathews
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. R. Jalal Kamali
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Moussa Mossad
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yousef Feltaous
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Izzat S. A. Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ravish Chatrath
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Francis Ogbonnaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Michael Baum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marta S. Lopes.

Additional information

Communicated by P. Langridge.

Electronic supplementary material

Below is the link to the electronic supplementary material.

122_2012_2030_MOESM1_ESM.xlsx

Supplementary material 1 Multi-environment QTL and percentage of variation accounted for yield (YLD), thousand kernel weight (TKW), grain number (GM2), days to heading and maturity (DH and DM), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTvg and CTgf), and early ground cover (EGC) analysed for each trait at the time across all environments (ENV). Marker, chromosome (Chr), position (Pos), QTL by environment interactions (QTLxE), and additive effects in each environment are shown (positive effects refer to the Seri parent whereas negative additive effects refer to the Babax parent). Environments include Darab (Dar), Sohag (Soh), Dongola (Don), Wad Medani (WD), Tel Hadya (THa), Mexico irrigated (MIR), Mexico drought (MD), Mexico heat (MH), Mexico heat plus drought (MHD), Karnal (Kar), and Ludihana (Lud). NA, not available. Figures in bold indicate significant QTL effects in one particular environment. (XLSX 15 kb)

122_2012_2030_MOESM2_ESM.xlsx

Supplementary material 2 Multi –trait QTL effects and percentage of variation accounted for yield (YLD), thousand kernel weight (TKW), grain number (GM2), days to heading and maturity (DH and DM), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTgf), and early ground cover (EGC) analysed for each environment at the time across all traits (multi-trait, single environment). Marker, chromosome (Chr), position (Pos), and additive effects for traits in each environment (ENV) are shown (positive effects refer to the Seri parent whereas negative additive effects refer to the Babax parent). Environments include Mexico irrigated (MIR), Mexico drought (MD), Mexico heat (MH), and Mexico heat plus drought (MHD). NA, not available. Figures in bold indicate significant QTL effects in one particular environment. (XLSX 16 kb)

122_2012_2030_MOESM3_ESM.xlsx

Supplementary material 3 Multi-trait QTL effects and percentage of variation accounted for yield (YLD), thousand kernel weight (TKW), grain number (GM2), days to heading and maturity (DH and DM), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTvg and CTgf), and early ground cover (EGC) analysed for each environment (ENV) at the time across all traits (multi-trait, single environment) using sister lines containing the Seri allele for marker 7D-acc/cat-10 on chromosome 7D-b. Marker, chromosome (Chr), position (Pos), and additive effects for each trait in each environment are shown (positive effects refer to the Seri parent whereas negative additive effects refer to the Babax parent). Environments include Mexico irrigated (MIR), Mexico drought (MD), Mexico heat (MH), and Mexico heat plus drought (MHD). NA, not available. Figures in bold indicate significant QTL effects in one particular environment. (XLSX 14 kb)

122_2012_2030_MOESM4_ESM.xlsx

Supplementary material 4 Multi –trait QTL effects and percentage of variation accounted for yield (YLD), thousand kernel weight (TKW), grain number (GM2), days to heading and maturity (DH and DM), plant height (PH), canopy temperature at the vegetative, and grain filling stages (CTvg and CTgf) and early ground cover (EGC) analysed for each environment (ENV) at the time across all traits (multi-trait, single environment) using sister lines containing the Babax allele for marker 7D-acc/cat-10 on chromosome 7D-b. Locus, linkage group (LG), position (Pos), and additive effects for each trait in each environment are shown (positive effects refer to the Seri parent whereas negative additive effects refer to the Babax parent). Environments included Mexico irrigated (MIR), Mexico drought (MD), Mexico heat (MH), and Mexico heat plus drought (MHD). NA, not available. Figures in bold indicate significant QTL effects in one particular environment. (XLSX 13 kb)

122_2012_2030_MOESM5_ESM.xlsx

Supplementary material 5 Marker scores of genotype SBS 21 (high yielding) and SBS 205 (low yielding) associated with QTL for yield (YLD), thousand kernel weight (TKW), grain number (GM2), days to heading and maturity (DH and DM), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTvg and CTgf), and early ground cover (EGC). Trait, marker, chromosome (Chr), position (Pos), and QTL by environment interaction (QTLxE) are shown. Scores 1 and 2 refer to the Seri and Babax parent, respectively. Additives effects (Add.Eff) for each genotype according to the observed allele, predicted QTL effects (Pred.QTL as the sum of all additive effects for each trait) and observed values for each trait (OBS VAL) are presented. (XLSX 12 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lopes, M.S., Reynolds, M.P., McIntyre, C.L. et al. QTL for yield and associated traits in the Seri/Babax population grown across several environments in Mexico, in the West Asia, North Africa, and South Asia regions. Theor Appl Genet 126, 971–984 (2013). https://doi.org/10.1007/s00122-012-2030-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00122-012-2030-4

Keywords

Search

Navigation