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Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions

Abstract

Grain yield and grain weight of wheat are often decreased by water-limitation in the north-eastern cropping belt of Australia. Based on knowledge that CIMMYT lines are well-adapted in this region, a recombinant inbred line (RIL) population between two elite CIMMYT bread wheats (Seri M82 and Babax) was evaluated under water-limited environments. Fourteen productivity traits were evaluated in 192 progeny in up to eight trials. For three aggregations of the environments (all, high yield or low yield), multiple quantitative trait loci (QTL) were detected, each explaining <15% of variation. Co-location of multiple trait QTL was greatest on linkage groups 1B-a, 1D-b, 4A-a, 4D-a, 6A-a, 6B-a, 7A-a and an unassigned linkage group. Two putative QTL (LOD > 3) from Seri (6D-b and UA-d) increased grain yield and co-located with a suggestive (2 < LOD < 3) and a putative QTL for increased stem carbohydrate content (WSC), respectively; the latter QTL also co-located with a putative anthesis QTL for earlier flowering. Both QTL were detected only in high yield (>4t ha−1) environments. A third increased grain yield QTL (7A-a) from Babax co-located with QTL for increased grain number. Six putative QTL increased grain weight and co-located with QTL for harvest index, grains per spike and spike number. Three putative QTL for increased grains per spike co-located with strong QTL for earlier flowering, increased grain weight and fewer spikes. A group of progeny that exceeded the mean grain yield and grain weight of commercial checks had an increased frequency of QTL for high WSC, large grain size, increased harvest index and greater height, but fewer stems, when compared to low yielding (20% less), low grain weight progeny. These findings were consistent with agronomic analyses of the germplasm and demonstrate that there should be opportunities to independently manipulate grain number and grain size which is typically difficult due to strong negative correlations.

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References

  1. Bernardo R (2004) What proportion of declared QTL in plants are false? Theor Appl Genet 109:410–424

    Article  Google Scholar 

  2. Brennan JP, Byth DE (1979) Genotype × environmental interactions for wheatyields and selection for widely adapted wheat genotypes. Aust J Agric Res 30:221–232

    Article  Google Scholar 

  3. Chapman SC (2008) Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. Euphytica 161:195–208

    Article  Google Scholar 

  4. Cooper M, Byth DE, DeLacy IH, Woodruff DR (1993) Predicting grain yield in Australian environments using data from CIMMYT international wheat performance trials. I. Potential for exploiting correlated responses to selection. Field Crops Res 32:305–322

    Article  Google Scholar 

  5. Cooper M, Woodruff DR, Eisemann RL, Brennan PS, DeLacy IH (1995) A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theor Appl Genet 90:492–502

    Article  Google Scholar 

  6. Doerge R (2002) Mapping and analysis of quantitative trait loci in experimental populations. Nat Rev Genet 3:43–52

    Article  CAS  PubMed  Google Scholar 

  7. Dreccer MF, van Herwaarden AF, Chapman SC (2009) Grain number and grain weight in wheat lines contrasting for stem water soluble carbohydrate concentration. Field Crops Res 112:43–54

    Article  Google Scholar 

  8. Ehdaie B, Alloush GA, Waines JG (2008) Genotypic variation in linear rate of grain growth and contribution of stem reserves to yield in wheat. Field Crops Res 106:34–43

    Article  Google Scholar 

  9. Hoisington DA (1992) Laboratory protocols. CIMMYT Applied Molecular Genetics Laboratory. CIMMYT, Mexico, D.F.

    Google Scholar 

  10. Huang XQ, Cloutier S, Lycar L, Radovanovic N, Humphreys DG, Noll JS, Somers DJ, Brown PD (2006) Molecular dissection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheats (Triticum aestivum L.). Theor Appl Genet 113:753–766

    Article  CAS  PubMed  Google Scholar 

  11. Isidore E, van Os H, Andrzejewski S, Bakker J, Barrena I, Bryan GJ, Caromel B, van Eck H, Ghareeb B, de Jong W, van Koert P, Lefebvre V, Milbourne D, Ritter E, van der Voort JR, Rousselle-Bourgeois F, van Vliet J, Waugh R (2003) Toward a marker-dense meiotic map of the potato genome, lessons from linkage group I. Genetics 165:2107–2116

    CAS  PubMed  Google Scholar 

  12. Koebner RMD (1995) Generation of PCR-based markers for the detection of rye chromatin in a wheat background. Theor Appl Genet 90:740–745

    Article  Google Scholar 

  13. Koebner RMD, Shepherd KW, Appels R (1986) Controlled introgression to wheat of genes from rye chromosome arm 1RS by induction of allosyndesis. 2. Characterization of recombinants. Theor Appl Genet 73:209–217

    Article  CAS  Google Scholar 

  14. Kumar N, Kulwal PL, Balvan HS, Gupta PK (2007) QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat. Mol Breed 19:163–177

    Article  Google Scholar 

  15. Li S, Jia J, Wei X, Zhang X, Li L, Chen H, Fan Y, Sun H, Zhao X, Lei T, Xu Y, Jiang F, Wang H, Li L (2007) A intervarietal genetic map and QTL analysis for yield traits in wheat. Mol Breed 20:167–178

    Article  Google Scholar 

  16. Lukaszewski AJ, Gustafson JP, Apolinarska B (1982) Transmission of chromosomes through the eggs and pollen of triticale × wheat F1 hybrids. Theor Appl Genet 63:49–55

    Article  Google Scholar 

  17. Mago R, Spielmeyer W, Lawrence GJ, Lagudah ES, Ellis JG, Pryor A (2002) Identification and mapping of molecular markers linked to rust resistance genes located on chromosome 1RS of rye using wheat-ray translocation lines. Theor Appl Genet 104:1317–1324

    Article  CAS  PubMed  Google Scholar 

  18. Marza F, Bai G-H, Carver BF, Zhou W–C (2006) Quantitative trait loci for yield and related traits in the wheat population Ning7840 × Clark. Theor Appl Genet 112:688–698

    Article  CAS  PubMed  Google Scholar 

  19. Mathews KL, Chapman SC, Trethowan R, Pfeiffer W, van Ginkel M, Crossa J, Payne T, DeLacy I, Fox PN, Cooper M (2007) Global adaptation patterns of Australian and CIMMYT spring bread wheat. Theor Appl Genet 115:819–835

    Article  PubMed  Google Scholar 

  20. Mathews KL, Malosetti M, Chapman SC, McIntyre CL, 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 

  21. McLaren CG, Ramos L, Lopez C, Eusebio W (2004) Applications of the genealogy management system. ICIS Technical Manual

  22. Miftahudin RK, Ma XF, Mahmoud AA, Layton J, Milla MAR, Chikmawati T, Ramalingam J, Feril O, Pathan MS, Momirovic GS, Kim S, Chema K, Fang P, Haule L, Struxness H, Birkes J, Yaghoubian C, Skinner R, McAllister J, Nguyen V, Qi LL, Echalier B, Gill BS, Linkiewicz AM, Dubcovsky J, Akhunov ED, Dvorák J, Dilbirligi M, Gill KS, Peng JH, Lapitan NLV, Bermudez-Kandianis CE, Sorrells ME, Hossain KG, Kalavacharla V, Kianian SF, Lazo GR, Chao S, Anderson OD, Gonzalez-Hernandez J, Conley EJ, Anderson JA, Choi DW, Fenton RD, Close TJ, McGuire PE, Qualset CO, Nguyen HT, Gustafson JP (2004) Analysis of expressed sequence tag loci on wheat chromosome group 4. Genetics 168:651–663

    Google Scholar 

  23. Miralles DJ, Slafer GA (2007) Sink limitations to yield in wheat, how could it be reduced? J Agric Sci 145:139–149

    Article  Google Scholar 

  24. Mohler V, Hsam SLK, Zeller FJ, Wenzel G (2001) An STS marker distinguishing the rye-derived powdery mildew resistance alleles at the Pm8/Pm17 locus of common wheat. Plant Breed 120:448–450

    Article  CAS  Google Scholar 

  25. Nagata K, Shimizu H, Terao T (2002) Quantitative trait loci for non-structural carbohydrate accumulation in leaf sheaths and culms of rice (Oryza sativa L.) and their effects on grain filling. Breed Sci 52:275–283

    Article  CAS  Google Scholar 

  26. Narasimhamoorthy B, Gill BS, Fritz AK, Nelson JC, Brown-Guedira GL (2006) Advanced backcross QTL analysis of a hard winter wheat × synthetic wheat population. Theor Appl Genet 112:787–796

    Article  CAS  PubMed  Google Scholar 

  27. 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 

  28. Peake A (2003) Inheritance of grain yield and effect of the 1BL/1RS translocation in three bi-parental wheat (Triticum aestivum L.) populations in production environments of north eastern Australia. School of Land and Food Sciences, The University of Queensland, Brisbane

  29. Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusic D, Waterman E, Weyen J, Schondelmaier J, Habash DZ, Farmer P, Saker L, Clarkson DT, Abugalieva A, Yessimbekova M, Turuspekov Y, Abugalieva S, Tuberosa R, Sanguineti M-C, Hollingon PA, Aragues R, Royo A, Dodig D (2005) A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 110:865–880

    Article  CAS  PubMed  Google Scholar 

  30. R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

    Google Scholar 

  31. Rajaram S, Mann CE, Ortiz-Ferrara G, Mujeeb-Kazi A (1983) Adaptation, stability and high yield potential of certain 1B/1R CIMMYT wheats. In: Sakamoto S (ed) The 6th international wheat genetics symposium. CIMMYT, Mexico City, Kyoto, Japan, pp 613–621

  32. Rattey A, Shorter R, Chapman SC, Dreccer MF, van Herwaarden AF (2009) Variation for biomass and grain components, and traits conferring improved yield and grain weight, in an elite recombinant inbred wheat population grown under variable drought conditions. Aust J Agric Res 60:717–729

    Google Scholar 

  33. Rebetzke GJ, Condon AG, Richards RA, Farquhar GD (2006) Inheritance of reduced carbon isotope discrimination in bread wheat (Triticum aestivum L.). Euphytica 150:97–106

    Article  CAS  Google Scholar 

  34. Rebetzke GJ, van Herwaarden AF, Jenkins C, Weiss M, Lewis D, Ruuska S, Tabe L, Fettel NA, Richards RA (2008) Quantitative trait loci for soluble stem carbohydrate production in wheat. Aust J Agric Res 59:891–905

    Article  CAS  Google Scholar 

  35. Schmidt AL, McIntyre CL, Thompson J, Seymour NP, Liu CJ (2005) Quantitative trait loci for root lesion nematode (Pratylenchus thornei) resistance in Middle-Eastern landraces and their potential for introgression into Australian bread wheat. Aust J Agric Res 56:1059–1068

    Article  CAS  Google Scholar 

  36. Singh NK (1985) The structure and genetic control of endosperm proteins in wheat and rye. PhD thesis. University of Adelaide

  37. Snape JW, Foulkes MJ, Simmonds J, Leverington M, Fish LJ, Wang Y, Ciavarrella M (2007) Dissecting gene × environmental effects on wheat yields via QTL and physiological analysis. Euphytica 154:401–408

    Article  Google Scholar 

  38. Takai T, Fukata Y, Shiraiwa T, Horie T (2005) Time-related mapping of quantitative trait loci controlling grain-filling of rice (Oryza sativa L.). J Exp Bot 56:2107–2118

    Article  CAS  PubMed  Google Scholar 

  39. Turner LB, Cairns AJ, Armstead IP, Ashton J, Skot K, Whittaker D, Humphreys MO (2006) Dissecting the regulation of fructan metabolism in perennial ryegrass (Lolium perenne) with quantitative trait locus mapping. New Phytol 169:45–58

    Article  CAS  PubMed  Google Scholar 

  40. Van Herwaarden AF, Angus JF, Richards RA (1998) ‘Haying off’, the negative grain yield response of dryland wheat to nitrogen fertiliser. II. Carbohydrate and protein dynamics. Aust J Agric Res 49:1083–1093

    Article  Google Scholar 

  41. Van Ooijen JW, Voorrips RE (2001) JoinMap 3.0, software for the calculation of genetic linkage maps. Plant Research International B_V, Wageningen

  42. Villareal RL, DelToro E, Mujeeb-Kazi A, Rajaram S (1995) The 1BL/1RS chromosome translocation effect on yield characteristics in a Triciticum aestivum L. cross. Plant Breed 114:497–500

    Article  Google Scholar 

  43. Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, Kilian A (2004) Diversity Arrays Technology (DArT) for whole-genome profiling of barley. Proc Natl Acad Sci 101:9915–9920

    Article  CAS  PubMed  Google Scholar 

  44. Yang DL, Jing RL, Chang XP, Li W (2007) Identification of quantitative trait loci and environmental interactions for accumulation and remobilization of water-soluble carbohydrates in wehat (Triticum aestivum L.) stems. Genetics 176:571–584

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This study was supported in part by the Australian Grains Research and Development Corporation. The authors are grateful for the excellent field technical assistance provided by Greg Roberts, Terry Collins, Philip van Drie, Kevin Niemeyer and Peter Harland. M. Ghaderi is grateful for the support of the Iranian Ministry of Science and Technology during his studies.

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Correspondence to C. Lynne McIntyre.

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Communicated by M. Sorrells.

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McIntyre, C.L., Mathews, K.L., Rattey, A. et al. Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions. Theor Appl Genet 120, 527–541 (2010). https://doi.org/10.1007/s00122-009-1173-4

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Keywords

  • Quantitative Trait Locus
  • Quantitative Trait Locus Analysis
  • Harvest Index
  • DArT Marker
  • Putative Quantitative Trait Locus