Advertisement

Euphytica

, Volume 209, Issue 3, pp 789–804 | Cite as

Genome-wide linkage mapping of QTL for physiological traits in a Chinese wheat population using the 90K SNP array

  • Fengmei Gao
  • Jindong Liu
  • Li Yang
  • Xiaoxia Wu
  • Yonggui Xiao
  • Xianchun Xia
  • Zhonghu HeEmail author
Article

Abstract

Many physiological traits are associated with grain yield and yield-related traits in wheat. The aim of the present study was to identify quantitative trait loci (QTL) for physiological traits, including ground cover, normalized difference in vegetation index and canopy temperature depression, and closely linked molecular markers for marker-assisted breeding. Two hundred and forty-six F8 recombinant inbred lines (RILs) come from the Zhou 8425B/Chinese Spring cross were genotyped using the high-density Illumina iSelect 90K single nucleotide polymorphism (SNP) assay. Field trials were conducted under irrigated conditions at Zhengzhou and Zhoukou in Henan province, during the 2012–2013 and 2013–2014 cropping seasons. Analysis of variance of physiological traits showed significant differences (P < 0.01) among RILs, and RILs × environment interactions. Ground cover at late spring (GC-S), normalized difference in vegetation index at late spring (NDVI-S), and ground cover pre-winter (GC-W), had moderate broad-sense heritabilities of 0.66, 0.52 and 0.40, respectively. GC-S, NDVI-S and canopy temperature depression at 10 days post anthesis (CTD-10) were positively correlated with GY. Using a high-density linkage map of 5636 polymorphic SNP markers and composite interval mapping, 24 QTL for GC, NDVI and CTD were identified on 12 chromosomes, explaining 3.4–14.6 % of the phenotypic variance. Five stable QTL were detected across three environments, viz. QGC-W.caas-7AL, QNDVI-S.caas-7AL, QGC-S.caas-3AS, QCTD-A.caas-5BS and QCTD-10.caas-5BS. In addition, 10 QTL clusters were observed on chromosomes 1AL, 2AL, 2BL, 3AS, 3B (2), 4BS, 5B, 7AS, and 7AL. The stable QTL and QTL clusters were linked to SNP markers, with genetic distances to the closest SNPs ranging from 0 to 2.0 cM; these could be used for marker-assisted selection to improve yield-related traits in wheat breeding.

Keywords

Molecular markers Quantitative trait loci Single nucleotide polymorphism Triticum aestivum 

Abbreviations

Chl-10

SPAD value of chlorophyll content at 10 days post anthesis

Chl-A

SPAD value of chlorophyll content at anthesis

CTD

Canopy temperature depression

CTD-10

Canopy temperature depression at 10 days post anthesis

CTD-A

Canopy temperature depression at anthesis

GC-S

Ground cover at late spring

GC-W

Ground cover pre-winter

GWAS

Genome-wide association study

GBS

Genotyping-by-sequencing

GY

Grain yield

KNS

Kernel number per spike

MAS

Marker-assisted selection

NDVI

Normalized difference in vegetation index

NDVI-S

Normalized difference in vegetation index at late spring

NDVI-W

Normalized difference in vegetation index pre-winter

PH

Plant height

QTL

Quantitative trait locus/loci

RIL

Recombinant inbred line

SN

Spike number per square meter

SNP

Single nucleotide polymorphism

SSR

Simple sequence repeat

S.D.

Standard deviation

TKW

Thousand kernel weight

Notes

Acknowledgments

We thank Prof. R. A. McIntosh, Plant Breeding Institute, University of Sydney, for review of this manuscript. This work was funded by the National Natural Science Foundation of China (31461143021), Beijing Municipal Science and Technology Project (D151100004415003), and the National High Technology Research and Development Program of China (2012AA10A308).

Supplementary material

10681_2016_1682_MOESM1_ESM.docx (457 kb)
Supplementary material 1 (DOCX 457 kb)

References

  1. Amani I, Fischer RA, Reynolds MP (1996) Canopy temperature depression association with yield of irrigated spring wheat cultivars in hot climate. J Agron Crop Sci 176:119–129CrossRefGoogle Scholar
  2. Araus JL, Slafer MP, Roya C, Serett MD (2008) Breeding for yield potential and stress adaptation. Crit Rev Plant Sci 27:377–412CrossRefGoogle Scholar
  3. Ball AD, Stapleym J, Dawson DA, Birkhead TR, Burke T, Slate J (2010) A comparison of SNPs and microsatellites as linkage mapping markers: lessons from the zebra finch (Taeniopygia guttata). BMC Genomics 11:218CrossRefPubMedPubMedCentralGoogle Scholar
  4. Bellundagi A, Singh GP, Prabhu KV, Arora A, Neelu J, Ramya P, Singh AM, Singh PK, Ahlawat A (2013) Early ground cover and other physiological traits as efficient selection criteria for grain yield under moisture deficit stress conditions in wheat (Triticum aestivum L.). Ind J Plant Physiol 18:277–281CrossRefGoogle Scholar
  5. Burton GW, Devane EM (1952) Estimating heritability in tall fescue from replicated colonial material. Agron J 45:478–481CrossRefGoogle Scholar
  6. Cabral AL, Jordan MC, McCartney CA, You FM, Humphreys DG, RacLachlan R, Pozniak CJ (2014) Identification of candidate genes, regions and markers for pre-harvest sprouting resistance in wheat (Triticum aestivum L.). BMC Plant Biol 114:340CrossRefGoogle Scholar
  7. Diab AA, Kantety RV, Ozturk NZ, Benscher D, Nachit MM, Sorrells ME (2007) Drought-inducible genes and differentially expressed sequence tags associated with components of drought tolerance in durum wheat. Sci Res Essay 3:9–26Google Scholar
  8. Edae EA, Byrne PF, Haley SD, Lopes MS, Reynolds MP (2014) Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 127:791–807CrossRefPubMedGoogle Scholar
  9. 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 6:1–9CrossRefGoogle Scholar
  10. Evett SR, Howell TA, Schneider AD, Upchurch DR, Wanjura DF (1996) Canopy temperature based automated irrigation control. In Camp CR (ed) Proceedings of the international conference evaporation and irrigation scheduling, San Antonio, TX. American Society of Agricultural Engineers, St Joseph, MI, pp 207–213Google Scholar
  11. FAO (2011) FAOSTAT. http://faostat3.fao.org/home/index.html#DOWNLOAD. Food and Agriculture Organization of the United Nations, Rome. Accessed 23 Jul 2014
  12. Gao FM, Wen WE, Liu JD, Rasheed A, Yin GH, Xia XC, He ZH (2015) Genome-wide linkage mapping of QTL for physiological traits in a RIL population derived from Zhou 8425B/Chinese Spring. Front Plant Sci 6:1099PubMedPubMedCentralGoogle Scholar
  13. Gupta PK, Rustgi S, Mir RR (2008) Array-based high-throughput DNA markers for crop improvement. Heredity 101:5–18CrossRefPubMedGoogle Scholar
  14. Hasheminasab H, Assad MT, Aliakbari A, Sahhafi SR (2012) Evaluation of some physiological traits associated with improved drought tolerance in Iranian wheat. Ann Biol Res 3:1719–1725Google Scholar
  15. Holland JB (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161CrossRefPubMedGoogle Scholar
  16. Huang XQ, Kempf H, Ganal MW, Röder MS (2004) Advanced backcross QTL analysis in progenies derived from a cross between a German elite winter wheat variety and a synthetic wheat (Triticum aestivum L.). Theor Appl Genet 109:933–943CrossRefPubMedGoogle Scholar
  17. Karimizadeh R, Mohammadi M (2011) Association of canopy temperature depression with yield of durum wheat genotypes under supplementary irrigated and rained conditions. Aust J Crop Sci 5:138–146Google Scholar
  18. Kumar S, Kumari P, Kumar U, Grover M, Singh AK, Singh R, Sengar RS (2013) Molecular approaches for designing heat tolerant wheat. J Plant Biochem Biotechnol 22:359–371CrossRefGoogle Scholar
  19. Li HH, Ye GY, Wang JK (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175:361–374CrossRefPubMedPubMedCentralGoogle Scholar
  20. Li XM, Chen XM, Xiao YG, Xia XC, Wang DS, He ZH, Wang HJ (2014a) Identification of QTLs for seedling vigor in winter wheat. Euphytica 198:199–209CrossRefGoogle Scholar
  21. Li XM, He ZH, Xiao YG, Xia XC, Trethowan R, Wang HJ, Che XM (2014b) QTL mapping for leaf senescence-related traits in common wheat under limited and full irrigation. Euphytica 203:569–582CrossRefGoogle Scholar
  22. Liu JD, He ZH, Wu L, Bai B, Wen WE, Xie CJ, Xia XC (2015) Genome-wide linkage mapping of QTL for adult-plant resistance to stripe rust in a Chinese wheat population Linmai 2 × Zhong 892. PLoS One (on-line)Google Scholar
  23. Lopes MS, Reynolds MP, McIntyre CL, Mathews KL, Jalal Kamali MR, Mossad M, Feltaous Y, Tahir ISA, Chatrath R, Ogbonnaya F, Baum M (2013) 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 126:971–984CrossRefPubMedGoogle Scholar
  24. Lu YL, Xu J, Yuan ZM, Hao ZF, Xie CX, Li XH, Shah T, Lan H, Zhang SH, Rong TZ, Xu YB (2012) Comparative LD mapping using single SNPs and haplotypes identifies QTL for plant height and biomass as secondary traits of drought tolerance in maize. Mol Breed 30:407–418CrossRefGoogle Scholar
  25. Mason RE, Hays DB, Mondal S, Ibrahim AMH, Basnet BR (2013) QTL for yield, yield components and canopy temperature depression in wheat under late sown field conditions. Euphytica 194:243–259CrossRefGoogle Scholar
  26. McCartney CA, Somers DJ, Humphreys DJ, Lukow O, Ames N, Noll J, Cloutier S, McCallum BD (2005) Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL 4452 × AC ‘Domain’. Genome 48:870–883CrossRefPubMedGoogle Scholar
  27. Olivares-Villegas JJ, Reynolds MP, McDonald GK (2007) Drought-adaptive attributes in the Seri/Babax hexaploid wheat population. Funct Plant Biol 34:189–203CrossRefGoogle Scholar
  28. Paliwal R, Marion SR, Kumar U, Srivastava JP, Joshi AK (2012) QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.). Theor Appl Genet 125:561–575CrossRefPubMedGoogle Scholar
  29. Pask AJD, Pietragalla J, Mullan DM, Reynolds MP (2012) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico CityGoogle Scholar
  30. Pinto RS, Reynolds MP (2015) Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution in bread wheat. Theor App Genet 128:575–585CrossRefGoogle Scholar
  31. Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivares-Villegas J-J, Chapman SC (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021CrossRefPubMedPubMedCentralGoogle Scholar
  32. Rashid A, Stark JC, Tanveer A, Mustafa T (1999) Use of canopy temperature measurements as a screening tool for drought tolerance in spring wheat. J Agron Crop Sci 182:231–238CrossRefGoogle Scholar
  33. Rebetzke GJ, Rattey AR, Farquhar G, Richards R, Condon AG (2013) Genomic regions for canopy temperature and their genetic association with stomatal conductance and grain yield in wheat. Funct Plant Biol 40:14–33CrossRefGoogle Scholar
  34. Reynolds MP, Ortiz-Monasterio JI, Mcnab A (2001) Application of physiology in wheat breeding. CIMMYT, Mexico CityGoogle Scholar
  35. Reynolds MP, Saint Pierre C, Saad ASI, Vargas M, Condon AG (2007) Evaluating potential genetic gains in wheat associated with stress-adaptive trait expression in elite genetic resources under drought and heat stress. Crop Sci 47:S172–S189CrossRefGoogle Scholar
  36. Royo C, Villegas D, Garcia Del Moral LF, Elhani S, Aparicio N, Rharrabti Y, Araus JL (2002) Comparative performance of carbon isotope discrimination and canopy temperature depression as predictors of genotypes differences in durum wheat yield in Spain. Aust J Agric Res 53:561–569CrossRefGoogle Scholar
  37. Russo MA, Ficco DBM, Laido G, Marone D, Papa R, Blanco A, Gadaleta A, Vita PD, Mastrangelo AM (2014) A dense durum wheat × T. dicoccum linkage map based on SNP markers for the study of seed morphology. Mol Breed 34:1579–1597CrossRefGoogle Scholar
  38. Saintenac C, Jiang D, Wang S, Akhunov E (2013) Sequence-based mapping of the polyploid wheat genome. G3-Genes Genomes Genet 3:1105–1114Google Scholar
  39. Sela H, Ezratim S, Ben-Yehuda P, Manisterski J, Akhunov E, Dvorak J, Breiman A, Korol A (2014) Linkage disequilibrium and association analysis of stripe rust resistance in wild emmer wheat (Triticum turgidum ssp. dicoccoides) population in Israel. Theor Appl Genet 127:2453–2463CrossRefPubMedGoogle Scholar
  40. Sharma RC, Crossa J, Velu G, Huerta-Espinoc J, Vargas M, Payneb TS, Singhb RP (2012) Genetic gains for YLD in CIMMYT spring bread wheat across international environments. Crop Sci 52:1522–1533CrossRefGoogle Scholar
  41. Stamm P, Ramamoorthy R, Kumar PP (2011) Feeding the extra billions: strategies to improve crops and enhance future food security. Plant Biotechnol Rep 5:107–120CrossRefGoogle Scholar
  42. Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP (2014) Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet 128:353–363CrossRefPubMedGoogle Scholar
  43. Tsilo TJ, Hareland GA, Simsek S, Chao SM, Anderson JA (2010) Genome mapping of kernel characteristics in hard red spring wheat breeding lines. Theor Appl Genet 121:717–730CrossRefPubMedGoogle Scholar
  44. Veldboom LR, Lee M (1996) Genetic mapping of quantitative trait loci in maize in stress and non-stress environments: I. Grain yield and yield components. Crop Sci 36:1310–1319CrossRefGoogle Scholar
  45. Wang SC, Wong D, Forrest K, Allen A, Chao SM, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L, Mastrangelo AM, Whan A, Stephen S, Barker G, Wieseke R, Plieske J, Lillemo M, Mather D, Appels R, Dolferus R, Brown-Guedira G, Korol A, Akhunova AR, Feuillet C, Salse J, Morgante M, Pozniak C, Luo MC, Dvorak J, Morell M, Dubcovsky J, Ganal M, Tuberosa R, Lawley C, Mikoulitch I, Cavanagh C, Edwards KJ, Hayden M, Akhunov E, International Wheat Genome Sequencing Consortium (2014) Characterization of polyploid wheat genomic diversity using a high-density 90000 single nucleotide polymorphism array. Plant Biotechnol J 12:87–96Google Scholar
  46. Xiao YG, Liu JJ, Xia XC, Chen XM, Reynolds MP, He ZH (2013) Genetic analysis of early vigour in winter wheat using digital imaging. Acta Agron Sin 39:1935–1943CrossRefGoogle Scholar
  47. Yang ZB, Bai ZY, Li XL, Wang P, Wu QX, Yang L, Li LQ, Li XJ (2012) SNP identification and allelic-specific PCR markers development for TaGW2, a gene linked to wheat kernel weight. Theor Appl Genet 125:1057–1068CrossRefPubMedGoogle Scholar
  48. Yu H, Xie W, Wang J, Xing Y, Xu CG, Li XH, Xiao JH, Zhang QF (2011) Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers. PLoS One 6:e17595CrossRefPubMedPubMedCentralGoogle Scholar
  49. Zhai SN, He ZH, Wen WE, Jin H, Liu JD, Zhang Y, Liu ZY, Xia XC (2015) Genome-wide linkage mapping of flour color-related traits and polyphenol oxidase activity in common wheat. Theor Appl Genet 129:377–394CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Fengmei Gao
    • 1
    • 2
    • 3
  • Jindong Liu
    • 1
  • Li Yang
    • 1
  • Xiaoxia Wu
    • 2
  • Yonggui Xiao
    • 1
  • Xianchun Xia
    • 1
  • Zhonghu He
    • 1
    • 4
    Email author
  1. 1.National Wheat Improvement Center, Institute of Crop ScienceChinese Academy of Agricultural Sciences (CAAS)BeijingChina
  2. 2.Key Laboratory of Soybean Biology, Soybean Research Institute, Ministry of EducationNortheast Agricultural UniversityHarbinChina
  3. 3.Keshan Agricultural Research InstituteHeilongjiang Academy of Agricultural SciencesKeshanChina
  4. 4.International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAASBeijingChina

Personalised recommendations