Skip to main content
Log in

Population structure in a wheat core collection and genomic loci associated with yield under contrasting environments

  • Published:
Genetica Aims and scope Submit manuscript

Abstract

A set of 96 winter wheat accessions sampled from a variety of geographic origins, including cultivars and breeding lines, were characterized with 46 genome-wide SSR loci for genetic diversity and population structure. The genetic diversity within these accessions was examined using a genetic distance-based and a model-based clustering method. The model-based analysis identified an underlying population structure comprising of four distinct sub-populations which corresponded well with distance-based groupings. Information on the population structure is taken into account in an association mapping study of grain yield from a 3-years field trial incorporating fully irrigated, rainfed and drought stress treatments. A total of 21 marker-grain yield associations (P < 0.01) were identified with nine SSR markers. Most associations were detected only in one to three environments (treatment/year combination), with an average R 2 value around 13 %. However, marker gwm484 (on chromosome 2D) was associated with yield in six environments, including irrigated, rainfed and drought stress treatments, suggesting it could be used to improve grain yield across a range of environments. Variation in grain yield at this locus was associated with earliness, early vigour, kernels per spikelet and harvest index. Microsatellite locus psp3200 (on chromosome 6D) was associated with yield in dry and hot environments, which was related to earliness, early vigour, productive tillering and total biomass per plant. Partial least squares regression, with nine environmental factors, showed that precipitation from tillering to maturity was the main environmental factor causing marker × environment associations for grain yield.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Aastveit A, Martens H (1986) ANOVA interaction interpreted by partial least squares regression. Biometrics 42:829–844

    Article  Google Scholar 

  • Austin RB (1980) Physiological limitations to cereals yields and ways of reducing them by breeding. In: Hurd RG, Biscoe PV, Dennis C (eds) Opportunities for increasing crop yields. Association of Applied Biology, Pitman, Boston, pp 3–19

    Google Scholar 

  • Breseghello F, Sorrells MS (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177. doi:10.1534/genetics.105.044586

    Article  PubMed  Google Scholar 

  • Buckler ES, Thornsberry JM (2002) Plant molecular diversity and applications of genomics. Curr Opin Plant Biol 5:107–111. doi:10.1016/S1369-5266(02)00238-8

    Article  PubMed  CAS  Google Scholar 

  • Campbell BT, Baenziger PS, Eskridge KM, Budak H, Streck NA, Weiss A, Gill KS, Erayman M (2004) Using environmental covariates to explain genotype × environment and QTL × environment interactions for agronomic traits on chromosome 3A of wheat. Crop Sci 44:620–627. doi:10.2135/cropsci2004.0620

    Google Scholar 

  • Chao S, Zhang W, Dubcovsky J, Sorrells M (2007) Evaluation of genetic diversity and genome-wide linkage disequilibrium among US wheat (Triticum aestivum L.) germplasm representing different market classes. Crop Sci 47:1018–1030. doi:10.2135/cropsci2006.06.0434

    Article  CAS  Google Scholar 

  • Crossa J, Vargas M, van Eeuwijk FA, Jiang C, Edmeades GO, Hoisington D (1999) Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables. Theor Appl Genet 99:611–625. doi:10.1007/s001220051276

    Article  PubMed  CAS  Google Scholar 

  • Crossa J, Burgueno J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, Singh RP, Trethowan R, Warburton M, Franco J, Reynolds M, Crouch JH, Ortiz R (2007) Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177:1889–1913. doi:10.1534/genetics.107.078659

    Article  PubMed  CAS  Google Scholar 

  • Cuthbert JL, Somers DJ, Brule-Babel AL, Brown PD, Crow GH (2008) Molecular mapping of quantitative trait loci for yield and yield components in spring wheat (Triticum aestivum L.). Theor Appl Genet 117:595–608. doi:10.1007/s00122-008-0804-5

    Article  PubMed  CAS  Google Scholar 

  • Dilbirligi M, Erayman M, Campbell BT, Randhawa HS, Baenziger PS, Dweikat I, Gill KS (2006) High-density mapping and comparative analysis of agronomically important traits on wheat chromosome 3A. Genomics 88:74–87. doi:10.1016/j.ygeno.2006.02.001

    Article  PubMed  CAS  Google Scholar 

  • Dodig D, Quarrie SA, Stanković S, Milijić S, Denčić S (2002) Characterising wheat genetic resources for responses to drought stress. In: Proceedings of the ICID international conference on ‘drought mitigation and prevention of land desertification’, 21–25 April 2002, Bled, Slovenia. 38doc.pdf, CD edition

  • Dodig D, Zorić M, Kobiljski B, Šurlan-Momirović G, Quarrie SA (2010) Assessing drought tolerance and regional patterns of genetic diversity among spring and winter bread wheat using simple sequence repeats and phenotypic data. Crop Pasture Sci 61:812–824. doi:10.1071/CP10001

    Article  CAS  Google Scholar 

  • Dreisigacker S, Zhang P, Warburton ML, Van Ginkel M, Hoisington D, Melchinger AE (2004) SSR and pedigree analyses of genetic diversity among CIMMYT wheat lines targeted to different megaenvironments. Crop Sci 44:381–388. doi:10.2135/cropsci2004.0381

    Article  CAS  Google Scholar 

  • El Mousadik A, Petit RJ (1996) High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor Appl Genet 92:832–839. doi:10.1007/BF00221895

    Article  Google Scholar 

  • Falush D, Stephens M, Prithchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587

    PubMed  CAS  Google Scholar 

  • Felsenstein J (1993) PHYLIP (phylogeny inference package). Version 3.5c. Department of Genetics, University of Washington, Seatle

  • Fischer RA, Maurer R (1978) Drought resistance in spring wheat cultivars: I. Grain yield responses. Aust J Agric Res 29:897–912. doi:10.1071/AR9780897

    Article  Google Scholar 

  • Flint-Garcia SA, Thuillet AC, Yu JM, Pressoir G, Romero SM, Mitchell SE, Doebley J, Kresovich S, Goodman MM, Buckler ES (2005) Maize association population, a high-resolution platform for quantitative trait locus dissection. Plant J 44:1054–1064. doi:10.1111/j.1365-313X.2005.02591.x

    Article  PubMed  CAS  Google Scholar 

  • Foulkes MJ, Snape JW, Shearman VJ, Reynolds MP, Gaju O, Sylvester-Bradley R (2007) Genetic progress in yield potential in wheat: recent advances and future prospects. J Agric Sci 145:17–29. doi:10.1017/S0021859607006740

    Article  CAS  Google Scholar 

  • Francia E, Tacconi G, Crosatti C, Barabaschi D, Bulgarelli D, Dall’Aglio E, Vale G (2005) Marker-assisted selection in crop plants. Plant Cell Tissue Organ Cul 82:317–342. doi:10.1007/s11240-005-2387-z

    Article  CAS  Google Scholar 

  • Guo Z, Song Y, Zhou R, Ren Z, Jia J (2010) Discovery, evaluation and distribution of haplotypes of the wheat Ppd-D1 gene. New Phytol 185:841–851. doi:10.1111/j.1469-8137.2009.03099.x

    Article  PubMed  CAS  Google Scholar 

  • Hai L, Wagner C, Friedt W (2007) Quantitative structure analysis of genetic diversity among spring bread wheats (Triticum aestivum L.) from different geographical regions. Genetica 130:213–225. doi:10.1007/s10709-006-9008-6

    Article  PubMed  CAS  Google Scholar 

  • Hopwood A, Oldroyd N, Fellows S, Ward R, Owen SA, Sullivan K (1997) Rapid quantification of DNA samples extracted from buccal scrapes prior to DNA profiling. Biotechniques 23:18–20

    PubMed  CAS  Google Scholar 

  • Huang XQ, Börner A, Röder MS, Ganal MW (2002) Assessing genetic diversity of wheat (Triticum aestivum L.) germplasm using microsatellite markers. Theor Appl Genet 105:699–707. doi:10.1007/s00122-002-0959-4

    Article  PubMed  CAS  Google Scholar 

  • Islam-Faridi MN, Worland AJ, Law CN (1996) Inhibition of ear emergence time and sensitivity to day-length determined by the group 6 chromosomes of wheat. Heredity 77:572–580. doi:10.1038/hdy.1996.184

    Article  Google Scholar 

  • Kirigwi FM, Van Ginkel M, Brown-Gedira 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. doi:10.1007/s11032-007-9100-3

    Article  CAS  Google Scholar 

  • Kobiljski B, Quarrie SA, Denčić S, Kirby J, Ivegeš M (2002) Genetic diversity of the Novi Sad wheat core collection revealed by microsatellites. Cellular Mol Biol Lett 7:685–694

    CAS  Google Scholar 

  • Kordenaeej A (2008) Mapping QTLs for yield and yield components under drought stress in bread wheat. PhD Thesis, University of Natural Resources and Applied Life Sciences (BOKU), Vienna, Austria

  • Kuchel H, Williams JK, Langridge P, Eagles HA, Jefferies SP (2007a) Genetic dissection of grain yield in bread wheat. I. QTL analysis. Theor Appl Genet 115:1029–1041. doi:10.1007/s00122-007-0628-8

    Article  PubMed  CAS  Google Scholar 

  • Kuchel H, Williams K, Langridge P, Eagles HA, JeVeries SP (2007b) Genetic dissection of grain yield in bread wheat. II. QTL-by-environment interaction. Theor Appl Genet 115:1015–1029. doi:10.1007/s00122-007-0629-7

    Article  PubMed  CAS  Google Scholar 

  • Kumar N, Kulwal PL, Balyan HS, Gupta PK (2007) QTL mapping for yield and yield contribution traits in two mapping populations of bread wheat. Mol Breed 19:163–177. doi:10.1007/s11032-006-9056-8

    Article  Google Scholar 

  • Lafitte HR, Yongsheng G, Yan S, Li ZK (2007) Whole plant responses, key processes, and adaptation to drought stress: the case of rice. J Exp Bot 58:169–175. doi:10.1093/jxb/erl101

    Article  PubMed  CAS  Google Scholar 

  • 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 20:167–168. doi:10.1007/s11032-007-9080-3

    Article  Google Scholar 

  • Maccaferri M, Sanguineti MC, Noli E, Tuberosa R (2005) Population structure and long-range linkage disequilibrium in a durum wheat elite collection. Mol Breed 15:271–289. doi:10.1007/s11032-004-7012-z

    Article  CAS  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. doi:10.1023/B:EUPH.0000040511.46388.ef

    Article  CAS  Google Scholar 

  • Matus IA, Hayes PM (2002) Genetic diversity in three groups of barley germplasm assessed by simple sequence repeats. Genome 45:1095–1106. doi:10.1139/G02-071

    Article  PubMed  CAS  Google Scholar 

  • McCartney CA, Somers DJ, Humphreys DG, Lukow O, Ames N, Noll J, Cloutier S, McCallum BD (2005) Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452 × ’AC Domain’. Genome 48:870–883. doi:10.1139/G05-055

    Article  PubMed  CAS  Google Scholar 

  • McIntyre CL, Mathews KyL, Rattey A, Chapman SC, Drenth J, Ghaderi M, Reynolds M, Shorter R (2010) 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. doi:10.1007/s00122-009-1173-4

    Article  PubMed  CAS  Google Scholar 

  • Minch E, Ruiz-Linares A, Goldstein D, Feldman M, Cavalli-Sforza LL (1997) Microsat: a computer program for calculating various statistics on microsatellite allele data. ver. 1.5d. Stanford University, Stanford CA, USA

  • Petit RJ, El Mousadik A, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conserv Biol 12:844–855. doi:10.1111/j.1523-1739.1998.96489.x

    Article  Google Scholar 

  • Piepho HP (2000) A mixed-model approach to mapping quantitative trait loci in barley on the basis of multiple environment data. Genetics 156:2043–2050

    PubMed  CAS  Google Scholar 

  • Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivares-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. doi:10.1007/s00122-010-1351-4

    Article  PubMed  Google Scholar 

  • Prasad B, Babar MA, Xu XY, Bai GH, Klatt AR (2009) Genetic diversity in the U.S. hard red winter wheat cultivars as revealed by microsatellite markers. Crop Pasture Sci 60:16–24. doi:10.1071/CP08052

    Article  CAS  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    PubMed  CAS  Google Scholar 

  • Quarrie SA, Dodig D, Pekić S, Kirby J, Kobiljski B (2003) Prospects for marker-assisted selection of improved drought responses in wheat. Bul J Plant Physiol, Special Issue, (Proceedings of the European Workshop on Environmental Stress and Sustainable Agriculture, Varna, Bulgaria) pp 83–95

  • 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 MC, Hollington 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. doi:10.1007/s00122-004-1902-7

    Article  PubMed  CAS  Google Scholar 

  • Quarrie SA, Dodig D, Kobiljski B, Kandić V, Savić J, Rančić D, Pekić Quarrie S (2011) Improving wheat yields using association mapping. In: Proceedings of the international scientific symposium of agriculture ‘Agrosym Jahorina 2011’, 10–12 November 2011, Jahorina, Bosnia and Herzegovina, 2–8. CD edition. http://www.agrosym.unssa.rs.ba

  • R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

    Google Scholar 

  • Rebetzke GJ, Ellis MH, Bonnett DG, Richards RA (2007) Molecular mapping of genes for coleoptile growth in bread wheat (Triticum aestivum L.). Theor Appl Genet 114:1173–1183. doi:10.1007/s00122-007-0509-1

    Article  PubMed  CAS  Google Scholar 

  • Reimer SO, Pozniak CJ, Clarke FR, Clarke JM, Somers DJ, Knox RE, Singh AK (2008) Association mapping of yellow pigment in an elite collection of durum wheat cultivars and breeding lines. Genome 51:1016–1025. doi:10.1139/G08-083

    Article  PubMed  CAS  Google Scholar 

  • Reynolds MP, Borlaug NE (2006) Impacts of breeding on international collaborative wheat improvement. J Agric Sci 144:3–17. doi:10.1017/S0021859606005867

    Article  Google Scholar 

  • Rice WR (1989) Analyzing tables of statistical tests. Evolution 43:223–225

    Article  Google Scholar 

  • Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resources 8:103–106. doi:10.1111/j.1471-8286.2007.01931.x

  • Snape JW, Laurie DA, Worland AJ (1998) Understanding the genetics of abiotic stress responses in cereals and possible strategies for their amelioration. Aspects Appl Biol 50:9–14

    Google Scholar 

  • Sneath PHA, Sokal RR (1973) Numerical taxonomy. Freeman, San Francisco

    Google Scholar 

  • Somers DJ, Isaac P, Edwards K (2004) A high-density microsatellite consensus map for bread wheat (Triticum aestivum L.). Theor Appl Genet 109:1105–1114. doi:10.1007/s00122-004-1740-7

    Article  PubMed  CAS  Google Scholar 

  • Somers DJ, Banks T, DePauw R, Fox S, Clarke J, Pozniak C, McCartney C (2007) Genome-wide linkage disequilibrium analysis in bread wheat and durum wheat. Genome 50:557–567. doi:10.1139/G07-031

    Article  PubMed  CAS  Google Scholar 

  • Stich B, Melchinger AE (2010) An introduction to association mapping in plants. CAB Rev Perspect Agric Veterinary Sci Nutr Nat Resour 5:1–9. doi:10.1079/PAVSNNR20105039

    Google Scholar 

  • Stich B, Piepho H-P, Schulz B, Melchinger AE (2008) Multi-trait association mapping in sugar beet (Beta vulgaris L.). Theor Appl Genet 117:947–954. doi:10.1007/s00122-008-0834-z

    Article  PubMed  Google Scholar 

  • Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J Roy Statist Soc Ser B 36:111–147

    Google Scholar 

  • Vargas M, Crossa J, Sayre K, Reynolds M, Ramirez ME, Talbot M (1998) Interpreting genotype × environment interaction in wheat by partial least squares regression. Crop Sci 38:679–689

    Article  Google Scholar 

  • Vargas M, Eeuwijk FA, Crossa J, Ribaut J-M (2006) Mapping QTLs and QTL × environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods. Theor Appl Genet 112:1009–1023. doi:10.1007/s00122-005-0204-z

    Article  Google Scholar 

  • von Korff M, Grando S, Del Greco A, This D, Baum M, Ceccarelli S (2008) Quantitative trait loci associated with adaptation to Mediterranean dryland conditions in barley. Theor Appl Genet 117:653–669. doi:10.1007/s00122-008-0787-2

    Article  Google Scholar 

  • Weir BS, Hill WG (2002) Estimating F-statistics. Annu Rev Genet 36:721–750. doi:10.1146/annurev.genet.36.050802.093940

    Article  PubMed  CAS  Google Scholar 

  • Whitt SR, Buckler ES (2003) Using natural allelic diversity to evaluate gene function. Methods Mol Biol 236:123–139. doi:10.1385/1-59259-413-1:123

    PubMed  CAS  Google Scholar 

  • Xie CX, Warburton M, Li MS, Li XH, Xiao MJ, Hao ZF, Zhao Q, Zhang SH (2008) An analysis of population structure and linkage disequilibrium using multilocus data in 187 maize inbred lines. Mol Breed 21:407–418. doi:10.1007/s11032-007-9140-8

    Article  Google Scholar 

  • Yan W, Rajcan I (2002) Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci 42:11–20. doi:10.2135/cropsci2002.1100

  • Yan WG, Li Y, Agrama HA, Luo D, Gao F, Lu X, Ren G (2009) Association mapping of stigma and spikelet characteristics in rice (Oryza sativa L.). Mol Breed 24:277–292. doi:10.1007/s11032-009-9290-y

    Google Scholar 

  • Yao J, Wang L, Liu L, Zhao C, Zheng Y (2009) Association mapping of agronomic traits on chromosome 2A of wheat. Genetica 137:67–75. doi:10.1007/s10709-009-9351-5

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We are grateful to Dr. Jean-Luc Jannink for insightful suggestions and to Dr. Marco Maccaferri for critically reviewing this manuscript. This work was supported by a Serbian Ministry of Education and Science grant (award no. TR31005/11 and TR31066) and an EU-FP7 Marie Curie Intra-European Fellowship award to D.D. (Grant Agreement Number 254064).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Miroslav Zorić.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 69 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zorić, M., Dodig, D., Kobiljski, B. et al. Population structure in a wheat core collection and genomic loci associated with yield under contrasting environments. Genetica 140, 259–275 (2012). https://doi.org/10.1007/s10709-012-9677-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10709-012-9677-2

Keywords

Navigation