Agarwal PK, Agarwal P, Reddy MK and Sopory SK (2006) Role of DREB transcription factors in abiotic and biotic stress tolerance in plants. Plant Cell Rep 25:1263-1274. [ Links ]
Aghaee-Sarbarzeh M, Ferrahi M, Singh S, Singh H, Friebe B, Gill BS and Dhaliwal HS (2002) Transfer of leaf and stripe rust-resistance genes from Aegilops triuncialis and Ae. Geniculata to bread wheat. Euphytica 127:377-382. [ Links ]
Ashraf M, Ozturk M and Athar HR (2009) Salinity and Water Stress: Improving Crop Efficiency. Springer, Berlin, pp. 1-243. [ Links ]
Assefa S (2000) Resistance to wheat leaf rust in Aegilops tauschii Coss and inheritance of resistance in hexaploid wheat. Genet Resour Crop Evol 47:135-140. [ Links ]
Bordes J, Ravel C, Le Gouis J, Charmet G and Balfourier F (2011) Use of global wheat core collection for association analysis of flour and dough quality traits. J Cereal Sci 54:137-147. [ Links ]
Bordes J, Ravel C, Jaubertie JP, Duperrier B, Gardet O, Heumez E, Pissavy AL, Charmet G, Le Gouis J and Balfourier F (2013) Genomic regions associated with the nitrogen limitation response revealed in a global wheat core collection. Theor Appl Genet 126:805-822. [ Links ]
Bouslama M and Schapaugh WT (1950) Stress tolerance in soybeans. I. Evaluation of three screening techniques for heat and drought tolerance. Crop Sci 24:933-937. [ Links ]
Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y and Buckler ES (2007) TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633-2635. [ Links ]
Breseghello F and Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165-1177. [ Links ]
Budak H, Kantar M and Yucebilgili Kurtoglu K (2013) Drought tolerance in modern and wild wheat. Sci World J 2013:548246. [ Links ]
Cox TS (1994) Leaf rust-resistance genes Lr41, Lr42, and Lr43 transferred from Triticum tauschii to common wheat. Crop Sci 34:39-43. [ Links ]
Cox TS and Hatchett JH (1994) Hessian fly resistance gene H26 transferred from Triticum tauschii to common wheat. Crop Sci 34:958-960. [ Links ]
Cox TS, Raupp WJ, Wilson DL, Gill BS, Leath S and Bockus WW (1992) Resistance to foliar diseases in a collection of Triticum tauschii germplasm. Plant Dis 76:1061-1064. [ Links ]
Crossa J, Burgueno J, Dreisickacker S, Vargas M, Herrera-Foessel SA, Lillemo M, Singh RP, Trethowan R, Warburton M, Franco J, et al. (2007) Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177:1889-1913. [ Links ]
Erena EA, Patrick PF, Byrne SD, Marta MS and Matthew MP (2013) Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 4:791-807. [ Links ]
Ergen NZ and Budak H (2009) Sequencing over 13,000 expressed sequence tags from six subtractive cDNA libraries of wild and modern wheats following slow drought stress. Plant Cell Environ 32:220-236. [ Links ]
Evanno G, Regnaut S and Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611-2620. [ Links ]
Fleury D, Jefferies S, Kuchel H and Langridge P (2010) Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot 61:3211-3222. [ Links ]
Friebe B, Jiang J, Raupp WJ, McIntSCh RA and Gill BS (1996) Characterization of wheat alien translocations conferring resistance to diseases and pests: Current status. Euphytica 71:59-83. [ Links ]
Habash DZ, Kehel Z and Nachit M (2009) Genomic approaches for designing durum wheat ready for climate change with a focus on drought. J Exp Bot 60:2805-2815. [ Links ]
Hoagland DR and Arnon IR (1950) The water-culture method for growing plants without soils. Circ Calif Agric Exp Stn 347:4-32. [ Links ]
Holland JB (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156-161. [ Links ]
Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li CY, Zhu CR, Lu TT, Zhang ZW, et al. (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genet 42:961-967. [ Links ]
Jan A, Maruyama K, Todaka D, Kidokoro S, Abo M, Yoshimura E, Shinozaki K, Nakashima K and Yamaguchi-Shinozaki K (2013) OsTZF1, a CCCH-tandem zinc finger protein, confers delayed senescence and stress tolerance in rice by regulating stress-related genes. Plant Physiol 161:1202-1216. [ Links ]
Jiang AL, Xu ZS, Zhao GY, Cui XY, Chen M, Li LC and Ma YZ (2014) Genome-Wide Analysis of the C3H Zinc Finger Transcription Factor Family and Drought Responses of Members in Aegilops tauschii. Plant Mol Biol 6:1241-1256. [ Links ]
Kang GZ, Ma HZ, Liu GQ, Han QX, Li CW and Guo TC (2013) Silencing of TaBTF3 gene impairs tolerance to freezing and drought stresses in wheat. Mol Genet Genomics 11:591-599. [ Links ]
Kump K, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, Zwonitzer JC, Kresovich S, McMullen MD, Ware D, et al. (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163-168. [ Links ]
Landjeva S, Neumann K and Lohwasser U (2008) Molecular mapping of genomic regions associated with wheat seedling growth under osmotic stress. Biol Plant 2:259-266. [ Links ]
Li GQ, Li ZF, Yang WY, Zhang Y, He ZH, Xu SC, Singh RP, Qu YY and Xia XC (2006) Molecular mapping of stripe rust resistance gene YrCH42 in Chinese wheat cultivar Chuanmai 42 and its allelism with Yr 24 and Yr26. Theor Appl Genet 112:1434-1440. [ Links ]
Liu XL, Li RZ, Chang XP and Jing RL (2013) Mapping QTLs for seedling root traits in a doubled haploid wheat population under different water regimes. Euphytica 189:51-66. [ Links ]
Ludlow MM and Muchow RC (1990) A critical evaluation of traits for improving crop yields in water-limited environments. Advan Agron 43:107-153. [ Links ]
Luo MC, Gu YQ, You FM, Deal KR, Ma Y, Hu Y, Huo N, Wang Y, Wang J, Chen S, et al. (2014) A 4-gigabase physical map unlocks the structure and evolution of the complex genome of Aegilops tauschii, the wheat D-genome progenitor. Proc Natl Acad Sci U S A 110:7940-7945. [ Links ]
Ma H, Singll RP and Muieeb-kazi A (1995) Resistance to stripe rust in Triticum turgidum, T. tauschii and their synthetic hexaploids. Euphytica 82:117-120. [ Links ]
Massman J, Cooper B, Horsley R, Neate S, Dill-Macky R, Chao S, Dong Y, Schwarz P, Muehlbauer GJ and Smith KP (2011) Genome-wide association mapping of Fusarium head blight resistance in contemporary barley breeding germplasm. Mol Breeding 27:439-454. [ Links ]
Mizoi J, Shinozaki K and Yamaguchi-Shinozaki K (2012) Review AP2/ERF family transcription factors in plant abiotic stress responses. Biochim Biophys Acta 1819:86-96. [ Links ]
Mujeeb-Kazi A, Rosas V and Roldan S (1996) Conservation of the genetic variation of Triticum tauschii in synthetic hexaploid wheats and its potential utilization for wheat improvement. Genet Resour Crop Evol 43:129-134. [ Links ]
Navakode S, Neumann K, Kobiljski B, Lohwasser U and Börner A (2014) Genome wide association mapping to identify aluminium tolerance loci in bread wheat. Euphytica 198:401-411. [ Links ]
Neumann K, Kobiljski B, Dencie S, Varshney RK and Borner A (2007) Genome-wide association mapping: A case study in bread wheat (Triticum aestivum L.). Mol Breed 27:37-58. [ Links ]
Nicotra AB and Davidson A (2010) Adaptive phenotypic and plant water use. Funct Plant Biol 37:117-127. [ Links ]
Pasam RK, Sharma R, Malosetti M, van Eeuwijk FA, Haseneyer G, Kilian B and Graner A (2012) Genome-wide association studies for agronomical traits in a worldwide spring barley collection. BMC Plant Biol 12:16-37. [ Links ]
Pritchard JK, Stephens M and Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 55:945-95. [ Links ]
Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele ND, Pljevljakusi CD, Waterman E, Weyen J, et al. (2005) A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring x SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 110:865-880. [ Links ]
Raupp WJ, Amri A, Hatchett JH, Gill BS, Wilson DL and Cox TS (1993) Chromosomal location of Hessian fly-resistance genes H22, H23 and H24 derived from Triticum tauschii in the D genome of wheat. J Hered 84:142-145. [ Links ]
Ravel C, Martre P, Romeuf I, Dardevet M, El-Malki R, Bordes J, Duchateau N, Brunel D, Balfourier F and Charmet G (2009) Nucleotide polymorphism in the wheat transcriptional activator Spa influences its pattern of expression and has pleiotropic effects on grain protein composition, dough viscoelasticity and grain hardness. Plant Physiol 151:33-44. [ Links ]
Richards RA, Rebetzke GJ, Condon AG and van Herwaarden AF (2002) Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereals. Crop Sci 42:111-121. [ Links ]
Phenotypic traits evaluation
Analysis of variance indicated significant differences (P < 0.05) due to the genotype, site, water regime and their interaction effects for all the studied traits. These and the Pearson’s correlations (r) were reported by Mwadzingeni et al. . High and positive correlations and high heritability estimates were detected for most of the traits considered in the current study. Spike length, number of spikelets per spike, plant height, number of kennels per spike, number of days-to-heading and thousand kernel weight had higher levels of genotypic variance (σ2g), hence high heritability values of > 50% (Table 1). The number of days-to-maturity and grain yield had moderate heritability estimates (20% ≤ H2 < 50%).
Population structure was constructed to reveal the genetic relationships and to aid genotype selection. Nine distinct populations were recognised (Fig 1) after the LnP (D) kept increasing from -766,307 at K = 1 to -627,026 (with a mean value of ln likelihood of -590,791) at K = 9. Fig 1 presents the population structure for K = 9 where each colour represents a different genetic cluster. The list of genotypes and the overall representation of membership of the sample in each of the 9 clusters are presented in Table 2. The expected heterozygosity of genes among individuals varied from 0.07 to 0.29 with fixation index (Fst) varying from 0.31 to 0.89 among clusters.
Table 2. Nine genetic clusters with their respective list of wheat genotypes, proportion of membership, expected heterozygosity and the mean values of Fst observed from the study population.
In the structure, Cluster 1 consisted of six and four genotypes from the heat and drought tolerance nurseries, respectively (Table 2). Cluster 2 consisted of only four genotypes from the heat tolerance nursery. This was followed by the largest group (Cluster 3) which comprised of 29 genotypes of which 21 were from the heat tolerance nursery while the remaining eight were from the drought tolerance nursery. Cluster 4 had only genotypes from the heat tolerance nurseries, while Clusters 5, 6 and 7 had mixtures of genotypes. All the local checks (LM61, LM64, LM66, LM67 and LM70) were grouped in Cluster 8, together with ten other genotypes including LM12 from the heat tolerance nursery and nine genotypes from the drought tolerance nursery (Table 2). Likewise, the last cluster contained the genotypes LM78 and LM94 from the drought tolerance nursery.
Linkage disequilibrium analysis revealed the presence of 597,871 loci pairs within a physical distance extending up to 16,356 bp. About 45,835 (7.67%) of loci pairs were in significant LD (P < 0.05). Further, 5,188 (0.87%) of the pairs were in complete LD (R2 = 1). Marker pairs in LD were observed over long distances, however, a clear and rapid decline in LD with distance was observed. Pearson’s correlation coefficients revealed negative correlation (r = -0.0813 between the linkage disequilibrium (R2) and the physical distance (bp); as well as between the P-value and R2 (r = -0.59), revealing the existence of linkage decay.
A total of 334 significant (P < 0.05) marker-trait associations (MTAs) were observed. S1 Table provides the list of significant (0.05 > P > 0.001) MTAs that could also have influence on respective traits. Only the MTAs that had P values < 0.001 (Table 3) were considered as significant for all traits except for grain yield, thousand seed weight and number of days-to-maturity where significant (P < 0.05) marker-trait associations were considered because the three traits are highly complex, often with moderate to low heritability . These markers explained > 20% of the total phenotypic variation observed on all respective traits. Of the MTAs that were considered significant, four loci were identified to be highly associated with the number of days-to-heading, explaining 24.96% to 37.77% of the total phenotypic variation. Two of these makers were located on chromosome 5A, while the other two were found on chromosomes 5B and 6B (Table 3). The number of days-to-heading were recorded immediately before imposing drought stress but the means from the stressed and non-stressed experiments were used separately for GWAS to check for repeatability.
Marker-trait- association analyses revealed association between specific phenotypes and genetic variants within a genome, which could lead to the discovery of genes controlling the traits. Two markers located on chromosomes 1A and 2D were associated with plant height under drought-stress. Under non-stressed condition, six markers were associated with plant height of which two were located on chromosome 2B and the rest were on chromosomes 5A, 5B, 6B, and 7B. These markers explained 23.75% to 28.8% of the variation in plant height. Spike length was associated with thirteen markers under drought-stressed condition explaining 22.17% to 31.96% of the total phenotypic variation; and eight markers under non-stressed condition; explaining 21.20% to 30.45% of the variation in spike length. The markers observed for this trait under drought-stress were from chromosomes 1B, 2B, 2D, 3A, 4B, 5B, 6A, 6B and 7A. Eight DArT markers were associated with spike length under non-stressed condition of which seven markers were consistent under both drought-stressed and non-stressed conditions from chromosomes 2B, 2D, 5B, 5B and 7A (Table 3). Under drought-stress, SPS was highly associated with eight markers located on chromosomes 6B, 2D, 2B, 5D, 1B and 4B; while under the same stress level, seven significant MTAs were recorded that were located on chromosomes 1B, 2D, 4B, 5A, 5B and 6B. Six of the markers, except for one located on chromosome 2B, one on 5A and one on 5B were consistent with the ones obtained under drought-stressed condition (Table 3).
The B genome had most of the significant MTAs observed for this trait. The marker 6B|079.586479380|3949288|3949288 explained the highest proportion of the phenotypic variation (41%) under drought-stressed condition while a marker on chromosome 2B explained the least proportion (28.06%) of the phenotypic variation observed under the non-stressed condition. Under drought-stressed condition, the number of kernels per spike was associated with two markers located on chromosomes 2D and 4A, explaining 30.04% and 30.24% of the observed phenotypic variation, in that order. Eight significant MTAs were detected under non-stressed condition on chromosomes 2D, 6B and 7A explaining 28.06% to 36.46% of the variation of the number of spikelets per spike, respectively. Three MTAs on chromosomes 6B, 7B and 5D were considered significant (0.05 > P > 0.001) for the number of days-to-maturity, thousand seed weight and grain yield accounting for 24.03%, 23.94% and 22.57% of the phenotypic variation, respectively. Table 4 summarises the number of DArTseq markers observed for each of the nine agronomic traits evaluated under drought stressed and non-stressed conditions. Subject to further validation, these markers will be useful for marker-assisted selection for respective traits under target growing conditions.
A pleiotropic locus is associated and affects the expression of more than one phenotypic trait. In this study, several pleiotropic loci were identified including the marker 5A|084.411633690|3534155|3534155 that was associated with DTH, PHT and SPS under non-stressed condition (Table 3). Days-to-heading, PHT and DTM under non-stressed condition; SPL under drought-stressed condition; and SPS under drought-stressed condition were associated with the marker 6B|079.586479380|3949288|3949288 located on chromosome 6B. On chromosome 2D, the locus 2D|128.146584600|4021827|4021827 was associated with PHT under drought-stress condition as well as SPL, SPS, and KPS under both drought-stressed and non-stressed conditions. Plant height and SPL under drought-stressed condition were associated with the marker 1B|063.445873190|3937163|3937163 on chromosome 1B. The marker 7A|065.934336980|1118335|1118335 was associated with SPL under both drought-stressed and non-stressed conditions as well as with KPS under non-stressed condition. Additionally, 5B|000.000000000|3023157|3023157 was associated with SPL under drought-stressed and non-stressed conditions as well as with SPS under drought-stressed condition only. Spike length and SPS under drought-stressed condition were associated with the marker 2B|108.086871100|1132117|1132117, while the marker 4B|042.180040830|1081624|1081624 was associated with SPL under drought-stressed condition only and SPS under both drought-stressed and non-stressed conditions. Further, the locus 5B|000.649324338|1209883|1209883 was associated with DTH and PHT under non-stressed condition as well as SPS under drought-stressed condition. Finally, 6B|031.043100140|1237876|1237876 was associated with SPL and KPS under drought-stressed condition. Blast searches of the marker 6B|031.043100140|1237876|1237876 on the National Center for Biotechnology Information (NCBI) (http://blast.ncbi.nlm.nih.gov/Blast.cgi) and GrainGenes (http://wheat.pw.usda.gov/GG2/blast.shtml) databases indicated that this marker has a sequence alignment that is 97% identical to the TaMFT gene that regulates seed dormancy on chromosome 3A (Nakamura et al. ; http://www.uniprot.org/uniprot/A0A0K2RW47).
Out of the 65 significant marker-trait associations observed, 25 trait-specific MTAs were differentiated. Chromosome 2B had four trait specific MTAs of which one was associated with spike length under drought-stress, two with plant height under non-stressed condition and one with spike length under non-stressed condition. Traits that were represented by at least one significant trait-specific marker-trait association under either of the two water conditions were days-to-heading, plant height, spike length, number of spikelet per spike, number of kernels per spike, days-to-maturity, and grain yield (Table 3).