Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis.

Puccinia spp. causing rust diseases in wheat and other cereals secrete several specialized effector proteins into host cells. Characterization of these proteins and their interaction with host's R proteins could greatly help to limit crop losses due to diseases. Prediction of effector proteins by combining the transcriptome analysis and multiple in-silico approaches is gaining importance in revealing the pathogenic mechanism. The present study involved identification of 13 Puccinia triticina (Pt) coding sequences (CDSs), through transcriptome analysis, that were differentially expressed during wheat-leaf rust interaction; and prediction of their effector like features using different in-silico tools. NCBI-BLAST and pathogen-host interaction BLAST (PHI-BLAST) tools were used to annotate and classify these sequences based on their most closely matched counterpart in both the databases. Homology between CDSs and the annotated sequences in the NCBI database ranged from 79 to 94% and with putative effectors of other plant pathogens in PHI-BLAST from 24.46 to 54.35%. Nine of the 13 CDSs had effector-like features according to EffectorP 3.0 (≥0.546 probability of these sequences to be effector). The qRT-PCR expression analysis revealed that the relative expression of all CDSs in compatible interaction (HD2329) was maximum at 11 days post inoculation (dpi) and that in incompatible interactions (HD2329 + Lr28) was maximum at 3 dpi in seven and 9 dpi in five CDSs. These results suggest that six CDSs (>0.8 effector probability as per EffectorP 3.0) could be considered as putative Pt effectors. The molecular docking and MD simulation analysis of these six CDSs suggested that candidate Lr28 protein binds more strongly to candidate effector c14094_g1_i1 to form more stable complex than the remaining five. Further functional characterization of these six candidate effectors should prove useful for a better understanding of wheat-leaf rust interaction. In turn, this should facilitate effector-based leaf rust resistance breeding in wheat.

Mapping of QTLs and meta-QTLs for Heterodera avenae Woll. resistance in common wheat (Triticum aestivum L.).

BACKGROUND: In hexaploid wheat, quantitative trait loci (QTL) and meta-QTL (MQTL) analyses were conducted to identify genomic regions controlling resistance to cereal cyst nematode (CCN), Heterodera avenae. A mapping population comprising 149 RILs derived from the cross HUW 468 × C 306 was used for composite interval mapping (CIM) and inclusive composite interval mapping (ICIM). RESULTS: Eight main effect QTLs on three chromosomes (1B, 2A and 3A) were identified using two repeat experiments. One of these QTLs was co-localized with a previously reported wheat gene Cre5 for resistance to CCN. Seven important digenic epistatic interactions (PVE = 5% or more) were also identified, each involving one main effect QTL and another novel E-QTL. Using QTLs earlier reported in literature, two meta-QTLs were also identified, which were also used for identification of 57 candidate genes (CGs). Out of these, 29 CGs have high expression in roots and encoded the following proteins having a role in resistance to plant parasitic nematodes (PPNs): (i) NB-ARC,P-loop containing NTP hydrolase, (ii) Protein Kinase, (iii) serine-threonine/tyrosine-PK, (iv) protein with leucine-rich repeat, (v) virus X resistance protein-like, (vi) zinc finger protein, (vii) RING/FYVE/PHD-type, (viii) glycosyl transferase, family 8 (GT8), (ix) rubisco protein with small subunit domain, (x) protein with SANT/Myb domain and (xi) a protein with a homeobox. CONCLUSION: Identification and selection of resistance loci with additive and epistatic effect along with two MQTL and associated CGs, identified in the present study may prove useful for understanding the molecular basis of resistance against H. avenae in wheat and for marker-assisted selection (MAS) for breeding CCN resistant wheat cultivars.

Identification and characterization of RuvBL DNA helicase genes for tolerance against abiotic stresses in bread wheat (Triticum aestivum L.) and related species.

Recombination UVB (sensitivity) like (RuvBL) helicase genes represent a conserved family of genes, which are known to be involved in providing tolerance against abiotic stresses like heat and drought. We identified nine wheat RuvBL genes, one each on nine different chromosomes, belonging to homoeologous groups 2, 3, and 4. The lengths of genes ranged from 1647 to 2197 bp and exhibited synteny with corresponding genes in related species including Ae. tauschii, Z. mays, O. sativa, H. vulgare, and B. distachyon. The gene sequences were associated with regulatory cis-elements and transposable elements. Two genes, namely TaRuvBL1a-4A and TaRuvBL1a-4B, also carried targets for a widely known miRNA, tae-miR164. Gene ontology revealed that these genes were closely associated with ATP-dependent formation of histone acetyltransferase complex. Analysis of the structure and function of RuvBL proteins revealed that the proteins were localized mainly in the cytoplasm. A representative gene, namely TaRuvBL1a-4A, was also shown to be involved in protein-protein interactions with ten other proteins. On the basis of phylogeny, RuvBL proteins were placed in two sub-divisions, namely RuvBL1 and RuvBL2, which were further classified into clusters and sub-clusters. In silico studies suggested that these genes were differentially expressed under heat/drought. The qRT-PCR analysis confirmed that expression of TaRuvBL genes differed among wheat cultivars, which differed in the level of thermotolerance. The present study advances our understanding of the biological role of wheat RuvBL genes and should help in planning future studies on RuvBL genes in wheat including use of RuvBL genes in breeding thermotolerant wheat cultivars.

GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat.

In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01357-5.

Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.).

In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and ß-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.

Genetics and breeding for resistance against four leaf spot diseases in wheat (Triticum aestivum L.).

In wheat, major yield losses are caused by a variety of diseases including rusts, spike diseases, leaf spot and root diseases. The genetics of resistance against all these diseases have been studied in great detail and utilized for breeding resistant cultivars. The resistance against leaf spot diseases caused by each individual necrotroph/hemi-biotroph involves a complex system involving resistance (R) genes, sensitivity (S) genes, small secreted protein (SSP) genes and quantitative resistance loci (QRLs). This review deals with resistance for the following four-leaf spot diseases: (i) Septoria nodorum blotch (SNB) caused by Parastagonospora nodorum; (ii) Tan spot (TS) caused by Pyrenophora tritici-repentis; (iii) Spot blotch (SB) caused by Bipolaris sorokiniana and (iv) Septoria tritici blotch (STB) caused by Zymoseptoria tritici.

QTL mapping for resistance against cereal cyst nematode (Heterodera avenae Woll.) in wheat (Triticum aestivum L.).

The resistance to cereal cyst nematode (Heterodera avenae Woll.) in wheat (Triticum aestivum L.) was studied using 114 doubled haploid lines from a novel ITMI mapping population. These lines were screened for nematode infestation in a controlled environment for two years. QTL-mapping analyses were performed across two years (Y1 and Y2) as well as combining two years (CY) data. On the 114 lines that were screened, a total of 2,736 data points (genotype, batch or years, and replication combinations) were acquired. For QTL analysis, 12,093 markers (11,678 SNPs and 415 SSRs markers) were used, after filtering the genotypic data, for the QTL mapping. Composite interval mapping, using Haley-Knott regression (hk) method in R/QTL, was used for QTL analysis. In total, 19 QTLs were detected out of which 13 were novel and six were found to be colocalized or nearby to previously reported Cre genes, QTLs or MTAs for H. avenae or H. filipjevi. Nine QTLs were detected across all three groups (Y1, Y2 and CY) including a significant QTL "QCcn.ha-2D" on chromosome 2D that explains 23% of the variance. This QTL colocalized with a previously identified Cre3 locus. Novel QTL, QCcn.ha-2A, detected in the present study could be the possible unreported homeoloci to QCcn.ha-2D, QCcn.ha-2B.1 and QCcn.ha-2B.2. Six significant digenic epistatic interactions were also observed. In addition, 26 candidate genes were also identified including genes known for their involvement in PPNs (plant parasitic nematodes) resistance in different plant species. In-silico expression of putative candidate genes showed differential expression in roots during specific developmental stages. Results obtained in the present study are useful for wheat breeding to generate resistant genetic resources against H. avenae.

Earth Biogenome Project: present status and future plans.

Earth Biogenome Project (EBP) is an ambitious project targeted to provide high-quality reference genome sequences for all 1.8 million named extant (living) eukaryote species. The project was launched on 1 November 2018 with an initial 2 years' pilot phase (2018-2020) followed by Phase I (2020-2023), during which genomes of 9400 species will be sequenced. The genomes of the remaining ~1.7 million species will be sequenced in a planned manner during Phase II (2024-2027) and Phase III (2028-2030). In view of the excitement generated and the progress already made, the subject was covered in a Special Feature of a recent issue of PNAS (25 January 2022). The present status and future plans of EBP along with challenges faced are briefly discussed in this article.

Meta-QTLs, ortho-MetaQTLs and candidate genes for grain Fe and Zn contents in wheat (Triticum aestivum L.).

Majority of cereals are deficient in essential micronutrients including grain iron (GFe) and grain zinc (GZn), which are therefore the subject of research involving biofortification. In the present study, 11 meta-QTLs (MQTLs) including nine novel MQTLs for GFe and GZn contents were identified in wheat. Eight of these 11 MQTLs controlled both GFe and GZn. The confidence intervals of the MQTLs were narrower (0.51-15.75 cM) relative to those of the corresponding QTLs (0.6 to 55.1 cM). Two ortho-MQTLs involving three cereals (wheat, rice and maize) were also identified. Results of MQTLs were also compared with the results of earlier genome wide association studies (GWAS). As many as 101 candidate genes (CGs) underlying MQTLs were also identified. Twelve of these CGs were prioritized; these CGs encoded proteins with important domains (zinc finger, RING/FYVE/PHD type, flavin adenine dinucleotide linked oxidase, etc.) that are involved in metal ion binding, heme binding, iron binding, etc. qRT-PCR analysis was conducted for four of these 12 prioritized CGs using genotypes which have differed for GFe and GZn. Significant differential expression in these genotypes was observed at 14 and 28 days after anthesis. The MQTLs/CGs identified in the present study may be utilized in marker-assisted selection (MAS) for improvement of GFe/GZn contents and also for understanding the molecular basis of GFe/GZn homeostasis in wheat. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01149-9.

GWAS for main effects and epistatic interactions for grain morphology traits in wheat.

In the present study in wheat, GWAS was conducted for identification of marker trait associations (MTAs) for the following six grain morphology traits: (1) grain cross-sectional area (GCSA), (2) grain perimeter (GP), (3) grain length (GL), (4) grain width (GWid), (5) grain length-width ratio (GLWR) and (6) grain form-density (GFD). The data were recorded on a subset of spring wheat reference set (SWRS) comprising 225 diverse genotypes, which were genotyped using 10,904 SNPs and phenotyped for two consecutive years (2017-2018, 2018-2019). GWAS was conducted using five different models including two single-locus models (CMLM, SUPER), one multi-locus model (FarmCPU), one multi-trait model (mvLMM) and a model for Q x Q epistatic interactions. False discovery rate (FDR) [P value -log10(p) ≥ 5] and Bonferroni correction [P value -log10(p) ≥ 6] (corrected p value < 0.05) were applied to eliminate false positives due to multiple testing. This exercise gave 88 main effect and 29 epistatic MTAs after FDR and 13 main effect and 6 epistatic MTAs after Bonferroni corrections. MTAs obtained after Bonferroni corrections were further utilized for identification of 55 candidate genes (CGs). In silico expression analysis of CGs in different tissues at different parts of the seed at different developmental stages was also carried out. MTAs and CGs identified during the present study are useful addition to available resources for MAS to supplement wheat breeding programmes after due validation and also for future strategic basic research. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01164-w.

BS-Seq reveals major role of differential CHH methylation during leaf rust resistance in wheat (Triticum aestivum L.).

Epigenetic regulation of the activity of defense genes during onset of diseases or resistance against diseases in plants is an active area of research. In the present study, a pair of wheat NILs for leaf rust resistance gene Lr28 (R) in the background of an Indian cultivar HD2329 (S) was used for a study of DNA methylation mediated regulation of gene expression. Leaf samples were collected at 0 h before (S0 and R0) and 96 h after inoculation (S96 and R96). The DNA samples were subjected to BS-Seq and sequencing data were used for identification of differentially methylated/demethylated regions/genes (DMRs and DMGs). Following four pairs of comparisons were used for this purpose: S0 vs S96; S0 vs R0; R0 vs R96; S96 vs R96. Major role of CHH methylation relative to that of CG and CHG methylation was observed. Some important observations include the following: (i) abundance of CHH methylation among DMRs; (ii) predominance of DMRs in intergenic region, relative to other genomic regions (promoters, exons, introns, TSS and TTS); (iii) abundance of transposable elements (TEs) in DMRs with CHH context; (iv) demethylation mediated high expression of genes during susceptible reaction (S0 vs S96) and methylation mediated low expression of genes during resistant reaction (R0 vs R96 and S96 vs R96); (v) major genes under regulation encode proteins, which differ from those encoded by genes regulated during susceptible reaction and (vi) ~ 500 DMGs carried differential binding sites for H3K4/K27me3 marks suggesting joint involvement of DNA and H3 methylation. Thus, CHH methylation either alone or in combination with histone methylation plays a major role in regulating the expression of genes involved in wheat-leaf rust interaction.

Dataset of nine agronomic traits in bread wheat phenotyped under irrigated and rain-fed environments.

Higher yield and broad adaptation to drought-prone environments are key targets of wheat breeding programs. This can be achieved through a complete knowledge of the genetic architecture of yield and its related traits. This brief article provides analysed mean data used in the research article entitled "QTL mapping for nine drought-responsive agronomic traits in bread wheat under irrigated and rain-fed environments" (Gahlaut et al., 2017). Phenotypic data were recorded on nine important agronomic traits on a doubled haploid (DH) mapping population derived from the cross Kukri/Excalibur. For recording this data, the mapping population was grown during three crop seasons (2010-11 to 2012-13) at four separate locations in India, both under irrigated and rain-fed environments. This dataset is valuable for wheat breeders to better understand the genetic basis of drought tolerance in wheat.

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS.

Genetic architecture of resistance to spot blotch in wheat was examined using a Genome-Wide Association Study (GWAS) involving an association panel comprising 303 diverse genotypes. The association panel was evaluated at two different locations in India including Banaras Hindu University (BHU), Varanasi (Uttar Pradesh), and Borlaug Institute for South Asia (BISA), Pusa, Samastipur (Bihar) for two consecutive years (2017-2018 and 2018-2019), thus making four environments (E1, BHU 2017-18; E2, BHU 2018-19; E3, PUSA, 2017-18; E4, PUSA, 2018-19). The panel was genotyped for 12,196 SNPs based on DArT-seq (outsourced to DArT Ltd by CIMMYT); these SNPs included 5,400 SNPs, which could not be assigned to individual chromosomes and were therefore, described as unassigned by the vendor. Phenotypic data was recorded on the following three disease-related traits: (i) Area Under Disease Progress Curve (AUDPC), (ii) Incubation Period (IP), and (iii) Lesion Number (LN). GWAS was conducted using each of five different models, which included two single-locus models (CMLM and SUPER) and three multi-locus models (MLMM, FarmCPU, and BLINK). This exercise gave 306 MTAs, but only 89 MTAs (33 for AUDPC, 30 for IP and 26 for LN) including a solitary MTA detected using all the five models and 88 identified using four of the five models (barring SUPER) were considered to be important. These were used for further analysis, which included identification of candidate genes (CGs) and their annotation. A majority of these MTAs were novel. Only 70 of the 89 MTAs were assigned to individual chromosomes; the remaining 19 MTAs belonged to unassigned SNPs, for which chromosomes were not known. Seven MTAs were selected on the basis of minimum P value, number of models, number of environments and location on chromosomes with respect to QTLs reported earlier. These 7 MTAs, which included five main effect MTAs and two for epistatic interactions, were considered to be important for marker-assisted selection (MAS). The present study thus improved our understanding of the genetics of resistance against spot blotch in wheat and provided seven MTAs, which may be used for MAS after due validation.

Meta-analysis reveals consensus genomic regions associated with multiple disease resistance in wheat (Triticum aestivum L.).

In wheat, meta-QTLs (MQTLs) and candidate genes (CGs) were identified for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the five diseases. As many as 493 QTLs were available from these studies, which were distributed in five diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103 QTLs; fusarium head blight (FHB), 184 QTLs; karnal bunt (KB), 66 QTLs; and loose smut (LS), 14 QTLs. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were verified using marker-trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identified which were further investigated for differential expression using data from five transcriptome studies, resulting in 194 differentially expressed candidate genes (DECGs). Among the DECGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for fine mapping and cloning of MDR genes and marker-assisted breeding. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01282-z.

WheatQTLdb V2.0: a supplement to the database for wheat QTL.

We recently developed a database for hexaploid wheat QTL (WheatQTLdb; www.wheatqtldb.net), which included 11,552 QTL affecting various traits of economic importance. However, that database did not include valuable QTL from other wheat species and/or progenitors of hexaploid wheat. Therefore, an updated and improved version of wheat QTL database (WheatQTLdb V2.0) was developed, which now includes information on hexaploid wheat (Triticum aestivum) and the following seven other related species: T. durum, T. turgidum, T. dicoccoides, T. dicoccum, T. monococcum, T. boeoticum, and Aegilops tauschii. WheatQTLdb V2.0 includes a much-improved list of QTL, including 27,518 main effect QTL, 202 epistatic QTL, and 1321 metaQTL. This newly released WheatQTLdb V2.0 also has additional valuable options to search and choose the QTL, category-wise, and trait-wise data for their use in research or breeding programs.

Meta-QTLs, ortho-MQTLs and candidate genes for nitrogen use efficiency and root system architecture in bread wheat (Triticum aestivum L.).

In wheat, meta-QTLs (MQTLs), ortho-MQTLs, and candidate genes (CGs) were identified for nitrogen use efficiency and root system architecture. For this purpose, 1788 QTLs were available from 24 studies published during 2006-2020. Of these, 1098 QTLs were projected onto the consensus map resulting in 118 MQTLs. The average confidence interval (CI) of MQTLs was reduced up to 8.56 folds in comparison to the average CI of QTLs. Of the 118 MQTLs, 112 were anchored to the physical map of the wheat reference genome. The physical interval of MQTLs ranged from 0.02 to 666.18 Mb with a mean of 94.36 Mb. Eighty-eight of these 112 MQTLs were verified by marker-trait associations (MTAs) identified in published genome-wide association studies (GWAS); the MQTLs that were verified using GWAS also included 9 most robust MQTLs, which are particularly useful for breeders; we call them 'Breeder's QTLs'. Some selected wheat MQTLs were further utilized for the identification of ortho-MQTLs for wheat and maize; 9 such ortho-MQTLs were available. As many as 1991 candidate genes (CGs) were also detected, which included 930 CGs with an expression level of > 2 transcripts per million in relevant organs/tissues. Among the CGs, 97 CGs with functions previously reported as important for the traits under study were selected. Based on homology analysis and expression patterns, 49 orthologues of 35 rice genes were also identified in MQTL regions. The results of the present study may prove useful for the improvement of selection strategy for yield potential, stability, and performance under N-limiting conditions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-01085-0.

Meta-QTLs and candidate genes for stripe rust resistance in wheat.

In bread wheat, meta-QTL analysis was conducted using 353 QTLs that were available from earlier studies. When projected onto a dense consensus map comprising 76,753 markers, only 184 QTLs with the required information, could be utilized leading to identification of 61 MQTLs spread over 18 of the 21 chromosomes (barring 5D, 6D and 7D). The range for mean R2 (PVE %) was 1.9% to 48.1%, and that of CI was 0.02 to 11.47 cM; these CIs also carried 37 Yr genes. Using these MQTLs, 385 candidate genes (CGs) were also identified. Out of these CGs, 241 encoded known R proteins and 120 showed differential expression due to stripe rust infection at the seedling stage; the remaining 24 CGs were common in the sense that they encoded R proteins as well as showed differential expression. The proteins encoded by CGs carried the following widely known domains: NBS-LRR domain, WRKY domains, ankyrin repeat domains, sugar transport domains, etc. Thirteen breeders' MQTLs (PVE > 20%) including four pairs of closely linked MQTLs are recommended for use in wheat molecular breeding, for future studies to understand the molecular mechanism of stripe rust resistance and for gene cloning.

WheatQTLdb: a QTL database for wheat.

During the last three decades, QTL analysis in wheat has been conducted for a variety of individual traits, so that thousands of QTL along with the linked markers, their genetic positions and contribution to phenotypic variation (PV) for concerned traits are now known. However, no exhaustive database for wheat QTL is currently available at a single platform. Therefore, the present database was prepared which is an exhaustive information resource for wheat QTL data from the published literature till May, 2020. QTL data from both interval mapping and genome-wide association studies (GWAS) have been included for the following classes of traits: (i) morphological traits, (ii) N and P use efficiency, (iii) traits for biofortification (Fe, K, Se, and Zn contents), (iv) tolerance to abiotic stresses including drought, water logging, heat stress, pre-harvest sprouting and salinity, (v) resistance to biotic stresses including those due to bacterial, fungal, nematode and insects, (vi) quality traits, and (vii) a variety of physiological traits, (viii) developmental traits, and (ix) yield and its related traits. For the preparation of the database, literature was searched for data on QTL/marker-trait associations (MTAs), curated and then assembled in the form of WheatQTLdb. The available information on metaQTL, epistatic QTL and candidate genes, wherever available, is also included in the database. Information on QTL in this WheatQTLdb includes QTL names, traits, associated markers, parental genotypes, crosses/mapping populations, association mapping panels and other useful information. To our knowledge, WheatQTLdb prepared by us is the largest collection of QTL (11,552), epistatic QTL (107) and metaQTL (330) data for hexaploid wheat to be used by geneticists and plant breeders for further studies involving fine mapping, cloning, and marker-assisted selection (MAS) during wheat breeding.