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Chickpea production is vulnerable to drought stress. Identifying the genetic components underlying drought adaptation is crucial for enhancing chickpea productivity. Here, we present the fine mapping and characterization of ‘QTL-hotspot’, a genomic region controlling chickpea growth with positive consequences on crop production under drought. We report that a non-synonymous substitution in the transcription factor CaTIFY4b regulates seed weight and organ size in chickpea. Ectopic expression of CaTIFY4b in Medicago truncatula enhances root growth under water deficit. Our results suggest that allelic variation in ‘QTL-hotspot’ improves pre-anthesis water use, transpiration efficiency, root architecture and canopy development, enabling high-yield performance under terminal drought conditions. Gene expression analysis indicated that CaTIFY4b may regulate organ size under water deficit by modulating the expression of GRF-INTERACTING FACTOR1 (GIF1), a transcriptional co-activator of Growth-Regulating Factors. Taken together, our study offers new insights into the role of CaTIFY4b and on diverse physiological and molecular mechanisms underpinning chickpea growth and production under specific drought scenarios.

Introduction Legume crops are an important source of protein and oil for human health and in fixing atmospheric N2 for soil enrichment. With an objective to accelerate much-needed genetic analyses and breeding applications, draft genome assemblies were generated in several legume crops; many of them are not high quality because they are mainly based on short reads. However, the superior quality of genome assembly is crucial for a detailed understanding of genomic architecture, genome evolution, and crop improvement. Objectives Present study was undertaken with an objective of developing improved chromosome-length genome assemblies in six different legumes followed by their systematic investigation to unravel different aspects of genome organization and legume evolution. Methods We employed in situ Hi-C data to improve the existing draft genomes and performed different evolutionary and comparative analyses using improved genome assemblies. Results We have developed chromosome-length genome assemblies in chickpea, pigeonpea, soybean, subterranean clover, and two wild progenitor species of cultivated groundnut (A. duranensis and A. ipaensis). A comprehensive comparative analysis of these genome assemblies offered improved insights into various evolutionary events that shaped the present-day legume species. We highlighted the expansion of gene families contributing to unique traits such as nodulation in legumes, gravitropism in groundnut, and oil biosynthesis in oilseed legume crops such as groundnut and soybean. As examples, we have demonstrated the utility of improved genome assemblies for enhancing the resolution of “QTL-hotspot” identification for drought tolerance in chickpea and marker-trait associations for agronomic traits in pigeonpea through genome-wide association study. Genomic resources developed in this study are publicly available through an online repository, ‘Legumepedia’. Conclusion This study reports chromosome-length genome assemblies of six legume s

Hybrids are extensively used in agriculture to deliver an increase in yield, yet the molecular basis of heterosis is not well understood. Global DNA methylation analysis, transcriptome analysis and small RNA profiling were aimed to understand the epigenetic effect of the changes in gene expression level in the two hybrids and their parental lines. Increased DNA methylation was observed in both the hybrids as compared to their parents. This increased DNA methylation in hybrids showed that majority of the 24‐nt siRNA clusters had higher expression in hybrids than the parents. Transcriptome analysis revealed that various phytohormones (auxin and salicylic acid) responsive hybrid‐MPV DEGs were significantly altered in both the hybrids in comparison to MPV. DEGs associated with plant immunity and growth were overexpressed whereas DEGs associated with basal defence level were repressed. This antagonistic patterns of gene expression might contribute to the greater growth of the hybrids. It was also noticed that some common as well as unique changes in the regulatory pathways were associated with heterotic growth in both the hybrids. Approximately 70% and 67% of down‐regulated hybrid‐MPV DEGs were found to be differentially methylated in ICPH 2671 and ICPH 2740 hybrid, respectively. This reflected the association of epigenetic regulation in altered gene expressions. Our findings also revealed that miRNAs might play important roles in hybrid vigour in both the hybrids by regulating their target genes, especially in controlling plant growth and development, defence and stress response pathways. The above finding provides an insight into the molecular mechanism of pigeonpea heterosis.

The genetic architecture of seed protein content (SPC) and its relationships to agronomic traits in pigeonpea is poorly understood. Accordingly, five F2 populations segregating for SPC and four agronomic traits (seed weight (SW), seed yield (SY), growth habit (GH) and days to first flowering (DFF)) were phenotyped and genotyped using genotyping-by-sequencing approach. Five high-density population-specific genetic maps were constructed with an average inter-marker distance of 1.6 to 3.5 cM, and subsequently, integrated into a consensus map with average marker spacing of 1.6 cM. Based on analysis of phenotyping data and genotyping data, 192 main effect QTLs (M-QTLs) with phenotypic variation explained (PVE) of 0.7 to 91.3% were detected for the five traits across the five populations. Major effect (PVE ≥ 10%) M-QTLs included 14 M-QTLs for SPC, 16 M-QTLs for SW, 17 M-QTLs for SY, 19 M-QTLs for GH and 24 M-QTLs for DFF. Also, 573 epistatic QTLs (E-QTLs) were detected with PVE ranging from 6.3 to 99.4% across traits and populations. Colocalization of M-QTLs and E-QTLs explained the genetic basis of the significant (P < 0.05) correlations of SPC with SW, SY, DFF and GH. The nature of genetic architecture of SPC and its relationship with agronomic traits suggest that genomics-assisted breeding targeting genome-wide variations would be effective for the simultaneous improvement of SPC and other important traits.

Tomato spotted wilt virus (TSWV) is a devastating disease to peanut growers in the South-eastern region of the United States. Newly released peanut cultivars in recent years are crucial as they have some levels of resistance to TSWV. One mapping population of recombinant inbred line (RIL) used in this study was derived from peanut lines of SunOleic 97R and NC94022. A whole genome re-sequencing approach was used to sequence these two parents and 140 RILs. A recombination bin-based genetic map was constructed, with 5,816 bins and 20 linkage groups covering a total length of 2004 cM. Using this map, we identified three QTLs which were colocalized on chromosome A01. One QTL had the largest effect of 36.51% to the phenotypic variation and encompassed 89.5 Kb genomic region. This genome region had a cluster of genes, which code for chitinases, strictosidine synthase-like, and NBS-LRR proteins. SNPs linked to this QTL were used to develop Kompetitive allele specific PCR (KASP) markers, and the validated KASP markers showed expected segregation of alleles coming from resistant and susceptible parents within the population. Therefore, this bin-map and QTL associated with TSWV resistance made it possible for functional gene mapping, map-based cloning, and marker-assisted breeding. This study identified the highest number of SNP makers and demonstrated recombination bin-based map for QTL identification in peanut. The chitinase gene clusters and NBS-LRR disease resistance genes in this region suggest the possible involvement in peanut resistance to TSWV.

Ascochyta blight (AB) is one of the major biotic stresses known to limit the chickpea production worldwide. To dissect the complex mechanisms of AB resistance in chickpea, three approaches, namely, transcriptome, small RNA and degradome sequencing were used. The transcriptome sequencing of 20 samples including two resistant genotypes, two susceptible genotypes and one introgression line under control and stress conditions at two time points (3rd and 7th day post inoculation) identified a total of 6767 differentially expressed genes (DEGs). These DEGs were mainly related to pathogenesis‐related proteins, disease resistance genes like NBS‐LRR, cell wall biosynthesis and various secondary metabolite synthesis genes. The small RNA sequencing of the samples resulted in the identification of 651 miRNAs which included 478 known and 173 novel miRNAs. A total of 297 miRNAs were differentially expressed between different genotypes, conditions and time points. Using degradome sequencing and in silico approaches, 2131 targets were predicted for 629 miRNAs. The combined analysis of both small RNA and transcriptome datasets identified 12 miRNA‐mRNA interaction pairs that exhibited contrasting expression in resistant and susceptible genotypes and also, a subset of genes that might be post‐transcriptionally silenced during AB infection. The comprehensive integrated analysis in the study provides better insights into the transcriptome dynamics and regulatory network components associated with AB stress in chickpea and, also offers candidate genes for chickpea improvement.

Background Terminal drought stress leads to substantial annual yield losses in chickpea (Cicer arietinum L.). Adaptation to water limitation is a matter of matching water supply to water demand by the crop. Therefore, harnessing the genetics of traits contributing to plant water use, i.e. transpiration rate and canopy development dynamics, is important to design crop ideotypes suited to a varying range of water limited environments. With an aim of identifying genomic regions for plant vigour (growth and canopy size) and canopy conductance traits, 232 recombinant inbred lines derived from a cross between ICC 4958 and ICC 1882, were phenotyped at vegetative stage under well-watered conditions using a high throughput phenotyping platform (LeasyScan). Results Twenty one major quantitative trait loci (M-QTLs) were identified for plant vigour and canopy conductance traits using an ultra-high density bin map. Plant vigour traits had 13 M-QTLs on CaLG04, with favourable alleles from high vigour parent ICC 4958. Most of them co-mapped with a previously fine mapped major drought tolerance “QTL-hotspot” region on CaLG04. One M-QTL was found for canopy conductance on CaLG03 with the ultra-high density bin map. Comparative analysis of the QTLs found across different density genetic maps revealed that QTL size reduced considerably and % of phenotypic variation increased as marker density increased. Conclusion Earlier reported drought tolerance hotspot is a vigour locus. The fact that canopy conductance traits, i.e. the other important determinant of plant water use, mapped on CaLG03 provides an opportunity to manipulate these loci to tailor recombinants having low/high transpiration rate and plant vigour, fitted to specific drought stress scenarios in chickpea.

Sterility mosaic disease (SMD) is one of the serious production constraints that may lead to complete yield loss in pigeonpea. Three mapping populations including two recombinant inbred lines and one F2, were used for phenotyping for SMD resistance at two locations in three different years. Genotyping-by-sequencing approach was used for simultaneous identification and genotyping of SNPs on above mentioned populations. In total, 212,464, 89,699 and 64,798 SNPs were identified in ICPL 20096 × ICPL 332 (PRIL_B), ICPL 20097 × ICP 8863 (PRIL_C) and ICP 8863 × ICPL 87119 (F2) respectively. By using high-quality SNPs, genetic maps were developed for PRIL_B (1,101 SNPs; 921.21 cM), PRIL_C (484 SNPs; 798.25 cM) and F2 (996 SNPs; 1,597.30 cM) populations. The average inter marker distance on these maps varied from 0.84 cM to 1.65 cM, which was lowest in all genetic mapping studies in pigeonpea. Composite interval mapping based QTL analysis identified a total of 10 QTLs including three major QTLs across the three populations. The phenotypic variance of the identified QTLs ranged from 3.6 to 34.3%. One candidate genomic region identified on CcLG11 seems to be promising QTL for molecular breeding in developing superior lines with enhanced resistance to SMD.

Peanut is an important crop, economically and nutritiously, but high production cost is a serious challenge to peanut farmers as exemplified by chemical spray to control foliar diseases such as leaf spots and thrips, the vectors of tomato spotted wilt virus (TSWV). The objective of this research was to map the quantitative trait loci (QTLs) for resistance to leaf spots and TSWV in one recombinant inbred line (RIL) mapping population of “Tifrunner × GT-C20” for identification of linked markers for marker-assisted breeding. Here, we report the improved genetic linkage map with 418 marker loci with a marker density of 5.3 cM/loci and QTLs associated with multi-year (2010–2013) field phenotypes of foliar disease traits, including early leaf spot (ELS), late leaf spot (LLS), and TSWV. A total of 42 QTLs were identified with phenotypic variation explained (PVE) from 6.36 to 15.6%. There were nine QTLs for resistance to ELS, 22 QTLs for LLS, and 11 QTLs for TSWV, including six, five, and one major QTLs with PVE higher than 10% for resistance to each disease, respectively. Of the total 42 QTLs, 34 were mapped on the A sub-genome and eight mapped on the B sub-genome suggesting that the A sub-genome harbors more resistance genes than the B sub-genome. This genetic linkage map was also compared with two diploid peanut physical maps, and the overall co-linearity was 48.4% with an average co-linearity of 51.7% for the A sub-genome and 46.4% for the B sub-genome. The identified QTLs associated markers and potential candidate genes will be studied further for possible application in molecular breeding in peanut genetic improvement for disease resistance.

Single nucleotide polymorphisms (SNPs) are the most abundant DNA sequence variation in the genomes which can be used to associate genotypic variation to the phenotype. Therefore, availability of a high-density SNP array with uniform genome coverage can advance genetic studies and breeding applications. Here we report the development of a high-density SNP array ‘Axiom_Arachis’ with 58 K SNPs and its utility in groundnut genetic diversity study. In this context, from a total of 163,782 SNPs derived from DNA resequencing and RNA-sequencing of 41 groundnut accessions and wild diploid ancestors, a total of 58,233 unique and informative SNPs were selected for developing the array. In addition to cultivated groundnuts (Arachis hypogaea), fair representation was kept for other diploids (A. duranensis, A. stenosperma, A. cardenasii, A. magna and A. batizocoi). Genotyping of the groundnut ‘Reference Set’ containing 300 genotypes identified 44,424 polymorphic SNPs and genetic diversity analysis provided in-depth insights into the genetic architecture of this material. The availability of the high-density SNP array ‘Axiom_Arachis’ with 58 K SNPs will accelerate the process of high resolution trait genetics and molecular breeding in cultivated groundnut.

Fusarium wilt (FW) is one of the most important biotic stresses causing yield losses in pigeonpea. Genetic improvement of pigeonpea through genomics-assisted breeding (GAB) is an economically feasible option for the development of high yielding FW resistant genotypes. In this context, two recombinant inbred lines (RILs) (ICPB 2049 × ICPL 99050 designated as PRIL_A and ICPL 20096 × ICPL 332 designated as PRIL_B) and one F2 (ICPL 85063 × ICPL 87119) populations were used for the development of high density genetic maps. Genotyping-by-sequencing (GBS) approach was used to identify and genotype SNPs in three mapping populations. As a result, three high density genetic maps with 964, 1101 and 557 SNPs with an average marker distance of 1.16, 0.84 and 2.60 cM were developed in PRIL_A, PRIL_B and F2, respectively. Based on the multi-location and multi-year phenotypic data of FW resistance a total of 14 quantitative trait loci (QTLs) including six major QTLs explaining >10% phenotypic variance explained (PVE) were identified. Comparative analysis across the populations has revealed three important QTLs (qFW11.1, qFW11.2 and qFW11.3) with upto 56.45% PVE for FW resistance. This is the first report of QTL mapping for FW resistance in pigeonpea and identified genomic region could be utilized in GAB.

Identification of candidate genomic regions associated with target traits using conventional mapping methods is challenging and time-consuming. In recent years, a number of single nucleotide polymorphism (SNP)-based mapping approaches have been developed and used for identification of candidate/putative genomic regions. However, in the majority of these studies, insertion–deletion (Indel) were largely ignored. For efficient use of Indels in mapping target traits, we propose Indel-seq approach, which is a combination of whole-genome resequencing (WGRS) and bulked segregant analysis (BSA) and relies on the Indel frequencies in extreme bulks. Deployment of Indel-seq approach for identification of candidate genomic regions associated with fusarium wilt (FW) and sterility mosaic disease (SMD) resistance in pigeonpea has identified 16 Indels affecting 26 putative candidate genes. Of these 26 affected putative candidate genes, 24 genes showed effect in the upstream/downstream of the genic region and two genes showed effect in the genes. Validation of these 16 candidate Indels in other FW- and SMD-resistant and FW- and SMD-susceptible genotypes revealed a significant association of five Indels (three for FW and two for SMD resistance). Comparative analysis of Indel-seq with other genetic mapping approaches highlighted the importance of the approach in identification of significant genomic regions associated with target traits. Therefore, the Indel-seq approach can be used for quick and precise identification of candidate genomic regions for any target traits in any crop species.

To accelerate genomics research and molecular breeding applications in chickpea, a high-throughput SNP genotyping platform 'Axiom® CicerSNP Array' has been designed, developed and validated. Screening of whole-genome resequencing data from 429 chickpea lines identified 4.9 million SNPs, from which a subset of 70 463 high-quality nonredundant SNPs was selected using different stringent filter criteria. This was further narrowed down to 61 174 SNPs based on p-convert score ≥0.3, of which 50 590 SNPs could be tiled on array. Among these tiled SNPs, a total of 11 245 SNPs (22.23%) were from the coding regions of 3673 different genes. The developed Axiom® CicerSNP Array was used for genotyping two recombinant inbred line populations, namely ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261). Genotyping data reflected high success and polymorphic rate, with 15 140 (29.93%; ICCRIL03) and 20 018 (39.57%; ICCRIL04) polymorphic SNPs. High-density genetic maps comprising 13 679 SNPs spanning 1033.67 cM and 7769 SNPs spanning 1076.35 cM were developed for ICCRIL03 and ICCRIL04 populations, respectively. QTL analysis using multilocation, multiseason phenotyping data on these RILs identified 70 (ICCRIL03) and 120 (ICCRIL04) main-effect QTLs on genetic map. Higher precision and potential of this array is expected to advance chickpea genetics and breeding applications.

Small insertions and deletions (InDels) are the second most prevalent and the most abundant structural variations in plant genomes. In order to deploy these genetic variations for genetic analysis in genus Arachis, we conducted comparative analysis of the draft genome assemblies of both the diploid progenitor species of cultivated tetraploid groundnut (Arachis hypogaea L.) i.e., Arachis duranensis (A subgenome) and Arachis ipaënsis (B subgenome) and identified 515,223 InDels. These InDels include 269,973 insertions identified in A. ipaënsis against A. duranensis while 245,250 deletions in A. duranensis against A. ipaënsis. The majority of the InDels were of single bp (43.7%) and 2–10 bp (39.9%) while the remaining were >10 bp (16.4%). Phylogenetic analysis using genotyping data for 86 (40.19%) polymorphic markers grouped 96 diverse Arachis accessions into eight clusters mostly by the affinity of their genome. This study also provided evidence for the existence of “K” genome, although distinct from both the “A” and “B” genomes, but more similar to “B” genome. The complete homology between A. monticola and A. hypogaea tetraploid taxa showed a very similar genome composition. The above analysis has provided greater insights into the phylogenetic relationship among accessions, genomes, sub species and sections. These InDel markers are very useful resource for groundnut research community for genetic analysis and breeding applications.

Genome complexity and narrow genetic base are the major problems that hinder achieving accelerated genetic gains in groundnut or peanut, a major source of vegetable oil (48%) and protein (25%). High density genotyping is a must-have genomic resource in a crop for use in several genetic and breeding applications. Availability of genome sequence for both the diploid genome progenitors of cultivated groundnut has provided an opportunity for discovery of structural variations in large-scale including single nucleotide polymorphisms (SNPs), the most abundant DNA sequence variation in the genomes. In this context, we developed a high-density SNP array ‘Axiom_Arachis’ with 58K SNPs with uniform genome coverage. We identified initially a total of 163,782 SNPs that included 118,860 SNPs (58,438 SNPs for A-genome and 60,422 SNPs for B-genome) from 30 tetraploids and 44,922 SNPs (39,937 SNPs for A-genome and 4,985 SNPs for B-genome) from 11 diploids. Finally, a total of 58,233 highly informative SNPs with genome specificity were used for array development. In addition to cultivated groundnuts (Arachis hypogaea), this array has fair representation of other diploids (A. duranensis, A. stenosperma, A. cardenasii, A. magna and A. batizocoi). Genotyping of the groundnut ‘Reference Set’ containing 300 genotypes identified 44,424 polymorphic SNPs and genetic diversity analysis provided in-depth insights into the genetic architecture of this material. In summary, this high-density SNP array will accelerate the process of trait dissection and molecular breeding including genomic selection for achieving higher genetic gains in groundnut.

Genomic selection (GS) allows safe phenotyping and reduces cost and shortening selection cycles. Incorporating of genotype × environment (G×E) interactions in genomic prediction models improves the predictive ability of lines performance across environments and in target environments. Phenotyping data on a set of 320 elite chickpea breeding lines on different traits (e.g., plant height, days to maturity, and seed yield), from three consecutive years for two different treatments at two locations were recorded. These lines were genotyped on DArTseq(1.6K) and Genotyping- by-Sequencing (GBS; 89K SNPs) platforms. Five different models were fitted, four of which included genomic information as main effects (baseline model) and/or G×E interactions. Three different cross-validation schemes that mimic real scenarios that breeders might face on fields were considered to assess the predictive ability of the models (CV2: incomplete field trials; CV1: newly developed lines; and CV0: new previously untested environments). Different prediction models gave different results for the different traits; however, some interesting patterns were observed. For CV1, analyzing yield seed interaction models improved baseline counterparts on an average between 55 and 92% using DArT and DArT combined with GBS data, respectively [between 9 and 112% for all traits]. While for CV2 these improvements varied b tween 65 and 102% [between 8 and 130% remaining traits]. In CV0, no clear advantage was observed considering the interaction term. These results suggest that GS models hold potential for breeder’s applications on chickpea cultivar improvements.

Terminal drought stress leads to substantial yield losses in chickpea (Cicer arietinum L.). Water conservation at vegetative growth (canopy conductivity and canopy size and development) allow plants to increase soil water extraction during grain filling and are hypothesised to help chickpea adaptation to water limited environments. Plant vigour and water saving traits were phenotyped in 232 recombinant inbred lines (RILs), derived from a cross between ICC4958 and ICC1882, at 28 days after sowing under well water conditions using a high throughput phenotyping platform. Different density genetic maps (241-SSRLow density, 1007-SSR+SNPs-High density and 1557-SNPs-Ultra high density) were used for QTLs identification. Several major QTLs (M-QTLs) for plant vigour traits (3D-leaf area, shoot biomass, plant height and growth related traits) were identified on CaLG04, and co-mapped with previously identified and fine mapped major drought tolerance QTL-hotspot region on CaLG04 (~300Kb).The canopy conductance trait (e.g Transpiration rate) had a M-QTL mapped on CaLG03 using ultra-high density bin markers. Plant vigour traits on CaLG04 and canopy conductance related traits on CaLG03 provide opportunity to manipulate these loci to tailor recombinants having lower transpiration rate and high plant vigour. This ideotype might be enhancing the water stress adaptation in chickpea. To test this hypothesis, a subset of 40 RILs contrasting for vigour and water use traits was tested in lysimeters and field under different water stress treatments. High vigour low water use lines had higher seed yield under severe water stress treatments than high vigour and high water use lines, validating the hypothesis.

A “QTL-hotspot” region of 7 Mb size for drought component traits was identified on CaLG04, using a recombinant inbred line (RIL) population (ICC 4958 × ICC 1882) in chickpea. Further, skimbased genotyping by sequencing (GBS) approach with large SNP markers delimited the “QTL-hotspot” region into two sub-regions; viz, “QTL-hotspot_a” of 139.22 kb and “QTL-hotspot_b” of 153.36 kb, on the kabuli draft genome sequence. In order to validate and identify more recombinations in the sub-regions for further refinement, a fine mapping population with 1,911 lines was developed. Flanking markers of the two “QTL-hotspot” sub-regions were converted to KASPar assays and used to screen the fine mapping population consisting of 1,911 lines. As a result, 19 F2:3 recombinant families were identified. These families were phenotyped for seed weight and other drought-related traits. Comparison of genotype and phenotype data identified a genomic region of ~113 Kb size within “QTL-hotspot_a” responsible for 100 seed weight (100SDW) and other drought-related traits in chickpea. Subsequently, a syntenic study between the refined “QTL-hotpsot” region and desi genome identified a 2 Mb region on Ca_LG_4 pseudomolecule. Whole genome re-sequencing (WGRS) analysis of selected lines from each recombinant family identified several non-synonymous and InDel mutations within important candidate genes. Functional validation of these genes will help decipher the mechanism of drought stress tolerance in chickpea.

Pigeonpea is a uniquely drought and heat tolerant crop that provides a major source of dietary protein to nearly a billion people in the tropical and semi-arid tropics of the world. Despite its importance as a source of dietary protein the genetic control of its seed protein content (SPC) is poorly understood. The present study used high density linkage maps to identify quantitative trait loci (QTL) associated with SPC and its relationship with 100-seed weight (HSW), seed yield (SY), days to first flower (DTFF) and growth habit (GH). Five F2 mapping populations segregating for SPS including ICP 11605 × ICP 14209, ICP 8863 × ICP 11605, HPL 24 × ICP 11605, ICP 5529 × ICP 11605 and ICP 8863 × ICPL 87119 were genotyped using genotyping-by-sequencing and phenotyped for the traits. The average inter-marker distance on the population-specific maps varied from 1.6cM to 3.5cM.On the basis of the population-specific and consensus linkage maps, 48main effect QTLs (M-QTLs)with phenotypic variance explained (PVE) ranging from 0.7 to 23.5% were detected across five populations, of which 15 M-QTLs were major (PVE≥10). Twenty seven of the M-QTLs could be collapsed into six consensus QTL regions. In addition, 34 epistatic QTLs (E-QTLs) with PVE ranging from 6.3% to 69.8% were detected across populations. Co-localization of M-QTLs and E-QTLs affecting SPC and the agronomic traits explained the genetic basis of the significant (P < 0.05) correlations of SPC with HSW (r2 = 0.22 to 0.30), SY (r2 = -0.18 to -0.28), DTFF (r2 = -0.17 to -0.31) and GH (r2 = 0.18 to 0.34). The quantitative nature of genetic control of SPC and its relationship with agronomic traits suggest that marker-assisted recurrent selection or genomic selection would be effective for the simultaneous improvement of SPC and other important traits.

Completely sequenced plant genomes provide scope for designing a large number of genome wide insertion–deletion (InDel) markers, which are useful in various aspects of crop breeding and genetic analysis. With the objective of developing InDel markers from pigeonpea genome, the re-sequencing data of eight MAGIC parental lines were used to identify InDels using Dindel software. As a result, a total of 102,181 InDels were identified. Of these 70158 InDels were found unique. The higher number of InDels were found in intergenic (43%) followed by upstream (26%) and downstream (24%) regions. A total of 6.93 % of Indels were found in the genic region. Out of 70158 InDels, 2,426 (1032 Insertions and 1394 deletions) with ≥20 bp size among different parental lines were selected. Average distribution of selected 2426 InDels was found 220 InDels/LG with maximum number of InDels on CcLG11 (385 InDels) and minimum number of In- Dels on CcLG05 (70 InDels). A set of 293 InDels could assess genetic diversity and establish phylogenetic relationships among 16 parental lines of different mapping populations. Validation of these primer pairs on parental lines of different mapping population resulted in higher amplification success rate (≥83%) with almost 52.04% polymorphism rate among parental lines on 3% agarose gel. The number of alleles per locus ranged from 2 to 9 with an average of 3.8 alleles. Further, to track the genome of parents in complex funnel crossing scheme of pigeonpea MAGIC population at 28-two way, 14-four way and 7-eigth way stages, we have identified unique InDel primers for each of the 8 MAGIC parents. The result showed that InDel markers with their high polymorphic potential in comparison to SSR markers would be preferred candidate markers in various marker-based applications in pigeonpea genetics and breeding.

To map resistance genes for Fusarium wilt (FW) and sterility mosaic disease (SMD) in pigeonpea, sequencing-based bulked segregant analysis (Seq-BSA) was used. Resistant (R) and susceptible (S) bulks from the extreme recombinant inbred lines of ICPL 20096 × ICPL 332 were sequenced. Subsequently, SNP index was calculated between R- and S-bulks with the help of draft genome sequence and reference-guided assembly of ICPL 20096 (resistant parent). Seq-BSA has provided seven candidate SNPs for FW and SMD resistance in pigeonpea. In parallel, four additional genotypes were re-sequenced and their combined analysis with R- and S-bulks has provided a total of 8362 nonsynonymous (ns) SNPs. Of 8362 nsSNPs, 60 were found within the 2-Mb flanking regions of seven candidate SNPs identified through Seq-BSA. Haplotype analysis narrowed down to eight nsSNPs in seven genes. These eight nsSNPs were further validated by re-sequencing 11 genotypes that are resistant and susceptible to FW and SMD. This analysis revealed association of four candidate nsSNPs in four genes with FW resistance and four candidate nsSNPs in three genes with SMD resistance. Further, In silico protein analysis and expression profiling identified two most promising candidate genes namely C.cajan_01839 for SMD resistance and C.cajan_03203 for FW resistance. Identified candidate genomic regions/SNPs will be useful for genomics-assisted breeding in pigeonpea.

A combination of two approaches, namely QTL analysis and gene enrichment analysis were used to identify candidate genes in the “QTL-hotspot” region for drought tolerance present on the Ca4 pseudomolecule in chickpea. In the first approach, a high-density bin map was developed using 53,223 single nucleotide polymorphisms (SNPs) identified in the recombinant inbred line (RIL) population of ICC 4958 (drought tolerant) and ICC 1882 (drought sensitive) cross. QTL analysis using recombination bins as markers along with the phenotyping data for 17 drought tolerance related traits obtained over 1–5 seasons and 1–5 locations split the “QTL-hotspot” region into two subregions namely “QTL-hotspot_a” (15 genes) and “QTL-hotspot_b” (11 genes). In the second approach, gene enrichment analysis using significant marker trait associations based on SNPs from the Ca4 pseudomolecule with the above mentioned phenotyping data and the candidate genes from the refined “QTL-hotspot” region showed enrichment for 23 genes. Twelve genes were found common in both approaches. Functional validation using quantitative real-time PCR (qRT-PCR) indicated four promising candidate genes having functional implications on the effect of “QTL-hotspot” for drought tolerance in chickpea.

Continued geological data collection and processing is necessary during the various phases of orebody development. This aspect has been very well established as different applications of geological knowledge in various areas of mine planning, production scheduling, blast pattern design, quality control, geotechnical design, limits excavation, dewatering and pit slope drainage are found. The role of geological information at the Mount Whaleback iron ore mine, Western Australia, is examined., Continued geological data collection and processing is necessary during the various phases of orebody development. This aspect has been very well established as different applications of geological knowledge in various areas of mine planning, production scheduling, blast pattern design, quality control, geotechnical design, limits excavation, dewatering and pit slope drainage are found. The role of geological information at the Mount Whaleback iron ore mine, Western Australia, is examined.