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The fluctuating climates, rising human population, and deteriorating arable lands necessitate sustainable crops to fulfil global food requirements. In the countryside, legumes with intriguing but enigmatic nitrogen-fixing abilities and thriving in harsh climatic conditions promise future food security. However, breaking the yield plateau and achieving higher genetic gain are the unsolved problems of legume improvement. Present study gives emphasis on 15 important legume crops, i.e., chickpea, pigeonpea, soybean, groundnut, lentil, common bean, faba bean, cowpea, lupin, pea, green gram, back gram, horse gram, moth bean, rice bean, and some forage legumes. We have given an overview of the world and India’s area, production, and productivity trends for all legume crops from 1961 to 2020. Our review article investigates the importance of gene pools and wild relatives in broadening the genetic base of legumes through pre-breeding and alien gene introgression. We have also discussed the importance of integrating genomics, phenomics, speed breeding, genetic engineering and genome editing tools in legume improvement programmes. Overall, legume breeding may undergo a paradigm shift once genomics and conventional breeding are integrated in the near future.

A meta‐analysis of quantitative trait loci (QTLs), associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits was conducted for the first time in pigeonpea (Cajanus cajan L.). Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 biparental populations). Of these 498, 203 QTLs were projected onto “PigeonPea_ConsensusMap_2022,” saturated with 10,522 markers, which resulted in the prediction of 34 meta‐QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54 cM) was 3.37 times lower than the CI of the initial QTLs (8.56 cM). Of the 34 MQTLs, 12 high‐confidence MQTLs with CI (≤5 cM) and a greater number of initial QTLs (≥5) were utilized to extract 2255 gene models, of which 105 were believed to be associated with different traits under study. Furthermore, eight of these MQTLs were observed to overlap with several marker‐trait associations or significant SNPs identified in previous genome‐wide association studies. Furthermore, synteny and ortho‐MQTL analyses among pigeonpea and four related legumes crops, such as chickpea, pea, cowpea, and French bean, led to the identification of 117 orthologous genes from 20 MQTL regions. Markers associated with MQTLs can be employed for MQTL‐assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Additionally, MQTLs may be subjected to fine mapping, and some of the promising candidate genes may serve as potential targets for positional cloning and functional analysis to elucidate the molecular mechanisms underlying the target traits.

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A meta-analysis of quantitative trait loci (QTLs) associated with following six major quality traits (i) arabinoxylan, (ii) dough rheology properties, (iii) nutritional traits, (iv) polyphenol content, (v) processing quality traits, and (vi) sedimentation volume was conducted in wheat. For this purpose, as many as 2458 QTLs were collected from the 50 mapping studies published during 2013-20. Of the total QTLs, 1126 QTLs were projected on to the consensus map saturated with 2,50,077 markers resulting into the identification of 110 meta-QTLs (MQTLs) with average confidence interval (CI) of 5.6 cM. These MQTLs had 18.84 times reduced CI compared to CI of initial QTLs. Fifty-one (51) MQTLs were also verified with the marker-trait associations (MTAs) detected in earlier genome-wide association studies (GWAS). Physical region occupied by a single MQTL ranged from 0.12 to 749.71 Mb with an average of 130.25 Mb. Candidate gene mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with >2 transcripts per million (TPM) expression in grains and related tissues which also included 44 high-confidence candidate genes known to be involved in the various cellular and biochemical processes related to quality traits. Further, nine functionally characterized wheat genes associated with grain protein content, high molecular weight glutenin and starch synthase enzymes were also found to be co-localized with some of the MQTLs. In addition, synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTLs syntenic to 16 rice MQTLs. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. These genes encoded proteins mainly belonging to the following families: starch synthase, glycosyl transferase, aldehyde dehydrogenase, SWEET sugar transporter, alpha amylase, glycoside hydrolase, glycogen debranching enzyme, protein kinase, peptidase, legumain and seed storage protein enzyme.

A meta-analysis of quantitative trait loci (QTLs) associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits, was conducted for the first time in pigeonpea. Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 bi-parental populations). Of these 498, 203 QTLs were projected onto "PigeonPea_ConsensusMap_2022," saturated with 10,522 markers, which resulted in the prediction of 34 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54 cM) was 3.37-times lower than the CI of the initial QTLs (8.56 cM). Of the 34 MQTLs, 12 high-confidence MQTLs with smaller CI (≤ 5 cM) and a greater number of initial QTLs (≥ 5) were utilized to extract 2,255 gene models, of which 105 were believed to be associated with different traits. The comparison of physical coordinates of MQTLs and marker trait associations reported from genome-wide association studies enabled the validation of eight MQTLs. Furthermore, synteny and ortho-MQTL analysis among pigeonpea and four related legumes crops such as chickpea, pea, cowpea, and french bean led to the identification 117 orthologous genes from 20 MQTL regions. Flanking markers of identified MQTLs can be employed for MQTL-assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Furthermore, genes identified in this study could be potential targets for positional cloning and functional analysis to unveil the molecular mechanisms underlying the target traits.

Background: The productivity of chickpea needs to be increased significantly to exploit the maximum benefit from the increasing world market demand for this crop. Hence, the objective of this study was to find suitable heterotic cross combinations for yield and yield related traits in chickpea. Methods: The present study on chickpea was conducted at Norman E. Borlaug Crop Research Centre, GBPUAT, Pantnagar, Uttarakhand, during rabi season of 2018-2019. It comprised a half-diallel set of seven diverse parents viz. PG5 and PG170 and five ICRISAT chickpea collections viz. ICC13124, ICC14778, ICC14815, ICC16348 and ICC16349 and their 21 F1 crosses were used for heterosis studies through 7×7 diallel analysis with respect to seed yield and other yield contributing traits. Result: Variance components of combining ability revealed that both additive and non-additive gene actions were important for yield and yield related traits. The parent ICC13124 was best general combiner for yield and related traits. It was also one of the parents in best specific combinations for seed yield. The crosses viz. PG170×ICC16349 (7.15), ICC13124×ICC14815 (3.22) and ICC13124×ICC14778 (3.00) were the best specific cross combinations for seed yield. The highest heterosis for seed yield was observed in cross PG170×ICC16349 (73.14%) followed by ICC13124×ICC14815 (43.17%) and ICC13124×ICC14778 (41.09%). The results showed direct association between the specific combining abilities of the crosses and heterotic response indicating the involvement of additive × additive type of interactions causing heterosis. Hence, the GCA, SCA and per se performance of the crosses should be given equal importance while selecting for heterotic combinations.