Legume Breeding in Transition: Innovation and Outlook

The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested–untested genotypes (TG, UG) in observed–unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (−30.4%) and residual (−39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

Pusa 391, a mega desi chickpea variety with medium maturity duration is extensively cultivated in the Central Zone of India. Of late, this variety has become susceptible to Fusarium wilt (FW), which has drastic impact on its yield. Presence of variability in the wilt causing pathogen, Fusarium oxysporum f.sp. ciceri (foc) across geographical locations necessitates the role of pyramiding for FW resistance for different races (foc 1,2,3,4 and 5). Subsequently, the introgression lines developed in Pusa 391 genetic background were subjected to foreground selection using three SSR markers (GA16, TA 27 and TA 96) while 48 SSR markers uniformly distributed on all chromosomes, were used for background selection to observe the recovery of recurrent parent genome (RPG). BC₁F₁ lines with 75–85% RPG recovery were used to generate BC₂F₁. The plants that showed more than 90% RPG recovery in BC₂F₁ were used for generating BC₃F₁. The plants that showed more than 96% RPG recovery were selected and selfed to generate BC₃F₃. Multi-location evaluation of advanced introgression lines (BC₂F₃) in six locations for grain yield (kg/ha), days to fifty percent flowering, days to maturity, 100 seed weight and disease incidence was done. In case of disease incidence, the genotype IL1 (BGM 20211) was highly resistant to FW in Junagarh, Indore, New Delhi, Badnapur and moderately resistant at Sehore and Nandyal. GGE biplot analysis revealed that IL1(BGM20211) was the most stable genotype at Junagadh, Sehore and Nandyal. GGE biplot analysis revealed that IL1(BGM 20211) and IL4(BGM 20212) were the top performers in yield and highly stable across six environments and were nominated for Advanced Varietal Trials (AVT) of AICRP (All India Coordinated Research Project on Chickpea) in 2018–19. BGM20211 and BGM 20212 recorded 29 and 28.5% average yield gain over the recurrent parent Pusa 391, in the AVT-1 and AVT-2 over five environments. Thus, BGM20211 was identified for release and notified as Pusa Manav/Pusa Chickpea 20211 for Madhya Pradesh, Gujarat and Maharashtra, Southern Rajasthan, Bundhelkhand region of Uttar Pradesh states by the Central Sub-Committees on Crop Standards, Notification and Release of Varieties of Agricultural Crops, Ministry of Agriculture and Farmers Welfare, Government of India, for commercial cultivation in India (Gazette notification number S.O.500 (E) dt. 29-1-2021).Such pyramided lines give resilience to multiple races of fusarium wilt with added yield advantage.

Legumes are rich in protein and phytochemicals and have provided a healthy diet for human beings for thousands of years. In recognition of the important role they play in human nutrition and agricultural production, the researchers have made great efforts to gain new genetic traits in legumes such as yield, stress tolerance, and nutritional quality. In recent years, the significant increase in genomic resources for legume plants has prepared the groundwork for applying cutting-edge breeding technologies, such as transgenic technologies, genome editing, and genomic selection for crop improvement. In addition to the different genome editing technologies including the CRISPR/Cas9-based genome editing system, this review article discusses the recent advances in plant-specific gene-editing methods, as well as problems and potential benefits associated with the improvement of legume crops with important agronomic properties. The genome editing technologies have been effectively used in different legume plants including model legumes like alfalfa and lotus, as well as crops like soybean, cowpea, and chickpea. We also discussed gene-editing methods used in legumes and the improvements of agronomic traits in model and recalcitrant legumes. Despite the immense opportunities genome editing can offer to the breeding of legumes, governmental regulatory restrictions present a major concern. In this context, the comparison of the regulatory framework of genome editing strategies in the European Union and the United States of America was also discussed. Gene-editing technologies have opened up new possibilities for the improvement of significant agronomic traits in legume breeding.