Association of functional markers with flowering time in lentil

Delineation of loci governing an extra-earliness trait in lentil (Lens culinaris Medik.) using the QTL-Seq approach

Developing early maturing lentil has the potential to minimize yield losses, mainly during terminal drought. Whole-genome resequencing (WGRS) based QTL-seq identified the loci governing earliness in lentil. The genetic analysis for maturity duration provided a good fit to 3:1 segregation (F2), indicating earliness as a recessive trait. WGRS of Globe Mutant (late parent), late-flowering, and early-flowering bulks (from RILs) has generated 1124.57, 1052.24 million raw and clean reads, respectively. The QTL-Seq identified three QTLs (LcqDTF3.1, LcqDTF3.2, and LcqDTF3.3) on chromosome 3 having 246244 SNPs and 15577 insertions/deletions (InDels) and 13 flowering pathway genes. Of these, 11 exhibited sequence variations between bulks and validation (qPCR) revealed a significant difference in the expression of nine candidate genes (LcGA20oxG, LcFRI, LcLFY, LcSPL13a, Lcu.2RBY.3g060720, Lcu.2RBY.3g062540, Lcu.2RBY.3g062760, LcELF3a, and LcEMF1). Interestingly, the LcELF3a gene showed significantly higher expression in late-flowering genotype and exhibited substantial involvement in promoting lateness. Subsequently, an InDel marker (I-SP-383.9; LcELF3a gene) developed from LcqDTF3.2 QTL region showed 82.35% PVE (phenotypic variation explained) for earliness. The cloning, sequencing, and comparative analysis of the LcELF3a gene from both parents revealed 23 SNPs and InDels. Interestingly, a 52 bp deletion was recorded in the LcELF3a gene of L4775, predicted to cause premature termination of protein synthesis after 4 missense amino acids beyond the 351st amino acid due to the frameshift during translation. The identified InDel marker holds significant potential for breeding early maturing lentil varieties.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Omics Path to Increasing Productivity in Less-Studied Crops Under Changing Climate-Lentil a Case Study

Conventional breeding techniques for crop improvement have reached their full potential, and hence, alternative routes are required to ensure a sustained genetic gain in lentils. Although high-throughput omics technologies have been effectively employed in major crops, less-studied crops such as lentils have primarily relied on conventional breeding. Application of genomics and transcriptomics in lentils has resulted in linkage maps and identification of QTLs and candidate genes related to agronomically relevant traits and biotic and abiotic stress tolerance. Next-generation sequencing (NGS) complemented with high-throughput phenotyping (HTP) technologies is shown to provide new opportunities to identify genomic regions and marker-trait associations to increase lentil breeding efficiency. Recent introduction of image-based phenotyping has facilitated to discern lentil responses undergoing biotic and abiotic stresses. In lentil, proteomics has been performed using conventional methods such as 2-D gel electrophoresis, leading to the identification of seed-specific proteome. Metabolomic studies have led to identifying key metabolites that help differentiate genotypic responses to drought and salinity stresses. Independent analysis of differentially expressed genes from publicly available transcriptomic studies in lentils identified 329 common transcripts between heat and biotic stresses. Similarly, 19 metabolites were common across legumes, while 31 were common in genotypes exposed to drought and salinity stress. These common but differentially expressed genes/proteins/metabolites provide the starting point for developing high-yielding multi-stress-tolerant lentils. Finally, the review summarizes the current findings from omic studies in lentils and provides directions for integrating these findings into a systems approach to increase lentil productivity and enhance resilience to biotic and abiotic stresses under changing climate.

Development of EST-SSR Markers Linked to Flowering Candidate Genes in Elymus sibiricus L. Based on RNA Sequencing

Elymus sibiricus L. is an important cold-season grass with excellent cold and drought tolerance, good palatability, and nutrition. Flowering time is a key trait that affects forage and seed yield. Development of EST-SSR (expressed sequence tag simple sequence repeat) markers based on flowering genes contributes to the improvement of flowering traits. In the study, we detected 155 candidate genes related to flowering traits from 10,591 unigenes via transcriptome sequencing in early- and late-flowering genotypes. These candidate genes were mainly involved in the photoperiodic pathway, vernalization pathway, central integrator, and gibberellin pathway. A total of 125 candidate gene-based EST-SSRs were developed. Further, 15 polymorphic EST-SSRs closely associated to 13 candidate genes were used for genetic diversity and population structure analysis among 20 E. sibiricus accessions, including two contrasting panels (early-flowering and late-flowering). Among them, primer 28366, designed from heading date 3a (HD3a), effectively distinguished early- and late-flowering genotypes using a specifically amplified band of 175 bp. The polymorphic information content (PIC) value ranged from 0.12 to 0.48, with an average of 0.25. The unweighted pair group method analysis (UPGMA) cluster and structure analysis showed that the 20 E. sibiricus genotypes with similar flowering times tended to group together. These newly developed EST-SSR markers have the potential to be used for molecular markers assisted selection and germplasm evaluation of flowering traits in E. sibiricus.

Prospects of next generation sequencing in lentil breeding

Lentil is an important food legume crop that has large and complex genome. During past years, considerable attention has been given on the use of next generation sequencing for enriching the genomic resources including identification of SSR and SNP markers, development of unigenes, transcripts, and identification of candidate genes for biotic and abiotic stresses, analysis of genetic diversity and identification of genes/ QTLs for agronomically important traits. However, in other crops including pulses, next generation sequencing has revolutionized the genomic research and helped in genomic assisted breeding rapidly and cost effectively. The present review discuss current status and future prospects of the use NGS based breeding in lentil.

Association mapping for mungbean yellow mosaic India virus resistance in mungbean (Vigna radiata L. Wilczek)

The present study aimed to detect the marker-trait association of a selected diverse panel of 127 mungbean genotypes against mungbean yellow mosaic India virus (MYMIV). Virus-specific primers pairs viz., AC-abut/AV-abut and BC-abut/BV-abut confirmed the involvement of MYMIV in yellow mosaic disease development and the same was validated through restriction digestion analysis. 256 genome-wide microsatellite markers were screened on a test panel in which 93 polymorphic markers were used in association studies. Population structure analysis led to formation of six distinct subpopulations. 1097 alleles were detected among 127 test genotypes whereas number of alleles ranged 2-22 and PIC values ranged 0.27-0.92%, indicating ample amount of variation at genome level. 15 microsatellite markers were detected as associated with MYMIV resistance, among them three microsatellites explained 11-14% phenotypic variation. The specific regions close to CEDG293, DMB-SSR008 and DMB-SSR059 associated with MYMIV resistance were detected, located on linkage group 2, 4 and 9 and may prove useful in marker-assisted mungbean improvement programme for enhancing MYMIV resistance.

Towards Exploitation of Adaptive Traits for Climate-Resilient Smart Pulses

Pulses are the main source of protein and minerals in the vegetarian diet. These are primarily cultivated on marginal lands with few inputs in several resource-poor countries of the world, including several in South Asia. Their cultivation in resource-scarce conditions exposes them to various abiotic and biotic stresses, leading to significant yield losses. Furthermore, climate change due to global warming has increased their vulnerability to emerging new insect pests and abiotic stresses that can become even more serious in the coming years. The changing climate scenario has made it more challenging to breed and develop climate-resilient smart pulses. Although pulses are climate smart, as they simultaneously adapt to and mitigate the effects of climate change, their narrow genetic diversity has always been a major constraint to their improvement for adaptability. However, existing genetic diversity still provides opportunities to exploit novel attributes for developing climate-resilient cultivars. The mining and exploitation of adaptive traits imparting tolerance/resistance to climate-smart pulses can be accelerated further by using cutting-edge approaches of biotechnology such as transgenics, genome editing, and epigenetics. This review discusses various classical and molecular approaches and strategies to exploit adaptive traits for breeding climate-smart pulses.