Agronomic performance of chickpea affected by drought stress at different growth stages

Susceptibility to drought stress has restrained chickpea productivity at a global level, and the development of drought-tolerant varieties is essential to maintain its productivity. Therefore, the present study was conducted to evaluate genetic divergence in selected genotypes of chickpea and their morpho-physiological responses under irrigated and stressed conditions to identify the traits that account for the better performance of these genotypes under stressed conditions, as well as genotypes with improved drought tolerance. The genotypes were evaluated for two years under irrigated and drought stressed conditions, and significant variation was found amongst the genotypes for different morpho-physiological and yield traits. The maximum reduction was observed for plant yield (33.23%) under stressed conditions. Principle component analysis (PCA)-based biplots and correlation studies established its strong positive correlation with relative water content (RWC), membrane stability index (MSI), chlorophyll index (CI), secondary branches (SB) and yield traits and negative correlations with drought susceptibility index (DSI), days to maturity (DM) and 100 seed weight (100 SW) under drought stress, suggesting their use in selecting drought-tolerant germplasm. Ten genotypes with high values of RWC, MSI, CI, SB, yield traits and lower DSI were identified as drought-tolerant and might serve as ideal donors in the forthcoming breeding of elite chickpea cultivars. The seed-filling stage began earlier in these genotypes, with significantly reduced days to maturity under stressed conditions. Our results indicate selection for earliness offers a promising strategy for the development of drought-tolerant chickpea cultivars

Legume Genomics: From Genomic Resources to Molecular Breeding

With an explosive growth rate, especially in developing countries, the world population of 7.2 billion is expected to reach 9.6 billion by 2050. There is a need to produce about 70% more food to feed this predicted population. Legumes form important constituents of a vegetarian diet and are rich sources of dietary protein (Duranti and Gius, 1997). Legumes comprise the third largest family of flowering plants and provide important sources of food, fodder, oil, and fiber products. Legume seeds typically contain 20 to 25% protein and are also a rich source of dietary fiber. In addition, legumes have the capability to fix atmospheric N2 with the help of symbiotic nitrogen fixing bacteria in root nodules, thereby reducing fertilizer use in agriculture, and the cost of nitrogen inputs by smallholder farmers in developing countries. Due to their higher protein content and other nutrients, legumes are considered important to confront malnutrition among resource-poor people in developing countries. In brief, legumes including beans (Phaseolus vulgaris), chickpea (Cicer arietinum), cowpea (Vigna unguiculata), lentils (Lens culinaris), pea (Pisum sativum), peanut (Arachis hypogaea), and soybean (Glycine max), etc. play an important role in ensuring food security, reducing poverty, improving human health and nutrition, and enhancing ecosystem resilience, especially in developing countries...

Genomic Selection for Crop Improvement: New Molecular Breeding Strategies for Crop Improvement

Genomic Selection for Crop Improvement serves as handbook for users by providing basic as well as advanced understandings of genomic selection. This useful review explains germplasm use, phenotyping evaluation, marker genotyping methods, and statistical models involved in genomic selection. It also includes examples of ongoing activities of genomic selection for crop improvement and efforts initiated to deploy the genomic selection in some important crops. In order to understand the potential of GS breeding, it is high time to bring complete information in the form of a book that can serve as a ready reference for geneticist and plant breeders

Evaluating dimensionality reduction for genomic prediction

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26,817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data

Genomic Selection for Crop Improvement: An Introduction

Marker-assisted selection (MAS) exploits the markers associated with traits of interest for selecting lines with superior alleles for developing improved lines. However use of MAS is restricted to simple traits due to its inability to handle complex traits. Advancements in genomics technologies have been able to dramatically reduce the cost of genotyping, enabling the use of genome-wide marker data for selecting lines with higher breeding value. Genomic selection (GS), a modern breeding approach that uses genome-wide marker data to estimate the breeding value and has the potential to address the complex traits. GS exploits the genotyping and phenotyping data on a training population to train the prediction models to calculate the genomic estimated breeding value (GEBV). GS has the capability to reduce selection cycle duration and increase selection accuracy, intensity, efficiency, and gains per unit of time, thereby enhancing the rate of genetic gains. Availability of cost-effective genotyping platforms has enabled the cost-effective generation of large-scale genotyping data, facilitating the deployment of GS in several crop species. This chapter provides an introduction to the book, highlighting the basic and advanced principles of GS breeding and its applications for crop improvement

Characterization of ASR gene and its role in drought tolerance in chickpea (Cicer arietinum L.)

Chickpea has a profound nutritional and economic value in vegetarian society. Continuous decline in chickpea productivity is attributed to insufficient genetic variability and different environmental stresses. Chickpea like several other legumes is highly susceptible to terminal drought stress. Multiple genes control drought tolerance and ASR gene plays a key role in regulating different plant stresses. The present study describes the molecular characterization and functional role of Abscissic acid and stress ripening (ASR) gene from chickpea (Cicer arietinum) and the gene sequence identified was submitted to NCBI Genbank (MK937569). Molecular analysis using MUSCLE software proved that the ASR nucleotide sequences in different legumes show variations at various positions though ASR genes are conserved in chickpea with only few variations. Sequence similarity of ASR gene to chickpea putative ABA/WDS induced protein mRNA clearly indicated its potential involvement in drought tolerance. Physiological screening and qRT-PCR results demonstrated increased ASR gene expression under drought stress possibly enabled genotypes to perform better under stress. Conserved domain search, protein structure analysis, prediction and validation, network analysis using Phyre2, Swiss-PDB viewer, ProSA and STRING analysis established the role of hypothetical ASR protein NP_001351739.1 in mediating drought responses. NP_001351739.1 might have enhanced the ASR gene activity as a transcription factor regulating drought stress tolerance in chickpea. This study could be useful in identification of new ASR genes that play a major role in drought tolerance and also develop functional markers for chickpea improvement

Super-Pangenome by Integrating the Wild Side of a Species for Accelerated Crop Improvement

The pangenome provides genomic variations in the cultivated gene pool for a given species. However, as the crop’s gene pool comprises many species, especially wild relatives with diverse genetic stock, here we suggest using accessions from all available species of a given genus for the development of a more comprehensive and complete pangenome, which we refer to as a super-pangenome. The super-pangenome provides a complete genomic variation repertoire of a genus and offers unprecedented opportunities for crop improvement. This opinion article focuses on recent developments in crop pangenomics, the need for a super-pangenome that should include wild species, and its application for crop improvement

Whole genome re-sequencing reveals genome-wide variations among parental lines of 16 mapping populations in chickpea (Cicer arietinum L.)

Background Chickpea (Cicer arietinum L.) is the second most important grain legume cultivated by resource poor farmers in South Asia and Sub-Saharan Africa. In order to harness the untapped genetic potential available for chickpea improvement, we re-sequenced 35 chickpea genotypes representing parental lines of 16 mapping populations segregating for abiotic (drought, heat, salinity), biotic stresses (Fusarium wilt, Ascochyta blight, Botrytis grey mould, Helicoverpa armigera) and nutritionally important (protein content) traits using whole genome re-sequencing approach. Results A total of 192.19 Gb data, generated on 35 genotypes of chickpea, comprising 973.13 million reads, with an average sequencing depth of ~10 X for each line. On an average 92.18 % reads from each genotype were aligned to the chickpea reference genome with 82.17 % coverage. A total of 2,058,566 unique single nucleotide polymorphisms (SNPs) and 292,588 Indels were detected while comparing with the reference chickpea genome. Highest number of SNPs were identified on the Ca4 pseudomolecule. In addition, copy number variations (CNVs) such as gene deletions and duplications were identified across the chickpea parental genotypes, which were minimum in PI 489777 (1 gene deletion) and maximum in JG 74 (1,497). A total of 164,856 line specific variations (144,888 SNPs and 19,968 Indels) with the highest percentage were identified in coding regions in ICC 1496 (21 %) followed by ICCV 97105 (12 %). Of 539 miscellaneous variations, 339, 138 and 62 were inter-chromosomal variations (CTX), intra-chromosomal variations (ITX) and inversions (INV) respectively. Conclusion Genome-wide SNPs, Indels, CNVs, PAVs, and miscellaneous variations identified in different mapping populations are a valuable resource in genetic research and helpful in locating genes/genomic segments responsible for economically important traits. Further, the genome-wide variations identified in the present study can be used for developing high density SNP arrays for genetics and breeding applications

Advances in Chickpea Genomic Resources for Accelerating the Crop Improvement

Chickpea plays a major role in food and nutritional security worldwide. Its productivity is severely affected by various biotic and abiotic stresses; hence development of stress resilience varieties that can yield higher under stress environment remains the call of the hour. Conventional breeding approaches clubbed with the genome information, commonly known as genomic-assisted breeding (GAB) have the potential to accelerate the crop improvement efforts. In order to deploy the GAB for crop improvement in chickpea, there was need to convert an orphan crop chickpea into the genomic resource-rich crop. Advent of sequencing technology has resulted in reduction of cost and led to development of huge genomic resources in chickpea. A variety of markers have been developed, used for various mapping studies including linkage mapping and association mapping and finally deployed for developing the superior varieties using GAB approached such as marker assisted backcrossing and genomic selection. The chapter reviews the journey of chickpea status from orphan crop with almost no marker resources to a genome resource-rich crop, which are being used for achieving the genetic gains at a momentum

Current Status and Prospects of Genomic Selection in Legumes

Legumes play a major role in food and nutritional security across the world. The current rate of genetic gains in legume breeding programs is not enough to meet the food and nutritional requirement of an ever increasing global population. To feed this growing population, it is essential to enhance the rate of genetic gains for increased productivity of these legumes. Genomics tools have great potential in developing improved cultivars faster and more precisely by deploying modern breeding approaches. Marker-assisted backcrossing (MABC) and marker-assisted recurrent selection (MARS) approaches have been successfully deployed in several legume crops for improving traits with simple genetic behaviour. However, it is difficult to address the complex traits using MABC and MARS as several large and small effect quantitative trait loci (QTLs) are involved in their expression. Genomic selection (GS) has potential to capture small and large effect genetic factors and deal with the complex traits. Over the last decade, large scale genomic resources have been developed in majority of the legume crops, which provide a perfect platform to deploy genome-wide information in selecting breeding material for enhancing the rate of genetic gain. Many legume breeders have already took initiatives towards deploying GS breeding by developing training populations, standardizing the GS models, studying effect of marker density, size of training population, and genotype and environment interaction. This chapter presents an overview on the current status of GS and presents the future prospects of its deployment in some legume breeding programs