Discerning molecular diversity and association mapping for phenological, physiological and yield traits under high temperature stress in chickpea (Cicer arietinum L.)

Meta QTL analysis for dissecting abiotic stress tolerance in chickpea

BACKGROUND

Chickpea is prone to many abiotic stresses such as heat, drought, salinity, etc. which cause severe loss in yield. Tolerance towards these stresses is quantitative in nature and many studies have been done to map the loci influencing these traits in different populations using different markers. This study is an attempt to meta-analyse those reported loci projected over a high-density consensus map to provide a more accurate information on the regions influencing heat, drought, cold and salinity tolerance in chickpea.

RESULTS

A meta-analysis of QTL reported to be responsible for tolerance to drought, heat, cold and salinity stress tolerance in chickpeas was done. A total of 1512 QTL responsible for the concerned abiotic stress tolerance were collected from literature, of which 1189 were projected on a chickpea consensus genetic map. The QTL meta-analysis predicted 59 MQTL spread over all 8 chromosomes, responsible for these 4 kinds of abiotic stress tolerance in chickpea. The physical locations of 23 MQTL were validated by various marker-trait associations and genome-wide association studies. Out of these reported MQTL, CaMQAST1.1, CaMQAST4.1, CaMQAST4.4, CaMQAST7.8, and CaMQAST8.2 were suggested to be useful for different breeding approaches as they were responsible for high per cent variance explained (PVE), had small intervals and encompassed a large number of originally reported QTL. Many putative candidate genes that might be responsible for directly or indirectly conferring abiotic stress tolerance were identified in the region covered by 4 major MQTL- CaMQAST1.1, CaMQAST4.4, CaMQAST7.7, and CaMQAST6.4, such as heat shock proteins, auxin and gibberellin response factors, etc. CONCLUSION: The results of this study should be useful for the breeders and researchers to develop new chickpea varieties which are tolerant to drought, heat, cold, and salinity stresses.

SSR Genotyping and Marker-Trait Association with Yield Components in a Kazakh Germplasm Collection of Chickpea (Cicer arietinum L.)

Genetic diversity and marker-trait association with yield-related components were assessed in 39 chickpea accessions from a germplasm collection with either spring or autumn-sown seeds in South-Eastern Kazakhstan. Chickpea accessions originated from Azerbaijan, Germany, Kazakhstan, Moldova, Russia, Türkiye, Ukraine, Syria, and the International Center for Agricultural Research in the Dry Areas (ICARDA). Eleven SSR markers were used for molecular genotyping. Yield and yield components were evaluated in nine traits in experiments with spring and autumn seed sowing. The number of alleles of polymorphic markers varied from 2 to 11. The greatest polymorphism was found in the studied chickpea genotypes using SSR marker TA22 (11 alleles), while NCPGR6 and NCPGR12 markers were monomorphic. In the studied chickpea accessions, unique alleles of the SSR loci TA14, TA46, TA76s, and TA142 were found that were not previously described by other authors. An analysis of correlation relationships between yield-related traits in chickpea revealed the dependence of yield on plant height, branching, and the setting of a large number of beans. These traits showed maximal values in experiments with chickpea plants from autumn seed sowing. An analysis of the relationship between the SSR markers applied and morphological yield-related traits revealed several informative markers associated with important traits, such as plant height, height to first pod, number of branches, number of productive nodes, number of pods per plant, hundred seed weight, seed weight per plant, and seed yield.

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.

Genome-Wide Association Analysis Reveals Trait-Linked Markers for Grain Nutrient and Agronomic Traits in Diverse Set of Chickpea Germplasm

Chickpea is an inexpensive source of protein, minerals, and vitamins to the poor people living in arid and semi-arid regions of Southern Asia and Sub-Saharan Africa. New chickpea cultivars with enhanced levels of protein, Fe and Zn content are a medium-term strategy for supplying essential nutrients for human health and reducing malnutrition. In the current study, a chickpea reference set of 280 accessions, including landraces, breeding lines, and advanced cultivars, was evaluated for grain protein, Fe, Zn content and agronomic traits over two seasons. Using a mid-density 5k SNP array, 4603 highly informative SNPs distributed across the chickpea genome were used for GWAS analysis. Population structure analysis revealed three subpopulations (K = 3). Linkage disequilibrium (LD) was extensive, and LD decay was relatively low. A total of 20 and 46 marker-trait associations (MTAs) were identified for grain nutrient and agronomic traits, respectively, using FarmCPU and BLINK models. Of which seven SNPs for grain protein, twelve for Fe, and one for Zn content were distributed on chromosomes 1, 4, 6, and 7. The marker S4_4477846 on chr4 was found to be co-associated with grain protein over seasons. The markers S1_11613376 and S1_2772537 co-associated with grain Fe content under NSII and pooled seasons and S7_9379786 marker under NSI and pooled seasons. The markers S4_31996956 co-associated with grain Fe and days to maturity. SNP annotation of associated markers were found to be related to gene functions of metal ion binding, transporters, protein kinases, transcription factors, and many more functions involved in plant metabolism along with Fe and protein homeostasis. The identified significant MTAs has potential use in marker-assisted selection for developing nutrient-rich chickpea cultivars after validation in the breeding populations.

Response of Physiological, Reproductive Function and Yield Traits in Cultivated Chickpea (Cicer arietinum L.) Under Heat Stress

Under global climate change, high-temperature stress is becoming a major threat to crop yields, adversely affecting plant growth, and ultimately resulting in significant yield losses in various crops, including chickpea. Thus, identifying crop genotypes with increased heat stress (HS) tolerance is becoming a priority for chickpea research. Here, we assessed the response of seven physiological traits and four yield and yield-related traits in 39 chickpea genotypes grown in normal-sown and late-sown environments [to expose plants to HS (>32/20°C) at the reproductive stage] for two consecutive years (2017-2018 and 2018-2019). Significant genetic variability for the tested traits occurred under normal and HS conditions in both years. Based on the tested physiological parameters and yield-related traits, GNG2171, GNG1969, GNG1488, PantG186, CSJ515, RSG888, RSG945, RVG202, and GNG469 were identified as promising genotypes under HS. Further, ten heat-tolerant and ten heat-sensitive lines from the set of 39 genotypes were validated for their heat tolerance (32/20°C from flowering to maturity) in a controlled environment of a growth chamber. Of the ten heat-tolerant genotypes, GNG1969, GNG1488, PantG186, RSG888, CSJ315, and GNG1499 exhibited high heat tolerance evidenced by small reductions in pollen viability, pollen germination, and pod set %, high seed yield plant^-1 and less damage to membranes, photosynthetic ability, leaf water status, and oxidative processes. In growth chamber, chlorophyll, photosynthetic efficiency, pollen germination, and pollen viability correlated strongly with yield traits. Thus, GNG1969, GNG1488, PantG186, RSG888, CSJ315, and GNG1499 genotypes could be used as candidate donors for transferring heat tolerance traits to high-yielding heat-sensitive varieties to develop heat-resilient chickpea cultivars.