High-density linkage map construction and mapping of seed trait QTLs in chickpea (Cicer arietinum L.) using Genotyping-by-Sequencing (GBS)

Genetic analysis of agronomic and physiological traits associated with latex yield revealed complex genetic bases in Hevea brasiliensis

Hevea brasiliensis, a natural rubber producing species, is widely cultivated due to its high rubber yield potential. Natural rubber is synthesised in the rubber particles of laticifers. Latex diagnosis (LD) was established to characterise the physiological state of the laticiferous system by measuring its physiological parameters, i.e., sucrose, inorganic phosphorous (Pi), thiols and total solid content (TSC). Rubber clones are often classified in three groups i.e., quick starters, medium starters and slow starters. To better understand the genetic bases of latex yield, a biparental population was generated from a cross between the quick-starter clone PB 260 and the medium-starter clone SP 217. LD was performed during the peak latex production season and used to calculate sucrose loading. The agronomic and physiological parameters associated with latex yield led to the classification of genotypes according to the rubber clonal typology and to the identification of quantitative trait loci (QTL) using a high-density map. Inorganic phosphorous content was positively associated with yield during the first year of production thus enabling identification of quick-starter clones. In addition, the LD-based clonal typology led to determine the long-term yield potential and the use of appropriate ethephon stimulation. QTL analysis successfully identified several QTLs related to yield, sucrose, Pi and TSC. One QTL related to sucrose loading was identified in the same position as the QTL for sucrose on linkage group 1. To our knowledge, this is the first study to report QTL analysis for this trait. The use of a high-density map enables the identification of genes underlying QTLs. Several putative genes underlying QTLs related to yield, sucrose and TSC were identified.

Phylogenetic relationships and genetic diversity of the Korean endemic Phedimus latiovalifolius (Crassulaceae) and its close relatives

Phedimus latiovalifolius (Y.N.Lee) D.C.Son & H.J.Kim is exclusively distributed in the high mountains in the Korean Peninsula, mainly along the Baekdudaegan mountain range. Despite its morphological and distributional distinction from other Phedimus Raf. species, its taxonomic identity and phylogenetic relationship with congeneric species remain unclear. This study employs genotyping-by-sequencing-derived genome-wide single nucleotide polymorphisms to establish the monophyly of P. latiovalifolius and its relationship with closely related species. Genetic diversity and population differentiation of P. latiovalifolius are also assessed to provide baseline genetic information for future conservation and management strategies. Our phylogenetic analyses robustly demonstrate the monophyletic nature of P. latiovalifolius, with P. aizoon (L.) 't Hart identified as its closest sister lineage. There is no genetic evidence supporting a hybrid origin of P. latiovalifolius from P. aizoon involving either P. ellacombeanus (Praeger) 't Hart or P. kamtschaticus (Fisch.) 't Hart. Population genetic analyses reveal two major groups within P. latiovalifolius. A higher genetic variation is observed in P. ellacombeanus than in the congeneric species. Notably, most of the genetic variation exists within P. latiovalifolius populations. Given its distribution and the potential role of Baekdudaegan as an East Asian Pleistocene refugia, P. latiovalifolius could be considered rare and endemic, persisting in the refugium across glacial/interglacial cycles.

Genetic Diversity and Population Structure of a Large USDA Sesame Collection

Sesame, Sesamum indicum L., is one of the oldest domesticated crops used for its oil and protein in many parts of the world. To build genomic resources for sesame that could be used to improve sesame productivity and responses to stresses, a USDA sesame germplasm collection of 501 accessions originating from 36 countries was used in this study. The panel was genotyped using genotyping-by-sequencing (GBS) technology to explore its genetic diversity and population structure and the relatedness among its accessions. A total of 24,735 high-quality single-nucleotide polymorphism (SNP) markers were identified over the 13 chromosomes. The marker density was 1900 SNP per chromosome, with an average polymorphism information content (PIC) value of 0.267. The marker polymorphisms and heterozygosity estimators indicated the usefulness of the identified SNPs to be used in future genetic studies and breeding activities. The population structure, principal components analysis (PCA), and unrooted neighbor-joining phylogenetic tree analyses classified two distinct subpopulations, indicating a wide genetic diversity within the USDA sesame collection. Analysis of molecular variance (AMOVA) revealed that 29.5% of the variation in this population was due to subpopulations, while 57.5% of the variation was due to variation among the accessions within the subpopulations. These results showed the degree of differentiation between the two subpopulations as well as within each subpopulation. The high fixation index (FST) between the distinguished subpopulations indicates a wide genetic diversity and high genetic differentiation among and within the identified subpopulations. The linkage disequilibrium (LD) pattern averaged 161 Kbp for the whole sesame genome, while the LD decay ranged from 168 Kbp at chromosome LG09 to 123 Kbp in chromosome LG05. These findings could explain the complications of linkage drag among the traits during selections. The selected accessions and genotyped SNPs provide tools to enhance genetic gain in sesame breeding programs through molecular approaches.

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.

The chickpea WIP2 gene underlying a major QTL contributes to lateral root development

Lateral roots are a major component of root system architecture, and lateral root count (LRC) positively contributes to yield under drought in chickpea. To understand the genetic regulation of LRC, a biparental mapping population derived from two chickpea accessions having contrasting LRCs was genotyped by sequencing, and phenotyped to map four major quantitative trait loci (QTLs) contributing to 13-32% of the LRC trait variation. A single- nucleotide polymorphism tightly linked to the locus contributing to highest trait variation was located on the coding region of a gene (CaWIP2), orthologous to NO TRANSMITTING TRACT/WIP domain protein 2 (NTT/WIP2) gene of Arabidopsis thaliana. A polymorphic simple sequence repeat (SSR) in the CaWIP2 promoter showed differentiation between low versus high LRC parents and mapping individuals, suggesting its utility for marker-assisted selection. CaWIP2 promoter showed strong expression in chickpea apical root meristem and lateral root primordia. Expression of CaWIP2 under its native promoter in the Arabidopsis wip2wip4wip5 mutant rescued its rootless phenotype to produce more lateral roots than the wild-type plants, and led to formation of amyloplasts in the columella. CaWIP2 expression also induced the expression of genes that regulate lateral root emergence. Our study identified a gene-based marker for LRC which will be useful for developing drought-tolerant, high-yielding chickpea varieties.

Development of an SNP marker set for marker-assisted backcrossing using genotyping-by-sequencing in tetraploid perilla

High-quality molecular markers are essential for marker-assisted selection to accelerate breeding progress. Compared with diploid species, recently diverged polyploid crop species tend to have highly similar homeologous subgenomes, which is expected to limit the development of broadly applicable locus-specific single-nucleotide polymorphism (SNP) assays. Furthermore, it is particularly challenging to make genome-wide marker sets for species that lack a reference genome. Here, we report the development of a genome-wide set of kompetitive allele specific PCR (KASP) markers for marker-assisted recurrent selection (MARS) in the tetraploid minor crop perilla. To find locus-specific SNP markers across the perilla genome, we used genotyping-by-sequencing (GBS) to construct linkage maps of two F2 populations. The two resulting high-resolution linkage maps comprised 2326 and 2454 SNP markers that spanned a total genetic distance of 2133 cM across 16 linkage groups and 2169 cM across 21 linkage groups, respectively. We then obtained a final genetic map consisting of 22 linkage groups with 1123 common markers from the two genetic maps. We selected 96 genome-wide markers for MARS and confirmed the accuracy of markers in the two F2 populations using a high-throughput Fluidigm system. We confirmed that 91.8% of the SNP genotyping results from the Fluidigm assay were the same as the results obtained through GBS. These results provide a foundation for marker-assisted backcrossing and the development of new varieties of perilla.

Genetic mapping of quantitative trait loci associated with drought tolerance in chickpea (Cicer arietinum L.)

Elucidation of the genetic basis of drought tolerance is vital for genomics-assisted breeding of drought tolerant crop varieties. Here, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphisms (SNPs) in recombinant inbred lines (RILs) derived from a cross between a drought tolerant chickpea variety, Pusa 362 and a drought sensitive variety, SBD 377. The GBS identified a total of 35,502 SNPs and subsequent filtering of these resulted in 3237 high-quality SNPs included in the eight linkage groups. Fifty-one percent of these SNPs were located in the genic regions distributed throughout the genome. The high density linkage map has total map length of 1069 cm with an average marker interval of 0.33 cm. The linkage map was used to identify 9 robust and consistent QTLs for four drought related traits viz. membrane stability index, relative water content, seed weight and yield under drought, with percent variance explained within the range of 6.29%-90.68% and LOD scores of 2.64 to 6.38, which were located on five of the eight linkage groups. A genomic region on LG 7 harbors quantitative trait loci (QTLs) explaining > 90% phenotypic variance for membrane stability index, and > 10% PVE for yield. This study also provides the first report of major QTLs for physiological traits such as membrane stability index and relative water content for drought stress in chickpea. A total of 369 putative candidate genes were identified in the 6.6 Mb genomic region spanning these QTLs. In-silico expression profiling based on the available transcriptome data revealed that 326 of these genes were differentially expressed under drought stress. KEGG analysis resulted in reduction of candidate genes from 369 to 99, revealing enrichment in various signaling pathways. Haplotype analysis confirmed 5 QTLs among the initially identified 9 QTLs. Two QTLs, qRWC1.1 and qYLD7.1, were chosen based on high SNP density. Candidate gene-based analysis revealed distinct haplotypes in qYLD7.1 associated with significant phenotypic differences, potentially linked to pathways for secondary metabolite biosynthesis. These identified candidate genes bolster defenses through flavonoids and phenylalanine-derived compounds, aiding UV protection, pathogen resistance, and plant structure.The study provides novel genomic regions and candidate genes which can be utilized in genomics-assisted breeding of superior drought tolerant chickpea cultivars.

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.

Construction of a genetic linkage map and QTL mapping of fruit quality traits in guava (Psidium guajava L.)

Guava (Psidium guajava L.) is an important fruit crop of the Indian sub-continent, with potential for improvements in quality and yield. The goal of the present study was to construct a genetic linkage map in an intraspecific cross between the elite cultivar 'Allahabad Safeda' and the Purple Guava landrace to identify the genomic regions responsible for important fruit quality traits, viz., total soluble solids, titratable acidity, vitamin C, and sugars. This population was phenotyped in field trials (as a winter crop) for three consecutive years, and showed moderate-to-high values of heterogeneity coefficients along with higher heritability (60.0%-97.0%) and genetic-advance-over-mean values (13.23%-31.17%), suggesting minimal environmental influence on the expression of fruit-quality traits and indicating that these traits can be improved by phenotypic selection methods. Significant correlations and strong associations were also detected among fruit physico-chemical traits in segregating progeny. The constructed linkage map consisted of 195 markers distributed across 11 chromosomes, spanning a length of 1,604.47 cM (average inter-loci distance of 8.80 markers) and with 88.00% coverage of the guava genome. Fifty-eight quantitative trait loci (QTLs) were detected in three environments with best linear unbiased prediction (BLUP) values using the composite interval mapping algorithm of the BIP (biparental populations) module. The QTLs were distributed on seven different chromosomes, explaining 10.95%-17.77% of phenotypic variance, with the highest LOD score being 5.96 for qTSS.AS.pau-6.2. Thirteen QTLs detected across multiple environments with BLUPs indicate stability and utility in a future breeding program for guava. Furthermore, seven QTL clusters with stable or common individual QTLs affecting two or more different traits were located on six linkage groups (LGs), explaining the correlation among fruit-quality traits. Thus, the multiple environmental evaluations conducted here have increased our understanding of the molecular basis of phenotypic variation, providing the basis for future high-resolution fine-mapping and paving the way for marker-assisted breeding of fruit-quality traits.

Breeding and Genomic Approaches towards Development of Fusarium Wilt Resistance in Chickpea

Chickpea is an important leguminous crop with potential to provide dietary proteins to both humans and animals. It also ameliorates soil nitrogen through biological nitrogen fixation. The crop is affected by an array of biotic and abiotic factors. Among different biotic stresses, a major fungal disease called Fusarium wilt, caused by Fusarium oxysporum f. sp. ciceris (FOC), is responsible for low productivity in chickpea. To date, eight pathogenic races of FOC (race 0, 1A, and 1B/C, 2-6) have been reported worldwide. The development of resistant cultivars using different conventional breeding methods is very time consuming and depends upon the environment. Modern technologies can improve conventional methods to solve these major constraints. Understanding the molecular response of chickpea to Fusarium wilt can help to provide effective management strategies. The identification of molecular markers closely linked to genes/QTLs has provided great potential for chickpea improvement programs. Moreover, omics approaches, including transcriptomics, metabolomics, and proteomics give scientists a vast viewpoint of functional genomics. In this review, we will discuss the integration of all available strategies and provide comprehensive knowledge about chickpea plant defense against Fusarium wilt.

The developmental dynamics in cool season legumes with focus on chickpea

Chickpea is one of the most widely consumed grain legume world-wide. Advances in next-generation sequencing and genomics tools have led to genetic dissection and identification of potential candidate genes regulating agronomic traits in chickpea. However, the developmental particularities and its potential in reforming the yield and nutritional value remain largely unexplored. Studies in crops such as rice, maize, tomato and pea have highlighted the contribution of key regulator of developmental events in yield related traits. A comprehensive knowledge on the development aspects of a crop can pave way for new vistas to explore. Pea and Medicago are the close relatives of genus Cicer and the basic developmental events in these legumes are similar. However, there are some distinct developmental features in chickpea which hold potential for future crop improvement endeavours. The global chickpea germplasm encompasses wide range of diversities in terms of morphology at both vegetative and reproductive stages. There is an immediate need for understanding the genetic and molecular basis of this diversity and utilizing them for the yield contributing trait improvement. The review discusses some of the key developmental events which have potential in yield enhancement and the lessons which can be learnt from model legumes in this regard.

Comparative transcriptomic and metabolite profiling reveals genotype-specific responses to Fe starvation in chickpea

Iron deficiency is a major nutritional stress that severely impacts crop productivity worldwide. However, molecular intricacies and subsequent physiological and metabolic changes in response to Fe starvation, especially in leguminous crops like chickpea, remain elusive. In the present study, we investigated physiological, transcriptional, and metabolic reprogramming in two chickpea genotypes (H6013 and L4958) with contrasting seed iron concentrations upon Fe deficiency. Our findings revealed that iron starvation affected growth and physiological parameters of both chickpea genotypes. Comparative transcriptome analysis led to the identification of differentially expressed genes between the genotypes related to strategy I uptake, metal ions transporters, reactive oxygen species-associated genes, transcription factors, and protein kinases that could mitigate Fe deficiency. Our gene correlation network discovered several putative candidate genes like CIPK25, CKX3, WRKY50, NAC29, MYB4, and PAP18, which could facilitate the investigation of the molecular rationale underlying Fe tolerance in chickpea. Furthermore, the metabolite analysis also illustrated the differential accumulation of organic acids, amino acids and other metabolites associated with Fe mobilization in chickpea genotypes. Overall, our study demonstrated the comparative transcriptional dynamics upon Fe starvation. The outcomes of the current endeavor will enable the development of Fe deficiency tolerant chickpea cultivars.

Construction of a genome-wide genetic linkage map and identification of quantitative trait loci for powdery mildew resistance in Gerbera daisy

Powdery mildew (PM) is a common fungal disease in many important crops. The PM caused by Podosphaera xanthii has been the most challenging problem in commercial Gerbera (Gerbera hybrida) production globally, often leading to severe losses of crop yield and quality. A small number of PM-resistant breeding lines and cultivars have been reported in Gerbera, but the underlying genetics for PM resistance in Gerbera is largely unknown. Scarcity of genomic resources such as genetic linkage maps and molecular markers has severely hindered the effort to understand the genetic basis and locate loci controlling PM resistance in Gerbera. This study aimed to construct a genome-wide genetic linkage map, identify quantitative trait loci (QTL), and molecular markers for PM resistance in Gerbera. A segregating mapping population was developed by crossing PM-resistant and -susceptible Gerbera breeding lines, genotyped by sequencing, and phenotyped for PM resistance. A genome-wide genetic linkage map constructed with 791 single polymorphic site (SNP) markers spans 1912.30 cM across 27 linkage groups (LG) and reaches a density of 1 marker per 2.42 cM. One major consistent QTL was discovered in LG16, explaining more than 16.6% of the phenotypic variance for PM resistance. The QTL was tagged with two flanking SNP markers. The availability of this genetic linkage map will be very useful for locating and tagging QTLs for other important traits in Gerbera, and the newly discovered QTL and SNP markers will enable development of molecular markers for improving Gerbera for resistance to PM.

Molecular Breeding and Drought Tolerance in Chickpea

Cicer arietinum L. is the third greatest widely planted imperative pulse crop worldwide, and it belongs to the Leguminosae family. Drought is the utmost common abiotic factor on plants, distressing their water status and limiting their growth and development. Chickpea genotypes have the natural ability to fight drought stress using certain strategies viz., escape, avoidance and tolerance. Assorted breeding methods, including hybridization, mutation, and marker-aided breeding, genome sequencing along with omics approaches, could be used to improve the chickpea germplasm lines(s) against drought stress. Root features, for instance depth and root biomass, have been recognized as the greatest beneficial morphological factors for managing terminal drought tolerance in the chickpea. Marker-aided selection, for example, is a genomics-assisted breeding (GAB) strategy that can considerably increase crop breeding accuracy and competence. These breeding technologies, notably marker-assisted breeding, omics, and plant physiology knowledge, underlined the importance of chickpea breeding and can be used in future crop improvement programmes to generate drought-tolerant cultivars(s).

An integrated transcriptome mapping the regulatory network of coding and long non-coding RNAs provides a genomics resource in chickpea

Large-scale transcriptome analysis can provide a systems-level understanding of biological processes. To accelerate functional genomic studies in chickpea, we perform a comprehensive transcriptome analysis to generate full-length transcriptome and expression atlas of protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs) from 32 different tissues/organs via deep sequencing. The high-depth RNA-seq dataset reveal expression dynamics and tissue-specificity along with associated biological functions of PCGs and lncRNAs during development. The coexpression network analysis reveal modules associated with a particular tissue or a set of related tissues. The components of transcriptional regulatory networks (TRNs), including transcription factors, their cognate cis-regulatory motifs, and target PCGs/lncRNAs that determine developmental programs of different tissues/organs, are identified. Several candidate tissue-specific and abiotic stress-responsive transcripts associated with quantitative trait loci that determine important agronomic traits are also identified. These results provide an important resource to advance functional/translational genomic and genetic studies during chickpea development and environmental conditions.

A full-length transcriptome and expression atlas of protein-coding genes and long non-coding RNAs is generated in chickpea. Components of transcriptional regulatory networks and candidate tissue-specific transcripts associated with quantitative trait loci are identified.

High-Density Linkage Mapping of Agronomic Trait QTLs in Wheat under Water Deficit Condition using Genotyping by Sequencing (GBS)

Improvement of grain yield is the ultimate goal for wheat breeding under water-limited environments. In the present study, a high-density linkage map was developed by using genotyping-by-sequencing (GBS) of a recombinant inbred line (RIL) population derived from the cross between Iranian landrace #49 and cultivar Yecora Rojo. The population was evaluated in three locations in Iran during two years under irrigated and water deficit conditions for the agronomic traits grain yield (GY), plant height (PH), spike number per square meter (SM), 1000 kernel weight (TKW), grain number per spike (GNS), spike length (SL), biomass (BIO) and harvest index (HI). A linkage map was constructed using 5831 SNPs assigned to 21 chromosomes, spanning 3642.14 cM of the hexaploid wheat genome with an average marker density of 0.62 (markers/cM). In total, 85 QTLs were identified on 19 chromosomes (all except 5D and 6D) explaining 6.06–19.25% of the traits phenotypic variance. We could identify 20 novel QTLs explaining 8.87–19.18% of phenotypic variance on chromosomes 1A, 1B, 1D, 2B, 3A, 3B, 6A, 6B and 7A. For 35 out of 85 mapped QTLs functionally annotated genes were identified which could be related to a potential role in drought stress.

Genome-wide analysis suggests the potential role of lncRNAs during seed development and seed size/weight determination in chickpea

The integrated transcriptome data analyses suggested the plausible roles of lncRNAs during seed development in chickpea. The candidate lncRNAs associated with QTLs and those involved in miRNA-mediated seed size/weight determination in chickpea have been identified. Long non-coding RNAs (lncRNAs) are important regulators of various biological processes. Here, we identified lncRNAs at seven successive stages of seed development in small-seeded and large-seeded chickpea cultivars. In total, 4751 lncRNAs implicated in diverse biological processes were identified. Most of lncRNAs were conserved between the two cultivars, whereas only a few of them were conserved in other plants, suggesting their species-specificity. A large number of lncRNAs differentially expressed between the two chickpea cultivars associated with seed development-related processes were identified. The lncRNAs acting as precursors of miRNAs and those mimicking target protein-coding genes of miRNAs involved in seed size/weight determination, including HAIKU1, BIG SEEDS1, and SHB1, were also revealed. Further, lncRNAs located within seed size/weight associated quantitative trait loci were also detected. Overall, we present a comprehensive resource and identified candidate lncRNAs that may play important roles during seed development and seed size/weight determination in chickpea.

Genetic mapping of QTLs for drought tolerance in chickpea (Cicer arietinum L.)

Chickpea yield is severely affected by drought stress, which is a complex quantitative trait regulated by multiple small-effect genes. Identifying genomic regions associated with drought tolerance component traits may increase our understanding of drought tolerance mechanisms and assist in the development of drought-tolerant varieties. Here, a total of 187 F8 recombinant inbred lines (RILs) developed from an interspecific cross between drought-tolerant genotype GPF 2 (Cicer arietinum) and drought-sensitive accession ILWC 292 (C. reticulatum) were evaluated to identify quantitative trait loci (QTLs) associated with drought tolerance component traits. A total of 21 traits, including 12 morpho-physiological traits and nine root-related traits, were studied under rainfed and irrigated conditions. Composite interval mapping identified 31 QTLs at Ludhiana and 23 QTLs at Faridkot locations for morphological and physiological traits, and seven QTLs were identified for root-related traits. QTL analysis identified eight consensus QTLs for six traits and five QTL clusters containing QTLs for multiple traits on linkage groups CaLG04 and CaLG06. The identified major QTLs and genomic regions associated with drought tolerance component traits can be introgressed into elite cultivars using genomics-assisted breeding to enhance drought tolerance in chickpea.

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.

Improving Drought Stress Tolerance in Ramie (Boehmeria nivea L.) Using Molecular Techniques

Ramie is one of the most significant fiber crops and contributes to good quality fiber. Drought stress (DS) is one of the most devastating abiotic factors which is accountable for a substantial loss in crop growth and production and disturbing sustainable crop production. DS impairs growth, plant water relation, and nutrient uptake. Ramie has evolved a series of defense responses to cope with DS. There are numerous genes regulating the drought tolerance (DT) mechanism in ramie. The morphological and physiological mechanism of DT is well-studied; however, modified methods would be more effective. The use of novel genome editing tools like clustered regularly interspaced short palindromic repeats (CRISPR) is being used to edit the recessive genes in crops to modify their function. The transgenic approaches are used to develop several drought-tolerant varieties in ramie, and further identification of tolerant genes is needed for an effective breeding plan. Quantitative trait loci (QTLs) mapping, transcription factors (TFs) and speed breeding are highly studied techniques, and these would lead to the development of drought-resilient ramie cultivars. The use of hormones in enhancing crop growth and development under water scarcity circumstances is critical; however, using different concentrations and testing genotypes in changing environments would be helpful to sort the tolerant genotypes. Since plants use various ways to counter DS, investigating mechanisms of DT in plants will lead to improved DT in ramie. This critical review summarized the recent advancements on DT in ramie using novel molecular techniques. This information would help ramie breeders to conduct research studies and develop drought tolerant ramie cultivars.

Genotyping-by-Sequencing Based Molecular Genetic Diversity of Pakistani Bread Wheat (Triticum aestivum L.) Accessions

Spring wheat (Triticum aestivum L.) is one of the most imperative staple food crops, with an annual production of 765 million tons globally to feed ∼40% world population. Genetic diversity in available germplasm is crucial for sustainable wheat improvement to ensure global food security. A diversity panel of 184 Pakistani wheat accessions was genotyped using 123,596 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing with 42% of the SNPs mapped on B, 36% on A, and 22% on D sub-genomes of wheat. Chromosome 2B contains the most SNPs (9,126), whereas 4D has the least (2,660) markers. The mean polymorphic information content, genetic diversity, and major allele frequency of the population were 0.157, 0.1844, and 0.87, respectively. Analysis of molecular variance revealed a higher genetic diversity (80%) within the sub-population than among the sub-populations (20%). The genome-wide linkage disequilibrium was 0.34 Mbp for the whole wheat genome. Among the three subgenomes, A has the highest LD decay value (0.29 Mbp), followed by B (0.2 Mbp) and D (0.07 Mbp) genomes, respectively. The results of population structure, principal coordinate analysis, phylogenetic tree, and kinship analysis also divided the whole population into three clusters comprising 31, 33, and 120 accessions in group 1, group 2, and group 3, respectively. All groups were dominated by the local wheat accessions. Estimation of genetic diversity will be a baseline for the selection of breeding parents for mutations and the genome-wide association and marker-assisted selection studies.

Novel Genomic Regions Linked to Ascochyta Blight Resistance in Two Differentially Resistant Cultivars of Chickpea

Ascochyta blight (AB), caused by the fungal pathogen Ascochyta rabiei, is a devastating foliar disease of chickpea (Cicer arietinum L.). The genotyping-by-sequencing (GBS)-based approach was deployed for mapping QTLs associated with AB resistance in chickpea in two recombinant inbred line populations derived from two crosses (AB₃₂₇₉ derived from ILC 1929 × ILC 3279 and AB₄₈₂ derived from ILC 1929 × ILC 482) and tested in six different environments. Twenty-one different genomic regions linked to AB resistance were identified in regions CalG02 and CalG04 in both populations AB₃₂₇₉ and AB₄₈₂. These regions contain 1,118 SNPs significantly associated with AB resistance (p ≤ 0.001), which explained 11.2-39.3% of the phenotypic variation (PVE). Nine of the AB resistance-associated genomic regions were newly detected in this study, while twelve regions were known from previous AB studies. The proposed physical map narrows down AB resistance to consistent genomic regions identified across different environments. Gene ontology (GO) assigned these QTLs to 319 genes, many of which were associated with stress and disease resistance, and with most important genes belonging to resistance gene families such as leucine-rich repeat (LRR) and transcription factor families. Our results indicate that the flowering-associated gene GIGANTEA is a possible key factor in AB resistance in chickpea. The results have identified AB resistance-associated regions on the physical genetic map of chickpea and allowed for the identification of associated markers that will help in breeding of AB-resistant varieties.

Genome-wide association studies dissect the genetic architecture of seed shape and size in common bean

Seed weight and size are important yield components. Thus, selecting for large seeds has been a key objective in crop domestication and breeding. In common bean, seed shape is also important since it influences industrial processing and plays a vital role in determining the choices of consumers and farmers. In this study, we performed genome-wide association studies on a core collection of common bean accessions to dissect the genetic architecture and identify genomic regions associated with seed morphological traits related to weight, size, and shape. Phenotypic data were collected by high-throughput image-based approaches, and utilized to test associations with 10,362 single-nucleotide polymorphism markers using multilocus mixed models. We searched within genome-associated regions for candidate genes putatively involved in seed phenotypic variation. The collection exhibited high variability for the entire set of seed traits, and the Andean gene pool was found to produce larger, heavier seeds than the Mesoamerican gene pool. Strong pairwise correlations were verified for most seed traits. Genome-wide association studies identified marker-trait associations accounting for a considerable amount of phenotypic variation in length, width, projected area, perimeter, and circularity in 4 distinct genomic regions. Promising candidate genes were identified, e.g. those encoding an AT-hook motif nuclear-localized protein 8, type 2C protein phosphatases, and a protein Mei2-like 4 isoform, known to be associated with seed size and weight regulation. Moreover, the genes that were pinpointed are also good candidates for functional analysis to validate their influence on seed shape and size in common bean and other related crops.

Identification of QTLs/ Candidate Genes for Seed Mineral Contents in Common Bean (Phaseolus vulgaris L.) Through Genotyping-by-Sequencing

Throughout the ages, the common bean has been consumed by humanity as an important food staple crop and source of nutrition on a global scale. Since its domestication, a wide spectrum of phenotypic and genotypic investigations have been carried out to unravel the potential of this crop and to understand the process of nutrient accumulation along with other desirable characteristics. The common bean is one of the essential legume crops due to its high protein and micronutrient content. The balance in micronutrients is critical for the growth and development of plants as well as humans. Iron (Fe), Zinc (Zn), Copper (Cu), Manganese (Mn), Magnesium (Mg), Calcium (Ca), and Molybdenum (Mo) are some of the important micronutrients present in legumes. Thus, we aimed to investigate the quantitative trait loci’s (QTLs)/single nucleotide polymorphisms (SNPs) to identify the candidate genes associated with micronutrients through genotyping by sequencing (GBS). In our investigation, through GBS we identified SNPs linked with traits and assessed seven micronutrients in 96 selected common bean genotypes for screening nutritionally rich genotypes. Among 96399 SNPs total identified through GBS, 113 SNPs showed significant phenotypic variance, ranging from 13.50 to 21.74%. SNPs associated with most of the seed micronutrients (Mg, Mn, Fe, Ca, Cu) were found on chr3 & chr11 (Mg, Mn, Mo, Ca, Zn). The findings from this study could be used for haplotype-based selection of nutritionally rich genotypes and for marker-assisted genetic enhancement of the common bean. Further, the identified SNPs for candidate genes/transporters associated with micronutrient content may pave the way for the enrichment of seeds by employing genomics-assisted breeding programs.

Population Genetics and Development of a Core Collection from Elite Germplasms of Xanthoceras sorbifolium Based on Genome-Wide SNPs

Xanthoceras sorbifolium is one of the most important species of woody oil. In this study, whole genome re-sequencing of 119 X. sorbifolium germplasms was conducted and, after filtering, 105,685,557 high-quality SNPs were identified, which were used to perform population genetics and core collection development analyses. The results from the phylogenetic, population structure, and principal component analyses showed a high level of agreement, with 119 germplasms being classified into three main groups. The germplasms were not completely classified based on their geographical origins and flower colors; furthermore, the genetic backgrounds of these germplasms were complex and diverse. The average polymorphsim information content (PIC) values for the three inferred groups clustered by structure analysis and the six classified color groups were 0.2445 and 0.2628, respectively, indicating a low to medium informative degree of genetic diversity. Moreover, a core collection containing 29.4% (35) out of the 119 X. sorbifolium germplasms was established. Our results revealed the genetic diversity and structure of X. sorbifolium germplasms, and the development of a core collection will be useful for the efficient improvement of breeding programs and genome-wide association studies.

A kabuli chickpea ideotype

The concept of 'crop ideotype' is coined as a desirable plant model expected to better perform for seed yield, oils and other useful characteristics when developed as a cultivar, and it consists of two major approaches, namely, (i) 'defect elimination', that is, integration of disease resistance to a susceptible genotype from a resistant genotype and (ii) 'selection for yield' by improving yield after crosses between desirable parents. For consideration of these approaches, here we introduced an ideotype in kabuli chickpea (Cicer arietinum L.) which is high-yielding, extra-large-seeded, and double- or multi-podded, has high plant height and imparipinnate-leafed traits, and is heat tolerant and resistant to ascochyta blight [Ascochyta rabiei (Pass.) Labr.], which causes considerable yield losses, via marker-assisted selection. F3 and F4 lines were evaluated for agro-morphological traits divided into six classes, namely, (i) imparipinnate-leafed and single-podded progeny, (ii) imparipinnate-leafed and double-podded progeny, (iii) imparipinnate-leafed and multi-podded progeny, (iv) unifoliolate-leafed and single-podded progeny, (v) unifoliolate-leafed and double-podded progeny, (vi) unifoliolate-leafed and multi-podded progeny. F3:4 lines having 100-seed weight ≥ 45 g and double- or multi-podded traits were additionally assessed for resistance to ascochyta blight using molecular markers including SCY17590 and CaETR-1. Superior lines having higher values than their best parents were determined for all studied traits indicating that economic and important traits including yield and seed size in chickpea could be improved by crossing suitable parents. Imparipinnate-leafed and multi-podded plants had not only the highest number of pods and seeds per plant but also the highest yield. On the other hand, imparipinnate-leafed and single podded progeny had the largest seed size, followed by imparipinnate-leafed and double-podded progeny. Multi-podded plants produced 23% more seed yield than that of single-podded plants, while multi-podded plants attained 7.6% more seed yield than that of double-podded plants. SCY17590 and CaETR-1 markers located on LG4 related to QTLAR2 and QTLAR1 were found in 14 lines among 152 F3:4 lines. Six superior lines were selected for being double- or multi-podded, imparipinnate-leafed, suitable for combine harvest, heat-tolerant, and resistant to ascochyta blight, and having both of two resistance markers and extra-large seeds as high as 50-60 g per 100-seed weight. Resistance alleles from two different backgrounds for resistance to ascochyta blight were integrated with double- or multi-podded kabuli chickpea lines having high yield, extra-large seeds, high plant height, imparipinnate-leaves and high heat tolerance, playing a crucial role for future demands of population and food security. These approaches seem to be applicable in ideotype breeding for other important crop plants.

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.

Chickpea (Cicer arietinum L.) as a Source of Essential Fatty Acids - A Biofortification Approach

Chickpea is a highly nutritious pulse crop with low digestible carbohydrates (40-60%), protein (15-22%), essential fats (4-8%), and a range of minerals and vitamins. The fatty acid composition of the seed adds value because fats govern the texture, shelf-life, flavor, aroma, and nutritional composition of chickpea-based food products. Therefore, the biofortification of essential fatty acids has become a nutritional breeding target for chickpea crop improvement programs worldwide. This paper examines global chickpea production, focusing on plant lipids, their functions, and their benefits to human health. In addition, this paper also reviews the chemical analysis of essential fatty acids and possible breeding targets to enrich essential fatty acids in chickpea (Cicer arietinum) biofortification. Biofortification of chickpea for essential fatty acids within safe levels will improve human health and support food processing to retain the quality and flavor of chickpea-based food products. Essential fatty acid biofortification is possible by phenotyping diverse chickpea germplasm over suitable locations and years and identifying the candidate genes responsible for quantitative trait loci mapping using genome-wide association mapping.

Development of Genome-Wide Functional Markers Using Draft Genome Assembly of Guava (Psidium guajava L.) cv. Allahabad Safeda to Expedite Molecular Breeding

Guava (Psidium guajava L.), a rich source of nutrients, is an important tropical and subtropical fruit of the Myrtaceae family and exhibits magnificent diversity. Genetic diversity analysis is the first step toward the identification of parents for hybridization, genetic mapping, and molecular breeding in any crop species. A diversity analysis based on whole-genome functional markers increases the chances of identifying genetic associations with agronomically important traits. Therefore, here, we sequenced the genome of guava cv. Allahabad Safeda on an Illumina platform and generated a draft assembly of ~304 MB. The assembly of the Allahabad Safeda genome constituted >37.95% repeat sequences, gene prediction with RNA-seq data as evidence identified 14,115 genes, and BLAST n/r, Interproscan, PfamScan, BLAST2GO, and KEGG annotated 13,957 genes. A comparative protein transcript analysis of tree species revealed the close relatedness of guava with Eucalyptus. Comparative transcriptomics-based SSR/InDel/SNP-PCR ready genome-wide markers in greenish-yellow skinned and white fleshed-Allahabad Safeda to four contrasting cultivars viz apple-color-skinned and white-fleshed-Lalima, greenish-yellow-skinned and pink-fleshed-Punjab Pink, purple-black-skinned and purple-fleshed-Purple Local and widely used rootstock-Lucknow-49 were developed. The molecular markers developed here revealed a high level of individual heterozygosity within genotypes in 22 phenotypically diverse guava cultivars. Principal coordinate, STRUCTURE clustering, and neighbor-joining-based genetic diversity analysis identified distinct clusters associated with fruit skin and flesh color. The genome sequencing of guava, functional annotation, comparative transcriptomics-based genome-wide markers, and genetic diversity analysis will expand the knowledge of genomes of climacteric fruits, facilitating trait-based molecular breeding and diversifying the nutritional basket.

Phylogeography Reveals Geographic and Environmental Factors Driving Genetic Differentiation of Populus sect. Turanga in Northwest China

Populus sect. Turanga (hereafter referred to as "Populus"), including Populus euphratica and Populus pruinosa, are the predominant tree species in desert riparian forests in northwestern China. These trees play key roles in maintaining ecosystem balance, curbing desertification, and protecting biodiversity. However, the distribution area of Populus forests has been severely diminished and degraded in recent years due to increased habitat destruction and human activity. Understanding the genetic diversity among Populus individuals and populations is essential for designing conservation strategies, but comprehensive studies of their genetic diversity in northwest China are lacking. Here, we assessed the population structures and genetic diversity of 1,620 samples from 85 natural populations of Populus (59 P. euphratica and 26 P. pruinosa populations) covering all of northwestern China using 120 single nucleotide polymorphism (SNP) markers. Analysis of population structure revealed significant differentiation between these two sister species and indicated that strong geographical distribution patterns, a geographical barrier, and environmental heterogeneity shaped the extant genetic patterns of Populus. Both P. euphratica and P. pruinosa populations in southern Xinjiang had higher genetic diversity than populations in other clades, perhaps contributing to local geographic structure and strong gene flow. Analysis of molecular variance (AMOVA) identified 15% variance among and 85% variance within subpopulations. Mantel tests suggested that the genetic variation among P. euphratica and P. pruinosa populations could be explained by both geographical and environmental distance. The genetic diversity of P. euphratica showed a significant negative correlation with latitude and longitude and a positive correlation with various environmental factors, such as precipitation of warmest quarter and driest month, temperature seasonality, and annual mean temperature. These findings provide insights into how the genetic differentiation of endangered Populus species was driven by geographical and environmental factors, which should be helpful for designing strategies to protect these genetic resources in the future.

Identification of stable heat tolerance QTLs using inter-specific recombinant inbred line population derived from GPF 2 and ILWC 292

Heat stress during reproductive stages has been leading to significant yield losses in chickpea (Cicer arietinum L.). With an aim of identifying the genomic regions or QTLs responsible for heat tolerance, 187 F8 recombinant inbred lines (RILs) derived from the cross GPF 2 (heat tolerant) × ILWC 292 (heat sensitive) were evaluated under late-sown irrigated (January-May) and timely-sown irrigated environments (November-April) at Ludhiana and Faridkot in Punjab, India for 13 heat tolerance related traits. The pooled ANOVA for both locations for the traits namely days to germination (DG), days to flowering initiation (DFI), days to 50% flowering (DFF), days to 100% flowering (DHF), plant height (PH), pods per plant (NPP), biomass (BIO), grain yield (YLD), 100-seed weight (HSW), harvest index (HI), membrane permeability index (MPI), relative leaf water content (RLWC) and pollen viability (PV)) showed a highly significant difference in RILs. The phenotyping data coupled with the genetic map comprising of 1365 ddRAD-Seq based SNP markers were used for identifying the QTLs for heat tolerance. Composite interval mapping provided a total of 28 and 23 QTLs, respectively at Ludhiana and Faridkot locations. Of these, 13 consensus QTLs for DG, DFI, DFF, DHF, PH, YLD, and MPI have been identified at both locations. Four QTL clusters containing QTLs for multiple traits were identified on the same genomic region at both locations. Stable QTLs for days to flowering can be one of the major factors for providing heat tolerance as early flowering has an advantage of more seed setting due to a comparatively longer reproductive period. Identified QTLs can be used in genomics-assisted breeding to develop heat stress-tolerant high yielding chickpea cultivars.

QTL Analysis of Stem Elongation and Flowering Time in Lettuce Using Genotyping-by-Sequencing

Lettuce plants tend to undergo floral initiation by elongation of flower stalks (bolting) under high-temperature and long-day conditions, which is a serious problem for summer lettuce production. Our objective was to generate a high-density genetic map using SNPs obtained from genotyping-by-sequencing (GBS) analysis of F5 recombinant inbred lines (RILs) and to map QTLs involved in stem growth and flowering time in lettuce. A set of 127 intra-specific RIL mapping populations derived from a cross between two varieties, green and red leaf lettuce, were used to identify QTLs related to the number of days from sowing to bolting (DTB), to flowering of the first flower (DTF), to seed-setting of the first flower (DTS), and the total number of leaves (LN), plant height (PH), and total number of branches of main inflorescence (BN) for two consecutive years. Of the 15 QTLs detected, one that controls DTB, DTF, DTS, LN, and PH detected on LG 7, and another QTL that controls DTF, DTS, and PH detected on LG 1. Analysis of the genomic sequence corresponding to the QTL detected on LG 7 led to the identification of 22 putative candidate genes. A consistent QTL related to bolting and flowering time, and corresponding candidate genes has been reported. This study will be valuable in revealing the genetic basis of stem growth and flowering time in lettuce.

Construction of a high-density genetic map and QTL analysis for yield, yield components and agronomic traits in chickpea (Cicer arietinum L.)

Unravelling the genetic architecture underlying yield components and agronomic traits is important for enhancing crop productivity. Here, a recombinant inbred line (RIL) population, developed from ICC 4958 and DCP 92–3 cross, was used for constructing linkage map and QTL mapping analysis. The RIL population was genotyped using a high-throughput Axiom^®CicerSNP array, which enabled the development of a high-density genetic map consisting of 3,818 SNP markers and spanning a distance of 1064.14 cM. Analysis of phenotyping data for yield, yield components and agronomic traits measured across three years together with genetic mapping data led to the identification of 10 major-effect QTLs and six minor-effect QTLs explaining up to 59.70% phenotypic variance. The major-effect QTLs identified for 100-seed weight, and plant height possessed key genes, such as C3HC4 RING finger protein, pentatricopeptide repeat (PPR) protein, sugar transporter, leucine zipper protein and NADH dehydrogenase, amongst others. The gene ontology studies highlighted the role of these genes in regulating seed weight and plant height in crop plants. The identified genomic regions for yield, yield components, and agronomic traits, and the closely linked markers will help advance genetics research and breeding programs in chickpea.

Genome resequencing reveals DNA polymorphisms associated with seed size/weight determination in chickpea

Crop productivity in legumes is determined by number and size/weight of seeds. To understand the genetic basis of seed size/weight in chickpea, we performed genome resequencing of 13 small- and 5 large-seeded genotypes using Illumina platform. Single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) differentiating small- and large-seeded genotypes were identified. A total of 17,902 SNPs and 2594 InDels located in promoter and/or coding regions that may contribute to seed size/weight were detected. Of these, 266 SNPs showed significant association with seed size/weight trait. Twenty-three genes including those involved in cell growth/division, encoding transcription factors and located within QTLs associated with seed size/weight harbored SNPs within transcription factor binding motif(s) and/or coding region. The non-synonymous SNPs were found to affect the mutational sensitivity and stability of the encoded proteins. Overall, we provided a high-quality SNP map for large-scale genotyping applications and identified candidate genes that determine seed size/weight in chickpea.

Genome-wide profiling of miRNAs during seed development reveals their functional relevance in seed size/weight determination in chickpea

MicroRNAs (miRNAs) are non-coding small RNAs that regulate gene expression at transcriptional and post-transcriptional levels. The role of miRNAs in seed development and seed size/weight determination is poorly understood in legumes. In this study, we profiled miRNAs at seven successive stages of seed development in a small-seeded and a large-seeded chickpea cultivar via small RNA sequencing. In total, 113 known and 243 novel miRNAs were identified. Gene ontology analysis revealed the enrichment of seed/reproductive/post-embryonic development and signaling pathways processes among the miRNA target genes. A large fraction of the target genes exhibited antagonistic correlation with miRNA expression. The sets of co-expressed miRNAs showing differential expression between the two cultivars were recognized. Known transcription factor (TF) encoding genes involved in seed size/weight determination, including SPL, GRF, MYB, ARF, HAIKU1, SHB1, KLUH/CYP78A5, and E2Fb along with novel genes were found to be targeted by the predicted miRNAs. Differential expression analysis revealed higher transcript levels of members of SPL and REVOLUTA TF families and lower expression of their corresponding miRNAs in the large-seeded cultivar. At least 19 miRNAs known to be involved in seed development or differentially expressed between small-seeded and large-seeded cultivars at late-embryogenesis and/or mid-maturation stages were located within known quantitative trait loci (QTLs) associated with seed size/weight determination. Moreover, 41 target genes of these miRNAs were also located within these QTLs. Altogether, we revealed important roles of miRNAs in seed development and identified candidate miRNAs and their target genes that have functional relevance in determining seed size/weight in chickpea.

MutMap Approach Enables Rapid Identification of Candidate Genes and Development of Markers Associated With Early Flowering and Enhanced Seed Size in Chickpea (Cicer arietinum L.)

Globally terminal drought is one of the major constraints to chickpea (Cicer arietinum L.) production. Early flowering genotypes escape terminal drought, and the increase in seed size compensates for yield losses arising from terminal drought. A MutMap population for early flowering and large seed size was developed by crossing the mutant line ICC4958-M3-2828 with wild-type ICC 4958. Based on the phenotyping of MutMap population, extreme bulks for days to flowering and 100-seed weight were sequenced using Hi-Seq2500 at 10X coverage. On aligning 47.41 million filtered reads to the CDC Frontier reference genome, 31.41 million reads were mapped and 332,395 single nucleotide polymorphisms (SNPs) were called. A reference genome assembly for ICC 4958 was developed replacing these SNPs in particular positions of the CDC Frontier genome. SNPs specific for each mutant bulk ranged from 3,993 to 5,771. We report a single unique genomic region on Ca6 (between 9.76 and 12.96 Mb) harboring 31, 22, 17, and 32 SNPs with a peak of SNP index = 1 for low bulk for flowering time, high bulk for flowering time, high bulk for 100-seed weight, and low bulk for 100-seed weight, respectively. Among these, 22 SNPs are present in 20 candidate genes and had a moderate allelic impact on the genes. Two markers, Ca6EF10509893 for early flowering and Ca6HSDW10099486 for 100-seed weight, were developed and validated using the candidate SNPs. Thus, the associated genes, candidate SNPs, and markers developed in this study are useful for breeding chickpea varieties that mitigate yield losses under drought stress.

Soybean BARCSoySNP6K: An assay for soybean genetics and breeding research

The limited number of recombinant events in recombinant inbred lines suggests that for a biparental population with a limited number of recombinant inbred lines, it is unnecessary to genotype the lines with many markers. For genomic prediction and selection, previous studies have demonstrated that only 1000-2000 genome-wide common markers across all lines/accessions are needed to reach maximum efficiency of genomic prediction in populations. Evaluation of too many markers will not only increase the cost but also generate redundant information. We developed a soybean (Glycine max) assay, BARCSoySNP6K, containing 6000 markers, which were carefully chosen from the SoySNP50K assay based on their position in the soybean genome and haplotype block, polymorphism among accessions and genotyping quality. The assay includes 5000 single nucleotide polymorphisms (SNPs) from euchromatic and 1000 from heterochromatic regions. The percentage of SNPs with minor allele frequency >0.10 was 95% and 91% in the euchromatic and heterochromatic regions, respectively. Analysis of progeny from two large families genotyped with SoySNP50K versus BARCSoySNP6K showed that the position of the common markers and number of unique bins along linkage maps were consistent based on the SNPs genotyped with the two assays; however, the rate of redundant markers was dramatically reduced with the BARCSoySNP6K. The BARCSoySNP6K assay is proven as an excellent tool for detecting quantitative trait loci, genomic selection and assessing genetic relationships. The assay is commercialized by Illumina Inc. and being used by soybean breeders and geneticists and the list of SNPs in the assay is an ideal resource for SNP genotyping by targeted amplicon sequencing.

Genome-Wide SNP Discovery and Mapping QTLs for Seed Iron and Zinc Concentrations in Chickpea (Cicer arietinum L.)

Biofortification through plant breeding is a cost-effective and sustainable approach towards addressing micronutrient malnutrition prevailing across the globe. Screening cultivars for micronutrient content and identification of quantitative trait loci (QTLs)/genes and markers help in the development of biofortified varieties in chickpea (Cicer arietinum L.). With the aim of identifying the genomic regions controlling seed Fe and Zn concentrations, the F2:3 population derived from a cross between MNK-1 and Annigeri 1 was genotyped using genotyping by sequencing approach and evaluated for Fe and Zn concentration. An intraspecific genetic linkage map comprising 839 single nucleotide polymorphisms (SNPs) spanning a total distance of 1,088.04 cM with an average marker density of 1.30 cM was constructed. By integrating the linkage map data with the phenotypic data of the F2:3 population, a total of 11 QTLs were detected for seed Fe concentration on CaLG03, CaLG04, and CaLG05, with phenotypic variation explained ranging from 7.2% (CaqFe3.4) to 13.4% (CaqFe4.2). For seed Zn concentration, eight QTLs were identified on CaLG04, CaLG05, and CaLG08. The QTLs individually explained phenotypic variations ranging between 5.7% (CaqZn8.1) and 13.7% (CaqZn4.3). Three QTLs for seed Fe and Zn concentrations (CaqFe4.4, CaqFe4.5, and CaqZn4.1) were colocated in the "QTL-hotspot" region on CaLG04 that harbors several drought tolerance-related QTLs. We identified genes in the QTL regions that encode iron-sulfur metabolism and zinc-dependent alcohol dehydrogenase activity on CaLG03, iron ion binding oxidoreductase on CaLG04, and zinc-induced facilitator-like protein and ZIP zinc/iron transport family protein on CaLG05. These genomic regions and the associated markers can be used in marker-assisted selection to increase seed Fe and Zn concentrations in agronomically superior chickpea varieties.

Next Generation Sequencing Based Forward Genetic Approaches for Identification and Mapping of Causal Mutations in Crop Plants: A Comprehensive Review

The recent advancements in forward genetics have expanded the applications of mutation techniques in advanced genetics and genomics, ahead of direct use in breeding programs. The advent of next-generation sequencing (NGS) has enabled easy identification and mapping of causal mutations within a short period and at relatively low cost. Identifying the genetic mutations and genes that underlie phenotypic changes is essential for understanding a wide variety of biological functions. To accelerate the mutation mapping for crop improvement, several high-throughput and novel NGS based forward genetic approaches have been developed and applied in various crops. These techniques are highly efficient in crop plants, as it is relatively easy to grow and screen thousands of individuals. These approaches have improved the resolution in quantitative trait loci (QTL) position/point mutations and assisted in determining the functional causative variations in genes. To be successful in the interpretation of NGS data, bioinformatics computational methods are critical elements in delivering accurate assembly, alignment, and variant detection. Numerous bioinformatics tools/pipelines have been developed for such analysis. This article intends to review the recent advances in NGS based forward genetic approaches to identify and map the causal mutations in the crop genomes. The article also highlights the available bioinformatics tools/pipelines for reducing the complexity of NGS data and delivering the concluding outcomes.

A high-density SNP-based linkage map using genotyping-by-sequencing and its utilization for improved genome assembly of chickpea (Cicer arietinum L.)

Genotyping-by-sequencing (GBS) allows rapid identification of markers for use in development of linkage maps, which expedite efficient breeding programs. In the present study, we have utilized GBS approach to identify and genotype single-nucleotide polymorphism (SNP) markers in an inter-specific RIL population of Cicer arietinum L. X C. reticulatum. A total of 141,639 raw SNPs were identified using the TASSEL-GBS pipeline. After stringent filtering, 8208 candidate SNPs were identified of which ~ 37% were localized in the intragenic regions followed by genic regions (~ 30%) and intergenic regions (~ 27%). We then utilized 6920 stringent selected SNPs from present study and 6714 SNPs and microsatellite markers available from previous studies for construction of linkage map. The resulting high-density linkage map comprising of eight linkage groups contained 13,590 markers which spanned 1299.14 cM of map length with an average marker density of 0.095 cM. Further, the derived linkage map was used to improve the available assembly of desi chickpea genome by anchoring 443 previously unplaced scaffolds onto eight linkage groups. The present efforts have refined anchoring of the desi chickpea genome assembly to 55.57% of the ~ 520 Mb of assembled desi genome. To the best of our knowledge, the linkage map generated in the present study represents one of the most dense linkage map developed for the crop till date. It will serve as a valuable resource for fine mapping and positional cloning of important quantitative trait loci (QTLs) associated with agronomical traits and also for anchoring and ordering of future genome sequence assemblies.

DNA methylation reprogramming during seed development and its functional relevance in seed size/weight determination in chickpea

Seed development is orchestrated via complex gene regulatory networks and pathways. Epigenetic factors may also govern seed development and seed size/weight. Here, we analyzed DNA methylation in a large-seeded chickpea cultivar (JGK 3) during seed development stages. Progressive gain of CHH context DNA methylation in transposable elements (TEs) and higher frequency of small RNAs in hypermethylated TEs during seed development suggested a role of the RNA-dependent DNA methylation pathway. Frequency of intragenic TEs was higher in CHH context differentially methylated region (DMR) associated differentially expressed genes (DEGs). CG context hyper/hypomethylation within the gene body was observed for most of DMR-associated DEGs in JGK 3 as compared to small-seeded chickpea cultivar (Himchana 1). We identified candidate genes involved in seed size/weight determination exhibiting CG context hypermethylation within the gene body and higher expression in JGK 3. This study provides insights into the role of DNA methylation in seed development and seed size/weight determination in chickpea.

Rajkumar et al. report progressive gain of CHH context DNA methylation in transposable elements during seed development in chickpea, of which hypermethylation is associated with small RNAs. The candidate genes that determine seed size/weight in chickpea show CG context hypermethylation in the gene body and higher expression in large-seeded cultivar.

Integrating genomics for chickpea improvement: achievements and opportunities

Integration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts. The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.

Serendipitous In Situ Conservation of Faba Bean Landraces in Tunisia: A Case Study

Cultivation of faba bean (Vicia faba L.) in Tunisia is largely based on improved varieties of the crop. However, a few farmers continue to produce local cultivars or landraces. The National Gene Bank of Tunisia (NGBT) recently launched a collection project for faba bean landraces, with special focus on the regions of the North West, traditionally devoted to cultivating grain legumes, and where around 80% of the total national faba bean cultivation area is located. The seed phenotypic features of the collected samples were studied, and the genetic diversity and population structure analyzed using simple sequence repeat markers. The genetic constitution of the present samples was compared to that of faba bean samples collected by teams of the International Center for Agricultural Research in the Dry Areas (ICARDA) in the 1970s in the same region, and stored at the ICARDA gene bank. The results of the diversity analysis demonstrate that the recently collected samples and those stored at ICARDA largely overlap, thus demonstrating that over the past 50 years, little genetic change has occurred to the local faba bean populations examined. These findings suggest that farmers serendipitously applied international best practices for in situ conservation of agricultural crops.

Detection of significant SNP associated with production and oil quality traits in interspecific oil palm hybrids using RARSeq

A RARSeq based Association mapping study was performed in a population of 104 Elaeis oleifera x E. guineensis hybrids of five origins with the aim of finding functional markers associated to six productive and 19 oil quality traits. For this purpose mRNA of each genotype was isolated and double stranded cDNA was synthesized. Following digestion with two restriction enzymes and adapter ligation, a size selected pool of barcoded amplicons was produced and sequenced using Illumina MiSeq. The obtained sequences were processed with a "snakemake" pipeline, filtered and missing values were imputed. For all traits except two significant effects of the origin was observed. Genetic diversity analyses revealed high variability within origins and an excess of heterozygosity in the population. Two GLM models with Q matrix or PCA matrix as covariates and two MLM models incorporating in addition a Kinship matrix were tested for genotype-phenotype associations using GAPIT software. Using unadjusted p values (< 0.01) 78 potential associations were detected involving 25 SNP and 20 traits. When applying FDR multiple testing with p <  0.05, 25 significant associations remained involving eight SNP and six quality traits. Four SNP were located in genes with a potential relevant biological meaning.

Updates on Genomic Resources in Chickpea for Crop Improvement

In recent years, rapid advancement has been done in generation of genomic resources for the important legume crop chickpea. Here, we provide an update on important advancements made on availability of genomic resources for this crop. The availability of reference genome and transcriptome sequences, and resequencing of several accessions have enabled the discovery of gene space and molecular markers in chickpea. These resources have helped in elucidating evolutionary relationship and identification of quantitative trait loci for important agronomic traits. Gene expression in different tissues/organs during development and under abiotic/biotic stresses has been interrogated. In addition, single-base resolution DNA methylation patterns in different organs have been analyzed to understand gene regulation. Overall, we provide a consolidated overview of available genomic resources of chickpea that may help in fulfilling the promises for improvement of this important crop.

Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions.

Characterization of genetic diversity and population structure in wheat using array based SNP markers

Genetic diversity is crucial for successful adaptation and sustained improvement in crops. India is bestowed with diverse agro-climatic conditions which makes it rich in wheat germplasm adapted to various niches. Germplasm repository consists of local landraces, trait specific genetic stocks including introgressions from wild relatives, exotic collections, released varieties, and improved germplasm. Characterization of genetic diversity is done using morpho-physiological characters as well as by analyzing variations at DNA level. However, there are not many reports on array based high throughput SNP markers having characteristics of genome wide coverage employed in Indian spring wheat germplasm. Amongst wheat SNP arrays, 35K Axiom Wheat Breeder's Array has the highest SNP polymorphism efficiency suitable for genetic mapping and genetic diversity characterization. Therefore, genotyping was done using 35K in 483 wheat genotypes resulting in 14,650 quality filtered SNPs, that were distributed across the B (~ 50%), A (~ 39%), and D (~ 10%) genomes. The total genetic distance coverage was 4477.85 cM with 3.27 SNP/cM and 0.49 cM/SNP as average marker density and average inter-marker distance, respectively. The PIC ranged from 0.09 to 0.38 with an average of 0.29 across genomes. Population structure and Principal Coordinate Analysis resulted in two subpopulations (SP1 and SP2). The analysis of molecular variance revealed the genetic variation of 2% among and 98% within subpopulations indicating high gene flow between SP1 and SP2. The subpopulation SP2 showed high level of genetic diversity based on genetic diversity indices viz. Shannon's information index (I) = 0.648, expected heterozygosity (He) = 0.456 and unbiased expected heterozygosity (uHe) = 0.456. To the best of our knowledge, this study is the first to include the largest set of Indian wheat genotypes studied exclusively for genetic diversity. These findings may serve as a potential source for the identification of uncharacterized QTL/gene using genome wide association studies and marker assisted selection in wheat breeding programs.

Genotyping by sequencing can reveal the complex mosaic genomes in gene pools resulting from reticulate evolution: a case study in diploid and polyploid citrus

BACKGROUND AND AIMS

Reticulate evolution, coupled with reproductive features limiting further interspecific recombinations, results in admixed mosaics of large genomic fragments from the ancestral taxa. Whole-genome sequencing (WGS) data are powerful tools to decipher such complex genomes but still too costly to be used for large populations. The aim of this work was to develop an approach to infer phylogenomic structures in diploid, triploid and tetraploid individuals from sequencing data in reduced genome complexity libraries. The approach was applied to the cultivated Citrus gene pool resulting from reticulate evolution involving four ancestral taxa, C. maxima, C. medica, C. micrantha and C. reticulata.

METHODS

A genotyping by sequencing library was established with the restriction enzyme ApeKI applying one base (A) selection. Diagnostic single nucleotide polymorphisms (DSNPs) for the four ancestral taxa were mined in 29 representative varieties. A generic pipeline based on a maximum likelihood analysis of the number of read data was established to infer ancestral contributions along the genome of diploid, triploid and tetraploid individuals. The pipeline was applied to 48 diploid, four triploid and one tetraploid citrus accessions.

KEY RESULTS

Among 43 598 mined SNPs, we identified a set of 15 946 DSNPs covering the whole genome with a distribution similar to that of gene sequences. The set efficiently inferred the phylogenomic karyotype of the 53 analysed accessions, providing patterns for common accessions very close to that previously established using WGS data. The complex phylogenomic karyotypes of 21 cultivated citrus, including bergamot, triploid and tetraploid limes, were revealed for the first time.

CONCLUSIONS

The pipeline, available online, efficiently inferred the phylogenomic structures of diploid, triploid and tetraploid citrus. It will be useful for any species whose reproductive behaviour resulted in an interspecific mosaic of large genomic fragments. It can also be used for the first generations of interspecific breeding schemes.