Development and evaluation of high-density Axiom® CicerSNP Array for high-resolution genetic mapping and breeding applications in chickpea

Celebrating Professor Rajeev K. Varshney's transformative research odyssey from genomics to the field on his induction as Fellow of the Royal Society

Professor Rajeev K. Varshney's transformative impact on crop genomics, genetics, and agriculture is the result of his passion, dedication, and unyielding commitment to harnessing the potential of genomics to address the most pressing challenges faced by the global agricultural community. Starting from a small town in India and reaching the global stage, Professor Varshney's academic and professional trajectory has inspired many scientists active in research today. His ground-breaking work, especially his effort to list orphan tropical crops to genomic resource-rich entities, has been transformative. Beyond his scientific achievements, Professor Varshney is recognized by his colleagues as an exemplary mentor, fostering the growth of future researchers, building institutional capacity, and strengthening scientific capability. His focus on translational genomics and strengthening seed system in developing countries for the improvement of agriculture has made a tangible impact on farmers' lives. His skills have been best utilized in roles at leading research centres where he has applied his expertise to deliver a new vision for crop improvement. These efforts have now been recognized by the Royal Society with the award of the Fellowship (FRS). As we mark this significant milestone in his career, we not only celebrate Professor Varshney's accomplishments but also his wider contributions that continue to transform the agricultural landscape.

Development of a next generation SNP genotyping array for wheat

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

Natural alleles of Mediator subunit genes modulate plant height in chickpea

Plant height (PH) is an important plant architectural trait targeted during Green Revolution to enhance crop yields. Identification of genes and natural alleles governing plant height without compromising agronomic performance can fill the lacuna of knowledge connecting ideal plant architecture with maximum achievable yield in chickpea. Through coherent strategy involving genome‐wide association study, QTL/fine mapping, map‐based cloning, molecular haplotyping, and downstream functional genomics, the current study identified two Mediator subunit genes namely, CaMED23 and CaMED5b and their derived natural alleles/haplotypes underlying the major QTLs and trans‐acting eQTLs regulating plant height in chickpea. Differential accumulation of haplotype‐specific transcripts of these two Mediator genes in corresponding haplotype‐introgressed near‐isogenic lines (NILs) correlates negatively with the plant height trait. Quantitative as well as qualitative estimation based on histology, scanning electron microscopy, and histochemical assay unraveled the reduced lengths and cell sizes of internodes along with compromised lignin levels in dwarf/semi‐dwarf chickpea NILs introgressed with superior CaMED23 and CaMED5b gene haplotypes. This observation, supported by global transcriptome profiling‐based diminished expression of various phenylpropanoid pathway genes upstream of lignin biosynthesis in dwarf/semi‐dwarf NILs, essentially links plant height with lignin accumulation. The identified molecular signatures in the Mediator subunit genes can be efficiently utilized to develop desirable dwarf/semi‐dwarf‐type chickpea cultivars without affecting their yield per plant via modulating lignin/phenylpropanoid biosynthesis.

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.

A multiplexed plant-animal SNP array for selective breeding and species conservation applications

Reliable and high-throughput genotyping platforms are of immense importance for identifying and dissecting genomic regions controlling important phenotypes, supporting selection processes in breeding programs, and managing wild populations and germplasm collections. Amongst available genotyping tools, single nucleotide polymorphism arrays have been shown to be comparatively easy to use and generate highly accurate genotypic data. Single-species arrays are the most commonly used type so far; however, some multi-species arrays have been developed for closely related species that share single nucleotide polymorphism markers, exploiting inter-species cross-amplification. In this study, the suitability of a multiplexed plant-animal single nucleotide polymorphism array, including both closely and distantly related species, was explored. The performance of the single nucleotide polymorphism array across species for diverse applications, ranging from intra-species diversity assessments to parentage analysis, was assessed. Moreover, the value of genotyping pooled DNA of distantly related species on the single nucleotide polymorphism array as a technique to further reduce costs was evaluated. Single nucleotide polymorphism performance was generally high, and species-specific single nucleotide polymorphisms proved suitable for diverse applications. The multi-species single nucleotide polymorphism array approach reported here could be transferred to other species to achieve cost savings resulting from the increased throughput when several projects use the same array, and the pooling technique adds another highly promising advancement to additionally decrease genotyping costs by half.

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.

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).

Recent advancement in OMICS approaches to enhance abiotic stress tolerance in legumes

The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.

Genome-wide association analysis to delineate high-quality SNPs for seed micronutrient density in chickpea (Cicer arietinum L.)

Chickpea is the most important nutrient-rich grain legume crop in the world. A diverse core set of 147 chickpea genotypes was genotyped with a Axiom(®)50K CicerSNP array and trait phenotyped in two different environments for four seed micronutrients (Zn, Cu, Fe and Mn). The trait data and high-throughput 50K SNP genotypic data were used for the genome-wide association study (GWAS). The study led to the discovery of genes/QTLs for seed Zn, Cu, Fe and Mn, concentrations in chickpea. The analysis of seed micronutrient data revealed significant differences for all four micronutrient concentrations (P ≤ 0.05). The mean concentrations of seed Zn, Cu, Fe and Mn pooled over the 2 years were 45.9 ppm, 63.8 ppm 146.1 ppm, and 27.0 ppm, respectively. The analysis of results led to the identification of 35 SNPs significantly associated with seed Zn, Cu, Fe and Mn concentrations. Among these 35 marker-trait associations (MTAs), 5 were stable (consistently identified in different environments), 6 were major (explaining more than 15% of the phenotypic variation for an individual trait) and 3 were both major and stable MTAs. A set of 6 MTAs, MTAs (3 for Mn, 2 for Fe, and 1 for Cu) reported by us during the present study have been also reported in the same/almost same genomic regions in earlier studies and therefore declared as validated MTAs. The stable, major and validated MTAs identified during the present study will prove useful in future chickpea molecular breeding programs aimed at enhancing the seed nutrient density of chickpea.

Genetic variation in CaTIFY4b contributes to drought adaptation in chickpea

Chickpea production is vulnerable to drought stress. Identifying the genetic components underlying drought adaptation is crucial for enhancing chickpea productivity. Here, we present the fine mapping and characterization of 'QTL-hotspot', a genomic region controlling chickpea growth with positive consequences on crop production under drought. We report that a non-synonymous substitution in the transcription factor CaTIFY4b regulates seed weight and organ size in chickpea. Ectopic expression of CaTIFY4b in Medicago truncatula enhances root growth under water deficit. Our results suggest that allelic variation in 'QTL-hotspot' improves pre-anthesis water use, transpiration efficiency, root architecture and canopy development, enabling high-yield performance under terminal drought conditions. Gene expression analysis indicated that CaTIFY4b may regulate organ size under water deficit by modulating the expression of GRF-INTERACTING FACTOR1 (GIF1), a transcriptional co-activator of Growth-Regulating Factors. Taken together, our study offers new insights into the role of CaTIFY4b and on diverse physiological and molecular mechanisms underpinning chickpea growth and production under specific drought scenarios.

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.

Genetic Augmentation of Legume Crops Using Genomic Resources and Genotyping Platforms for Nutritional Food Security

Recent advances in next generation sequencing (NGS) technologies have led the surge of genomic resources for the improvement legume crops. Advances in high throughput genotyping (HTG) and high throughput phenotyping (HTP) enable legume breeders to improve legume crops more precisely and efficiently. Now, the legume breeder can reshuffle the natural gene combinations of their choice to enhance the genetic potential of crops. These genomic resources are efficiently deployed through molecular breeding approaches for genetic augmentation of important legume crops, such as chickpea, cowpea, pigeonpea, groundnut, common bean, lentil, pea, as well as other underutilized legume crops. In the future, advances in NGS, HTG, and HTP technologies will help in the identification and assembly of superior haplotypes to tailor the legume crop varieties through haplotype-based breeding. This review article focuses on the recent development of genomic resource databases and their deployment in legume molecular breeding programmes to secure global food security.

A Comprehensive Review on Chickpea (Cicer arietinum L.) Breeding for Abiotic Stress Tolerance and Climate Change Resilience

Chickpea is one of the most important pulse crops worldwide, being an excellent source of protein. It is grown under rain-fed conditions averaging yields of 1 t/ha, far from its potential of 6 t/ha under optimum conditions. The combined effects of heat, cold, drought, and salinity affect species productivity. In this regard, several physiological, biochemical, and molecular mechanisms are reviewed to confer tolerance to abiotic stress. A large collection of nearly 100,000 chickpea accessions is the basis of breeding programs, and important advances have been achieved through conventional breeding, such as germplasm introduction, gene/allele introgression, and mutagenesis. In parallel, advances in molecular biology and high-throughput sequencing have allowed the development of specific molecular markers for the genus Cicer, facilitating marker-assisted selection for yield components and abiotic tolerance. Further, transcriptomics, proteomics, and metabolomics have permitted the identification of specific genes, proteins, and metabolites associated with tolerance to abiotic stress of chickpea. Furthermore, some promising results have been obtained in studies with transgenic plants and with the use of gene editing to obtain drought-tolerant chickpea. Finally, we propose some future lines of research that may be useful to obtain chickpea genotypes tolerant to abiotic stress in a scenario of climate change.

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.

Translational Chickpea Genomics Consortium to Accelerate Genetic Gains in Chickpea (Cicer arietinum L.)

The Translational Chickpea Genomics Consortium (TCGC) was set up to increase the production and productivity of chickpea (Cicer arietinum L.). It represents research institutes from six major chickpea growing states (Madhya Pradesh, Maharashtra, Andhra Pradesh, Telangana, Karnataka and Uttar Pradesh) of India. The TCGC team has been engaged in deploying modern genomics approaches in breeding and popularizing improved varieties in farmers’ fields across the states. Using marker-assisted backcrossing, introgression lines with enhanced drought tolerance and fusarium wilt resistance have been developed in the genetic background of 10 elite varieties of chickpea. Multi-location evaluation of 100 improved lines (70 desi and 30 kabuli) during 2016–2017 and 2018–2019 enabled the identification of top performing desi and kabuli lines. In total, 909 Farmer Participatory Varietal Selection trials were conducted in 158 villages in 16 districts of the five states, during 2017–2018, 2018–2019, and 2019–2020, involving 16 improved varieties. New molecular breeding lines developed in different genetic backgrounds are potential candidates for national trials under the ICAR-All India Coordinated Research Project on Chickpea. The comprehensive efforts of TCGC resulted in the development and adoption of high-yielding varieties that will increase chickpea productivity and the profitability of chickpea growing farmers.

Development and application of the Faba_bean_130K targeted next-generation sequencing SNP genotyping platform based on transcriptome sequencing

KEY MESSAGE

Large-scale faba bean transcriptome data are available, and the first genotyping platform based on liquid-phase probe targeted capture technology was developed for genetic and molecular breeding studies. Faba bean (Vicia faba L., 2n = 12) is an important food legume crop that is widely grown for multiple uses worldwide. However, no reference genome is currently available due to its very large genome size (approximately 13 Gb) and limited single nucleotide polymorphism (SNP) markers as well as highly efficient genotyping tools have been reported for faba bean. In this study, 16.7 billion clean reads were obtained from transcriptome libraries of flowers and leaves of 102 global faba bean accessions. A total of 243,120 unigenes were de novo assembled and functionally annotated. Moreover, a total of 1,579,411 SNPs were identified and further filtered according to a selection pipeline to develop a high-throughput, flexible, low-cost Faba_bean_130K targeted next-generation sequencing (TNGS) genotyping platform. A set of 69 Chinese faba bean accessions were genotyped with the TNGS genotyping platform, and the average mapping rate of captured reads to reference transcripts was 93.14%, of which 53.23% were located in the targeted regions. The TNGS genotyping results were validated by Sanger sequencing and the average consistency rate reached 93.6%. Comprehensive population genetic analysis was performed on the 69 Chinese faba bean accessions and identified four genetic subgroups correlated with the geographic distribution. This study provides valuable genomic resources and a reliable genotyping tool that could be implemented in genetic and molecular breeding studies to accelerate new cultivar development and improvement in faba bean.

Genetic diversity analysis of Korean peanut germplasm using 48 K SNPs 'Axiom_Arachis' Array and its application for cultivar differentiation

Cultivated peanut (Arachis hypogaea) is one of the important legume oilseed crops. Cultivated peanut has a narrow genetic base. Therefore, it is necessary to widen its genetic base and diversity for additional use. The objective of the present study was to assess the genetic diversity and population structure of 96 peanut genotypes with 9478 high-resolution SNPs identified from a 48 K 'Axiom_Arachis' SNP array. Korean set genotypes were also compared with a mini-core of US genotypes. These sets of genotypes were used for genetic diversity analysis. Model-based structure analysis at K = 2 indicated the presence of two subpopulations in both sets of genotypes. Phylogenetic and PCA analysis clustered these genotypes into two major groups. However, clear genotype distribution was not observed for categories of subspecies, botanical variety, or origin. The analysis also revealed that current Korean genetic resources lacked variability compared to US mini-core genotypes. These results suggest that Korean genetic resources need to be expanded by creating new allele combinations and widening the genetic pool to offer new genetic variations for Korean peanut improvement programs. High-quality SNP data generated in this study could be used for identifying varietal contaminant, QTL, and genes associated with desirable traits by performing mapping, genome-wide association studies.

Root-omics for drought tolerance in cool-season grain legumes

Root traits can be exploited to increase the physiological efficiency of crop water use under drought. Root length, root hairs, root branching, root diameter, and root proliferation rate are genetically defined traits that can help to improve the water productivity potential of crops. Recently, high-throughput phenotyping techniques/platforms have been used to screen the germplasm of major cool-season grain legumes for root traits and their impact on different physiological processes, including nutrient uptake and yield potential. Advances in omics approaches have led to the dissection of genomic, proteomic, and metabolomic structures of these traits. This knowledge facilitates breeders to improve the water productivity and nutrient uptake of cultivars under limited soil moisture conditions in major cool-season grain legumes that usually face terminal drought. This review discusses the advances in root traits and their potential for developing drought-tolerant cultivars in cool-season grain legumes.

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.

Assessment of cold tolerance in chickpea (Cicer spp.) grown under cold/freezing weather conditions of North-Western Himalayas of Jammu and Kashmir, India

Chickpea is one of the most important grain legume crops in the world. India is the largest producer, consumer as well as importer of chickpea. Cold stress (temperature < 15 °C) is one of the important abiotic stresses limiting chickpea production by hampering its growth and vigor at all phenological stages. This study was aimed to characterize a diverse set of 366 chickpea genotypes for cold tolerance and identify most promising cold tolerant chickpea genotypes in the Western-Himalayas of Jammu and Kashmir, India. The 366 genotypes used during the present study including genotypes belonging to cultivated, primary and secondary gene pools of chickpea. Two important approaches were used including visual screening under field conditions and screening under controlled conditions by measuring cell membrane stability through electrolyte leakage tests. The analysis of trait data collected through both the approaches led to the identification of five most promising/candidate cold tolerant chickpea genotypes including one wild genotype "Ortan-066" from secondary gene pool species (C. echinospermum), one wild genotype "Cudi 1-022" from primary gene pool species (C. reticulatum) and three genotypes (IC 116783, ICC 15200 and AGBLG 170004) from the cultivated species (Cicer arietinum). Wild genotype "Ortan-066" was found best cold tolerance source with the mean Cold Tolerance Rating (CTR) of 2 and Electrolyte Leakage Index (ELI) of 10.82%, followed by wild genotype "Cudi 1-022" (CTR = 3, ELI = 18.89%), and three cultivated genotypes viz., IC 116783, ICC 15200 and AGBL-G-170004, with the mean CTR of 3 and an estimated mean ELI of 21.26%, 21.58% and 21.94%, respectively. The promising, candidate cold tolerant genotypes identified during the present study could be used in chickpea breeding programs aimed at improving cold tolerance of cultivated chickpea worldwide. The candidate lines can be also used for developing bi-parental mapping populations, wild × cultivated introgression lines, transcriptomics and for differential expression analysis of cold tolerant genes in chickpea.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-00997-1.

Integration of Infinium and Axiom SNP array data in the outcrossing species Malus × domestica and causes for seemingly incompatible calls

Background

Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls.

Results

Concordance among the duplicate individuals was on average of 97.1% across 10,295 SNPs. Of these SNPs, 35% had discordant call(s) that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe.

Conclusions

The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07565-7.

Introgression of "QTL-hotspot" region enhances drought tolerance and grain yield in three elite chickpea cultivars

With an aim of enhancing drought tolerance using a marker-assisted backcrossing (MABC) approach, we introgressed the "QTL-hotspot" region from ICC 4958 accession that harbors quantitative trait loci (QTLs) for several drought-tolerance related traits into three elite Indian chickpea (Cicer arietinum L.) cultivars: Pusa 372, Pusa 362, and DCP 92-3. Of eight simple sequence repeat (SSR) markers in the QTL-hotspot region, two to three polymorphic markers were used for foreground selection with respective cross-combinations. A total of 47, 53, and 46 SSRs were used for background selection in case of introgression lines (ILs) developed in genetic backgrounds of Pusa 372, Pusa 362, and DCP 92-3, respectively. In total, 61 ILs (20 BC₃ F₃ in Pusa 372; 20 BC₂ F₃ in Pusa 362, and 21 BC₃ F₃ in DCP 92-3), with >90% recurrent parent genome recovery were developed. Six improved lines in different genetic backgrounds (e.g. BGM 10216 in Pusa 372; BG 3097 and BG 4005 in Pusa 362; IPC(L4-14), IPC(L4-16), and IPC(L19-1) in DCP 92-3) showed better performance than their respective recurrent parents. BGM 10216, with 16% yield gain over Pusa 372, has been released as Pusa Chickpea 10216 by the Central Sub-Committees on Crop Standards, Notification and Release of Varieties of Agricultural Crops, Ministry of Agriculture and Farmers Welfare, Government of India, for commercial cultivation in India. In summary, this study reports introgression of the QTL-hotspot for enhancing yield under rainfed conditions, development of several introgression lines, and release of Pusa Chickpea 10216 developed through molecular breeding in India.

New resources for genetic studies in maize (Zea mays L.): a genome-wide Maize6H-60K single nucleotide polymorphism array and its application

Despite the availability of numerous molecular markers in maize, effective evaluation of all types of germplasm resources, accurate identification of varieties and analysis of a large number of materials in a timely, low-cost manner is challenging. Here, we present Maize6H-60K, a genome-wide single nucleotide polymorphism (SNP) array to facilitate maize genotyping. We first identified 160 million variants by sequencing data of 388 representative inbreds and then tiled 200 000 high-quality variants on a screening array. These variants were further narrowed down to 61 282 using stringent filtering criteria. Among the 60 000 markers, 21 460 SNPs (35%) were within genic regions and 12 835 (21%) were located in coding regions. To assess their effectiveness, 329 inbreds, 221 hybrids, 34 parent-offspring sets and six breeding samples were genotyped. Overall, 48 972 SNPs (80%) were categorized into the highest quality class, that of 'poly high resolution'. A total of 54 658 (89.29%) and 53 091 (86.73%) SNPs had minor allele frequency values ≥ 0.20 in inbreds and hybrids respectively. A linkage disequilibrium (LD) analysis revealed that LD decline was in equilibrium when r² was between 0.10 and 0.15, which corresponds to a physical distance of 400-600 kb. UPGMA clustering analysis divided the 329 inbred lines into nine groups that were consistent with known pedigrees. A background analysis of breeding materials indicated that the 60 000 markers were suitable for evaluation of breeding populations constructed by materials between or within heterotic groups. The developed Maize6H-60K array should be an important tool in maize genetic studies, variety identification and molecular breeding.

Genomic resources in plant breeding for sustainable agriculture

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

Development of a highly efficient 50K single nucleotide polymorphism genotyping array for the large and complex genome of Norway spruce (Picea abies L. Karst) by whole genome resequencing and its transferability to other spruce species

Norway spruce (Picea abies L. Karst) is one of the most important forest tree species with significant economic and ecological impact in Europe. For decades, genomic and genetic studies on Norway spruce have been challenging due to the large and repetitive genome (19.6 Gb with more than 70% being repetitive). To accelerate genomic studies, including population genetics, genome‐wide association studies (GWAS) and genomic selection (GS), in Norway spruce and related species, we here report on the design and performance of a 50K single nucleotide polymorphism (SNP) genotyping array for Norway spruce. The array is developed based on whole genome resequencing (WGS), making it the first WGS‐based SNP array in any conifer species so far. After identifying SNPs using genome resequencing data from 29 trees collected in northern Europe, we adopted a two‐step approach to design the array. First, we built a 450K screening array and used this to genotype a population of 480 trees sampled from both natural and breeding populations across the Norway spruce distribution range. These samples were then used to select high‐confidence probes that were put on the final 50K array. The SNPs selected are distributed over 45,552 scaffolds from the P. abies version 1.0 genome assembly and target 19,954 unique gene models with an even coverage of the 12 linkage groups in Norway spruce. We show that the array has a 99.5% probe specificity, >98% Mendelian allelic inheritance concordance, an average sample call rate of 96.30% and an SNP call rate of 98.90% in family trios and haploid tissues. We also observed that 23,797 probes (50%) could be identified with high confidence in three other spruce species (white spruce [Picea glauca], black spruce [P. mariana] and Sitka spruce [P. sitchensis]). The high‐quality genotyping array will be a valuable resource for genetic and genomic studies in Norway spruce as well as in other conifer species of the same genus.

Development of a dense genetic map and QTL analysis for pod borer Helicoverpa armigera (Hübner) resistance component traits in chickpea (Cicer arietinum L.)

Genetic enhancement for resistance against the pod borer, Helicoverpa armigera is crucial for enhancing production and productivity of chickpea. Here we provide some novel insights into the genetic architecture of natural variation in H. armigera resistance in chickpea, an important legume, which plays a major role in food and nutritional security. An interspecific recombinant inbred line (RIL) population developed from a cross between H. armigera susceptible accession ICC 4958 (Cicer arietinum) and resistant accession PI 489777 (Cicer reticulatum) was evaluated for H. armigera resistance component traits using detached leaf assay and under field conditions. A high-throughput AxiomCicerSNP array was utilized to construct a dense linkage map comprising of 3,873 loci and spanning a distance of 949.27 cM. Comprehensive analyses of extensive genotyping and phenotyping data identified nine main-effect QTLs and 955 epistatic QTLs explaining up to 42.49% and 38.05% phenotypic variance, respectively, for H. armigera resistance component traits. The main-effect QTLs identified in this RIL population were linked with previously described genes, known to modulate resistance against lepidopteran insects in crop plants. One QTL cluster harbouring main-effect QTLs for three H. armigera resistance component traits and explaining up to 42.49% of the phenotypic variance, was identified on CaLG03. This genomic region, after validation, may be useful to improve H. armigera resistance component traits in elite chickpea cultivars.

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.

Genetic Dissection and Identification of Candidate Genes for Salinity Tolerance Using Axiom®CicerSNP Array in Chickpea

Globally, chickpea production is severely affected by salinity stress. Understanding the genetic basis for salinity tolerance is important to develop salinity tolerant chickpeas. A recombinant inbred line (RIL) population developed using parental lines ICCV 10 (salt-tolerant) and DCP 92-3 (salt-sensitive) was screened under field conditions to collect information on agronomy, yield components, and stress tolerance indices. Genotyping data generated using Axiom^®CicerSNP array was used to construct a linkage map comprising 1856 SNP markers spanning a distance of 1106.3 cM across eight chickpea chromosomes. Extensive analysis of the phenotyping and genotyping data identified 28 quantitative trait loci (QTLs) explaining up to 28.40% of the phenotypic variance in the population. We identified QTL clusters on CaLG03 and CaLG06, each harboring major QTLs for yield and yield component traits under salinity stress. The main-effect QTLs identified in these two clusters were associated with key genes such as calcium-dependent protein kinases, histidine kinases, cation proton antiporter, and WRKY and MYB transcription factors, which are known to impart salinity stress tolerance in crop plants. Molecular markers/genes associated with these major QTLs, after validation, will be useful to undertake marker-assisted breeding for developing better varieties with salinity tolerance.

Nested-association mapping (NAM)-based genetic dissection uncovers candidate genes for seed and pod weights in peanut (Arachis hypogaea)

Multiparental genetic mapping populations such as nested-association mapping (NAM) have great potential for investigating quantitative traits and associated genomic regions leading to rapid discovery of candidate genes and markers. To demonstrate the utility and power of this approach, two NAM populations, NAM_Tifrunner and NAM_Florida-07, were used for dissecting genetic control of 100-pod weight (PW) and 100-seed weight (SW) in peanut. Two high-density SNP-based genetic maps were constructed with 3341 loci and 2668 loci for NAM_Tifrunner and NAM_Florida-07, respectively. The quantitative trait locus (QTL) analysis identified 12 and 8 major effect QTLs for PW and SW, respectively, in NAM_Tifrunner, and 13 and 11 major effect QTLs for PW and SW, respectively, in NAM_Florida-07. Most of the QTLs associated with PW and SW were mapped on the chromosomes A05, A06, B05 and B06. A genomewide association study (GWAS) analysis identified 19 and 28 highly significant SNP-trait associations (STAs) in NAM_Tifrunner and 11 and 17 STAs in NAM_Florida-07 for PW and SW, respectively. These significant STAs were co-localized, suggesting that PW and SW are co-regulated by several candidate genes identified on chromosomes A05, A06, B05, and B06. This study demonstrates the utility of NAM population for genetic dissection of complex traits and performing high-resolution trait mapping in peanut.

Fine mapping and gene cloning in the post-NGS era: advances and prospects

Improvement in traits of agronomic importance is the top breeding priority of crop improvement programs. Majority of these agronomic traits show complex quantitative inheritance. Identification of quantitative trait loci (QTLs) followed by fine mapping QTLs and cloning of candidate genes/QTLs is central to trait analysis. Advances in genomic technologies revolutionized our understanding of genetics of complex traits, and genomic regions associated with traits were employed in marker-assisted breeding or cloning of QTLs/genes. Next-generation sequencing (NGS) technologies have enabled genome-wide methodologies for the development of ultra-high-density genetic linkage maps in different crops, thus allowing placement of candidate loci within few kbs in genomes. In this review, we compare the marker systems used for fine mapping and QTL cloning in the pre- and post-NGS era. We then discuss how different NGS platforms in combination with advanced experimental designs have improved trait analysis and fine mapping. We opine that efficient genotyping/sequencing assays may circumvent the need for cumbersome procedures that were earlier used for fine mapping. A deeper understanding of the trait architectures of agricultural significance will be crucial to accelerate crop improvement.

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.

Developing Climate-Resilient Chickpea Involving Physiological and Molecular Approaches With a Focus on Temperature and Drought Stresses

Chickpea is one of the most economically important food legumes, and a significant source of proteins. It is cultivated in more than 50 countries across Asia, Africa, Europe, Australia, North America, and South America. Chickpea production is limited by various abiotic stresses (cold, heat, drought, salt, etc.). Being a winter-season crop in northern south Asia and some parts of the Australia, chickpea faces low-temperature stress (0-15°C) during the reproductive stage that causes substantial loss of flowers, and thus pods, to inhibit its yield potential by 30-40%. The winter-sown chickpea in the Mediterranean, however, faces cold stress at vegetative stage. In late-sown environments, chickpea faces high-temperature stress during reproductive and pod filling stages, causing considerable yield losses. Both the low and the high temperatures reduce pollen viability, pollen germination on the stigma, and pollen tube growth resulting in poor pod set. Chickpea also experiences drought stress at various growth stages; terminal drought, along with heat stress at flowering and seed filling can reduce yields by 40-45%. In southern Australia and northern regions of south Asia, lack of chilling tolerance in cultivars delays flowering and pod set, and the crop is usually exposed to terminal drought. The incidences of temperature extremes (cold and heat) as well as inconsistent rainfall patterns are expected to increase in near future owing to climate change thereby necessitating the development of stress-tolerant and climate-resilient chickpea cultivars having region specific traits, which perform well under drought, heat, and/or low-temperature stress. Different approaches, such as genetic variability, genomic selection, molecular markers involving quantitative trait loci (QTLs), whole genome sequencing, and transcriptomics analysis have been exploited to improve chickpea production in extreme environments. Biotechnological tools have broadened our understanding of genetic basis as well as plants' responses to abiotic stresses in chickpea, and have opened opportunities to develop stress tolerant chickpea.

Segregation distortion: Utilizing simulated genotyping data to evaluate statistical methods

Segregation distortion is the phenomenon in which genotypes deviate from expected Mendelian ratios in the progeny of a cross between two varieties or species. There is not currently a widely used consensus for the appropriate statistical test, or more specifically the multiple testing correction procedure, used to detect segregation distortion for high-density single-nucleotide polymorphism (SNP) data. Here we examine the efficacy of various multiple testing procedures, including chi-square test with no correction for multiple testing, false-discovery rate correction and Bonferroni correction using an in-silico simulation of a biparental mapping population. We find that the false discovery rate correction best approximates the traditional p-value threshold of 0.05 for high-density marker data. We also utilize this simulation to test the effect of segregation distortion on the genetic mapping process, specifically on the formation of linkage groups during marker clustering. Only extreme segregation distortion was found to effect genetic mapping. In addition, we utilize replicate empirical mapping populations of wheat varieties Avalon and Cadenza to assess how often segregation distortion conforms to the same pattern between closely related wheat varieties.

An Axiom SNP genotyping array for Douglas-fir

BACKGROUND

In forest trees, genetic markers have been used to understand the genetic architecture of natural populations, identify quantitative trait loci, infer gene function, and enhance tree breeding. Recently, new, efficient technologies for genotyping thousands to millions of single nucleotide polymorphisms (SNPs) have finally made large-scale use of genetic markers widely available. These methods will be exceedingly valuable for improving tree breeding and understanding the ecological genetics of Douglas-fir, one of the most economically and ecologically important trees in the world.

RESULTS

We designed SNP assays for 55,766 potential SNPs that were discovered from previous transcriptome sequencing projects. We tested the array on ~ 2300 related and unrelated coastal Douglas-fir trees (Pseudotsuga menziesii var. menziesii) from Oregon and Washington, and 13 trees of interior Douglas-fir (P. menziesii var. glauca). As many as ~ 28 K SNPs were reliably genotyped and polymorphic, depending on the selected SNP call rate. To increase the number of SNPs and improve genome coverage, we developed protocols to 'rescue' SNPs that did not pass the default Affymetrix quality control criteria (e.g., 97% SNP call rate). Lowering the SNP call rate threshold from 97 to 60% increased the number of successful SNPs from 20,669 to 28,094. We used a subset of 395 unrelated trees to calculate SNP population genetic statistics for coastal Douglas-fir. Over a range of call rate thresholds (97 to 60%), the median call rate for SNPs in Hardy-Weinberg equilibrium ranged from 99.2 to 99.7%, and the median minor allele frequency ranged from 0.198 to 0.233. The successful SNPs also worked well on interior Douglas-fir.

CONCLUSIONS

Based on the original transcriptome assemblies and comparisons to version 1.0 of the Douglas-fir reference genome, we conclude that these SNPs can be used to genotype about 10 K to 15 K loci. The Axiom genotyping array will serve as an excellent foundation for studying the population genomics of Douglas-fir and for implementing genomic selection. We are currently using the array to construct a linkage map and test genomic selection in a three-generation breeding program for coastal Douglas-fir.

Mapping Quantitative Trait Loci for Carotenoid Concentration in Three F2 Populations of Chickpea

Quantitative trait locus (QTL) analyses for carotenoids in chickpea were completed for three F₂ populations. A moderate number of QTLs and candidate genes associated with carotenoid concentration in chickpea seeds were identified. Green cotyledon color is positively associated with provitamin A carotenoids. Three F₂ populations derived from crosses between cultivars with green and yellow cotyledon colors were used to identify quantitative trait loci (QTLs) associated with carotenoid components in chickpea (Cicer arietinum L.) seeds developed by the Crop Development Centre (CDC). Carotenoids including violaxanthin, lutein, zeaxanthin, β-cryptoxanthin, and β-carotene were assessed in the F_2:3 seeds via high-performance liquid chromatography (HPLC). In the 'CDC Jade' × 'CDC Frontier' population, 1068 bin markers derived from the 50K Axiom CicerSNP array were mapped onto eight linkage groups (LGs). Eight QTLs, including two each for β-carotene and zeaxanthin and one each for total carotenoids, β-cryptoxanthin, β-carotene, and violaxanthin were identified in this population. In the 'CDC Cory' × 'CDC Jade' population, 694 bin markers were mapped onto eight LGs and one partial LG. Quantitative trait loci for β-cryptoxanthin, β-carotene, violaxanthin, lutein, and total carotenoids were identified on LG8. A map with eight LGs was developed from 581 bin markers in the third population derived from the 'ICC4475' × 'CDC Jade' cross. One QTL for β-carotene and four QTLs, one each for β-cryptoxanthin, β-carotene, lutein, and total carotenoids, were identified in this population. The highest phenotypic variation explained by the QTLs was for β-carotene, which ranged from 58 to 70% in all three populations. A major gene for cotyledon color was mapped on LG8 in each population. A significant positive correlation between cotyledon color and carotenoid concentration was observed. Potential candidate genes associated with carotenoid components were obtained and their locations on the chickpea genome are presented.

Insight Into the Prospects for the Improvement of Seed Starch in Legume-A Review

In addition to proteins and/or oils, mature seeds of most legume crops contain important carbohydrate components, including starches and sugars. Starch is also an essential nutritional component of human and animal diets and has various food and non-food industrial applications. Starch is a primary insoluble polymeric carbohydrate produced by higher plants and consists of amylose and amylopectin as a major fraction. Legume seeds are an affordable source of not only protein but also the starch, which has an advantage of being resistant starch compared with cereal, root, and tuber starch. For these reasons, legume seeds form a good source of resistant starch-rich healthy food with a high protein content and can be utilized in various food applications. The genetics and molecular details of starch and other carbohydrate components are well studied in cereal crops but have received little attention in legumes. In order to improve legume starch content, quality, and quantity, it is necessary to understand the genetic and molecular factors regulating carbohydrate metabolism in legume crops. In this review, we assessed the current literature reporting the genetic and molecular basis of legume carbohydrate components, primarily focused on seed starch content. We provided an overview of starch biosynthesis in the heterotrophic organs, the chemical composition of major consumable legumes, the factors influencing starch digestibility, and advances in the genetic, transcriptomic, and metabolomic studies in important legume crops. Further, we discussed breeding and biotechnological approaches for the improvement of the starch composition in major legume crops. The information reviewed in this study will be helpful in facilitating the food and non-food applications of legume starch and provide economic benefits to farmers and industries.

Development of an Axiom Sugarcane100K SNP array for genetic map construction and QTL identification

An Axiom Sugarcane100K SNP array has been designed and successfully utilized to construct the sugarcane genetic map and to identify the QTLs associated with SCYLV resistance. To accelerate genetic studies in sugarcane, an Axiom Sugarcane100K single-nucleotide polymorphism (SNP) array was designed and customized in this study. Target enrichment sequencing 300 sugarcane accessions selected from the world collection of sugarcane and related grass species yielded more than four million SNPs, from which a total of 31,449 single-dose (SD) SNPs and 68,648 low-dosage (33,277 SD and 35,371 double dose) SNPs from two datasets, respectively, were selected and tiled on Affymetrix Axiom SNP array. Most of selected SNPs (91.77%) were located within genic regions (12,935 genes), with an average of 7.1 SNPs/gene according to sorghum gene models. This array was used to genotype 469 sugarcane clones, including one F₁ population derived from the cross between Green German and IND81-146, one selfing population derived from CP80-1827, and 11 diverse sugarcane accessions as controls. Results of genotyping revealed a high polymorphic SNP rate (77.04%) among the 469 samples. Three linkage maps were constructed by using SD SNP markers, including a genetic map for Green German with 3482 SD SNP markers spanning 3336 cM, a map for IND81-146 with 1513 SD SNP markers spanning 2615 cM, and a map for CP80-1827 with 536 SD SNP markers spanning 3651 cM. Quantitative trait loci (QTL) analysis identified 18 QTLs controlling Sugarcane yellow leaf virus resistance segregating in the two mapping populations, harboring 27 disease-resistant genes. This study demonstrated the successful development and utilization of a SNP array as an efficient genetic tool for high-throughput genotyping in highly polyploid sugarcane.

Genomics, genetics and breeding of tropical legumes for better livelihoods of smallholder farmers

Legumes are important components of sustainable agricultural production, food, nutrition and income systems of developing countries. In spite of their importance, legume crop production is challenged by a number of biotic (diseases and pests) and abiotic stresses (heat, frost, drought and salinity), edaphic factors (associated with soil nutrient deficits) and policy issues (where less emphasis is put on legumes compared to priority starchy staples). Significant research and development work have been done in the past decade on important grain legumes through collaborative bilateral and multilateral projects as well as the CGIAR Research Program on Grain Legumes (CRP-GL). Through these initiatives, genomic resources and genomic tools such as draft genome sequence, resequencing data, large-scale genomewide markers, dense genetic maps, quantitative trait loci (QTLs) and diagnostic markers have been developed for further use in multiple genetic and breeding applications. Also, these mega-initiatives facilitated release of a number of new varieties and also dissemination of on-the-shelf varieties to the farmers. More efforts are needed to enhance genetic gains by reducing the time required in cultivar development through integration of genomics-assisted breeding approaches and rapid generation advancement.

Resistance to Plant-Parasitic Nematodes in Chickpea: Current Status and Future Perspectives

Plant-parasitic nematodes constrain chickpea (Cicer arietinum) production, with annual yield losses estimated to be 14% of total global production. Nematode species causing significant economic damage in chickpea include root-knot nematodes (Meloidogyne artiella, M. incognita, and M. javanica), cyst nematode (Heterodera ciceri), and root-lesion nematode (Pratylenchus thornei). Reduced functionality of roots from nematode infestation leads to water stress and nutrient deficiency, which in turn lead to poor plant growth and reduced yield. Integration of resistant crops with appropriate agronomic practices is recognized as the safest and most practical, economic and effective control strategy for plant-parasitic nematodes. However, breeding for resistance to plant-parasitic nematodes has numerous challenges that originate from the narrow genetic diversity of the C. arietinum cultigen. While levels of resistance to M. artiella, H. ciceri, and P. thornei have been identified in wild Cicer species that are superior to resistance levels in the C. arietinum cultigen, barriers to interspecific hybridization restrict the use of these crop wild relatives, as sources of nematode resistance. Wild Cicer species of the primary genepool, C. reticulatum and C. echinospermum, are the only species that have been used to introgress resistance genes into the C. arietinum cultigen. The availability of genomic resources, including genome sequence and re-sequence information, the chickpea reference set and mini-core collections, and new wild Cicer collections, provide unprecedented opportunities for chickpea improvement. This review surveys progress in the identification of novel genetic sources of nematode resistance in international germplasm collections and recommends genome-assisted breeding strategies to accelerate introgression of nematode resistance into elite chickpea cultivars.

A new genomic tool for walnut (Juglans regia L.): development and validation of the high-density Axiom™ J. regia 700K SNP genotyping array

Over the last 20 years, global production of Persian walnut (Juglans regia L.) has grown enormously, likely reflecting increased consumption due to its numerous benefits to human health. However, advances in genome-wide association (GWA) studies and genomic selection (GS) for agronomically important traits in walnut remain limited due to the lack of powerful genomic tools. Here, we present the development and validation of a high-density 700K single nucleotide polymorphism (SNP) array in Persian walnut. Over 609K high-quality SNPs have been thoroughly selected from a set of 9.6 m genome-wide variants, previously identified from the high-depth re-sequencing of 27 founders of the Walnut Improvement Program (WIP) of University of California, Davis. To validate the effectiveness of the array, we genotyped a collection of 1284 walnut trees, including 1167 progeny of 48 WIP families and 26 walnut cultivars. More than half of the SNPs (55.7%) fell in the highest quality class of 'Poly High Resolution' (PHR) polymorphisms, which were used to assess the WIP pedigree integrity. We identified 151 new parent-offspring relationships, all confirmed with the Mendelian inheritance test. In addition, we explored the genetic variability among cultivars of different origin, revealing how the varieties from Europe and California were differentiated from Asian accessions. Both the reconstruction of the WIP pedigree and population structure analysis confirmed the effectiveness of the Applied Biosystems™ Axiom™ J. regia 700K SNP array, which initiates a novel genomic and advanced phase in walnut genetics and breeding.

Genome-wide SNP discovery for development of high-density genetic map and QTL mapping of ascochyta blight resistance in chickpea (Cicer arietinum L.)

A high-density linkage map of chickpea using 3430 SNPs was constructed and used to identify QTLs and candidate genes for ascochyta blight resistance in chickpea. Chickpea cultivation in temperate conditions is highly vulnerable to ascochyta blight infection. Cultivation of resistant cultivars in combination with fungicide application within an informed disease management package is the most effective method to control ascochyta blight in chickpeas. Identifying new sources of resistance is critical for continued improvement in ascochyta blight resistance in chickpea. The objective of this study was to identify genetic loci and candidate genes controlling the resistance to ascochyta blight in recombinant inbred lines derived from crossing cultivars Amit and ICCV 96029. The RILs were genotyped using the genotyping-by-sequencing procedure and Illumina^® GoldenGate array. The RILs were evaluated in the field over three site-years and in three independent greenhouse experiments. A genetic map with eight linkage groups was constructed using 3430 SNPs. Eight QTLs for resistance were identified on chromosomes 2, 3, 4, 5 and 6. The QTLs individually explained 7-40% of the phenotypic variations. The QTLs on chromosomes 2 and 6 were associated with the resistance at vegetative stage only. The QTLs on chromosomes 2 and 4 that were previously reported to be conserved across diverse genetic backgrounds and against different isolates of Ascochyta rabiei were confirmed in this study. Candidate genes were identified within the QTL regions. Their co-localization with the underlying QTLs was confirmed by genetic mapping. The candidate gene-based SNP markers would lead to more efficient marker-assisted selection for ascochyta blight resistance and would provide a framework for fine mapping and subsequent cloning of the genes associated with the resistance.

Development of a highly efficient Axiom™ 70 K SNP array for Pyrus and evaluation for high-density mapping and germplasm characterization

Background

Both a source of diversity and the development of genomic tools, such as reference genomes and molecular markers, are equally important to enable faster progress in plant breeding. Pear (Pyrus spp.) lags far behind other fruit and nut crops in terms of employment of available genetic resources for new cultivar development. To address this gap, we designed a high-density, high-efficiency and robust single nucleotide polymorphism (SNP) array for pear, with the main objectives of conducting genetic diversity and genome-wide association studies.

Results

By applying a two-step design process, which consisted of the construction of a first ‘draft’ array for the screening of a small subset of samples, we were able to identify the most robust and informative SNPs to include in the Applied Biosystems™ Axiom™ Pear 70 K Genotyping Array, currently the densest SNP array for pear. Preliminary evaluation of this 70 K array in 1416 diverse pear accessions from the USDA National Clonal Germplasm Repository (NCGR) in Corvallis, OR identified 66,616 SNPs (93% of all the tiled SNPs) as high quality and polymorphic (PolyHighResolution). We further used the Axiom Pear 70 K Genotyping Array to construct high-density linkage maps in a bi-parental population, and to make a direct comparison with available genotyping-by-sequencing (GBS) data, which suggested that the SNP array is a more robust method of screening for SNPs than restriction enzyme reduced representation sequence-based genotyping.

Conclusions

The Axiom Pear 70 K Genotyping Array, with its high efficiency in a widely diverse panel of Pyrus species and cultivars, represents a valuable resource for a multitude of molecular studies in pear. The characterization of the USDA-NCGR collection with this array will provide important information for pear geneticists and breeders, as well as for the optimization of conservation strategies for Pyrus.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5712-3) contains supplementary material, which is available to authorized users.

InDel markers: An extended marker resource for molecular breeding in chickpea

Chickpea is one of the most important food legumes that holds the key to meet rising global food and nutritional demand. In order to deploy molecular breeding approaches in crop improvement programs, user friendly and cost effective marker resources remain prerequisite. The advent of next generation sequencing (NGS) technology has resulted in the generation of several thousands of markers as part of several large scale genome sequencing and re-sequencing initiatives. Very recently, PCR based Insertion-deletions (InDels) are becoming a popular gel based genotyping solution because of their co-dominant, inexpensive, and highly polymorphic nature. With an objective to expand marker resources for genomics assisted breeding (GAB) in chickpea, whole genome re-sequencing data generated on five parental lines of one interspecific (ICC 4958 × PI 489777) and two intra-specific (ICC 283 × ICC 8261 and ICC 4958 × ICC 1882) mapping populations, were used for identification of InDels. A total of 231,658 InDels were identified using Dindel software with default parameters. Further, a total of 8,307 InDels with ≥20 bp size were selected for development of gel based markers, of which primers could be designed for 7,523 (90.56%) markers. On average, markers appeared at a frequency of 1,038 InDels/LG with a maximum number of markers on CaLG04 (1,952 InDels) and minimum on CaLG08 (360 InDels). In order to validate these InDels, a total of 423 primer pairs were randomly selected and tested on the selected parental lines. A high amplification rate of 80% was observed ranging from 46.06 to 58.01% polymorphism rate across parents on 3% agarose gel. This study clearly reflects the usefulness of available sequence data for the development of genome-wide InDels in chickpea that can further contribute and accelerate a wide range of genetic and molecular breeding activities in chickpea.

Toward the sequence-based breeding in legumes in the post-genome sequencing era

Efficiency of breeding programs of legume crops such as chickpea, pigeonpea and groundnut has been considerably improved over the past decade through deployment of modern genomic tools and technologies. For instance, next-generation sequencing technologies have facilitated availability of genome sequence assemblies, re-sequencing of several hundred lines, development of HapMaps, high-density genetic maps, a range of marker genotyping platforms and identification of markers associated with a number of agronomic traits in these legume crops. Although marker-assisted backcrossing and marker-assisted selection approaches have been used to develop superior lines in several cases, it is the need of the hour for continuous population improvement after every breeding cycle to accelerate genetic gain in the breeding programs. In this context, we propose a sequence-based breeding approach which includes use of independent or combination of parental selection, enhancing genetic diversity of breeding programs, forward breeding for early generation selection, and genomic selection using sequencing/genotyping technologies. Also, adoption of speed breeding technology by generating 4-6 generations per year will be contributing to accelerate genetic gain. While we see a huge potential of the sequence-based breeding to revolutionize crop improvement programs in these legumes, we anticipate several challenges especially associated with high-quality and precise phenotyping at affordable costs, data analysis and management related to improving breeding operation efficiency. Finally, integration of improved seed systems and better agronomic packages with the development of improved varieties by using sequence-based breeding will ensure higher genetic gains in farmers' fields.

Development and Application of High-Density Axiom Cajanus SNP Array with 56K SNPs to Understand the Genome Architecture of Released Cultivars and Founder Genotypes

As one of the major outputs of next-generation sequencing (NGS), a large number of genome-wide single-nucleotide polymorphisms (SNPs) have been developed in pigeonpea [ (L.) Huth.]. However, SNPs require a genotyping platform or assay to be used in different evolutionary studies or in crop improvement programs. Therefore, we developed an Axiom SNP array with 56K SNPs uniformly distributed across the genome and assessed its utility in a genetic diversity study. From the whole-genome resequencing (WGRS) data on 104 pigeonpea lines, ∼2 million sequence variations (SNPs and insertion-deletions [InDels]) were identified, from which a subset of 56,512 unique and informative sequence variations were selected to develop the array. The Axiom SNP array developed was used for genotyping 103 pigeonpea lines encompassing 63 cultivars released between 1960 and 2014 and 40 breeding, germplasm, and founder lines. Genotyping data thus generated on 103 pigeonpea lines provided 51,201 polymorphic SNPs and InDels. Genetic diversity analysis provided in-depth insights into the genetic architecture and trends in temporal diversity in pigeonpea cultivars. Therefore, the continuous use of the high-density Axiom SNP array developed will accelerate high-resolution trait mapping, marker-assisted breeding, and genomic selection efforts in pigeonpea.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Grain legumes form an important component of the human diet, provide feed for livestock, and replenish soil fertility through biological nitrogen fixation. Globally, the demand for food legumes is increasing as they complement cereals in protein requirements and possess a high percentage of digestible protein. Climate change has enhanced the frequency and intensity of drought stress, posing serious production constraints, especially in rainfed regions where most legumes are produced. Genetic improvement of legumes, like other crops, is mostly based on pedigree and performance-based selection over the past half century. To achieve faster genetic gains in legumes in rainfed conditions, this review proposes the integration of modern genomics approaches, high throughput phenomics, and simulation modelling in support of crop improvement that leads to improved varieties that perform with appropriate agronomy. Selection intensity, generation interval, and improved operational efficiencies in breeding are expected to further enhance the genetic gain in experimental plots. Improved seed access to farmers, combined with appropriate agronomic packages in farmers' fields, will deliver higher genetic gains. Enhanced genetic gains, including not only productivity but also nutritional and market traits, will increase the profitability of farming and the availability of affordable nutritious food especially in developing countries.

Revolution in Genotyping Platforms for Crop Improvement

In the past decade, the application of high-throughput sequencing to crop genotyping has given rise to novel platforms capable of genotyping tens of thousands of genome-wide DNA markers. Coupled with the decreasing costs of sequencing, this rapid increase in markers allows accelerated and highly accurate genotyping of entire crop populations and diversity sets using single nucleotide polymorphisms (SNPs). These revolutionary advances accelerate crop improvement by facilitating a more precise connection of phenotype to genotype through association studies, linkage mapping and diversity analysis. The platforms driving the advances in genotyping are array technologies and genotyping by sequencing (GBS) methods, which include both low-coverage whole genome resequencing (skim sequencing) and reduced representation sequencing (RRS) approaches. Here, we outline and compare these genotyping platforms and provide a perspective on the promising future of crop genotyping. While SNP arrays provide high quality, simple handling, and unchallenging analysis, the lower cost of RRS and the greater data volume produced by skim sequencing suggest that use of GBS will become more prevalent in crop genomics as sequencing costs decrease and data analysis becomes more streamlined. Graphical Abstract.