Genomewide association studies for 50 agronomic traits in peanut using the 'reference set' comprising 300 genotypes from 48 countries of the semi-arid tropics of the world

Understanding the impacts of drought on peanuts (Arachis hypogaea L.): exploring physio-genetic mechanisms to develop drought-resilient peanut cultivars

Peanut is a vital source of protein, particularly in the tropical regions of Asian and African countries. About three-quarters of peanut production occurs worldwide in arid and semi-arid regions, making drought an important concern in peanut production. In the US about two-thirds of peanuts are grown in non-irrigated lands, where drought accounts for 50 million USD loss each year. The looming threat of climate change exacerbates this situation by increasing erratic rainfall. Drought not only reduces yield but also degrades product quality. Peanuts under drought stress exhibit higher levels of pre-harvest aflatoxin contamination, a toxic fungal metabolite detrimental to both humans and animals. One way to sustain peanut production in drought-prone regions and address pre-harvest aflatoxin contamination is by developing drought-tolerant peanut cultivars, a process that can be accelerated by understanding the underlying physiological and genetic mechanisms for tolerance to drought stress. Different physiological attributes and genetic regions have been identified in drought-tolerant cultivars that help them cope with drought stress. The advent of precise genetic studies, artificial intelligence, high-throughput phenotyping, bioinformatics, and data science have significantly improved drought studies in peanuts. Yet, breeding peanuts for drought tolerance is often a challenge as it is a complex trait significantly affected by environmental conditions. Besides technological advancements, the success of drought-tolerant cultivar development also relies on the identification of suitable germplasm and the conservation of peanut genetic variation.

Multi-locus genome wide association study uncovers genetics of fresh seed dormancy in groundnut

Pre-harvest sprouting (PHS) in groundnut leads to substantial yield losses and reduced seed quality, resulting in reduced market value of groundnuts. Breeding cultivars with 14-21 days of fresh seed dormancy (FSD) holds promise for precisely mitigating the yield and quality deterioration. In view of this, six multi-locus genome-wide association study (ML-GWAS) models alongside a single-locus GWAS (SL-GWAS) model were employed on a groundnut mini-core collection using multi season phenotyping and 58 K "Axiom_Arachis" array genotyping data. A total of 9 significant SNP-trait associations (STAs) for FSD were detected on A01, A04, A08, A09, B02, B04, B05, B07 and B09 chromosomes using six ML-GWAS models. Additionally, the SL-GWAS model identified 38 STAs across 14 chromosomes of groundnut. A single STA on chromosome B02 (qFSD-B02-1) was consistently identified in both ML-GWAS and SL-GWAS models. Furthermore, candidate gene mining identified nine high confidence genes viz., Cytochrome P450 705 A, Dormancy/auxin associated family protein, WRKY family transcription factor, Protein kinase superfamily protein, serine/threonine protein phosphatase, myb transcription factor, transcriptional regulator STERILE APETALA-like, ethylene-responsive transcription factor 7-like and F-box protein interaction domain protein as prime regulators involved in Abscisic acid/Gibberellic acid signaling pathways regulating dormancy/germination. In addition, three of the allele-specific markers developed from the identified STAs were validated across a diverse panel. These markers hold potential for increasing dormancy in groundnut through marker-assisted selection (MAS). Thus, this research offers insights into genetic and molecular mechanisms underlying groundnut seed dormancy in addition to providing markers and donors for breeding future varieties with 2-3 weeks of FSD.

Using cross-country datasets for association mapping in Arachis hypogaea L

Groundnut (Arachis hypogaea L.) is one of the most important climate-resilient oil crops in sub-Saharan Africa. There is a significant yield gap for groundnut in Africa because of poor soil fertility, low agricultural inputs, biotic and abiotic stresses. Cross-country evaluations of promising breeding lines can facilitate the varietal development process. The objective of our study was to characterize popular test environments in Uganda (Serere and Nakabango) and Malawi (Chitala and Chitedze) and identify genotypes with stable superior yields for potential future release. Phenotypic data were generated for 192 breeding lines for yield-related traits, while genotypic data were generated using skim-sequencing. We observed significant variation (p < 0.001; p < 0.01; p < 0.05) across genotypes for all yield-related traits: days to flowering (DTF), pod yield (PY), shelling percentage, 100-seed weight, and grain yield within and across locations. Nakabango, Chitedze, and Serere were clustered as one mega-environment with the top five most stable genotypes being ICGV-SM 01709, ICGV-SM 15575, ICGV-SM 90704, ICGV-SM 15576, and ICGV-SM 03710, all Virginia types. Population structure analysis clustered the genotypes in three distinct groups based on market classes. Eight and four marker-trait associations (MTAs) were recorded for DTF and PY, respectively. One of the MTAs for DTF was co-localized within an uncharacterized protein on chromosome 13, while another one (TRv2Chr.11_3476885) was consistent across the two countries. Future studies will need to further characterize the candidate genes as well as confirm the stability of superior genotypes across seasons before recommending them for release.

Dissecting genomic regions and underlying candidate genes in groundnut MAGIC population for drought tolerance

BACKGROUND

Groundnut is mainly grown in the semi-arid tropic (SAT) regions worldwide, where abiotic stress like drought is persistent. However, a major research gap exists regarding exploring the genetic and genomic underpinnings of tolerance to drought. In this study, a multi-parent advanced generation inter-cross (MAGIC) population was developed and evaluated for five seasons at two locations for three consecutive years (2018-19, 2019-20 and 2020-21) under drought stress and normal environments.

RESULTS

Phenotyping data of drought tolerance related traits, combined with the high-quality 10,556 polymorphic SNPs, were used to perform multi-locus model genome-wide association study (GWAS) analysis. We identified 37 significant marker-trait associations (MTAs) (Bonferroni-corrected) accounting, 0.91- 9.82% of the phenotypic variance. Intriguingly, 26 significant MTAs overlap on four chromosomes (Ah03, Ah07, Ah10 and Ah18) (harboring 70% of MTAs), indicating genomic hotspot regions governing drought tolerance traits. Furthermore, important candidate genes associated with leaf senescence (NAC transcription factor), flowering (B3 domain-containing transcription factor, Ulp1 protease family, and Ankyrin repeat-containing protein), involved in chlorophyll biosynthesis (FAR1 DNA-binding domain protein), stomatal regulation (Rop guanine nucleotide exchange factor; Galacturonosyltransferases), and associated with yield traits (Fasciclin-like arabinogalactan protein 11 and Fasciclin-like arabinogalactan protein 21) were found in the vicinity of significant MTAs genomic regions.

CONCLUSION

The findings of our investigation have the potential to provide a basis for significant MTAs validation, gene discovery and development of functional markers, which could be employed in genomics-assisted breeding to develop climate-resilient groundnut varieties.

Advances in the evolution research and genetic breeding of peanut

Peanut is an important cash crop used in oil, food and feed in our country. The rapid development of sequencing technology has promoted the research on the related aspects of peanut genetic breeding. This paper reviews the research progress of peanut origin and evolution, genetic breeding, molecular markers and their applications, genomics, QTL mapping and genome selection techniques. The main problems of molecular genetic breeding in peanut research worldwide include: the narrow genetic resources of cultivated species, unstable genetic transformation and unclear molecular mechanism of important agronomic traits. Considering the severe challenges regarding the supply of edible oil, and the main problems in peanut production, the urgent research directions of peanut are put forward: The de novo domestication and the exploitation of excellent genes from wild resources to improve modern cultivars; Integration of multi-omics data to enhance the importance of big data in peanut genetics and breeding; Cloning the important genes related to peanut agronomic traits and analyzing their fine regulation mechanisms; Precision molecular design breeding and using gene editing technology to accurately improve the key traits of peanut.

Diallel analysis, maternal effect and heritability in groundnut for yield components and oil content

Background

Groundnut is one of the world's major food and oil crops. Being sources of nutrition and vegetable oil, rich in affordable and digestible protein, it is a strategic crop in Burkina Faso for food security, nutrition, and cash income. Understanding the nature of gene effect and genetic variation affecting yield and yield component traits will contribute to designing appropriate breeding methods for groundnut improvement and increase selection efficiency in Burkina Faso.

Methods

In 2018, a total of 30 F2 progenies were generated through a 6 x 6 full diallel mating using six different and contrasting varieties. In 2019, parents and progenies were evaluated in a lattice square design in 3 replications at ICRISAT-Mali experimental field to assess the general combining ability (GCA) and specific combining ability (SCA) effects, the inheritance and the maternal and reciprocal effects for yield component traits (YCT) and oil content (OC).

Results

Significant variabilities were observed among the parental genotypes and their F2 progenies for DTH, PSR, HPW, PL, PWD, SL, SWD, and OAC. Mean performance of the six parents were HPW (117.05g), HSW (57.24 g), PYH (1914.76), SYH (1312.73), PL (2.52), PWD (1,19), SL (1.38), SWD (0.83), OC (49.43), OAC (50.43) and LAC (33.61). Parent QH243C presented the highest value for SWD (1.02 cm) and OAC (60.76) while the parent ICGV09195 had the highest value of OC (50.36). Chalimbana presented the highest value of HPW (169.61 g), PL (2.98 cm), PWD (1. 41 cm), and SL (1.57 cm) while CG7 presented the highest value for HSW (75. 14 g), and SYH (1639.28 kg). Both YCT and OC are controlled by additive and non-additive gene effects with a predominance of additive gene action for HSW, SL, and SWD, whereas HPW, PL, PWD, and OAC were found to be more controlled by non-additive gene effects. Maternal effects as well as nuclear and cytoplasmic interaction effects were observed for both YCT and OC indicating that YCT and OC are influenced by a combination of genetic factors from both the maternal parent and the nuclear genome, as well as cytoplasmic factors such as mitochondrial DNA. Broad sense heritability ranged from 3.76 % to 91.56 %, and higher broad sense heritability values were recorded for pod length (91.56 %), hundred pod weight (83.71 %) and pod width (80.95 %).

Conclusion

The study yields valuable insights into the inheritance of YCT and OC. The parents, Chalimbana and CG7, showed promise as good combiners for both yield component traits and oil content when used as male parents while TE3, Sh470P and QH243C can be used as female for the oil content and its components (oleic and linoleic content).

Designing future peanut: the power of genomics-assisted breeding

KEY MESSAGE

Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

Molecular tagging of seed size using MITE markers in an induced large seed mutant with higher cotyledon cell size in groundnut

A large seed mutant, TG 89 having a 76.7% increment in hundred kernel weight in comparison to its parent TG 26, was isolated from an electron beam-induced mutagenized population. Studies based on environmental scanning electron microscopy of both parent and mutant revealed that the mutant seed cotyledon had significantly bigger cell size than parent. A mapping population with 122 F2 plants derived from the mutant and a distant normal seed genotype (ICGV 15007) was utilized to map the QTL associated with higher HKW. Bulk segregant analysis revealed putative association of three markers with this mutant large seed trait. Further, genotyping of F2 individuals with polymorphic markers detected 14 linkage groups with a map distance of 1053 cM. QTL analysis revealed a significant additive major QTL for the mutant large seed trait on linkage group A05 explaining 12.7% phenotypic variation for the seed size. This QTL was located between flanking markers AhTE333 and AhTE810 having a map interval of 4.7 cM which corresponds to 90.65 to 107.24 Mbp in A05 chromosome, respectively. Within this genomic fragment, an ortholog of the BIG SEEDS 1 gene was found at 102,476,137 bp. Real-time PCR revealed down-regulation of this BIG SEEDS 1 gene in the mutant indicating a loss of function mutation giving rise to a large seed phenotype. This QTL was validated in 11 advanced breeding lines having large seed size from this mutant but with varied genetic backgrounds. This validation showcased a highly promising selection accuracy of 90.9% for the marker-assisted selection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03909-0.

Integrating genomics and genome editing for orphan crop improvement: a bridge between orphan crops and modern agriculture system

Domestication of orphan crops could be explored by editing their genomes. Genome editing has a lot of promise for enhancing agricultural output, and there is a lot of interest in furthering breeding in orphan crops, which are sometimes plagued with unwanted traits that resemble wild cousins. Consequently, applying model crop knowledge to orphan crops allows for the rapid generation of targeted allelic diversity and innovative breeding germplasm. We explain how plant breeders could employ genome editing as a novel platform to accelerate the domestication of semi-domesticated or wild plants, resulting in a more diversified base for future food and fodder supplies. This review emphasizes both the practicality of the strategy and the need to invest in research that advances our understanding of plant genomes, genes, and cellular systems. Planting more of these abandoned orphan crops could help alleviate food scarcities in the challenge of future climate crises.

Genetic mapping identified major main-effect and three co-localized quantitative trait loci controlling high iron and zinc content in groundnut

Malnutrition is a major challenge globally, and groundnut is a highly nutritious self-pollinated legume crop blessed with ample genomic resources, including the routine deployment of genomic-assisted breeding. This study aimed to identify genomic regions and candidate genes for high iron (Fe) and zinc (Zn) content, utilizing a biparental mapping population (ICGV 00440 × ICGV 06040;). Genetic mapping and quantitative trait locus (QTL) analysis (474 mapped single-nucleotide polymorphism loci; 1536.33 cM) using 2 seasons of phenotypic data together with genotypic data identified 5 major main-effect QTLs for Fe content. These QTLs exhibited log-of-odds (LOD) scores ranging from 6.5 to 7.4, explaining phenotypic variation (PVE) ranging from 22% (qFe-Ah01) to 30.0% (qFe-Ah14). Likewise, four major main effect QTLs were identified for Zn content, with LOD score ranging from 4.4 to 6.8 and PVE ranging from 21.8% (qZn-Ah01) to 32.8% (qZn-Ah08). Interestingly, three co-localized major and main effect QTLs (qFe-Ah01, qZn-Ah03, and qFe-Ah11) were identified for both Fe and Zn contents. These genomic regions harbored key candidate genes, including zinc/iron permease transporter, bZIP transcription factor, and vacuolar iron transporter which likely play pivotal roles in the accumulation of Fe and Zn contents in seeds. The findings of this study hold potential for fine mapping and diagnostic marker development for high Fe and Zn contents in groundnut.

GWAS with principal component analysis identify QTLs associated with main peanut flavor-related traits

Peanut flavor is a complex and important trait affected by raw material and processing technology owing to its significant impact on consumer preference. In this research, principal component analysis (PCA) on 33 representative traits associated with flavor revealed that total sugars, sucrose, and total tocopherols provided more information related to peanut flavor. Genome-wide association studies (GWAS) using 102 U.S. peanut mini-core accessions were performed to study associations between 12,526 single nucleotide polymorphic (SNP) markers and the three traits. A total of 7 and 22 significant quantitative trait loci (QTLs) were identified to be significantly associated with total sugars and sucrose, respectively. Among these QTLs, four and eight candidate genes for the two traits were mined. In addition, two and five stable QTLs were identified for total sugars and sucrose in both years separately. No significant QTLs were detected for total tocopherols. The results from this research provide useful knowledge about the genetic control of peanut flavor, which will aid in clarifying the molecular mechanisms of flavor research in peanuts.

Dissection of the Genetic Basis of Resistance to Stem Rot in Cultivated Peanuts (Arachis hypogaea L.) through Genome-Wide Association Study

Peanut (Arachis hypogaea) is an important oilseed and cash crop worldwide, contributing an important source of edible oil and protein for human nutrition. However, the incidence of stem rot disease caused by Athelia rolfsii poses a major challenge to peanut cultivation, resulting in significant yield losses. In this study, a panel of 202 peanut accessions was evaluated for their resistance to stem rot by inoculating plants in the field with A. rolfsii-infested oat grains in three environments. The mean disease index value of each environment for accessions in subsp. fasitigiate and subsp. hypogaea showed no significant difference. Accessions from southern China displayed the lowest disease index value compared to those from other ecological regions. We used whole-genome resequencing to analyze the genotypes of the accessions and to identify significant SNPs associated with stem rot resistance through genome-wide association study (GWAS). A total of 121 significant SNPs associated with stem rot resistance in peanut were identified, with phenotypic variation explained (PVE) ranging from 12.23% to 15.51%. A total of 27 candidate genes within 100 kb upstream and downstream of 23 significant SNPs were annotated, which have functions related to recognition, signal transduction, and defense response. These significant SNPs and candidate genes provide valuable information for further validation and molecular breeding to improve stem rot resistance in peanut.

An Overview of Mapping Quantitative Trait Loci in Peanut (Arachis hypogaea L.)

Quantitative Trait Loci (QTL) mapping has been thoroughly used in peanut genetics and breeding in spite of the narrow genetic diversity and the segmental tetraploid nature of the cultivated species. QTL mapping is helpful for identifying the genomic regions that contribute to traits, for estimating the extent of variation and the genetic action (i.e., additive, dominant, or epistatic) underlying this variation, and for pinpointing genetic correlations between traits. The aim of this paper is to review the recently published studies on QTL mapping with a particular emphasis on mapping populations used as well as traits related to kernel quality. We found that several populations have been used for QTL mapping including interspecific populations developed from crosses between synthetic tetraploids and elite varieties. Those populations allowed the broadening of the genetic base of cultivated peanut and helped with the mapping of QTL and identifying beneficial wild alleles for economically important traits. Furthermore, only a few studies reported QTL related to kernel quality. The main quality traits for which QTL have been mapped include oil and protein content as well as fatty acid compositions. QTL for other agronomic traits have also been reported. Among the 1261 QTL reported in this review, and extracted from the most relevant studies on QTL mapping in peanut, 413 (~33%) were related to kernel quality showing the importance of quality in peanut genetics and breeding. Exploiting the QTL information could accelerate breeding to develop highly nutritious superior cultivars in the face of climate change.

Limitations and advantages of using metabolite-based genome-wide association studies: focus on fruit quality traits

In the last decades, linkage mapping has help in the location of metabolite quantitative trait loci (QTL) in many species; however, this approach shows some limitations. Recently, thanks to the most recent advanced in high-throughput genotyping technologies like next-generation sequencing, metabolite genome-wide association study (mGWAS) has been proposed a powerful tool to identify the genetic variants in polygenic agrinomic traits. Fruit flavor is a complex interaction of aroma volatiles and taste being sugar and acid ratio key parameter for flavor acceptance. Here, we review recent progress of mGWAS in pinpoint gene polymorphisms related to flavor-related metabolites in fruits. Despite clear successes in discovering novel genes or regions associated with metabolite accumulation affecting sensory attributes in fruits, GWAS incurs in several limitations summarized in this review. In addition, in our own work, we performed mGWAS on 194 Citrus grandis accessions to investigate the genetic control of individual primary and lipid metabolites in ripe fruit. We have identified a total of 667 associations for 14 primary metabolites including amino acids, sugars, and organic acids, and 768 associations corresponding to 47 lipids. Furthermore, candidate genes related to important metabolites related to fruit quality such as sugars, organic acids and lipids were discovered.

Genetic mapping identifies genomic regions and candidate genes for seed weight and shelling percentage in groundnut

Seed size is not only a yield-related trait but also an important measure to determine the commercial value of groundnut in the international market. For instance, small size is preferred in oil production, whereas large-sized seeds are preferred in confectioneries. In order to identify the genomic regions associated with 100-seed weight (HSW) and shelling percentage (SHP), the recombinant inbred line (RIL) population (Chico × ICGV 02251) of 352 individuals was phenotyped for three seasons and genotyped with an Axiom_Arachis array containing 58K SNPs. A genetic map with 4199 SNP loci was constructed, spanning a map distance of 2708.36 cM. QTL analysis identified six QTLs for SHP, with three consistent QTLs on chromosomes A05, A08, and B10. Similarly, for HSW, seven QTLs located on chromosomes A01, A02, A04, A10, B05, B06, and B09 were identified. BIG SEED locus and spermidine synthase candidate genes associated with seed weight were identified in the QTL region on chromosome B09. Laccase, fibre protein, lipid transfer protein, senescence-associated protein, and disease-resistant NBS-LRR proteins were identified in the QTL regions associated with shelling percentage. The associated markers for major-effect QTLs for both traits successfully distinguished between the small- and large-seeded RILs. QTLs identified for HSW and SHP can be used for developing potential selectable markers to improve the cultivars with desired seed size and shelling percentage to meet the demands of confectionery industries.

Genome-wide association studies reveal novel loci for resistance to groundnut rosette disease in the African core groundnut collection

KEY MESSAGE

We identified markers associated with GRD resistance after screening an Africa-wide core collection across three seasons in Uganda Groundnut is cultivated in several African countries where it is a major source of food, feed and income. One of the major constraints to groundnut production in Africa is groundnut rosette disease (GRD), which is caused by a complex of three agents: groundnut rosette assistor luteovirus, groundnut rosette umbravirus and its satellite RNA. Despite several years of breeding for GRD resistance, the genetics of the disease is not fully understood. The objective of the current study was to use the African core collection to establish the level of genetic variation in their response to GRD, and to map genomic regions responsible for the observed resistance. The African groundnut core genotypes were screened across two GRD hotspot locations in Uganda (Nakabango and Serere) for 3 seasons. The Area Under Disease Progress Curve combined with 7523 high quality SNPs were analyzed to establish marker-trait associations (MTAs). Genome-Wide Association Studies based on Enriched Compressed Mixed Linear Model detected 32 MTAs at Nakabango: 21 on chromosome A04, 10 on B04 and 1 on B08. Two of the significant markers were localised on the exons of a putative TIR-NBS-LRR disease resistance gene on chromosome A04. Our results suggest the likely involvement of major genes in the resistance to GRD but will need to be further validated with more comprehensive phenotypic and genotypic datasets. The markers identified in the current study will be developed into routine assays and validated for future genomics-assisted selection for GRD resistance in groundnut.

Genome-wide association mapping for LLS resistance in a MAGIC population of groundnut (Arachis hypogaea L.)

The identified 30 functional nucleotide polymorphisms or genic SNP markers would offer essential information for marker-assisted breeding in groundnut. A genome-wide association study (GWAS) on component traits of LLS resistance in an eight-way multiparent advance generation intercross (MAGIC) population of groundnut in the field and in a light chamber (controlled conditions) was performed via an Affymetrix 48 K single-nucleotide polymorphism (SNP) 'Axiom Arachis' array. Multiparental populations with high-density genotyping enable the detection of novel alleles. In total, five quantitative trait loci (QTLs) with marker - log10(p value) scores ranging from 4.25 to 13.77 for the incubation period (IP) and six QTLs with marker - log10(p value) scores ranging from 4.33 to 10.79 for the latent period (LP) were identified across the A- and B-subgenomes. A total of 62 markers‒trait associations (MTAs) were identified across the A- and B-subgenomes. Markers for LLS scores and the area under the disease progression curve (AUDPC) recorded for plants in the light chamber and under field conditions presented - log10 (p value) scores ranging from 4.22 to 27.30. The highest number of MTAs (six) was identified on chromosomes A05, B07 and B09. Out of a total of 73 MTAs, 37 and 36 MTAs were detected in subgenomes A and B, respectively. Taken together, these results suggest that both subgenomes have equal potential genomic regions contributing to LLS resistance. A total of 30 functional nucleotide polymorphisms or genic SNP markers were detected, among which eight genes were found to encode leucine-rich repeat (LRR) receptor-like protein kinases and putative disease resistance proteins. These important SNPs can be used in breeding programmes for the development of cultivars with improved disease resistance.

Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut (Arachis hypogaea L.) germplasm

Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in ≤ 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48 K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation.

Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance

Climate change is significantly impacting agricultural production worldwide. Peanuts provide food and nutritional security to millions of people across the globe because of its high nutritive values. Drought and heat stress alone or in combination cause substantial yield losses to peanut production. The stress, in addition, adversely impact nutritional quality. Peanuts exposed to drought stress at reproductive stage are prone to aflatoxin contamination, which imposes a restriction on use of peanuts as health food and also adversely impact peanut trade. A comprehensive understanding of the impact of drought and heat stress at physiological and molecular levels may accelerate the development of stress tolerant productive peanut cultivars adapted to a given production system. Significant progress has been achieved towards the characterization of germplasm for drought and heat stress tolerance, unlocking the physiological and molecular basis of stress tolerance, identifying significant marker-trait associations as well major QTLs and candidate genes associated with drought tolerance, which after validation may be deployed to initiate marker-assisted breeding for abiotic stress adaptation in peanut. The proof of concept about the use of transgenic technology to add value to peanuts has been demonstrated. Advances in phenomics and artificial intelligence to accelerate the timely and cost-effective collection of phenotyping data in large germplasm/breeding populations have also been discussed. Greater focus is needed to accelerate research on heat stress tolerance in peanut. A suits of technological innovations are now available in the breeders toolbox to enhance productivity and nutritional quality of peanuts in harsh environments. A holistic breeding approach that considers drought and heat-tolerant traits to simultaneously address both stresses could be a successful strategy to produce climate-resilient peanut genotypes with improved nutritional quality.

Two decades of association mapping: Insights on disease resistance in major crops

Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.

The Key to the Future Lies in the Past: Insights from Grain Legume Domestication and Improvement Should Inform Future Breeding Strategies

Crop domestication is a co-evolutionary process that has rendered plants and animals significantly dependent on human interventions for survival and propagation. Grain legumes have played an important role in the development of Neolithic agriculture some 12,000 years ago. Despite being early companions of cereals in the origin and evolution of agriculture, the understanding of grain legume domestication has lagged behind that of cereals. Adapting plants for human use has resulted in distinct morpho-physiological changes between the wild ancestors and domesticates, and this distinction has been the focus of several studies aimed at understanding the domestication process and the genetic diversity bottlenecks created. Growing evidence from research on archeological remains, combined with genetic analysis and the geographical distribution of wild forms, has improved the resolution of the process of domestication, diversification and crop improvement. In this review, we summarize the significance of legume wild relatives as reservoirs of novel genetic variation for crop breeding programs. We describe key legume features, which evolved in response to anthropogenic activities. Here, we highlight how whole genome sequencing and incorporation of omics-level data have expanded our capacity to monitor the genetic changes accompanying these processes. Finally, we present our perspective on alternative routes centered on de novo domestication and re-domestication to impart significant agronomic advances of novel crops over existing commodities. A finely resolved domestication history of grain legumes will uncover future breeding targets to develop modern cultivars enriched with alleles that improve yield, quality and stress tolerance.

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.

Improvement of heat stress tolerance in soybean (Glycine max L), by using conventional and molecular tools

The soybean is a significant legume crop, providing several vital dietary components. Extreme heat stress negatively affects soybean yield and quality, especially at the germination stage. Continuous change in climatic conditions is threatening the global food supply and food security. Therefore, it is a critical need of time to develop heat-tolerant soybean genotypes. Different molecular techniques have been developed to improve heat stress tolerance in soybean, but until now complete genetic mechanism of soybean is not fully understood. Various molecular methods, like quantitative trait loci (QTL) mapping, genetic engineering, transcription factors (TFs), transcriptome, and clustered regularly interspaced short palindromic repeats (CRISPR), are employed to incorporate heat tolerance in soybean under the extreme conditions of heat stress. These molecular techniques have significantly improved heat stress tolerance in soybean. Besides this, we can also use specific classical breeding approaches and different hormones to reduce the harmful consequences of heat waves on soybean. In future, integrated use of these molecular tools would bring significant results in developing heat tolerance in soybean. In the current review, we have presented a detailed overview of the improvement of heat tolerance in soybean and highlighted future prospective. Further studies are required to investigate different genetic factors governing the heat stress response in soybean. This information would be helpful for future studies focusing on improving heat tolerance in soybean.

Utility values and its time to deterioration in breast cancer patients after diagnosis and during treatments

BACKGROUND

The potential effects of breast cancer (BC) on health-related quality of life (HRQoL) should be considered in clinical and policy decision-making, as the economic burden of BC management is currently assessed. In the last decades, time-to-HRQoL score deterioration (TTD) has been proposed as an approach to the analysis of longitudinal HRQoL in oncology. The main objectives of the current study were to investigate the evolution of the utility values in BC patients after diagnosis and during follow-ups and to evaluate the TTD in utility values among women in all stages of BC.

METHODS

Health-state utility values (HSUV) were assessed using the EuroQol 5-Dimension 3-Level at diagnosis, at the end of the first hospitalization and 3 and 6 months after the first hospitalization. For a given baseline score, HSUV was considered to have deteriorated if this score decreased by ≥ 0.08 points of the EQ-5D utility index score and ≥ 7 points of the EQ visual analogue scale. TTD curves were calculated using the Kaplan-Meier estimation method.

RESULTS

Overall 381 patients were enrolled between February 2006 and February 2008. The highest proportions of respondents at the baseline and all follow-ups reporting some and extreme problems were in pain discomfort and anxiety/depression dimensions; more than 80% of patients experienced a deterioration in EQ-5D utility index score and EQ VAS score with a median TTD of 3.15 months and 6.24 Months, respectively.

CONCLUSIONS

BC patients undergoing therapy need psychological support to cope with their discomfort, pain, depression, anxiety, and fear during the process of diagnosis and treatment to improve their QoL.

Detection of a major QTL and development of KASP markers for seed weight by combining QTL-seq, QTL-mapping and RNA-seq in peanut

Combining QTL-seq, QTL-mapping and RNA-seq identified a major QTL and candidate genes, which contributed to the development of KASP markers and understanding of molecular mechanisms associated with seed weight in peanut. Seed weight, as an important component of seed yield, is a significant target of peanut breeding. However, relatively little is known about the quantitative trait loci (QTLs) and candidate genes associated with seed weight in peanut. In this study, three major QTLs on chromosomes A05, B02, and B06 were determined by applying the QTL-seq approach in a recombinant inbred line (RIL) population. Based on conventional QTL-mapping, these three QTL regions were successfully narrowed down through newly developed single nucleotide polymorphism (SNP) and simple sequence repeat markers. Among these three QTL regions, qSWB06.3 exhibited stable expression, contributing mainly to phenotypic variance across environments. Furthermore, differentially expressed genes (DEGs) were identified at the three seed developmental stages between the two parents of the RIL population. It was found that the DEGs were widely distributed in the ubiquitin-proteasome pathway, the serine/threonine-protein pathway, signal transduction of hormones and transcription factors. Notably, DEGs at the early stage were mostly involved in regulating cell division, whereas DEGs at the middle and late stages were primarily involved in cell expansion during seed development. The expression patterns of candidate genes related to seed weight in qSWB06.3 were investigated using quantitative real-time PCR. In addition, the allelic diversity of qSWB06.3 was investigated in peanut germplasm accessions. The marker Ah011475 has higher efficiency for discriminating accessions with different seed weights, and it would be useful as a diagnostic marker in marker-assisted breeding. This study provided insights into the genetic and molecular mechanisms of seed weight in peanut.

Insights into the Genomic Architecture of Seed and Pod Quality Traits in the U.S. Peanut Mini-Core Diversity Panel

Traits such as seed weight, shelling percent, percent sound mature kernels, and seed dormancy determines the quality of peanut seed. Few QTL (quantitative trait loci) studies using biparental mapping populations have identified QTL for seed dormancy and seed grade traits. Here, we report a genome-wide association study (GWAS) to detect marker–trait associations for seed germination, dormancy, and seed grading traits in peanut. A total of 120 accessions from the U.S. peanut mini-core collection were evaluated for seed quality traits and genotyped using Axiom SNP (single nucleotide polymorphism) array for peanut. We observed significant variation in seed quality traits in different accessions and different botanical varieties. Through GWAS, we were able to identify multiple regions associated with sound mature kernels, seed weight, shelling percent, seed germination, and dormancy. Some of the genomic regions that were SNP associated with these traits aligned with previously known QTLs. For instance, QTL for seed dormancy has been reported on chromosome A05, and we also found SNP on the same chromosome associated with seed dormancy, explaining around 20% of phenotypic variation. In addition, we found novel genomic regions associated with seed grading, seed germination, and dormancy traits. SNP markers associated with seed quality and dormancy identified here can accelerate the selection process. Further, exploring the function of candidate genes identified in the vicinity of the associated marker will assist in understanding the complex genetic network that governs seed quality.

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.

Genome-wide association studies: assessing trait characteristics in model and crop plants

GWAS involves testing genetic variants across the genomes of many individuals of a population to identify genotype–phenotype association. It was initially developed and has proven highly successful in human disease genetics. In plants genome-wide association studies (GWAS) initially focused on single feature polymorphism and recombination and linkage disequilibrium but has now been embraced by a plethora of different disciplines with several thousand studies being published in model and crop species within the last decade or so. Here we will provide a comprehensive review of these studies providing cases studies on biotic resistance, abiotic tolerance, yield associated traits, and metabolic composition. We also detail current strategies of candidate gene validation as well as the functional study of haplotypes. Furthermore, we provide a critical evaluation of the GWAS strategy and its alternatives as well as future perspectives that are emerging with the emergence of pan-genomic datasets.

Dissection of the Genetic Basis of Yield-Related Traits in the Chinese Peanut Mini-Core Collection Through Genome-Wide Association Studies

Peanut is an important legume crop worldwide. To uncover the genetic basis of yield features and assist breeding in the future, we conducted genome-wide association studies (GWAS) for six yield-related traits of the Chinese peanut mini-core collection. The seed (pod) size and weight of the population were investigated under four different environments, and these traits showed highly positive correlations in pairwise combinations. We sequenced the Chinese peanut mini-core collection using genotyping-by-sequencing approach and identified 105,814 high-quality single-nucleotide polymorphisms (SNPs). The population structure analysis showed essentially subspecies patterns in groups and obvious geographical distribution patterns in subgroups. A total of 79 significantly associated loci (P < 4.73 × 10^-7) were detected for the six yield-related traits through GWAS. Of these, 31 associations were consistently detected in multiple environments, and 15 loci were commonly detected to be associated with multiple traits. Two major loci located on chromosomal pseudomolecules A06 and A02 showed pleiotropic effects on yield-related traits, explaining ∼20% phenotypic variations across environments. The two genomic regions were found 46 putative candidate genes based on gene annotation and expression profile. The diagnostic marker for the yield-related traits from non-synonymous SNP (Aradu-A06-107901527) was successfully validated, achieving a high correlation between nucleotide polymorphism and phenotypic variation. This study provided insights into the genetic basis of yield-related traits in peanut and verified one diagnostic marker to facilitate marker-assisted selection for developing high-yield peanut varieties.

Genome wide association mapping for heat tolerance in sub-tropical maize

Background

Heat tolerance is becoming increasingly important where maize is grown under spring season in India which coincide with grain filling stage of crop resulting in tassel blast, reduced pollen viability, pollination failure and barren ears that causes devastating yield losses. So, there is need to identify the genomic regions associated with heat tolerance component traits which could be further employed in maize breeding program.

Results

An association mapping panel, consisting of 662 doubled haploid (DH) lines, was evaluated for yield contributing traits under normal and natural heat stress conditions. Genome wide association studies (GWAS) carried out using 187,000 SNPs and 130 SNPs significantly associated for grain yield (GY), days to 50% anthesis (AD), days to 50% silking (SD), anthesis-silking interval (ASI), plant height (PH), ear height (EH) and ear position (EPO) were identified under normal conditions. A total of 46 SNPs strongly associated with GY, ASI, EH and EPO were detected under heat stress conditions. Fifteen of the SNPs was found to have common association with more than one trait such as two SNPs viz. S10_1,905,273 and S10_1,905,274 showed colocalization with GY, PH and EH whereas S10_7,132,845 SNP associated with GY, AD and SD under normal conditions. No such colocalization of SNP markers with multiple traits was observed under heat stress conditions. Haplotypes trend regression analysis revealed 122 and 85 haplotype blocks, out of which, 20 and 6 haplotype blocks were associated with more than one trait under normal and heat stress conditions, respectively. Based on SNP association and haplotype mapping, nine and seven candidate genes were identified respectively, which belongs to different gene models having different biological functions in stress biology.

Conclusions

The present study identified significant SNPs and haplotype blocks associated with yield contributing traits that help in selection of donor lines with favorable alleles for multiple traits. These results provided insights of genetics of heat stress tolerance. The genomic regions detected in the present study need further validation before being applied in the breeding pipelines.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07463-y.

Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explore the distribution of recombination and potential chromosomal rearrangements. This linkage map was then used to identify Quantitative Trait Loci (QTL) for seed and pod traits that were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

Identification of QTLs for resistance to leaf spots in cultivated peanut (Arachis hypogaea L.) through GWAS analysis

Two QTLs on ChrB09 significantly associated with both early and late leaf spots were identified by genome-wide association study in the US peanut mini-core collection. Early leaf spot (ELS) and late leaf spot (LLS) are two serious peanut diseases in the USA, causing tens of millions of dollars of annual economic losses. However, the genetic factors underlying resistance to those diseases in peanuts have not been well-studied. We conducted a genome-wide association study for the two peanut diseases using Affymetrix version 2.0 SNP array with 120 genotypes mainly coming from the US peanut mini-core collection. A total of 46 quantitative trait loci (QTLs) were identified with phenotypic variation explained (PVE) from 10.19 to 24.11%, in which eighteen QTLs are for resistance to ELS and 28 QTLs for LLS. Among the 46 QTLs, there were four and two major QTLs with PVE higher than 16.99% for resistance ELS and LLS, respectively. Of the six major QTLs, five were located on the B sub-genome and only one was on the A sub-genome, which suggested that the B sub-genome has more potential resistance genomic regions than the A sub-genome. In addition, two genomic regions on chromosome B09 were found to provide significant resistance to both ELS and LLS. A total of 74 non-redundant genes were identified as resistance genes, among which, twelve candidate genes were in significant genomic regions including two candidate genes for both ELS and LLS, and other ten candidate genes for ELS. The QTLs and candidate genes obtained from this study will be useful to breed peanuts for resistances to the diseases.

PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

Dissection of the genetic basis of oil content in Chinese peanut cultivars through association mapping

Background

Peanut is one of the primary sources for vegetable oil worldwide, and enhancing oil content is the main objective in several peanut breeding programs of the world. Tightly linked markers are required for faster development of high oil content peanut varieties through genomics-assisted breeding (GAB), and association mapping is one of the promising approaches for discovery of such associated markers.

Results

An association mapping panel consisting of 292 peanut varieties extensively distributed in China was phenotyped for oil content and genotyped with 583 polymorphic SSR markers. These markers amplified 3663 alleles with an average of 6.28 alleles per locus. The structure, phylogenetic relationship, and principal component analysis (PCA) indicated two subgroups majorly differentiating based on geographic regions. Genome-wide association analysis identified 12 associated markers including one (AGGS1014_2) highly stable association controlling up to 9.94% phenotypic variance explained (PVE) across multiple environments. Interestingly, the frequency of the favorable alleles for 12 associated markers showed a geographic difference. Two associated markers (AGGS1014_2 and AHGS0798) with 6.90–9.94% PVE were verified to enhance oil content in an independent RIL population and also indicated selection during the breeding program.

Conclusion

This study provided insights into the genetic basis of oil content in peanut and verified highly associated two SSR markers to facilitate marker-assisted selection for developing high-oil content breeding peanut varieties.

An integrated research framework combining genomics, systems biology, physiology, modelling and breeding for legume improvement in response to elevated CO2 under climate change scenario

How unprecedented changes in climatic conditions will impact yield and productivity of some crops and their response to existing stresses, abiotic and biotic interactions is a key global concern. Climate change can also alter natural species' abundance and distribution or favor invasive species, which in turn can modify ecosystem dynamics and the provisioning of ecosystem services. Basic anatomical differences in C3 and C4 plants lead to their varied responses to climate variations. In plants having a C3 pathway of photosynthesis, increased atmospheric carbon dioxide (CO2) positively regulates photosynthetic carbon (C) assimilation and depresses photorespiration. Legumes being C3 plants, they may be in a favorable position to increase biomass and yield through various strategies. This paper comprehensively presents recent progress made in the physiological and molecular attributes in plants with special emphasis on legumes under elevated CO2 conditions in a climate change scenario. A strategic research framework for future action integrating genomics, systems biology, physiology and crop modelling approaches to cope with changing climate is also discussed. Advances in sequencing and phenotyping methodologies make it possible to use vast genetic and genomic resources by deploying high resolution phenotyping coupled with high throughput multi-omics approaches for trait improvement. Integrated crop modelling studies focusing on farming systems design and management, prediction of climate impacts and disease forecasting may also help in planning adaptation. Hence, an integrated research framework combining genomics, plant molecular physiology, crop breeding, systems biology and integrated crop-soil-climate modelling will be very effective to cope with climate change.

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.

Profiling of Nutraceuticals and Proximates in Peanut Genotypes Differing for Seed Coat Color and Seed Size

A total of 60 genotypes of peanut comprising 46 genotypes selected from ICRISAT mini core collection and 14 elite cultivars with differing kernel color and size were used to profile the nutritional parameters such as proximates (moisture, fat, ash, crude protein, crude fiber, carbohydrate content) and nutraceuticals (total polyphenol content and total antioxidant activity). The genotypes showed varied kernel color ranging from white to purple. Kernel skin color was quantified using colorimetry, and the color parameters were expressed as CIELAB color parameters. In total, nine morphological traits, six yield related traits, eight nutritional traits and eleven color parameters were observed across 60 genotypes. The sixty genotypes were grouped into ten clusters based on the color strength. Among them, Cluster-III with dark red seeds had the maximum fat content and total polyphenol content (TPC). Cluster-VI with light pink colored seeds had high antioxidant activity (AOA) and Cluster-X with white colored seeds had highest moisture and crude protein content. Color strength (K/S) was found to be positively correlated with TPC. Another color parameter, redness/greenness (a*) was found to be positively correlated with AOA. However, seed size was positively correlated with the crude protein content, but not with any other nutritional traits under study. The population studies based on the genotypic data indicated two distinct groups pertaining to botanical types of peanut. The marker-trait association (MTA) using single marker analysis indicated 75 major MTAs for most of the nutritional traits except for moisture content. The markers associated with nutritional parameters and other important yield related traits can further be utilized for genomics-assisted breeding for nutrient-rich peanuts.

Functional Biology and Molecular Mechanisms of Host-Pathogen Interactions for Aflatoxin Contamination in Groundnut (Arachis hypogaea L.) and Maize (Zea mays L.)

Aflatoxins are secondary metabolites produced by soilborne saprophytic fungus Aspergillus flavus and closely related species that infect several agricultural commodities including groundnut and maize. The consumption of contaminated commodities adversely affects the health of humans and livestock. Aflatoxin contamination also causes significant economic and financial losses to producers. Research efforts and significant progress have been made in the past three decades to understand the genetic behavior, molecular mechanisms, as well as the detailed biology of host-pathogen interactions. A range of omics approaches have facilitated better understanding of the resistance mechanisms and identified pathways involved during host-pathogen interactions. Most of such studies were however undertaken in groundnut and maize. Current efforts are geared toward harnessing knowledge on host-pathogen interactions and crop resistant factors that control aflatoxin contamination. This study provides a summary of the recent progress made in enhancing the understanding of the functional biology and molecular mechanisms associated with host-pathogen interactions during aflatoxin contamination in groundnut and maize.

QTL identification for seed weight and size based on a high-density SLAF-seq genetic map in peanut (Arachis hypogaea L.)

BACKGROUND

The cultivated peanut is an important oil and cash crop grown worldwide. To meet the growing demand for peanut production each year, genetic studies and enhanced selection efficiency are essential, including linkage mapping, genome-wide association study, bulked-segregant analysis and marker-assisted selection. Specific locus amplified fragment sequencing (SLAF-seq) is a powerful tool for high density genetic map (HDGM) construction and quantitative trait loci (QTLs) mapping. In this study, a HDGM was constructed using SLAF-seq leading to identification of QTL for seed weight and size in peanut.

RESULTS

A recombinant inbred line (RIL) population was advanced from a cross between a cultivar 'Huayu36' and a germplasm line '6-13' with contrasting seed weight, size and shape. Based on the cultivated peanut genome, a HDGM was constructed with 3866 loci consisting of SLAF-seq and simple sequence repeat (SSR) markers distributed on 20 linkage groups (LGs) covering a total map distance of 1266.87 cM. Phenotypic data of four seed related traits were obtained in four environments, which mostly displayed normal distribution with varied levels of correlation. A total of 27 QTLs for 100 seed weight (100SW), seed length (SL), seed width (SW) and length to width ratio (L/W) were identified on 8 chromosomes, with LOD values of 3.16-31.55 and explaining phenotypic variance (PVE) from 0.74 to 83.23%. Two stable QTL regions were identified on chromosomes 2 and 16, and gene content within these regions provided valuable information for further functional analysis of yield component traits.

CONCLUSIONS

This study represents a new HDGM based on the cultivated peanut genome using SLAF-seq and SSRs. QTL mapping of four seed related traits revealed two stable QTL regions on chromosomes 2 and 16, which not only facilitate fine mapping and cloning these genes, but also provide opportunity for molecular breeding of new peanut cultivars with improved seed weight and size.

GWAS Discovery Of Candidate Genes for Yield-Related Traits in Peanut and Support from Earlier QTL Mapping Studies

Peanut (Arachis hypogaea L.) is one of the most important oil crops worldwide, and its yet increasing market demand may be met by genetic improvement of yield related traits, which may be facilitated by a good understanding of the underlying genetic base of these traits. Here, we have carried out a genome-wide association study (GWAS) with the aim to identify genomic regions and the candidate genes within these regions that may be involved in determining the phenotypic variation at seven yield-related traits in peanut. For the GWAS analyses, 195 peanut accessions were phenotyped and/or genotyped; the latter was done using a genotyping-by-sequencing approach, which produced a total of 13,435 high-quality single nucleotide polymorphisms (SNPs). Analyses of these SNPs show that the analyzed peanut accessions can be approximately grouped into two big groups that, to some extent, agree with the botanical classification of peanut at the subspecies level. By taking this genetic structure as well as the relationships between the analyzed accessions into consideration, our GWAS analyses have identified 93 non-overlapping peak SNPs that are significantly associated with four of the studied traits. Gene annotation of the genome regions surrounding these peak SNPs have found a total of 311 unique candidate genes. Among the 93 yield-related-trait-associated SNP peaks, 12 are found to be co-localized with the quantitative trait loci (QTLs) that were identified by earlier related QTL mapping studies, and these 12 SNP peaks are only related to three traits and are almost all located on chromosomes Arahy.05 and Arahy.16. Gene annotation of these 12 co-localized SNP peaks have found 36 candidates genes, and a close examination of these candidate genes found one very interesting gene (arahy.RI9HIF), the rice homolog of which produces a protein that has been shown to improve rice yield when over-expressed. Further tests of the arahy.RI9HIF gene, as well as other candidate genes especially those within the more confident co-localized genomic regions, may hold the potential for significantly improving peanut yield.

Identification of major consensus QTLs for seed size and minor QTLs for pod traits in cultivated groundnut (Arachis hypogaea L.)

Hundred kernel weight is an important indicator for large-seeded genotype selection. A recombinant inbred line population was used to decipher the genetic architecture of seed size and three pod traits in cultivated groundnut based on the phenotypic data from six and three environments, respectively. The study revealed a consensus major QTL for HKW in B07 group that explained 10.5-23.9% phenotypic variation due to seed size. Further, two other minor QTLs were identified in B03 and B08 group for the seed size. Two minor QTLs for pod beak were positioned in B03 and A08. A minor QTL for pod reticulation was also mapped in the same map interval with the pod beak QTL in A08. Another minor QTL for pod constriction was co-mapped with the minor QTL for HKW in B08. The other minor QTL for pod constriction was placed in the neighboring map interval with the consensus QTL for seed size in B07 that suggests linkage of pod constriction with large seed trait. Analysis of the flanking markers profile in 71 cultivated groundnut genotypes revealed a strong association of pPGPseq_2E06 marker with large seed trait.

Modernising breeding for orphan crops: tools, methodologies, and beyond

In spite of the limited investment in orphan crops, access to new technologies such as bioinformatics and low-cost genotyping opens new doors to modernise their breeding effectively. Innovation in plant breeding is imperative to meet the world's growing demand for staple food and feed crops, and orphan crops can play a significant role in increasing productivity and quality, especially in developing countries. The short breeding history of most orphan crops implies that genetic gain should be achievable through easy-to-implement approaches such as forward breeding for simple traits or introgression of elite alleles at key target trait loci. However, limited financial support and access to sufficient, relevant and reliable phenotypic data continue to pose major challenges in terms of resources and capabilities. Digitalisation of orphan-crop breeding programmes can help not only to improve data quality and management, but also to mitigate data scarcity by allowing data to be accumulated and analysed over time and across teams. Bioinformatics tools and access to technologies such as molecular markers, some of them provided as services via specific platforms, allow breeders to implement modern strategies to improve breeding efficiency. In orphan crops, more marker-trait associations relevant to breeding germplasm are generally needed, but implementing digitalization, marker-based quality control or simple trait screening and introgression will help modernising breeding. Finally, the development of local capacities-of both people and infrastructure-remains a necessity to ensure the sustainable adoption of modern breeding approaches.

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.

Genetics, genomics and breeding of groundnut (Arachis hypogaea L.)

Groundnut is an important food and oil crop in the semiarid tropics, contributing to household food consumption and cash income. In Asia and Africa, yields are low attributed to various production constraints. This review paper highlights advances in genetics, genomics and breeding to improve the productivity of groundnut. Genetic studies concerning inheritance, genetic variability and heritability, combining ability and trait correlations have provided a better understanding of the crop's genetics to develop appropriate breeding strategies for target traits. Several improved lines and sources of variability have been identified or developed for various economically important traits through conventional breeding. Significant advances have also been made in groundnut genomics including genome sequencing, marker development and genetic and trait mapping. These advances have led to a better understanding of the groundnut genome, discovery of genes/variants for traits of interest and integration of marker-assisted breeding for selected traits. The integration of genomic tools into the breeding process accompanied with increased precision of yield trialing and phenotyping will increase the efficiency and enhance the genetic gain for release of improved groundnut varieties.

Crop genome-wide association study: a harvest of biological relevance

With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.

Adapting legume crops to climate change using genomic approaches

Our agricultural system and hence food security is threatened by combination of events, such as increasing population, the impacts of climate change, and the need to a more sustainable development. Evolutionary adaptation may help some species to overcome environmental changes through new selection pressures driven by climate change. However, success of evolutionary adaptation is dependent on various factors, one of which is the extent of genetic variation available within species. Genomic approaches provide an exceptional opportunity to identify genetic variation that can be employed in crop improvement programs. In this review, we illustrate some of the routinely used genomics-based methods as well as recent breakthroughs, which facilitate assessment of genetic variation and discovery of adaptive genes in legumes. Although additional information is needed, the current utility of selection tools indicate a robust ability to utilize existing variation among legumes to address the challenges of climate uncertainty.

Sequencing Analysis of Genetic Loci for Resistance for Late Leaf Spot and Rust in Peanut (Arachis hypogaea L.)

The aim of this study was to identify candidate resistance genes for late leaf spot (LLS) and rust diseases in peanut (Arachis hypogaea L.). We used a double-digest restriction-site associated DNA sequencing (ddRAD-Seq) technique based on next-generation sequencing (NGS) for genotyping analysis across the recombinant inbred lines (RILs) derived from a cross between a susceptible line, TAG 24, and a resistant line, GPBD 4. A total of 171 SNPs from the ddRAD-Seq together with 282 markers published in the previous studies were mapped on a genetic map covering 1510.1 cM. Subsequent quantitative trait locus (QTL) analysis revealed major genetic loci for LLS and rust resistance on chromosomes A02 and A03, respectively. Heterogeneous inbred family-derived near isogenic lines and the pedigree of the resistant gene donor, A. cardenasii Krapov. & W.C. Greg., including the resistant derivatives of ICGV 86855 and VG 9514 as well as GPBD 4, were employed for whole-genome resequencing analysis. The results indicated the QTL candidates for LLS and rust resistance were located in 1.4- and 2.7-Mb genome regions on A02 and A03, respectively. In these regions, four and six resistance-related genes with deleterious mutations were selected as candidates for LLS and rust resistance, respectively. These delimited genomic regions may be beneficial in breeding programs aimed at improving disease resistance and enhancing peanut productivity.

Genetic imprints of domestication for disease resistance, oil quality, and yield component traits in groundnut (Arachis hypogaea L.)

Ploidy difference between wild Arachis species and cultivated genotypes hinder transfer of useful alleles for agronomically important traits. To overcome this genetic barrier, two synthetic tetraploids, viz., ISATGR 1212 (A. duranensis ICG 8123 × A. ipaensis ICG 8206) and ISATGR 265-5A (A. kempff-mercadoi ICG 8164 × A. hoehnei ICG 8190), were used to generate two advanced backcross (AB) populations. The AB-populations, namely, AB-pop1 (ICGV 91114 × ISATGR 1212) and AB-pop2, (ICGV 87846 × ISATGR 265-5A) were genotyped with DArT and SSR markers. Genetic maps were constructed for AB-pop1 and AB-pop2 populations with 258 loci (1415.7 cM map length and map density of 5.5 cM/loci) and 1043 loci (1500.8 cM map length with map density of 1.4 cM/loci), respectively. Genetic analysis identified large number of wild segments in the population and provided a good source of diversity in these populations. Phenotyping of these two populations identified several introgression lines with good agronomic, oil quality, and disease resistance traits. Quantitative trait locus (QTL) analysis showed that the wild genomic segments contributed favourable alleles for foliar disease resistance while cultivated genomic segments mostly contributed favourable alleles for oil quality and yield component traits. These populations, after achieving higher stability, will be useful resource for genetic mapping and QTL discovery for wild species segments in addition to using population progenies in breeding program for diversifying the gene pool of cultivated groundnut.