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Iron deficiency chlorosis is an important abiotic stress affecting groundnut production worldwide in calcareous and alkaline soils with a pH of 7.5–8.5. To identify genomic regions controlling iron deficiency chlorosis resistance in groundnut, the recombinant inbred line population from the cross TAG 24 × ICGV 86031 was evaluated for associated traits like visual chlorosis rating and SPAD chlorophyll meter reading across three crop growth stages for two consecutive years. Thirty‐two QTLs were identified for visual chlorosis rating (3.9%–31.8% phenotypic variance explained [PVE]) and SPAD chlorophyll meter reading [3.8%–11% PVE] across three stages over 2 years. This is the first report of identification of QTLs for iron deficiency chlorosis resistance‐associated traits in groundnut. Three major QTLs (>10% PVE) were identified at severe stage, while majority of other QTLs were having small effects. Interestingly, two major QTLs for visual chlorosis rating at 60 days (2013) and 90 days (2014) were located at same position on LG AhXIII. The identified QTLs/markers after validation across diverse genetic material could be used in genomics‐assisted breeding. [ABSTRACT FROM AUTHOR]

Key message: Groundnut has entered now in post-genome era enriched with optimum genomic and genetic resources to facilitate faster trait dissection, gene discovery and accelerated genetic improvement for developing climate-smart varieties. Cultivated groundnut or peanut (Arachis hypogaea), an allopolyploid oilseed crop with a large and complex genome, is one of the most nutritious food. This crop is grown in more than 100 countries, and the low productivity has remained the biggest challenge in the semiarid tropics. Recently, the groundnut research community has witnessed fast progress and achieved several key milestones in genomics research including genome sequence assemblies of wild diploid progenitors, wild tetraploid and both the subspecies of cultivated tetraploids, resequencing of diverse germplasm lines, genome-wide transcriptome atlas and cost-effective high and low-density genotyping assays. These genomic resources have enabled high-resolution trait mapping by using germplasm diversity panels and multi-parent genetic populations leading to precise gene discovery and diagnostic marker development. Furthermore, development and deployment of diagnostic markers have facilitated screening early generation populations as well as marker-assisted backcrossing breeding leading to development and commercialization of some molecular breeding products in groundnut. Several new genomics applications/technologies such as genomic selection, speed breeding, mid-density genotyping assay and genome editing are in pipeline. The integration of these new technologies hold great promise for developing climate-smart, high yielding and more nutritious groundnut varieties in the post-genome era. [ABSTRACT FROM AUTHOR]

Summary: Bacterial wilt, caused by Ralstonia solanacearum, is a devastating disease affecting over 350 plant species. A few peanut cultivars were found to possess stable and durable bacterial wilt resistance (BWR). Genomics‐assisted breeding can accelerate the process of developing resistant cultivars by using diagnostic markers. Here, we deployed sequencing‐based trait mapping approach, QTL‐seq, to discover genomic regions, candidate genes and diagnostic markers for BWR in a recombination inbred line population (195 progenies) of peanut. The QTL‐seq analysis identified one candidate genomic region on chromosome B02 significantly associated with BWR. Mapping of newly developed single nucleotide polymorphism (SNP) markers narrowed down the region to 2.07 Mb and confirmed its major effects and stable expressions across three environments. This candidate genomic region had 49 nonsynonymous SNPs affecting 19 putative candidate genes including seven putative resistance genes (R‐genes). Two diagnostic markers were successfully validated in diverse breeding lines and cultivars and could be deployed in genomics‐assisted breeding of varieties with enhanced BWR. [ABSTRACT FROM AUTHOR]

Summary: Cultivated peanut (Arachis hypogaea L.) is an important grain legume providing high‐quality cooking oil, rich proteins and other nutrients. Shelling percentage (SP) is the 2nd most important agronomic trait after pod yield and this trait significantly affects the economic value of peanut in the market. Deployment of diagnostic markers through genomics‐assisted breeding (GAB) can accelerate the process of developing improved varieties with enhanced SP. In this context, we deployed the QTL‐seq approach to identify genomic regions and candidate genes controlling SP in a recombinant inbred line population (Yuanza 9102 × Xuzhou 68‐4). Four libraries (two parents and two extreme bulks) were constructed and sequenced, generating 456.89–790.32 million reads and achieving 91.85%–93.18% genome coverage and 14.04–21.37 mean read depth. Comprehensive analysis of two sets of data (Yuanza 9102/two bulks and Xuzhou 68‐4/two bulks) using the QTL‐seq pipeline resulted in discovery of two overlapped genomic regions (2.75 Mb on A09 and 1.1 Mb on B02). Nine candidate genes affected by 10 SNPs with non‐synonymous effects or in UTRs were identified in these regions for SP. Cost‐effective KASP (Kompetitive Allele‐Specific PCR) markers were developed for one SNP from A09 and three SNPs from B02 chromosome. Genotyping of the mapping population with these newly developed KASP markers confirmed the major control and stable expressions of these genomic regions across five environments. The identified candidate genomic regions and genes for SP further provide opportunity for gene cloning and deployment of diagnostic markers in molecular breeding for achieving high SP in improved varieties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Peanut genomics is very challenging due to its inherent problem of genetic architecture. Blockage of gene flow from diploid wild relatives to the tetraploid; cultivated peanut, recent polyploidization combined with self pollination, and the narrow genetic base of the primary genepool have resulted in low genetic diversity that has remained a major bottleneck for genetic improvement of peanut. Harnessing the rich source of wild relatives has been negligible due to differences in ploidy level as well as genetic drag and undesirable alleles for low yield. Lack of appropriate genomic resources has severely hampered molecular breeding activities, and this crop remains among the less-studied crops. The last five years, however, have witnessed accelerated development of genomic resources such as development of molecular markers, genetic and physical maps, generation of expressed sequenced tags (ESTs), development of mutant resources, and functional genomics platforms that facilitate the identification of QTLs and discovery of genes associated with tolerance/resistance to abiotic and biotic stresses and agronomic traits. Molecular breeding has been initiated for several traits for development of superior genotypes. The genome or at least gene space sequence is expected to be available in near future and this will further accelerate use of biotechnological approaches for peanut improvement. [Copyright &y& Elsevier]

Genomic regions governing water deficit stress tolerance were identified in peanut using a recombinant inbred line (RIL) population derived from an elite variety TMV 2 and its narrow leaf mutant TMV 2-NLM, which was evaluated over six-seasons at Dharwad (non-stress) and Tirupati (water-stress) in India. Stress condition could differentiate the RILs much better than the non-stress condition for the physiological traits. A linkage map with 700 markers was used to identify the quantitative trait loci (QTLs). Three sets of best linear unbiased predictions (BLUPs) were estimated for the drought tolerance traits for the rainy and post-rainy seasons at Dharwad and post-rainy seasons at Tirupati, and employed for single marker analysis, composite interval mapping and multiple QTL mapping. Of the 305 significant marker-trait associations for the 11 traits, only 21 were of major effect for pod yield per plant (PYPP), specific dry weight at 70 days after sowing (SDW_70) and specific leaf area at 70 DAS (SLA_70). Three major main effect QTLs were identified for PYPP with the highest phenotypic variance explained (PVE) of 10.5%. Nine QTLs with the highest PVE of 18.4% were identified for SDW_70, of which four QTLs were also governing SLA_70 with the highest PVE of 15.7%. A few of them were also involved in epistatic interactions, and formed multiple QTL mapping models. Five major QTLs for SDW_70 were stable over both the locations. Candidate genes with SNPs and AhMITE1 insertion were identified for the major QTL regions. A rare nonsynonymous SNP at Ah02_1558700 within the gene ArahyW1P0U6 governing PYPP was detected. Functional analysis of these candidate genes may be useful for future genetic modifications in addition to validating and using the linked markers for improving drought tolerance in peanut. [ABSTRACT FROM AUTHOR]

Iron deficiency chlorosis (IDC) under calcareous and alkaline soils is a significant abiotic stress affecting the growth and yield of peanut. In this study, the genomic regions governing IDC tolerance were mapped using a recombinant inbred line (RIL) population derived from TMV 2 (susceptible to IDC) and TMV 2-NLM (tolerant to IDC), which was phenotyped during the rainy seasons of 2019 and 2020 in the iron-deficient calcareous plots. The best linear unbiased prediction (BLUP) values for IDC tolerance traits like visual chlorotic rating (VCR), and SPAD chlorophyll meter reading (SCMR) were used for QTL analysis along with a genetic map carrying 700 GBS-derived SNP, AhTE and SSR markers. In total, 11 and 12 main-effect QTLs were identified for VCR and SCMR, respectively. Among them three QTLs were major with the phenotypic variance explained (PVE) of 10.3–34.4% for VCR, and two QTL were major for SCMR with PVE of 11.5–11.7%. A region (159.3–178.3 cM) on chromosome Ah13 carrying two QTLs (one each for VCR and SCMR) was consistent with the previous report. A SNP marker, Ah14_138037990 identified from single marker analysis for VCR was located in the intronic region of the gene Arahy.QA0C1, which is important for protein-binding. Overall, this study identified new QTLs and also validated QTL for IDC tolerance. These genomic resources could be useful for genomics-assisted breeding of peanut for IDC tolerance. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]