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Peanut (Arachis hypogaea L.) is a great plant protein source for human diet since it has high protein content in the kernel. Therefore, seed protein content (SPC) is considered a major agronomic and quality trait in peanut breeding. However, few genetic loci underlying SPC have been identified in peanuts, and the underlying regulatory mechanisms remain unknown, limiting the effectiveness of breeding for high-SPC peanut varieties. In this study, a major QTL (qSPCB10.1) controlling peanut SPC was identified within a 2.3 Mb interval in chromosome B10 by QTL-seq using a recombinant inbred line population derived from parental lines with high and low SPCs, respectively. Sequence comparison, transcriptomic analysis, and annotation analysis of the qSPCB10.1 locus were performed. Six differentially expressed genes with sequence variations between two parents were identified as candidate genes underlying qSPCB10.1. Further locus interaction analysis revealed that qSPCB10.1 could not affect the seed oil accumulation unless qOCA08.1XH13 was present, a high seed oil content (SOC) allele for a major QTL underlying SOC. In summary, our study provides a basis for future investigation of the genetic basis of seed protein accumulation and facilitates marker-assisted selection for developing high-SPC peanut genotypes. [ABSTRACT FROM AUTHOR]

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops. [ABSTRACT FROM AUTHOR]

[Display omitted] • A major QTL for aflatoxin production resistance was identified in a 1.98 Mbp genomic region by both NGS-based QTL-seq approach and genetic linkage analysis. • A gene namely AhAftr1 , annotated as "NB-LRRs protein gene" with structural variation (SV) in the LRRs domain between parental lines was identified by fine-mapping using a secondary segregation mapping population. • Transgenic experiments confirmed that the SV of AhAftr1 confers aflatoxin production resistance. • RNA-Seq and differential gene expression analysis indicated that AhAftr1 might be involved in disease resistance via the ETI pathway. • Thirty-six lines were identified from a special panel of germplasm accessions and breeding lines by using AFTR.Del.A07, which was developed based on the SV, and their aflatoxin content were decreased by over 77.67% compared to the susceptible control Zhonghua12. Peanut is susceptible to infection of Aspergillus fungi and conducive to aflatoxin contamination, hence developing aflatoxin-resistant variety is highly meaningful. Identifying functional genes or loci conferring aflatoxin resistance and molecular diagnostic marker are crucial for peanut breeding. This work aims to (1) identify candidate gene for aflatoxin production resistance, (2) reveal the related resistance mechanism, and (3) develop diagnostic marker for resistance breeding program. Resistance to aflatoxin production in a recombined inbred line (RIL) population derived from a high-yielding variety Xuhua13 crossed with an aflatoxin-resistant genotype Zhonghua 6 was evaluated under artificial inoculation for three consecutive years. Both genetic linkage analysis and QTL-seq were conducted for QTL mapping. The candidate gene was further fine-mapped using a secondary segregation mapping population and validated by transgenic experiments. RNA-Seq analysis among resistant and susceptible RILs was used to reveal the resistance pathway for the candidate genes. The major effect QTL qAFTRA07.1 for aflatoxin production resistance was mapped to a 1.98 Mbp interval. A gene, AhAftr1 (Arachis hypogaea Aflatoxin resistance 1), was detected structure variation (SV) in leucine rich repeat (LRR) domain of its production, and involved in disease resistance response through the effector-triggered immunity (ETI) pathway. Transgenic plants with overexpression of AhAftr1 (ZH6) exhibited 57.3% aflatoxin reduction compared to that of AhAftr1 (XH13). A molecular diagnostic marker AFTR.Del.A07 was developed based on the SV. Thirty-six lines, with aflatoxin content decrease by over 77.67% compared to the susceptible control Zhonghua12 (ZH12), were identified from a panel of peanut germplasm accessions and breeding lines through using AFTR.Del.A07. Our findings would provide insights of aflatoxin production resistance mechanisms and laid meaningful foundation for further breeding programs. [ABSTRACT FROM AUTHOR]

High sugar content in peanut seeds is one of the major breeding objectives for peanut flavor improvement. In order to explore the genetic control of sugar accumulation in peanut kernels, we constructed a recombinant inbred line population of 256 F2:6-7 lines derived from the Luhua11 × 06B16 cross. A highresolution genetic map was constructed with 3692 bin markers through whole genome re-sequencing. The total map distance was 981.65 cM and the average bin marker distance was 0.27cM. A major stable QTL region (qSCB09/qSSCB09) was identified on linkage group (LG) B09 associated with both sucrose content (SC) and soluble sugar content (SSC) explaining 21.51-33.58% phenotypic variations. This major QTL region was consistently detected in three environments and mapped within a physical interval of 1.56 Mb on chromosome B09, and six candidate genes were identified. These results provide valuable information for further map-based cloning of favorable allele for sugar content in peanut. [ABSTRACT FROM AUTHOR]

Drought is a destructive abiotic stress that affects all critical stages of peanut growth such as emergence, flowering, pegging, and pod filling. The development of a drought-tolerant variety is a sustainable strategy for long-term peanut production. The U.S. mini-core peanut germplasm collection was evaluated for drought tolerance to the middle-season drought treatment phenotyping for pod weight, pod count, relative water content (RWC), specific leaf area (SLA), leaf dry matter content (LDMC), and drought rating. A genome-wide association study (GWAS) was performed to identify minor and major QTLs. A total of 144 QTLs were identified, including 18 significant QTLs in proximity to 317 candidate genes. Ten significant QTLs on linkage groups (LGs) A03, A05, A06, A07, A08, B04, B05, B06, B09, and B10 were associated with pod weight and pod count. RWC stages 1 and 2 were correlated with pod weight, pod count, and drought rating. Six significant QTLs on LGs A04, A07, B03, and B04 were associated with RWC stages 1 and 2. Drought rating was negatively correlated with pod yield and pod count and was associated with a significant QTL on LG A06. Many QTLs identified in this research are novel for the evaluated traits, with verification that the pod weight shared a significant QTL on chromosome B06 identified in other research. Identified SNP markers and the associated candidate genes provide a resource for molecular marker development. Verification of candidate genes surrounding significant QTLs will facilitate the application of marker-assisted peanut breeding for drought tolerance. [ABSTRACT FROM AUTHOR]

Screening for nitrogen (N)-efficient germplasm to achieve high yield and high N efficiency is an important strategy to enhance the sustainability of modern agriculture. In this study, 127 peanut (Arachis hypogaea L.) germplasm resources were comprehensively evaluated by seedling hydroponics and field. At the seedling stage, with the range of low-nitrogen screening concentrations gradually narrowed through a comprehensive membership function analysis, standard normal distribution test, and variance analysis, we found that 0.15 mM N for 24 days could be the optimal condition for evaluating the N efficiency of peanuts. Through principal component analysis and correlation analysis, dry matter weight, root/shoot ratio, N content, N accumulation, N-use efficiency, and N use index were considered to be the N efficiency parameters, and a regression mathematical model was established accordingly. In the field, peanut genotypes that differ in resistance to low-nitrogen stress were evaluated by a yield nitrogen efficiency index under normal nitrogen and no nitrogen applications to verify the results at the seedling stage. Based on the multiple phenotypic analysis, N-efficient and N-inefficient peanut genotypes among germplasm were screened, and a comprehensive evaluation system was established to provide the theoretical basis for peanut breeding and cultivation techniques. [ABSTRACT FROM AUTHOR]

Arachis lutescens Krapov. & Rigoni 1958 is an important species due to their potentially extensive applications for cultivated peanut breeding. The whole chloroplast genome of A. lutescens was successfully assembled and annotated for the first time. The complete chloroplast genome of A. lutescens is a typically circular structure of 156,398 bp with a GC content of 36.3%. It comprises a large single-copy (LSC) region of 85,950 bp, a small single-copy (SSC) region of 18,800 bp, and two inverted repeat regions (IRs) of 25,824 bp, each. The plastome of A. lutescens contains a total of 125 genes, including 81 protein-coding genes, 36 tRNAs, and eight rRNAs. The phylogenetic analysis strongly supports the close relationship between A. lutescens and cultivated peanut clades. This study contributes to our understanding of the molecular characteristics and evolutionary relationships of this plant species. [ABSTRACT FROM AUTHOR]

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively. [ABSTRACT FROM AUTHOR]

Products from peanut seeds are nutritious for human and/or animal consumption. Peanut seed nutritional quality is determined by the genotype (G) of cultivars or accessions, environmental conditions (E), and their interactions (G × E). To evaluate the effects of genotype, environment, and their interactions on seed nutritional quality, 52 peanut germplasm accessions, which vary in oil content and fatty acid composition, were planted at three locations (Citra, FL; Byron, GA; and Clovis, NM) for 2 years (2017 and 2018). The harvested seeds were analyzed for protein, oil, and fatty acid composition using an N analyzer, nuclear magnetic resonance, and gas chromatography, respectively. Significant effects of accession, FAD2 gene, where FAD is fatty acid desaturase, location, year, and their interactions on the investigated traits were evaluated. The average oil content from accessions grown in Florida (51.1%) or Georgia (50.7%) was significantly higher than those grown in New Mexico (45.8%), whereas the average protein content from accessions grown in Florida (23.2%) was significantly lower than those grown in New Mexico (25.4%) or Georgia (24.8%). This is expected as there is a negative correlation between oil and protein content. After genotyping and classification (A/A, A/W, and G/W) for the FAD2A/FAD2B genes, the nutritional quality variations were further evaluated at the levels of the specific haplotype of FAD2A/FAD2B and explicit environmental index. The average prediction accuracy of seed nutritional quality trait values from the prediction model demonstrated that predicting trait values in new environments is feasible. The results of this study provide insights into the seed nutritional analysis, germplasm evaluation, and peanut breeding, cultivation, and production to peanut breeders, curators, farmers, and product processors. Core Ideas: Genotype, environment, and their interactions affect peanut seed nutritional quality.The FAD2A/FAD2B gene (F) effect significantly alters seed fatty acid composition.Location (L) effect significantly changes seed oil and protein content.Photothermal time may be a good parameter for representing the growing location environmental conditions for data analysis.Recommendation was given to farmers and processors on how to cultivate the right peanut varieties and purchase the right seeds from specific regions. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Peanut pod rot is one of the major plant diseases affecting peanut production and quality over China, which causes large productivity losses and is challenging to control. To improve the disease resistance of peanuts, breeding is one significant strategy. Crucial preventative and management measures include grading peanut pod rot and screening high-contributed genes that are highly resistant to pod rot should be carried out. A machine vision-based grading approach for individual cases of peanut pod rot was proposed in this study, which avoids timeconsuming, labor-intensive, and inaccurate manual categorization and provides dependable technical assistance for breeding studies and peanut pod rot resistance. The Shuffle Attention module has been added to the YOLOv5s (You Only Look Once version 5 small) feature extraction backbone network to overcome occlusion, overlap, and adhesions in complex backgrounds. Additionally, to reduce missing and false identification of peanut pods, the loss function CIoU (Complete Intersection over Union) was replaced with EIoU (Enhanced Intersection over Union). The recognition results can be further improved by introducing grade classification module, which can read the information from the identified RGB images and output data like numbers of non-rotted and rotten peanut pods, the rotten pod rate, and the pod rot grade. The Precision value of the improved YOLOv5s reached 93.8%, which was 7.8%, 8.4%, and 7.3% higher than YOLOv5s, YOLOv8n, and YOLOv8s, respectively; the mAP (mean Average Precision) value was 92.4%, which increased by 6.7%, 7.7%, and 6.5%, respectively. Improved YOLOv5s has an average improvement of 6.26% over YOLOv5s in terms of recognition accuracy: that was 95.7% for non-rotted peanut pods and 90.8% for rotten peanut pods. This article presented a machine vision-based grade classification method for peanut pod rot, which offered technological guidance for selecting high-quality cultivars with high resistance to pod rot in peanut. [ABSTRACT FROM AUTHOR]

Peanut is a leguminous crop with an indeterminate growth habit that will continuously flower during the entire reproductive development stage. Flowering redundancy adversely affects the yield and quality of peanut. In this study, eight flowering and five yield-related traits were collected and comprehensively evaluated from a diverse germplasm panel consisting of 232 peanut accessions, aiming to provide a theoretical basis for improving the flowering habit and yield for future peanut breeding efforts. As a result, large phenotypic diversity was observed in 13 traits. Most of the traits suggested high heritability, except high effective flowering duration (HEFD), days to 100% flower cessation (DTC100), and yield per plant (YPP). Days to 90% flower cessation (DTC90), days to DTC100, flowering duration (FD), and low effective flowering duration (LEFD) showed significant negative correlations with 100-seed weight, 100-pod weight, YPP, average weight per pod, and shelling percentage. Principal component analysis (PCA) suggested that flowering redundancy traits as well as yield-related traits contributed more to the first three PCs when compared to other traits. This study addresses the lack of peanut flowering phenotypic and genetic diversity and lays the foundation for in-depth research on redundancy-related genes in peanut flowering. [ABSTRACT FROM AUTHOR]

Wild relatives comprise a diverse genetic source from which to introduce beneficial alleles for peanut breeding. However, when using wild species for breeding, unadapted agronomic characteristics are often co‐inherited along with favorable traits. Previously, an advanced backcross population was developed from a cross between Arachis hypogaea and the wild‐derived induced allotetraploid (Arachis batizocoi × Arachis stenosperma)4x to incorporate nematode resistance genes into elite peanut. We observed additional wild alleles unintentionally inherited throughout the genome, possibly causing variation in several pod and seed characteristics. To identify the wild introgressions controlling the variation in pod constriction (PC), a critical trait for peanut's acceptability and marketability, we performed an association analysis using 419 SNPs and phenotypic data of 72 BC3F2/3 families. An introgression on the top of chromosome B08 (pod constriction‐quantitative trait locus [PC‐QTL], 3.7 Mbp) derived from A. batizocoi was found to be controlling 47.3% of the PC variation. To confirm the PC‐QTL, six BC3F4 families harboring the wild loci in a homozygous or heterozygous state were selected and used for segregation analysis. All progenies from the backcross families carrying the homozygous allele had severely constricted pods, whereas progenies from families with the heterozygous allele were segregated for PC. Finally, analyses using end‐point genotyping with four linked SNP assays confirmed the association of the PC‐QTL with PC. The present work studied PC and developed DNA markers to assist the selection against constricted pods while retaining the new and beneficial wild‐derived genes. Core Ideas: In peanut, pod and seed traits are important for yield and quality.When incorporating beneficial traits from wild germplasm, unadapted wild pod and seed traits are also inherited.A major pod constriction quantitative trait locus was discovered in a backcross population harboring wild‐derived segments.Molecular markers developed and validated can be used for selection against wild‐type deep pod constriction. [ABSTRACT FROM AUTHOR]

A-genome Arachis species (AA; 2n = 2x = 20) are commonly used as secondary germplasm sources in cultivated peanut breeding, Arachis hypogaea L. (AABB; 2n = 4x = 40), for the introgression of various biotic and abiotic stress resistance genes. Genome doubling is critical to overcoming the hybridization barrier of infertility that arises from ploidy-level differences between wild germplasm and cultivated peanuts. To develop improved genome doubling methods, four trials of various concentrations of the mitotic inhibitor treatments colchicine, oryzalin, and trifluralin were tested on the seedlings and seeds of three A-genome species, A. cardenasii, A. correntina, and A. diogoi. A total of 494 seeds/seedlings were treated in the present four trials, with trials 1 to 3 including different concentrations of the three chemical treatments on seedlings, and trial 4 focusing on the treatment period of 5 mM colchicine solution treatment of seeds. A small number of tetraploids were produced from the colchicine and oryzalin gel treatments of seedlings, but all these tetraploid seedlings reverted to diploid or mixoploid states within six months of treatment. In contrast, the 6-h colchicine solution treatment of seeds showed the highest tetraploid conversion rate (6–13% of total treated seeds or 25–40% of surviving seedlings), and the tetraploid plants were repeatedly tested as stable tetraploids. In addition, visibly and statistically larger leaves and flowers were produced by the tetraploid versions of these three species compared to their diploid versions. As a result, stable tetraploid plants of each A-genome species were produced, and a 5 mM colchicine seed treatment is recommended for A-genome and related wild Arachis species genome doubling. [ABSTRACT FROM AUTHOR]

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data thatwere used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs. [ABSTRACT FROM AUTHOR]

Background: Calmodulins (CaMs)/CaM-like proteins (CMLs) are crucial Ca2+-binding sensors that can decode and transduce Ca2+ signals during plant development and in response to various stimuli. The CaM/CML gene family has been characterized in many plant species, but this family has not yet been characterized and analyzed in peanut, especially for its functions in response to Ralstonia solanacearum. In this study, we performed a genome-wide analysis to analyze the CaM/CML genes and their functions in resistance to R. solanacearum. Results: Here, 67, 72, and 214 CaM/CML genes were identified from Arachis duranensis, Arachis ipaensis, and Arachis hypogaea, respectively. The genes were divided into nine subgroups (Groups I-IX) with relatively conserved exon‒intron structures and motif compositions. Gene duplication, which included whole-genome duplication, tandem repeats, scattered repeats, and unconnected repeats, produced approximately 81 pairs of homologous genes in the AhCaM/CML gene family. Allopolyploidization was the main reason for the greater number of AhCaM/CML members. The nonsynonymous (Ka) versus synonymous (Ks) substitution rates (less than 1.0) suggested that all homologous pairs underwent intensive purifying selection pressure during evolution. AhCML69 was constitutively expressed in different tissues of peanut plants and was involved in the response to R. solanacearum infection. The AhCML69 protein was localized in the cytoplasm and nucleus. Transient overexpression of AhCML69 in tobacco leaves increased resistance to R. solanacearum infection and induced the expression of defense-related genes, suggesting that AhCML69 is a positive regulator of disease resistance. Conclusions: This study provides the first comprehensive analysis of the AhCaM/CML gene family and potential genetic resources for the molecular design and breeding of peanut bacterial wilt resistance. [ABSTRACT FROM AUTHOR]

Peanuts (Arachis hypogaea L.) are important high-protein and oil-containing legume crops adapted to arid to semi-arid regions. The yield and quality of peanuts are complex quantitative traits that show high environmental influence. In this study, a recombinant inbred line population (RIL) (Valencia-C × JUG-03) was developed and phenotyped for nine traits under two environments. A genetic map was constructed using 1323 SNP markers spanning a map distance of 2003.13 cM. Quantitative trait loci (QTL) analysis using this genetic map and phenotyping data identified seventeen QTLs for nine traits. Intriguingly, a total of four QTLs, two each for 100-seed weight (HSW) and shelling percentage (SP), showed major and consistent effects, explaining 10.98% to 14.65% phenotypic variation. The major QTLs for HSW and SP harbored genes associated with seed and pod development such as the seed maturation protein-encoding gene, serine-threonine phosphatase gene, TIR-NBS-LRR gene, protein kinase superfamily gene, bHLH transcription factor-encoding gene, isopentyl transferase gene, ethylene-responsive transcription factor-encoding gene and cytochrome P450 superfamily gene. Additionally, the identification of 76 major epistatic QTLs, with PVE ranging from 11.63% to 72.61%, highlighted their significant role in determining the yield- and quality-related traits. The significant G × E interaction revealed the existence of the major role of the environment in determining the phenotype of yield-attributing traits. Notably, the seed maturation protein-coding gene in the vicinity of major QTLs for HSW can be further investigated to develop a diagnostic marker for HSW in peanut breeding. This study provides understanding of the genetic factor governing peanut traits and valuable insights for future breeding efforts aimed at improving yield and quality. [ABSTRACT FROM AUTHOR]

This article presents a study on the use of gene editing to enhance the nutritional quality of peanuts. Peanuts are an important crop for addressing malnutrition and ensuring food security. The study focuses on reducing the levels of very long chain fatty acids (VLCFAs) in peanuts, as high levels of these fatty acids are associated with health issues. The researchers used the CRISPR/Cas9 system to genetically disrupt specific genes in peanuts, resulting in peanut mutants with significantly reduced VLCFA content. The findings of this study have implications for improving traits in peanut breeding programs, such as yield, quality, and stress resistance. [Extracted from the article]

Cultivated peanut or groundnut (Arachis hypogaea L.) is a grain legume grown in many developing countries by smallholder farmers for food, feed, and/or income. The speciation of the cultivated species, that involved polyploidization followed by domestication, greatly reduced its variability at the DNA level. Mobilizing peanut diversity is a prerequisite for any breeding program for overcoming the main constraints that plague production and for increasing yield in farmer fields. In this study, the Groundnut Improvement Network for Africa assembled a collection of 1,049 peanut breeding lines, varieties, and landraces from 9 countries in Africa. The collection was genotyped with the Axiom_Arachis2 48K SNP array and 8,229 polymorphic single nucleotide polymorphism (SNP) markers were used to analyze the genetic structure of this collection and quantify the level of genetic diversity in each breeding program. A supervised model was developed using dapc to unambiguously assign 542, 35, and 172 genotypes to the Spanish, Valencia, and Virginia market types, respectively. Distance-based clustering of the collection showed a clear grouping structure according to subspecies and market types, with 73% of the genotypes classified as fastigiata and 27% as hypogaea subspecies. Using STRUCTURE , the global structuration was confirmed and showed that, at a minimum membership of 0.8, 76% of the varieties that were not assigned by dapc were actually admixed. This was particularly the case of most of the genotype of the Valencia subgroup that exhibited admixed genetic heritage. The results also showed that the geographic origin (i.e. East, Southern, and West Africa) did not strongly explain the genetic structure. The gene diversity managed by each breeding program, measured by the expected heterozygosity, ranged from 0.25 to 0.39, with the Niger breeding program having the lowest diversity mainly because only lines that belong to the fastigiata subspecies are used in this program. Finally, we developed a core collection composed of 300 accessions based on breeding traits and genetic diversity. This collection, which is composed of 205 genotypes of fastigiata subspecies (158 Spanish and 47 Valencia) and 95 genotypes of hypogaea subspecies (all Virginia), improves the genetic diversity of each individual breeding program and is, therefore, a unique resource for allele mining and breeding. [ABSTRACT FROM AUTHOR]

Sclerotium rolfsii is a necrotrophic fungus that causes devastating stem rot disease on peanuts under high temperature and humidity conditions. Developing more resistant stem rot varieties is a better disease management strategy from an economic and environmental point of view. In this study, 105 peanut accessions from the USDA mini-core collection were screened for resistance against stem rot under growth chamber and greenhouse conditions at the New Mexico State University Agricultural Science Center in Clovis, NM, USA. The accessions were inoculated with a virulent isolate of S. rolfsii, and disease development was monitored and scored at 5, 7, 9, 11, and 17-days post-inoculation (DPI). Mean disease score, lesion length and broad sense heritability were calculated, and cluster analysis was performed. Though the accessions were not significantly different for the mean disease parameters, numerical differences were observed in disease score and lesion length among the accessions at 17-DPI. Eleven accessions showed moderate resistance and performed better than stem rot resistant commercial cultivar, 'G03L', and ninety-four accessions were susceptible based on mean disease scores under both growing conditions. Low broad-sense heritability for disease score (0.05 to 0.06) and lesion length (0.03 to 0.06) under different environments indicates that most of the variance for these traits is due to environmental effects, and that stem rot resistance can be a complex trait. The identified resistant accessions can be used for genotyping and finding major QTLs through genome-wide association mapping to dissect genetic basis of stem rot resistance in peanuts. This information would be helpful to the peanut breeding program to develop more resistant varieties. [ABSTRACT FROM AUTHOR]

To explore the correlation between peanut seed biochemical quality and stress resistance under germination stress, seed samples of 90 peanut genotypes were subjected to different types of stress including salt (0.12 mol/L NaCl), alkali (0.1 mol/L NaHCO3, pH 8.3), drought (15% PEG 6000), and low temperature (2°C soaking for 72 h). Germination indicators and peanut seed biochemical quality were analyzed by canonical correlation analysis. It was discovered that, oleic acid was positively correlated with vigor index under salt stress, while oleic acid was negatively correlated with germination percentage under low temperature stress. Oleic acid was beneficial to peanut seed germination under salt stress, but not conducive to germination under low temperature stress. There was a positive correlation between palmitic acid and germination index under drought stress, and palmitic acid was beneficial to peanut seed germination under drought stress. Vitamin E was beneficial to seed germination under alkali stress. The above results were expected to be helpful for exploring the mechanism of stress resistance during peanut germination, and also for peanut stress-resistant breeding. [ABSTRACT FROM AUTHOR]

The production and breeding of peanuts was restricted by the frequently extreme climatic conditions in Guangdong province, China. To understand the influence of drought and pluvial climates on peanut traits and yield, a phenotypic investigation of seventy peanut cultivars was conducted from 2018 to 2022; comprehensive field meteorological data collection, and typical drought (2021) and pluvial (2022) climates were recorded. The results revealed that the cultivars achieved the highest single plant pod weight (SPPW) and single plant seed weight (SPSW) of 61.03 g and 45.84 g, respectively, in drought conditions, followed by the control, and finally the pluvial. The SPPW, SPSW and eight agronomy traits exhibited significant differences across the different climatic conditions. Correlation analysis revealed the yield traits and key yield-related traits were positively or negatively correlated with soil water content (SWC), total global radiation (TGR), total precipitation (TP) and total net radiation (TNR). The intermediate and Spanish type cultivars were more stable and productive than the other botanical types of cultivars, commercial varieties exhibited better performance than landraces, and seven cultivars were identified with good production potential, under drought and pluvial conditions. Our study showed that pluvial climate was detrimental to peanut yield, and the SPPW and SPSW were significantly influenced by climates with genotype differences. [ABSTRACT FROM AUTHOR]

Peanut yield in southern China is usually limited by calcium deficiency in soil. Most previous studies have found that small-seed varieties showed higher tolerance than large-seed varieties (e.g. Virginia type) under calcium deficiency, however, our preliminary research found that sensitive varieties also existed in small-seed counterparts. Few studies have been conducted to characterize low-calcium tolerance among small-seed germplasms with genetic diversity, and the differences in physiological characteristics between sensitive and tolerant varieties has not been reported yet. Thus, in order to better understand such differences, the current study firstly collected and characterized a diversity germplasm panel consisting of 50 small-seed peanut genotypes via a 2-year field trial, followed by the physiological characterization in sensitive (HN032) and tolerant (HN035) peanut genotypes under calcium deficiency. As a result, the adverse effects brought by calcium deficiency on calcium uptake and distribution in HN032 was much larger than HN035. In details, calcium uptake in the aboveground part (leaves and stems) was reduced by 16.17% and 33.66%, while in the underground part (roots and pods), it was reduced by 13.69% and 68.09% under calcium deficiency for HN035 and HN032, respectively; The calcium distribution rate in the pods of HN035 was 2.74 times higher than HN032. The utilization efficiency of calcium in the pods of HN035 was 1.68 and 1.37 times than that of HN032 under calcium deficiency and sufficiency, respectively. In addition, under calcium deficiency conditions, the activities of antioxidant enzymes SOD, POD, and CAT, as well as the MDA content, were significantly increased in the leaves of HN032, peanut yield was significantly reduced by 22.75%. However, there were no significant changes in the activities of antioxidant enzymes, MDA content, and peanut yield in HN035. Therefore, higher calcium absorption and utilization efficiency may be the key factors maintaining peanut yield in calcium-deficient conditions for tolerant genotypes. This study lays a solid foundation for selecting low-calcium tolerant varieties in future peanut breeding. [ABSTRACT FROM AUTHOR]

Summary: Cultivated peanut (Arachis hypogaea L.) is an important oil and cash crop. Pod size is one of the major traits determining yield and commodity characteristic of peanut. Fine mapping of quantitative trait locus (QTL) and identification of candidate genes associated with pod size are essential for genetic improvement and molecular breeding of peanut varieties. In this study, a major QTL related to pod size, qAHPS07, was fine mapped to a 36.46 kb interval on chromosome A07 using F2, recombinant inbred line (RIL) and secondary F2 populations. qAHPS07 explained 38.6%, 23.35%, 37.48%, 25.94% of the phenotypic variation for single pod weight (SPW), pod length (PL), pod width (PW) and pod shell thickness (PST), respectively. Whole genome resequencing and gene expression analysis revealed that a RuvB‐like 2 protein coding gene AhRUVBL2 was the most likely candidate for qAHPS07. Overexpression of AhRUVBL2 in Arabidopsis led to larger seeds and plants than the wild type. AhRUVBL2‐silenced peanut seedlings represented small leaves and shorter main stems. Three haplotypes were identified according to three SNPs in the promoter of AhRUVBL2 among 119 peanut accessions. Among them, SPW, PW and PST of accessions carrying Hap_ATT represent 17.6%, 11.2% and 26.3% higher than those carrying Hap_GAC，respectively. In addition, a functional marker of AhRUVBL2 was developed. Taken together, our study identified a key functional gene of peanut pod size, which provides new insights into peanut pod size regulation mechanism and offers practicable markers for the genetic improvement of pod size‐related traits in peanut breeding. [ABSTRACT FROM AUTHOR]

Cultivated peanut (Arachis hypogaea L.) is an important economic and oilseed crop worldwide, providing high-quality edible oil and high protein content. Seed size/weight and oil content are two important determinants of yield and quality in peanut breeding. To identify key regulators controlling these two traits, two peanut cultivars with contrasting phenotypes were compared to each other, one having a larger seed size and higher oil content (Zhonghua16, ZH16 for short), while the second cultivar had smaller-sized seeds and lower oil content (Zhonghua6, ZH6). Whole transcriptome analyses were performed on these two cultivars at four stages of seed development. The results showed that ~40% of the expressed genes were stage-specific in each cultivar during seed development, especially at the early stage of development. In addition, we identified a total of 5356 differentially expressed genes (DEGs) between ZH16 and ZH6 across four development stages. Weighted gene co-expression network analysis (WGCNA) based on DEGs revealed multiple hub genes with potential roles in seed size/weight and/or oil content. These hub genes were mainly involved in transcription factors (TFs), phytohormones, the ubiquitin–proteasome pathway, and fatty acid synthesis. Overall, the candidate genes and co-expression networks detected in this study could be a valuable resource for genetic breeding to improve seed yield and quality traits in peanut. [ABSTRACT FROM AUTHOR]

In order to improve the breeding efficiency of edible peanut quality breeding and optimize the evaluation system, 21 traits (including appearance, nutritional, sensory quality) and 17 amino acid components of 10 edible peanut varieties were measured and analyzed. Results showed that the variation coefficient of appearance quality was in order of kernel length > 100-kernel weight > kernel width, the variation coefficient of nutritional quality was in order of sucrose content > oleic acid content > tocopherol > protein content > oil content. Amino acid analysis showed that amino acids composition in peanut kernel was complete, and the firstly restrict amino acid was Met + Cys, which had great improvement potential. Path analysis and decision analysis showed that the effect on protein content was in order of Asp> Gly> His> Pro> Tyr> Phe. There were significant correlations between sensory quality traits. Boiling sensory was significantly correlated with protein content. Roasting sensory was significantly correlated with sucrose and oleic acid content. According to principal component analysis, 6 principal components (cumulative contribution rate of 91.69%) were extracted. 9 quality traits were screened as key evaluation indexes, and 3 excellent comprehensive quality varieties were selected to provide reference for breeding of edible peanut quality. [ABSTRACT FROM AUTHOR]

Peanut is a high-yield economic crop with rich nutrition for both food and oil. It contains abundant nutrients as protein, sucrose, fructose and glucose. However, the evaluation of its taste characteristics still needs data support. The taste index data of 33 fresh edible peanut materals (including 9 cultirars and 24 advanced lines from Jihua 02-1-4 and Zhonghua 26 cross) were collected by Tastes Sensing System (TS-5000Z). Principal component analysis (PCA) was performed to evaluate and identify 5 basic tastes (sourness, sweetness, bitterness, saltiness and umami) and astringency. Through the testing of peanut samples, it was found that bitterness, astringency, umami, saltiness and sweetness were effective taste indicators of peanut samples. Through PCA analysis of effective taste indexes, saltiness, umami and sweetness contributed more to the first principal component, and saltiness, umami and bitterness contributed more to the second principal component. Nine varieties, including Fuhua 1, were significantly different from TC strains. When TC1-24 was separated by PCA clustering, umami, bitterness and sweetness contributed more to the first principal component, saltiness contributed the most to the second principal component, followed by bitterness. The PCA clustering analysis of 9 peanut varieties showed that saltiness contributed the most to the first principal component, followed by astringency; and sweetness contributed the most to the second principal component, followed by astringency aftertaste and bitterness. About bitterness, TC20-24 had the highest value of bitterness between 6.5 to 7.0, and the bitterness of other varieties was below 6.5. About astringency, TC5 and TC6 had low astringency and aftertaste-A, cultivar HTH and HZZ had the highest aftertaste-A, and these of other varieties were between 3.0 to 4.5. About sweetness, SLH had the highest value, while HZZ had the lowest. There were 16 varieties with sweetness value above 21, and most samples of TC strain had higher sweetness value. About umami and saltiness, 9 varieties and TC17, TC18 and TC19 had low values, TC20 to TC24 had relatively low values, and other TC strains had higher values. The umami and saltiness of JH403, JH43, JTH1 and JTH2 were close, and their umami and saltiness were the lowest. Through electronic tongue technology, it could concluded that taste indexes of fresh edible peanut are mainly umami, sweetness, saltiness, bitterness and astringency (aftertaste). And it might be viable to quickly and truly evaluate the taste of fresh edible peanut without statistical analysis and modeling, for breeding new peanut varieties with high sugar and high protein. [ABSTRACT FROM AUTHOR]

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.

Background: Peanut is an economically-important oilseed crop and needs a large amount of calcium for its normal growth and development. Calcium deficiency usually leads to embryo abortion and subsequent abnormal pod development. Different tolerance to calcium deficiency has been observed between different cultivars, especially between large and small-seed cultivars. Results: In order to figure out different molecular mechanisms in defensive responses between two cultivars, we treated a sensitive (large-seed) and a tolerant (small-seed) cultivar with different calcium levels. The transcriptome analysis identified a total of 58 and 61 differentially expressed genes (DEGs) within small-seed and large-seed peanut groups under different calcium treatments, and these DEGs were entirely covered by gene modules obtained via weighted gene co-expression network analysis (WGCNA). KEGG enrichment analysis showed that the blue-module genes in the large-seed cultivar were mainly enriched in plant-pathogen attack, phenolic metabolism and MAPK signaling pathway, while the green-module genes in the small-seed cultivar were mainly enriched in lipid metabolism including glycerolipid and glycerophospholipid metabolisms. By integrating DEGs with WGCNA, a total of eight hub-DEGs were finally identified, suggesting that the large-seed cultivar concentrated more on plant defensive responses and antioxidant activities under calcium deficiency, while the small-seed cultivar mainly focused on maintaining membrane features to enable normal photosynthesis and signal transduction. Conclusion: The identified hub genes might give a clue for future gene validation and molecular breeding to improve peanut survivability under calcium deficiency. [ABSTRACT FROM AUTHOR]

Background: Pod size is an important yield target trait for peanut breeding. However, the molecular mechanism underlying the determination of peanut pod size still remains unclear. Results: In this study, two peanut varieties with contrasting pod sizes were used for comparison of differences on the transcriptomic and endogenous hormonal levels. Developing peanut pods were sampled at 10, 15, 20, 25 and 30 days after pegging (DAP). Our results showed that the process of peanut pod-expansion could be divided into three stages: the gradual-growth stage, the rapid-growth stage and the slow-growth stage. Cytological analysis confirmed that the faster increase of cell-number during the rapid-growth stage was the main reason for the formation of larger pod size in Lps. Transcriptomic analyses showed that the expression of key genes related to the auxin, the cytokinin (CK) and the gibberellin (GA) were mostly up-regulated during the rapid-growth stage. Meanwhile, the cell division-related differentially expressed genes (DEGs) were mostly up-regulated at 10DAP which was consistent with the cytological-observation. Additionally, the absolute quantification of phytohormones were carried out by liquid-chromatography coupled with the tandem-mass-spectrometry (LC–MS/MS), and results supported the findings from comparative transcriptomic studies. Conclusions: It was speculated that the differential expression levels of TAA1 and ARF (auxin-related), IPT and B-ARR (CK-related), KAO, GA20ox and GA3ox (GA-related), and certain cell division-related genes (gene-LOC112747313 and gene-LOC112754661) were important participating factors of the determination-mechanism of peanut pod sizes. These results were informative for the elucidation of the underlying regulatory network in peanut pod-growth and would facilitate further identification of valuable target genes. [ABSTRACT FROM AUTHOR]

Cultivated peanut (Arachis hypogaea L.) is one of the most important oilseed crops worldwide. Pod-related traits, including pod length (PL), pod width (PW), ratio of PL to PW (PL/W) and 100-pod weight (100-PW), are crucial factors for pod yield and are key target traits for selection in peanut breeding. However, the studies on the natural variation and genetic mechanism of pod-related traits are not clear in peanut. In this study, we phenotyped 136 peanut accessions for four pod-related traits in two consecutive years and genotyped the population using a re-sequencing technique. Based on 884,737 high-quality single nucleotide polymorphisms (SNPs), genome-wide association studies (GWAS) were conducted for four pod-related traits using a fixed and random model uniform cyclic probability (FarmCPU) model. The results showed that a total of 36 SNPs were identified by GWAS, among which twenty-one, fourteen and one SNPs were significantly associated with PL, PL/W and 100-PW, respectively. The candidate regions where the four peak SNPs (10_76084075, 11_138356586, 16_64420451, and 18_126782541) were located were used for searching genes, and nineteen candidate genes for pod-related traits were preliminarily predicted based on functional annotations. In addition, we also compared the expression patterns of these nineteen candidate genes in different tissues of peanut, and we found that eight genes were specifically highly expressed in tender fruit, immature pericarp, or seed, so we considered these genes to be the potential candidate genes for pod-related traits. These results enriched the understanding of the genetic basis of pod-related traits and provided an important theoretical basis for subsequent gene cloning and marker-assisted selection (MAS) breeding in peanut. [ABSTRACT FROM AUTHOR]

Peanut seeds are susceptible to Aspergillus flavus infection, which has a severe impact on the peanut industry and human health. However, the molecular mechanism underlying this defense remains poorly understood. The aim of this study was to analyze the changes in differentially expressed genes (DEGs) and differential metabolites during A. flavus infection between Zhonghua 6 and Yuanza 9102 by transcriptomic and metabolomic analysis. A total of 5768 DEGs were detected in the transcriptomic study. Further functional analysis showed that some DEGs were significantly enriched in pectinase catabolism, hydrogen peroxide decomposition and cell wall tissues of resistant varieties at the early stage of infection, while these genes were differentially enriched in the middle and late stages of infection in the nonresponsive variety Yuanza 9102. Some DEGs, such as those encoding transcription factors, disease course-related proteins, peroxidase (POD), chitinase and phenylalanine ammonialyase (PAL), were highly expressed in the infection stage. Metabolomic analysis yielded 349 differential metabolites. Resveratrol, cinnamic acid, coumaric acid, ferulic acid in phenylalanine metabolism and 13S-HPODE in the linolenic acid metabolism pathway play major and active roles in peanut resistance to A. flavus. Combined analysis of the differential metabolites and DEGs showed that they were mainly enriched in phenylpropane metabolism and the linolenic acid metabolism pathway. Transcriptomic and metabolomic analyses further confirmed that peanuts infected with A. flavus activates various defense mechanisms, and the response to A. flavus is more rapid in resistant materials. These results can be used to further elucidate the molecular mechanism of peanut resistance to A. flavus infection and provide directions for early detection of infection and for breeding peanut varieties resistant to aflatoxin contamination. [ABSTRACT FROM AUTHOR]

Arachis hypogaea is an allotetraploid crop widely grown in the world. Wild relatives of genus Arachis are the rich source of genetic diversity and high levels of resistance to combat pathogens and climate change. The accurate identification and characterization of plant resistance gene, nucleotide binding site leucine rich repeat receptor (NLRs) substantially contribute to the repertoire of resistances and improve production. In the current study, we have studied the evolution of NLR genes in genus Arachis and performed their comparative genomics among four diploids (A. duranensis, A. ipaensis, A. cardenasii, A. stenosperma) and two tetraploid (wild: A. monticola and domesticated: A. hypogaea) species. In total 521, 354, 284, 794, 654, 290 NLR genes were identified from A. cardenasii, A. stenosperma and A. duranensis, A. hypogaea, A. monticola and A. ipaensis respectively. Phylogenetic analysis and classification of NLRs revealed that they belong to 7 subgroups and specific subgroups have expanded in each genome leading towards divergent evolution. Gene gain and loss, duplication assay reveals that wild and domesticated tetraploids species have shown asymmetric expansion of NLRome in both sub-genome (AA and BB). A-subgenome of A. monticola exhibited significant contraction of NLRome while B-subgenome shows expansion and vice versa in case of A. hypogaea probably due to distinct natural and artificial selection pressure. In addition, diploid species A. cardenasii revealed the largest repertoire of NLR genes due to higher frequency of gene duplication and selection pressure. A. cardenasii and A. monticola can be regarded as putative resistance resources for peanut breeding program for introgression of novel resistance genes. Findings of this study also emphasize the application neo-diploids and polyploids due to higher quantitative expression of NLR genes. To the best of our knowledge, this is the first study that studied the effect of domestication and polyploidy on the evolution of NLR genes in genus Arachis to identify genomic resources for improving resistance of polyploid crop with global importance on economy and food security. [ABSTRACT FROM AUTHOR]

Peanut growth, development, and eventual production are constrained by biotic and abiotic stresses resulting in serious economic losses. To understand the response and tolerance mechanism of peanut to biotic and abiotic stresses, high-throughput Omics approaches have been applied in peanut research. Integrated Omics approaches are essential for elucidating the temporal and spatial changes that occur in peanut facing different stresses. The integration of functional genomics with other Omics highlights the relationships between peanut genomes and phenotypes under specific stress conditions. In this review, we focus on research on peanut biotic stresses. Here we review the primary types of biotic stresses that threaten sustainable peanut production, the multi-Omics technologies for peanut research and breeding, and the recent advances in various peanut Omics under biotic stresses, including genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics and phenomics, for identification of biotic stress-related genes, proteins, metabolites and their networks as well as the development of potential traits. We also discuss the challenges, opportunities, and future directions for peanut Omics under biotic stresses, aiming sustainable food production. The Omics knowledge is instrumental for improving peanut tolerance to cope with various biotic stresses and for meeting the food demands of the exponentially growing global population. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Key message: AhyHOF1, likely encoding a WRI1 transcription factor, plays critical roles in peanut oil synthesis. Although increasing the oil content of peanut to meet growing demand has long been a primary aim of breeding programs worldwide, the mining of genetic resources to achieve this objective has obviously lagged behind that of other oil crops. In the present study, we developed an advanced recombinant inbred line population containing 192 F9:11 families derived from parents JH5 and KX01-6. We then constructed a high-resolution genetic map covering 3,706.382 cM, with an average length of 185.32 cM per linkage group, using 2840 polymorphic SNPs. Two stable QTLs, qCOA08_1 and qCOA08_2 having the highest contributions to genetic variation (16.1% and 20.7%, respectively), were simultaneously detected in multiple environments and closely mapped within physical intervals of approximately 2.9 Mb and 1.7 Mb, respectively, on chromosome A08. In addition, combined analysis of whole-genome and transcriptome resequencing data uncovered a strong candidate gene encoding a WRI1 transcription factor and differentially expressed between the two parents. This gene, designated as High Oil Favorable gene 1 in Arachis hypogaea (AhyHOF1), was hypothesized to play roles in oil accumulation. Examination of near-inbred lines of #AhyHOF1/#Ahyhof1 provided further evidence that AhyHOF1 increases oil content, mainly by affecting the contents of several fatty acids. Taken together, our results provide valuable information for cloning the favorable allele for oil content in peanut. In addition, the closely linked polymorphic SNP markers within qCOA08_1 and qCOA08_2 loci may be useful for accelerating marker-assisted selection breeding of peanut. [ABSTRACT FROM AUTHOR]

Key message: QTLs for growth habit are identified on Arahy.15 and Arahy.06 in peanut, and diagnostic markers are developed and validated for further use in marker-assisted breeding. Peanut is a unique legume crop because its pods develop and mature underground. The pegs derive from flowers following pollination, then reach the ground and develop into pods in the soil. Pod number per plant is influenced by peanut growth habit (GH) that has been categorized into four types, including erect, bunch, spreading and prostrate. Restricting pod development at the plant base, as would be the case for peanut plants with upright lateral branches, would decrease pod yield. On the other hand, GH characterized by spreading lateral branches on the ground would facilitate pod formation on the nodes, thereby increasing yield potential. We describe herein an investigation into the GH traits of 521 peanut recombinant inbred lines grown in three distinct environments. Quantitative trait loci (QTLs) for GH were identified on linkage group (LG) 15 between 203.1 and 204.2 cM and on LG 16 from 139.1 to 139.3 cM. Analysis of resequencing data in the identified QTL regions revealed that single nucleotide polymorphism (SNP) or insertion and/or deletion (INDEL) at Arahy15.156854742, Arahy15.156931574, Arahy15.156976352 and Arahy06.111973258 may affect the functions of their respective candidate genes, Arahy.QV02Z8, Arahy.509QUQ, Arahy.ATH5WE and Arahy.SC7TJM. These SNPs and INDELs in relation to peanut GH were further developed for KASP genotyping and tested on a panel of 77 peanut accessions with distinct GH features. This study validates four diagnostic markers that may be used to distinguish erect/bunch peanuts from spreading/prostrate peanuts, thereby facilitating marker-assisted selection for GH traits in peanut breeding. [ABSTRACT FROM AUTHOR]

'NemaTAM II' (Reg. no. CV‐152, PI 699122) is a high‐yielding, high‐oleic fatty acid, runner‐type peanut (Arachis hypogaea L. ssp. hypogaea var. hypogaea) cultivar with resistance to root‐knot nematode (RKN). The cultivar was developed by the Texas A&M AgriLife Research peanut breeding program to provide growers with a nematode resistant replacement option for the former 'Webb' cultivar, which had excellent nematode resistance but also had a very large vine size that made harvest difficult for peanut farmers. It was tested under the experimental designation of Tx144342 and was released on 11 Jan. 2021. NemaTAM II maintains the resistance to RKN of Webb, has proven to yield equal to or better than Webb, and has higher grade potential. NemaTAM II has a shorter canopy than Webb, which leads to easier digging and inverting at harvest than its predecessor. Core Ideas: NemaTAM II is a new root‐knot nematode resistant peanut cultivar adapted to Texas.NemaTAM II has a lower growth habit than the previous cultivar Webb, which aids in digging.NemaTAM II has a higher grade potential than the previous cultivar Webb.NemaTAM II has yield potential equal or better than the cultivar Georgia 14N.NemaTAM II has Sclerotinia minor resistance equal to the previous cultivar Webb. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Peanuts (Arachis hypogaea L.) are an allotetraploid grain legume mainly cultivated by poor farmers in Africa, in degraded soil and with low input systems. Further understanding nodulation genetic mechanisms could be a relevant option to facilitate the improvement of yield and lift up soil without synthetic fertilizers. We used a subset of 83 chromosome segment substitution lines (CSSLs) derived from the cross between a wild synthetic tetraploid AiAd (Arachis ipaensis × Arachis duranensis)4× and the cultivated variety Fleur11, and evaluated them for traits related to BNF under shade-house conditions. Three treatments were tested: without nitrogen; with nitrogen; and without nitrogen, but with added0 Bradyrhizobium vignae strain ISRA400. The leaf chlorophyll content and total biomass were used as surrogate traits for BNF. We found significant variations for both traits specially linked to BNF, and four QTLs (quantitative trait loci) were consistently mapped. At all QTLs, the wild alleles decreased the value of the trait, indicating a negative effect on BNF. A detailed characterization of the lines carrying those QTLs in controlled conditions showed that the QTLs affected the nitrogen fixation efficiency, nodule colonization, and development. Our results provide new insights into peanut nodulation mechanisms and could be used to target BNF traits in peanut breeding programs. [ABSTRACT FROM AUTHOR]

Introduction: Fungal foliar diseases can severely affect the productivity of the peanut crop worldwide. Late leaf spot is the most frequent disease and a major problem of the crop in Brazil and many other tropical countries. Only partial resistance to fungal diseases has been found in cultivated peanut, but high resistances have been described on the secondary gene pool. Methods: To overcome the known compatibility barriers for the use of wild species in peanut breeding programs, we used an induced allotetraploid (Arachis stenosperma x A. magna)4x, as a donor parent, in a successive backcrossing scheme with the high-yielding Brazilian cultivar IAC OL 4. We used microsatellite markers associated with late leaf spot and rust resistance for foreground selection and high-throughput SNP genotyping for background selection. Results: With these tools, we developed agronomically adapted lines with high cultivated genome recovery, high-yield potential, and wild chromosome segments from both A. stenosperma and A. magna conferring high resistance to late leaf spot and rust. These segments include the four previously identified as having QTLs (quantitative trait loci) for resistance to both diseases, which could be confirmed here, and at least four additional QTLs identified by using mapping populations on four generations. Discussion: The introgression germplasm developed here will extend the useful genetic diversity of the primary gene pool by providing novel wild resistance genes against these two destructive peanut diseases. [ABSTRACT FROM AUTHOR]

Peanut is susceptible to Aspergillus flavus infection, and the consequent aflatoxin contamination has been recognized as an important risk factor affecting food safety and industry development. Planting peanut varieties with resistance to aflatoxin contamination is regarded as an ideal approach to decrease the risk in food safety, but most of the available resistant varieties have not been extensively used in production because of their low yield potential mostly due to possessing small pods and seeds. Hence, it is highly necessary to integrate resistance to aflatoxin and large seed weight. In this study, an RIL population derived from a cross between Zhonghua 16 with high yield and J 11 with resistance to infection of A. flavus and aflatoxin production, was used to identify quantitative trait locus (QTL) for aflatoxin production (AP) resistance and hundred-seed weight (HSW). From combined analysis using a high-density genetic linkage map constructed, 11 QTLs for AP resistance with 4.61–11.42% phenotypic variation explanation (PVE) and six QTLs for HSW with 3.20–28.48% PVE were identified, including three major QTLs for AP resistance (qAFTA05.1, qAFTB05.2 and qAFTB06.3) and three for HSW (qHSWA05, qHSWA08 and qHSWB06). In addition, qAFTA05.1, qAFTB06.3, qHSWA05, qHSWA08 and qHSWB06 were detected in multiple environments. The aflatoxin contents under artificial inoculation were decreased by 34.77–47.67% in those segregated lines harboring qAFTA05.1, qAFTB05.2 and qAFTB06.3, while the HSWs were increased by 47.56–49.46 g in other lines harboring qHSWA05, qHSWA08 and qHSWB06. Conditional QTL mapping indicated that HSW and percent seed infection index (PSII) had no significant influence on aflatoxin content. Interestingly, the QT 1059 simultaneously harboring alleles of aflatoxin content including qAFTA05.1 and qAFTB05.2, alleles of PSII including qPSIIB03.1, qPSIIB03.2, and qPSIIB10 and alleles of HSW including qHSWA05, qHSWB06, qHSWA08 had better resistance to A. flavus infection and to toxin production and higher yield potential compared with the two parents of the RIL. The above identified major loci for AP resistance and HWS would be helpful for marker-assisted selection in peanut breeding. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. Fast and accurate are always the goals of the plant 3D model reconstruction research. Results: We proposed a 3D reconstruction method based on dual RGB-D cameras for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits' height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was also carried out during the experiment processing. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result showed that the average accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. Conclusions: The reconstruction method for the 3D model of the peanut plant described in this paper is capable of rapidly and accurately establishing a 3D model of the peanut plant while also meeting the modeling requirements for other species' breeding processes. This study offers a potential tool to further explore the 3D model for improving traits and agronomic qualities of plants. [ABSTRACT FROM AUTHOR]

Peanut (Arachis hypogaea L.) is an oil and economic crop of vital importance, and peanut pod is the key organ influencing the yield and processing quality. Hence, the Pod-related traits (PRTs) are considered as important agronomic traits in peanut breeding. To broaden the variability of PRTs in current peanut germplasms, three elite peanut cultivars were used to construct Ethyl methane sulfonate (EMS)-induced mutant libraries in this study. The optimal EMS treatment conditions for the three peanut varieties were determined. It was found that the median lethal dose (LD50) of EMS treatment varied greatly among different genotypes. Finally, the EMS-induced peanut mutant libraries were constructed and a total of 124 mutant lines for PRTs were identified and evaluated. Furthermore, "M-8070", one of the mutant lines for pod constriction, was re-sequenced via high-throughput sequencing technology. The genome-wide variations between "M-8070" and its wild parent "Fuhua 8" (FH 8) were detected. 2994 EMS-induced single nucleotide polymorphisms (SNPs) and 1188 insertion-deletions (InDels) between "M-8070" and its wild parent were identified. The predominant SNP mutation type was C/G to T/A transitions, while the predominant InDel mutation type was "1-bp". We analyzed the distribution of identified mutations and annotated their functions. Most of the mutations (91.68% of the SNPs and 77.69% of the InDels) were located in the intergenic region. 72 SNPs were identified in the exonic region, leading to 27 synonymous, 43 nonsynonymous and 2 stop-gain variation for gene structure. 13 Indels were identified in the exonic region, leading to 4 frame-shift, 8 non-frame-shift and 1 stop-gain variations of genes. These mutations may lead to the phenotypic variation of "M-8070". Our study provided valuable resources for peanut improvement and functional genomic research. [ABSTRACT FROM AUTHOR]

Keywords: Peanut (Arachis hypogaea L.); CRISPR/Cas9; base-editing; ALS; herbicide-resistant EN Peanut (Arachis hypogaea L.) CRISPR/Cas9 base-editing ALS herbicide-resistant 1923 1925 3 09/20/23 20231001 NES 231001 Genome editing has been employed to introduce accurate and predictable genetic mutations in plants, gaining wide acceptance due to its high efficiency, precision, usability and significant potential for crop improvement (Manghwar I et al i ., [5]). The gained herbicide resistance appears to be gene dosage-dependent, whereby the T SB 2 sb plants with simultaneous base conversions in both I AhALS2-A i and I AhALS2-B i genes grew and thrived upon herbicide application, while those with mutations in only one I ALS i exhibited no newly grown leaves (Figure 1f). Creation of herbicide-resistance in allotetraploid peanut using CRISPR/Cas9-meditated cytosine base-editing. [Extracted from the article]