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Agronomic and physiological traits in peanut (Arachis hypogaea) are important to breeders for selecting high‐yielding and resilient genotypes. However, direct measurement of these traits is labor‐intensive and time‐consuming. This study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress. Two different approaches were evaluated. The first approach employed eighty narrowband vegetation indices as input features for an ensemble model that included K‐nearest neighbors, support vector regression, random forest, and multi‐layer perceptron (MLP). The second approach utilized mean and standard deviation of canopy spectral reflectance per band. The resultant 400 features were used to train a deep learning (DL) model consisting of one‐dimensional convolutional layers followed by an MLP regressor. Predictions of the agronomic traits obtained using feature learning and DL (R2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models (R2 = 0.44–0.61, sMAPE = 27%–59%). In contrast, the ensemble model had a slightly better performance in predicting physiological traits (R2 = 0.35–0.57; sMAPE = 37%–70%) compared to the results obtained from the DL model (R2 = 0.36–0.52; sMAPE = 47%–64%). The results showed that the combination of UAV‐based hyperspectral imaging and ML techniques have the potential to assist breeders in rapid screening of genotypes for improved yield and drought tolerance in peanut. Core Ideas A drought experiment was conducted for an F1 population of peanut lines at the pod‐filling stage. Unmanned aerial vehicles‐based hyperspectral imagery data were collected 14, 18, and 29 days after drought. Machine learning models were used to predict three agronomic and two physiological traits. Both machine and deep learning methods explained ∼50% of the variation in the dataset. The overall best prediction accuracy occurred 18 days after drought stress imposition.

Peanut diseases, such as leaf spot and spotted wilt caused by Tomato Spotted Wilt Virus, can significantly reduce yield and quality. Application of marker assisted plant breeding requires the development and validation of different types of DNA molecular markers. Nearly 10,000 SSR-based molecular markers have been identified by various research groups around the world, but less than 14.5% showed polymorphism in peanut and only 6.4% have been mapped. Low levels of polymorphism limit the application of marker assisted selection (MAS) in peanut breeding programs. Insertion/deletion (InDel) markers have been reported to be more polymorphic than SSRs in some crops. The goals of this study were to identify novel InDel markers and to evaluate the potential use in peanut breeding. Forty-eight InDel markers were developed from conserved sequences of functional genes and tested in a diverse panel of 118 accessions covering six botanical types of cultivated peanut, of which 104 were from the U.S. mini-core. Results showed that 16 InDel markers were polymorphic with polymorphic information content (PIC) among InDels ranged from 0.017 to 0.660. With respect to botanical types, PICs varied from 0.176 for fastigiata var., 0.181 for hypogaea var., 0.306 for vulgaris var., 0.534 for aequatoriana var., 0.556 for peruviana var., to 0.660 for hirsuta var., implying that aequatoriana var., peruviana var., and hirsuta var. have higher genetic diversity than the other types and provide a basis for gene functional studies. Single marker analysis was conducted to associate specific marker to disease resistant traits. Five InDels from functional genes were identified to be significantly correlated to tomato spotted wilt virus (TSWV) infection and leaf spot, and these novel markers will be utilized to identify disease resistant genotype in breeding populations.

The segregation of 141 polymorphic expressed sequence tag-simple sequence repeat (EST-SSR) markers in an F1 intergeneric citrus population was studied to build the first extensive EST maps for the maternal sweet orange and paternal Poncirus genomes. Of these markers, 122 were found segregating in sweet orange, 59 in Poncirus, and 40 in both. Eleven linkage groups with 113 markers in sweet orange, 8 with 45 markers in Poncirus, and 13 with 123 markers in the cross pollinator (CP) consensus of both, were constructed. About 775.8 cM of sweet orange genome and 425.7 cM of Poncirus genome were covered. Through comparison of shared markers, three cases were found where two linkage groups in one map apparently were colinear with one group of the other map; Poncirus linkages Ar1a and Ar1b and consensus linkages CP1a and CP1b, were both collinear with one sweet orange linkage, Sa1, as were sweet orange Sa3a and Sa3b with Poncirus Ar3 and consensus CP3, and sweet orange Sa7a and Sa7b, and consensus CP7a and CP7b with Poncirus Ar7. These EST-SSR markers are particularly useful for constructing comparative framework maps for related genera because they amplify orthologous genes to provide anchor points across taxa. All SSR primers are freely available to the citrus community.

Early leaf spot (ELS) and late leaf spot (LLS) are major fungal diseases of peanut that can severely reduce yield and quality. Development of acceptable genetic resistance has been difficult due to a strong environmental component and many major and minor QTLs. Resistance genes (R-genes) are an important component of plant immune system and have been identified in peanut. Association of specific R-genes to leaf spot resistance will provide molecular targets for marker-assisted breeding strategies. In this study, advanced breeding lines from different pedigrees were evaluated for leaf spot resistance and 76 candidate R-genes expression study was applied to susceptible and resistant lines. Thirty-six R-genes were differentially expressed and significantly correlated with resistant lines, of which a majority are receptor like kinases (RLKs) and receptor like proteins (RLPs) that sense the presence of pathogen at the cell surface and initiate protection response. The largest group was receptor-like cytoplasmic kinases (RLCKs) VII that are involved in pattern-triggered kinase signaling resulting in the production reactive oxygen species (ROS). Four R-genes were homologous to TMV resistant protein N which has shown to confer resistance against tobacco mosaic virus (TMV). When mapped to peanut genomes, 36 R-genes were represented in most chromosomes except for A09 and B09. Low levels of gene-expression in resistant lines suggest expression is tightly controlled to balance the cost of R-gene expression to plant productively. Identification and association of R-genes involved in leaf spot resistance will facilitate genetic selection of leaf spot resistant lines with good agronomic traits.

Peanut (Arachis hypogaea L.) is an important food and oilseed crop worldwide. Yield and quality can be significantly reduced by foliar fungal diseases, such as early and late leaf spot diseases. Acceptable levels of leaf spot resistance in cultivated peanut have been elusive due to environmental interactions and the proper combination of QTLs in any particular peanut genotype. Resistance gene analogs, as potential resistance (R)-genes, have unique roles in the recognition and activation of disease resistance responses. Novel R-genes can be identified by searches for conserved domains such as nucleotide binding site, leucine rich repeat, receptor like kinase, and receptor like protein from expressed genes or through genomic sequences. Expressed R-genes represent necessary plant signals in a disease response. The goals of this research are to identify expressed R-genes from cultivated peanuts that are naturally infected by early and late spot pathogens, compare these to the closest diploid progenitors, and evaluate specific gene expression in cultivated peanuts. Putative peanut R-genes (381) were available from a public database (NCBI). Primers were designed and PCR products were sequenced. A total of 214 sequences were produced which matched to proteins with the corresponding R-gene motifs. These R-genes were mapped to the genome sequences of Arachis duranensis and Arachis ipaensis, which are the closest diploid progenitors for tetraploid cultivated peanut, A. hypogaea. Identification and association of specific gene-expression will elucidate potential disease resistance mechanism in peanut and may facilitate the selection of breeding lines with high levels of leaf spot resistance.

Isolation of good quality RNA and DNA from seeds is difficult due to high levels of polysaccharides, polyphenols, and lipids that can degrade or co-precipitate with nucleic acids. Standard RNA extraction methods utilizing guanidinium–phenol–chloroform extraction has not shown to be successful. RNA isolation from plant seeds is a prerequisite for many seed specific gene expression studies and DNA is necessary in marker-assisted selection and other genetic studies. We describe a modified method to isolate both RNA and DNA from the same seed tissue and have been successful with several oil seeds including peanut, soybean, sunflower, canola, and oil radish. An additional LiCl precipitation step was added to isolate both RNA and DNA from the same seed tissues. High quality nucleic acids were observed based on A 260 /A 280 and A 260 /A 230 ratios above 2.0 and distinct bands on gel-electrophoresis. RNA was shown to be suitable for reverse transcriptase polymerase chain reaction based on actin or 60S ribosomal primer amplification and DNA was shown to have a single band on gel-electrophoresis analysis. This result shows that RNA and DNA isolated using this method can be appropriate for molecular studies in peanut and other oil containing seeds.

Background Peanut ( Arachis hypogaea L.) is a grain legume crop that originated from South America and is now grown around the world. Peanut growth habit affects the variety’s adaptability, planting patterns, mechanized harvesting, disease resistance, and yield. The objective of this study was to map the quantitative trait locus (QTL) associated with peanut growth habit-related traits by combining the genome-wide association analysis (GWAS) and bulked segregant analysis sequencing (BSA-seq) methods. Results GWAS was performed with 17,223 single nucleotide polymorphisms (SNPs) in 103 accessions of the U.S. mini core collection genotyped using an Affymetrix version 2.0 SNP array. With a total of 12,342 high-quality polymorphic SNPs, the 90 suggestive and significant SNPs associated with lateral branch angle (LBA), main stem height (MSH), lateral branch height (LBL), extent radius (ER), and the index of plant type (IOPT) were identified. These SNPs were distributed among 15 chromosomes. A total of 597 associated candidate genes may have important roles in biological processes, hormone signaling, growth, and development. BSA-seq coupled with specific length amplified fragment sequencing (SLAF-seq) method was used to find the association with LBA, an important trait of the peanut growth habit. A 4.08 Mb genomic region on B05 was associated with LBA. Based on the linkage disequilibrium (LD) decay distance, we narrowed down and confirmed the region within the 160 kb region (144,193,467–144,513,467) on B05. Four candidate genes in this region were involved in plant growth. The expression levels of Araip.E64SW detected by qRT-PCR showed significant difference between ‘Jihua 5’ and ‘M130’. Conclusions In this study, the SNP (AX-147,251,085 and AX-144,353,467) associated with LBA by GWAS was overlapped with the results in BSA-seq through combined analysis of GWAS and BSA-seq. Based on LD decay distance, the genome range related to LBA on B05 was shortened to 144,193,467–144,513,467. Three candidate genes related to F-box family proteins ( Araip.E64SW , Araip.YG1LK , and Araip.JJ6RA ) and one candidate gene related to PPP family proteins ( Araip.YU281 ) may be involved in plant growth and development in this genome region. The expression analysis revealed that Araip.E64SW was involved in peanut growth habits. These candidate genes will provide molecular targets in marker-assisted selection for peanut growth habits.

Many plant ESTs have been sequenced as an alternative to whole genome sequences, including peanut because of the genome size and complexity. The US peanut research community had the historic 2004 Atlanta Genomics Workshop and named the EST project as a main priority. As of August 2011, the peanut research community had deposited 252,832 ESTs in the public NCBI EST database, and this resource has been providing the community valuable tools and core foundations for various genome-scale experiments before the whole genome sequencing project. These EST resources have been used for marker development, gene cloning, microarray gene expression and genetic map construction. Certainly, the peanut EST sequence resources have been shown to have a wide range of applications and accomplished its essential role at the time of need. Then the EST project contributes to the second historic event, the Peanut Genome Project 2010 Inaugural Meeting also held in Atlanta where it was decided to sequence the entire peanut genome. After the completion of peanut whole genome sequencing, ESTs or transcriptome will continue to play an important role to fill in knowledge gaps, to identify particular genes and to explore gene function.

Cultivated peanut ( Arachis hypogaea ) is one of the most widely grown food legumes in the world, being valued for its high protein and unsaturated oil contents. Drought stress is one of the major constraints that limit peanut production. This study’s objective was to identify the drought-responsive genes preferentially expressed under drought stress in different peanut genotypes. To accomplish this, four genotypes (drought tolerant: C76-16 and 587; drought susceptible: Tifrunner and 506) subjected to drought stress in a rainout shelter experiment were examined. Transcriptome sequencing analysis identified that all four genotypes shared a total of 2,457 differentially expressed genes (DEGs). A total of 139 enriched gene ontology terms consisting of 86 biological processes and 53 molecular functions, with defense response, reproductive process, and signaling pathways, were significantly enriched in the common DEGs. In addition, 3,576 DEGs were identified only in drought-tolerant lines in which a total of 74 gene ontology terms were identified, including 55 biological processes and 19 molecular functions, mainly related to protein modification process, pollination, and metabolic process. These terms were also found in shared genes in four genotypes, indicating that tolerant lines adjusted more related genes to respond to drought. Forty-three significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways were also identified, and the most enriched pathways were those processes involved in metabolic pathways, biosynthesis of secondary metabolites, plant circadian rhythm, phenylpropanoid biosynthesis, and starch and sucrose metabolism. This research expands our current understanding of the mechanisms that facilitate peanut drought tolerance and shed light on breeding advanced peanut lines to combat drought stress.

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