Peanut Mini Core Collection Research Articles

Genetic dissection of fatty acid components in the Chinese peanut (Arachis hypogaea L.) mini-core collection under multi-environments.

Peanut (Arachis hypogaea L.) is an important source of edible oil and protein for human nutrition. The quality of peanut seed oil is mainly determined by the composition of fatty acids, especially the contents of oleic acid and linoleic acid. Improving the composition of fatty acids in the seed oil is one of the main objectives for peanut breeding globally. To uncover the genetic basis of fatty acids and broaden the genetic variation in future peanut breeding programs, this study used genome-wide association studies (GWAS) to identify loci associated with target traits and developed diagnostic marker. The contents of eight fatty acid components of the Chinese peanut mini-core collection were measured under four environments. Using the phenotypic information and over one hundred thousand single nucleotide polymorphisms (SNPs), GWAS were conducted to investigate the genetics basis of fatty acids under multi-environments. Overall, 75 SNPs were identified significant trait associations with fatty acid components. Nineteen associations were repeatedly identified in multiple environments, and 13 loci were co-associated with two or three traits. Three stable major associated loci were identified, including two loci for oleic acid and linoleic acid on chromosome A09 [mean phenotypic variation explained (PVE): 38.5%, 10.35%] and one for stearic acid on B06 (mean PVE: 23%). According to functional annotations, 21 putative candidate genes related to fatty acid biosynthesis were found underlying the three associations. The allelic effect of SNP A09-114690064 showed that the base variation was highly correlated with the phenotypic variation of oleic acid and linoleic acid contents, and a cost-effective Kompetitive allele-Specific PCR (KASP) diagnostic marker was developed. Furthermore, the SNP A09-114690064 was found to change the cis-element CAAT (-) in the promoter of ahFAD2A to YACT (+), leading dozens of times higher expression level. The enhancer-like activity of ahFAD2A promoter was identified that was valuable for enriching the regulation mechanism of ahFAD2A. This study improved our understanding on the genetic architecture of fatty acid components in peanut, and the new effective diagnostic marker would be useful for marker-assisted selection of high-oleic peanut breeding.

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

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

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

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

Identification of potential QTLs and genes associated with seed composition traits in peanut (Arachis hypogaea L.) using GWAS and RNA-Seq analysis

Cultivated peanut (Arachis hypogaea L.) is a major oilseed crop providing edible oil and protein. Oil quality is determined by fatty acid composition including the ratio of oleic acid (C18:1) and linoleic acid (C18:2). A genome-wide association study with 13,382 single nucleotide polymorphisms (SNPs) was conducted to investigate the genetics basis of oil, protein, eight fatty acid concentrations, and O/L ratio (ratio of oleic and linoleic acid) using a diverse panel of 120 genotypes mainly selected from the U.S. peanut mini core collection grown in two years. A total of 178 significant quantitative trait loci (QTLs) associated with those seed composition traits were identified with phenotypic variation explained (PVE) from 18.35% to 27.56%. RNA-Seq analysis identified 282 DEGs (differentially expressed genes) within the 1Mb of the significant QTLs for seed composition traits. Among those 282 genes, sixteen candidate genes for seed fatty acid metabolism and protein synthesis were screened according to the gene functions.

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

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

Resistance to Athelia rolfsii and Web Blotch in the U.S. Mini-core Collection

ABSTRACT Athelia rolfsii (=Sclerotium rolfsii) is a soilborne fungus that causes the disease commonly known as southern blight, southern stem rot, stem rot, and white mold. Despite the fact that A. rolfsii is one of the most destructive pathogens of peanut, the U.S. germplasm collection has not been evaluated for resistance to this pathogen. Therefore, 71 of the 112 accessions comprising the U.S. peanut mini-core collection were evaluated in the field for resistance to southern blight in 2016 to 2018 in Oklahoma. Moderate to low levels of southern blight were observed, but four accessions—CC125, CC208, CC559, and CC650—had low levels of disease in 2017 and 2018, the most favourable years for A. rolfsii. Ratings for web blotch, a yield-limiting foliar disease in some production areas caused by Didymella arachidicola, were also taken in 2017 and 2018, when outbreaks occurred. Five entries—CC287, CC155, CC149, CC812, and CC559—had between 10% and 20% disease in 2018, a year when over half of the mini-core accessions exhibited between 50% and 93% disease. Because cultivated peanut in the U.S. has a narrow genetic base, these results will be useful to breeders seeking additional sources of resistance to A. rolfsii and web blotch.

GWAS and Coexpression Network Reveal Ionomic Variation in Cultivated Peanut.

Peanut is an important legume providing products with nutrient dense including mineral nutrition. However, the genetic basis underlying the variations in the mineral composition in peanut is still unknown. Genome-wide association studies (GWAS) of the concentrations of 13 mineral elements combined with coexpression network were performed using a diverse panel of 120 genotypes mainly selected from the U.S. peanut mini core collection. A total of 36 significant quantitative trait loci (QTLs) associated with five elemental concentrations were identified with phenotypic variation explained (PVE) from 18.35% to 27.56%, in which 24 QTLs were for boron (B), 2 QTLs for copper (Cu), 6 QTLs for sodium (Na), 3 QTLs for sulfur (S), and 1 QTL for zinc (Zn). A total of 110 nonredundant candidate causal genes identified were significantly associated with elemental accumulation, which included one high-priority overlap (HPO) candidate gene arahy.KQD4NT, an important elemental/metal transporter gene located on LGB04 with position 5,413,913-5,417,353.

Evaluation of linkage disequilibrium, population structure, and genetic diversity in the U.S. peanut mini core collection

BackgroundDue to the recent domestication of peanut from a single tetraploidization event, relatively little genetic diversity underlies the extensive morphological and agronomic diversity in peanut cultivars today. To broaden the genetic variation in future breeding programs, it is necessary to characterize germplasm accessions for new sources of variation and to leverage the power of genome-wide association studies (GWAS) to discover markers associated with traits of interest. We report an analysis of linkage disequilibrium (LD), population structure, and genetic diversity, and examine the ability of GWA to infer marker-trait associations in the U.S. peanut mini core collection genotyped with a 58 K SNP array.ResultsLD persists over long distances in the collection, decaying to r2 = half decay distance at 3.78 Mb. Structure within the collection is best explained when separated into four or five groups (K = 4 and K = 5). At K = 4 and 5, accessions loosely clustered according to market type and subspecies, though with numerous exceptions. Out of 107 accessions, 43 clustered in correspondence to the main market type subgroup whereas 34 did not. The remaining 30 accessions had either missing taxonomic classification or were classified as mixed. Phylogenetic network analysis also clustered accessions into approximately five groups based on their genotypes, with loose correspondence to subspecies and market type. Genome wide association analysis was performed on these lines for 12 seed composition and quality traits. Significant marker associations were identified for arachidic and behenic fatty acid compositions, which despite having low bioavailability in peanut, have been reported to raise cholesterol levels in humans. Other traits such as blanchability showed consistent associations in multiple tests, with plausible candidate genes.ConclusionsBased on GWA, population structure as well as additional simulation results, we find that the primary limitations of this collection for GWAS are a small collection size, significant remaining structure/genetic similarity and long LD blocks that limit the resolution of association mapping. These results can be used to improve GWAS in peanut in future studies – for example, by increasing the size and reducing structure in the collections used for GWAS.

Identifying Sclerotinia Blight Resistance in U.S. Peanut Mini‐core Collection

CSA NewsVolume 63, Issue 7 p. 14-14 Society Science Identifying Sclerotinia Blight Resistance in U.S. Peanut Mini-core Collection First published: 05 July 2018 https://doi.org/10.2134/csa2018.63.0709Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume63, Issue7July 2018Pages 14-14 RelatedInformation

Population structure and association mapping to detect QTL controlling tomato spotted wilt virus resistance in cultivated peanuts

Tomato spotted wilt (TSW) is a serious virus disease of peanut in the United States. Breeding for TSWV resistance would be facilitated by the implementation of marker-assisted selection in breeding programs; however, genes associated with resistance have not been identified. Association mapping is a type of genetic mapping that can exploit relationships between markers and traits in many lineages. The objectives of this study were to examine genetic diversity and population structure in the U.S. peanut mini-core collection using simple sequence repeat (SSR) markers, and to conduct association mapping between SSR markers and TSWV resistance in cultivated peanuts. One hundred and thirty-three SSR markers were used for genotyping 104 accessions. Four subpopulations, generally corresponding to botanical varieties, were classified by population structure analysis. Association mapping analysis indicated that five markers: pPGPseq5D5, GM1135, GM1991, TC23C08, and TC24C06, were consistently associated with TSW resistance by the Q, PCA, Q+K, and PCA+K models. These markers together explained 36.4% of the phenotypic variance. Moreover, pPGPseq5D5 and GM1991 were associated with both visual symptoms of TSWV and ELISA values with a high R2. The potential of these markers for use in a marker-assisted selection program to breed peanut for resistance to TSWV is discussed.

Seed Protein Percentage and Mineral Concentration Variability and Correlation with Other Seed Quality Traits in the U.S. Peanut Mini-Core Collection

Seed Protein Percentage and Mineral Concentration Variability and Correlation with Other Seed Quality Traits in the U.S. Peanut Mini-Core Collection

Seed Protein Percentage and Mineral Concentration Variability and Their Correlation with Other Seed Quality Traits in the U.S. Peanut Mini-Core Collection

ABSTRACT Protein percentage and mineral concentrations are important parameters for determining the seed nutrition quality. Although the U.S. peanut mini-core collection is the important genetic resources for peanut breeding programs, the variability in protein percentage and mineral concentrations for this mini-core has not been well evaluated. The lack of information may hinder its optimum utilization. The seeds from this mini-core were collected from two field seasons. Their protein percentage and mineral concentrations of 95 accessions were determined by nitrogen analysis and inductively coupled plasma – optical emission spectrometry, respectively. Significant variability in the seed protein percentage among accessions was revealed, ranging from 20.6 to 30.4%, with an average of 26.2%. Significantly higher variability in plant micronutrient mineral concentrations (more than two-fold for B, Cu, Fe, Mn, Mo, Na, Ni, and Zn) than in macronutrient mineral concentrations (less than two-fold for K, Mg, P, and S) was also identified among accessions. Calcium however was an exception, demonstrating 3.7-fold variability among the accessions evaluated. Three accessions (PI 497517, PI 493547, and PI 429429) were identified as lines containing high seed levels of both Fe and Zn. Correlation coefficients were also determined among 28 investigated seed chemical composition traits, using data from a previous study with the same samples. Protein percentage was significantly negatively correlated with seed weight, oil, and oleate percentage. Several mineral elements (Fe, Mg, Mn, and Zn) were also significantly negatively correlated with oleate percentage. The results from this study will be useful for peanut nutrition breeding and food product development.

Molecular marker development from transcript sequences and germplasm evaluation for cultivated peanut (Arachis hypogaea L.).

Molecular markers are important tools for genotyping in genetic studies and molecular breeding. The SSR and SNP are two commonly used marker systems developed from genomic or transcript sequences. The objectives of this study were to: (1) assemble and annotate the publicly available ESTs in Arachis and the in-house short reads, (2) develop and validate SSR and SNP markers, and (3) investigate the genetic diversity and population structure of the peanut breeding lines and the U.S. peanut mini core collection using developed SSR markers. An NCBI EST dataset with 252,951 sequences and an in-house 454 RNAseq dataset with 288,701 sequences were assembled separately after trimming. Transcript sequence comparison and phylogenetic analysis suggested that peanut is closer to cowpea and scarlet bean than to soybean, common bean and Medicago. From these two datasets, 6455 novel SSRs and 11,902 SNPs were identified. Of the discovered SSRs, 380 representing various SSR types were selected for PCR validation. The amplification rate was 89.2%. Twenty-two (6.5%) SSRs were polymorphic between at least one pair of four genotypes. Sanger sequencing of PCR products targeting 110 SNPs revealed 13 true SNPs between tetraploid genotypes and 193 homoeologous SNPs within genotypes. Eight out of the 22 polymorphic SSR markers were selected to evaluate the genetic diversity of Florida peanut breeding lines and the U.S. peanut mini core collection. This marker set demonstrated high discrimination power by displaying an average polymorphism information content value of 0.783, a combined probability of identity of 10(-11), and a combined power of exclusion of 0.99991. The structure analysis revealed four sub-populations among the peanut accessions and lines evaluated. The results of this study enriched the peanut genomic resources, provided over 6000 novel SSR markers and the credentials for true peanut SNP marker development, and demonstrated the power of newly developed SSR markers in genotyping peanut germplasm and breeding materials.

Resistance to late leaf spot and rust diseases in ICRISAT’s mini core collection of peanut (Arachis hypogaea L.)

Late leaf spot (LLS) (Phaeoisariopsis personata) and rust (Puccinia arachidis) are major foliar diseases of peanut causing significant losses worldwide. Identification and infusion of resistance into peanut cultivars is important in the management of these diseases. The present study therefore aimed at screening the peanut mini core collection to identify potential sources of resistance to these diseases. Two separate field experiments were conducted for screening LLS and rust under artificial epiphytotic conditions during rainy seasons of 2012 and 2013 at ICRISAT, Patancheru, India. The trials were laid in a randomized complete block design on beds with three replications. Data on LLS and rust disease severities were collected using 1 to 9 scales at 75, 90 and 105 days after sowing (DAS), and pod yields were recorded at harvest. Results indicate significant variations among accessions for LLS and rust resistance. Mean of 2 years study revealed that 53 accessions were moderately resistant (MR), 86 accessions were susceptible (S) and 45 accessions were highly susceptible (HS) to LLS. For rust disease, 10 accessions were resistant (R), 115 accessions were with ‘MR’ reaction and 59 accessions with susceptible (S) reaction. Six superior accessions in terms of combined disease resistance and yield (ICGs 4389, 6993, 11426, 4746, 6022, 11088) were selected and the disease progress curves, for each, were generated. Highest yields were recorded with ICG 11426 in LLS and rust plots. Overall, our results indicate that these six accessions can be potential sources of LLS and rust resistance.

Postemergence Herbicide Tolerance Variation in Peanut Germplasm

Although herbicide tolerance is not usually evaluated until the final stages of breeding programs, this trait is very important for grower adoption of new peanut cultivars. Understanding herbicide tolerance of breeding lines could help breeders develop selection strategies that maximize herbicide tolerance in new commercial cultivars. However, little is known about herbicide tolerance variability in peanut germplasm. Thirty-five randomly selected breeding lines from the peanut mini-core collection and cultivars ‘Florida-07’ and ‘Georgia-06G’ were evaluated for tolerance to 11 herbicides under greenhouse conditions. Variation among peanut lines in herbicide tolerance, measured as dry weight reductions (DWR), was similar across herbicides and was normally distributed. Florida-07 and Georgia-06G were in the lower two quartiles of injury and DWR among the evaluated peanut lines. Dose–response experiments showed that the most tolerant breeding lines had I50(the rate required to cause 50% injury) and GR50(the rate required to reduce dry weight 50%) values 0.4 to 2.5 times higher than the most susceptible lines, depending on the herbicide. A breeding line had a dicamba GR5013 times higher than the most susceptible line and 2.8 and 4.7 times higher than Florida-07 and Georgia-06G, respectively. The most tolerant lines were consistently tolerant to herbicides with different mechanisms of action, suggesting that nontarget site mechanisms are more likely to be responsible for the tolerance than target-site mutations. These results confirmed peanut-breeding programs would greatly benefit from screening breeding lines for tolerance to key herbicides and developing an herbicide-tolerance catalog. This information can be used when designing new crosses to reduce the risk of developing cultivars with low herbicide tolerance especially considering that one-half of the breeding lines exhibited lower tolerance than the commercial cultivars.

Multiple Resistant and Nutritionally Dense Germplasm Identified from Mini Core Collection in Peanut

ABSTRACTPeanut (Arachis hypogaea L.) is extensively grown by resource‐poor farmers in the semiarid tropics where many abiotic and biotic stresses limit the crop's productivity and seed quality. Peanut cultivars with enhanced host‐plant resistance, adaptation to abiotic stress, input‐use efficiency, and yield potential will maximize yield gains and minimize inputs to sustain production. The peanut mini core collection was evaluated for agronomic traits in multienvironment trials at Patancheru, India. The published information on 184 mini core accessions revealed 28 accessions resistant to abiotic stress, 30 resistant to biotic stress, and 18 that were agronomically desirable but susceptible to stresses, while 16 were seed nutrient dense. The mini core is part of the composite collection, which was previously genotyped using SSRs. The agronomic evaluation, stress response, and nutritional information together with genotyping data were used to identify genetically diverse germplasm with agronomically beneficial traits: ICG 12625 (resistance to drought, low temperature, late leaf spot [LLS], Aspergillus flavus Link, bacterial wilt; high oil and good oil quality) and ICG 442 (resistance to drought, salinity, P deficiency); ICG 12625 and ICG 2381 (resistance to rust, A. flavus; good oil quality); ICG 12697 (resistance to LLS, rust, A. flavus) and ICG 6022 (resistance to early leaf spot [ELS], LLS); ICG 14710 (high oil, Fe, Zn) and ICG 7963 (high protein, Fe, Zn); ICG 11426 (resistance to ELS, LLS, rust) and ICG 5221 (high Fe and Zn and good oil quality). Accessions with adaptation to rainy and/or post‐rainy environments were ICG# 434, 5745, 8285, 10036, 11088, 11651, 12625, 15042, and 15419. These accessions are ideal genetic resources that may be used to develop agronomically superior and nutritionally enhanced peanut cultivars with multiple resistances to abiotic and biotic stresses.

Diversity characterization and association analysis of agronomic traits in a Chinese peanut (Arachis hypogaeaL.) mini‐core collection

Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A peanut (Arachis hypogaea L.) mini-core collection in China comprising 298 accessions was genotyped using 109 simple sequence repeat (SSR) markers, which identified 554 SSR alleles and phenotyped for 15 agronomic traits in three different environments, exhibiting abundant genetic and phenotypic diversity within the panel. A model-based structure analysis assigned all accessions to three groups. Most of the accessions had the relative kinship of less than 0.05, indicating that there were no or weak relationships between accessions of the mini-core collection. For 15 agronomic traits in the peanut panel, generally the Q + K model exhibited the best performance to eliminate the false associated positives compared to the Q model and the general linear model-simple model. In total, 89 SSR alleles were identified to be associated with 15 agronomic traits of three environments by the Q + K model-based association analysis. Of these, eight alleles were repeatedly detected in two or three environments, and 15 alleles were commonly detected to be associated with multiple agronomic traits. Simple sequence repeat allelic effects confirmed significant differences between different genotypes of these repeatedly detected markers. Our results demonstrate the great potential of integrating the association analysis and marker-assisted breeding by utilizing the peanut mini-core collection.

Registration of Purified Accessions for the U.S. Peanut Mini-Core Germplasm Collection

Many accessions of the USDA peanut (Arachis hypogaea L.) germplasm collection are heterogeneous. Advances in genomics technology have highlighted the need for collections of homogeneous accessions. The objectives of this research were to purify accessions of the USDA mini-core collection and to characterize this collection for morphological traits. Twenty seeds of 104 accessions (Reg. No. GP-131 to GP-234) of the peanut mini-core collection were planted at Dawson, GA, in 2008 under irrigated conditions. The seeds from five phenotypical uniform plants were harvested, bulked, and planted in Headland, AL, in 2009 to continue the purification process. In 2010, homogenous seeds of each accession were planted in Headland, AL, to increase seed counts for chemical analysis and genotyping. In addition to morphological characters and seed chemical composition, the accessions were evaluated for spotted wilt resistance caused by a Tomato spotted wilt virus (TSWV) and early leaf spot resistance caused by Cercospora arachidicola (Hori), which is a major disease that significantly affects peanut production in the southeast. The morphological and chemical data, along with images of pod and seed traits, were entered in the Germplasm Resources Information Network (GRIN); detailed information can be found at http://www.ars-grin.gov/cgi-bin/npgs/html/csr.pl?PEANUT. The results showed that after purification, these accessions not only preserved similar genetic variation in the collection but also became more homogeneous compared to the original accessions. A small number of seed of the purified accession can be obtained for research and breeding purposes through the National Plant Germplasm System (http://www.ars-grin.gov/npgs/orders.html).

Oil, Fatty Acid, Flavonoid, and Resveratrol Content Variability and FAD2A Functional SNP Genotypes in the U.S. Peanut Mini-Core Collection

Peanut seeds contain high amounts of oil and protein as well as some useful bioactive phytochemicals which can contribute to human health. The U.S. peanut mini-core collection is an important genetic resource for improving seed quality and developing new cultivars. Variability of seed chemical composition within the mini-core was evaluated from freshly harvested seeds for two years. Oil, fatty acid composition, and flavonoid/resveratrol content were quantified by NMR, GC, and HPLC, respectively. Significant variability was detected in seed chemical composition among accessions and botanical varieties. Accessions were further genotyped with a functional SNP marker from the FAD2A gene using real-time PCR and classified into three genotypes with significantly different O/L ratios: wild type (G/G with a low O/L ratio <1.7), heterozygote (G/A with O/L ratio >1.4 but <1.7), and mutant (A/A with a high O/L ratio >1.7). The results from real-time PCR genotyping and GC fatty acid analysis were consistent. Accessions with high amounts of oil, quercetin, high seed weight, and O/L ratio were identified. The results from this study may be useful not only for peanut breeders, food processors, and product consumers to select suitable accessions or cultivars but also for curators to potentially expand the mini-core collection.

Heat stress screening of peanut (Arachis hypogaea L.) seedlings for acquired thermotolerance

Heat can be one of the major abiotic stresses that adversely affect crop production worldwide at different stages of development. As field screening for heat tolerance can be inconsistent and seasonally-limited, it is important to develop a reliable protocol under controlled conditions that allows simultaneous screening of multiple genotypes. The objective of this research was to develop a straightforward laboratory protocol using acquired thermotolerance (ATT) in peanut seedlings as a measure of one mechanism of heat stress tolerance. Sixteen genotypes, including selected accessions of the US peanut minicore collection along with standard checks, were evaluated for acquired themotolerance in two independent experiments. A change in the temperature sensitivity of chlorophyll accumulation was used as an indicator of acquired thermotolerance. Pre-incubation at 38°C for 4 h before the 30-min 50°C challenge triggered the acquired thermotolerance system of the leaf disks, resulting in chlorophyll accumulation upon exposure to light. There was considerable variation among genotypes for ATT in both experiments. Genotypic ranking for mean ATT values were highly correlated (0.949) in both experiments. The effect of seed weight on ATT was not significant. This method is relatively simple and inexpensive and can be used to screen a large number of genotypes.