RETN) is a gene coding for proinflammatory adipokine called resistin secreted by macrophages in humans. Single nucleotide polymorphisms (SNPs) in RETN are linked to obesity and insulin resistance in various populations. Using dbSNP, 78 nonsynonymous SNPs (nsSNPs) were retrieved and tested on a PredictSNP 1.0 megaserver. Among these, 15 nsSNPs were predicted as highly deleterious and thus subjected to further analyses, such as conservation, posttranscriptional modifications, and stability. The 3D structure of human resistin was generated by homology modeling using Swiss model. Root-mean-square deviation (RMSD), hydrogen bonds (h-bonds), and interactions were estimated. Furthermore, UTRscan served to identify UTR functional SNPs. Among the 15 most deleterious nsSNPs, 13 were predicted to be highly conserved including variants in posttranslational modification sites. Stability analysis predicted 9 nsSNPs  which can decrease protein stability with at least three out of the four algorithms used in this study. These nsSNPs were chosen for structural analysis. Both variants C51Y and C104Y showed the highest RMS deviations  which were confirmed by the important decrease in total h-bonds. The analysis of hydrophobic and hydrophilic interactions showed important differences between the native protein and the 9 mutants, particularly I32S, G79C, and C104Y. Six SNPs in the 3′UTR  were predicted to be implicated in polyadenylation signal. This study revealed 9 highly deleterious SNPs located in the human RETN gene coding region and 6 SNPs within the 3′UTR that may alter the protein structure. Interestingly, these SNPs are worth to be analyzed in functional studies to further elucidate their effect on metabolic phenotype occurrence.Resistin ( Genomic variation understanding is one of the major challenges of current genomics research field, due to the enormous number of genetic variations in the human genome. Single nucleotide polymorphisms (SNPs) represent the most abundant genetic variations throughout the human genome ranging between 3 and 5 million in each individual . Mostly,SNPs that change the encoded amino acids are called nonsynonymous single nucleotide polymorphisms (nsSNPs). Nonsynonymous SNPs, forming about half of all genetic changes related to human diseases, can influence resulting protein structure and/or function with either neutral or deleterious effects , 5.Moreover, the study of noncoding DNA is also important because it contains the majority of reported SNPs in human genome. Polymorphisms in 5′ and 3′ untranslated regions (UTRs) are of major interest because they can affect gene expression and posttranscriptional and posttranslational activities and thus be of functional relevance , 7.RETN) is located on chromosome 19p13.2. It was shown that resistin is linked to several inflammatory disorders including obesity, type 2 diabetes, cardiovascular disease, and asthma [Resistin is a proinflammatory adipokine which belongs to the cysteine-rich C-terminal domain proteins called resistin-like molecules (RELMs) and mainly secreted by adipocytes in rodents and macrophages in humans , 9. The d asthma –13. Thisd asthma –16. Circd asthma .RETN showed a significant association with circulating resistin levels. Beckers et al. identified the first missense mutation C78S in resistin in a morbidly obese proband and his obese mother. This finding encourages the study of variants in the RETN gene coding region to elucidate their involvement in pathogenesis [RETN gene and anthropometric variables and alterations related to obesity revealed inconsistent results [Moreover, a case-control study on type 1 diabetes mellitus patients showed that the combination of insulin and Rosiglitazone decreased resistin and leptin levels significantly . Geneticogenesis . It was ogenesis . However results , 20–23.RETN gene in multiple inflammatory diseases, particularly metabolic abnormalities, we conducted a computational analysis using nsSNP effect predictors like SIFT, PolyPhen, PANTHER, PhD-SNP and PredictSNP. Most deleterious nsSNPs were further analyzed by conservation and stability tools. Finally, a structural analysis was conducted in order to identify the most functionally deleterious SNPs in coding and untranslated regions.Basing on the importance of RETN gene was collected from dbSNP (http://www.ncbi.nlm.nih.gov/snp/). The amino acid sequence of the protein (NCBI accession: NP_001180303) was retrieved from the NCBI protein database (http://www.ncbi.nlm.nih.gov/protein). The theoretical structure of resistin (PDB ID: 1LV6) was abandoned since it was not in agreement with the crystal structure available for mouse resistin now.The SNP information of http://loschmidt.chemi.muni.cz/predictsnp1/) [PredictSNP1.0 (ctsnp1/)  was usedSIFT (Sorting Intolerant from Tolerant) predicts whether an amino acid substitution affects the protein function based on sequence homology and the physical properties of amino acids . SIFT tahttp://consurf.tau.ac.il/) was used to analyze amino acid sequence conservation. This web-based algorithm predicts the crucial functional regions of a protein by estimating the degree of amino acid conservation based on multiple sequence alignment. The grade range from 1 to 9 estimates the extent of conservation of the amino acid throughout evolution. Therefore, grade 9 represents the most highly conserved residue, and the numbers descend to 1 representing the least conserved region. This tool analyzes the conservation at the nucleotide and amino acid levels.A ConSurf web server (http://www.modpred.org/) was used to predict posttranslational modification (PTM) sites; the server consists of a set of bootstrapped logistic regression models for each type of PTM, retrieved from 126,036 nonredundant PTM sites verified experimentally, the literature, and from the databases [A ModPred web server (atabases . Resultshttp://folding.biofold.org/cgi-bin/i-mutant2.0), which is a support vector machine (SVM) web-based tool used for the automatic prediction of changes in protein stability due to SNP. It provides the predicted free energy change value (DDG) and the sign of the prediction as increase or decrease. DDG value is calculated from the unfolding Gibbs free energy value of the mutated protein minus the unfolding Gibbs free energy value of the wild type in kcal/mol. DDG > 0 means that the protein stability increased, and DDG < 0 means that the protein stability decreased [The change in protein stability due to nsSNPs was predicted using I-Mutant2.0 (ecreased .http://mupro.proteomics.ics.uci.edu/). This server is based on two machine learning methods: support vector machines and neural networks. Both of them were trained on a large mutation dataset and showed accuracy above 84%.The stability was also checked by a MUpro tool  have crucial roles in degradation, translation, and localization of mRNAs as well as the regulation of protein-protein interaction. We used the UTRscan web server lidation , 34. To https://swissmodel.expasy.org). The model has a QMEAN of -1.83 and a sequence identity of 55.56%  with PDB code 1RGX  as a temf 55.56% .UCSF Chimera was used to confirm the corresponding positions of the SNPs and to construct the 15 mutant models . It is aThe energy minimization of the wild type and mutant structures was performed by NOMAD-Ref server Gromacs-based as a default force field; we used conjugate gradient method for the 3D structure optimization .UCSF Chimera served again to check RMS deviation by superimposing both native and mutant structures. In addition, this tool served to calculate total h-bond values for each structure.COCOMAPS (bioCOmplexes COntact MAPS) is a web application to effectively analyze and visualize the interface in biological protein-protein complexes by making use of intermolecular contact maps. The input file was the resistin homology model in PDB format. In our study, we used COCOMAPS to analyze the interaction between the three monomers of resistin protein . To achihttp://string-db.org) is a database of known and predicted protein interactions, which currently covers 9,643,763 proteins from 2031 organisms. This database provides a critical assessment and integration of protein-protein interactions including direct  and indirect  associations [STRING . It contained a total of 1075 SNPs. Out of which, 78 were nsSNPs, 35 were coding synonymous SNPs, 339 were located in the noncoding region, which comprises 18 SNPs in the 5′UTR, 35 SNPs were in the 3′UTR, and 287 were in the intronic region.The A total of 78 nsSNPs were selected for our investigation. This SNP collection was analyzed with various in silico prediction tools to measure their effects on pathogenicity and to find out disease-associated SNPs. All nsSNPs which were obtained from SNP database were loaded to PredictSNP1.0, and all available integrated tools were selected for prediction. Fifteen nsSNPs were predicted as deleterious by all integrated tools, except for nsSNPAnalyzer and PANTHER that did not give any prediction for any mutation. According to SNAP, a total of 38 nsSNPs out of 54 were predicted to be deleterious (70.37%), followed by MAPP with 37 deleterious nsSNPs (68.51%), PolyPhen-2 with 31 nsSNPs (57.40%), PolyPhen-1 with 25 nsSNPs (46.29%), SIFT with 26 nsSNPs (48.15%), and PhD-SNP with 18 nsSNPs (33.33%). The nsSNPs predicted as deleterious are listed in The results of ConSurf analysis showed that 13 deleterious missense SNPs are located in highly conserved regions, with conservation values ranging between 7 and 9, which suggests that these positions are important for the resistin integrity. Among these, three residues were predicted to be exposed and functional, five others were predicted to be buried and structural, two buried residues and one exposed residue." while we should mention at the beginning of the paragraph that "11 deleterious missence SNPs are located in highly conserved regions", because we mentionned just after this that conservation values are ranging between 7 and 9 so we excluded G71 ( score: 4) and R84 (score: 6). The position 84 was predicted as moderately conserved, and the position 71 was predicted as variable residue; therefore, they were not selected for structural analysis.ModPred was used to predict posttranslational modification sites present within the human resistin protein. Only PTMs with high or medium confidence were discussed. In the native protein, position R84 was predicted as a site of ADP-ribosylation, W98 as a site of C-linked glycosylation or proteolytic cleavage, and C103 and C104 as disulfide linkage sites. After mutagenesis, C51 appeared as a site of amidation with the change of Cys to Tyr, while the position W98 changed to a disulfide linkage site with the change of Trp to Cys. Regarding the position C104, it was predicted that the change of Cys to Tyr conferred an amidation site with a high confidence. The results of ModPred are shown in We analyzed the 13 missense substitutions predicted as deleterious from the previous steps with the I-Mutant2.0. and MUpro web server. nsSNPs predicted to decrease stability with both tools were selected for further structural analysis. The results are showed in Using the X-ray crystal structure (1rgx) as a template, we modeled the 3D structure of native human resistin using the Swiss model web server. The RMSD values associated with the 9 mutants are given in The interface contacts between the amino acids present within the resistin trimer were studied using COCOMAPS. Variation in the number of different types of interactions was observed between the native and 9 resistin mutants; the results are given in Regarding the number of hydrophilic-hydrophilic interactions, the native complex participated with 262 hydrophilic-hydrophilic interactions. The mutant complexes I32S, C51Y, G79C, and C104Y, showed a significant increase in the number of hydrophilic-hydrophilic interactions with 286, 266, 277, and 266 interactions, respectively, which indicate a reduction in the hydrophobicity of these mutant trimers. In addition, the mutant complex C103G showed a significant increase in the number of hydrophobic-hydrophobic interactions indicating the increase of its hydrophobicity.Moreover, we found that the C51Y mutant trimer interacts with only 75 residues of chain C forming the trimer complex while in the native complex, chain C interacts with 78 residues. This small deviation may disrupt resistin trimer formation.The UTRscan server was used to predict the effect of UTR SNPs on transcriptional motif. Six SNPs in the 3′UTR, namely, rs920569876, rs74176247, rs1447199134, rs943234785, rs76346269, and rs78048640, were predicted to be in polyadenylation sites and thus may be responsible for pathological phenotypes. Results are given in Protein-Protein Interactions Using STRING. Prediction of protein-protein interactions indicated that resistin interacts with 10 proteins including insulin (INS), insulin receptor (INSR), leptin (LEP), adiponectin (ADIPOQ), peroxisome proliferator-activated receptor gamma (PPARG), tumor necrosis factor (TNF), interleukin 6 (TNF), nicotinamide phosphoribosyltransferase (NAMPT), nuclear receptor subfamily 3 group C member 1 (NR3C1), and ghrelin/obestatin prepropeptide (GHRL) (e (GHRL) .RETN gene. However, population-based association studies on RETN remain insufficient. Therefore, the current study is aimed at investigating the structural and functional impact of the SNPs present in the RETN gene using a thorough computational analysis. A total of 78 nsSNPs in RETN gene were retrieved from dbSNP database.With the growth of SNP number in databases, it becomes difficult to determine SNPs contributing in disease development. Thus, computational analysis can help to select a limited number of prioritizing deleterious SNPs for genetic disease screening . Among tRETN gene.The methods used in this study revealed the importance of using various algorithms with different prediction capacities to estimate the effect of variations on structural and functional levels. Using a single bioinformatic tool to predict potentially pathogenic nsSNPs may not be significant . Hence, Fifteen nsSNPs were predicted to be the most deleterious SNPs by these tools. In addition, using the ConSurf web server, 13 of them were predicted to be highly conserved with conservation scores ranging between 7 and 9. Moreover, PTM site prediction showed that W98 is a site for ADP-ribosylation and proteolytic cleavage in the wild type, while after mutation (W98C), it became a site for disulfide linkage with the gain of Cys residue; this may induce conformational restrictions on the protein, altering strongly its folding, function, and stability. Inversely, C103 and C104 residues were predicted as a PTM sites that lost disulfide linkages. Although disulfide bond 3D structure and interactions are highly conserved in nature, their loss may affect the protein folding. Nine nsSNPs were significantly predicted to affect stability. These results suggested that I32S, C51Y, G58E, G58R, C78S, G79C, W98C, C103G, and C104Y may be the structurally and functionally most significant SNPs in human resistin. The variant C104Y possessed the highest RMSD value, i.e., 1.308 Å followed by C51Y with an RMSD of 1.137 Å. Moreover, these variants had a significant loss of hydrogen bonds compared to the wild type. Interestingly, C51, C78, C103, and C104 residues are located in the highly conserved cysteine-rich C-terminus of resistin as all proteins of the resistin-like family share a common C-terminus domain with invariant spacing between cysteine positions (1C-X11-2C-X8-3C-X-4C-X3-5C-X10-6C-X-7C-X-8C-X9-9C-10C) , 47. FigRETN gene was investigated in this work by assessing the impact of deleterious SNPs in coding and untranslated regions through a computational approach. In a total of 78 missense SNPs, 15 were predicted as the most deleterious using PredictSNP. From which, 9 nsSNPs were predicted as highly conserved and affect protein stability. The structural analysis revealed high RMSD scores for both C104Y and C51Y variants, respectively, with a loss of the total hydrogen bonds. Six UTR SNPs were predicted to be in polyadenylation sites. Hence, we concluded that 9 nsSNPs I32S, C51Y, G58E, G58R, C78S, G79C, W98C, C103G, and C104Y and 6 substitutions in the 3′UTR, namely, rs920569876, rs74176247, rs1447199134, rs943234785, rs76346269, and rs78048640, could be important candidates in the pathological process of resistin particularly in metabolic pathways.


Gossypium spp.) is a leading natural fiber crop and an important source of vegetable protein and oil for humans and livestock. To investigate the genetic architecture of seed nutrients in upland cotton, a genome-wide association study (GWAS) was conducted in a panel of 196 germplasm resources under three environments using a CottonSNP80K chip of 77,774 loci. Relatively high genetic diversity (average gene diversity being 0.331) and phenotypic variation  were detected in this panel. Correlation analysis revealed that the well-documented negative association between seed protein (PR) and oil may be to some extent attributable to the negative correlation between oleic acid (OA) and PR. Linkage disequilibrium (LD) was unevenly distributed among chromosomes and subgenomes. It ranged from 0.10–0.20 Mb (Chr19) to 5.65–5.75 Mb (Chr25) among the chromosomes and the range of Dt-subgenomes LD decay distances was smaller than At-subgenomes. This panel was divided into two subpopulations based on the information of 41,815 polymorphic single-nucleotide polymorphism (SNP) markers. The mixed linear model considering both Q-matrix and K-matrix [MLM(Q+K)] was employed to estimate the association between the SNP markers and the seed nutrients, considering the false positives caused by population structure and the kinship. A total of 47 SNP markers and 28 candidate quantitative trait loci (QTLs) regions were found to be significantly associated with seven cottonseed nutrients, including protein, total fatty acid, and five main fatty acid compositions. In addition, the candidate genes in these regions were analyzed, which included three genes, Gh_D12G1161, Gh_D12G1162, and Gh_D12G1165 that were most likely involved in the control of cottonseed protein concentration. These results improved our understanding of the genetic control of cottonseed nutrients and provided potential molecular tools to develop cultivars with high protein and improved fatty acid compositions in cotton breeding programs through marker-assisted selection.Cotton ( Gossypium spp.) is the leading natural fiber crop for the manufacture of textiles and an important source of vegetable oil and protein for humans and livestock  Merrill  were detected  or chromosome regions conferring seed nutrients and composition in oilseed crops, including Arabidopsis thaliana , Taian, China in 2014 and 2016, and in Ling County, Dezhou, China in 2015. The 196 cotton accessions were planted at two experiment sites in a randomized complete block design with three replicates. Each replicate had one row that was 8-m long. The row space was 80 cm, and the average plant space was 33 cm. The planting date was April 24 in 2014 and 2015 and April 28 in 2016. Cultural practices followed local recommendations.http://www.elementar.de/en/products/nprotein-analysis/rapid-n-exceed.html). The mean value of three replicates was used for further analysis. Phenotypic traits across multiple environments were estimated using the best linear unbiased predictions (BLUPs) based on a linear model  (mg/g) in seed kernels and five fatty acids, including myristic acid (MA), palmitic acid (PA), stearic acid (SA), oleic acid (OA), and linoleic acid (LA), as percentages of TA were determined by gas chromatography  according to Lian et al. . In thisRRID:SCR_002865). Descriptive statistics was performed using the BLUPed traits values  to remove the SNPs with a call rate of < 90% and a minor allele frequency (MAF) < 0.05. A final set of 41,815 SNP markers was retained for further analysis.Genomic DNA was extracted from young leaf tissue with the Qiagen DNeasy Plant Kit. The DNA quantity and quality were measured with NanoDrop 2000 and agarose gel electrophoresis. Genotyping was conducted at the Beijing Compass Biotechnology Co., Ltd. using the CottonSNP80K array (Illumina)  was used to calculate the polymorphic information content (PIC) of the SNP markers, gene diversity and genetic distances among accessions, and to plot the unweighted pair group method with arithmetic means (UPGMA) phylogenetic tree using Nei's genetic distance method ] and the ad hoc statistic ΔK were calculated using STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/structureHarvester/), which is a program for visualizing the STRUCTURE output and implementing the Evanno method  were also used to adjust the population structure. TASSEL v5.2.40 (RRID:SCR_012837) was also used to calculate the parameter r2 .Principal component analysis (PCA) and the K matrix calculated with TASSEL v5.2.40 (RRID:SCR_012837) was employed and the genome-wide association mapping was performed implementing six models: the naive general linear model (GLM), the general linear model considering the Q-matrix [GLM (Q)], the general linear model considering the PCA-matrix , the mixed linear model considering the K-matrix [MLM (K)], the mixed linear model considering both Q-matrix and K-matrix [MLM (Q + K)], and the mixed linear model considering both PCA-matrix and K-matrix [MLM (PCA + K)]. The threshold to define a significant association between the marker and trait was set at a probability level of –log(p) ≥ 3.8.Genotypic and phenotypic data were jointly analyzed for determining the marker-trait associations. For this purpose, the software package TASSEL v5.2.40 (Gossypium hirsutum AD1 genome NAU-NBI assembly v1.1 (annot v1.1) (https://www.cottongen.org/tools/jbrowse). Moreover, the identification of the specific expressed genes in relevant tissues of the candidate regions was based on the G. hirsutum (TM-1) gene expression database  variation among the five fatty acids  during the 3 years, whereas the observed variations in LA (linoleic acid) and PR  were not significant. The results also indicated that the environment was responsible for a sizeable portion of the observed total variations in oil and the fatty acids levels, and the interaction between the genotype and the environment for oil concentration was larger than that for the total protein content.The results of ANOVA of the traits for 3 years 2014–2016) are listed in Table 4–2016 arThe BLUPed phenotypic values of MA, PA, SA, OA, LA, TA, and PR followed a normal distribution Figure , with meAll the 196 accessions were genotyped using the CottonSNP80K chip with 77,774 SNPs. Of the 77,774 SNP loci, 55,660 (71.57%) were polymorphic. After the removal of SNPs with a call rate of < 90% or with minor allele frequencies (MAFs) < 5%, 41,815 (53.76%) polymorphic SNP markers were finally screened out and used to assess the population structure (Q), relative kinship (K), and GWAS analysis. These filtered SNPs provided a whole genome-wide  coverage, with a mean distance of 46.26 kb ~K (from K = 1 to K = 20)  Figure . However2 Figure , suggest2 Figure . Third, 2 Figure . The K m2 Figure , in whic2 Figure .To determine the most appropriate model for association analysis, six common models, namely, the GLM model, the GLM (Q) model, the GLM(PCA) model, the MLM (K) model, the MLM (PCA + K) model, and the MLM (Q + K) model, were compared and shown using a quantile-quantile (Q-Q) plot Figures –10B. In 2) ranged from 7.3 to 13.3%, with an average of 9.5%.The BLUPed traits and 41,815 SNP markers were used in the association analysis. With the Q+K model, 47 significantly associated SNP markers (–log(p) > 3.80) were identified for seven cottonseed nutrient traits Table –10A. In If the distance between the lead SNP and following SNP markers was less than the LD decay distance among each chromosome or the pairwise r2 (the LD statistic) between the lead SNP and the following SNP markers was >0.1, then these SNP markers were set as a confidence interval for a QTL. Consequently, from the 47 associated SNP markers Table , 28 QTLsqGhPR-c26 region contained the maximum of 11 significant SNP markers (Table G. hirsutum (TM-1) genome, and a total of 1,789 genes were localized to these riveted regions  variation, and the minimum P-value was 0.0000167. Combining the QTL qGhPR-c26 (D12:338644336..0094524) and the LD heatmap (TM78688_TM78690 – TM78740_TM78747: D12:38600352..38927683)  Figure , the can) Figure . All thena Table . Of thesna Table , S5.According to the TM-1 gene expression database , which enhances the quality of cottonseed oil with high OA  effects have been reported Kohel, , 2010. RG. hirsutum  was selected by comparing six models using the quantile–quantile analysis. The results indicated that this strategy was effective. The 196 accessions were assigned to two subpopulations based on the peak of Δs Figure . These rs Figure . This iss Figure . FinallyZea mays, Glycine max, Oryza sativa, and Brassica napus, the LD decay distances were <100 kb, <600 kb, <1 Mb, and <6 Mb, respectively , was detected  for cottonseed oil  contained 8 genes (Gh_D12G1160 - Gh_D12G1167) annotated with Arabidopsis thaliana, SwissProt, InterProtscan, and GO function (Table A. thaliana (Lee et al., In addition to the four stable QTLs mentioned above, some new QTLs including 18 for seed oil and fatty acid composition and six for seed protein were also identified in this work and their stability needs to be verified. Besides comparison of QTLs identified in different works, candidate gene function or annotation analysis is an alternative method widely used in preliminary verification of the detected QTLs. The annotation information of the candidate genes in the QTL regions Table  will no on Table . The canIn future verification studies, these candidate genes might be identified by detecting the expression of these genes during the different development stages of target regions and/or by inhibiting or overexpressing these genes.X-LS and X-ZS designed the experiments. YY and X-LS wrote the manuscript. XW, HX, LW, QW, JT, WF, and GZ helped in collecting phenotypic data. MS helped analyze the results and revised the manuscript. YY, XW, and LW performed most of the experiments and contributed equally to this work. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Arachis hypogaea L.) is one of the most important oil crops worldwide, and its yet increasing market demand may be met by genetic improvement of yield related traits, which may be facilitated by a good understanding of the underlying genetic base of these traits. Here, we have carried out a genome-wide association study (GWAS) with the aim to identify genomic regions and the candidate genes within these regions that may be involved in determining the phenotypic variation at seven yield-related traits in peanut. For the GWAS analyses, 195 peanut accessions were phenotyped and/or genotyped; the latter was done using a genotyping-by-sequencing approach, which produced a total of 13,435 high-quality single nucleotide polymorphisms (SNPs). Analyses of these SNPs show that the analyzed peanut accessions can be approximately grouped into two big groups that, to some extent, agree with the botanical classification of peanut at the subspecies level. By taking this genetic structure as well as the relationships between the analyzed accessions into consideration, our GWAS analyses have identified 93 non-overlapping peak SNPs that are significantly associated with four of the studied traits. Gene annotation of the genome regions surrounding these peak SNPs have found a total of 311 unique candidate genes. Among the 93 yield-related-trait-associated SNP peaks, 12 are found to be co-localized with the quantitative trait loci (QTLs) that were identified by earlier related QTL mapping studies, and these 12 SNP peaks are only related to three traits and are almost all located on chromosomes Arahy.05 and Arahy.16. Gene annotation of these 12 co-localized SNP peaks have found 36 candidates genes, and a close examination of these candidate genes found one very interesting gene (arahy.RI9HIF), the rice homolog of which produces a protein that has been shown to improve rice yield when over-expressed. Further tests of the arahy.RI9HIF gene, as well as other candidate genes especially those within the more confident co-localized genomic regions, may hold the potential for significantly improving peanut yield.Peanut ( Arachis hypogaea L.) has been planted in more than 100 countries  [As one important source of edible oil, the cultivated peanut (fao.org) . Neverthfao.org) ,4. Yield-related traits, such as hundred-seed/pod weight and mature pod number per plant, are mostly quantitative traits, which have been found to be governed by multiple loci and are influenced by environmental factors ,6. UnderArachis’ with 58K SNPs that has a genome-wide coverage has been developed from 41 peanut accessions and some wild peanut relatives [GWAS has been used more and more often to study the genetic base of important traits in peanut ,13,14,15elatives  identifihttps://www.peanutbase.org/) [To further explore the genetic resources embedded within cultivated peanut, in the present study, 195 peanut accessions were genotyped using the GBS method, which has been made possible by the recent release of peanut whole genome sequence data (se.org/) ,24. Thesse.org/) ,13,14, aA. hypogaea var. hypogaea, 30 var. hirsuta, 56 var. vulgaris, 18 var. fastigiata plus nine irregular types , and the broad-sense heritability (H2) for each trait was estimated using the R package “lem4” [A total of 165 key germplasms of the 195 analyzed peanut accessions were planted at three different locations  in May in China during 2013, 2014, and 2016. Each accession was represented by 34–40 plants that were grown in a two-row plot (5.00 m long and 0.80 m wide). Seven different yield-related traits were evaluated for each accession: Hundred-seed weight (SW), hundred-pod weight (PW), yield per plant (YP), mature pod number per plant (MPP), pod number per plant (PNP), pod branch number per plant (PBP), and total branch number per plant (TBP). Each yield-related trait for each harvested accession was repeatedly measured three times for each study location and year, and these three measurements were then averaged out, so in total, nine mean values (3 locations × 3 years) for each trait of each accession were acquired. To minimize environmental effects, one BLUP (best linear unbiased prediction) value were estimated for each trait of each accession from the nine mean values obtained above, and it was these BLUP values that would be used later on in the GWAS analyses (cf. ). The coe “lem4” .®2.0 fluorometer . Each DNA sample has a concentration of no less than 50 ng/μl and a total DNA of more than 2 μg.Fresh leaves per accession were collected at Shandong Peanut Research Institute, Shandong Academy of Agriculture Science, and it was from these leaves that DNA was extracted using the DNeasy Plant Mini Kit . The extracted DNA was firstly electrophoresized and visualized in agarose gel containing Super GelRed , and then had their quality and concentration measured by the Nanodrop™ 2000 spectrophotometer  and the QubitEcoR I and Nia III, New England Biolabs, Ipswich, MA, USA) to perform digestion reactions on the above-prepared DNA samples [The genotyping by sequencing (GBS) libraries were constructed using a double digest GBS approach that employed a rare–common pair of restriction enzymes ,29 (EcoR samples . The dighttps://www.peanutbase.org/data/public/Arachis_hypogaea/Tifrunner.gnm1.KYV3/) using BWA v0.6.2 (-t 4 –M –k 32 –r 1 –c 1) [The raw reads generated from Illumina sequencing were filtered to get high-quality reads by removing adapter contamination, reads with ≥10% unidentified nucleotides, and reads with >50% low Phred scores (≤ 10). The acquired high-quality reads were then mapped onto a peanut reference genome ( 1 –c 1) .https://software.broadinstitute.org/gatk) [SNP calling was performed for all samples using the GATK’s Unified Genotyper (rg/gatk) . The iderg/gatk) .K) were tested using a cross-validation procedure, and the one with the lowest cross-validation error was chosen as the best K value  [Based on the SNPs that were identified above, a phylogenetic tree was constructed by a neighbor-joining (NJ) method as implemented in PHYLIP v3.69 , with thual.pdf) . The matual.pdf) .K = 4) of ancestral populations, while the K-matrix was acquired from SPAGeDi v1.5, and both matrices are based on all the high-quality SNPs identified in the present study. For each trait, the best model out of the tested four based on Q-Q plot was accepted as the final model. We used Bonferroni corrected p-value to take the multiple testing problems into consideration, and a p-value of 0.05/  or less was required to establish the significance. A candidate genome region that may be responsible for the studied yield-related traits was defined as the most strongly associated SNP (thereafter referred to as peak SNP) plus a 200kb-long genomic region that centered on the peak SNP [Genome-wide association study (GWAS) analyses of the seven aforementioned yield-related traits based on the acquired high-quality SNPs were conducted using TASSEL v5.0 ,39. Fourpeak SNP .www.omicshare.com/tools), and provided information about which biological processes/KEGG pathways these genes were enriched in. To accommodate the multiple testing problem in the enrichment analyses, FDR adjusted p-values were calculated with a value of 0.05 being the significance threshold.The candidate genome regions identified above were compared with the QTLs from previous relevant QTL/GWAS mapping studies ,12,13,14To reveal the genetic base of seven yield-related traits, a total of 195 peanut accessions were genotyped using the genotyping-by-sequencing (GBS) approach. Sequencing of the GBS libraries produced approximately 1695 million clean reads, which were of high quality (93.1% reads with a Phred score >30) and had a GC content of 37.6–40.2%. About 98.88% of those clean reads were successfully mapped to the peanut reference genome. These clean reads were deposited in the sequence read archive database under SRA accession: PRJNA525244.Ts/Tv) ratio for the analyzed peanut genome data was 1.94  in var. hypogaea, with those of var. fastigiata (π = 0.00011) and var. vulgaris (π = 0.00018) being in the middle.Overall, the levels of genome-wide nucleotide diversity (π) of the irregular accession group (π = 0.00042) being higher than those of the four studied peanut botanical varieties (π = 0.00006–0.00025). To be specific for the botanical varieties, the highest level of nucleotide diversity (π = 0.00025) was found in A. hypogaea ssp. hypogaea (var. hypogaea and var. hirsuta), while the second group mostly comprised accessions from A. hypogaea ssp. fastigiata (var. vulgaris and var. fastigiata), but also with a considerable proportion of members coming from ssp. hypogaea (mostly var. hypogaea) (A neighbor-joining (NJ) tree inferred from the acquired SNPs showed that the analyzed peanut accessions could be approximately classified into two major groups, with the first group being dominated by samples from ypogaea) e,d.K: 1–10) of ancestral populations, K = 4 represented the most sensible choice according to cross-validation error value: It has the lowest value  prevailed the genetic makeups of var. vulgaris, var. fastigiata and ca. 40% of var. hypogaea. For K = 3, again var. hirsuta and a big part of var. hypogaea dominated the first two ancestral populations (green and red) while the rest of the peanut accessions constituted the majority of the third population (blue). Overall, the results of both K = 3 and 4 agreed well with that of K = 2; for the latter, the first population was mostly composed of individuals from var. hirsuta and a big part of var. hypogaea, whereas the second ancestral population had var. vulgaris, var. fastigiata and ca. 40% of the var. hypogaea individuals as its main members.Results from the principal component analysis (PCA) were in accordance with those of the phylogenetic analysis c,f. The st value b, howeveas small b. For K r) estimates being, or less than, 0.45, except those for the trait pairs YP–PBP/MPP/SW/PW (0.60–0.66), as well as SW–PW (0.91) and MPP–PBP (0.93) (2H) for SW (H2 = 0.72) and PW (0.63) were the highest among the seven agronomic traits, while those for PBP (0.04), PNP (0.14), and MPP (0.19) were the lowest, and those for TBP (0.54) and YP (0.39) were in the middle , hundred-pod weight (PW), yield per plant (YP), mature pod number per plant (MPP), pod number per plant (PNP), pod branch number per plant (PBP), and total branch number per plant (TBP)) were estimated. All seven traits were found to follow the normal distribution without any significant skewness and kurtosis . The corP (0.93) . The broe middle .Genome-wide association analyses of the phenotypically characterized peanut accessions were conducted to see if any of the acquired SNPs had variants that were associated with the considered yield-related traits. Significantly associated SNPs were found for four of the studied agronomic traits , with the MLM and GLM models being the best statistical models, respectively, for traits SW, PW, PBP, and for trait YP based on the Q–Q plots . It shouH2 = 0.72) among the considered traits, a total of 38 peak SNPs were found to be significantly associated with it  (H2 = 0.63), 23 significantly associated peak SNPs  have been identified from chromosomes Arahy.07 (1 SNP), Arahy.15 (1 SNP), Arahy.18 (1 SNP), and Arahy.19 (20 SNPs), and it is worth to be noted that the p-value for one peak SNP on Arahy.19 was < 10−6 (H2 = 0.39) compared to SW and PW, 29 associated peak SNPs  had been identified from chromosomes Arahy.04 (1 SNP), Arahy.05 (2 SNP), Arahy.06 (1 SNP), Arahy.09 (1 SNP), Arahy.11 (2 SNPs), Arahy.15 (2 SNPs), Arahy.16 (4 SNPs), Arahy.18 (3 SNPs), and Arahy.19 (13 SNPs). On each of the chromosomes Arahy.09, Arahy.15, Arahy.16, and Arahy.19, one peak SNP was found to have a p-value smaller than 10−6. In addition, 26 significant peak SNPs that were significantly associated with PBP (2H = 0.04) were found on chromosome Arahy.01 (2 SNPs), Arahy.05 (20 SNPs), Arahy.06 (1 SNP), Arahy.12 (2 SNPs), and Arahy.19 (1 SNP). Among these identified SNP peaks, rs9144 on chromosome Arahy15, rs11390 on chromosome Arahy18, as well as rs11866, rs12090, rs12695, rs12720, and rs12730 on chromosome Arahy.19 were all associated with traits SW, PW, and YP simultaneously.For SW that was estimated to show the highest heritability ( × 10−6) . Three o × 10−6) . For PW s < 10−6 . For YP To sum it up, there were 93 non-overlapping peak SNPs that had been identified to be associated with the studied yield-related traits, and annotation of the 200kb genome regions that centered on these peak SNPs found a total of 311 unique preliminary candidate associated genes (CAGs) .Gene annotation of the 200kb genomic regions that centered on the peak SNPs, which had been identified to be associated with the studied yield-related traits, found 107 candidate associated genes (CAGs) for trait PBP, 70 CAGs for PW, 132 CAGs for SW, and 88 CAGs for YP . GO enriRegarding the KEGG pathway enrichment analyses, the two significant pathways enriched in the CAGs for PBP were autophagy  and oxidative phosphorylation . There was only one significantly enriched pathway  for PW (genetic information processing). For SW, four pathways were significantly enriched among the associated CAGs: Non-homologous end-joining (ko03450) and proteasome (ko03050) (both were Genetic information processing), as well as tyrosine metabolism (ko00350) and nitrogen metabolism (ko00910) (both belonging to Metabolism). No pathway was significant for YP .The previous QTL mapping/GWAS studies (using different sets of peanut accessions comparing to the present study) that identified significant QTL regions for traits PBP, SW, PW, and YP have been summarized , and theTheses earlier reported QTLs for traits SW, PW, YP, and PBP were then compared with the genomic regions that had been identified to be associated with the same traits in the present GWAS study. None of the earlier reported QTLs for PBP were co-located with those identified in the present study. However, a total of 12 co-localized genomic regions were found for traits SW , PW , and YP , among which one was located on chromosome Arahy.04 (rs2123), five on Arahy.05 , and six on Arahy.16  . It is wp value < 0.05). Ten of the enriched pathways were involved in metabolic pathways   . GO enriThe cultivated peanut is an important oilseed crop, which is widely cultivated across tropical, subtropical, and warm temperate area ,42. It oT, AG) is therefore conformationally more favorable than that generated by transversions  [The genotyping in this study was performed with the genotyping-by-sequencing approach that uses genome sequencing  to complete the genome-wide molecular marker (SNP) discovery and genotyping at the same time . This hi AT, GT) . Moreovehypogaea and ssp. fastigiata), which can be further sorted out into six botanical varieties [hypogaea: var. hirsuta and var. hypogaea; ssp. fastigiata: var. vulgaris and var. fastigiata) and almost equally represent the two subspecies , while the other major group more or less matches up with ssp. fastigiata and is composed of individuals from var. vulgaris and var. fastigiata, as well as ca. 40% of the var. hypogaea individuals. Perhaps the unexpected behaviour of the ca. 40% of the var. hypogaea individuals that are genetically more closely related to ssp. fastigiata instead of ssp. hypogaea , which is not surprising considering that traits YP, SW, and PW are highly correlated with each other r > 0.6, . Genes rarahy.RI9HIF gene (predicted to produce high affinity nitrate transporter 2.4 in A. hypogaea)  may deserve more attention. This is because one rice homolog (OsNRT2.3) of this gene produces a protein (OsNRT2.3b) that has been shown to improve yield in rice when over expressed [arahy.RI9HIF or at some other CAGs especially in the co-localized genomic regions significantly influences peanut yield needs further investigation.A close examination of these 36 CAGs around the 12 relatively confident QTLs for yield found that the xpressed ,57. WhetBy carrying out GWAS analyses, we have identified 93 non-overlapping SNP peaks that are significantly associated with four yield-related traits in peanut, and gene annotation of the genomic regions surrounding these SNP peaks identified 311 unique candidate genes. A comparison with earlier related QTL mapping/GWAS studies show that 12 of the 93 yield-related-trait-associated SNP peaks are co-localized with earlier identified QTLs, and 36 candidate genes have been identified from these 12 co-localized genomic regions.