Ascochyta rabiei (syn. Phoma rabiei) has the potential to destroy the chickpea industry worldwide, due to limited sources of genetic resistance in the cultivated gene pool, high evolutionary potential of the pathogen and challenges with integrated disease management. Therefore, the deployment of stable genetic resistance in new cultivars could provide an effective disease control strategy. To investigate the genetic basis of AB resistance, genotyping-by-sequencing based DArTseq-single nucleotide polymorphism (SNP) marker data along with phenotypic data of 251 advanced breeding lines and chickpea cultivars were used to perform genome-wide association (GWAS) analysis. Host resistance was evaluated seven weeks after sowing using two highly aggressive single spore isolates (F17191-1 and TR9571) of A. rabiei. GWAS analyses based on single-locus and multi-locus mixed models and haplotyping trend regression identified twenty-six genomic regions on Ca1, Ca4, and Ca6 that showed significant association with resistance to AB. Two haplotype blocks (HB) on chromosome Ca1; HB5 (992178–1108145 bp), and HB8 (1886221–1976301 bp) were associated with resistance against both isolates. Nine HB on the chromosome, Ca4, spanning a large genomic region (14.9–56.6 Mbp) were also associated with resistance, confirming the role of this chromosome in providing resistance to AB. Furthermore, trait-marker associations in two F3 derived populations for resistance to TR9571 isolate at the seedling stage under glasshouse conditions were also validated. Eighty-nine significantly associated SNPs were located within candidate genes, including genes encoding for serine/threonine-protein kinase, Myb protein, quinone oxidoreductase, and calmodulin-binding protein all of which are implicated in disease resistance. Taken together, this study identifies valuable sources of genetic resistance, SNP markers and candidate genes underlying genomic regions associated with AB resistance which may enable chickpea breeding programs to make genetic gains via marker-assisted/genomic selection strategies.Ascochyta blight (AB), caused by a necrotrophic fungus,  Cicer arietinum L.) is an annual legume of the family Fabaceae and provides a healthy source of protein, carbohydrates with a low glycemic index, vitamins, and minerals for human consumption. Chickpea also provides rotational benefit at a whole-farm level, due to its ability to fix atmospheric nitrogen and break the cycles of pests, disease and weeds. However, its production is constrained by several abiotic and biotic stress factors, especially diseases. Ascochyta blight (AB), caused by a necrotrophic fungus, Ascochyta rabiei (syn. Phoma rabiei) is a major endemic disease of chickpea worldwide. In Australia, this disease causes an average of $4.8 million loss to the chickpea industry annually and without the current control measures, the losses are estimated to be $39.7 million  for resistance have been identified on Ca1 , Ca2 Ch,b, Ca3 , Ca4 St, Ca5 Sa, C6 Sab,b, Ca7 , and Ca8Chickpea Breeding Australia (CBA) has been incorporating genetic resistance from several diverse sources for the last two decades and developing improved varieties for growers. This set of breeding germplasm provides an excellent opportunity to identify alleles/loci for traits of interest, including AB resistance, that have accumulated as a result of recombination and selection under diverse pathogen populations. In addition, breeding populations offer several advantages; any QTL identified will be of direct relevance for the resistance breeding pipeline . Also, aA. rabiei. Of them, two genomic regions on Ca1: HB5 (992178–1108145 bp) and HB8 (1886221–1976301 bp) were detected with both isolates. We also validated genomic loci on chromosome Ca1, associated with resistance to TR9571 isolate in two F3 populations derived from crosses involving moderately resistant breeding line, CICA1841. The SNPs/haplotypes identified herein will provide chickpea breeders and prebreeders the genotypes with favorable haplotypes as parents and further accumulate diverse and novel alleles from domestic and wild accessions using genomic selection strategies to develop cultivars with an optimum and stable level of resistance against the most damaging necrotrophic fungus.In this study, we investigated (i) the extent of genetic variation for AB resistance in the advanced Australian breeding germplasm against highly aggressive isolates and (ii) identified SNPs/haplotypes and underlying putative candidate genes associated with resistance at the adult plant stage. Utilizing 251 advanced breeding lines, phenotypic, and genotyping-by-sequencing based DArTseq-SNP marker data, we identified 26 genomic regions for resistance to two highly aggressive isolates of n = 251) of advanced Australian breeding germplasm; comprising 227 lines from stage 2 (S2) and stage 3 (S3) of the breeding cycle, 20 commercial cultivars, three FLIP (Food Legume Improvement Program) lines and one AB resistant Iranian landrace (ICC3996) was selected for this study (3 populations derived from PBA Drummond (highly susceptible to AB)/CICA1841 (moderately resistant to AB) and CBA Captain (susceptible to AB)/CICA1841 (moderately resistant to AB) crosses, were selected to verify QTL associated with resistance. All three parents of the F3 population were included in the GWAS set. Seed of all genotypes was obtained from New South Wales Department of Primary Industries (CBA), Tamworth, Australia.A GWAS set (is study . Stage 2A. rabiei (Cicer echinospermum interspecific breeding line and three FLIP lines). Two Australian chickpea commercial cultivars; Howzat (highly susceptible) and Genesis 090 (moderately resistant) were included as “reference checks” in all experiments  in a randomized complete block design with three replicates. The two highly aggressive isolates; F17191-1  and TR9571  were used to assess disease response and identify genomic regions associated with resistance to these isolates. Both isolates belong to the same pathotype group (Group 5) but genetically relate to different clusters; TR9571 (Cluster E), F17191-1 (Cluster C) .A. rabiei isolate, F17191-1 (Experiment 1) or TR9571 (Experiment 2). Plants were spray inoculated to runoff at a concentration of 1 × 106 pycnidiospores mL–1. After inoculation, the plants were maintained under high humidity with overhead misting, applied at a rate of 30 s every half an hour. Plants were watered using sprinklers as required until disease assessment. At 48 days after inoculation, host-pathogen interaction response was recorded at the adult plant stage on a pot basis (genotype) assessing the fraction of main stems broken, the fraction of the main stems with lesions, the fraction of side branches with lesions and the fraction of leaf area diseased. These data were converted to score of 1–9 as follows: 1 = no disease; 2 = no stem infection, 2% foliage infected; 3 = <5% stems broken, 30% main stems lesions, 10% foliage infected; 4 = 15% main stems broken, 60% main stem lesions, 25% minor stems lesions, 20% foliage infected; 5 = 40% main stems broken, 80% main stem lesions, 65% minor stems lesions, 50% leaves infected; 6 = 50% main stems broken, 100% stem lesions, 85% minor stems lesions, 60% leaves infected; 7 = 75% main stems broken, 100% stem lesions, 96% minor stem lesions, 75% leaves; 8 = 100% main stems broken, 100% stem lesions, 100% minor stems lesions, 90% leaves infected, and 9 = plant dead (100% infection).Seven-week-old seedlings (with an average of six fully developed nodes) were separately inoculated with 3 populations; PBA Drummond/CICA1841 (240 lines) and CBA Captain/CICA1841 (240 lines), including parents , four check cultivars , a Syrian landrace, ICC3996, and twelve breeding lines was performed under glasshouse conditions at Tamworth, Australia (Latitude: −31.1333 Longitude: 150.9500) using the TR9571 isolate. The 10-day old seedlings were inoculated using 5 × 105 pycnidiospores mL–1 and were maintained under high humidity with misters for 24 h following inoculation, at a rate of one minute every half an hour. After which, the misting was set for three minutes once per day until disease assessment. Disease infection was scored 14 days after inoculation using a modified 1–9 disease rating scale based on the disease reaction of an individual plant at the seedling stage. The 1–9 disease score is described as follows: 1 = no disease; 2 = few leaf lesions – small, 3 = few leaf lesions – large; 4 = 3 or less infected whole leaves, no stem lesions; 5 = more than 3 infected whole leaves, with small stem lesions (<1 cm); 6 = more than three infected whole leaves, with large stem lesions (>1 cm); 7 = numerous large stem lesions, but still some green leaves and growing point dead; 8 = leaves dead, stem green, growing point dead; and 9 = plant dead, no green tissue.Phenotypic assessment of two FAll statistical analyses were conducted with R version 4.0.5 statistical software . The pacThe generalized linear mixed model with logit link of the binomial family was applied to satisfy the disease measure scale (fraction) for main stem broken (MSB), branch diseased (BD) and stem lesions (SL). The predicted mean of four disease measures against two isolates is depicted as box plots. Correlation coefficients for eight variables were calculated to evaluate the association among four disease measures with two isolates.H2) of all measures of AB was calculated using the following equation: Broad-sense heritability (ed means . In addiin silico DArT (presence/absence markers) and (ii) DArTseq SNP (codominant). In this study, only DArTseq SNP markers were used for GWAS analyses. A total of 3,918 DArTseq SNP markers were polymorphic in the GWAS set. A sub-set of 2,130 high-quality DArTseq-SNP markers with a high call rate (>85%) and minor allele frequency (>2%) were further utilized for GWAS. Six kompetitive allele-specific PCR (KASP) markers associated with AB QTL on Ca2 and Ca4 reported by 1 to predict the physical position of each marker.Genomic DNA was extracted from 10-day old seedlings as described previously . All ger3 population derived from PBA Drummond/CICA1841 (165 lines) and both parents were genotyped with DArTseq markers as described above. For CBA Captain/CICA1841 F3 population (224 lines), a subset of 45 moderately resistant (AB score 4–5) and 45 susceptible (AB score = 7–8) including parents, were genotyped using DArTseq markers. Marker data filtering was applied as described above for the GWAS set and 390 and 576 DArTseq-SNP markers generated from PBA Drummond/CICA1841 and CBA Captain/CICA1841 populations, respectively were used for QTL analyses.The F2 A plot of the first two principal components was created to visualize the possible population structure of the GWAS set. A kinship matrix to control for the relatedness among chickpea cultivars/breeding lines was computed from the identity-by-state distances matrix as executed in the SVS package Version8.6.0 . The extent of linkage disequilibrium (LD) based on adjacent pair-wise r2 values between high-quality SNPs from the GWAS set and physical distances between these SNPs was estimated using the Synbreed package in R (r2 as responses (y) and pair-wise physical distances (Mbp) as predictor variables as described by r2 = 0.2) was measured using the stochastic gamma model with inverse link  ≥ 5.0, and Bonferroni P-value cut-off ≤ 0.01. In addition, haplotype trend regression (HTR) was performed which takes one or more block(s) of genotypic markers and for each block of markers, estimates the haplotypes for these markers, then regresses their by-sample haplotype probabilities against a dependent variable . Haplotypes block and HTR analyses were performed using an algorithm implemented in SVS v8.1.5. HB were constructed for Ca1 to Ca8 chromosomes using SNPs with a MAF ≥ 0.05, ninety-eight (98%) percent upper confidence intervals of the “D” values and the lower boundary to 0.70. Haplotype frequencies were estimated using the Expectation Maximization (EM) algorithm with a frequency threshold of 0.01, EM convergence tolerance (0.0001) and 1,000 iterations. HTR analysis was performed based on stepwise regression with forward elimination, pre-computed HB as described above, and the first three principal components as fixed covariates. False Discovery Rate (FDR) and Bonferroni corrected P-values were imputed, and significant HB were called based on Bonferroni and FDR P-value cut-off ≤0.01 and ≤0.001, respectively. Linear marker regression was performed to verify QTL associated with AB resistance in two F3 populations derived from PBA Drummond/CICA1841 (390 SNP) and CBA Captain/CICA1841 (576 SNP) using SVS package. Furthermore, the PC  of the GWAS set and each F3 population were estimated using common set of DArTseq SNP markers in the SVS package to gauge the relatedness between them. The first two PC, explaining most of genotypic variance were plotted to show clustering of GWAS and F3 populations.To identify genome-wide associations, both population structure  and kinship matrix (random effects) were accounted for in this study. GWAS was conducted using efficient mixed-model association expedited (EMMAX) single-locus (SLMM) and multi-locus mixed model (MLMM) in SVS V_8.6.0 software to identify the association between AB resistance (measured as disease score) and SNP markers . MLMM us10P values of all SNPs. QQ plots of the observed −10 Plog values (Y-axis) and the expected −log10P (X-axis) were plotted to check for genomic inflation.Genomic regions associated with AB resistance were designated as chromosome name followed by the physical location (bp) of the DArTseq SNP based on kabuli reference genome (v 2.6.3) to aid in QTL comparison in this study and across previous studies. Manhattan plots were generated using the −logTo compare the genomic regions identified in this study with the previously reported AB QTL, the information provided by A. rabiei, TR9571 and F17191-1, using four measures of disease assessment: disease score, main stem breakages, stem lesions and branches diseased. Most disease measures had high heritability values, ranging from 52 to 82%  had higher correlations with MSB and BD but the same had low correlations with SL . A moderA total of 2,130 high-quality DArTseq SNP and six KASP markers were selected for genetic analyses; of which 1,915 SNPs could be anchored on all eight chromosomes (Ca1–Ca8) of the reference chickpea genome assembly . AnchoreC. echinospermum interspecific line) grouped with desi S2 breeding lines, which had C. echinospermum in their pedigrees, whereas landrace ICC3996 grouped with desi genotypes . It varied among chromosomes and decayed at a faster rate in Ca4  as compared with Ca6   and each of the eight chickpea chromosomes (Ca1–Ca8) were estimated to gain insight into the extent of genetic diversity in the breeding germplasm. The decreasing trend of the LD decay curve with the increase in physical distance (Mbp) was observed based on a stochastic gamma model with an inverse link . The LD .26 Mbp) , reflect10P values ≥ 5.0 and FDR ≤ 3.9E–3) were identified for AB resistance against F17191-1 and TR9571 isolates on chromosomes Ca1 and Ca4  and multi-locus mixed model (MLMM) while accounting for both genetic structure (PCA) and relatedness (identity-by-descent matrix) to reduce false positives and correct any spurious associations. The QQ-plots and Manhattan plots generated for SNP associations are presented in  and Ca4 . A SNP o and Ca4 . Our resentified . The proentified .We further used DArTseq and KASP SNP markers to estimate haplotypes and identified HB associated with AB resistance using HTR. A total of 215 HB were identified . The chr10P-value = 10.43–19.15) for AB resistance, accounting for moderate to high phenotypic variation  with resistance to TR9571 isolate , Ca2 (17214916–18485579 bp), and Ca7 (2352759 bp and 23532379–23744945 bp) were significantly associated  were localized in a large genomic interval spanning 1057756–2545315 bp . Ca1 genulations , 5. Thisulations , 5.3 populations were searched against the CDC Frontier genome assembly v2.6.3 using BLASTn. Candidate genes that map within the 20 kb region up and downstream of the significant SNPs associated with resistance are presented (The putative candidate genes underlying sixty-one genomic loci anchored on the Ca1 35), Ca2 (10), Ca4 (11), Ca6 (2), and Ca7 (3), identified using SNP and haplotype-based GWAS and in two Fresented , 5, 7. I, Ca2  were performed to determine association between SNP markers and AB resistance. Phenotypic expression of AB disease is difficult to quantify, as the fungus infects leaf and stem tissue, and pods and seeds, therefore resistance among GWAS accessions using four different methods of disease assessment was initially evaluated. All disease measures: disease score, main stem breakages, stem lesions and branches diseased showed moderate to high correlations. For GWAS, a standardized disease score  used by the majority of the scientific community, was adopted. A small proportion of genotypes (4%) showed moderate resistance (disease scores of ≤3) to the two highly aggressive Australian isolates suggesting that useful variation exists within chickpea breeding germplasm and could be utilized for genetic analysis and resistance breeding purposes. Some of these sources of resistance are currently being used as parents for developing new cultivars resistant to highly aggressive A. rabiei isolates in CBA.In this study, genetic analyses of advanced (GWAS set of 251 genotypes) and early generation breeding accessions of the Australian chickpea breeding program (PBA/CBA)  and haplotype-based (HTR) approaches to identify genome-wide SNP associations for resistance to AB in the Australian breeding germplasm , 3. The Our analysis detected a higher number of significant SNP associations using HTR compared to SLMM and MLMM thus providing more detailed marker-trait association. These results are in agreement with previous studies, which demonstrated that haplotype analysis captures associations, which are not detected using single-SNP based approaches as haplotypes are dependent on the mutational and recombinational history of the QTL and the near-by markers . Haploty3 populations revealed a large genomic region on Ca1 (1.10–3.5 Mbp) that was also detected by MLMM (Ca1: 1781336) and HTR   , 5. The 1976301) . These fA. rabiei interaction. Our results provide a basis to further investigate molecular mechanisms and associated gene pathways involved in a complex and an important pathosystem.We identified 325 candidate genes mapped within 20 kb up– and downstream of the SNP markers on Ca1, Ca2, Ca4, Ca6, and Ca7 and at least 70 candidate genes (89 SNPs) had the associated SNP within the gene sequence. The most significant genomic region on Ca1 associated with resistance to both isolates’ harbors three genes; Ca07002 , and Ca07021 and Ca07022 (NADPH–dependent quinone oxidoreductase). In Arabidopsis, CaMBP may play a critical role in the cross-talk of multiple signaling pathways in the plant . Quinone3 population (Ca2: 17214916–18485579) are within the QTL intervals which have been previously identified in the mapping populations derived from ICCV96029 × Amit and ICCV96029 /CDC-Luna  genes, LOC101513119 (18.2 Mbp) and LOC101493700 (18.3 Mbp) map within the Ca2 genomic region  which showed association with resistance to AB. These results corroborate with previous findings suggesting that chromosome Ca4 is an AB QTL hotspot region . Li et a3 population. Eighty-nine significantly associated SNPs with resistance to AB were located within the candidate genes; some of these genes are implicated in disease resistance in plants. The role of these candidate genes needs to be established to understand chickpea-Ascochyta interactions and develop strategies to enhance resistance in commercial cultivars. Both common and different SNP loci identified in this study offer the opportunity to combine the alleles associated with resistance to different isolates (genetic clustering and pathogenicity) to develop cultivars with “broad-spectrum” resistance to multiple A. rabiei isolates. It remains to be validated if the same or different genomic regions are associated with resistance/susceptibility to isolates based on their genetic clustering and pathogenicity grouping.In summary, 26 genomic regions associated with resistance to AB in a GWAS set were identified and at least seven SNP associations were traced in CBA Captain/CICA1841 FThe original contributions presented in the study are included in the article/RR and KH planned the study and designed the experiments. AW, RR, and ND phenotyped the F3 populations. JD and MK-K phenotyped the GWAS set. RR and MR analyzed the data. KM developed the disease score system. NS and RR identified the candidate genes. RR performed GWAS analysis and wrote the manuscript. All authors read and approved this manuscript for publication.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


P450 genes and podocytes-marker genes. Out of 31 SNPs, 28 were accurately located using the mentioned codes.•SNP-locator code locates any SNP in promoters, upstream regulatory regions, exons and introns.•The SNP-locator code requires the FASTA genomic sequence of the studied gene and the genetic variant nomenclature at the cDNA level.•G4-overlap code maps the SNP to the overlapping or the closest G4 sequence.G-quadruplexes (G4s) are non-canonical DNA and RNA secondary structures that control gene regulation. A single nucleotide polymorphism (SNP) is a small genetic variation occurring within a DNA sequence and accounting for the variabilities between individuals. While the majority of SNPs, especially those frequent in the population, are considered as benign genetic variations, few others can lead to diseases. SNPs occurring in G4 sequences were reported to modulate gene regulation. In order to find overlaps between predicted G4 sequences and SNPs located in the genomic regions, we developed two complementary computational python codes (SNP-locator and G4-overlap). The codes map a mutation to the overlapping/closest G4 sequences, based on the genetic variant name and the FASTA format of the corresponding gene. We validated these two codes on a set of 31 SNP variants occurring in cytochromes   Specifications tableMethod detailsThe double helix B-DNA form can fold into other structures such as G-quadruplexes Single nucleotide Polymorphisms (SNPs) are small genetic changes occurring within a DNA sequence. Genetic variants arising in the coding or non-coding regions may have strong influence on gene regulation and protein function and may, in some cases, be linked to clinical manifestations ,8. SeverThe python program (version 3.9.7) must be installed on the computer user.SNP-locator: For the first code, a text file containing the genomic sequence of the desired gene in FASTA format and the genetic variant written as per the format set by the Human Genome Variation Society (at the cDNA level) are required. The SNP-locator code calculates the distance between the start codon and the SNP by creating two variables one for the total distance (distance) and the other for counting the base pairs in the exons (exon). Then, the lines in the text file are read until the incremented exon variable becomes equal to the distance SNP-start codon in cDNA and the total distance is given as an output.The code takes three main inputs: The path of the folder that contains the genomic file, the name of the genomic file and the location of the mutation at the cDNA level.If the mutation is in the intron, the code takes two additional inputs to track the location and the distance of the SNP in the intron being either ahead /forward (represented with “+”) or behind/backwards (represented with” - “) from the cDNA variant.CYP2D6 gene.SNP-locator code determines the SNP by marking it with quotations and displays the distance between the SNP and the start codon. G4-overlap: To determine the overlapping G4 sequence or the closest one to the SNP, an additional text file containing the predicted G4 sequences by G4Hunter as well as the output of the SNP-locator code are required. The G4Hunter application used is http://bioinformatics.cruk.cam.ac.uk/G4Hunter/.The user has to save the results obtained from the first code in a text file as represented in Before executing the code, the section below “seqnames” in the txt file obtained from G4Hunter has to be modified such that for every new gene the name has to be presented alone and followed by a “>” symbol . For eveWhen executing the code, the user inputs the path of the folder in which the results of the first code are located and the G4-sequences exist, along with their respective file names .Fig. DThThe distance of the overlapping or closest G4 sequence to the first codon is computed following several steps. First, the starts of the G4 sequences are subtracted from the position of the start codon in the genomic text file to obtain the distance between each G4 sequence and the start codon. Then, the distance between the SNP variant and the new computed distance for every G4 sequence is measured and stored in a list. Finally, the minimum distance from the list is extracted, and the whole process is repeated for all the SNP variants. The output given is the start and the minimum distance of the chosen G4 sequence for each gene .https://github.com/Marc-shebaby/SNP-G4-overlaps.git) and can be run on a personal computer.All source code are available on GitHub  and declare any financial interests/personal relationships which may affect your work in the box belowthe appropriate statement below (please .☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Please declare any financial interests/personal relationships which may be considered as potential competing interests here.☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:


Pythium ultimum causes seed rot and pre-emergence damping-off of chickpea (Cicer arietinum L.). The pathogen has been controlled for several decades using the fungicide metalaxyl as seed treatment but has re-emerged as a severe problem with the detection of metalaxyl-resistant isolates of the pathogen from infested fields in the United States Pacific Northwest. The objective of this study was to identify genetic markers and candidate genes associated with resistance to P. ultimum in an interspecific recombinant inbred line population (CRIL-7) derived from a cross between C. reticulatum (PI 599072) x C. arietinum (FLIP 84-92C) and conduct genome-wide association studies (GWAS) for disease resistance using a chickpea diversity panel consisting of 184 accessions. CRIL-7 was examined using 1029 SNP markers spanning eight linkage groups. A major QTL, “qpsd4-1,” was detected on LG 4 that explained 41.8% of phenotypic variance, and a minor QTL, “qpsd8-1,” was detected on LG8 that explained 4.5% of phenotypic variance. Seven candidate genes were also detected using composite interval mapping including several genes previously associated with disease resistance in other crop species. A total of 302,902 single nucleotide polymorphic (SNP) markers were used to determine population structure and kinship of the diversity panel. Marker–trait associations were established by employing different combinations of principal components (PC) and kinships (K) in the FarmCPU model. Genome-wide association studies detected 11 significant SNPs and seven candidate genes associated with disease resistance. SNP Ca4_1765418, detected by GWAS on chromosome 4, was located within QTL qpsd4-1 that was revealed in the interspecific CRIL-7 population. The present study provides tools to enable MAS for resistance to P. ultimum and identified genomic domains and candidate genes involved in the resistance of chickpea to soilborne diseases.The soilborne oomycete plant pathogen  Cicer arietinum L) is one of the most historically significant field crops, being among the eight “founder crops” domesticated by Neolithic societies 8,000–12,000 years ago in the “Fertile Crescent” of present-day Iraq, Syria, and Turkey (Phaseolus vulgaris L.) and peas (Pisum sativum L.) . Chickpea is produced in more than 50 nations, with India being the largest producer, accounting for approximately 68% of global production . There are two major market classes of chickpea based on seed traits; “Desi” chickpea, which have a “teardrop”-shaped seed and a pigmented seed coat, and “Kabuli” chickpea, which has an “owl head” shape, a light beige or cream-colored seed coat, and is typically larger than the Desi chickpea (Chickpea (d Turkey . Its impchickpea .Ascochyta rabiei (Pass.) Lab.  and Northern Plains (Montana and North Dakota). In 2019, more than 194,000 tonnes of chickpea were produced in the United States, with an estimated value greater than $116 million . However, several diseases challenge farmers in the United States, including Ascochyta blight caused by s.) Lab. . RecentlPythium includes several soilborne species that cause seed and seedling diseases across a wide range of crops , common bean (Phaseolus vulgaris L.), and pea (Pisum sativum L.)  Fitzp. and P. deliense Meurs isolated from soybean (Glycine max L.) and dry bean (Phaseolus vulgaris L.), respectively. Increased production costs and concerns about the development of metalaxyl-resistant P. ultimum suggest that effective disease control in the future may require other approaches, including the use of disease-resistant chickpea cultivars.The genus of crops . P. ultiivum L.) . Seed roon State . Historion State . Howeverprouting . Subsequobserved . Greenho control . HoweverDesi chickpeas were resistant to P. ultimum and all Kabuli chickpeas tested were susceptible  x C. arietinum (FLIP 84–92C) was used to conduct QTL analysis. PI 599072 is a Desi type and is resistant to P. ultimum, whereas FLIP 84-92C is a disease-susceptible Kabuli type. The RILs were increased under greenhouse conditions by single seed descent to F7  that included 177 chickpea recombinant inbred lines (RIL) derived from an interspecific cross nt to F7 . Genome-cessions .P. ultimum strain PT410 (P. ultimum isolate (PT410) under controlled growth chamber conditions  at Washington State University, Pullman, United States. Five seeds were planted in 10-cm pots containing 70 g of soil mix infested with 25,000 CFU of P. ultimum oospores for each entry. Myles and Sierra chickpea cultivars were used as resistant and susceptible checks, respectively . Broad-sense heritability (H2) was calculated based on the average mean seedling emergence values from repeated experiments with R software (http://www.R-project.org/) using MME-based algorithms . For processing reads, demultiplexing of all library groups was done using Illumina bcl2fastq 2.17.1.14 software, followed by demultiplexing of library groups into samples according to their barcodes. Quality trimming was done by discarding low-quality reads with a final length <20 bases, and filtered data were used to call SNPs. The filtered, high-quality reads from each sample were aligned to the chickpea reference genome (Cicer arietinum CDC Frontier whole-genome assembly v1.0) (p ≤ 0.0001) were excluded. However, SNP that were slightly distorted (0.0001 ≤ p ≤ 0.05) from the Mendelian ratio were included in the linkage map.Genomic DNA was extracted from young leaves of both parents and 177 RILs. Single nucleotide polymorphic (SNP) markers were detected using the Genotyping by Sequencing (GBS) approach. The libraries from the parental lines and RILs were prepared using ly v1.0) . The varly v1.0) . SNP marThese filtered markers were used to construct linkage groups (LGs) using the “Map” function in QTL IciMaping 4.1 and were assigned numbers (LG1–LG8) based on the genomic position of SNP markers. LGs with unlinked markers were removed from further construction. The remaining SNP were grouped with a logarithm-of-odds (LOD) threshold of 9.0. Recombination counting and ordering (RECORD) and “COUNT” (number of recombination events) algorithms were used in ordering and rippling. The linkage map and the best linear unbiased predictions (BLUP) value of phenotypic data of the CRIL-7 population from repeated experiments were used for QTL analysis. QTL was detected with composite inclusive composite interval mapping of additive (ICIM-ADD) function in QTL IciMaping 4.1 . The thr2 was calculated for all SNPs across each chromosome of the chickpea genome. SNPs with significant r2 values (p < 0.001) were considered informative and were pruned using a linkage disequilibrium (LD) pruning method implemented in PLINK software v1.09 using “—indep-pairwise 50 5 0.5” command line in Linux  software. The ADMIXTURE tool uses a model-based algorithm to estimate the ancestry of unrelated individuals . The nump-values of all SNPs  . Genes located within a 100-Kb region centered on a significant SNP were selected as candidate genes. The SNPeff tool (http://snpeff.sourceforge.net/) was used to detect genome coordinates of candidate genes and amino acid changes due to the SNPs  was considered a random effect, and population structure based on the number of principal components (PC) that explained 25%–50% of the total phenotypic variance was considered a fixed effect. For marker–trait associations, models with different combinations of the population (PC)/admixture(Q) and family (K) structures were applied using the GAPIT package of R software: FarmCPU with Kinship (K), FarmCPU with Kinship (K) + PC(2), FarmCPU with Kinship (K) + PC(3), FarmCPU with Kinship (K) + PC(4), FarmCPU with Kinship (K) + PC(5), and FarmCPU with Kinship (K) + Admixture (Q) . The besall SNPs . The finthe SNPs .P. ultimum in a repeated experiment. A summary of descriptive statistics of disease reactions is presented in P. ultimum. The mean of FLIP 84-92C was ≤0.3 in both experiments, indicating susceptibility to P. ultimum. The means of all RILs in experiments 1 and 2 were 1.7 and 1.8, respectively. Transgressive segregants were observed among RILs in both experiments. RIL effects, experiment effects, and their interaction effects were all significant  x C. arietinum (FLIP 84–92C)]. After filtering, 1,029 SNP markers were used to construct a linkage map and were assigned to the eight chickpea linkage groups (LG1–LG8)  . The eigLG1–LG8) . LG 8 waLG1–LG8) . Gaps inP. ultimum, QTL qpsd4-1 on LG 4, and QTL qpsd8-1 on LG 8. qpsd4-1 explained 46.5% of total variance with LOD = 25.2, whereas qpsd8-1 explained 4.5% of the total phenotypic variance with LOD = 3.3  reference genome on the Pulse Crop database was used for identifying candidate genes , missing data, and duplicate markers, and 229,965 SNPs were removed. Further linkage disequilibrium (LD) pruning removed 367,133 SNPs. After filtering and pruning, a total of 302,902 polymorphic SNPs remained for GWAS .Desi and Kabuli classes  explained 25% of the total variance, and the first five principal components (PC) explained 50% of the total variance. Graphs plotted using the first two PC explained the distribution of genotypes within different subpopulations . In ADMI classes , while wp-value of 1.65 × 10−7 was also detected within QTL qpsd4-1 that was revealed in the interspecific CRIL-7 population. Among the 35 significant SNPs, 29 were found in intergenic regions and 6 within genic regions (The greatest number of significant SNPs was observed on chromosome 4 (Ca4) . Marker  regions . Seven c regions .GBS can be used to generate large-scale SNPs that are abundant in the genome allowing for the construction of high-density genetic maps and higher statistical power in association studies . The CRIP. ultimum, qpsd4-1, was detected on LG 4  into 1-aminocyclopropane-1-carboxylate (ACC), which is the precursor of ethylene and acts as a signaling molecule to regulate plant growth and reduce stress response  Heynh mutants where plants with reduced ACC production showed greater disease susceptibility  in Oryza sativa and Pseudomonas syringae pv tomato (pto) in Solanum lycopersicum  into a random effect and a fixed-effect model, which reduces false positives and false negatives caused by kinship and population structure and gives highly significant SNP markers . The beslections  correspoBlumeria graminis f. sp. hordei (Bgh) in barley (Hordeum vulgare L.) (Zea mays L.) and wheat (Triticum aestivum L.) demonstrated that caffeoyl-CoA O-methyltransferase conferred resistance against southern leaf blight, gray leaf spot, and sharp eyespot disease, respectively  Heynh., Nicotiana sp., and Cucumis sativus L.  cluster in part of the A. thaliana. The LUPR1 gene has been associated with resistance to oomycetes Hyaloperonospora parasitica and Py. infestans  protein  . Gene Cagare L.) . Ca_1727ectively . Ca_0462tivus L. . Ca_2274y. sojae . The Ca_nfestans . The Ca_thaliana . Additio protein .P. ultimum. We also identified 35 SNPs and 14 candidate genes associated with disease resistance based on the GWAS of a chickpea diversity panel. SNP Ca4_1765418, detected by GWAS on chromosome 4, was located within QTL qpsd4-1 that was revealed in the interspecific CRIL-7 population. These findings suggest this region of the genome should be examined more closely to identify genes conditioning disease resistance. Significant QTLs must be validated in different chickpea populations before the markers can be widely used for breeding. The present study provides tools to enable MAS for resistance to P. ultimum and identified genomic domains and candidate genes involved in the resistance of chickpea to soilborne diseases.In this study, we used an interspecific chickpea population to identify one major and one minor QTL associated with resistance to


The three-dimensional (3D) structure analysis was performed on the nsSNP Y316S, V386G, E387G, C389G, and C391G nsSNP in the TGF-β domain of chicken BMP2 and H344P, S347P, V357A nsSNP in the TGF-β domain of chicken BMP4 protein that was anticipated to be harmful and of high risk. The ability of the proteins to perform variety of tasks interact with other molecules depends on their tertiary structural composition. The current analysis revealed the four most damaging variants , highly conserved and functional and are located in the TGF-beta domain of BMP2 and BMP4. The amino acid substitutions E387G, C389G, and C391G are discovered in the binding region. It was observed that the mutations in the TGF-beta domain caused significant changes in its structural organization including the substrate binding sites. Current findings will assist future research focused on the role of these variants in BMP function loss and their role in skeletal disorders, and this will possibly help to develop practical strategies for treating bone-related conditions.BMPs (bone morphogenetic proteins) are multipurpose (transforming growth factor)TGF-superfamily released cytokines. These glycoproteins, acting as disulfide-linked homo- or heterodimers, are highly potent regulators of bone and cartilage production and repair, cell proliferation throughout embryonic development, and bone homeostasis in the adults. Due to the fact that genetic variation might influence structural functions, this study is aimed to determine the pathogenic effect of nonsynonymous single-nucleotide polymorphisms (nsSNPs) in BMP genes. The implications of these variations, investigated using computational analysis and molecular models of the mature TGF- Bone mineralization involves preosteoblastic cells, physicochemical reactions, and an organic matrix framework . Plasma Additionally, researchers found that the size of the defect, the function of pre-osteogenic cells, and the mechanisms that regulate their growth, maturation, and process all had an impact on bone regeneration . The migThe growth differentiation factor (GDF 5) plays a significant role in bone and joint development . BMP13 mOsteoblast abnormalities include brittleness, spontaneous fractures, disturbed fracture healing, scoliosis, kyphosis, developmental ossification, postnatal skeletal overgrowth at non-skeletal locations periosteal growth, changed quantity of bone mass, and an aberrant quality of bone matrix. There are several types of chondrodysplasia chondrodysplastic disease, dwarfism, unusual long-term growth (brachypodism), faulty or accelerated chondrogenesis, and problems in the cartilage template vascularization . FailureA study examined the walking abilities (gait scores) of traditional broilers and discovered that 77.4 per cent of birds had an irregular gait, with 5.5 percent having significant gait impairments (gait score 2). Besides that, 4.7 percent of the broilers were found to have tibial dyschondroplasia, and 6.5 percent were found to have inflammation of the hock joint tendon sheath (tenosynovitis) . LamenesThis work examined possible detrimental SNPs in the BMP2 and BMP4 genes of chicken, which might impair the protein's structural and dynamic integrity. This study is aimed to study the role of BMP in skeletal development and to find a series of missense mutations in chickens. The appearance of these variations, investigated using computational analysis and molecular models of the mature TGF-beta domain, revealed how the mutations might impact BMP protein function. The current study showed that GDF3 plays a crucial and evolutionarily role in the skeletal development and new multi-allelic inheritance of BMP variations in developmental illness.http://www.ncbi.nlm.nih.gov/snp/), and Ensembl database . Each SNP has its unique reference sequence ID (rsID) (supplementary file). BMP genes' amino acid sequence and the BMP genes' sequence were obtained from the UniProt database (Q90751 and Q90752). BMP genes' sequence was also obtained from the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/gene/). This information used for further computational analysis.The SNP data for the BMP genes were obtained from the National Center for Biotechnology Information, USA (NCBI) dbSNP  (BMP2-ENSGALG00000029301 and BMP4-ENSGALG00000012429). To find the most detrimental SNPs, in silico harmful SNP prediction algorithms were applied. Among these techniques were the sorting intolerant from tolerant (SIFT) [The SNP database (dbSNP) of the NCBI contains extensive information on single nucleotide variants in every gene sequence. This database has been utilized to gather important BMP2 and BMP4 gene variations and their rs IDs. SNPs were extracted from Ensembl genome browser using the Chicken genome  , polymort (SIFT) , consenst (SIFT) . PolyPhet (SIFT) . The Mutt (SIFT) . PROVEANt (SIFT) . The efft (SIFT) . The PhDt (SIFT) . The mutt (SIFT) .http://www.modpred.org/) was used to predict post-translational modification sites within the chicken BMP sequences. It is a sequence-based predictor of proteins' PTM  sites. The server comprises 34 ensembles of logistic regression models; each was trained independently a cluster of 126,036 non-redundant experimentally confirmed sites for 23 different polymorphisms culled from Adhoc literature and public sources [The ConSurf online tool was used to analyze the native BMP2 and BMP4 proteins , 34. Thi sources .http://www.sbg.bio.ic.ac.uk/phyre2/html) and the Swiss model (http://swissmodel.expasy.org) [http://consurftest.tau.ac.il) [Z-score to categorize the protein sequence as excellent (Z − score > 7.5) or poor (Z − score < 7.5) [The crystal structures of chicken BMP2 and BMP4 were generated with the web applications Phyre2 (asy.org) , 39. Basu.ac.il) , 41, theu.ac.il) , 42 wereu.ac.il) . This see < 7.5) . The HOPe < 7.5) .In this study, the FTSite server, an energy-based approach that correctly identifies the binding sites of around 94% of the apoproteins from two test sets, was used to find alternative binding site prediction methods , 46, 47.http://asia.ensembl.org/Gallus_gallus/) (BMP2-ENSGALG00000029301 and BMP4-ENSGALG00000012429) and retrieved 5 SNPs in the 3′UTR area, 8 SNPs in the 5′UTR region, 57 SNPs in the noncoding region, and 11 missense variations in chicken BMP2 gene. The BMP4 gene contains 24 SNPs in the noncoding region and 18 missense variations. These 11 missense mutations of BMP2 and 18 missense mutations of BMP4, or nsSNP, were submitted to various SNP prediction tools to find out their effect. This resulted in a classification of nsSNP as either neutral or neutral detrimental to the structure and function of the BMPs  can be firmly regarded as significant candidates in the pathogenesis of skeletal disorders associated with BMP dysfunction. This will aid in discovering effective drugs and developing precision therapies. Wet lab experiments are necessary to determine the influence of these polymorphisms on the structure and function of the proteins. Also, understanding the pathogenesis of leg and skeletal abnormalities is important for better understanding the broiler leg illness. It is also important for reducing the economic loss caused by this disease, which is of considerable relevance and value.


The availability of large-scale genomic data resources makes it very convenient to mine and analyze genes that are related to important agricultural traits in rice. Pan-genomes have been constructed to provide insight into the genome diversity and functionality of different plants, which can be used in genome-assisted crop improvement. Thus, a pan-genome comprising all genetic elements is crucial for comprehensive variation study among the heat-resistant and -susceptible rice varieties. In this study, a rice pan-genome was firstly constructed by using 45 heat-tolerant and 15 heat-sensitive rice varieties. A total of 38,998 pan-genome genes were identified, including 37,859 genes in the reference and 1141 in the non-reference contigs. Genomic variation analysis demonstrated that a total of 76,435 SNPs were detected and identified as the heat-tolerance-related SNPs, which were specifically present in the highly heat-resistant rice cultivars and located in the genic regions or within 2 kbp upstream and downstream of the genes. Meanwhile, 3214 upregulated and 2212 downregulated genes with heat stress tolerance-related SNPs were detected in one or multiple RNA-seq datasets of rice under heat stress, among which 24 were located in the non-reference contigs of the rice pan-genome. We then mapped the DEGs with heat stress tolerance-related SNPs to the heat stress-resistant QTL regions. A total of 1677 DEGs, including 990 upregulated and 687 downregulated genes, were mapped to the 46 heat stress-resistant QTL regions, in which 2 upregulated genes with heat stress tolerance-related SNPs were identified in the non-reference sequences. This pan-genome resource is an important step towards the effective and efficient genetic improvement of heat stress resistance in rice to help meet the rapidly growing needs for improved rice productivity under different environmental stresses. These findings provide further insight into the functional validation of a number of non-reference genes and, especially, the two genes identified in the heat stress-resistant QTLs in rice. The growth of the world population needs our best efforts to increase crop production by 100% before 2050 . One of Indica and Japonica subspecies [Japonica rice cultivars, Akitakomachi, Nipponbare, Hitomebore, and Todorokiwase, are classified as heat-tolerant genotypes [Indica cultivars, IR24, IR36, Ciherang, ADT36, BG90-2, Dular, Huanghuazhan, AUS17, M9962, Sonalee, Carreon, Dular, N22, OS4, P1215936, HT54, Sintiane Diofor, and AUS16, are known as heat-tolerant genotypes [Japonica–Indica cross breeding, has also shown considerable heat tolerance during the booting stage and the flowering stage [The heat tolerance of plants refers to the ability of plants to avoid and endure high-temperature adversity. The tolerance of high temperatures in rice germplasm resources has been identified in both bspecies ,6. The Jenotypes ,8 while enotypes ,11. In menotypes . Giza178Indica and Japonica sub-species, and identified 966 new heat stress-resistant loci linked with the panicle length and number of spikelets [Apart from the thermotolerance phenotypic studies, genetic studies have been conducted to dissect and understand the mechanisms of heat stress resistance and discover heat-resistant genes or quantitative trait loci (QTLs) and apply them to thermotolerance breeding. Multiple genetic studies have shown that the heat tolerance of rice is a multigenic trait that varies with the development stages and plant tissues ,15. Withpikelets . Lafargepikelets . Similarpikelets .Brassica [Access to plant genomes has revolutionized the opportunities to discover specific genes and their subsequent associated traits. The efforts to dissect the genetic architecture of agronomically important traits in rice, such as QTL, GWAS, and genomic prediction, have been carried out primarily at the level of SNPs ,36. ThesBrassica ,41,42,43Brassica ,45,46.Thus, a pan-genome comprising all genetic elements is crucial for comprehensive variation study among the heat stress-resistant and -susceptible rice varieties. Therefore, in this paper, we firstly constructed a rice pan-genome reference from 60 heat-responsive rice cultivars using a pan-genome iterative mapping and assembly approach. Secondly, we detected the presence and absence of variation (PAV) and SNPs in the tested rice accessions. Thirdly, we identified the SNPs specific to the highly resistant rice cultivars and compared the results with the outcome of the comparative transcriptome analysis we performed on multiple RNA-seq datasets to detect the potential candidate heat stress tolerance genes in the non-reference sequences. Finally, the heat stress tolerance genes were also identified by mapping the pan-genes to known heat stress-tolerant QTLs in multiple rice reference genomes. The genome resequencing data of 60 rice varieties with different heat stress tolerance was downloaded from the 3000 rice genomes project (3K RGP) and other heat stress-related studies. The heat stress response of each variety was identified from previously conducted studies and the heat stress response of each variety was collected from each respective study . Based oAdmixture, 6 Indica, and 7 Japonica in the heat-susceptible group while the heat-resistant group was composed of 1 Aus, 2 Admixture, 11 Japonica, and 31 Indica rice varieties.By using the iterative mapping and assembly approach , the panwww.plantgdb.org (accessed on 21 February 2021)) and 4 rice RNA-seq data sets  and plant proteins  from NCBI were used as evidence. Finally, functional annotations of the predicted genes were performed using the Blast2GO tool [The pan-genome was constructed by mapping the sequence reads individually to the Nipponbare reference genome by using Bowtie2 v2.4.2 with (-I 0 -X 1000) options , and the2GO tool  and the 2GO tool  functionWe performed gene presence and absence analysis on the 56 rice cultivars with a read depth of greater than 10×. We first aligned the raw reads of these 56 rice varieties to the pan-genome sequence using bowtie2 v2.4.2 with (-I 0 -X 1000) options . Then, thttps://archive.gramene.org/qtl/ (accessed on 2 March 2021)). BLAST was used to map the marker positions on the twelve cultivated Asian rice reference genomes ) was run with the options “–gzip” and “–split-spot” to split the fastq reads. Residual adaptor sequences at both 5′ and 3′ ends were removed from the raw reads using the default parameters of the Fastp trimming and cleaning tool [The RNA-seq datasets were downloaded from the SRA archive of the NCBI database. First, the fastq-dump tool available in the SRA-Toolkit version 2.8.2 ) online phylogenetic plotting tool [Variants were identified based on the GATK best practices for SNP/Indel discovery . GATK veing tool . In this research work, we gathered 60 rice accessions with different tolerance of heat stress. Based on the criteria mentioned in the data collection section of the Material and Methods, the heat stress response recorded during panicle development and spikelet fertility, the rice cultivars were classified as 3 highly resistant, 36 resistant, 6 moderately resistant, 10 susceptible, and 5 highly susceptible to heat stress. The heat-responsive rank and genome resequencing depth are provided in the The genome resequencing data of these 60 rice varieties was used to build the rice pan-genome. After mapping the short read sequences to the Nipponbare genome, a total of 525 Mb non-reference sequences were obtained. The removal of contaminants (non-green plant sequences) and redundant contigs resulted in 38,189 non-reference contigs with a total length of 71,740,214 bp. In the final assembled non-reference sequences, using ab initio gene prediction tools and additional RNA-seq data, protein sequences, and EST sequences, a total of 1141 fully annotated genes were predicted . The PAV analysis was conducted on the whole-genome resequencing reads of 56 rice accessions with a sequencing depth greater than 10x. Subsequently, the presence and absence profile of each gene was calculated using the SGSgeneloss package . The majJaponica, containing 8 susceptible and 10 tolerant accessions. In total, 38,601 pan-genome genes were shared in this clade while 159 genes were present only in the resistant accessions. On the other hand, in the second cluster, the majority of the rice accessions belonged to Indica, including 29 tolerant and 7 susceptible accessions. Similarly, 38,860 genes were shared in these accessions and 370 genes were unique to the resistant accessions. The number of heat-resistant accessions sharing these unique genes varied from 1 to 14.The PAV-based relationships of the rice cultivars were accessed using the Jaccard similarity index. The Jaccard similarity index of the genes’ presence/absence variation varied between 0.9 and 1, suggesting that there was a close relationship between the different accessions . HoweverGO-based enrichment analysis of the predicted genes showed that 491 of them were involved in biological processes, among which the significantly enriched category included 38.9% involved in the response to stress, 20% in involved in biological regulation, and 11.8% in signal transduction functions. In the molecular function category, 535 genes were identified in which the significant category included 59% associated with binding and 45.7% associated with catalytic activity. In total, 485 of the predicted genes were also annotated to be involved in the cellular component functions, in which the genes associated with the organelle part (53%), membrane part (10%), and protein-containing complex (8%) were among the significantly enriched gene categories .We identified a large number of variants (SNPs) by mapping the heat stress-responsive cultivars’ whole-genome sequence reads to the pan-genome reference using the GATK tools. After filtering out the low-quality SNPs, SNPs with MAF ≤0.05, and SNPs with missing genotypes over 80%, a total of 5,059,798 biallelic SNPs were detected, among which 191,187 SNPs were identified in the non-reference contigs. It was observed that chromosome 1 had the highest number of SNPs , followed by chromosomes 4, 8, and 11. On the other hand, chromosome 9 contained the fewest number of SNPs . The SNP density comparison revealed that chromosome 8 had the highest number of SNPs per Kbp with 26.27/Kbp, followed by chromosome 10, 11, and 12, and chromosome 3 had the lowest density of SNPs per Kbp with 17.12/Kbp. The SNP density in the non-reference contigs (8.72/Kbp) was less than that in the reference genome (21.69/Kbp). The SNPs in each variety varied from 98,704 in HINUKARI to 2,211,361 in IR36 . N22 hadIndica, Japonica, Aus, and Admix. However, the phylogenetic relationships based on the SNPs grouped the varieties into two major clusters, which are in agreement with their population classification. The first cluster contained 19 rice varieties, in which the majority belonged to Japonica species, whereas the second cluster included the remaining 41 rice varieties, with the majority belonging to Indica species  tree was constructed using the SNPs. The accessions were categorized into the rice sub-species  species . To furt species . It was  species .The classification of the above 5,059,798 SNPs illustrates that the highest number of SNPs were located in the intergenic regions (45%), followed by the upstream (29.9%), downstream (14.4%), exonic (3.92%), and intronic (3.83%) regions . MissensTo further identify the heat tolerance-related variants and candidate genes, we investigated the genes that harbored the SNPs specific to the heat-resistant rice cultivars. Subsequently, we placed more emphasis on the high-impact SNPs, SNPs in the genic regions, and SNPs located within 2 kbp upstream and downstream of the genes. Using these criteria, we identified a total of 146,773 SNPs, including 2427 SNPs in 435 non-reference genes. Additionally, we performed further variant filtering to screen the SNPs specific to the highly heat-resistant cultivars. As a result, 76,435 SNPs were identified to be specific to the highly resistant cultivars, including 827 SNPs in 187 non-reference genes, which were named heat tolerance-related SNPs. The 76,435 SNPs were annotated as 162 high-impact SNPs , 5046 moderate-impact SNPs (non-synonymous), 66,575 modifier SNPs, and 4458 low-impact SNPs . Four RNA-seq datasets of rice under heat stress, PRJNA633211, PRJNA610667, PRJNA604026, and PRJNA508820, were downloaded from the NCBI database. Each dataset contained the transcriptome data of two rice cultivars with a contrasting response to heat stress and each sample had a minimum of two replicas . After tTo further investigate the genes associated with heat stress tolerance, we screened the DEGs with heat tolerance-related SNPs in all the datasets and excluded the DEGs with a contradicting expression profile among the different studies. Consequently, we were able to identify 3214 upregulated and 2212 downregulated genes with heat tolerance-related SNPs in one or more RNA-seq datasets . Among t(maker_00000041) was upregulated in two RNA-seq datasets of rice under heat stress, with 99.8% similarity to the ORUFI11G23900.1 gene in Oryza rufipogon. The cysteine-rich receptor-like protein kinase 6 (maker_00001878) gene upregulated in two RNA-seq datasets was homologous to the OBART07G17510.1 gene in Oryza barthii, with an identity of 81.4%. The thiol methyltransferase 2 domain-containing protein (maker_00001393) gene was homologous to the OGLUM03G40460.1 gene in Oryza glumipatula. The sulfotransferase (maker_00000647) gene was identified to be homologous to ONIVA11G17360.1 in Oryza nivara, with a similarity index of 100.0%  in rice. The positions of 63 heat stress-tolerant QTLs were retrieved from previous research. After filtering the overlapping QTL regions, 46 heat stress-tolerant QTL regions were selected . In ordeBrassica napus [The sequencing and assembly of the rice genome have allowed tremendous progress in rice genotyping and gene identification. Multiple studies have been conducted on rice using the pan-genome approach to mine the overall variation in rice cultivars, such as Zhao et al. (2018) , Wang etca napus , and ideca napus  and sorgca napus . Here, iVerticillium longisporum disease resistance in Brassica napus [Overall, 20.79% of the rice pan genomic genes were variable genes, and the PAV-based classification of the tested rice varieties was in agreement with the SNP-based cluster. A total of 159 and 370 unique genes were found in the resistant varieties in the two PAV-based clusters, respectively , demonstca napus . TherefoIndica and Japonica. Previous studies, such as Xu et al. (2020), found significant variation in the LOC_Os12g39840 (SLG1) gene between Japonica and Indica species, which confer high-temperature tolerance in Indica rice [With the rapid development of next-generation sequencing technologies, it is now much more reliable to discover DNA polymorphisms at a genome-wide scale, which plays a vital role in unraveling the genetic basis of phenotypic differences. As a result, the variant analysis using the pan-genome reference enabled us to discover 191,187 additional SNPs from the genetically diverse rice accessions. Recent pan-genomic studies, such as the studies by Ruperao et al. (2021) on the sorghum pan-genome , Zhao etica rice . Given tica rice , the hea2+ sensor, was involved in heat stress tolerance in a number of plant species, including Arabidopsis [Arabidopsis was enhanced by multiple stresses, especially heat and drought [Genome-wide SNP markers have been used to identify stress-resistant genes in plants in previous studies, including Li et al. (2017), Silva et al. (2012), and Xu et al. (2014), etc. ,75,76. Tbidopsis ,78,79. P drought . Sulfotr drought . Other g drought ,84,85,86Several heat-resistant QTLs in rice have been identified in previous research . In thisWe constructed and characterized the rice pan-genome using the rice reference genome and the whole-genome resequencing reads of 60 heat stress-responsive rice varieties. The pan-genome had 38,898 genes, which were categorized into core and variable genes according to the presence and absence variation. The results showed that PAV in the pan-genome contributed to the phenotypic diversity of the heat stress-responsive rice cultivars. Consequently, 3214 upregulated and 2212 downregulated genes with heat tolerance-related SNPs were identified by combining the strategy of SNP and transcriptomic analysis. Twenty-four DEGs with heat resistance-related SNPs were located in the non-reference contigs of the pan-genome, among which most were annotated as stress-responsive genes in rice. Two DEGs with heat resistance-related SNPs in the non-reference contigs were mapped to the known heat-resistant QTLs. Overall, the results of this study provide further insight for researchers on the functional validation of these heat stress resistance candidate genes.


A genetic diversity analysis and identification of plant germplasms and varieties are important and necessary for plant breeding. Deoxyribonucleotide (DNA) fingerprints based on genomic molecular markers play an important role in accurate germplasm identification. In this study, Specific-Locus Amplified Fragment Sequencing (SLAF-seq) was conducted for a sugarcane population with 103 cultivated and wild accessions. In total, 105,325 genomic single nucleotide polymorphisms (SNPs) were called successfully to analyze population components and genetic diversity. The genetic diversity of the population was complex and clustered into two major subpopulations. A principal component analysis (PCA) showed that these accessions could not be completely classified based on geographical origin. After filtration, screening, and comparison, 192 uniformly-distributed SNP loci were selected for the 32 chromosomes of sugarcane. An SNP complex genotyping detection system was established using the SNaPshot typing method and used for the precise genotyping and identification of 180 sugarcane germplasm samples. According to the stability and polymorphism of the SNPs, 32 high-quality SNP markers were obtained and successfully used to construct the first SNP fingerprinting and quick response codes (QR codes) for sugarcane. The results provide new insights for genotyping, classifying, and identifying germplasm and resources for sugarcane breeding Saccharum spp.), a C4 plant, is mainly distributed in subtropical and tropical areas [Sugarcane  technologies has caused the number of genetic variants that can be found in a single experiment to exponentially grow . The sugeference . The SLAeference . A powereference . It alsoSeveral common SNP genotyping methods based on electrophoresis, such as CAPS (cleaved amplified polymorphic sequence) , dCAPS , is based on the fluorescence-labeling single base extension principle, also known as small sequencing. The Multiplex SNaPshot panels system, whilst slightly more expensive than other available typing techniques, such as KASP, has the advantages of good consistency, high throughput, and strong site flexibility. SNaPshot is an easily integrable SNP typing option for forensic laboratories that has readily available forensic human and non-human DNA assays . At presarieties . In meloarieties . In tea arieties .Phaseolus vulgaris L.), 754 markers were used to define clustering trees for 708 genotypes, and a fingerprint recognition platform was constructed [In sugarcane, many improved cultivars and modern varieties were developed by both public and private organizations . Howeverstructed . In tobastructed .In this study, SLAF-seq data from 103 sugarcane germplasm resources were used for genetic diversity analysis, and SNP sites that have good polymorphisms that were uniformly distributed across the entire genome were identified by filtering and screening. A total of 43 markers, including 32 core SNPs and 11 candidate SNPs, were successfully developed by SNaPshot technology and a fingerprint for 180 sugarcane varieties was constructed.Saccharum spontaneum L.). A total of 180 sugarcane accessions were used for core SNP marker verification and DNA fingerprinting construction. These materials were comprised of breeding lines, landraces, commercial hybrids, and wild germplasm. They were provided by the Key Lab of Sugarcane Biology and Genetic Breeding , Nanning Germplasm Resources Nursery of Guangxi University , and Hainan Sugarcane Breeding Farm   .A total of 103 sugarcane samples were subjected to simplified genome sequencing using SLAF-sequence. For each germplasm, about 0.5 g of fresh, young, and veinless leaves were extracted with the cetyl ammonium bromide (CTAB) method . NanoDro®  and the sequence of fragment lengths 464–494 was defined as a SLAF-tag. The principles of the selective digestion plan are as follows: (1) The proportion of digestion SLAF tags located in repeat sequences should be as low as possible; (2) the SLAF tags were evenly distributed on the genome as far as possible; (3) the consistency of the fragment length should be maintained with the specific test system [We selected the reference genome of sugarcane for electronic digestion prediction . DNA samt system ; and (4)t system  sequenciS. spontaneum genome AP85-441, ftp://apple.fafu.edu.cn/SsponAnnotation, access on 4 May 2019) using BWA 0.7.10-r789 software [Grouping and genotyping of SLAF-seq data were carried out by the procedures described by Sun et al.  Briefly,software . The reasoftware  and SAMtsoftware  were appsoftware  was usedsoftware  softwareK ranging from 1 to 10. The cross-validation error rate of the K value was analyzed. The phylogenic trees from the SNP data were constructed by the neighbor-joining method with a bootstrap value of 1000 using MEGA 7.0 software [https://www.evolgenius.info/evolview/#/treeview, accessed on 13 April 2022 ).TASSEL software was used to calculate eigenvectors and eigenvalues according to SNP differences between individuals. PCA of the SNP data of 103 accessions was performed using the R 4.2.1 software package, SNPrelate . A populsoftware . The visBetween 4 to 8 SNPs were evenly selected according to the length of each chromosome. The following steps were performed to convert it to SNP markers: (1) TBtools software was used to compare the sugarcane reference genome and select a 200-bp sequence above and below the SNP locus. (2) The BLAST function in NCBI was used to detect the homology of sequences. The sequence with multiple homology SLAF tags was eliminated. (3) Specific primers were designed according to the flanking sequence of the target SNPs to amplify the fragment containing the SNP locus. Exonuclease I (Exo I) and shrimp alkaline phosphatase (SAP)  were added to the amplification product, and the primer sequences and the remaining dNTPs in the reaction system were digested. (4) Using the purified amplified product as a template, a unidirectional extension primer was designed according to the upstream or downstream sequence adjacent to the site. The first base at the 3‘ end of the primer was adjacent to the site to be tested, and the Tm value was above 50 °C. Poly C or Poly T of different lengths were added to the 5′ end of the primer.2 (25 mM), 2 μL10× of PCR Buffer, 2.4 μL of dNTP (2.5 mm), 1.0 μL of Probe Mix, 0.2 μL of HotStarTaq (5 U/μL), and 12.2 μL of ddH2O. The PCR reaction procedures were as follows: pre-denaturation at 95 °C for 2 min, denaturation at 94 °C for the 20 s, annealing at 65 °C for 40 s (−0.5 °C/cycle), extension for 90 s at 72 °C for 11 cycles; 94 °C denaturation for 20 s, 59 °C annealing for 30 s, 72 °C extension for 90 s for 24 cycles; and the last 72 °C extension for 2 min. PCR products were purified by reference to ABI’s SNaPshot technology. The extended PCR reaction procedure is as follows: pre-denaturation at 96 °C for 1 min, denaturation at 96 °C for 10 s, annealing at 52 °C for 5 s, and extension at 60 °C for the 30 s carried out for 28 cycles. Finally, ABI’s PRISM3730 sequencer was used for SNP genotyping and GeneMapper 4.1 [SNPs were detected by ABI’s SNaPshot Multiplex Kit. The total reaction system was a 20-μL PCR reaction system, including 1 μL of genomic DNA (10 ng/μL) 1.2 μL of MgClpper 4.1  softwarehttp://cli.im/, accessed on 17 May 2022), was used to generate the QR codes for sugarcane. The genotypes based on the SNP barcodes were entered, and the QR codes were automatically generated. When the barcode is scanned, the genotype of each accession is shown.Based on the SNP information obtained from the above analysis, 4-8 SNP loci were selected on each chromosome and 192 SNP markers were selected in total. Primers were developed by SNaPshot technology. Those primers with existing polymorphic differences were selected as core SNP markers, and primers with existing poor polymorphisms were regarded as candidate markers. The core SNPs were used to obtain genotypic data from the sugarcane germplasm accessions, and the online software, Caoliaoerweima  process and samtools. The identified SNPs were filtered, and 105,325 high-quality SNPs were obtained for subsequent analyses. An analysis of the types of predicted mutations associated with these SNPs showed that of the 6 possible single-base mutations, C/T and A/G transitions were the most frequent, accounting for 39.59% and 39.51% of the total, respectively. Of the 105,325 SNPs, 83,317 were transitions and 22,008 were transversions  using the cross-validation error rate. The cross-validation error-validation error rate was lowest when K = 2, which shows that the 103 sugarcane accessions can indeed be grouped into two clusters , was used to encode the genotypic data for the core SNPs in the 180 sugarcane accessions. A QR code fingerprint was constructed for each sugarcane line. The fingerprints contained corresponding information, such as variable name, type, and botanical classification. QR codes that are easily scanned and obtained using a mobile internet device, such as a cellphone, were translated from the fingerprints for these accessions. For example, the QR code for FuNong 41 shown in The overall predictive accuracy of the 32-SNP barcode was 100% for the 180 sugarcane germplasms. The barcode can distinguish all materials obviously and effectively . The onlUsing the genotyping data of the 32 core SNP markers, a genetic distance matrix for the 180 sugarcane accessions was calculated. The genetic distance between sugarcane accessions ranged from 0 to 0.743, and the mean genetic distance was 0.133. The value for the genetic distances was mainly distributed in the range of 0–0.1 and 0.1–0.2 , indicatSSR markers have been widely utilized in plant genomic mapping, selection identification, association analysis, and marker-assisted breeding over the past three decades. SSR markers may undergo mutations along the length of the SSR loci during PCR amplification or as a result of DNA enzyme slippage and duplication, which can lead to enormous numbers of shutter bands during polymorphism detection that make it difficult to interpret bands and construct multiple SSR detection systems. In contrast, SNPs have a low mutation rate and might be applicable in high-throughput automatic detection. The establishment of a SNP complicated typewriting system exploitation multiplex PCR and exposure typewriting strategies is comparatively easy. In this study, 7869,726 SNP loci were found to facilitate SLAF sequencing, and 32 high-quality SNP markers were identified to create sugarcane fingerprints. When compared to mass spectrometry or digital PCR strategies, this application can simultaneously examine several SNP sites within the same reaction tube. In addition, the capillary ionophoresis analysis of microsequencing merchandise using SNaPshot is quite practical, easy, and straightforward.The application of the highly-developed and popular SLAF-sequence simplified genome sequencing approach has been extensively documented for a variety of crops, including Chinese lou onions , sweet pThe advantages and downsides of the aforementioned two strategies are weighed against our level of economic tolerance and the sugarcane genomic characteristics to arrive at the best possible combination. The tetraploid genome used in our experimental study only comprises 32 chromosomes. The cultivated species, on the other hand, are a product of multi-generational selection and are derived from tropical species. The genetic data on chromosomes is extensive and intricate. The cultivated species, nonetheless, were chosen over multiple generations and descend from tropical species. There is a wealth of complex genetic data on the chromosomes. At the moment, the genome data for tropical species have been made public, and the genome assembly data for other materials will soon follow. Additionally, this technology system has a great deal of room for improvement. We may be capable of directly obtaining specific SNP loci associated with genes as heavy sequencing technologies reduce costs as well as the initiation of other tag classification methods, and even some functional gene sequences of related reports. This will help to use fewer molecular marker materials to determine similarities and differences. Moreover, the authenticity and efficiency will be improved compared to the current level.Recently, the genus for sugarcane and its relatives was preserved and wont to guide sugarcane breeding. The hybrid varieties, hybrid oldsters, and intermediate materials obtained through genetic improvement are various. These accessions are evaluated for victimization in completely different classes, like landraces, historical and up-to-date cultivars, breeding materials, and wild germplasms. To facilitate subsequent breeding and application of germplasms, an associated understanding of the genetic relationships, and therefore the structure of population biology among varieties/accessions at the genomic level, is required.S. spontaneum L., a wild material, is gathered in a single cluster, whereas cultivar materials are distributed among different communities. In the long-term domestic and breeding process, the genetic background of different wild accessions is introduced to the genome of cultivated species [Using the nucleotide sequences of the variant site SNPs obtained by SLAF for the 103 sugarcane accessions in this study, the results for PCA, genetic population analysis, and phylogenetic analysis were consistent. The clustering results showed that the wild germplasm fell into a single cluster, which also reflected the substantial genetic differences between wild germplasms and cultivars. However, cultivated accessions were distributed in different groups, indicating that these cultivars have no obvious correlation according to geographical region. The genetic background of the population is complex and diverse.  species . These sDNA fingerprints, which were developed in 1986 by Jefferys, have the advantage of being fast and accurate, and are based on DNA sequence polymorphisms at multiple loci distributed across the genome. The fingerprint map for sugarcane accessions is rich in polymorphisms and has a high degree of individual specificity and environmental stability that allows different individuals to be discriminated. DNA fingerprinting is a powerful tool to identify varieties and is also suitable for identifying plant germplasm resources. RFLP, AFLP, SSR, and SNP have been successively used to construct fingerprints. SSR fingerprinting is often used for variety identification and analysis due to its good repeatability, simplicity, ease of operation, and mostly co-dominant markers. Pan et al. developed the SSR marker-based molecular identification database for sugarcane , which pSolanum lycopersicum L.), a custom-made Illumina SNP-panel was used to genotype 214 accessions and examine polymorphism patterns [SNP-based high-throughput assay techniques and data analysis are based on the genetic characteristics of “binary variants”, in which two different fluorophores are used to label two different alleles and fluorescence detection systems that can effectively distinguish between two homozygous genotypes and one heterozygous genotype. To meet the needs of large-scale detection and fingerprint database construction, multiple SNP markers must be developed and screened to achieve the ideal ability to identify different varieties. In wheat, SNP markers were compared with other DNA markers to identify SNPs that have the necessary characteristics for high-throughput assays that have high accuracy . In tomapatterns . This stThe complexity of species genomes, marker types, and marker detection techniques, as well as varying numbers, influence the choice of key fingerprint-identifying markers. In the present study, SNaPshot technology was used to detect SNPs, and 32 core SNP loci were used to create fingerprints for 180 sugarcane germplasm resources, such that each variety had its unique fingerprint code. Markers for SNaPshot are highly polymorphic and have an enhanced capacity to identify different germplasms, thus providing a powerful resource for genotyping and identifying sugarcane cultivars. SNaPshot also represents a useful method for classifying sugarcane germplasm and ensuring the authenticity and purity of varieties. The QR codes provide convenient access to information on which decisions for sugarcane breeding programs can be made.Identification and screening of new varieties or cultivars are important in crop breeding. Traditionally, new varieties are identified using morphological characteristics. However, this approach has several disadvantages, including long identification cycles, high costs, and the confounding effects of environmental factors. Moreover, the genetic background of modern sugarcane cultivars became narrower since only a few elite parental lines have been used. In turn, the existing morphological genetic variation decreased and was more limited in terms of variety identification. Therefore, the use of DNA molecular markers to identify germplasm on a molecular level is critical. Through SLAF sequencing, we obtained 192 SNPs using filtering and screening, and of these 32 core markers, 11 candidate markers were successfully developed. Fingerprints for 180 sugarcane accessions were successfully constructed using 32 core markers.Variety identification should be accurate, efficient, simple, and have a high throughput. The detection method should be suitable for automation, which is a requirement for a variety of identification technologies in the future. SNP markers can be used to identify existing sugarcane resources, laying a foundation for the standardization of sugarcane and its application in subsequent molecular genetics and breeding programs. At present, high-throughput detection of SNP markers in large sample sets has shown advantages for the rapid identification of different varieties. In the future, high-throughput SNP detection technology will have broad applications for the fingerprinting and identification of newly-developed sugarcane varieties.In this study, SLAF-seq was used to analyze the population structure and genetic diversity of 103 sugarcane germplasms. The population could be divided into two well-separated clusters with no significant correlation between the distribution of sugarcane germplasm and its geographical origin. SNaPshot multiplex technology was used to develop 43 SNP markers (32 core SNPs and 11 candidate SNPs) for germplasm identification and fingerprint construction. Precision, high flux, and excellent site adaptation are benefits of the SLAF-sequence and Multiplex SNaPshot Panels system. Given the complexity of the sugarcane genome, the research goal of creating fingerprints, and the need to identify sugarcane germplasm resources, this technique is highly innovative. The fingerprints of 180 sugarcane germplasm could be constructed based on the 32 core SNPs. The results of this study provide a useful approach for genotyping, classifying, and identifying germplasm and resources in sugarcane breeding.


Phaseolus vulgaris) is one of the major legume crops cultivated worldwide. Bacterial wilt (BW) of common bean (Curtobacterium flaccumfaciens pv. flaccumfaciens), being a seed-borne disease, has been a challenge in common bean producing regions. A genome-wide association study (GWAS) was conducted to identify SNP markers associated with BW resistance in the USDA common bean core collection. A total of 168 accessions were evaluated for resistance against three different isolates of BW. Our study identified a total of 14 single nucleotide polymorphism (SNP) markers associated with the resistance to BW isolates 528, 557, and 597 using mixed linear models (MLMs) in BLINK, FarmCPU, GAPIT, and TASSEL 5. These SNPs were located on chromosomes Phaseolus vulgaris [Pv]02, Pv04, Pv08, and Pv09 for isolate 528; Pv07, Pv10, and Pv11 for isolate 557; and Pv04, Pv08, and Pv10 for isolate 597. The genomic prediction accuracy was assessed by utilizing seven GP models with 1) all the 4,568 SNPs and 2) the 14 SNP markers. The overall prediction accuracy (PA) ranged from 0.30 to 0.56 for resistance against the three BW isolates. A total of 14 candidate genes were discovered for BW resistance located on chromosomes Pv02, Pv04, Pv07, Pv08, and Pv09. This study revealed vital information for developing genetic resistance against the BW pathogen in common bean. Accordingly, the identified SNP markers and candidate genes can be utilized in common bean molecular breeding programs to develop novel resistant cultivars.Common bean ( Phaseolus vulgaris L.) is an important legume crop known for its edible seeds and pods worldwide , affects production of common bean in a major way due to its seed-borne nature and is caused by various isolates of Cff (Vigna unguiculata), common bean (Phaseolus vulgaris), mung bean (Vigna radiata), pea (Pisum sativum), and soybean (Glycine max)  . BW was ine max)  and was ine max) , Canada ine max) , and in ine max) .via infected seeds  of the total accessions were collected from Mexico. The remaining 83 accessions were collected from Guatemala (20), Colombia (18), Costa Rica (10), Nicaragua (10), Ecuador (9), El Salvador (5), Honduras (4), and Peru (7) .Cff pathogen in this study. The yellow (BW_528), orange (BW_557), and purple (BW_597) isolates of the BW pathogen were previously recovered and maintained from infected common beans in Nebraska .The 168 common bean accessions were tested for the three isolates of BW. The experiment was conducted using the cotyledonary node inoculation method  by HowarIn brief, 7–8 seeds were sown at a depth of 2.5 cm using 15 cm plastic pots with a standard potting mix. The seedlings were thinned to five plants upon emergence. The 7- to 10-day-old seedlings were inoculated with the respective isolate using a sterile needle. The inoculated seedlings were then incubated at a daily temperature of 28°C/22°C for 16 h per day and 8 h per night photoperiod in a greenhouse. A total of 9–12 plants per accession were used for evaluation for each isolate. In addition, ten plants for the resistant and susceptible controls were included for each BW isolate. The symptoms were evaluated 4 weeks post inoculations. Data were recorded as average severity for the replicated plants for each isolate. A 2-month cycle was used to evaluate the germplasm for each isolate individually.A standard rating scale from 1 to 4 was used to evaluate the plants, with 1 as highly resistant demonstrating no wilt or discoloration, 2 being moderately resistant with wilt or discoloration at one of the unifoliolate leaves, 3 showing wilt or discoloration on both unifoliolate leaves but asymptomatic on the 1st trifoliolate leaf, and 4 as highly susceptible with wilt or discoloration on the 1st trifoliolate leaf .https://datadryad.org/stash/dataset/doi:10.25338/B8KP45) for genotyping. SNP filtering was conducted with removal of SNPs; data missing rate >20%, heterogeneous >10%, and MAF  <5%.The common bean core set was genotyped  using BAThe phenotypic data for the three BW isolates resistance was analyzed using ANOVA and GLM functions in JMP Genomics 7 . The meahttps://github.com/jiabowang/GAPIT3).The principal component analysis (PCA) and genetic diversity were analyzed using GAPIT 3 (genomic association and prediction integrated tool version 3) by setting PCA = 2 to 10 and NJ tree = 2 to 10, and phylogenetic trees were drawn using the neighbor-joining (NJ) method . A threshold LOD [log10(p)] value >3.0 was used to select significant SNP markers associated with resistance to the BW_528, BW_557, and BW_597 isolates. Squared correlation coefficient (R2) was used to calculate the linkage disequilibrium (LD) between the markers.The phenotypic and genotypic data obtained for the 168 common bean core collection was subjected to genome-wide association mapping using the mixed linear model (MLM) methods in TASSEL 5 . The comPvulgaris 442_v2.1 available on the Phytozome website  was used to retrieve the candidate genes from the reference annotation of the common bean genome.The SNP regions were subjected to candidate gene discovery analysis for the identification of candidate genes spanning the 50 kb (50 kb on each side of SNP) regions around the significant SNPs. The Andean whole-genome reference sequence In addition to the identification of SNPs associated with BW resistance, the effect of these SNPs markers was also evaluated by using seven genomic prediction (GP) models. The ridge regression (RR); best linear unbiased prediction (BLUP) rrBLUP analysis; Bayesian models: Bayes A, Bayes B, Bayes ridge regression (BRR), and Bayes LASSO (BL); random forest (RF); and support vector machines (SVM) were deployed to assess GP .https://www.r-project.org). The Pearson correlation coefficient (r) was used to estimate the prediction accuracy (PA) using the GEBVs and observed values  skewed to resistance  to each The distribution of resistance scores after inoculation of 165 accessions with the BW_557 isolate was skewed toward the left side with score 1 of BW resistance . Seven aBased on the phenotypic analysis, PI203958 may be a good candidate for resistance to all three BW isolates. PI310611 was resistant to two BW isolates (BW_528 and BW_557) . SimilarThe three subpopulations  were well-differentiated with red, green, and blue colors  in 168 cOur analysis identified a total of 21 R-lines . The ideCollectively, 14 SNPs were associated with resistance to BW_528, BW_557, and BW_597, respectively, based on the four MLM models in TASSEL 5, FarmCPU, GAPIT, and BLINK using the 4,568 SNPs . The idet-tests for the 14 SNP markers are listed in The The GWAS panel for BW_528 was subjected to four MLM analyses in TASSEL 5; a QQ-plot distribution was obtained for the observed vs. expected LOD values. Based on MLM, the distribution of QQ-plot between the observed vs. expected LOD value showed divergence from the expected distribution . A similThese findings indicate the presence of SNPs at LOD scores greater than two to be associated with resistance to the BW_528 isolates .The TASSEL analysis showed a Manhattan plot for the MLM model with only one significant SNP (ss715648247 on chromosome Pv (04) had LOD close to 3  and other four SNPs with LOD >2.0 on Pv02, Pv04, Pv08, and Pv09, respectively, indicating a weak association for resistance to the BW_528 . On the The combined results from all the four models of MLM in GAPIT, MLM in FarmCPU, BLINK, and TASSEL showed a total of five SNPs associated with resistance to the BW_528 isolate . The twoBased on MLM in TASSEL 5, the distribution of QQ-plot between the observed vs. expected LOD values showed divergence from the expected distribution. A similar trend was observed for the MLM QQ-plot obtained from the cMLM analysis from GAPIT and the MLM analysis from FarmCPU, and BLINK. The QQ-plots obtained from GAPIT, BLINK, and FarmCPU showed a larger divergence between the observed vs. expected values at LOD >2.5 . This inThe TASSEL analysis showed the Manhattan plot for the MLM model with SNPs associated with BW_550 resistance, being indicated as dots with LOD value greater than 3 to be located on chromosome Pv11 . On the The combined results from all the four models of MLM in GAPIT, FarmCPU, BLINK, and TASSEL showed a total of five SNPs associated with resistance to the BW_557 isolate. Three SNPs, ss715647928, ss715648425, and ss715642582, were located on chromosome Pv07 with the latter two located closely together on positions 14,455,236 and 14,750,979 bp, respectively . The othThe MLM QQ-plot distribution between the observed vs. expected LOD, obtained using the MLM in TASSEL 5, showed divergence from the expected distribution. A similar trend was observed for the MLM QQ-plot obtained from the cMLM analysis in GAPIT, and the MLM analysis in FarmCPU and BLINK . The QQ-The TASSEL analysis showed the Manhattan plot for the MLM model with only one SNP associated with resistance to the BW_597 isolate (LOD >3) located on chromosomes Pv04 and Pv08 . The MLMThe combined results from all the four models of MLM in GAPIT, FarmCPU, BLINK, and TASSEL showed a total of four SNPs associated with resistance in common bean for the BW_597 isolate . The twoPhvul.002G041600, Phvul.004G076900, Phvul.004G119800, Phvul.008G107000, Phvul.009G210400, and Phvul.002G041000) were identified as candidate genes for resistance to the BW_528 isolate. Out of these six genes, five genes  were located within the 50 kb region of the associated SNP  region, while one gene model (Phvul.002G041000) included the SNP itself  were located within the 50 kb distance of the identified SNPs , while one gene model, Phvul.007G104500 include the identified SNP (ss715647928). The Phvul.007G112100 encoded a disease-associated Leucine-rich repeat protein kinase family protein positioned at 14,476,334 to 14,479,522 bp on chromosome Pv07  were located on chromosome Pv08, within the 50 kb region of ss715647896 and ss71564199 SNPs, respectively  all the 4,568 SNPs were used and between 0.40 and 0.53 when 2) using the 14 selected SNPs. Similarly, the seven models resulted in a range of 0.37–0.46 when 1) all 4,568 SNPs were used for BW_557 in comparison to the reduced range of 0.30–0.44 when 2) using the selected 14 SNPs. A slightly higher range of average “r” value from 0.41 to 0.47 and 0.43 to 0.52 were observed for BW_597 when 1) all the 4,568 SNPs and 2) the 14 selected SNPs were used, respectively, in combination with the seven GP models . The resThe general trend of PA was higher when a greater number of SNPs  was utilized in combination with the seven models in comparison to the use of 14 selected SNPs for BW_528 and BW_557. However, observing the PA for individual models, the RF model indicated slightly higher PA when 14 SNPs set was used for BW_557 resistance. Conversely, the PA followed a general trend of higher range for the average values of (r) and for each of the individual models when using a lower number of SNPs (14 selected SNPs) .The population structure and genetic diversity analyses in this study indicated the presence of three subpopulations  among the tested germplasm as examined by the tool GAPIT 3 . HistoriThe current study was focused on identifying SNP markers associated with resistance to the three isolates of BW in common beans. The phenotypic and genotypic data from the 168 accessions of the common bean core collection was subjected to the four MLM models in GAPIT, BLINK, FarmCPU, and TASSEL 5 to carry out GWAS analysis for each of the BW isolates. A total of 14 SNP markers were associated with resistance to the three different BW isolates, including five SNP markers for BW isolate 528, five SNP markers for BW isolate 557, and four SNP markers for BW isolate 597 . These SArachis hypogaea L.) with identification of four QTLs on chromosome 4 , AFLP (amplified fragment length polymorphism), and SCAR (sequence characterized amplified region) markers to map QTLs for BW resistance  and genemosome 4 .Pvulgaris v1.0_218 reference genome sequence (from Andean accession G19833) for various races   . Thus, tPhvul.002G041000 and Phvul.004G154100, on chromosome Pv02 for BW_528 resistance associated with SNPs ss715647803 and ss715649344. The Phvul.002G041000 gene encodes a nuclease-related domain (NERD), associated with BW resistance to BW isolate 597 in common bean. Other reported genes  by using all 4,568 SNPs and 2) by using the 14 selected SNPs, in combination with seven GP models 1) rrBLUP, 2) Bayes A, 3) Bayes B, 4) BL, 4) BRR, 5) RF, 6) SVM, and 7) BL for each of the three BW isolates.The average (r) calculated for all the SNPs and the 14 SNPs  indicateMoreover, GS, based on the estimation of PA through use of Pearson’s correlation coefficient (r) between the observed values and GEBV, has been employed to assess 481 common bean elite lines for resistance to environmental stress with reported prediction abilities between 0.6 and 0.8 for various traits . SeveralOur study successfully tested 168 common bean accessions from the USDA NPGS based on public phenotypic resistance data for genome-wide association study (GWAS) and genomic prediction (GP). A total of 14 SNPs, on chromosome Pv02, Pv04, Pv07, Pv08, Pv09, Pv010, and Pv011 with 14 candidate genes, and 21 lines with potential resistance to the BW_528, BW_557, and BW_597 types were identified as a result of this study. The different SNPs and candidate genes identified for each isolate can be pyramided to enhance resistance to multiple isolates of BW. Moreover, the identified SNPs and candidate genes can be further explored and employed using genome editing and breeding techniques to develop common bean cultivars with enhanced resistance to the three BW isolates.