Salt stress, a major plant environmental stress, is a critical constraint for rice productivity. Dissecting the genetic loci controlling salt tolerance in rice for improving productivity, especially at the flowering stage, remains challenging. Here, we conducted a genome-wide association study (GWAS) of salt tolerance based on exome sequencing of the Thai rice accessions.Oryza sativa L.) accessions belonging to the indica subspecies were evaluated. The rice accessions were subjected to exome sequencing, resulting in 112,565 single nucleotide polymorphisms (SNPs) called with a minor allele frequency of at least 5%. LD decay analysis of the panel indicates that the average LD for SNPs at 20 kb distance from each other was 0.34 (r2), which decayed to its half value (~ 0.17) at around 80 kb. By GWAS performed using mixed linear model, two hundred loci containing 448 SNPs on exons were identified based on the salt susceptibility index of the net photosynthetic rate at day 6 after salt stress; and the number of panicles, filled grains and unfilled grains per plant. One hundred and forty six genes, which accounted for 73% of the identified loci, co-localized with the previously reported salt quantitative trait loci (QTLs). The top four regions that contained a high number of significant SNPs were found on chromosome 8, 12, 1 and 2. While many are novel, their annotation is consistent with potential involvement in plant salt tolerance and in related agronomic traits. These significant SNPs greatly help narrow down the region within these QTLs where the likely underlying candidate genes can be identified.Photosynthetic parameters and cell membrane stability under salt stress at the flowering stage; and yield-related traits of 104 Thai rice  contains supplementary material, which is available to authorized users. Stress caused by salinity is one of the most serious environmental factors, which inhibits plant growth and decreases crop productivity worldwide . PrimaryOryza sativa L.), one of the world’s most important cereal crops, is classified as a salinity sensitive crop . For yield-related traits, the numbers of tillers (TIL), panicles (PAN), filled grains (FG) and unfilled grains (UFG) per pot were recorded at the end of experiment.Photosynthetic parameters consisting of net photosynthetic rate ( Ebercon  was usedO. sativa ‘Nipponbare’ database (Michigan State University [MSU] Rice Genome Annotation Project). The capture libraries were cleaned using AMPure  and amplified by PCR using post-capture primers. The final yields were quantified by Bioanalyzer . Exome-capture libraries (18–23) were pooled in each lane and sequenced using the Illumina HiSeq2000’s protocol in the Illumina genome analyzer .Rice gDNA was extracted from leaf tissue using the Genomic DNA Mini Kit (Plant)  and the amount of DNA was quantified using a spectrophotometer. For the exome library preparation, gDNA was fragmented using dsDNA Fragmentase . The sheared DNA was modified using an End Repaired enzyme (New England Biolabs) and deoxyadenosine was added at the 3′ end using a Klenow fragment (New England Bio-labs). Each of the unique DNA barcodes  was joined to DNA in each library using DNA ligase (New England Biolabs). Pre-capture libraries were hybridized with the capture probes of the rice exome region, which were designed based on the Statistical analyses were performed with IBP SPSS ver.22 . Analysis of variance (ANOVA) was carried out to assess the effects of genotype, environment, and G × E interactions using the general linear model procedure. Duncan’s Multiple Range Test was used to compare the mean value for tests of significance. Cluster analysis among physiological responses was performed by JMP ver. 11  and R ‘corrplot’ package .heterozygous, minimum coverage and minimum percentage of each of the two observed major basecalls were 5 and 20, respectively and minimum total coverage was 10; for a position to be called homozygous, minimum coverage was 6 or 3 if positions with the minimum coverage of 6 were present in at least 10 accessions. SNP density was visualized using R 'CMplot' (https://github.com/YinLiLin/R-CMplot).The short-sequence reads from the Illumina Genome Analyzer were grouped into the correct categories using the pipeline created by Missirian et al. . The ricTo estimate the number of subgroups in the panel to select the appropriate statistical model for association between the phenotypic and genomic data, analysis of the population structure within the rice population was performed using EIGENSOFT version 6.0.1, which used principal component analysis (PCA) to model ancestry differences in a population , 48. Popr2) in frequency across a pair of SNP loci. The r2 values between pairs of SNPs were calculated using the command in PLINK [r2 values within each bin were averaged. To visualize the result, the r2 values were sorted and plotted against the physical distance [Linkage disequilibrium (LD) analysis was assessed by computing the correlation  of all SNPs using the R ‘qqman’ package [p-value of SNP marker was corrected for multiple tests by calculating q-value  of each trait. SNPs with the q-value lower than 0.05 was selected as significant marker.To identify loci underlying the genetic regulation of traits mentioned above, SNPs were removed from the analysis by PLINK 1.07  if their package . The p-vhttp://www.plantstress.com/files/qtls_for_resistance.htm#salinity.The list of candidate genes from GWA mapping was compared with the salt QTL mapping that was previously reported by Hu et al. , PatishtPN (− 49%); stomatal conductance, gs (− 50%); transpiration rate, E (− 43%), and cell membrane stability, CMS (− 18%) when compared with the control condition , which decayed to its half value (~ 0.17) at around 80 kb  and the lowest r2 was found on chromosome 11 (r2 = 0.27).For LD decay analysis of the panel, the binned q-value  < 0.05 were considered as truly significant. Given that an FDR adjusted p-value threshold of 0.05 means that 5% of significant tests would result in false positives and the number of spurious associations was greatly reduced. Altogether, 448 significant SNPs were found from GWA mapping of four traits, and the list of SNP positions, alternate SNPs and candidate loci were presented in Additional file PN.day6), which was located on chromosome 10  on chromosome 12, which associated with grain yield (GY) of rice under salt stress at reproductive stage. On chromosome 3, 28 significant SNPs were located in qGP3 (RM49–RM6712), which involved germination percentage (GP) of rice under salt stress [qPL3.1 s (RM520–RM570) and qSHL-3 (RM7000–RM7389). These QTLs were correlated with the panicle length (PL) and shoot length (SHL) [qDSS2.1 (RM109-RM110) containing 19 significant SNPs identified in this study.Overall, GWAS mapping identified 448 significant SNPs in the exome, which were located on 200 genes  , 58. FinPN; stomatal conductance, gs; transpiration rate, E; and intercellular CO2 concentration, Ci), cell membrane stability, CMS and four parameters of yield-related traits . On average, the stability indices of photosynthesis performance and CMS decreased under salt stress, while increases of Ci were found, which were similar to the yield parameter, UFG . This enzyme, common to bacteria, plants and humans, shares a common catalytic center, a heme with an iron coordinated to the thiolate of a conserved cysteine . They oxng stage  reported than WT .MAIF1 reduced inhibition of root growth and tolerance under salt stress compared with WT [OsMsr9, a novel rice putative F-box containing protein, especially in the panicle. Overexpression of OsMsr9 increased root length, shoot length and survival rate under salt stress [Interestingly, several novel candidate loci with 144 significant SNPs identified from this GWA mapping were found on chromosome 10, in which no salt QTL was reported. This represents the highest density of significant SNPs found in the same LD block Fig. c. Intere with WT . Salt int stress .p-value (9.04 × 10− 11) found on chromosome 12 of GWA mapping of UFG  in enhancing oxidative stress has been reported in the plant model Arabidopsis [AtUSP conferred a strong tolerance to oxidative stress, primarily via its chaperone function.Moreover, SNP with the lowest  in rice . Furtherbidopsis . They foindica Thai accessions. Altogether, the significant SNPs were located on 200 loci distributed among all rice chromosomes. Our GWA mapping was highly consistent with previous salt tolerance QTL mapping studies conducted in bi-parental populations. Overall, more than 73% of the candidate genes controlling salt tolerance identified in our GWAS overlap with the salt QTLs. While many are novel, their annotation is consistent with potential involvement in plant salt tolerance and in related agronomic traits. These significant SNPs greatly help narrow down the region within these QTLs where the likely underlying candidate genes can be identified. Knowledge on the varieties with high salt tolerance, as well as the associated SNPs from this study, will be useful for future improvement of rice yield productivity under salt stress.High quality genotyping data from high-throughput sequencing combined with robust statistical analysis, enables GWA mapping of complex quantitative traits. We conducted GWAS for salt tolerance during rice reproduction based on high-density SNPs in exon regions using Additional file 1:Table S1. List of all rice accessions used for SNP genotyping. Rice accessions are tabled with their geographical locations, GS No., parental lines/parental relations and heterozygosity. (XLSX 19 kb)Additional file 2:Table S2. List of rice accessions and phenotypic data used for association mapping. The average values of four biological replicates are shown. (XLSX 50 kb)Additional file 3:Figure S1. Frequency distribution of PN: net photosynthetic rate; gs: stomatal conductance; E: transpiration rate; Ci: intercellular CO2 concentration, CMS: cell membrane stability, TIL: number of tillers per plant; PAN: number of panicles per plant; FG: number of filled grains per plant and UFG: number of unfilled grains per plant for 104 rice accessions under control (a and b) and salt stress conditions (c and d). (PDF 306 kb)Additional file 4:Table S3. Pearson’s correlation coefficients of the phenotypic traits measured in 104 rice accessions. Data are from salt susceptible indices of each trait. Shaded values are significant at p < 0.05, p < 0.01, or p < 0.001, darker shading indicates higher significance. (XLSX 11 kb)Additional file 5:Figure S2. Population structure of 190 rice association panels, which consisted mostly of the indica accessions. (TIF 5827 kb)Additional file 6:Table S4. List of significant SNPs from GWA mapping for various traits under control and stress conditions using compressed MLM. Details are also given for MSU Locus ID and their putative functions from where the respective SNP was selected. (XLSX 39 kb)Additional file 7:Figure S3. The peak regions on rice chromosomes containing significant SNPs from GWAS of PAN (a and b) and UFG (c and d). The pair-wise LD for the SNP of the lowest p-value (red letters) is indicated as r2 values, where the markers were divided into binds of 5 kb and the r2 values were averaged and shown as blue bars; the darkest blue represents a value of 1 and the lightest blue represents a value of 0. The dotted lines denote the regions containing LD blocks that the significant SNPs reside. Examples of other significant SNPs are shown in green letters. Note that the diagram of r2 values represents all neighboring SNPs present in that region, while it is not proportional to the physical distance of the chromosome. (TIFF 9604 kb)


Oryza species have salt-tolerant and salt-sensitive genotypes; however, very few studies have investigated the genetic architecture responsible for photosynthetic efficiency under salinity stress in cultivated rice.Photosynthetic efficiency might be a key factor determining plant resistance to abiotic stresses. Plants can sense when growing conditions are not favorable and trigger an internal response at an early stage before showing external symptoms. When a high amount of salt enters the plant cell, the membrane system and function of thylakoids in chloroplasts could be destroyed and affect photosynthetic performance if the salt concentration is not regulated to optimal values. 0 and NPQ were higher in Japonica subspecies, ΦPSII of Indica varieties was higher than that in other subgroups, which suggested that the variation in photosynthetic efficiency was extensively regulated under salt treatment in diverse cultivated rice. Two significant regions on chromosome 5 were identified to associate with the fraction of open PSII centers (qL) and the minimum chlorophyll fluorescence (F0). These regions harbored genes related to senescence, chloroplast biogenesis and response to salt stress are of interest for future functional characterization to determine their roles in regulating photosynthesis.We used an imaging-based chlorophyll fluorometer to monitor eight rice varieties that showed different salt tolerance levels for four consecutive days under control and salt conditions. An analysis of the changes in chlorophyll fluorescence parameters clearly showed the maximum quantum efficiency of PSII in sensitive varieties was significantly reduced after NaCl treatment when compared to tolerant varieties. A panel of 232 diverse rice accessions was then analyzed for chlorophyll fluorescence under salt conditions, the results showed that chlorophyll fluorescence parameters such as FRice plant is very sensitive to salinity stress, especially at young seedling stage. Our work identified the distribution pattern of chlorophyll fluorescence parameters in seedlings leaf and their correlations with salt tolerance level in a diverse gene pool. We also revealed the complexity of the genetic architecture regulating rice seedling photosynthetic performance under salinity stress, the germplasm analyzed in this study and the associated genetic information could be utilized in rice breeding program.The online version of this article (10.1186/s12870-019-1983-8) contains supplementary material, which is available to authorized users. Photosynthesis is an essential process that generate energy to support plant growth. Components participating in photosynthetic machinery, such as photosynthetic pigments, photosystems, electron transport systems, gas-exchange processes and enzymes involved in carbon metabolism, are important for photosynthetic efficiency and could be potentially affected by abiotic stresses .In recent years, technology to detect chlorophyll fluorescence in leaves has advanced rapidly, and its power to reflect photosynthetic efficiency in vivo needs to be examined. Light energy captured by chlorophyll molecules is used in photochemical reactions to drive photosynthesis or, if in excess, dissipated as heat  or emitted as chlorophyll fluorescence . These tWhen a high amount of salt was accumulated in the plant cell for a period of time, the membrane permeability and function of thylakoids in chloroplasts was damaged ; a gradu2 fixation (gas-exchange) per unit leaf area, production of carbohydrates or dry mass per plant, and chlorophyll fluorescence. To investigate the genetic characteristics of plant photosynthetic efficiency, researchers analyzed the properties of diverse genotypes: the response of photosynthetic light use efficiency (ΦPSII) to different light conditions in Arabidopsis  was calculated as described by Genty et al. . The qua0 (minimum fluorescence)Fm Fv/Fm , Fv/Fm = (Fm-F0)/FmFm′-Ft)/Fm′ΦPSII (effective PSII quantum yield), ΦPSII = (Fm′-Ft)/(Fm′-F0’) * (F0’/Ft)qL , qL = (Fm-Fm′)/Fm′NPQ , NPQ = (FY(II) = (Fm′-Fs)/Fm′Y(NO) = 1/(NPQ + 1 + qL(Fm/Fo-1))Y(NPQ) = 1-Y(II)-1/(NPQ + 1 + qL(Fm/Fo-1))Y(II) + Y(NO) + Y(NPQ) = 1The definition and calculation of six major chlorophyll fluorescence parameters are described below:To quantify and compare the symptoms of salt toxicity among rice varieties, we modified the International Rice Research Institute (IRRI) standard evaluation system (SES)  accordinY = Xβ + ε, where Y was phenotype data, X was genotype data, β was the SNP effect and ε was random effects. The P model was represented by Y = Xβ + Pγ + ε, where P was the population structure and γ was the effect of the population structure. The K model was represented by Y = Xβ + Zu + ε, where u was the random effect of kinship and Z was a coincidence matrix. The P + K model was represented by Y = Xβ + Pγ + Zu + ε. The population structure was analyzed by principal component analysis [Four statistical models were applied to identify the association between SNPs and trait variations: naïve, P, K, and P + K models. The naïve model was represented by analysis . Associaanalysis  and R/GAanalysis .2 > 0.7) between the most significant SNP and its neighboring SNPs, and the candidate genes within the region were identified using Gramene BioMart (http://ensembl.gramene.org/biomart). The SNP position was based on IRGSP 1.0 annotation.The LD between the most significant SNP and its neighboring SNPs was estimated using Tagger in Haploview 4.2 . The canPearson correlation was used to estimate the level of correlation between traits. The normality of each trait was tested using the Shapiro-Wilk method. The genetic distance between the diverse accessions was calculated using the “euclidean” method, and agglomerative hierarchical clustering using the “complete” method was applied to evaluate the relationship between traits and rice accession genetic backgrounds and was plotted as a heatmap. All analyses were conducted using publicly available R packages.Additional file 1:Table S1. Phenotypic evaluation to score the visual symptoms of salt toxicity at the seedling stage (DOCX 14 kb)Additional file 2:Table S2. The quantum yield calculated from the ratio of treatment to control (T/C) values of each parameter in eight varieties (XLSX 14 kb)Additional file 3:Table S3. Chlorophyll fluorescence and injury score of 232 accessions and their subpopulation assignment (XLSX 33 kb)Additional file 4:Table S4. Correlation between chlorophyll florescence parameters and injury score (IS) in the global panel (232 varieties) (DOCX 16 kb)Additional file 5:Table S5. The genes inside the LD region of the most significant SNP detected in the F0 GWAS (XLSX 75 kb)Additional file 6:Table S6. The genes inside the LD region of the most significant SNP detected in the qL GWAS (XLSX 112 kb)Additional file 7:Figure S1. Daily changes in chlorophyll fluorescence parameters  in eight rice varieties under control (red line) and salt (blue line) conditions. Each data point represents the average value of 3 replicates. (PPTX 10924 kb)Additional file 8:Figure S2. Box plot of six chlorophyll fluorescence measurements and injury score in six subpopulations and two subspecies. Student’s t-test was used to determine whether any two subpopulations or two subspecies were different. *, **, and **** represent the significance level at P < 0.05, 0.01 and 0.0001, respectively. (PDF 979 kb)Additional file 9:Figure S3. Genetic relationship and phenotypic distributions of varieties in five subpopulations. Each column represents a single variety, and the sample ID is labeled at the bottom of the heatmap. Each row represents the chlorophyll fluorescence parameter and injury score of each variety. The colors close to red indicate that the value of each chlorophyll fluorescence parameter is higher than its average value over all accessions; for the injury score, the red represents the lower injury scores, which suggest that the variety is more tolerant to salt toxicity. The color key of the Z-score was calculated from the distance between the raw score and the population mean in units of the standard deviation. (PDF 1686 kb)Additional file 10:Figure S4. Distribution of six chlorophyll fluorescence parameters in Japonica varieties. The normality of each parameter was examined using the Shapiro-Wilks test. The P-value of each test is provided. (PPTX 7609 kb)Additional file 11:Figure S5. Distribution of six chlorophyll fluorescence parameters in Indica varieties. The normality of each parameter was examined using the Shapiro-Wilks test. The P-value of each test is provided. (PPTX 7606 kb)Additional file 12:Figure S6. Genome-wide association analysis of six chlorophyll fluorescence parameters in 232 diverse varieties or in two subspecies (Japonica and Indica). Manhattan plot showing the significance of each SNP tested by the appropriate statistical models described in “Methods”. (PDF 4610 kb)Additional file 13: Figure S7. Genome-wide association analysis of qL in 232 diverse varieties and LD pattern of the most significant SNP. (a) Manhattan plot showing the significance of each SNP tested by a mixed linear model. (b) The most significant SNP is at 26,473,167 bp. The dot color of each SNP represents its LD with the most significant SNP, and the decay of LD is bordered by two vertical dashed lines. The location of the OsCBL4 gene is marked with a blue bar. (PPTX 14234 kb)


The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets.S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation.A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.The online version of this article (10.1186/s12864-019-6057-7) contains supplementary material, which is available to authorized users. Genomic technologies provide unprecedented opportunities to reveal the history of crop domestication, to discover novel genetic diversity, and to understand the genetic basis of economically important traits, collectively contributing to crop improvement and food security . One of The variant discovery pipeline for WGS dataset can be roughly divided into four steps: read mapping, variant calling, variant filtering, and imputation. Sequence aligners for the read mapping step can be grouped according to their indexing methodologies . ProgramOver the past decade, extensive progress in human genomic studies has developed and applied machine-learning based variant filtering methods  which usA major challenge for crop genomics is the ability to accurately and comprehensively characterize genetic diversity in domesticated crops, diverse landraces, and wild crop relatives. Genetic diversity in plants can be much greater than that found in human genomes. These sources of diversity, especially in the wild species, provide a reservoir of genetic variation for future crop improvement –27. For https://www.ncbi.nlm.nih.gov/grc/human). Once released, however, most plant reference genomes see little improvement, resulting in references that are less accurate and less complete than that found in humans. Other key challenges are the large amounts of repetitive sequences, structural variations and, in some crops, complex polyploid genomes [The second challenge for variant discovery in plant genomics is the quality of available reference genomes. The human reference genome has been in a constant state of improvement for decades  and SAMtools mpileup (SAMtools-mpileup) using domesticated tomatoes, wild relatives and simulated genomic datasets. We show that as diversity and genome complexity increased, the ability of these algorithms to identify variants varied strongly. In addition, the inadequacy of a single reference genome was uncovered after a cross-reference comparison was performed. Finally, we evaluated the performance of machine learning based variant filtering method and reference panel assisted imputation methods on the high diversity plant datasets.The performance of three different aligners, BWA-MEM, Bowtie2, and SOAP2, was evaluated using Illumina paired-end read datasets from 52 domesticated tomato, 30 related wild relatives , false positive (FP) and false negative (FN) alignments. Simulated datasets were derived from the reference genome by permuting fragment sizes, and number of SNPs or size of small indels per read. For all alignment methods, the ratio of FP alignment increased as more SNPs or indels were introduced per read Fig. c-d when S. lycopersicum, 2 S. pennellii and 2 other wild relatives) were aligned to both S. lycopersicum and Solanum pennellii reference genomes [S. lycopersicum coordinate to the S. pennellii coordinate, the read was considered to be syntenic. If the positions did not match, the read was considered non-syntenic. BWA-MEM was able to align approximately 4.22 times more reads per sample than Bowtie2  of these alignments were considered as syntenic compared to 88.17% (SD ± 1.59%) of Bowtie2 alignments.To indirectly determine the true vs false positive rates of BWA-MEM and Bowtie2 in real data, one million randomly selected reads from six samples  was found between GATK-HC and SAMtools-mpileup identified SNP variants.Four variant datasets were produced from the permutation of the aligners, Bowtie2-tuned, and BWA-MEM, and the variant callers SAMtools-mpileup and GATK-HC using 52 domesticated and 30 wild tomatoes. Results showed nearly a two-fold difference in the number of unfiltered SNPs ranging from 69.2 M to 133.7 M. A greater difference in the variant count in wild species was observed than that found in domesticated ones  [The large increase in the number of SNPs in wild samples was expected due to both greater distance from the domesticated reference genome and increased diversity relative to the domesticated samples. Expectedly, as the distance from the reference genome increased, a greater proportion of reads was unmapped. The variants in these unmapped reads, especially in the wild species, could represent “missing diversity”. To test this hypothesis, we evaluated how variants discovery in these 82 tomato samples were changed by mapping reads to a wild reference genome (nnellii) .S. pennellii reference was performed under identical settings as above. As previously seen, BWA-MEM showed the highest mapping percentage and SOAP2 showed the lowest  as opposed to 34.22% (or 94.87%) against the S. lycopersicum reference using SOAP2-tuned (or BWA-MEM). Interestingly, the 82 samples, except for the S. pennellii sample, had similar IBS distances to the reference genome. As with the S. lycopersicum reference, alignment percentage to the S. pennellii reference was inversely proportional to IBS distance to the reference genome  were identified from 8 domesticated tomato samples when S. pennellii reference genome was used for variant discovery  on metrics such as read depth, strand bias, and variant quality and so on, a newly implemented machine-learning based (ML) variant filtering , and a cTwo methods were used to indirectly infer the quality of filtered datasets: recapitulation of diversity estimates generated by a “gold standard” set of 22,336,965 SNPs (See details in Methods) in the form of PCA  between the original masked and imputed genotypes was calculated to estimate imputation accuracy.Missing genotypes, possibly due technical limitations, are commonly resolved via imputation. In human studies, standard imputation methods leverage linkage disequilibrium (LD) and reference panels . Beagle Solanum pimpinellifolium (PIM) samples  tomato samples Fig. a or the The ability to accurately and comprehensively identify genetic variation is a critical step for studying diversity, trait mapping and breeding in plant genomics. Many plant studies involve high levels of genetic diversity and, in some instances, incorporating distantly related varieties and wild relatives. Neither of these conditions are common in human studies, and as such pipelines designed and evaluated on humans may perform differently than expected. Therefore, we evaluated programs that are commonly used by plant genomic studies on SNP discovery steps including read alignment, variant calling, variant filtering and missing data imputation in the context of plant diversity discovery.One of the first computational steps in the variant discovery pipeline is the alignment of reads to a suitable reference genome. Previous aligner evaluation studies have been performed using either human or microbial genomic datasets , 46, whiBWA-MEM’s increased sensitivity may come at a cost in that, as the number of SNPs or size of INDELs per read increased, the false positive rate also became slightly higher than that of Bowtie2-tuned Fig. c-d. The The next step in an analysis pipeline is variant calling. Comparisons between aligner-variant caller combinations indicated that the alignment algorithm had a greater impact on the number of variants discovered than the variant caller used. For a given aligner, SAMtools-mpileup and GATK-HC had similar results in the total number of SNP identified in the real tomato genomic dataset. This further emphasizes the importance of selecting an aligner appropriate to the goals of the experiment, especially when high diversity samples such as wild relatives and related species are included in the study. According to the simulation results, GATK-HC was able to identify more true positive variants at higher precision ratio in most population-based variant discovery cases. Especially in the high diversity population simulation, GATK-HC was more preferred than SAMtools-mpileup because SAMtools-mpileup resulted very low recall ratio in both SNP and INDEL detection. In the simulation analysis, the size of INDELs identified by GATK-HC had larger range than those identified by SAMtools-mpileup and ground truth INDELs, which partially explained why GATK-HC had lower precision in the INDEL identification than SAMtools-mpileup. One of the possible explanations is that GATK-HC performs local-assembly to identify the haplotypes whereas SAMtools-mpileup only utilizes read alignments. Plant genomes, in general, are rich in repetitive sequences which are difficult to assemble correctly using short reads. Therefore, the local assembly strategy taken by GATK-HC will not only identify true variants, but also generate false positive variants, INDELs especially. Consistent with a previous research , SAMtoolIn general, we recommend GATK-HC for variant calling and filtering for several reasons. First of all, GATK-HC outperformed SAMtools-mpileup in most of our situation tests resulting a higher precision and recall ratio for SNP and INDEL detection. Second, GATK-HC allows rapid incorporation of multiple samples into a dataset without needing to recall genotypes for all samples, even previously genotyped ones, from aligned reads by using the GVCF system. This saves considerable time and computational expense when adding samples to a dataset. The third reason to recommend GATK-HC is that it supports multi-thread processing which is not available in the SAMtools-mpileup. Taking the advantage of high-performance clusters, multi-thread feature can significantly save processing time especially for large studies. Finally, the GATK package supports sophisticated machine learning based variant filtering (VQSR) which showed superior performance than empirical hard cutoffs. We did, however, find situations that SAMtools-mpileup is more preferable depending on the goal of the study. For example, for a low diversity population with very low sequencing coverage (1x), SAMtools-mpileup was able to identify more true SNPs than GATK-HC but at the cost of lower precision. If the purpose of the experiment is to identify as many true positive SNPs as possible, then SAMtools-mpileup could be used in this particular situation. Another situation that SAMtools-mpileup may be preferable is identifying SNPs from a closely related sample. According to the simulation results from single samples, SAMtools-mpileup resulted slightly higher precision and recall values than GATK-HC results when the mutation rate was lower than 0.05. If the experiment aims at charactering SNPs in a genotype that is closely related to the reference genome, SAMtools-mpileup could be used in this particular situation.Variant filtering is the third step in a diversity assessment pipeline. Three approaches to this were evaluated: hard filtering of various quality metrics, machine learning as implemented in GATK, and a combined approach. The combined approach which utilized hard filtered SolCap markers as the training dataset showed significant improvements over other variant filtering methods. According to the PCA plots Fig. a-c and LThe final step in the variant discovery pipeline is imputation. Reference panels are routinely employed in human studies, but these have not been routinely employed in plant genomics. To evaluate the importance of a reference panel for imputation, Beagle v4.1  was usedS. lycopersicum genome, and vice versa. These results indicated the inadequacy of single reference genome for comprehensive variant discovery. It also indicated that employing multiple reference genomes could identify additional sources of diversity that went undetected when using a single reference. These results have implications for the utility of pan-genomes. Multiple references or pan genomes would likely increase the detection of “missing diversity” that is due primarily to PAV between samples. Moreover, using a distantly related reference genome may allow the detection of SNPs that would be undetected using a closely related reference genome. These species-specific, fixed variants have implications in the evolutionary history of plant species such as domestication events. To date, several crop pan-genomes have been reported [The effect of presence-absence variation on identifying missing genetic diversity is a special concern in studies that include high diversity samples. This issue can be seen from the results of the cross-reference experiment. Up to 11.15% of the variations identified using the wild reference could not be mapped back to the domesticated reported –52 that reported . The mosIn conclusion, we found that BWA-MEM was better overall at detecting more true-positive alignments, especially in distantly related samples, while Bowtie2 was better at minimizing the incorrect alignments. Incorporating multiple reference genomes gave a more complete picture of variations, especially when the samples showed considerable presence-absence variations. For filtering, the optimal approach found in our test was to incorporate a combination of machine learning and hard filtering, in which a set of “known” SNPs was used as the training set for machine learning. This requires a panel of known, high-quality SNPs, however, which may be unavailable for many plant species. Finally, the importance of high-quality reference panels was emphasized during the imputation step especially when genotype imputation was challenging due to small LD blocks or not enough samples. Above all, the computational pipeline to discover variation from plant sequencing data will depend upon the diversity of the datasets, whether the goals of the experiment benefit from higher sensitivity or accuracy, the depth of sequence coverage, and the availability of external resources such as reference panels and gold-standard SNPs.http://broadinstitute.github.io/picard/). Simulated tomato sequencing reads were generated from the S. lycopersicum reference genome. A custom Python script was used to introduce from 0 to 20 SNPs per read, fragment sizes ranging from 200 to 10,000 nt, and INDELs ranging from 0 to 40 nt. In order to evaluate the performance of BWA-MEM on multiple crop species, simulation of the Illumina sequencing reads was also performed on rice, soybean, tomato, maize and wheat using mason (version 2.0.9) [We used publicly available 602 WGS datasets representing 514 domesticated and 88 related wild species of tomato. The data were retrieved from the NCBI BioProjects under accession PRJNA259308, PRJNA353161 and PRJEB5235. The raw sequence data was quality trimmed using Trimmomatic (version 0.36)  with then 2.0.9) . The mutDifferent aligners were evaluated using high-coverage datasets from PRJEB5235 and simulated datasets. BWA-MEM (version 0.7.17-r1188), SOAP2 (version 2.21), SOAP2-tuned, Bowtie2 (version 2.3.3.1) and Bowtie2-tuned were tested. SOAP2-tuned was used with the following options: -m 100 -× 888 -s 35 -l 32 -v 3 . Bowtie2Eighty-two high-coverage datasets from PRJEB5235 was used for SNP discovery comparisons. SNPs were called with SAMtools-mpileup (version 1.9) and GATK-HC (version 3.8–0-ge9d806836) using BWA-MEM and Bowtie2-tuned alignments. In GATK, variants were firstly identified by HaplotypeCaller using the option --emitRefConfidence GVCF, and then joint genotyping was performed using GenotypeGVCFs. In SAMtools-mpileup, genotyping was done in one step and the option -C 50 was used as recommended in the manual. Only polymorphic SNPs were used as data for the Venn diagram. Simulated datasets with known variants were generated for tomato, rice, soybean, maize using mason. Each crop species was simulated at different coverages  and mutation rates . In addition to individual simulated datasets, population-level simulated datasets were also generated with varied diversity (low diversity: 0.001 mutation rate and high diversity: 0.1 mutation rate), population size  and sequencing coverage . SAMtools-mpileup and GATK-HC were evaluated on both individual and population simulated datasets by comparing the precision and recall ratios. The functional annotations of the variants were predicted by snpEff (version 4.3) .Solanum pimpinellifolium samples (PIM), and 36 distantly related wild species (WILD). The one hundred domesticated samples from PRJNA353161 only, 15 DOM-REF samples from PRJEB5235 only, 50 PIM samples and 36 WILD samples were randomly selected for generating simulated datasets. Polymorphic SNPs in each dataset were randomly masked using a custom Python script if there were more than 7 reads supporting the genotypes. Both Beagle v4.1 and 2-step imputation methods were used to impute missing genotypes in five simulated datasets. The concordance R2 ratio between genotyped and imputed values were calculated as imputation accuracy using BCFtools [Beagle v4.1  direct iBCFtools .https://gatkforums.broadinstitute.org/gatk/discussion/2806/howto-apply-hard-filters-to-a-call-set). For INDELs, hard filtering was performed using “QD <2 || FS > 200 || ReadPosRankSum < -20”, as suggested by the GATK tutorial. The machine learning based methods, for both SNPs and INDELs, followed the GATK Best Practice Workflow (https://software.broadinstitute.org/gatk/documentation/article.php?id=2805). To summarize, the first step was to build a variant recalibration model using the program VariantRecalibrator. In the real tomato genomic dataset, SolCap and filtered SolCap markers were used as the training dataset with prior likelihood set to 90 and 95%, respectively. In the simulated dataset, 30% of the simulated gold standard variants were used as the training dataset with the prior likelihood set to 95%. All the annotations generated by GATK-HC including coverage, coverage by depth, FisherStrand, StrandOddsRatio, MappingQualityRankSumTest, ReadPosRankSumTest, RMSMappingQuality and InbreedingCoeff, were used to build the recalibration model. The second step was to apply the recalibration model to variants using the program ApplyRecalibration with the option --ts_filter_level 99.9. Polymorphic SNPs in the first 10 million base pairs of Chromosome 1 were selected to test the performance of different filtering methods. PCA was performed using SNPrelate after LD pruning (R2 > 0.2). LD decay was calculated using the PopLDdecay package [The 602 tomato datasets were used to generate raw SNPs using BWA-MEM and GATK-HC pipeline. Hard-filtered, machine-learning based and combined filtering methods were individually applied to the raw dataset. The parameters used for hard-filtering included QualByDepth (QD < 2), FisherStrand (FS > 60), RMSMappingQuality (MQ < 40) and StrandOddsRatio (SOR > 3.0), which were suggested by the GATK hard filtering tutorial Additional file 2:Table S2. Summary of alignment time (XLSX 39 kb)Additional file 3:Table S3. Summary of synteny analysis (XLSX 11 kb)Additional file 4Table S4. Functional annotation summary of variants identified by different variant calling programs (XLSX 9 kb)Additional file 5:Table S5. Summary of different variant filtering results (XLSX 33 kb)Additional file 6:Figure S1. Alignment time and length comparisons of different aligners. Figure S2. Evaluation of variant calling programs using real and simulated plant genomic datasets. Figure S3. Evaluation of different variant calling programs on simulated single genomic dataset. Figure S4. Cross-reference comparison on SNP identification. Figure S5. Machine-learning based variant filtering. Figure S6. Quantitative comparison between machine learning and hard-filtering using simulated dataset. Figure S7. Comparison between direct and two-step imputation (DOCX 27699 kb)


Subsequent large-scale validation and genotyping of these SNPs discovered 619 desi accessions-derived (DAD) SNPs, 531 kabuli accessions-derived (KAD) SNPs, 884 multiple accessions-derived (MAD) SNPs and 1083 two accessions (desi ICC 4958 and kabuli CDC Frontier)-derived (TAD) SNPs that were mapped on eight chromosomes. These informative SNPs were annotated in coding/non-coding regulatory sequence components of genes. The MAD-SNPs were efficient to detect high intra-specific polymorphic potential and wide natural allelic diversity level including high-resolution admixed-population genetic structure and precise phylogenetic relationship among 291 desi and kabuli accessions. This signifies their effectiveness in introgression breeding and varietal improvement studies targeting useful agronomic traits of chickpea. Six trait-associated genes with SNPs including quantitative trait nucleotides (QTNs) in combination explained 27.5% phenotypic variation for seed yield per plant (SYP). A pentatricopeptide repeat (PPR) gene with a synonymous-coding SNP/QTN significantly associated with SYP trait was found most-promising in chickpea. The essential information delineated can be of immense utility in genomics-assisted breeding applications to develop high-yielding chickpea cultivars.We discovered 2150  Cicer arietinum) is a self-pollinated, diploid (2n = 16) and economically important legume food crop rich in human dietary proteins1. The chickpea is primarily represented by its desi and kabuli cultivars exhibiting a distinct differentiation in both agro-morphological and architecture traits, which genomes have been sequenced recently5. Substantial enhancement of its yield and productivity by developing high-yielding a/biotic stress tolerant and climate-ready cultivars is essential at present to sustain the global food security. To accomplish these objectives, many significant efforts involving classical genetics as well as genomics-assisted breeding strategies like quantitative trait loci (QTL) mapping, fine-mapping/map-based cloning and association analysis have been made to decipher the inheritance pattern and quantitative dissection of complex yield and stress tolerance traits for genetic improvement of chickpea30. However, these are constrained by narrow genetic base and low intra-specific marker polymorphism among desi and kabuli accessions due to combined strong impact of four major domestication bottlenecks in chickpea as compared to other food crop plants35. To overcome the aforesaid limitations, development and high-throughput genotyping of large-scale sequence-based informative markers like single nucleotide polymorphism (SNPs) exhibiting a higher potential of polymorphism among cultivated desi and kabuli accessions is a prerequisite for their use in genomics-assisted breeding applications and genetic improvement of chickpea.Chickpea (desi and kabuli) and wild accessions are found efficient in fast discovery of numerous SNPs at a genome-wide scale in chickpea44. The large-scale validation and high-throughput genotyping of these SNPs mined from limited number of sequenced accessions using diverse array-based genotyping assays commonly exhibit less potential of inter-/intra-specific polymorphism and natural allelic diversity among cultivated and wild accessions to be deployed efficiently for genetic enhancement of chickpea27. However, degree of these polymorphic and diversity potential can be enriched by using the genotyping information of SNPs derived from whole genome resequencing data that are generated from a large number of desi, kabuli and wild accessions of chickpea. Essentially, this approach requires huge cost, time and resources for developing and genotyping of SNPs at a whole genome/gene level in chickpea. In this regard, NGS-based low sequencing-depth genome coverage sequencing strategies like genotyping-by-sequencing (GBS) assay seems quite promising for rapid discovery and genotyping of genome-wide SNPs simultaneously among numerous cultivated (desi and kabuli) and wild accessions in order to drive high-throughput genetic analysis in chickpea with sub-optimal expense of cost, labour and resources27. However, these low genome coverage sequencing assays often generate erroneous and non-uniform SNP genotyping data across accessions, thereby further large-scale revalidation and genotyping of GBS-derived SNPs is needed currently prior to their use in genetic improvement of chickpea. In this perspective, high-throughput validation and genotyping of numerous GBS-derived high-quality SNPs discovered by assembling all the sequence reads generated from large number of accessions belonging to each of the desi and kabuli cultivars individually (cultivar-wise) can be useful for their deployment in genomics-assisted crop improvement of chickpea. In view of aforementioned prospects, efforts were made in the current study for large-scale validation and high-throughput genotyping of informative SNPs, discovered by desi and kabuli cultivar-wise individual assembling of the sequence reads, generated from 92 accessions using GBS assay at a genome-wide scale in chickpea -led genome and transcriptome sequencing of multiple cultivated -, kabuli accessions-derived (KAD)- and multiple accessions-derived (MAD)-SNPs which were further compared with two accessions (ICC 4958 and CDC Frontier) derived (TAD) SNPs for evaluating the potential of these developed markers in large-scale genetic analysis in chickpea  sequence depth of coverage. This exertion overall produced 3.3 to 5.7 million of high-quality sequence reads per accession covering on an average 5.4-fold (X) sequence depth of coverage. More than 85% of de-multiplexed high-quality sequence reads generated from accessions were mapped separately (desi and kabuli cultivar-wise) to unique physical locations on desi reference genome4. The desi and kabuli cultivar-wise reference genome-based GBS analysis altogether detected 2150 (DAD-SNPs) and 2199 (KAD-SNPs) high-quality SNPs  from the accessions which were further validated through PCR amplicons-based sequencing assay. For large-scale validation and high-throughput genotyping, a representative set of high-quality 672 DAD-, 576 KAD- and 1152 TAD-SNPs differentiating 39 desi, 53 kabuli and 2 (ICC 4958 and CDC Frontier) accessions, respectively, were selected based on their uniform physical distribution on eight chromosomes. These physically mapped SNPs were genotyped using the genomic DNA of 291 desi and kabuli chickpea accessions through a 34-plex high-throughput matrix assisted laser desorption ionization-time of flight (MALDI-TOF) Sequenom Mass Array genotyping assay. The optimized multiplex assay successfully validated 619 DAD-, 531 KAD- and 1083 TAD-SNPs based on their homozygous and heterozygous allele discrimination as evident from the call cluster plots of low and high mass homo- and hetero-zygote yields with a 92–94% genotyping success rate  followed by biological process  and cellular component . The transcription factor (TF) genes with MAD- and TAD-SNPs belonging to TF families like basic helix-loop-helix (bHLH), basic leucine zipper (bZIP) and APETALA2 ethylene-responsive element binding proteins (AP2-EREBP) were found predominant.The detailed structural annotation of 884 MAD- and 1083 TAD-SNPs exhibited the occurrence of 664 (75.1%) and 837 (77.3%) SNPs in 436 and 461 chickpea genes, respectively Fig. –S4. The desi and kabuli chickpea accessions  than that of 102 kabuli accessions . Notably, a higher potential of MAD-SNPs followed by DAD-SNPs and a lower potential of TAD-SNPs for detecting polymorphism among accessions belonging to desi and kabuli cultivars was observed  and kabuli (0.58) chickpea  and kabuli (0.31) chickpea.The molecular diversity and population genetic structure among 291 kabuli and 29 desi accessions while POP II comprised of 129 desi and 60 kabuli accessions. The K value outcome with the best replicate was ascertained by estimating the average LnP(D) (log-likelihood) (exhibiting the greatest apparent inflection) and the second order statistics of ΔK . All these SNPs detected a higher polymorphic potential in POP II (mean PIC: 0.31–0.43) than that of POP I. A significant population divergence based on pair-wise FST (P < 0.001) was estimated as 0.69 between POP I and POP II. Highest FST-based population differentiation of 0.30 was observed within POP II. All 291 desi and kabuli chickpea accessions clearly belonged to a structured population in which 69% of their inferred ancestry was derived from one model-based population and the remaining 31% had an admixed ancestry  value of 2 as POP and POP II Fig. . The POPdesi and kabuli chickpea accessions (association panel). The use of these SNPs classified these desi and kabuli accessions into aforesaid two distinct population groups (POP I and POP II)  was observed among accessions belonging to the association panel based on multi-environments field data. A normal frequency distribution of quantitative SYP trait among accessions of an association panel was evident  and replicated multi-location/years seed yield per plant (SYP) field phenotyping data of 291 −6  among desi and kabuli accessions belonging to an association panel. The combined SYP trait phenotypic variation explained by all significant six gene-based SNP loci was 27.5%. Notably, one synonymous SNP derived from a gene encoding pentatricopeptide repeat (PPR)-containing protein exhibited strong association (24.2% PVE and 1.2 × 10−8 P) with SYP trait in chickpea  cut-off ≤0.05 using the genotyping data of 1967 SNPs identified six genomic loci exhibiting significant association with SYP trait at a P ≤ 10−6 Fig. . Interesome Fig. . A maximome Fig. . The SYPdesi and kabuli accessions. To overcome these, large-scale discovery and genotyping of informative sequence-based markers like SNPs differentiating the maximum number of desi and kabuli accessions together and exhibiting high intra-specific potential among cultivated accessions using a user-friendly, rapid and cost-effective genotyping assay is essential at a genome-wide scale45. This will essentially accelerate the generation of genotyping information of numerous genome-wide SNP markers differentiating large-scale accessions for high-resolution association mapping and efficient dissection of complex quantitative traits in order to drive genetic enhancement of chickpea. In this perspective, validation and genotyping of numerous non-erroneous and robust SNPs that are discovered by desi and kabuli cultivar-wise individual assembling of low-sequencing depth-coverage sequence reads generated from numerous germplasm accessions using GBS assay, can be an efficient alternative genome-wide strategy in chickpea. Adopting this strategy, the current study has developed diverse kinds of genome-wide 1967 SNPs including 884 DAD-, KAD-, MAD-SNPs and 1083 TAD-SNPs to evaluate their potential for discriminating cultivated desi and kabuli accessions and exhibiting association with SYP traits in chickpea. The structurally and functionally annotated informative natural SNP allelic variants discovered by us can be utilized for rapid identification of functionally relevant molecular tags associated with multiple agronomic traits in chickpea. These developed SNPs will further serve as a useful genomic resource for cultivar-wise selection of informative SNPs differentiating desi and kabuli accessions at a genome-wide scale to expedite high-throughput genetic analysis in chickpea.Genomics-assisted crop improvement of chickpea is usually hindered due to its narrow genetic base and low intra-specific polymorphism among cultivated desi and kabuli chickpea germplasm accessions was much higher as compared to that reported in earlier studies with SNP markers48. Nevertheless, the nucleotide diversity measured by DAD-, KAD- and MAD-SNPs among 189 desi and 102 kabuli accessions was almost comparable to that documented using the genome-wide resequencing- and GBS-derived SNP markers45. The level of molecular diversity detected especially by MAD-SNPs among 291 cultivated desi and kabuli chickpea accessions is much higher as compared to that documented earlier with SNP markers48. Distinct differentiation and clustering of 291 accessions into two major population groups are in accordance with their cultivar-specific origin, pedigree relationships and parentage that well-corresponded with previous studies of Upadhyaya et al.49 and Kujur et al.45.The intra-specific polymorphic potential especially detected by SNPs among 291 cultivated desi and kabuli accessions. The continuous crop genetic improvement breeding efforts with an aim to develop high-yielding stress tolerant desi and kabuli cultivars may influence their domestication pattern and genetic structure immensely therefore, detection of numerous admixture traces among these cultivated 291 desi and kabuli chickpea accessions is expected. The determination of high-resolution population genetic structure among accessions is vital to assess the accuracy (non-spurious) of robust SNPs associated with a particular agronomic trait in crop plants. In this context, high-resolution admixed population genetic structure can essentially be effective in detecting non-spurious genomic loci associated significantly with target traits in chickpea.The clustering of accessions into a specific population group was strongly influenced by geographical origin as well as adaptive environment rather than their known parentage and pedigree. The observed admixed population genetic structure inferred the occurrence of multiple domestication events along with strong evolutionary bottleneck among desi and kabuli chickpea accessions can be deployed in marker-aided introgression breeding for varietal improvement targeting multiple traits of agronomic importance in chickpea.While trying to evaluate the potential of the developed markers in explaining molecular diversity and population genetic structure in chickpea it was observed that large-scale genome-wide MAD-SNPs discovered by especially comparing the low-sequencing depth-coverage sequence-reads of multiple accessions exhibited a higher potential of intra-specific polymorphism as well as a wider natural allelic diversity than that of two accessions (TAD-SNPs). Therefore, these informative MAD-SNPs with the requisite potential to establish precise phylogenetic relationships and determine admixed population genetic structure among 291 desi and kabuli chickpea accessions at a genome-wide scale to evaluate their potential for marker-trait association and identify potential genomic loci and natural allelic variants governing complex seed yield quantitative trait in chickpea. The high-resolution association study altogether detected six genomic SNP loci including QTNs significantly associated with SYP traits. None of these six major SYP trait-associated QTNs identified has been documented by earlier association and QTL mapping studies in chickpea. This infers the novelty of the detected gene-based natural SNP allelic variants in governing seed yield trait in chickpea. Essentially, the five protein-coding genes with synonymous and non-synonymous SNP loci associated with SYP trait delineated by high-resolution association mapping have functional relevance for quantitative dissection of complex seed yield traits in chickpea. These SYP trait-associated SNPs will be of immense use in establishing marker-trait linkages and identification of natural allelic variants in the genes regulating SYP trait, thereby can accelerate the marker-assisted selection to develop desirable cultivars with high seed yield.The current investigation developed numerous informative DAD-, KAD-, MAD- and TAD-SNPs differentiating 291 precisely field-phenotyped 56. Essentially, the role of delineated most-promising SYP trait-associated PPR genes with synonymous SNPs in regulating fertility restoration, heterosis breeding and hybrid seed production is well-documented in crop plants including legumes57. Modulation of their gene expression can thereby enhance the overall seed yield. Henceforth, functionally relevant molecular signatures including candidate genes, transcription factors and natural SNP alleles delineated in the present study can essentially be deployed for genomics-assisted crop improvement to develop high seed-yielding cultivars of chickpea.The delineated genes encoding response regulator, arginine decarboxylase, Mediator transcription factor and PPR protein with synonymous and non-synonymous coding and intronic SNPs associated with SYP trait in chickpea are known to control gene expression dynamics and transcriptional regulatory pathways underlying growth, development and yield traits in multiple crop plantsC. arietinum desi (189 accessions) and kabuli (102) cultivars were selected from available chickpea reference core/mini-core germplasm collections49 as per Kujur et al.45  belonging to 45 Table . Of thesea Table . The gendesi and kabuli chickpea accessions using GBS assay were acquired from NCBI SRA database (Accession ID SRX845396) and de-multiplexed into individual sequences by using their unique barcodes in accordance with Kujur et al.45. The high-quality sequence reads of 92 accessions after filtering with NGS QC Toolkit58, were grouped into desi (39 accessions) and kabuli (53) cultivar-wise separately and further mapped individually on desi chickpea genome sequences4 using BWA59. The SNPs were mined by desi and kabuli cultivar-wise comparison of the mapped sequences among accessions using Samtools (http://www.htslib.org) and subsequently high-quality SNPs supported by at least 10 sequence reads  were screened following Srivastava et al.30. A set of chromosome-wise uniformly mapped high-quality SNPs exhibiting differentiation only between two desi (ICC 4958) and kabuli (CDC Frontier) cultivars (which draft genomes are sequenced) were screened for further analysis. The structural and functional annotation of SNPs in diverse coding (synonymous and non-synonymous substitutions) and non-coding  sequence components of genes and genomes (chromosomes/pseudomolecules and unanchored scaffolds) were performed as per the desi (CGAP v2.04) genome annotation.The raw FASTQ sequence reads generated by sequencing of 92 desi accessions (39)-derived (DAD) SNPs, kabuli accessions (53)-derived (KAD) SNPs, multiple accessions (92 desi and kabuli)-derived (MAD) SNPs and two accessions (ICC 4958 and CDC Frontier)-derived (TAD) SNPs were genotyped in the genomic DNA of 291 chickpea accessions using Sequenom MALDI-TOF MassARRAY assay (http://www.sequenom.com). The SNP loci along with their flanking 100-bp sequences were curated and a 34-plex MassARRAY multiplex iPLEX assay including adapter-ligated pre-amplification and unextended/nucleotide extension PCR primers targeting each SNP were designed by MassARRAY Assay Design software v3.1 following the criteria of Saxena et al.61. The multiplex PCR amplification, incubation, primer extension, resin clean-up and mass spectrometry-based SNP genotyping were carried out as per the manufacturer’s instructions of Sequenom iPLEX Gold amplification kit. The SNP genotyping data detected in iPLEX spectrochip bio-arrays were analysed by a MassARRAY Typer 3.4. The genotyping information of SNPs based on allele-specific differences in mass between extension products was visualized and documented among 291 chickpea accessions following Saxena et al.61.For genotyping of SNPs, desi and kabuli chickpea accessions. The amplicons were sequenced and high-quality sequences were aligned and compared among accessions to detect SNPs following Kujur et al.8 and Saxena et al.60.For validation of high-quality SNPs, primer-pairs were designed targeting 100-bp either side flanking sequences of 192 representative SNP loci and PCR amplified using the genomic DNA of 48 selected 62 to estimate the per cent polymorphism and PIC (polymorphism information content) among 291 desi and kabuli chickpea accessions. The SNP genotyping information was further analysed on a 100-kb non-overlapping sliding window approach of TASSEL v5.063 (http://www.maizegenetics.net) to measure one of the major nucleotide diversity parameter-θπ among chickpea accessions as per Kujur et al.45 and Bajaj et al.18.The genotyping data of DAD-, KAD-, MAD- and TAD-SNPs were analysed with PowerMarker v3.51desi and kabuli chickpea accessions. The cluster analysis among accessions was carried out based on Nei’s genetic distance64 using the neighbour-joining (NJ) method (with 1000 bootstrap replicates) of PowerMarker, and unrooted phylogenetic tree was constructed employing MEGA v7.065.The genotyping data of SNPs were used to assess the molecular diversity and for establishing phylogenetic relationships among 291 66 as per Kujur et al.45. The optimum population number (K) was determined following the ad hoc66 and delta K67 methods. Using the optimum K, the population structure model representing better relationships among chickpea accessions was determined. Diverse population genetic parameters including genetic divergence (FST) and degree of admixture among different population groups were estimated.To determine the population structure among 291 chickpea accessions, the genotyping data of SNPs were analysed in a model-based program STRUCTURE v2.3.4desi (189) and kabuli (102) germplasm accessions were planted in the experimental field with 35 × 10 cm row-to-row and plant-to-plant spacing, respectively, following standard agronomic practices. These accessions were grown in the field as per ‘alpha-lattice’ design with two replications during crop season for two consecutive years at two diverse geographical locations  of India. Four selected desi (ICCV 10 and ICCV 93954) and kabuli (ICC 12968 and Annigeri) chickpea accessions sown after every 10 individual plants were served as reference in the field experimental design to ascertain the homogeneity of association panel across two environments. Seed yield per plant (SYP) was estimated by measuring the average weight (g) of fully matured dried seeds (at 10% moisture content) harvested from 10–15 representative plants of each accession. The genetic inheritance pattern of SYP trait based on diverse statistical parameters including coefficient of variation (CV), broad-sense heritability (H2) and frequency distribution were estimated in an association panel as per Bajaj et al.18 and Das et al.23.For large-scale phenotyping, an association panel consisting of 291 diverse desi and kabuli accessions belonging to an association panel of chickpea. To perform association analysis, the compressed mixed linear model (CMLM)  and population parameters previously determined (P3D) interfaces of GAPIT were utilized as per Thudi et al.14, Kujur et al.10 and Kumar et al.65. To determine the accuracy of SNP marker-trait association, we performed the quantile-quantile (Q-Q) plot-based false discovery rate (FDR cut-off ≤0.05) corrections for multiple comparisons between observed/expected −log10(P)-values and adjusted P-value threshold of significance in accordance with Benjamini and Hochberg68. The SNP loci associated with SYP trait at a lowest FDR adjusted P-value (threshold P < 1 × 10−6) and highest R2 were identified to be highly significant in chickpea. Following FDR-controlling method of model with the SNPs and adjusted P-value, R2 representing the magnitude of SNP marker-SYP trait association was estimated. The trait association outcomes were revalidated through a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model of GWAS as per Wen et al.69. The QTNs with LOD >3.0 and P-value < 0.005 were identified to be significantly associated with studied trait.For efficient trait association mapping, we employed the environment-wise SYP phenotyping data measured across two diverse geographical locations and experimental years from each of the selected accession for estimating the mean SYP trait value in an individual accession. The SNP genotyping data was correlated with multi- environments replicated field phenotyping data of SYP trait as well as population structure (Q), kinship (K) matrix and principal component analysis (PCA) (P) information of 291 Table S1Table S2Table S3Table S4Table S5Table S6


The common bean is one of the most important staples in many areas of the world. Extensive phenotypic and genetic characterization of unexplored bean germplasm are still needed to unlock the breeding potential of this crop. Dissecting genetic control of flowering time is of pivotal importance to foster common bean breeding and to develop new varieties able to adapt to changing climatic conditions. Indeed, flowering time strongly affects yield and plant adaptation ability. The aim of this study was to investigate the genetic control of days to flowering using a whole genome association approach on a panel of 192 highly homozygous common bean genotypes purposely developed from landraces using Single Seed Descent. The phenotypic characterization was carried out at two experimental sites throughout two growing seasons, using a randomized partially replicated experimental design. The same plant material was genotyped using double digest Restriction-site Associated DNA sequencing producing, after a strict quality control, a dataset of about 50 k Single Nucleotide Polymorphisms (SNPs). The Genome-Wide Association Study revealed significant and meaningful associations between days to flowering and several SNP markers; seven genes are proposed as the best candidates to explain the detected associations. Achieving food security is one of the most important challenges to face in the next three decades. FAO’s 2017 prospects’ revision on the world’s population growth reports an expected growth of the population of more than 2 billion people by 2050 . AccordiIn this context, grain legumes are generally regarded as key commodities for improving food security as they are a relatively inexpensive source of amino acids and other important nutrients such as minerals, when compared to livestock and dairy products . In addiPhaseolus vulgaris L., 2n = 2x = 22) is one of the most important staples in the world, produced over an area of 18 million hectares with a total production of 12 million tons per year , initially proposed as a modification of the classical bulk breeding scheme to overcome the problem of natural selection , represeAlthough bi-parental mapping has been successful in identifying many significant Quantitative Trait Loci (QTL) mapped to wide intervals in the common bean genome, our knowledge of genes controlling certain traits is still limited . In factin silico simulations to optimize the technique and to map the markers) and of collections of genetically diverse pure lines  makes Next Generation Genotyping (NGG) extremely attractive. Among the possible different NGG strategies  (Pisum sativum) (Lupinus angustifolius) . In cterized . Moreovencatula) , pea (Pisativum)  and narrifolius)  have beeP. vulgaris few studies on flowering time variation and control have been carried out to date. QTL mapping studies detected some genomic regions associated with the trait  and 84 Mesoamerican  accessions were included. Europe is the most represented geographical area in the panel (153 accessions) followed by South and Central America . Italy accounts the highest number of accessions followed by Turkey, Spain, Netherlands, and Portugal. A heatmap representing the origin of the materials is reported in Starting from the above described collection, 181 common bean highly homozygous genotypes  were obtained applying SSD for at least 5 consecutive generations under isolated conditions. The 181 lines together with 11 cultivars, included as controls, constitute the diversity panel used in this study accounting a total of 192 lines (NCBI BioSample accessions from SAMN12035168 to SAMN12035359). Further details about lines within the panel, including the genebank from which each accession has been originally obtained, are reported in The phenological characterization of the 192 genotypes was carried out for two consecutive seasons (2016 and 2017) at: (i) DSA3-UNIPG experimental field located in Sant’Andrea d’Agliano, Perugia, Italy  (hereafter PG) and (ii) CREA-CI experimental field located in Anzola dell’Emilia, Bologna, Italy  (hereafter BO). In 2016 plant material was only evaluated in PG while in 2017 in both PG and BO. In both years sowing has been carried out in May: the 4th (PG_2016), the 11th (PG_2017), and the 12th (BO_2017).dtf) was recorded as days between sowing and the opening of the first flower  + (2 × 6)]. In PG, the 222 common bean samples were grown in 6 adjacent blocks (fixed size of 1 column × 37 rows) covered by anti-insect net; in BO the same samples were arranged in 3 adjacent blocks (fixed size of 1 column × 74 rows). Plants were grown in a net covered nursery supplied with an automatic drip-irrigation system throughout the entire duration of the trials (May to mid-October). For each sample days to flowering  of the genotype effect were calculated in GenStat® . DNA integrity was evaluated after 1% agarose gels (Euro Clone) stained with ethidium bromide electrophoresis. DNA samples were then diluted to 30 ng/μl for following genotyping.Genomic DNA was isolated from young leaf tissues, collected from 15 days-old single seedlings, using the TissueLyser II (Qiagen) and the DNeasy 96 plant kit (Qiagen) according to the procedure provided by the manufacturer. DNA concentration and quality were estimated using UV-Vis spectrophotometry  and ligated to barcode adapters. Samples were than pooled on multiplexing batches and bead purified. For each pool, target fragment distribution was collected on BluePippin instrument . Gel eluted fraction was amplified with oligo primers that introduce TruSeq indexes and subsequently bead purified. The resulting libraries were than checked with both Qubit 2.0 Fluorimeter  and Bioanalyzer DNA assay . Libraries were processed with Illumina cBot for cluster generation on the flow cell, following the manufacturer’s instruction and sequenced with V4 chemistry pair end 125 bp mode on HiSeq2500 instrument .A double digest Restriction-site Associated DNA sequencing (ddRAD-seq) approach was used for genotyping. The library preparation and the sequencing were carried out by IGAtech . Before starting the procedure, a further check of the DNA concertation was produced using a fluorimetric assay to further normalize and uniform the samples. The libraries were produced using a custom protocol (IGAtech), with minor modifications in respect to the one implemented by Peterson and colleagues . In siliome v1.0 . Since tDemultiplexing of raw Illumina sequences was performed using Stacks v 2.0  and subsSeveral quality control steps were performed on the SNP dataset using PLINK v1.09  and TASSr2 ≥ 0.3) were removed. In order to detect the population stratification of the developed panel, a Bayesian clustering approach was used. The number of clusters was initially tested in STRUCTURE v.2.3.4  ( v.2.3.4  assuming v.2.3.4  implemen v.2.3.4 . Accordi v.2.3.4  and (ii) v.2.3.4 .P. vulgaris is characterized by a strong genetic structure  implemented in TASSEL v 5.2 that includes corrections for both population structure (Q) and kinship (K). In fact, the use of such model was necessary as tructure . The thrp-values were then plotted, as –log10(p) to produce a Manhattan plot using the R package “CMplots”  using the online tool BLASTP  available at: https://www.arabidopsis.org/Blast/.The resulting CMplots” . The corCMplots”  and by b v. 12.1 . In ordep-values. Pairwise LD between markers within the windows (r2) were calculated using HaploView 4.2 (A raw estimation of LD decay was obtained dividing the size of common bean genome (bp) by the number of independent recombination blocks within the panel, calculated according to View 4.2 ; the samdtf during the three experiments. In BO-2017, the spatial analysis was more efficient than the completely randomized design with a superior efficiency of the spatial model CrdL  of 22.4% over the Completely randomized design (Crd); the Crd was the best model for BLUPs calculation from data collected in PG-2016 and 2017. Summary statistics of BLUPs are reported in dtf showed high broad sense heritability (He2B) in all trials  and 0.93 (P < 0.001), respectively. The full BLUPs dataset is available in A total of 648 (97.3%) common bean samples were successfully characterized for l trials . Distribl trials ; on the l trials . Simple en years  and expeen years  revealedhttps://www.ebi.ac.uk/ena/data/view/PRJEB33063.The ddRAD-seq genotyping generated a dataset of 106,072 polymorphic loci, of those 99.3%  were mapped on the reference genome v.1.0 (Pvulgaris_218_v1.0.fasta) . The fulAfter quality control, no genotype was excluded and a dataset of 49,518 SNPs markers evenly distributed over the 11 common bean chromosomes was retained for association analyses. A graphical representation of SNPs’ distribution over the eleven bean chromosomes is reported in r2 ≥ 0.3) a dataset of 2,518 SNP was generated and used to perform STRUCTURE and cryptic relatedness analyses . A graphic representation of the genetic structure of the panel is reported in q ≥ 0.8 , resulted in a threshold equal to 5.4 (–log10(p)). GWAS results showed that multiple regions are associated with dtf in the common bean genome  was obtained for SNP 123164_60 on chromosome Pv08. Significant associations were also found for SNPs 66929_307, 17455_7, 95297_22, 59746_63, 59746_36, 116028_71, and 17777_7. In total, 8 significant SNPs for dtf were identified in 4 different common bean chromosomes: Pv01, Pv04, Pv06, and Pv08  and TAIR, resulted on the identification of 7 possible candidates.P. vulgaris reference genome, the sequenced fragment containing SNP 123164_60 produced multiple hits making the discovery of an associated candidate gene rather complex. However, our analysis detected a relevant gene, Phvul.008G149900, located 100 kb upstream of a highly significant hit. Even if no functional annotation was found on the common bean reference genome for this gene, it is noteworthy that its encoded protein is highly similar to Arabidopsis At3G12810. This protein, similar to ATP dependent chromatin-remodeling proteins of the ISWI family, is encoded by Photoperiod-Independent Early flowering 1 (PIE1) that is involved in multiple flowering pathways.When aligned to the Phvul.004G112100 is our best candidate to explain the phenotypic variation associated with this marker. The gene encodes for a NAD(P)-binding Rossmann-fold superfamily protein, carrying out oxidoreductase activity in the chloroplast.Located only 50 kb downstream of the SNP 66929_307, on Pv04, Phvul.001G227200. In this case, the SNP is located in the first intron of the gene. Phvul.001G227200 is homologous of the A. thaliana At1G56260, also known as Meristem Disorganization 1 (MD1) that is required for the maintenance of stem cells through a reduction in DNA damage  as displayed in The search of the best candidate gene for marker SNP 17455_7 resulted in the identification of A damage . Phvul.0Phvul.006G215800 resulted as the best candidate to explain the effect of the SNP 95297_22 detected on Pv06. The gene encodes for Potassium Channel AKT2/3.Phvul.004G085100 is the best candidate gene explaining the effect of the markers. The homolog gene in Arabidopsis encodes for a sucrose transporter protein: AtSUC2.The two significant SNPs detected on Pv04 (SNP 59746_36 and 59746_63) co-localize on the same chromosome region being separated by 27 bp only. Located 40 kb upstream of the signal, Phvul.008G055400 as the most meaningful candidate gene associated to the SNP 116028_71. The gene encodes for a Leucine-Rich Repeat Receptor-Like Protein, also known as Clavata2 (CLV2).Finally, we identified Phvul.001G236000 are in the same recombination block showing the association between the marker and the identified gene (Phvul.001G227200); it is noteworthy that several recombination events occurred between the two markers. A clear association was also observed for SNPs 59746_36 and 59746_63 with Phvul.004G085100  showed an early-flowering phenotype  on chromosome Pv04. In particular, PvTFLx is located 2 Mb downstream our best candidate suggesting that this region harbors different genes involved in flowering time control.henotype  suggestiPhvul.001G227200, homolog of MD1 in Arabidopsis, was the resulting candidate to explain the significance of the SNP 17455_7. Interestingly, in A. thaliana mutants of this gene showed several development defects such as abnormal phyllotaxy and plastochron, stem fasciation and reduced root growth  contains the two candidate genes detected in this study on Pv01 together with Phvul.001G221100 that encodes for Phytocrome A  causes dwarfism, delayed development and it has been reported that such plants can occasionally flower, but never produce viable seeds  causes altered flower development, late flowering or interrupted flowering caused by a temporary termination of the main inflorescence flower meristem . The exploitation of the genes identified in this research will hopefully allow the development of new common bean varieties able to better adapt to changing climatic conditions.As from the discussed evidences and related bibliographic records, homolog of most of the genes that we propose as candidates for explaining observed flowering time variation in the studied common bean panel are involved in different pathways regulating flowering in https://www.ebi.ac.uk/ena/data/view/PRJEB33063.NCBI BioSample accessions of the 192 common bean lines from: SAMN12035168 to SAMN12035359. The full genotyping dataset of the same samples is available at: VN and LR conceived and designed the experiments. LC and AC performed plant phenotyping. LR, LC, and AC performed phenotypic data analyses. LR and LC performed GWAS and candidate gene data analyses. AC and VN contributed to phenotyping costs. VN funded reagents, materials, and analysis tools. All authors wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.