Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. We demonstrate HapTree-X’s feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10×  faster than other tools. The advantage of HapTree-X’s ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases. Haplotype reconstruction of distant genetic variants is problematic in short-read sequencing. Here, the authors describe HapTree-X, a probabilistic framework that uses differential allele-specific expression to better reconstruct paternal haplotypes from diploid and polyploid genomes. This partial view can hide important interactions between nearby variants, and impede the search for understanding the molecular basis of human disease2. For instance, if an individual contains disease-risk variants in two different exons of the same gene, the genotype alone does not reveal whether both disease-associated mutations impact the same allele, thus leaving one functional copy, or whether they impact different alleles, leading to no functional copies of the gene. Such examples of compound heterozygosity have been associated with multiple diseases, including cerebral palsy, deafness, and haemochromatosis3. However, many additional examples likely remain undetectable given the lack of haplotype phasing information in the vast majority of disease association studies. The dearth of accurate haplotype phasing information can impact our ability to recognize optimal host-donor matches in organ transplantation, and also impede studies of human genetic variation, human population history reconstruction, ancestry determination for a given individual, and the study of genome evolution across individuals and across species4.The two primary technologies for modern genetic association studies, genotyping arrays for common variants and next-generation sequencing for rare variants, are both limited to inferring only the genotype of an individual, but not in stitching these genetic differences into phased haplotypes6, but these apply mostly to traditional linkage studies and not to modern genome-wide association studies and rare variant sequencing studies, where relatedness is generally not known. More recently, large-scale population sequencing and genotyping studies such as HapMap7 and 1000 Genomes Project2 have provided experimentally phased or computationally phased reference genomes that can be used for phasing common variants11, but these maps are ineffective for de novo mutations or rare variants that are typically not well-represented, or accurately phased in these references.Methods for inferring phase information are traditionally based on pedigree information within large families16. However, many such methods are severely limited by the short sequence length for heterozygous SNP distances that exceed read fragment length. For some of these methods, speed and memory usage is also an issue16. Two exceptions—the recent proximity-ligation (Hi-C)17 and long-read sequencing  based methods19 that enable longer-range phasing—yet still require specialized technologies that are expensive and that suffer from high error rates. On the other hand, some high-throughput sequencing technologies—especially transcriptome sequencing via RNA-seq—are affordable, widely available, already established and standardized, and allow longer-range phasing within genes by leveraging the fact that the transcriptomic distance between SNPs may be less than the genomic distance.More specialized computational methods for phasing operate on sequencing data alone and are able to phase rare and de novo mutations as they rely on sequencing reads that span two or more heterozygous SNPs21. However, these methods are still contiguity-based, relying on sequencing reads that span two or more heterozygous SNPs. Moreover, even the range of paired-end RNA-seq based phasing is limited by read fragment length in the presence of multiple or long intermediary exons that are devoid of heterozygous variants  more reads than other NGS-based methods can utilize, and increases the total phased block length up to 25% as compared to the other tools. We also show how DASE-based phasing of SNPs within genes with multiple isoforms can be theoretically achieved  information within RNA-seq data. We follow the intuition that DASE in the transcriptome can be exploited to improve phasing because SNP alleles within maternal and paternal haplotypes of a gene are present in the read data at asymmetric frequencies due to the gene’s differential haplotypic expression (DHE). Phasing based upon differentially expressed allele frequencies additionally allows the use of reads covering only one heterozygous SNP, as opposed to existing methods which discard this information and rely solely on sequence contiguity Fig. . Concept14—a maximum likelihood contig-based phaser that makes use of reads that span multiple SNPs—by non-trivially adapting the HapTree probabilistic model to now incorporate RNA-seq-specific priors that describe the correlation of allele-specific imbalance at SNP loci, allowing the construction of longer phased haplotype blocks. Furthermore, HapTree-X preserves the unique properties of HapTree, such as polyploid phasing, while adding capabilities such as incorporating long-range sequencing technologies22 and RNA-seq read data. HapTree-X also has greater scalability and is significantly faster than HapTree due to algorithmic and engineering improvements that reduce redundant computation as well as parallelization capabilities provided by the bioinformatics domain-specific language Seq23.HapTree-X generalizes prior work, HapTree12, exome sequencing, 10X long-range sequencing22, and RNA-seq without DASE21), it also is able to incorporate more complex diverse data as long as the user can give a reasonable prior about the underlying data; this usage case is demonstrated below where DASE-based phasing can phase reads covering only a single SNP.Not only does our general model readily integrate existing contiguity-based sequencing data that provides pairs of linked SNPs 12, HapCUT215, and phASER21. For benchmarking, we utilized the well-studied GM12878 sample, using cytosol, nucleus, and whole-cell RNA-seq data from the GM12878 lymphoblastoid cell-line from ENCODE CSHL Long RNA-seq track24, whole exome sequencing data from 1000 Genomes Project and a WGS sample from Illumina Platinum Genomes25; GENCODE release 19 was used as the reference gene annotation. We also included the K562 chronic myelogenous leukemia cell line RNA-seq data and validated it with the recently validated phasing ground truth dataset from ENCODE26. Lastly, we used five in-house sequenced Genome in a Bottle (GIAB)28 RNA-seq samples: NA12878, NA24143, NA24149, NA24385, and NA24631 and 10X Genomics’ publicly available GIAB samples and compared phased haplotype blocks to the gold-standard GIAB validation phases  not only on a whole-genome Platinum NA12878 dataset, but also on two high coverage 10× datasets that were aligned by the EMA aligner14.Finally, we note that the polyploid capabilities of HapTree-X are identical to those of HapTree . For these reasons, we refer readers to the original HapTree results for polyploid phasingIn addition to accurate results, we demonstrate significant speed improvements over other phasing methods tested Table —HapTree-HapTree-X can be added downstream to any pre-existing RNA-seq processing pipeline to output phased haplotype blocks. HapTree-X takes as input RNA-seq read alignment files (SAM/BAM format), a standard VCF file containing the individual’s genotype, and a gene annotation that specifies the boundaries of genes and their exons. Finally, we note that HapTree-X can easily incorporate different technologies during the phasing.Incorporating DASE into phasing enables HapTree-X to increase the number of phased SNPs and the length of phased blocks within genes in RNA-seq data. While DASE had modest impact on low-coverage GIAB samples, increasing the total phase length by only 1%, increased coverage on GM12878 samples caused DASE to increase the total phase length to 5% over other tools and HapTree-X with DASE turned off. The DASE effect is much stronger in joint exome and RNA-seq analysis: we observed up to a 12% increase in total phase length. We noticed that DASE performs the best on the K562 leukemia cell line, where the total phase length went up by 25%. We provide a theoretical explanation of this effect by showing that accuracy increases exponentially with FPKM depth-coverage—fragments per kilobase of transcript per million mapped reads , KANK1 , LNPEP (autism spectrum disorders34), MED28 (breast cancer35), DDR1 (schizophrenia36), SPRN (Creutzfeldt-Jakob disease37), STEAP2 (prostate cancer38), ZNF765 , and N4BP2L2 (arsenic poisoning40) genes are shown in Fig. HapTree-X links SNP pairs in the GM12878 dataset that could not be phased by sequence contiguity-based methods. Such phased SNPs enable us to better phase genes that have clinical associations with various diseases; a few significant examples that show 41. All of these samples were low-coverage RNA-seq samples, and thus could not benefit from DASE as much as GM12878 samples. Nevertheless, HapTree-X phased more SNPs in all cases than the other methods, and DASE consistently (17 out of 30 samples) improved phasing of the long BCR gene, which has a causal relationship to chronic myeloid leukemia43 , RNA-seq datasets are currently cheaper, more prevalent and contain abundant long-range phasing information via splicing and DASE that is currently underutilized. Notwithstanding the inherent limitations of RNA-seq data that reduce the scope of DASE-enabled optimizations, such as the small transcriptome size and gene-restricted phases, we show that such data still harbors enough valuable information to significantly improve phasing quality of both RNA-only and joint DNA-RNA analyses, with no impact on the computational resources.45. We also expect to see further validation of HapTree-X’s theoretical framework as the coverage of RNA-seq and the size of the ground truth datasets expand: DASE phases better if the coverage is higher, and the large portion of SNPs phased by any of the evaluated tools are not currently validated by the GIAB project . Finally, we are looking to expand our DASE theoretical framework to other problems, as other kinds of data—such as barcoded reads—exhibit similar biases that can be in principle modeled by the same theoretical framework.In the near future, we plan to extend HapTree-X to single-cell RNA-seq datasets that are rapidly becoming more affordable and commonThe fast access to more-comprehensively phased gene regions opens the door for further understanding of the relationship between genotype and phenotype in biomedical disease research. Our conceptual advance, as well as our implementation, will greatly benefit researchers who analyze large amounts of DNA and RNA sequencing data, regardless of the technology.HapTree-X is a Bayesian haplotype reconstruction framework which simultaneously employs read overlap information (through read contiguity or read barcodes) and optional DASE for haplotype phasing. HapTree-X outputs phased haplotype blocks, given an input of read alignment files (BAM/SAM), a VCF file containing the individual’s genotype, and an optional gene model which specifies the genes (and their exons) within the genome. It is able to take multiple lines of evidence  at the same time for improved phasing.14, we determine a haplotype of maximal likelihood which depends on the DASE present in the RNA-seq data, as well as the sequence-contiguity information within the reads.The HapTree-X pipeline is initiated by determining which genes are expressed using the gene model and RNA-seq data. For each of these genes, a maximum likelihood expression bias (DHE) is computed. Furthermore, we determine which SNPs within those genes have high likelihood of concordant expression; we phase only those SNPs. For reads containing only such SNPs, we assign to them the computed expression bias of the gene they cover; for all other reads, we assign a non-biased expression. Finally, applying a generalized version of HapTreeβ = 0.9, then it would be straightforward to guess the haplotypes as in Table Using DASE for phasing presents major challenges. Consider a simple example, presented in Table β = 0.9, and must instead be inferred from the same expression data. Furthermore, the integration of these data with reads covering multiple SNPs, as well as the complications arising from multiple genes and splicing makes this inference highly nontrivial.However, we overcome the difficulty that the underlying DHE is unknown, often not as drastically high as R), DHE (ε). Applying Bayes’ rule, we can reduce this problem to determining the haplotype H which maximizes the product over all reads R of the probability of observing each read r, given H is the true haplotype:We present a Bayesian mathematical framework for estimating r, we partition the SNPs covered by r into A and D  and take the product of the probabilities of agreement and disagreement, along with the assumed rate of expression (see below for further context and details of notation):To compute this probability, for each read H = , which contain the sequence of variant alleles inherited from each parent of the individual. As homozygous SNPs are irrelevant for phasing, we restrict ourselves to heterozygous SNPs (from now on referred to simply as an SNP) and we denote the set of these SNPs as S. We assume these SNPs to be biallelic, and because of these restrictions, H0 and H1 may be expressed as binary sequences, where a 0 denotes the reference allele and a 1 the alternative allele; H0 and H1 are complement sequences. Let H[s] =  denote the alleles present at s, for s ∈ S.The goal of phasing is to recover the unknown haplotypes (haploid genotypes), 46). The set of all reads is denoted as R. Given a set of SNP loci S, we define a read r ∈ R as a vector with entries r[s] ∈ {0, 1, −}, for s ∈ S, where a 0 denotes the reference allele, a 1 the alternative allele, and  −  that the read does not overlap s or that it contains an allele that is not observed in the genotype of locus s (likely due to a sequencing error). We say a read r ∈ R contains an SNP s if r[s] ≠ −  and we let size of a read r, ∣r∣, refer to the number of SNPs it contains. For each read r and for each SNP locus s, we assume a probability of opposite allele information r[s] equal to εr,s and represent these error probabilities as a matrix ε. We assume these errors to be independent from one another. We denote the sequence of observed nucleotides of a fragment simply as a read (independent from single/paired-end reads). We assume each read is mapped accurately and uniquely to the reference genome, and moreover that each read is sampled independently , upon the set of SNP loci S and read set R, we define a read graph such that there is a vertex for each SNP locus s ∈ S and an edge between any two vertices r containing both s and In genomic read data, all G that specifies the genes (and their exons) within the genome. We used GENCODE v19 annotation for our experiments on NA12878. For each g ∈ G, we assume that the haplotypes  restricted to g are expressed at rates β0, β1 respectively due to DHE. The phasing blocks correspond to the SNPs in genes g ∈ G, though we will see that some SNPs are not phased due to insufficient probability of concordant expression. Two distinct genes H we mean the gene haplotype, that is H restricted to the SNPs within g.To phase using differential expression (DASE-based phasing), we assume the existence of some gene annotation The final blocks to be phased by HapTree-X integrating both contig and DASE-based phasing are defined as the connected components of a joint read graph. The vertices are the SNPs phased by either method, and there is an edge between any two S, given the read data R, and sequencing error rates ε. Furthermore, suppose we knew for each read r, the likelihood that r was sampled from Hi  of set of SNPs H, reads R, error rates ε, and expression rates H being the true phase is given byGiven a haplotype H, we may define a relative likelihood measure, RL. Note that R, and of Since H], we assume a potential parallel bias, ρ ≥ 0.5, which results in a distribution on H such that adjacent SNPs are independently believed to be phased in parallel (00) or (11) with probability ρ and switched (01) or (10) with probability 1 − ρ. When ρ = 0.5 we have the uniform distribution on H. The general prior distribution on H in terms of ρ isP(H) and S(H) denote the number of adjacent SNPs that are parallel and switched in H, respectively. Given the above model, as each r ∈ R independent, we may expand For the prior P.If we knew the matrix g, containing SNPs Sg, the corresponding reads Rg have expression biases r ∈ Rg. Let β of g responsible for producing Rg. To do so, we formulate an HMM and use the forward algorithm to compute relative likelihoods of R given β, ε.For a fixed gene Rg, containing only reads of size one, so that s) are independent from r ∈ Rg to a uniformly random SNP s, and include this restricted read of size one (r∣s) in r∣ = 1, then r = r∣s, by definition.) Therefore, To achieve the conditional independence required in an HMM, we define β, given β, and therefore H H; since β, and since f has a unique local maxima for β ∈ , we can apply Newton-Rhapson method to determine β of maximum likelihood.Our goal is to determine the maximum likelihood  in Eqs. . This meH correspond to the hidden states, Sg. The observation at time s in this context is s. The emission distributions are as follows:H[s] is H restricted to s.To set this problem in the framework of an HMM, we let the haplotypes H in Eq.  is that of maximal likelihood. In doing so, we see the role played by concordant expression, and motivate its use as a probabilistic measure for determining which SNPs we believe we may phase with high accuracy.H+, a haplotype solution of a gene g, of maximum likelihood given β and ε. Let r[s] = v where v ∈ {0, 1}. Provided error rates are constant (say ϵ) and ϵ < 0.5, and assuming a uniform prior distribution (ρ = 0.5), we can show a solution of maximum likelihood is H+ ought to correctly recover the true haplotypes.Under a certain set of conditions, we derive H+ is of maximal likelihood, we introduce the terms concordant expression and discordant expression. We say R and H have concordant expression at s if β0 > β1, we expect to see the allele H0[s] expressed more than the allele H1[s] in Rg,s (concordant expression).To prove H+ as a solution which assumes concordant or equal expression at every SNP s. Because we assume uniform priors, Sg in the following way:We may now equivalently define (see Eq. ), and siH+ is of maximal likelihood, it only remains to show that concordant expression is at least as likely as discordant expression, as intuition suggests. Let γi = βi(1 − ϵ) + (1 − βi)ϵ, then as in Eq.  expression at s(H+[s]) with that of discordant  expression at s (H−[s].) For ease of notation, let vi = wi1−. Since ϵ < 0.5, we have γ0 > γ1; H+, which proves the inequality.Let s, we now measure the probability of concordant expression at that SNP, and only phase when that probability is sufficiently high, in order to determine which SNPs can be phased with high accuracy. This probability of concordant expression can be immediately derived from Eq.  denote the event of concordant expression at s, thenN reads, an expression bias β, and a constant error rate ϵ, we compute likelihood of concordant expression using the standard binomial distribution B by equating successes in the binomial model to observations of the majority allele, expressed with bias γ0 :X corresponds to the number of successes and μ = E[X] = Nβ. This bound shows that the probability of concordant expression increases exponentially with the coverage (N).Having shown that the solution of maximal likelihood under mild conditions is, intuitively, that which has concordant expression at each SNP locus from Eq. . We assuN, the Binomial Distribution B converges to the normal distribution We remark for large β however, which we may only estimate. We therefore also require that for any gene g to be phased by DASE , the DASE within the gene (at s) must be sufficiently unlikely to have been generated by uniform DHE (β = 0.5) .Now that we have a method for determining the likelihood of concordant expression, we can require any SNP loci to have a sufficiently high probability of concordant expression in order for HapTree-X to attempt to phase that SNP. The likelihood of concordant expression is dependent on m, M, whereg. For a single SNP s, we writeWe compute an upper bound on this probability using a two-sided binomial test applied to total allele counts M heads and at most m tails is computed below. Let N = m + M, then the upper bound based on the two-sided binomial test isBThe likelihood of at least Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Brassica juncea) is a major source of vegetable oil in the Indian subcontinent. The seed cake left after the oil extraction is used as livestock feed. We examined the genetic architecture of oil, protein, and glucosinolates by conducting a genome-wide association study (GWAS), using an association panel comprising 92 diverse genotypes. We conducted trait phenotyping over 2 years at two levels of nitrogen (N) application. Genotyping by sequencing was used to identify 66,835 loci, covering 18 chromosomes. Genetic diversity and phenotypic variations were high for the studied traits. Trait performances were stable when averaged over years and N levels. However, individual performances differed. General and mixed linear models were used to estimate the association between the SNP markers and the seed quality traits. Population structure, principal components (PCs) analysis, and discriminant analysis of principal components (DAPCs) were included as covariates to overcome the bias due to the population stratification. We identified 16, 23, and 27 loci associated with oil, protein, and glucosinolates, respectively. We also established LD patterns and haplotype structures for the candidate genes. The average block sizes were larger on A-genome chromosomes as compared to the B- genome chromosomes. Genetic associations differed over N levels. However, meta-analysis of GWAS datasets not only improved the power to recognize associations but also helped to identify common SNPs for oil and protein contents. Annotation of the genomic region around the identified SNPs led to the prediction of 21 orthologs of the functional candidate genes related to the biosynthesis of oil, protein, and glucosinolates. Notable among these are: LACS5 (A09), FAD6 (B05), ASN1 (A06), GTR2 (A06), CYP81G1 (B06), and MYB44 (B06). The identified loci will be very useful for marker-aided breeding for seed quality modifications in B. juncea.Indian mustard ( Brassicaceae. These plants are cultivated as vegetables, fodder, edible oilseeds, or biofuel crops. Of these, Indian mustard (B. juncea L. Czern & Coss) is the most widely cultivated oilseed crop in India, with very high acreage (6.9 million hectares) and production (7.2 million metric tonnes) . It is also cultivated in China, Southern Russia, and the Caspian steppes as a condiment, vegetable, and oilseed crop. B. juncea is an allotetraploid  and it evolved through multiple hybridization events between B. rapa  and B. nigra  , protease inhibitors  . Breedin2n = 16) . Palmiti2n = 16) . Breedin2n = 16) . High ol2n = 16) . Complexcic acid  and two iptomics . A high  content . Seed ca content . The prihibitors , Ca2+ deproteins , and dehproteins , are welN-hydroxysulfates), a endogenous allelochemical group and a subset of secondary metabolites, are only present in the family Brassicaceae , a major limiting factor for Brassica production , comprising 100 accessions (S7/S8 inbred lines), constituted the experimental materials for the present investigations. These accessions were evaluated in the field trials conducted during the winters of 2015–2016 (Y1) and 2016–2017 (Y2) as per the alpha lattice design with two doses of nitrogen (N) application (N0: no added N and N100: added N @100 kg/ha). The experiment was conducted in the oilseeds research area, Punjab Agricultural University, Ludhiana, India . Nitrogen was added in the form of urea (46% N). For N100 treatment, urea was applied in two split doses – half at the time of sowing and the remaining half at the stem elongation stage.Oil, protein, and glucosinolates content in intact seeds were estimated by Near Infrared Reflectance Spectroscopy (NIRS), Model 6500 spectrophotometer, Foss-NIR Systems, Inc., Silver Spring, MD, United States. Existing NIRS-based calibration models  were useStatistical software SAS v9.3 was used for analyses of variance (ANOVA) to assess the significance of variance due to genotype, replication, years, N-levels, and all possible interactions between these . The following model was used for this purpose.Pijkl is the phenotypic value of accession, i noted by the lth N-level in year k, μ the overall mean, Gi the effect of the accession i, Rj the effect of replication j, Yk the effect of year k, Nl the effect of N-level l, (Y × G)kj the interaction term between year k and genotype j, (N × G)lj the interaction between N-level l and genotype j, (Y × N)kl the interaction between year k and N-level l, (R × N)jl the interaction term between replication j and N-level l, and eijkl the random independent and identically distributed residual term. This approach was also used for characterizing the influence of the N-levels on the trait observations: multi-year and within-year models through the use of linear mixed models. Best unbiased linear predictors (BLUPs) were assessed to obtain datasets across the years and N-levels. These estimates and predictors extracted from both year and both N-level models were then used as input data for GWAS. Pairwise Pearson’s correlation coefficients (r) between the traits were estimated by using the R package “Hmisc”1. We visualized r network value with the R package “q-graph.”where ® HiSeq platform, which was outsourced to Novogene (HK) Company Limited, Hong Kong. GBS data was carried out for only 92 genotypes, as DNA from the remaining eight samples failed the quality test. SNP data calling was carried out using NGSEP (Next Generation Sequencing Experience Platform) GBS pipeline2  analysis was also used to reduce false positives by reducing the effects of population stratification which were implemented in the R package “adegenet.”Different covariates, i.e., population structure, principal components (PCs) analysis, and discriminant analysis of principal components (DAPCs), are often used to overcome the bias due to the population stratification on outcomes from GWAS. These allow for adjustments for population stratification. Population structure analysis was performed by using STRUCTURE v2.3.4 , with th5 with default settings to identify marker trait associations. Three methods  were used with Q, PCs, and DAPCs as covariates. Quantile-Quantile (Q-Q) plots were performed with –log10(P) of each observed SNP and the expected P value. These allowed us to select best fit algorithm and only corresponding SNPs were retained for further analysis. The significance of the association between SNPs and traits was assessed based on an arbitrary threshold of –log10(P) ≥ 3.Data for protein, oil, and glucosinolates content were normalized by arcsine transformation. Genome-wide association analysis was performed on normalized values of 92 genotypes and 66,835 SNPs. Phenotypic data were pooled across 2 years for N0, N100, and NP  levels. Kinship matrix data was generated using the MVP-package of the software R. Q, PCs, and DAPCs analysis were used as covariates in different GWAS analysis algorithms to reduce false positives by reducing the effects of population stratification. Marker trait associations (MTA’s) were estimated by MVP A. thaliana database. Annotation allowed us to identify candidates in the vicinity of peak SNPs. SNPs, common across N treatments, were also depicted. LD was estimated between annotated SNPs, calculating the square value of correlation coefficient (r2) between all pairs of markers by software TASSEL v5.2  analysis was carried out by using the software Haploview v4.2 . It uses6. The diversity panel was first stratified by using software PLINK v1.9  model, optimized to detect associations when some studies have an effect and others do not have any effect  values were generally high, but these were lower during Y1 as compared to Y2 at both N levels. PTN ranged from 23 to 35% over environments. TM117 (35%) and MCP-12-227 (34%) revealed maximum seed PTN for N0Y1, while MCP-12-227, TM117, DT 70, and PBR-357 had higher PTN (≥34%) for N0Y2. DJ-1-2 DT2 and BAUSAM-2 had high PTN (≥34%) at N100Y1. In comparison, DJ-1-2 DT2 and TM117 had maximum PTN (≥33%) at N100Y2. H2 for PTN ranged from 35 to 86% over years and N-levels. H2 was lower during Y1 as contrasted to Y2 at both N-levels. Average GSL values varied and these were lower by∼11 and 14 μmol/g during Y2 compared to Y1. The lowest GSL values were noted for the genotypes, JM-06006 (38 μmol/g) and JM-06026 (27.7 μmol/g) for N0Y2 and N100Y2 respectively. H2 values were lower at N100 compared to N0. Similar to OIL and PTN, H2 values for GSLs were lower for Y1 as compared to Y2. Oil and PTN were inversely correlated. GSL and PTN were positively associated at N0 . This gene was also envisaged close (0.38 kb) to A09_14737522. Another gene, LACS5 (long-chain acyl-coA synthetase 5), encoding a long-chain-fatty-acid-CoA ligase, was identified near SNPs A09_24082148, A09_24082198, and A09_24083336. We annotated FAD6 at a distance of 1.64 kb from the nearest of the associated SNPs: B05_5272794, B05_5272795, and B05_5281484. At1g06090, a gene encoding delta-9 desaturase-like 1 protein (SNP in the intron), and ADS1 (2.31 kb) were called on chromosome B06 (B06_19315729). GWAS also allowed for recognition of four SNPs  for association with DIR1, a gene encoding bifunctional inhibitor/lipid-transfer protein/seed storage 2S albumin superfamily protein, annotated at a distance of 3.83 kb from peak SNP.We recognized fifteen MTA’s involving chromosomes A04, A06, A09, B05, B06, and B08 . The phePASTICCINO-1 (pas1) was anticipated at 1.46 kb from SNPs A04_12420732 and A04_12425239. This gene encodes peptidylprolyl isomerase. We also envisage AtP4H3 with SNPs (A06_9257535 and A06_9257568) within the exon. This gene codes for procollagen proline 4-dioxygenase. The associated SNPs were recognized in the exonic region of the gene. Three significant SNPs – A06_15534622, A06_15534718, and A06_15534750 – were present at a distance of 1.08 kb from ASN1, a gene encoding glutamine dependent-asparagine synthetase. GTR2 was envisioned at a distance of 6.84 kb from the closest of the associated SNPs: A06_17974981, A06_17975007, A06_17975009, and A06_17975010. At2g41640, encoding glycosyltransferase family 61 protein, was predicted at a distance of 9.24 kb from SNPs A06_27490668, A06_27490671, and A06_27490693. We also detected PSP1 (PHOSPHOSERINE PHOSPHATASE 1) on the chromosome A09 at a distance of 3.71 kb from SNP (A09_30208138). This gene was also envisioned next to the peak SNP on the chromosome B03.We found 23 MTA’s on chromosomes A04, A06, A09, and B03 . The defHB16 and SK1 at the respective distances of 2.29 and 3.35 kb from the peak SNPs. Both these encode shikimate kinase, which catalyzes a step of shikimate pathway for the synthesis of aromatic amino acids . We also established a gene AT2G35450 on chromosome A05 at a distance of 1.2 kb from SNP A05_5070339. Chorismate mutase1 (CM1) was predicted close to two SNPs on chromosome A06 at a distance of 1.7 kb and three SNPs for chromosome B04 at a distance of 6.51 kb from the respective peak SNPs. Two significant SNPs, B03_19283625 and B03_19283626, were associated with JMT, encoding jasmonate O-methyltransferase. Three SNPs  were detected within the exon of the predicted gene, LINC4. This gene encodes branched chain-amino acid-transaminase. CYP81G1 and MYB44 were envisioned at distances of 12.97 and 15.71 kb from the respective significant SNPs present on chromosome B06.We identified 22 MTA’s involving chromosomes A04, A05, A06, B03, B04, and B06 . The pher2 values. The remaining 16 SNPs produced three haplotype blocks on the chromosomes A04, B04, and B06 for GSLs.Haplotype analysis and pair-wise LD estimation was performed in the panel using 60 SNPs annotated for OIL, PTN, and GSL . ThirteeAt1g06090 and ADS1 were predicted repeatedly at both N levels (meta p = 1.67 × 10–7) with B06_19315729. DIR1, predicted on chromosome B08, was significant at both N0 and NP in meta-analysis while this was significant at NP in GWAS. For PTN  were significant at N0 and NP following meta-analysis. These were significant at N100 alone for GWAS. SNP linked to At2g41640, a gene encoding glycosyltransferase family 61 protein, was significant at N0 and NP in meta-analysis (meta p = 8.72 × 10–6). It was significant only at N0 level following GWAS. PSP1, with A09_30208138 as associated SNP, was significant at N0 and NP during meta-analysis (meta p = 6.28 × 10–6). For glucosinolates, HB16 and SK1 were present close to eight SNPs associated with chromosome A04. These were significant at NP during GWAS. We recorded the highest but non-significant m values  for NP . Almost similar trends were recorded for LINC4, CYP81G1, and MYB44 on the chromosome B06.For meta-analysis, we looked into 21 candidate genes that were significantly associated with OIL, PTN, and GSL. Meta-analysis allowed reconfirmation of most GWAS-SNPs for OIL and PTN, but with higher mapping resolution and an improved number of significant variants (over N levels) . At1g060 For PTN , PASTICC) for NP . A similIn silico analysis of the genome sequences of B. napus and B. juncea allowed identification of multiple copies of the genes that predicted seed quality traits on the A genome common to both B. juncea and B. napus  encodes delta-12 desaturase that aids in fatty acid desaturation  was envisaged on chromosome A06. It is required in nitrogen storage and transport . It is an enzyme of shikimate pathway that directs carbon from the central metabolism pool to a broad range of secondary metabolites  was envisioned on chromosome B06. MYB44 regulates the expression of most GSL biosynthesis genes in partnership with EIN2  with a coding sequence comparable to MYB44. Three major QTLs are known for GSL content in B. napus (MYB28 on chromosomes A09, C02, and C09 (Glucosinolates are the most extensively studied defense-related secondary metabolites, produced exclusively in the family sicaceae . Very la(B. rapa  and B. nB. napus . ExpressB. napus . We recoabolites . CM1, prynthesis . JMT wasbidopsis . LINC4 wlutamate . We ideninolates . MYB44 (ith EIN2 . In siliB. napus . These c and C09 .in silico analysis of the reference genome sequence revealed multiple copies of predicted genes on different A- and B- genome chromosomes. We were also able to establish LD patterns and haplotype structures for the candidate genes. The average block sizes were larger on A-genome chromosomes as compared to the B- genome chromosomes. Genetic associations differed over N levels and meta-analysis of GWAS datasets not only improved the power to detect associations but also helped to identify common SNPs. N0 proved better to unravel subtle variations for OIL. In contrast, evaluation at N100 appeared suitable for investigating PTN.To summarize, we identified 21 orthologs of the functional candidate genes related to the biosynthesis of OIL, PTN, and GSL. As was expected for a polyploid crop, PRJNA639209.The datasets generated for this study can be found in the NCBI under bioproject SB developed genetic resources and designed and supervised the research, edited the manuscript. VS helped in the conduct of field trials. SS performed biochemical analysis. JA conducted statistical and bioinformatics analysis. BB helped with statistical analysis. MS and AS annotated the results. MS, AS, HK, JA, and NK interpreted the results and wrote the manuscript. All authors have read and approved the version of 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.


Many of these markers have been investigated for various forensic purposes, such as individual identification, deconvolution of DNA mixtures, or forensic ancestry inference. SNP-STR is a compound biomarker composed of a single nucleotide polymorphism (SNP) and a closely linked short tandem repeat polymorphism (STR), and possess the advantages of both SNPs and STRs. In addition, in conjunction with a polymerase chain reaction (PCR) technique based on the amplification refractory mutation system (ARMS), SNP-STRs can be used for forensic unbalanced DNA mixture analysis based on capillary electrophoresis (CE), which is the most commonly used platform in worldwide forensic laboratories. Our previous research reported 11 SNP-STRs, but few of them are derived from the commonly used STR loci, for which existing STR databases can be used as a reference. For maximum compatibility with existing DNA databases, in this study, we screened 18 SNP-STR loci, of which 14 were derived from the expanded CODIS core loci set. Stable and sensitive SNP-STR multiplex PCR panels based on the CE platform were established. Assays on simulated two-person DNA mixtures showed that all allele-specific primers could detect minor DNA components in 1:500 mixtures. Population data based on 113 unrelated Chengdu Han individuals were investigated. A Bayesian framework was developed for the likelihood ratio (LR) evaluation of SNP-STR profiling results obtained from two-person mixtures. Furthermore, we report on the first use of SNP-STRs in casework to show the advantages and limitations for use in practice. Compared to 2.86 × 10 Microhaplotypes has been revealed the abilities in different forensic application purposes, including individual identification, mixture recognition , and ancMassively parallel sequencing (MPS) technology can directly detect each strand’s allele combinations separately. However, the higher costs and lack of consistent nomenclature, reporting standards, and existing national DNA database infrastructure to support statistical calculations, impose practical challenges for the introduction of MPS for routine forensic procedures . CapillaOur previous research reported 11 SNP-STR markers based on a CE platform . FurtherIn order to achieve maximum compatibility with existing DNA databases, and further explore the capacity of SNP-STR markers to help analyze unbalanced DNA mixtures, we aimed to develop further SNP-STR loci based on commonly used STR loci. Eighteen novel SNP-STRs based on the expanded CODIS core loci set and other commonly used STR kits were developed. Stable and sensitive SNP-STR multiplex PCR panels based on the CE platform were established. The capabilities of the SNP allele-specific ARMS primers of these loci were evaluated using simulated binary unbalanced mixtures. Forensic parameters were estimated based on a survey of 113 individuals from the southwest Chinese Han population. Furthermore, as the likelihood ratio (LR) has become the most commonly used method by forensic communities to determine the weight of evidence , 2012, aIn our previous study, we developed SNP-STRs with ARMS technology to analyze unbalanced DNA mixtures , 2015. F2N2 + M2N2 ; (ii) “informative genotype 2,” the SNP genotype of the major DNA component is homozygous and the minor DNA component is heterozygous, with a probability of 2M3N + 2MN3. (iii) “informative genotype 3” represents the configuration when both the contributors are homozygous-MM, no alleles will be observed if we analyze the trace sample using the N-primer. The probability is M4 + N4; and (iv) when the SNP genotype of the major DNA component is heterozygous-MN, no specific SNP allele for the minor DNA component exists, which is called the “uninformative genotype.” The probability is 2M3N + 4M2N2 + 2MN3.Single nucleotide polymorphism-STR allele-specific primers allow for the selected amplification of the minor contributor’s genotype, as long as it has alleles that are absent in the major contributor’s genotype. For a two-person unbalanced mixture, the possible genotype configuration of the two contributors is represented by four scenarios see : (i) “in2N2 + M2N2 + 2M3N + 2MN3, which can be defined as the I value (probability of informative genotypes). This value can be used to assess the capability of the SNP-STR marker to target the minor DNA components in binary DNA mixtures that contain high background levels of major DNA components .Official permission to further analyze crime-related DNA samples and to present the corresponding data was obtained by the HI-TECH Industrial Sub-branch of Chengdu Municipal Public Security Bureau, Sichuan, China.Peripheral blood samples were collected from 113 unrelated Chinese Han individuals in Chengdu, China. Casework DNA samples from a sexual assault were used to report the first application of SNP-STR markers. A vaginal swab of the victim (No. 831-9) and reference blood samples of the victim (No. 831-1) and suspect (No. 831-2) were collected by the HI-TECH Industrial Sub-branch of Chengdu Municipal Public Security Bureau, Sichuan, China.Genomic DNA was isolated using a whole blood extraction kit  according to the manufacturer’s instructions and quantified spectrophotometrically using a NanoDrop 1000 Spectrophotometer .I > 0.04; (ii) SNP located within 400 bp of STR region; and (iii) targeted amplicons shorter than 550 bp.Single nucleotide polymorphism-STR loci were selected based on commonly used worldwide STRs, including those from the expanded CODIS core loci set, as well as other STRs available in commercial kits, such as the GlobalFiler PCR Amplification Kit (Thermo Fisher Scientific) and AGCU Expressmarker 22 kit  . The scrThe STRs mentioned above were searched for using the University of California Santa Cruz  Genome Browser. SNPs in the flanking regions of STRs were filtered by dense “Base Position” and pack “STS Markers” in “Mapping and Sequencing” and full “Common SNPs (147)” with MAFs between 0.02 and 0.5 in “Variation.” All other options were hidden. The selected SNPs were confirmed to meet the screening criteria using the dbSNP database . The SNP with the largest MAF or located closest to the STR was used in the following steps when there was more than one SNP around the STR locus.1. Allele-specific primers were designed by introducing a deliberate mismatch at the antepenultimate or penultimate base at the 3’ end of the forward primers. Two allele-specific forward primers and one standard reverse primer were obtained for each SNP-STR locus.General forward primers with the 3′ end starting with the SNP and a reverse primer downstream of the STR for one SNP-STR locus were designed using the Primer3 web version 4.0.0The SNP-STR multiplex was established to determine genotypes based on amplicon length and fluorophore label for each locus. The concentration of each primer was adjusted according to the capillary electrophoretic profiles of the amplification products generated by the primer mix reactions.Singleplex PCR amplifications were performed in 10 μL reactions containing 5 μL Multiplex PCR Mix , 0.5 μL primer mix , 3.5 μL nuclease-free water, and 1 μL genomic DNA (1 ng/μL). Thermocycling was performed in a Mastercycler Nexus Gradient  under the following conditions: initial denaturation at 95°C for 15 min; 31 cycles of 30 s at 94°C, 90 s at 58°C, and 60 s at 72°C; followed by a final extension step of 30 min at 60°C. The reaction and cycling conditions for the multiplex PCR amplifications using the primer mix were the same as those used for singleplex amplifications.The PCR products were separated and detected by capillary electrophoresis using a 3130XL Genetic Analyser  by adding 1 μL of product to 9 μL of a 40:1 mixture of HiDi GeneScan 600 LIZ Size Standard (Thermo Fisher Scientific). Electrophoretic conditions were 9 s at 3 kV for injection and 1,500 s at 15 kV for the run. The initial data were analyzed using GeneMapper ID-X v1.2 software (Thermo Fisher Scientific), with the peak height threshold for each fluorescent dye set at 50 RFU.2O.A total of 113 DNA samples were profiled using the primer mix for the SNP-STR multiplex. Amplification of the positive control was performed using 1 ng ‘9948-control’ DNA . The negative control was a no-template control (NTC) consisting of ddHForward primers (data not show) for Sanger sequencing were based on SNP-STR allele-specific primers for amplification using the reverse primers and obtain products containing SNP and STR loci between reactive primers. Three samples with SNP genotypes that were homozygous for both alleles and one heterozygote for each SNP-STR locus were amplified using sequencing primers. The resultant PCR products were sequenced from both directions using the Sanger method  after being recycled from polyacrylamide gel electrophoresis-separated bands. STR genotypes in the SNP-STR multiplex for some of the tested samples were confirmed using commercial STR kits .DNA samples with heterozygous SNP allele calls for each SNP-STR were selected to assess the minimum amount of template required for each allele-specific primer to obtain a positive profile. DNA samples diluted to 0.1, 0.05, 0.025, 0.01, and 0.005 ng/μL were amplified using the singleplex PCR conditions described in section Genotyping SNP-STRs.Peaks that were one repeat smaller or larger than the true allele (±0.5 bases) were considered stutter peaks. The stutter percentage of each primer was calculated by dividing the stutter peak height (n ± 1 repeat unit) by the associated allele peak height with 113 unrelated Chinese Han individuals. Peak height threshold of stutters was set to 20 RFU.To assess the capability of each allele-specific primer to target the minor DNA components in extremely unbalanced binary mixtures, mixtures with a series of different ratios  were made (the amount of minor DNA template was fixed at 0.05 ng). For all the simulated mixtures, the major contributor was homozygous for a given SNP, while the minor contributor was heterozygous. Some of the DNA mixtures were genotyped using commercial STR kits  for comparison.It is important to note that for SNP-STR analysis of the mixture, the only information needed from the main contributor’s DNA is its SNP-heterozygosity or homozygosity. Suppose a mixed trace sample was collected from the victim, DNA of the victim and the suspect was both available. For SNP-STR analyses under this scenario, the steps were as follows: (i) amplify the reference DNA of the victim and suspect using the SNP-STR multiplex; (ii) select informative markers for which the major DNA contributor is SNP homozygous. The primers specific to the opposite SNP alleles are then used to target its amplification from trace samples; and (iii) assess the LR value of the DNA results.For the traditional method, autosomal STR profiles of the trace sample and reference samples were obtained using the AGCU Expressmarker 22 kit  according to the manufacturer’s instructions. The SNP-STR genotype of the reference DNA was tested through SNP-STR multiplex PCR amplifications. Detection of the selected informative markers from the trace sample was based on Singleplex PCR amplifications. The reaction and cycling conditions, as well as the electrophoretic condition and data analysis method were the same as described in section PCR Conditions and Genotyping. A threshold of 50 RFU was used for peak calling.I) were estimated, using the formula I = 2M2N2 + 2M3N + 2MN3  would be informative based on the cumulative binomial distribution of 40 trials (markers). Forensic parameters of SNP-STR and STR loci were assessed by calculating the matching probability (MP) and power of discrimination (DP) using Powerstats v1.2 .Allele frequencies of each SNP-STR locus and corresponding SNPs were calculated for all 113 individuals. Exact tests for Hardy-Weinberg Equilibrium (HWE) as well as for linkage disequilibrium between all pairs of markers in the multiplex were performed using ARLEQUIN statistical software v3.5 . To evalN + 2MN3 , in whic2.Likelihood ratio is an established method that evaluates two contrasting hypotheses (prosecution vs. defense) . Cereda Likelihood ratio values for autosomal STR typing results were calculated using EuroForMix . PopulatI value of these loci, calculated using the MAFs in dbSNP, were in the range of 0.123–0.375. All SNPs of the 19 SNP-STRs were verified to be biallelic, according to the dbSNP database.Nineteen SNP-STRs were identified that met the screening criteria . The I vTwo SNP allele-specific forward primers with mismatches introduced using the ARMS technique and one reverse primer were obtained for each SNP-STR locus. All target amplicons were shorter than 522 bp. High amplification efficiency was achieved with primers designed for 18 of the 19 SNP-STRs meeting the screening criteria. The rs11063971-VWA locus was excluded due to poor amplification. Details are listed in Three multiplex panels for 18 SNP-STRs were established: Panel-A, Panel-B, and Panel-C . The conTo examine the specificity of SNP-STR primers, samples were selected from the 113 individuals and analyzed using Sanger sequencing. The SNP and STR genotypes of these samples were concordant with the Sanger results. Thirty-six allele-specific primers for the 18 SNP-STR loci were assessed. The primers for rs11642858C-D16S539 and rs17077990G-D3S1358 showed a positive result for amplification of DNA at the small amount of 0.005 ng. Primers for rs2070018C-FGA, rs25768A-D5S818, rs4847015T-D1S1656, rs7962284C-D12S391, rs7786079C-D7S820, rs2246512G-D10S1248, rs6736691A-D2S1338, rs58390469C-D2S441, and rs2325399G-D6S1043 required 0.01 ng DNA. Primers for rs9531308C-D13S317, rs7962284T-D12S391, rs7275705C-Penta D, rs7786079A-D7S820, rs57346531A-D8S1179, rs2246512A-D10S1248, rs6736691C-D2S1338, and rs9362476T-SE33 required at least 0.05 ng of template to generate a positive profile. The sensitivity of the remaining 17 primers reached 0.025 ng . SupplemThe simulated mixture tests required that the major DNA component was SNP homozygous for a given SNP-STR locus. Four primers  were not tested because there were no homozygous genotypes for these SNPs according to the survey of 113 individuals. Among the 32 allele-specific primers tested, 26 successfully amplified the minor DNA without a masking effect from the major DNA in the mixtures at ratios from 1:50 to 1:500, three  did so at ratios from 1:50 to 1:200, two (rs9362476C-SE33 and rs25768A-D5S818) did so at ratios from 1:50 to 1:100, and one (rs2070018T-FGA) did so at a ratio of 1:50 . SupplemOwing to confusion with the minor contributor, a high percentage of stutter may make the analysis of the mixture more difficult. In this study, the stutter percentage was calculated by dividing the stutter peak height (n ± 1 repeat unit) by the associated allele peak height with 113 samples. The results of the average stutter percentage and SD for each locus are listed in p) of the HWE test are listed in p-values for rs17651965-CSF1PO, rs6736691-D2S1338, and rs9362476-SE33 were all less than 0.05. There was no significant linkage disequilibrium among the SNP-STR combinations located on the same chromosome after Bonferroni correction (p < 0.0003).Haplotype frequencies are listed in I) for each informative genotype combination, which were calculated using the observed SNP MAF to estimate the probability of detecting minor DNA in a mixture, are given in I values of 0.0724, 0.202, and 0.379 for three kind of informative genotype combinations, and a combined I value of 0.747, 0.984, and 0.999989, respectively, which means there is an approximate 74.7% probability of obtaining at least one informative genotype 1 marker of the minor contributor’s DNA in a two-person mixture using this set of SNP-STR loci, and 98.4 and 99.9989% for informative genotype 2 and informative genotype 3, respectively.The probabilities of informative genotypes . The autosomal STR profile of the trace sample had a maximum of four alleles at only one locus (vWA). Three loci  were shown to have three alleles, respectively. It can be inferred as a two-person mixture based on the maximum allele count . The STR7.In this case study, the SNP genotypes of the victim and suspect constituted one locus of informative genotype 1, eight loci of informative genotype 2, six loci of informative genotype 3, and three loci of the uninformative genotype. For the trace sample, all informative alleles were successfully detected by using allele-specific primers targeting minor contributor’s alleles. The loci that belonged to informative genotype 3 showed no peaks, as expected. The SNP-STR alleles of the trace sample and suspect corresponded to all of these markers. The average and combined LR values of SNP-STR informative alleles for this casework were calculated using the Bayesian model mentioned previously, and the results are shown in Profiling results of the trace sample using allele-specific primers targeting minor contributor’s alleles are shown in 7.The LRs of SNP-STR informative alleles for this casework were calculated using the Bayesian model mentioned above, and the results are shown in Our laboratory has previously reported 11 SNP-STRs, but few of the linked STRs are derived from commonly used databases . The disI value, which is based on the minor allele frequency of the SNP . DIP-STRs also enable the specific detection of minor DNA even when the amount of major DNA is 1,000-fold higher . However–24 and >0.999999999999999, respectively, while the CMP and CPD of the 18 STRs were 4.56 × 10–22 and >0.999999999999999, respectively. In the HWE test, p values for three of the SNP-STR loci were less than 0.05, which may have been caused by the selection of samples from a relatively small group.The number of alleles at each locus was increased by the combination of STRs and SNPs and unrelated to the MAF of the SNP. According to a population survey of 113 individuals, there were 10 rs7786079-D7S820 haplotypes, which is an increase of four compared to the six alleles of D7S820. The MAF of rs7786079 was 0.079. While the MAF of rs58390469 was 0.4599, there were 11 rs58390469-D2S441 haplotypes, which is an increase of only three compared to the eight alleles of D2S441. The incorporation of SNPs linked to STRs increased the forensic performance. The cumulative match probability (CMP) and the cumulative power of discrimination (CPD) of the 18 SNP-STRs were 2.87 × 10I value was lower than that of the DIP-STR loci selected from across the genome (0.33)  . When thY-STR is commonly used in the analysis of forensic mixtures, especially in male-female mixtures from sexual assault cases. Given paternal inheritance, Y-STR haplotypes can only be used to exclude unrelated males and cannot be used for personal identification . Some stThe successful detection of SNP-STR alleles in unbalanced two-person mixtures depends on how the SNP genotypes of the mixture contributors compare to each other. Distinct allelic configurations are observed for each marker in practice. The evaluation processes may become tedious if this needs to be done manually. The use of the Bayesian model could make the process easier. It can also help to make evaluative procedures less prone to possible errors. OOBN, developed for DIP-STR analysis by Cereda et al., has the advantage of being able to deal with cases where the genetic information of further individuals (other than the suspect) needs to be considered  . However6) to extremely strong support of the defense hypothesis (LR ≤ 1 × 10–6), given the evidence , which belonged to informative genotype 2, had the highest LR (30.525). This is understandable because the allele frequency of C22.2 of rs2070018-FGA (0.018) is much smaller than those of A11 and A14 of rs58390469-D2S441 . An LR > 1 could be obtained even when no alleles were detected for the trace sample when the two contributors were both homozygous SNPs of the same kind. The LR value can range from extremely strong support of the prosecution hypothesis  for the publication of any potentially identifiable images or data included in this article.HJ, LW, WL, and LZ designed this study. HJ and LW wrote the manuscript. ML and YT conducted the sample collection. RZ and SQ conducted the experiment. JW and LaZ analyzed the results. All authors reviewed 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.


Thecaphora frezii is a severe fungal disease currently endemic to Argentina and Brazil. The identification of smut resistant germplasm is crucial in view of the potential risk of a global spread. In a recent study, we reported new sources of smut resistance and demonstrated its introgression into elite peanut cultivars. Here, we revisited one of these sources (line I0322) to verify its presence in the U.S. peanut germplasm collection and to identify single nucleotide polymorphisms (SNPs) potentially associated with resistance. Five accessions of Arachis hypogaea subsp. fastigiata from the U.S. peanut collection, along with the resistant source and derived inbred lines were genotyped with a 48K SNP peanut array. A recently developed SNP genotyping platform called RNase H2 enzyme-based amplification (rhAmp) was further applied to validate selected SNPs in a larger number of individuals per accession. More than 14,000 SNPs and nine rhAmp assays confirmed the presence of a germplasm in the U.S. peanut collection that is 98.6% identical  to the resistant line I0322. We report this germplasm with accompanying genetic information, genotyping data, and diagnostic SNP markers.Peanut smut caused by  Thecaphora frezii Carranza & Lindquist is a severe soil borne disease endemic to Argentina and Brazil , landraces, advanced breeding lines, and elite peanut varieties, led to the identification of resistant germplasm (Arachis hypogaea L. (2n = 4x = 40), then used simple-sequence repeats and Insertion/Deletion markers to confirm its introgression into elite peanut cultivars. These landraces were originally collected from South America as part of international efforts that involved several expeditions to the peanut center of origin  and 655 accessions from 66 wild Arachis species  and tetraploid (A. hypogaea subspecies hypogaea and fastigiata) species (The recent development of the 48K SNP peanut array (Axiom_Arachis2) has provided a set of single-nucleotide polymorphism markers that can be used to examine genome-wide patterns of genetic variation in diploid and tetraploid ermplasm , 2018. T species .A. hypogaea subsp. fastigiata, the smut resistant line I0322, and the 16 RILs (The objectives of the present study were to: (1) verify whether the source of a smut resistant line used by  16 RILs . A PCR a 16 RILs  was furtA. hypogaea subsp. fastigiata var. fastigiata obtained from the U.S. Department of Agriculture, Agricultural Research Service, National Plant Germplasm System (NPGS), Plant Genetic Resources Conservation Unit peanut (Arachis) collection in Griffin, GA, PI 497287, PI 497283, PI 497284, PI 497285, and PI 497286 ; and (2) a smut resistant line I0322, along with 16 RILs (resistant F2:5-7 lines) derived from the cross I0322 × Guasu (JS31411), provided by the Criadero El Carmen, General Cabrera, Córdoba, Argentina (A. hypogaea subsp. fastigiata collected in Bolivia as part of the Arachis germplasm collections conducted in South America between 1976 and 1983 (This study utilized two sources of plant DNA: (1) five accessions of rgentina . I0322 wand 1983 . This geand 1983 , all of polyploid . All samples with a dish quality control (dQC) value ≥ 0.82 and QC call rate ≥ 0.97 were considered to have passed the first quality control. A more stringent metric threshold was further applied to assign SNPs to the PolyHighResolution (PHR) category using a Fisher’s Linear Discriminant (FLD) ≥ 5.6 and a FLD for the homozygous genotype cluster (HomFLD) ≥ 10 (the default for polyploids is ≥ 3.6). Genotype data were extracted only from the PHR conversion type. Additional filtering steps were performed to remove SNPs called as heterozygous, having > 10% missing genotype calls, or showing divergence between technical replicates. The genome location of SNPs was based on the Axiom Arachis2 SNP array, as described in the Table S1 of A. hypogaea cv. Tifrunner reference genome (https://peanutbase.org).Two plants per PI accession were grown in the greenhouse at the USDA-ARS National Peanut Research Laboratory (NPRL) in Dawson, GA, and young leaves from individual plants were used for DNA extraction. DNA was extracted from freeze-dried leaves using the QIAGEN DNeasy Plant Mini Kit , quantified by spectrophotometry , and adjusted to a concentration of 30 ng/μL. DNA from I0322 and 16 RILs was provided by Criadero El Carmen . All samx and one from individual y are identical in state, we used the measure of allele sharing distances (ASDs)  . The dis package  in R ver package . One of  package . The sigbionj function in ape and the glPca in adegenet packages as implemented in Genetic relationships among accessions were further assessed in a broader context using an existing array-based SNP dataset consisting of 20 genotypes (Table S2 in A. hypogaea reference genome sequence at the probe target sites (https://peanutbase.org). For each assay, rhAmp utilizes two allele specific primers and a locus specific primer . Fifteen ng of sample DNA were used in 10 µL reactions with the following parameters: a pre reading stage 30 s 60 °C; enzyme activation 10 min 95 °C; then 40 cycles of: 10 s 95 °C, 30 s 60 °C, 20 s 68 °C; and a final post reading stage of 30 s at 60 °C. Thermal cycling was performed in a QuantStudio 7 Flex Real-Time PCR System . Each accession was represented by five biological replicates, which included the original DNA samples used on the array as a reference. The total number of rhAmp assays was  + (9 SNPs × 1 genotype (I0322) × 2 technical replicates) + 80 no-template controls (NTC) = 548. Analyses were performed with the QuantStudio Real-Time PCR Software v1.3  using the general workflow for genotyping experiments.P < 0.01 were removed. The coefficient of determination (adjusted R2) was used to measure the proportion of phenotypic variation explained by the SNP marker (R2 (P < 0.05), the analysis of variance and Tukey test were applied to determine allele effects. Disease incidence (IN) and disease severity index (DI) mean values were extracted from Table 2 and Table S1 of lm, aov, and LTukey functions (r2) and haplotype structure at significant SNP loci was estimated and visualized with Haploview version 4.2 . The analysis used only SNPs that were polymorphic between the parental lines (I0322 and Guasu) and segregated in a 1:1 ratio. Marker deviation from the expected ratio was determined by a chi-square test of goodness of fit, and those SNPs with a P marker . For thounctions . The extsion 4.2 .Out of the 47,837 SNPs available from the 48K Axiom Arachis2 SNP array , 14,837 A. hypogaea cv. Tifrunner (https://peanutbase.org). In this study, four of the PI accessions (NPRL-S118–NPRL-S121) and the resistant line exhibited heterozygous calls at all 45 loci, while accession NPRL-S122 was homozygous. The high sequence similarity between sub-genomes suggest that most heterozygous calls in this region are homoeologous SNPs. Although, some heterozygous calls may also be indicative of paralogous SNPs due to duplicated gene copies. This region is enriched for disease resistance and defense response genes of the Toll/Interleukin-1 Receptor (TIR)-Nucleotide Binding Site (NBS)-Leucine-Rich Repeat (LRR), Chitinase, F-box, and WD repeat-containing proteins families (The PI accessions and the resistant line (I0322) were highly homozygous with an overall number of 137 heterozygous calls. Most of these heterozygous calls were dispersed in the genome, which are most likely genotyping errors. However, a group of 45 SNPs occurred in continuous blocks, mapped to the distal end of homoeologous group 4 (Arahy.04/Arahy.14) of ifrunner . This reifrunner . BLAST sfamilies .NPRL-118 and I0322) to 0.271 (between NPRL-S120 and NPRL-S122). In terms of the proportion of shared alleles, these values translated into 0.986 and 0.729, respectively . In contrast, ASD values for all pairwise comparisons that included accession NPRL-S122 were statistically highly significant  . Thus, it is likely that NPRL-S122 is one of the landraces of A. hypogaea subsp. fastigiata var. peruviana documented for Bolivia (fastigiata type.Genetic distances (based on ASDs) ranged from 0.014 (between ectively . Among tectively . The neiectively . The resectively . However t-test) . Morphol t-test) , which i Bolivia , which wTo help understand the genetic relationships between the PI accessions and the resistant line I0322, the distribution of the number of SNP differences between each accession and I0322 was plotted along the 20 chromosomes . The freA. hypogaea L. subsp. fastigiata)  from the five genotypes in the cluster.To assess genotype relationships in a broader context, the five PI accessions and the resistant line were compared with 20 peanut genotypes from tigiata) . NeverthSingle nucleotide polymorphisms for the rhAmp assays were selected from a set of 4,247 SNPs showing polymorphism in at least one pairwise comparison. A final set of nine rhAmp assays was chosen to genotype 52 DNA samples . The assays targeted unlinked SNP loci from nine chromosome regions of the peanut genome . The cal1 hybrids.The SNP profile of the resistant line I0322 was identical to the five biological replicates of NPRL-S118 at the nine loci interrogated by the rhAmp assays. In a broader context, three of these loci  are informative (genotype-specific) to discriminate accession NPRL-S118 from all runner type cultivars assessed in our comparative analysis  and coulThe assays further uncovered genetic variation within accessions. Two biological replicates within accession NPRL-S120 (P < 0.01) accounted for less than 4% (138) of the 4,399 segregating SNPs. A final set of 4,261 markers were used in the association analysis, all of which were polymorphic between the two parental lines (P < 0.05) at six independent regions of the A  and B  sub-genomes  for IN and 0.62 (P < 0.001) for DI. Alleles associated with resistance derived from the resistant parent I0322 (r2 > 0.80) with AX-177643001, AX-147259639, AX-177644036, and AX-177642482 (A. hypogaea cv. Tifrunner reference genome (https://peanutbase.org).Markers with significant segregation distortion  to the resistant line I0322 used by This study confirmed the presence of a germplasm in the U.S. peanut collection that is 98.6% identical  or failing to detect minor effects  Chitwood) resistance, originated from the wild A-genome species Arachis cardenasii Krapov. & W.C. Greg and introgressed into cultivated peanut by backcrossing , however, we speculated that accession NPRL-S122 belongs to the A. hypogaea L. subsp. hypogaea var. peruviana described for Bolivia  germplasm line I0322;  was pres10.7717/peerj.10581/supp-1Supplemental Information 1Scale bar at the bottom of the dendrogram indicates the proportion of loci for which individuals differ. Scale bar below each seed and pod image corresponds to 1 cm.Click here for additional data file.10.7717/peerj.10581/supp-2Supplemental Information 2The number of SNP differences between each of the PI accessions and the resistant line I0322 were plotted against the physical position of the chromosome.Click here for additional data file.10.7717/peerj.10581/supp-3Supplemental Information 3The plot is based on 4,261 SNPs, which were polymorphic between the parental lines.Click here for additional data file.10.7717/peerj.10581/supp-4Supplemental Information 4Asterisks indicate the SNP allele from the resistant parent (I0322).Click here for additional data file.10.7717/peerj.10581/supp-5Supplemental Information 5Allele primers: numbers 1 and 2; locus specific primers: LS.Click here for additional data file.10.7717/peerj.10581/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.10581/supp-7Supplemental Information 7AA:0, BB:2, No call:-1. SNPs related to Figure 1 and Table S5 are indicated with asterisk.Click here for additional data file.10.7717/peerj.10581/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.10581/supp-9Supplemental Information 9A. hypogaea cv. Tifrunner reference genome and Axiom Arachis2 SNP array.SNP location is based on both, the Click here for additional data file.10.7717/peerj.10581/supp-10Supplemental Information 10The underlined names are the 20 genotypes extracted from Click here for additional data file.10.7717/peerj.10581/supp-11Supplemental Information 11A. hypogaea cv. Tifrunner reference genome.SNP physical location is based on the Click here for additional data file.


Glycine soja) selections and 429 cultivated (Glycine max) varieties (landraces and elites). This data was used to identify 7.8 million SNPs, to predict SNP effects relative to genic regions, and to identify the genetic structure, relationships, and linkage disequilibrium. We found evidence of distinct, mostly independent selection of lineages by particular geographic location. Among cultivated varieties, we identified numerous highly conserved regions, suggesting selection during domestication. Comparisons of these accessions against the whole U.S. germplasm genotyped with the SoySNP50K iSelect BeadChip revealed that over 95% of the re-sequenced accessions have a high similarity to their SoySNP50K counterparts. Probable errors in seed source or genotype tracking were also identified in approximately 5% of the accessions.We report characteristics of soybean genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild ( Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13568552 Glycine max (L.) Merr.] is one of the major grain legumes and oil seeds cultivated worldwide, particularly in Asia and the Americas. The cultivated soybean, G. max, was domesticated from its wild relative, G. soja, around the Eleventh Century B.C, in Eastern China1. Cultivated soybean spread to other locations through Asia shortly following domestication, and was then introduced into the United States in 17651. Soybean lost genetic diversity through domestication-related genetic bottlenecks, while the wild relative G. soja, growing in various environmental conditions, retained significant genetic diversity3.Soybean [4, followed by high-quality assemblies of several other soybean accessions, including elite lines and wild soybean accessions10. This genomic sequencing has been complemented by resequencing projects , designed to assess genetic variation across broader samplings of soybean germplasm13.The first reference genome for cultivated soybean was released in 201011). All of the reads were mapped onto the Williams 82 reference genome assembly and approximately 7.8 million single nucleotide polymorphisms (SNPs) were identified from the mapped reads. The G. max Williams 82 assembly was used as reference because that is most widely used and accepted. We compare these results from the chip-based genotyping of the same genotypes from the SoySNP50K iSelect BeadChip14 results, helping validate both results, while also identifying several probable errors in germplasm tracking or labeling. The genotypic data was also used to evaluate genetic relationships and population structure. We provide the variant data and associated analyses as major resources for other researchers, toward better understanding of soybean diversity for continued genetic improvement of soybeans.To map patterns of genome-wide variation, population structure, and to facilitate identification of the genetic basis of agronomic traits in soybean, 481 accessions from the USDA Soybean Germplasm Collection were re-sequenced. This included modern cultivars, traditional landraces, and wild species accessions, from throughout the range of both domesticated and wild soybean  and G. soja (81) lines for further studies. The core set maintained 98.7% and 96.1% of the diversity of the entire cultivated and wild soybean collections, respectively14. To maximize diversity without losing genetic information, we selected a total of 481 diverse soybean accessions from the core mentioned above (429 of G. max and 52 of G. soja) for sequencing and analysis . The germplasm of the 481 accessions comes from 37 countries, with the largest numbers coming from China (223), Japan (56), the U.S. (54), South Korea (44), the Russian Federation (26), and North Korea (14).The USDA Soybean Germplasm Collection contains 20,035 accessions, including 1,168 15. Soybean samples were tested for the heterogeneity using the SoySNP6K Illumina Infinium BeadChips 16 and the samples with below 10% heterogeneity were selected for whole-genome sequencing. A total of 5 µg of genomic DNA from each soybean line was used to construct the sequencing library, following the Illumina sequencing protocols. Paired-end sequencing libraries with an insert size of ~300 bp were sequenced using an Illumina HiSeq. 2000 sequencer at the Beijing Genomics Institute (BGI). The 50 most diverse lines from the core set were sequenced at a 40x genome equivalent and the remaining samples were sequenced at 15x coverage . This generated a total of 7.5 TB of raw next generation sequencing (NGS) reads data from all samples.Soybean seeds were obtained from the Soybean Stock Center (USDA-GRIN). For each accession 50 seeds were planted, in a greenhouse at the University of Missouri. After two weeks (V1 growth stage) a minimum of 24 plants per accession were selected based on morphological homogeneity  for leaf sample collection. Leaf samples were flash frozen and high quality high molecular weight DNA extractions were conducted using a standard protocol4 (Wm82.a2) (a widely used reference assembly), downloaded from Phytozome (www.phytozome.net)17, was used for mapping. We built the PGen18 multi-step SNP identification workflow using the Pegasus19 workflow management system (Pegasus-WMS), for quality checks, alignment of reads, variants calling, variants filtration, and VCF merging. The workflow takes paired-end and single-end Fastq reads as input, and performs data quality checks using FastQC20. Filtered, high-quality reads are then aligned against the reference genome using BWA21. Picard Tools22 is also used at this step to locate duplicate molecules and assign all reads into groups (using default parameters). The bwa command is ‘bwa mem -t 12 -M ref.fastq paired_read1.fastq paired_read2.fastq > aln.sam’.The reference genome for the soybean cultivar Williams 8223 version gatk-2.5-2-gf57256b. Filtering criteria are provided in the INFO fields in the VCF file. Important abbreviations in this section are: QD, quality by depth; FS, Fisher strand values; and MQ, mapping quality of variants. Detected variants were then filtered using the criteria “QD < 26.0 || FS > 60.0 || MQ < 40.0” for SNPs and “QD < 26.0 || FS > 200.0 || MQ < 40.0” for indels. Additional filtering can also be applied by modifying the configuration file of a PGen workflow. Outputs were generated as BAM and VCF standard formats that were stored in the CyVerse data store24 and accessed via the SoyKB database25 via the NGS re-sequencing data browser26, and for download at the SoyBase Data Store27. SNPs were assigned IDs using the script “assign_name.awk” available at https://github.com/soybase/SoySNP-Names. Missing data accounted for 1% and heterozygous SNPs accounted for 0.5% of the total dataset . SNPs were annotated using SnpEff 3.028 . In SoyKB the data is also loaded into the SNPViz 2.0 tool29 for interactive exploration of accession relationships using SNPs in selected genomic regions.After the sequence alignment, SNPs and indels were called using the Haplotype caller algorithm from the Genome Analysis Toolkit (GATK)30). To generate an alignment suitable for phylogenetic reconstruction, every 5th SNP was selected, giving an alignment length of 4,518 characters . This moderate matrix size permits maximum likelihood phylogenetic reconstruction and also takes advantage of the SNP distribution in the SoySNP50K set, as the SoySNP50K SNPs were chosen relative to LD, with greater density at chromosome ends and less density in the high-LD pericentromere, and avoiding closely-spaced SNPs. A tag was added to the genotype identifiers to indicate country of origin . A maximum likelihood phylogenetic tree was calculated using FastTree31, version 2.1.8, with default nucleotide parameters. Tree visualizations were generated using the Archaeopteryx tree viewer32.25,496 SNPs in common with the SoySNP50K iSelect BeadChip-derived data were used to develop the phylogenetic tree  between pairwise comparisons of all SNPs across a chromosome  was used to determine the best K that explained the structure in the collection based on model complexity. Structure was visualized , available for interactive use at https://soybase.org/gcvit. Soybean accession Essex (PI 548667) was selected to be the reference genotype due to its importance in soybean breeding programs. Bin size is set to 500,000 with the right side of the chromosomes set to display type “heatmap” showing SNP differences between the selected accessions and Essex. Heatmap base color set to white going from min-max.Chromosomal similarity heatmaps displayed in Fig. 40. Heterozygous SNPs were treated as missing data before creating a similarity matrix. Within the merged VCF file, missing data and heterozygous SNPs combined accounted for <1.5% . The R package SNPRelate41 was used to create a similarity matrix of the merged datasets and compare the accessions to one another. The script extractTop-Match.pl, available on GitHub42, was used to extract the top matches for a given PI in a similarity matrix .The two VCF files were merged on common SNP positions using BCFtools43. The sequencing data have been deposited in the NCBI Sequence Read Archive projects SRP06224544 and SRP10518345. We used 376 accessions raw data (331 G. max and 45 G. soja) from the SRP10518345 and selected 105 accessions (98 G. max and 7 G. soja) from the SRP06224544 for the analysis. Variant data and accession information are available at the SoyBase46 project page30 and the associated Data Store27. The variant data are also accessible for interactive exploration at SoyKB26. Also, all data including the detailed information of the accessions, phylogenetic tree, variant data are available at the National Agricultural Library Ag Data Commons (10.15482/USDA.ADC/1518301)43 and at figshare11.The authors declare that all data reported here are fully and freely available from the date of publication. All major data products are indicated in the Data Dictionary at the National Ag Library project data collection47, which contains genotype information for the entire USDA soybean germplasm collection48, based on common SNP positions. The R program SNPRelate41 was used to create a similarity matrix of all of the lines in the VCF file. In soybean, there is known variation between accessions with the same name49, however, it is expected that the top match for each of the 481 accessions would be the accession with the same PI name from the SoySNP50K dataset. The similarity scores between the re-sequenced accessions and its SoySNP50K counterpart ranged from 99–55%, with an average similarity score of 0.987. Of the 481 accessions, 441 had a 99% or greater similarity to their expected match, while 20 lines had a similarity between 90–99% to their expected match . Of the 461 accessions that had a similarity >90% to their counterpart, 407 accessions had the same PI identifier as the top match from the SoySNP50K. In 54 cases, the top hit from the SoySNP50K was not the expected PI accession number, but the difference in percent identity between the expected accession and top match was within 3%. These results indicate genetic redundancy for some groups of genotypes in the USDA soybean germplasm collection.All but 5 of the 481 accessions sequenced in this study have a counterpart in the U.S. germplasm collection (sharing the same PI accession number). The VCF file was merged with the SoySNP50K VCF file from the SoyBase Data StoreThe 5 accessions that did not have a counterpart in the SoySNP50K dataset had similarity <90% to all other lines, indicating that these lines are unique to this study. Fifteen accessions were labeled as mis-assigned, as these accessions had a >3% similarity difference between the expected top match and what was observed, indicating an error in the accession’s identity. These differences could be due to the fact that some accessions were collected at markets that included a mixture of seed. This explanation may apply for PIs PI 407262, PI 424079, PI 437160, PI 639586, PI 360957, PI 628913, PI 639559B and their respective top matches: PI 407264, PI 424048, PI 437153 A, PI 639610, PI 379563, PI 628917, PI 639558. For all of these cases, the PIs and their top matches all have the same country of origin. For example, PI 407262 has common name K42-A and is from South Korea, its top match is PI 407264, also from South Korea, and with a similar common name of K42-C. The top match of accession PI 548402 is to PI 438497. These accessions have the same common name, ‘Peking’, which could have led to a mis-labeling, as there are 7 lines in the USDA Germplasm Resources Information Network (GRIN) with the common name ‘Peking’. This also illustrates that two accessions with the same common name may not be genetically identical – or even particularly similar. Considering accessions that had a 90-99% similarity to their U.S. counterpart, we found that in the SoySNP50K data, there are two accessions that reference soybean line Hutcheson (‘Hutcheson’ and ‘PI 518664’). There are differences between these lines due to ‘Hutcheson” being grown out for multiple generations in a lab after seed was obtained from GRIN, while PI 518664 was sourced directly from GRIN . We suspect that a 90-99% similarity score to the SoySNP50K counterpart could be due to this event when 2 accessions have been grown out separately for multiple generations and inadvertent selection occurs.11). This 6.5 Tb data was combined with 1 Tb (105 selected accessions) of publicly available raw data3. Sequence reads were aligned to soybean reference genome Wm82.a2 (downloaded from Phytozome), using the BWA aligner21. The mapping rate varied from 97.52%-99.50%, averaging 99.14% in the G. max lines and 97% in G. soja . From the mapped sequence a total of 7,869,806 SNPs were identified.Resequencing of 376 accessions yielded 680 billion 125-bp paired-end reads, resulting in 6.5Tb of high-quality raw data , 125,602 as having a “low” effect (causing a synonymous mutation), 146,236 as having a “moderate” effect (causing a non-disruptive change in the protein), and 7,638 as having a “high” effect (causing a disruptive change in the protein). The 7,638 SNPs predicted as having a “high” effect are in 5,987 genes . The SNPs, along with their snpEff annotation, can be viewed on the SoyBase genome browser (https://soybase.org/gb2/gbrowse/gmax2.0/) under the “naturally occurring sequence variants” track, as “USB481”.The snpEff programG. max and G. soja data files glyma.Wm82.gnm2.div.G787.USB481_nosoja.vcf.gz and glyma.Wm82.gnm2.div.G787.Soja.vcf.gz at the SoyBase Data Store27, giving the respective snpEFF files at that location. The G. max results indicated 4,661,844 SNPs as a “modifier”, 76,352 as having a “low” effect, 82,567 as having a “moderate” effect, and 4,041 as having a “high” effect. The 4,041 SNPs as having a “high” effect fall into 3,420 genes. The G. soja results indicated 9,356,860 SNPs as a “modifier”, 187,992 as having a “low” effect, 212,196 as having a “moderate” effect), and 10,326 as having a “high” effect.SnpEff was run on the The variant data for the 481 diverse soybean accessions have many potential uses. We illustrate with four analyses: calculation of linkage disequilibrium decay; visualization of regional conservation and difference between accessions; and phylogenetic and structural analyses. Online tools for visualizing similarities and differences for the 481-accession data set are described below. The phylogenetic analysis and associated files are also available for interactive exploration.48 were used to survey the rate of LD decay and population structure in the collection. The LD for each chromosome and the average LD across all 20 chromosomes is indicated in Table G. max accessions is 173 kb, at r2 = 0.2. . There is no clear correlation (r2 = 0.33) between chromosome size and LD decay distance  was used to identify genomic regions that differ between accessions. A small subset of cultivated and wild accessions were selected randomly to compare against the elite cultivar Essex (PI 548677)  were reported by Han et al.50. Also noteworthy is a region in the middle of chromosome 15, where Essex shares a genomic region with accessions PI 209332, PI 548359 (Kingwa), and PI 567357, but is different between all other accessions, suggesting selective introgressions. Highly similar results are produced using different elite cultivars as a reference and comparing other landrace and wild cultivars. For example, if Lee (PI 548656) is used as the reference instead of Essex, we see the exact same results on Gm05 and Gm20. Results can be tested/confirmed/explored using the GCViT tool38 at SoyBase and selecting the USB481 dataset.The tool GCViT 14 were used to develop a phylogenetic tree showing similarity relationships among accessions : Japanese (green), Korean (orange), Chinese (blue), U.S. (light blue), and all other countries in gray. This suggests that phylogenetically distinct lineages arose within particular geographic locations, with relatively limited genetic exchange between, for example, central China and Japan. The accessions near the top  occur together, as expected considering their genetic distance from G. max.To complement the phylogenetic analysis, a genomic Structure plot for all 481 accessions was generated based on a cluster size of K = 5 Fig. . The ordG. soja). 7.8 million SNPs have been identified and more than 5,900 genes with high effect changes have been discovered among the germplasm collection. These changes will be of use in soybean breeding programs.In this study, we have presented re-sequencing data of 481 diverse accessions, including 52 wildtypes (Among the USDA soybean germplasm collection, we have identified 15 accessions that are potentially misnamed which will help other researchers avoid errors in their analyses. Illustration of the data with phylogenetic and structure analyses highlights the history of soybean domestication through mostly independent selection in numerous locations across Asia.The results presented here help build a more complete history of the US soybean breeding programs, which in turn will guide future efforts in soybean breeding.Supplementary Figure 1