Solanum tuberosum L.) are crucial for potato breeding. Late blight disease is one of the most significant diseases hindering potato production. The traits of late blight tolerance were evaluated for 284 potato cultivars to identify loci significantly associated with the late blight tolerance trait. Of all, 37 and 15 were the most tolerant to disease, and 107 and 30 were the most susceptible. A total of 22,489 high-quality single-nucleotide polymorphisms and indels were identified in 284 potato cultivars. All the potato cultivars were clustered into eight subgroups using population structure analysis and principal component analysis, which were consistent with the results of the phylogenetic tree analysis. The average genetic diversity for all 284 potato cultivars was 0.216, and the differentiation index of each subgroup was 0.025–0.149. Genome-wide linkage disequilibrium (LD) analysis demonstrated that the average LD was about 0.9 kb. A genome-wide association study using a mixed linear model identified 964 loci significantly associated with the late blight tolerance trait. Fourteen candidate genes for late blight tolerance traits were identified, including genes encoding late blight tolerance protein, chitinase 1, cytosolic nucleotide-binding site–leucine-rich repeat tolerance protein, protein kinase, ethylene-responsive transcription factor, and other potential plant tolerance-related proteins. This study provides novel insights into the genetic architecture of late blight tolerance traits and will be helpful for late blight tolerance in potato breeding.Uncovering the genetic basis and optimizing the late blight tolerance trait in potatoes ( Solanum tuberosum L.) is a major food crop produced worldwide  in 2018 and 2019 with 10 replicates of each accession in one row.Phytophthora infestans used in this study were collected from different potato planting areas in Qinghai Province with physiologically dominant species, mainly 3, 4, and 10. The susceptible potatoes were cut into slices, approximately 0.5-cm thick. These slices were inoculated with the pathogen, placed in culture dishes in an 18°C incubator, incubated in the dark, and covered with moist filter papers. After 5 days, the collected sporangia on these slices were filtered using nylon and steel mesh. The concentration of Phytophthora infestans was adjusted to 50,000 per milliliter.The in vitro inoculation with Phytophthora infestans after the seedlings had 10 leaves. Each picked leaf of the 284 potato accessions was inoculated with 15 μL of Phytophthora infestans and placed in an 18°C humidified incubator with 8 h darkness and 16 h light every day. Then, the indoor investigation for late blight tolerance in potatoes was evaluated  method  was used1 to filter the original sequencing data, count the total number of reads obtained from sequencing, assign the reads to each individual based on the barcodes designed using the AFSM technology, and count the number of reads in each individual. Bowtie2 software  of the potato population materials with the detected SNPs as inputs  of each material in each subgroup was used to construct the population-genetic structure matrix. VCFtools software6 was used to calculate the genetic diversity (π) and population pairwise F-statistics (FST)  . Accordiectively . In the R2 was used to evaluate the LD relationship between each pair of polymorphic sites throughout the genome, and the value of R2 was calculated using PopLDdecay software , the value of software  for highIn this study, data from 22,489 high-quality SNPs and indels were typed to perform GWAS on this population’s severity (lesion diameter) and tolerance grade. We used a compressed mixed linear model of TASSEL 5.0 software  for corrP-value, the qqman package of R software was used to draw the Manhattan plot and QQ plot. SAMtools was used to manually verify regions significantly correlated with the reordered read results of the potato reference genome PGSC_DM_v4.03.7The threshold for the significance of tolerance grade was set at 0.05. Inputting the physical location of the SNP in the potato genome and its Based on potato SNP annotation and LD decay and according to the functional annotation of the loci, the genes on which the loci were located were used as candidate genes. If a locus was located upstream and downstream of other genes simultaneously, the upstream and downstream genes were also used as candidate genes. If a locus was located in the intergenic region, the upstream and downstream genes closest to the locus were used as candidate genes. A mixed linear model was used to perform association analysis on the traits of late blight.–ΔΔCt method was used to calculate the relative expression levels.Primers were designed based on the coding region sequence (CDS) of the candidate genes, and the actin gene was the internal reference . A fluorP-value > 0.001, and 20,382 high-quality SNPs and 2,107 indels were obtained. Annotation of the high-quality SNPs and indels showed that 18,683 (83.08%) were in intergenic regions; 3,806 (16.92%) were in the gene regions of the genome of which 951 were in the untranscribed areas, 2,796 were in introns, and only 1,682 SNPs were in the coding areas. In the coding regions, 771 SNPs produced silent mutations, and 911 SNPs produced missense mutations at a ratio of 1.18  > 0.05 and Hardy–Weinberg equilibrium  of 1.18 .Among the 284 materials tested, 37 resources with disease area less than 3% or asymptomatic and disease grade 1 were selected. The area of the disease spot was 3–10%, forming an anaphylactic dead spot; there was no chlorotic halo around it; and there were 15 resources with disease grade 2. There were 30 resources with disease grade 3 with white mycelium visible on the disease spot surface, and the area of the disease spot was 10–30%. The disease spot was water-soaked and surrounded by a chlorotic halo. There were 107 resources with disease grade 4, and the area of the disease spot was 30–60%. There were obvious chlorotic circles and white mold layers around the disease spot. There were 95 resources with disease grade 5; the lesion area was more than 60%, and a large amount of white mildew appeared on the lesion’s surface .K-value was calculated  was assumed to be each integer from 1 to 12, and the cross-validation (CV) error of each lculated . When K PCA was conducted using all high-quality SNPs and indels. The calculation and analysis process was conducted by R software . After tQ-value of each material in these eight subgroups, each material was classified into the subgroup with the largest Q-value  of all 284 potato germplasm resources was 0.216, and the genetic diversity index of the eight subgroups was between 0.164 and 0.250. Among them, subgroup 8 had the lowest genetic diversity index (0.164), and 6 had the highest (0.250) . These dF-statistics (FST), a measure of population differentiation, was used to evaluate the degree of difference between subgroups of the 284 potato germplasm resources  and subgroups 3 and 7 had the lowest FST (0.025). Subgroups 2 and 3, subgroups 2 and 7, subgroups 3 and 7, subgroups 3 and 8, and subgroups 7 and 8 were relatively weakly differentiated, and their genetic relationships were relatively close. In contrast, there was a moderate degree of differentiation between other subgroups.The pairwise population esources . It was P < 0.05 was set as the threshold to determine the significant loci for the disease tolerance grade, and 14 candidate genes were obtained after annotating genes located at or near the significant loci.The mixed linear model analysis performed correlation analysis on the disease tolerance grade . A totalR2 = 0.2 as the decay threshold, the results show that LD decreases with increased physical distance between SNPs. When the coefficient of determination R2 = 0.2 is taken as the attenuation threshold. The attenuation distance from subgroups 1–8 was about 13.4, 1.3, 0.1, 0.7, 0.3, 1.9, 1.3, and 0.6 kb, respectively, and the attenuation distance of the whole population was about 0.9 kb. It was much lower than that of cultivated rice (123 kb), cultivated soybean (133 kb), and cultivated maize (30 kb), and slightly lower than that of cultivated cassava (8 kb) and a maize-inbred line population (1.5 kb)  .According to the LD, we found 14 candidate genes. Most of the candidate genes were within 10 kb of the mutation site, and the largest was no more than 20 kb. Phytophthora infestans in vitro for 5 days, the green leaves around the plaque and leaves without inoculation were used. Then, their relative expression levels were calculated  have been discovered, and these 11 major R genes have been successfully located on the potato genetic map . Combinain vitro. Among them, the candidate gene encoding the chitinase 1 gene may be involved in the immune response, and chitinase can improve the tolerance of plants to fungi (In this study, 19 candidate genes, respectively, were found by the GWAS of the disease severity grade identified from isolated potato leaves affected by late blight to fungi . The GWAto fungi . Two resto fungi . This sthttps://ngdc.cncb.ac.cn/search/?dbId=bioproject&q=PRJCA005945&page=1, PRJCA005945.The original contributions presented in the study are publicly available. This data can be found here: FW, ZX, and JW conceived and designed the experiments. FW and LZ conducted the experiments work. ZX, MZ, and LZ analyzed the experimental results. FW and MZ wrote the manuscript. ZX and JW reviewed and contributed to improve it and revised the last version of the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Microbes and their viruses are hidden engines driving Earth’s ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed.- and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity (π and θ), and selective pressures (pN/pS and Tajima’s D) within and fixation indices (FST) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations  that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity (π) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better “bet hedge” in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method  may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences.Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro- and microdiversity across microbial communities.These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macroVideo abstractThe online version contains supplementary material available at 10.1186/s40168-022-01231-0. Microbiology has experienced a revolution as sequencing and computational advances have enabled the cultivation-independent study of microbial and viral communities across diverse ecosystems. These studies have revealed the importance of the “microbiome” and its viruses as critical drivers whose metabolisms and impacts alter nutrient, metabolite and energy flows that dictate human health and ecosystem outputs e.g., –3). Prag. Prag3])A major challenge when assembling fragmented DNA from complex communities is assembling short-reads into biologically meaningful “genomes” that represent ecologically and evolutionarily relevant populations. At this point, however, there are several improved population definitions that account for ecological and evolutionary theory  and haveFirst, population genetic methods rely on defined genotypes of individuals within a population with equal coverage across each base within a sequence. These are conditions not satisfied in metagenomes as their populations have unequal coverage and are assembled from many individuals within a population. To date, researchers have developed many methods to overcome these issues. The most common ones try to resolve each individual’s genotype within the population by linking single nucleotide polymorphisms (SNPs) into strain-level genotypes [second obstacle is that analyzing both macro- and microdiversity in these modern datasets has scaling and standardization issues, and user-friendly bioinformatic tools are not yet available. For the latter, while several bioinformatic tools have emerged, they require intensive data manipulation prior to use and few do more than one type of analysis  all of MetaPop’s functions and parameters are called in a single command-line that processes and analyzes the input data from start to finish (with the option to run steps independently), and (3) it improves adaptive selection (pN/pS) results by determining if SNPs are linked at the codon level. MetaPop is fully documented and maintained by the developers at https://github.com/metaGmetapop/metapop.To fill this gap, we introduce the multi-functional bioinformatic pipeline MetaPop, written in python and R and bundled in a bioconda package, that analyzes and visualizes microbial and viral community metagenomic sequence data at both the inter-(macrodiversity) and intra-(microdiversity) population levels. MetaPop can be easily utilized by beginner microbiologists with little training. In this sense, the pipeline complements existing bioinformatic pipelines, such as Anvi’o  and metaPre-processingMacrodiversity and codon bias analysesMicrodiversity analysesMetaPop has three inputs: 1) a genome FASTA file, (2) a tab-delimited file of the number of reads or base pairs per metagenomic library, and (3) one BAM file per metagenomic library of read alignments (mappings) to the reference genomes. The reference genomes should be assembled microbial or viral contigs that represent populations. Currently, MetaPop only works for haploid organisms and works best for bacteria, archaea, and dsDNA phages. MetaPop is best applied when each BAM file is derived using reads from a single metagenomic community—as defined by the user—rather than reads pooled from multiple communities, in order to prevent the formation of hybrid populations that could violate underlying assumptions of population genetic inferences. The definition of a single community will need to be defined by the user and the question that they are trying to answer. Commonly, single communities in the context of population genetics are defined as samples taken from two different locations or different time points from the same site. With these inputs, MetaPop analyzes the data in three steps  percent alignment and read length for every read in each sample, (ii) percent of positions covered by reads and average depth of coverage for each genome, per BAM file, (iii) the raw and normalized genome abundances, calculated (iv) α- and (v) β-diversity values, (vi) genes with different codon biases, (vii) single nucleotide variant (SNV) calls and pileup files over SNV positions, (viii) called single nucleotide polymorphisms (SNPs), split into those which appear on genes and those which appear in intergenic regions, (x) linked SNP results, and (xi) intra- and (xii) inter-population genetic calculations. The visualization output include (i) overall summaries of read filtering, (ii) summary plots showing BAM file genome coverage and depth statistics, (iii) a heatmap of normalized genome abundances, (iv) scatterplots of α-diversity values, (v) ordination plots of β-diversity results, (vi) bar plots of codon position of detected SNPs, (vii) visualization of nucleotide diversity and codon bias per each gene for each genome, and identification of positively selected genes, and (viii) heatmaps of FST per genome across samples.To clearly and explicitly lay out the capabilities of MetaPop compared to existing complementary bioinformatic pipelines , 42, 44,Though user-customizable, MetaPop defaults to a 95% nucleotide identity (ID) cut-off, but can be changed by the user, to define population boundaries, guided by studies exploring sequence space boundaries between microbial and dsDNA viral populations –29, 47. Once a population is detected by these above criteria, MetaPop calculates its relative abundance based on mean nucleotide coverage across the genome (see the “within community) indices—richness, Chao1, ACE, Shannon’s H, Simpsons, inverse Simpsons, Fisher, and Pielou’s J—for that community are calculated. With multiple BAM input files, β-diversity (between community) indices—Jaccard, Bray-Curtis, and centered log-ratio-transformed Euclidean distances—between all communities are also calculated. Importantly, MetaPop also outputs the raw abundances, so that the user can normalize their own data.Macrodiversity is the measure of population diversity within a community. While some diversity measurements rely strictly on the presence or absence of populations (such as richness and Jaccard distances), many rely on the relative abundances of populations between communities . Importantly, metrics that rely on relative abundances have been shown to be more robust for metagenomic data because they are less susceptible to uneven sampling of rare taxa . Thus, tMicrobial and viral populations often have distinct codon biases for translational optimization . Genes wMicrodiversity is the measure of genetic diversity within a population. In natural communities, where populations are represented at different abundance levels, only genomes with enough data can be evaluated. Thus, by default, genomes with < 70% length of their genome covered and < 10× average read depth coverage are excluded from these analyses to ensure that there is enough coverage to accurately call SNPs and to assess contig-level microdiversity. The 10× value was selected because prior work revealed that downsampling read depth to 10× did not statistically significantly impact downstream microdiversity calculations . While dIdentified SNPs are cross-referenced with gene calls and assigned as either genic or non-genic. If genic, their position within each codon per gene is determined. Due to redundancy at the third position in codons that allows multiple codons to code for the same amino acid, most true SNPs should be at the 3rd position of the codon. MetaPop outputs all of the SNPs called and their codon positions if genic. MetaPop will issue a warning if there are more SNPs in the 1st and 2nd positions of a codon. Lastly, the global verified consensus allele per each SNP position is replaced as the consensus allele in each reference genome.SNPs are tested for local linkage at the codon level to identify codon variants by evaluating their co-presence within reads in each BAM file. Multiple programs try to link SNPs across the genome into strain haplotypes using the reads –41, 43. p-value < 0.05, φ > 0) meaning the SNPs occur together as a set disproportionately, “independent” , meaning the presence of one of the candidates excludes some of all of the others in that set disproportionately, or as “ambiguous,” meaning that they occur together or separately at apparent random, or that there is insufficient data to classify them otherwise. In ambiguous cases, SNPs are treated as independent for downstream analyses.In order for MetaPop to link the SNPs at the codon level, SNPs that localize in the same codon on the same gene are selected as candidates for linked SNP identification. The original reads covering the positions of the candidates from their respective genomes are collected from each BAM file, and the codons relevant to the candidates are extracted from those reads. SNPs within the same codon are tested in pairs. If more than two SNPs occur within a codon, pairwise tests are performed between each combination of pairs. A contingency table of the frequencies of the extracted codons with both SNPs, the number of extracted codons with one SNP, the number of extracted codons with the other SNP, and those containing no SNPs is produced. Fisher’s exact tests and phi coefficients are calculated. The linked SNP candidates are classified as either “linked” —expected nucleotide diversity  and the —are calg et al.  equationAM, FST . The synthetic dataset is composed of 30 mock, bacterial metagenomic communities of different known-proportions of strains  dataset2  that share > 80% ANI and a more distantly related, rare, species-level population (B. subtilis)  Fig. . This taS. aureus and S. epidermidis species, respectively, and a 0.26-fold change average difference of the B. subtilis which is simulated to represent a rare taxon across the communities  and β-diversity (Bray-Curtis dissimilarity) did not significantly differ from the actual values . Thus, despite the minor fold change differences in community composition and difficulty in accurately detecting the abundances of rare taxa, MetaPop is generally able to accurately assess the community composition and macrodiversity biological trends.Across the 30 communities, bacterial population relative abundances were almost identical to the simulated proportions with only a 0.98 and 1.08 mean fold change differences of the ies Fig. A. This fies Fig. A. MetaPoS. aureus  of the genes detected to have outlier codon usage have no known function. Of the remaining 45 annotated genes MetaPop detected with outlier codon usages, 20% are genes on known mobile elements (so prone to HGT) or are thought to be horizontally transferred and 47% are known to be highly expressed  per sample by randomly subsampling the π values of different viral populations in each sample and averaging those values. We replicated these methods with the π values calculated using MetaPop where SNPs were called locally when they had a differential base with a PHRED score ≥ 30  were slightly higher (median 1.33 fold-change) than the original GOV2 average π , indicating that despite higher average π and slight fluctuations in average π derived from the random subsampling process, the biological microdiversity patterns are still being captured due to a systematic adjustment consistent with calculations from fragmented genomes that derive from metagenomic datasets.Importantly, MetaPop calculates al. 2013  which dial. 2013  equatione π Fig. A, left. π values. Global SNP calling, for example, incorporates all SNP loci that were identified in any sample in the dataset into the π calculation for each sample , which will increase π. Using the GOV2 dataset, we see just that with PHRED ≥ 30 global SNP derived average π having a median 4.32-fold increase from the original GOV2 average π values calculated using PHRED ≥ 30 and local SNP calls . Further, analyses of larger microdiversity trends across ecological zones in the ocean defined in the original GOV2 analyses were also able to be replicated using both PHRED score cut-offs testing and a global and local approach  and PHRED score  can also impact downstream lls Fig. B, right lls Fig. E, right.lls Fig. C, left alls Fig. D, left. ach Fig. F. Taken Using the two biological datasets, we evaluated the impact of MetaPop’s novel codon-constrained SNP linkages on pN/pS selection analyses. The original pN/pS equation  calculatn = 248) with enough coverage to evaluate selection had ≥ 1 codon with putatively linked SNP in at least one sample  displayed positive selection, and when linking SNPs, we observed that 26.9% of the genes (n = 63) displayed positive selection  genes under positive selection using the codon-constrained SNP linkages, compared with 26.9% (n = 271) with PHRED ≥ 20  with enough coverage to evaluate selection had ≥ 1 codon with putatively linked SNP  to 24.56%  , and the microdiversity average π .To demonstrate the value of adding estimates of microdiversity to a researcher’s toolkit, we used MetaPop to re-analyze the gut viromes of autistic children that underwent fecal microbiota transfer (FMT) and their neurotypical peers. Eighty percent of autistic children suffer gastrointestinal problems, so understanding how the gut microbiota differs between autistic and neurotypical children may be important for treating this symptom of autism . Previou = 0.89; ). Thus fπ) is significantly lower in autistic children than within their neurotypical peers , paralleling our previous bacterial macrodiversity findings [π can indicate two biological outcomes: (i) more viruses from different populations are actively infecting host bacteria resulting in population expansion and more mutations, or (ii) more viral populations naturally maintain higher levels of microdiveristy in their standing populations. Either way, having a higher level of microdiveristy for a viral population can be an adaptive advantage because it better allows populations to “bet hedge” if their environment or hosts change [p = 0.026). Thus, we hypothesize that increased average π is beneficial in the gut virome because it allows viral populations to better adapt to their changing environments and hosts. Increased diversity at either the macro- and microdiversity level has consistently been shown to be important for maintaining ecosystem functions and services , wi, wiπ) isπ is beneficial, we expected to see that all of the responders would have increased viral microdiversity. Instead, we saw an interesting pattern  to the post-FMT (week 10) gut viral microdiversity . To simulate different computational power, from a standard laptop to desktop computer to a small-sized computer cluster, we ran MetaPop using 4, 6, and 14 cores, using 8, 16, and 64 GB of memory (RAM), respectively see Fig. A. The muTo assess both processing time and computational resource consumption, we first wanted to determine what steps in the MetaPop pipeline were rate-limiting. Across both the synthetic and biological datasets, the pre-processing and the SNP calling portion of the microdiversity section took the most processing time Fig. B. These R2 =0.985 (autism virome), R2 = 0.9564 (GOV2); Fig. R2 = 0.885 (synthetic group 1) and R2 = 0.841(synthetic group 2); Fig. With the knowledge of the rate-limiting steps, we then assessed the effect of BAM file size and the amount of computational power supplied on processing times. BAM files size is impacted by the number and length of reference genomes and the original number of reads. The synthetic and biological datasets differ substantially in these values resulting in vastly different BAM file sizes and allowing us to test MetaPop across a range of BAM file sizes, with the average synthetic dataset’s BAM file size equal to 320 megabytes (MB), the average autistic biological dataset’s BAM file size equal to 1 gigabyte (GB), and the average GOV2 biological dataset’s BAM file size equal to 6.7 GB. The processing time of the rate-limiting steps were nearly linear functions of BAM file size across the biological datasets , wh, whFirsts . Finalls  solve t- and microdiversity in metagenomic data. It combines both classical community ecology metrics with the full suite of population genetics parameters in a single integrated pipeline. While many of its functions are already available in existing pipelines [MetaPop is a fast and scalable pipeline for the analyses of both macroipelines , 42, 44,- and microdiversity across microbially impacted ecosystems.Molecular biology and sequencing technology advances have advanced the microbiologist’s toolkit from 16S rRNA gene analyses to metagenomics and changed questions we could ask from “who is there” to also add “what could they be doing” and “with whom might they interact”. Now, with further technological advances and by democratizing microdiversity analyses, we open a new window into the study of complex communities such that we can now ascertain “what populations have high microdiversity levels” and “which genes are under selection.” While studying microdiversity has been hard due to lack of data and ease of toolkit, we are entering an era where such data and toolkits are available such that our understanding of these new biological windows will provide new insights into how complex systems work. MetaPop gives scientists an ideal toolkit to explore the dual impact of macroS. aureus , three distinct strains of S. epidermidis , and a single strain of B. subtilis [S. aureus , one strain of S. epidermidis  as the population-level genome representatives. For the GOV2 dataset, we used the Tara Oceans 131 viromes from GOV2 and did not process the Malaspina viromes. The 488,130 viral populations identified in [bioRxiv version of [We chose three previously published datasets, a synthetic dataset representing mock bacterial communities  and two subtilis . Becausebly from ), and onified in  were usersion of  as the prsion of . The resIn order to computationally evaluate and benchmark MetaPop, we emulated the resource environment of several likely computational platforms and attempted to process both the synthetic and biological datasets using these resources. The average BAM file size of the mock synthetic dataset BAM is 320 MB and has 30 samples, and of the biological dataset BAM is 1013 MB, with a total of 49 samples. The chosen computational scales reflect a fairly typical laptop computer, with 4 processing cores and 8 GB of RAM, a desktop computer with 6 cores and 16 GB or RAM, and a supercomputing environment using all 14 cores on one of the Ohio Super Owens  nodes aThe Owens nodes are each equipped with a Intel Xeon e5 2680 v4 Broadwell processor, which has 14 cores, and each node shares identical RAM and data storage characteristics. This provides parity between the differing scales of the computing environment, rendering maximum permissible memory and allocated cores the sole difference affecting runtime and memory usage. Finally, the OSC process manager terminates the execution of code which exceeds the supplied memory of any given job, meaning that exceeding specified RAM results in a failure of MetaPop to complete, just as it would in an environment actually limited by such resources. While the manager would also terminate a job which exceeded a specified runtime, we supplied each instance with excessive runtime so that this would not be a factor.As it runs, MetaPop outputs timings for its five core components, namely preprocessing, SNP calling/refinement, linked SNP read mining and linkage calculations, calculation of microdiversity, and calculation of macrodiversity. In addition to the per-component timings, MetaPop outputs timings for each individual sample in sections of the code where each file is processed independently from the others. In both cases, these outputs include a date and time of start and finish for each step, and provide accurate timing for both the overall runtimes of each processing phase and for the time needed to run individual samples through preprocessing and SNP calling. MetaPop’s overall runtime was calculated as a simple sum of the per-component runtimes.In order to profile memory usage during the various phases of MetaPop, we relied on computational resource logs produced by the Ohio Supercomputer. These files report a variety of computational resource consumption statistics associated with a particular task, which includes the peak memory footprint for any collection of processes contained within a single job on the supercomputer. This approach answers the most pertinent question for users: what is the minimum RAM that is required to run a dataset of a given scale through MetaPop with a particular number of cores.H, and Peilou’s J) between the observed MetaPop values and the expected actual values across the 30 mock communities were compared using Wilcoxon tests in the R package “ggpubr.” β-(Bray-Curtis dissimilarity) diversity calculated distances calculated across all 30 communities then compared using a Mantel’s test in the R package “vegan.” Lastly, FastANI [In order to assess MetaPop’s ability to resolve macrodiversity, we compared MetaPop’s predicted relative abundances to the known relative abundances in the 30 mock communities . Importa FastANI  using deStaphylococcus aureus strains in the 30 mock communities [S. aureus are well studied to their clinical relevance and there is a great deal of knowledge about the different genes and elements that have been horizontally acquired within their genomes [We chose to evaluate MetaPop’s ability to pull out genes with different codon bias usage by examining the munities . S. aure genomes , 70 and, genomes , 74). Inπ) per sample in the 131 GOV2 samples based on MetaPop’s calculated nucleotide diversity (π) and compared it to the published microdiversity values [π was calculated by randomly selecting the π values of 100 viral populations and then averaging their values . This ws  values, the microdiversity (average π), and the percentage of genomes under positive selection for all the autistic and neurotypical children prior to FMT treatment were compared using Wilcoxon tests in the R package “ggpubr.” To calculate microdiveristy for each sample, average π was calculated by randomly selecting the π values of 10 viral populations and then averaging their values . This wAdditional file 1: Figure S1. Heatmap showing % average nucleotide identities (ANI) similarities among the different strains and populations in the 30 mock communities. Figure S2. Validating MetaPop’s macrodiversity and codon bias analyses. (A) Tornado plot showing the relative abundances of Staphylococcus aureus, Staphylococcus epidermidis, and Bacillus subtilis across the 30 mock communities in the actual synthesized community and as determined by MetaPop. Bar charts contained within the gray bar to the left of the tornado plot reveal the number strains per each bacterial species, with three being the highest number of strains per species. (B) Boxplots showing median and quartiles of different α-diversity indices  compared between the actual and MetaPop derived abundances. The Wilcoxon test p-values above are the result from comparing actual and MetaPop derived α-diversity indices. (C) Heatmaps of β-diversity Bray-Curtis dissimilarity distances calculated using the actual and MetaPop derived abundances. Figure S3. Genome map of genes with outlier codon usage in ST5 Staphylococcus aureus ECT-R2. Figure S4. Validating MetaPops’s microdiversity analyses using the Global Oceans Virome 2 dataset.  Line plots sorted by the original average nucleotide diversity (π) values from [Tara Oceans stations in the GOV2 dataset derived from the (A) original GOV2 values versus MetaPops’s PHRED≥30 local SNP calls, (B) original GOV2 values versus MetaPops’s PHRED≥30 global SNP calls, (C) original GOV2 values versus MetaPops’s PHRED≥20 global SNP calls, (D) MetaPops’s PHRED≥20 global SNP calls versus MetaPops’s PHRED≥30 global SNP calls, and (E) MetaPops’s PHRED≥30 global SNP calls versus MetaPops’s PHRED≥30 local SNP calls. The dashed line in the scatter plot represents the linear regression.  Bar plots showing the biological microdiveristy trends across the ecological zones defined in [Figure S5. Scatterplots with Loess smoothing displaying runtime per sample for the rate-limiting part of MetaPop  as a factor of file size in megabytes on (left and right) the biological datasets and (center) the synthetic dataset.ues from  and (A-EAdditional file 2: Table S1. Mock communities data actual and metapop derived abundances. Table S2. Biological virome dataset population abundances. Table S3. Full codon bias usage results for all genes in Staphylococcus aureus ECT-R2.


Several maize breeding programs in India have developed numerous inbred lines but the lines have not been characterized using high-density molecular markers. Here, we studied the molecular diversity, population structure, and linkage disequilibrium (LD) patterns in a panel of 314 tropical normal corn, two sweet corn, and six popcorn inbred lines developed by 17 research centers in India, and 62 normal corn from the International Maize and Wheat Improvement Center (CIMMYT). The 384 inbred lines were genotyped with 60,227 polymorphic single nucleotide polymorphisms (SNPs). Most of the pair-wise relative kinship coefficients (58.5%) were equal or close to 0, which suggests the lack of redundancy in the genomic composition in the majority of inbred lines. Genetic distance among most pairs of lines (98.3%) varied from 0.20 to 0.34 as compared with just 1.7% of the pairs of lines that differed by <0.20, which suggests greater genetic variation even among sister lines. The overall average of 17% heterogeneity was observed in the panel indicated the need for further inbreeding in the high heterogeneous genotypes. The mean nucleotide diversity and frequency of polymorphic sites observed in the panel were 0.28 and 0.02, respectively. The model-based population structure, principal component analysis, and phylogenetic analysis revealed three to six groups with no clear patterns of clustering by centers-wise breeding lines, types of corn, kernel characteristics, maturity, plant height, and ear placement. However, genotypes were grouped partially based on their source germplasm from where they derived. Zea mays L.) is one of the most valuable crops cultivated in the world for its diverse uses in food, feed, starch, textile, pharmaceutical, cosmetic industries, oil, protein, alcoholic, and bio-ethanol production [Maize  based on genotyping by sequencing (GBS) technology  has becoHigh-density SNPs via GBS are providing good opportunities to the researchers for low-cost and rapid turnaround time to quickly genotype their germplasm for different purposes, including genetic diversity assessment, mapping genes and quantitative trait loci (QTL) using bi-parental populations and genome-wide association mapping (GWAS) panels, and developing improved varieties using marker-assisted and genomic selection ,26,27. LWe used a total of 60,227 polymorphic SNPs, each with a minimum allele frequency of 0.05 and a maximum missing data of 20%, to understand the genetic diversity, population structure, and LD across 384 inbred lines originated from CIMMYT (62) and 17 research institutions/centers in India (322) . The polThe 60,227 polymorphic SNPs cover 2057.3 Mb of the maize genome with each chromosome ranging in length from 110.77 Mb (Chr-9) to 307.0 Mb (Chr-1) with an average of 205.73 Mb per chromosome . The totAs shown in Most of the pair-wise relative kinship values (58.5%) were equal or close to 0 and only 19.3% of the pairs were above 0.05 A, indicaThe neighbor-joining phylogenetic trees revealed four major clusters that consisted of 90, 89, 129, and 76 lines . The 62 The model-based population structure analysis conducted using 60,227 SNPs showed the highest ΔK peak at K = 6 , which sr2) values computed from 60,277 polymorphic SNPs ranged from 0 to 1. The distribution pattern of LD values between pairs of SNPs ranging from 0.2 to 1.0 is given in r2 decreased rapidly with increasing physical distance  to 0.75 (0.2–0.3 kb on Chr-10). The highest mean LD was observed on Chr-3 (0.52) and minimum on Chr-2 (0.42). The overall mean LD value across the genome was 0.46.LD . However, the percent heterogeneity of CIMMYT lines was higher than reported before [Genetic purity (homogeneity) in inbred lines is very important in maize breeding because it directly affects the ability of the line to be used as a parent in new line development and hybrid formation. In the current study, overall average of 17% heterogeneity was observed in the panel which is relatively high. It emphasizes the purification of heterogeneous lines either by ear to row selection or re-selection from the original seeds lots of the line. Relatively normal field corn genotypes of ICAR-IIMR Ludhiana and CIMMYT were showing low heterogeneity as compared to the lines from the centers and quality protein maize lines of the IIMR.Most of the IIMR quality protein lines are still in the advanced stage of development  reported in the literature at different physical intervals varied from 0.14 to 0.75, which may be due to differences in population size and the number of markers [Population size and the number of markers play a significant role in LD values: the lesser the numbers of markers the larger would be the LD values . Our int markers ,38,39. I markers ,38,39. S markers ,40.The identification of haplotype blocks in the genome can further improve the power of QTL detection in an association mapping panel. Haplotype blocks of long-length sequences that possess more polymorphic SNPs tend to harvest large diversity among the genotypes. In this study, we observed different sizes and numbers of haplotype blocks across the chromosomes that consisted of variable numbers of SNPs. On Chr-3, for example, we observed the highest total blocks length and relatively more numbers of blocks and SNPs. Simultaneously, the overall mean LD value across the physical distances was also highest on this chromosome. LD, haplotype blocks, SNPs numbers, and diversity in the genome are interrelated and are important for GWAS study. Rather than studying the whole genome, it is advisable to first examine for a preliminary association using a panel carrying more haplotype blocks. Once a specific region gets detected, then small haplotype blocks in the same regions can be explored further for fine mapping. These findings on LD and haplotype blocks suggest that the current panel of 384 inbred lines has sufficient genetic variability for all parameters and therefore can be considered for GWAS.viz., normal field corn (281), quality protein maize (95), popcorn (6), and sweet corn (2) with different kernel colors , maturity groups , plant height , and ear placement  , and seventeen maize research institutes/centers in India (322). The panel consisted of different types of corns, nd high) . The higApeKI  restriction endonuclease enzyme as described in previous studies [® 2.0 fluorometer was used to determine the concentration of the library, which was then diluted to a concentration of 1 ng/uL. Libraries with an appropriate insert of 328 bases and an effective concentration of more than 2 nM were used for sequencing. The sequencing was done using Illumina TrueSeq v3.0 pair-end sequencing with read lengths of 151 bp on HiSeq×10 Platform. Ninety-five samples (plus a blank negative control) were sequenced in one lane. The reversible terminator-based method was followed that enables the detection of a single base when they were incorporated in growing DNA strands.GBS Libraries were prepared by digesting 0.5 μg genomic DNA of each of the lines with  studies ,10. Briehttps://sourceforge.net/projects/picard/files/picard-tools/1.48/, accessed on 8 March 2022), [The sequencing generated a total of ~17.58 million raw reads, which were saved into FASTQ files.Quality control steps were followed to filter the raw data set by removing reads with a quality score of <20reads, by checking for the perfectly matched barcode with the expected four base remnants of the enzyme cut site, minor allele frequencies. Reads with minimum quality control (Q) score of 20 were considered for further analysis. These reads were sorted and de-multiplexed according to their barcode. The filtered sequences from each genotype were aligned to the maize reference genome using the Burrows-Wheeler alignment tool (BWA)  for SNPsh 2022), . FreeBayh 2022),  was usedhttps://web.stanford.edu/group/pritchardlab/structure_software/release_versions/v2.3.4/html/structure.html, accessed on 8 March 2022) using K values ranging from 2 to 9, three independent runs at 150,000 Markov Chain Monte Carlo (MCMC) iterations, and 100,000 burn-in periods. Genotypes with membership probability values of ≥0.50 were assigned to the same group, while those with ≤0.50 probability were treated as mixed [ln Pr (X|K)) for the run. Relative kinship and identity by state-based genetic distance between a pair of inbred lines, and principal component analysis (PCA) were done using TASSEL v5.2.80 . The genetic distance matrix was used to construct a neighbor-joining phylogenetic tree in TASSEL. The Newick file from TASSEL was visualized using interactive Tree Of Life (iTOL) [https://itol.embl.de/, accessed on 8 March 2022). The first three principal components from PCA were plotted using R platform. We used seven categorical variables in the PCA and phylogenetic trees, which included the research centers that developed the inbred lines, group membership from population structure analysis, type of corn, kernel color, maturity group, plant height, and ear height placement.The population structure was determined using the model-based STRUCTURE v.2.3.4 software   . The first data set consisted of all the 60,227 polymorphic SNPs, each with a minor allele frequency of 0.05. We then created two additional data sets by further filtering the SNPs using a minor allele frequency of 0.10 and 0.20. The LD values were computed by calculating pairwise squared allele-frequency correlations (r2) between the SNPs in the genome [https://www.maizegenetics.net/tassel, accessed on 8 March 2022).The linkage disequilibrium (LD) analysis was carried out using three data sets in Tomahawk software (e genome . The LD e genome ,49. The This study investigated the genetic diversity, relationship, population structure, and LD in a panel of 384 maize inbred lines using 60,277 polymorphic SNPs. Our study revealed the presence of large genetic variation among the inbred lines and no clear grouping among categorical variables. The inbred lines grouped partially on the basis of their pedigree. The presence of relatively high heterogeneous lines in the panel required special attention of the breeder for their purification depending on the purpose of the breeding program and the intended use of the inbred lines.The germplasm characterized in the current study is an important resource for breeding and genomics studies in India. The high-density genotype data and results presented in this study would be very useful for different purposes, including parental selection for new breeding programs, heterotic grouping, and genome-wide association studies.


Feeding behavior traits are an essential part of livestock production. However, the genetic base of feeding behavior traits remains unclear in Pekin ducks. This study aimed to determine novel loci related to feeding behavior in Pekin ducks.P-value < 4.74E-06) SNPs for feeding behavior traits were discovered, and four of them reached the genome-wide significance level . One genome-wide significance locus associated with daily meal times was located in a 122.25 Mb region on chromosome 2, which was within the intron of gene ubiquitin-conjugating enzyme E2 E2 (UBE2E2), and could explain 2.64% of the phenotypic variation. This locus was also significantly associated with meal feed intake, and explained 2.72% of this phenotypic variation.In this study, the feeding information of 540 Pekin ducks was recorded, and individual genotype was evaluated using genotyping-by-sequencing methods. Genome-wide association analysis (GWAS) was conducted for feeding behavior traits. Overall, thirty significant (This study is the first GWAS for feeding behavior traits in ducks. Our results provide a list of candidate genes associated with feeding behavior, and also help to better understand the genetic mechanisms of feeding behavior patterns in ducks.The online version contains supplementary material available at 10.1186/s12864-021-07668-1. Animal behavior refers to the instant response of animals to changes in their living environment or variations in their internal physiological conditions. Therefore, feeding behavior is of great significance for maintaining individual survival and population development. Feeding behavior patterns have been studied intensively in different animals, including mice , zebra fFeeding behavior traits are also very vital in Pekin ducks. For one thing, feeding behavior can be an effective way to track animal performance and health status. For another thing, researchers had found that there were strong associations between feeding behavior and feed efficiency in broilers and ducks , 9. NowaThe goal of this study was to investigate the genetic markers associated with feeding behavior in Pekin ducks. Previous researchers had shown that genotyping-by-sequencing (GBS) could be an efficient approach to genotype in ducks –14. In tThe feeding behavior information was collected from 540 ducks. The descriptive statistics of phenotypic record are shown in Table P-value.A total of 1 TB clean reads were produced and 99.15% of these were aligned to the reference genome. Through imputation and quality control, 1,899,988 SNPs were attained for association analysis. The frequency of SNPs along the genome and estimated genomic inflation factors are illustrated in supplementary data , Fig. S2Estimation of genetic parameters using genomic information is summarized in Table P-value < 4.74E-06) among 9 different chromosomes were identified .The results of loci-based analysis are shown in Fig. ed Table , includiUBE2E2), which could explain 3.42 and 2.74% of phenotypic variation, respectively.For NM and MFI, a common peak of genome-wide significant SNPs was identified on chromosome 2 due to the strong genetic correlations between them Fig. b, c. TheUBE2E2 on chromosome 2 was also associated with MD, accounting for 2.38% phenotypic variance. The most significant SNP for DFI was in the upstream region of RB binding protein 8 (RBBP8), with a significance of 1.07E-06. Our results also revealed that the top SNP for TFT was suggested to be associated with eyes shut homolog (EYS).As for the other feeding traits, the SNP located in the intron of P-values of previous significant SNPs for NM on chromosome 2 were lower than the potential significance level. No significant association was found after conditional analysis. So we could infer that the other significant SNPs in this region gained the genome-wide significance due to their close linkage relationships with the top associated SNP.According to Fig. ARHGEF10L), which was associated with DFI in ducks in the current study, mapped to body weight QTL in chicken studies. UBE2E2, a common candidate gene for NM, MFI, and MD, is associated with Wattles weight QTL in chickens.A total of 25 candidate genes were detected by GWAS analysis. We performed QTL annotation of these 25 chicken ortholog genes using the chicken QTL database. Seventeen genes were annotated with QTL information in chickens. The QTL information for five genes was consistent with the feeding behavior traits collected in this study Table . Rho guaGenetic parameters play a vital role in designing the animal breeding plan, and genetic parameter estimation using the relationship matrix based on whole-genome genotypes can be an efficient way to achieve this goal . As showMoreover, NM had relatively strong negative genetic and phenotypic correlations (≤ − 0.63) with MFI and MD in this study. Similar intensely negative phenotypic and genetic relationships (ranging from − 0.49 to − 0.93) were obtained in our former work . It suggUBE2E2 is a common candidate gene for the top SNPs of NM, MFI, and MD. UBE2E2 belongs to the UBE2E enzymes family and can lead to the ubiquitination of specific proteins. Mizukami, et al. [UBE2E2 in rat liver cells could facilitate cell proliferation. Yaagoubi, et al. [UBE2E2 accounted for metabolic syndrome in the Moroccan population. It is also reported that UBE2E2 is involved in adipocyte development and type 2 diabetes in humans [Feeding behavior traits are complex and are controlled by a collaboration of various biological processes, including energy metabolism, neurological development, and muscle system movement. In this study, , et al.  found th, et al.  discovern humans , 21. ComRBBP8 and synaptosome associated protein 47 (SNAP47). Mumtaz, et al. [RBBP8 mutations led to microcephaly, intellectual disability, and short stature in Pakistani people. Li, et al. [RBBP8, which was closely associated with body weight at the age of 120 days in purebred Wengshang Barred chicken. Münster-Wandowski, et al. [SNAP47 had a distinctive location in glutamatergic neurons in the mouse. In comparison with these results, we inferred that these two genes might regulate body weight gain and neuron activity in Pekin ducks. Meanwhile, one significant locus located in the intron of CXADR like membrane protein (CLMP) was also obtained in this study, contributing to 1.46% of the phenotypic variation. CLMP encodes a type I transmembrane protein and takes part in cell-cell adhesion. Previous researchers had shown that this gene was essential for normal intestinal development, and mutations in the gene were associated with congenital short bowel syndrome [CLMP and feed intake in ducks.As for DFI, two significant SNPs were detected on chromosome 2, and the candidate genes were , et al.  revealed, et al.  found on, et al.  pointed syndrome –27. Our EYS. EYS was reported to play an important role in degeneration of retinitis photoreceptors in humans [EYS might be associated with visual perception in Pekin ducks. Additionally, EGF like domain multiple 6 (EGFL6), cadherin 2 (CDH2), and EH domain containing 4 (EHD4) were also closely associated with TFT in our results. EGFL6 is a member of the epidermal growth factor repeat superfamily, which coordinates the regulation of bone remodeling and glucose homeostasis in humans [CDH2 produces a classical cadherin that can be crucial for bone growth and osteoblast differentiation in mice [CDH2 is associated with knee osteoarthritis in children [EHD4 took part in the activation of ATPases in Caenorhabditis elegans. And EHD2, a paralog of EHD4, was identified to be differentially abundant at 4 and 24 h in postmortem normal breast and woody breast in commercial broilers (P < 0.05), which might imply that EHD2 took part in the breast muscle myopathies process [For TFT, the most significant SNP was located downstream of n humans , 29. Then humans , 31. CDH in mice . The mutchildren , 34. Melchildren  reported process . IntegraIL1RAPL1) was associated with feed conversion ratio (FCR) and the candidate gene for residual feed intake (RFI) was ring finger protein 17 (RNF17). Deng et al. [ATP11A) was closely associated with body weight, dressed weight, eviscerated weight, foot weight, and wing weight in Pekin ducks. Compared with their results, the 25 candidate genes for feeding behavior traits in this study didn’t have any overlap with them. This might due to the fact that the genetic correlations between feeding behavior traits  and feed efficiency traits (FCR and RFI) were relatively low, which ranged from − 0.03 to 0.20 [Moreover, as we had previously conducted a genome-wide association study for feed efficiency traits in a fat strain of Pekin ducks, Zhu et al.  found thg et al.  also ideIn this study, the genetic parameters of feeding behavior traits were estimated, and the related genomic variations were identified. We obtained 30 significant SNPs using mixed-linear models, which localized to 25 candidate genes for the five different traits studied. The results of this study contributed to the sparse knowledge of feeding behavior traits in Pekin ducks and helped to achieve balanced breeding in the future.All of the experimental ducks were reared under consistent conditions at Beijing Golden Star Duck Inc. Briefly, ducks were provided ad libitum water and commercial diets: a starter diet (from 1 day to 18 day of age) containing 19% crude protein (CP), 12.81 MJ/kg dietary metabolizable energy (ME), and a grower diet containing 17.1% CP and 11.95 MJ/kg ME during our observation period. A total of 540 19-day-old ducks  were divided into three batches , with an interval of 7 days between each batch. At six weeks of age, ducks were blood sampled via the caudal tibia vein after fasting for six hours.Combined with radio frequency identification (RFID) technology and electronic feeders, feeding information of each duck was collected during the test period. The observation period for feeding behavior started at the age of 19 days and ended at the age of 42 days. The details of the recording process and the summary of different feeding behavior traits followed the same methods in our previous research . OverallMseI . Fragments ranging from 550 to 580 bp, including adapter sequence, were sequenced using an Illumina HiSeq2500 instrument. The data were deposited in the NCBI sequence read archive (SRP155579).Genomic DNA was extracted using a phenol-chloroform-based method, and genotyping was performed with a GBS method, which was first applied in our previous research . Briefly2 > 0.98 for low-quality filtering. We identified a subset of tagging SNPs that passed the following thresholds: minor allele frequency (MAF) > 0.05, sample call rate ≥ 0.95, and SNP call rate ≥ 0.95 with PLINK.Overall, a total of 1 TB clean reads were generated, together with an average sequencing depth of 1.5X for each duck. Clean data was mapped to a reference genome using BWA (v 0.7.17) , and thehttps://mirrors.tuna.tsinghua.edu.cn/CRAN/, TUNA Team, Tsinghua University, Beijing, China), and covariates with P-value < 0.05 were included in a mixed linear regression model as the fixed effects  for principal component analysis (PCA) , and the top three eigenvectors were implemented in association analysis to account for the effect of population stratification. The effects of covariates, including sex and batch, on quantitative phenotypes were assessed with analysis of variance (ANOVA) using R software  algorithm in GCTA (v 1.26.0) , which iWhere y is a vector of the normalized phenotype, a is the general mean, X is the matrix of fixed effects , B is the vector of corresponding coefficients; g is the accumulated effect of all SNPs except those on the chromosome where the candidate SNP is located , e is the vector of random residuals.The SNP-based genetic parameters for feeding behavior and the contribution of the phenotypic variances for significant SNPs were estimated using the GREML algorithm in GCTA (v 1.92.2) . The linWhere y is a vector of phenotypic value, W is the matrix of fixed variables, and Z represents the matrix of random variables, which refers to the effects of all SNPs on phenotype; e stands for random residuals, I is an identity matrix, b and u are the corresponding coefficients of fixed and random effects, respectively; λ) was calculated with R package qqman (0.1.4) [The genomic inflation factor ( (0.1.4) . Bonferr (0.1.4) . TherefoP-value that passed the significance level. GWAS does not distinguish a genuine causal locus from those statistically significant loci within a strong linkage disequilibrium (LD) region. Therefore, in order to characterize potential candidate genes responsible for a trait, we conducted a LD analysis and inferred the haplotype blocks containing peak SNPs by LDBlockShow [The conditional analysis was conducted to examine the potential associated SNPs that might be masked by a strong signal. Briefly, the initial region was tested with the strongest SNP covariate. Then, association analysis conditioning on the selected SNP was implemented to search for the top SNP iteratively one by one via a stepwise model selection procedure until no SNP had a conditional lockShow .P-value < 4.74E-06), we looked for the nearest candidate genes using BEDTools [For all the SNPs that exceeded a potential significant threshold (BEDTools . Due to BEDTools  using chAdditional file 1 Table S1.Additional file 2 Fig. S1-Fig. S3.


Cucumis sativus L.) is cultivated worldwide, and it is essential to produce enough high-quality seeds to meet demand. Pre-harvest sprouting (PHS) in cucumber is a critical problem and causes serious damage to seed production and quality. Nevertheless, the genetic basis and molecular mechanisms underlying cucumber PHS remain unclear. QTL-seq is an efficient approach for rapid quantitative trait loci (QTL) identification that simultaneously takes advantage of bulked-segregant analysis (BSA) and whole-genome resequencing. In the present research, QTL-seq analysis was performed to identify QTLs associated with PHS in cucumber using an F2 segregating population.Cucumber (qPHS4.1 and qPHS5.1, respectively. Subsequently, SNP and InDel markers selected from the candidate regions were used to refine the intervals using the extended F2 populations grown in the 2016 and 2017 seasons. Finally, qPHS4.1 was narrowed to 0.53 Mb on chromosome 4 flanked by the markers SNP-16 and SNP-24 and was found to explain 19–22% of the phenotypic variation in cucumber PHS. These results reveal that qPHS4.1 is a major-effect QTL associated with PHS in cucumber. Based on gene annotations and qRT-PCR expression analyses, Csa4G622760 and Csa4G622800 were proposed as the candidate genes.Two QTLs that spanned 7.3 Mb on Chromosome 4 and 0.15 Mb on Chromosome 5 were identified by QTL-seq and named These results provide novel insights into the genetic mechanism controlling PHS in cucumber and highlight the potential for marker-assisted selection of PHS resistance breeding.The online version contains supplementary material available at 10.1186/s12864-021-07548-8. Cucumis sativus L.) is an economically important vegetable globally. In 2018, cucumber was grown on 1,984,518 ha worldwide, and the cultivated area in China accounted for 52.72% of this area (www.fao.org/faostat/en). It is necessary to produce enough excellent-quality cucumber seeds to meet demand, especially in China. However, pre-harvest sprouting (PHS), also known as vivipary, a critical trait describing the untimely germination of seeds inside maternal fruits under certain conditions, severely decreases seed yields and quality [Cucumber  , 3. PHS Oryza sativa), wheat (Triticum aestivum), maize (Zea mays), barley (Hordeum vulgare) and tomato (Solanum lycopersicum). To date, in rice, more than 165 QTLs associated with seed dormancy or PHS resistance and located on different chromosomes have been identified [SD1 and SD2 on chromosome 5H, contributed the major effects on seed dormancy [SD1 was a major regulator of dormancy [SD2 was identified to prevent PHS [Regarding the genetic and molecular basis of seed dormancy and PHS resistance, extensive QTLs or genes for this trait have been identified in cereal crops and other vegetables, such as rice  and gene cloning analysis.Our previous studies have revealed that PHS was controlled by one major gene of additive-dominance effects plus additive-dominance polygene (D-1 model) via the method of mixed major-gene plus polygenes inheritance model . However1, F2 populations were sown directly into soil in the greenhouse on April 15 each year. For plant management, two female flowers were self-pollinated, and all the other female flowers and lateral branches were removed from each plant. The pollination date was recorded on labels hung on the peduncles of the fruits. The seeds in the cucumber fruits were harvested at 45 days after pollination (DAP), and the numbers of germinated seeds and total seeds were counted immediately. The PHS rate (%) was calculated as  × 100%. The average PHS rates of two cucumber fruits grown on the same plant were used for QTL analysis.The seeds of the resistant parent Q12, susceptible parent P60, and their F1, F2 populations  and showed a skewed normal distribution  and S-parent (P60), were subjected to whole-genome resequencing (WGRS) using the Illumina HiSeq4000 platform, and 36.83 Gb raw data was generated. The clean data were mapped to the cucumber reference genome  [Based on the phenotypic data of Fong; V2)  using thong; V2) , and 36.qPHS4.1 and qPHS5.1, respectively. In qPHS4.1, several loci with the highest Δ (SNP/InDel-index) value equal to ‘1’ were detected. Conversely, qPHS5.1 region was harboring loci with the lowest Δ (SNP/InDel-index) value equal to ‘-1’. These results indicated that the QTLs were associated with PHS in cucumber. qPHS4.1 conferred a partial level of PHS resistance in the resistant donor Q12, while qPHS5.1 provided partial resistance for the parent P60.Using GATK 3.8 software , a totalqPHS5.1, there were only two InDels detected in upstream. The other variations were identified in qPHS4.1interval.These two regions contained 443 SNPs and 124 InDels, of which 272 SNPs and 82 InDels were found to be intergenic, 70 SNPs and 19 InDels intronic, 4 SNPs synonymous, 6 SNPs nonsynonymous, 39 SNPs and 11 InDels in upstream and 47 SNPs and 10 InDels in downstream (Table Based on the gene annotation via ANNOVAR Version 2013Aug23) software , genes c013Aug23 2 individuals grown in 2016 and 2017 for 62 SNP and/or InDel markers selected from the qPHS4.1 and qPHS5.1 intervals, respectively. Finally, twenty-nine markers in qPHS4.1 were accurately genotyped and applied to construct the local genetic linkage maps by JoinMap 4.0 software [2 population. As shown in Table 2 population, two peak SNP loci, SNP-17 (LOD = 13.89) and SNP-24 (LOD = 16.06), were detected  in Q12 but decreased slightly in P60 from the 34 DAP stage to 40 DAP stage. These results suggested that Csa4G622760 and Csa4G622800 gene expression levels were higher in resistant cucumbers than in susceptible cucumbers before PHS occurred. Subsequently, accompanying the occurrence of PHS, its gene expression levels decreased significantly in resistant cucumbers compared to susceptible cucumbers. Therefore, we hypothesized that Csa4G622760 and Csa4G622800 are possible candidate genes involved in PHS in cucumber, but further functional analysis of these genes needs to be conducted.Taken together, these data show that the expression levels of the three genes were both down-regulated in Q12 and P60 with increasing ripeness of cucumber fruits. The 2 population derived from the two parents Q12 and P60, which showed opposite extremes of PHS phenotypes. Q12 is a typical resistant line in which PHS never occurs in favorable environments, while PHS occurs in the P60 line  is a critical problem that causes devastating losses to seed yields and quality  and wide2 population grown in 2017. Two QTLs associated with PHS, qPHS4.1 and qPHS5.1, were initially identified, which spanned 7.3 Mb on chromosome 4 and 0.15 Mb on chromosome 5, respectively. The predicted regions in R-pool and S-pool appeared as mirror images [qPHS4.1 was derived from the resistant donor Q12 and qPHS5.1 provided PHS resistance for P60. However, P60 was identified to be a susceptible genotype to PHS. Therefore, qPHS5.1 could be a putative minor-effect QTL for PHS.The application of high-throughput next-generation sequencing technology promotes the development of rapid molecular marker discovery and physical map construction. QTL-seq is a new method that combines next-generation sequencing with BSA for the rapid detection of QTLs and links molecular markers associated with traits of interest. It was first developed by Takagi et al. and applied in rice . Since tr images  in Fig. 2 population grown in 2016 was consistent with the findings from the 2017 season, which indicated that the experimental results were reliable and accurate. Subsequently, the regions from the two seasons were found to overlap. Therefore, qPHS4.1 was refined and narrowed down to 0.53 Mb on Chromosome 4. Unfortunately, qPHS5.1 was unmapped by JoinMap 4.0 and MapQTL version 6 software. This result demonstrated that qPHS4.1 was a major-effect QTL controlling PHS in cucumber and qPHS5.1 was a merely minor-effect QTL. We supposed that only two InDel markers detected from QTL-seq were used in the validation. We need to develop more molecular markers to further analyze the minor-effect involved in qPHS5.1 controlling PHS. As a major-effect QTL, qPHS4.1 was identified to explain about 20% of the phenotypic variation. The available tightly linked markers in qPHS4.1 can be used in MAS to promote the breeding process. We propose the introgression of qPHS4.1 could provide a partial level of PHS resistance for a susceptible background genotype and decrease the PHS rate of susceptible cucumber lines in a certain extent.And then, traditional QTL mapping methods were conducted to validate and narrow down the candidate regions. The phenotype identification and QTL mapping using the extended FqPHS4.1 region, a total of 39 candidate genes was identified by ANNOVAR software. Based on gene expression analysis by qRT-PCR, two genes, Csa4G622760 and Csa4G622800, containing upstream polymorphic SNPs, were considered candidate PHS regulating genes in cucumber  and P60  were crossed to obtain F1. F1 plants were self-pollinated to generate an F2 segregating population. P1 and P2 populations were evaluated for PHS in the experimental farm of the Tianjin Kernel Cucumber Research Institute  in 2016. Significance test was conducted between P1 and P2 populations. The F2 population was evaluated in 2016 (328 plants) and 2017 (299 plants) seasons. All the plants were grown in greenhouse conditions under whole-day light exposure. The day/night temperature in the greenhouse was controlled at 28–35 °C/15–26 °C.The high-generation inbred cucumber lines Q12 . Q12 and P60 genomic DNA were used to construct the P1 pool and P2 pool. Based on the phenotype data of F2 individuals grown in the 2017 season . According to the protocol, briefly, DNA samples were randomly fragmented by sonication to a size of 350 bp, then DNA fragments were end polished, A-tailed, and ligated with the full-length adapter for Illumina sequencing with further PCR amplification. PCR products were purified (AMPure XP system) and libraries were analyzed for size distribution by Agilent2100 Bioanalyzer and quantified using real-time PCR [http://www.novogene.com/).The genomic DNA of Q12, P60 and Ftime PCR . These lp = 0.05). By examining the Δ (SNP/InDel-index), the candidate genomic regions harboring high average Δ (SNP/InDel-index) values exceeding the confidence intervals and containing variations with SNP/InDel-index = ‘0’ or ‘1’ were defined as predicted regions for association with PHS. SNP/InDel-index was equal to ‘0’ or ‘1’ when the candidate variations in pools were entirely from P1 or P2, respectively, and the corresponding Δ (SNP/InDel-index) was equal to ‘-1’ or ‘1’.The raw sequencing data were filtered to get high-quality clean reads by removing the reads with ≥10% unidentified nucleotides, removing the reads with > 50% bases having phred quality < 5 and the reads with > 10 nucleotides aligned to the adapter. The clean reads obtained from four pools were aligned to the cucumber reference genome   using thThe ANNOVAR (Version 2013Aug23) software was used to annotate the candidate genes in the regions .1 plants. Then, polymorphic SNP and InDel markers were used to genotype the extended F2 individuals sown in the 2016 season and 2017 season. This validation was performed using Hi-SNP high-throughput genotyping method . The specific multiplex PCR primers of the markers were designed by Primer 3 online software  based on the cucumber reference genome  [2 individuals sown in 2016 (328 plants) and 2017 (299 plants), regional linkage maps were constructed using JoinMap 4.0 software [2 individuals, QTL analysis was performed by the software MapQTL version 6 [To verify the candidate SNP and InDel markers and narrow down the regions identified by QTL-seq, significant SNPs and InDels in the candidate regions were first selected and validated in the two parents and their Fong; V2)   , 46. Bassoftware  with thesoftware , respectersion 6 . The “MQhttp://www.icugi.org/cgi-bin/ICuGI/index.cgi), the functions of candidate PHS-associated genes that contained non-synonymous or upstream/downstream variations were predicted.According to the further narrowed region of the QTLs, effective SNPs or InDels associated with PHS were identified. Based on the Cucurbit Genomics Database (tubulin gene (GenBank ID: AF044573.1) was used as the reference gene for normalization of the relative expression of the candidate genes. The relative expression levels of the target genes were calculated using the 2−ΔΔCt method [t-test was used to check the significant differences in expression levels among the samples.We used qRT-PCR to investigate the relative expression levels of the candidate genes between the two parents. The cucumber cavity flesh tissues surrounding the seeds at 34 DAP and 40 DAP were sampled, and RNA was extracted using TRNzol Universal Reagent following the manufacturer’s protocol . The RNA quality was evaluated by agarose gel electrophoresis. cDNA was synthesized using a RevertAid First Strand cDNA Synthesis Kit . Primers for candidate genes were designed by Primer 3 and synthesized by Sangon Biotech (Shanghai) Co., Ltd. . Details of the primer sequences are presented in Table t method . The expAdditional file 1: Table S1. Phenotypic evaluation of pre-harvest sprouting trait for parents and their F1 and F2 populations.Additional file 2: Table S2. Candidate genes by annotation and loci generated from QTL-seq.Additional file 3: Table S3. Physical position, genetic distance, LOD values and variations explained generated from software MapQTL version 6.Additional file 4: Table S4. Detailed multiplex PCR Primers of the Markers.


Aegilops tauschii Coss. (D-genome) diversity and delineates synthetic and tetraploid wheat from other wheat, as well as tetraploid species and subgroups. The content includes SNP tagging key trait loci in wheat and barley, as well as direct connections to other genotyping platforms and legacy datasets. The utility of the array is enhanced through the web-based tool, Pretzel (https://plantinformatics.io/) which enables the content of the array to be visualized and interrogated interactively in the context of numerous genetic and genomic resources to be connected more seamlessly to research and breeding. The array is available for use by the international wheat and barley community.Array-based single nucleotide polymorphism (SNP) genotyping platforms have low genotype error and missing data rates compared to genotyping-by-sequencing technologies. However, design decisions used to create array-based SNP genotyping assays for both research and breeding applications are critical to their success. We describe a novel approach applicable to any animal or plant species for the design of cost-effective imputation-enabled SNP genotyping arrays with broad utility and demonstrate its application through the development of the Illumina Infinium Wheat Barley 40K SNP array Version 1.0. We show that the approach delivers high quality and high resolution data for wheat and barley, including when samples are jointly hybridised. The new array aims to maximally capture haplotypic diversity in globally diverse wheat and barley germplasm while minimizing ascertainment bias. Comprising mostly biallelic markers that were designed to be species-specific and single-copy, the array permits highly accurate imputation in diverse germplasm to improve the statistical power of genome-wide association studies (GWAS) and genomic selection. The SNP content captures tetraploid wheat (A- and B-genome) and  Triticum aestivum L.) and barley (Hordeum vulgare L.) for various applications including genome-wide association studies (GWAS), characterization of genetic resources, marker-assisted breeding, and genomic selection (High-density genotyping arrays that simultaneously interrogate thousands of single nucleotide polymorphisms (SNPs) have proven to be a powerful tool in genetic studies. The first generation of these have been widely used in wheat , they have high setup costs. The latter has presented significant challenges for the development of SNP arrays that can comprehensively serve both research and breeding applications; researchers have traditionally preferred high SNP density (which creates a high genotyping cost per sample but low cost per data point), while breeders typically only want a minimally sufficient marker density. This challenge drove us to develop a general approach to SNP array design that specifically takes into consideration the need for low-cost genotyping across a wide range of research and breeding applications, with the aim to seamlessly connect research to breeding.Pretzel  diversity, an additional 149 accessions selected from the global collection contained in the associated VCF file3 to expand the diversity captured and 102 historical breeding lines from the InterGrain commercial wheat breeding program4. The first two sets of accessions maximally captured genetic diversity among 6,087 globally diverse wheat accessions comprising landraces, varieties, synthetic derivatives, and novel trait donor lines (Ae. tauschii) to three Australian varieties: Yitpi, Annuello, and Correll , domesticated emmer (T. turgidum ssp. dicoccocum), and T. turgidum genotypes including durum landraces and cultivars were used to select tetraploid wheat specific SNP.The Infinium 90K wheat SNP genotypes reported by H. vulgare landraces as well as H. spontaneum  clustering (which left 157 H. vulgare-like accessions), the resulting SNP list was used to call SNP genotypes in the 120 InterGrain historical breeding lines. After filtering for > 60% call rate and > 5% MAF , 932,098 SNPs were used for LD analysis.In barley, SNP discovery was performed as described by r2) based on the allele frequency in the global barley or wheat diversity panel (excluding the synthetic derivatives) between two SNPs was considered as a measure of LD.Linkage disequilibrium analysis for the filtered SNP was performed using PLINK  at the cTo maximize the number of SNPs assayed for a given number of probes on the bead chip array, A/T and C/G variants (Infinium Type I SNP which require two probes) were avoided. To maximize SNP scorability and genotype calling accuracy, polymorphism underlying the 50-mer oligonucleotide SNP probe sequences were also avoided as they are known to cause shifts in SNP cluster position . For tagr2 value from the LD analysis), removed all SNPs linked to the selected tSNPs from the remaining list of SNPs, as well as all SNPs linked to any SNP in the selected tSNP set to avoid directly tagging any SNP at r2 ≥ 0.9 more than once, before repeating the process until a target number of tSNP was reached. This process ensured that the set of tSNP selected was the minimum set required to tag the most SNPs at r2 ≥ 0.90. Specifically, for a given set of SNPs S = {1s,s2,…} and function r2 defining the Pearson correlation coefficient squared ∀si,sj ∈ S, we define the tSNP set for s_i at q to be:A custom algorithm was used to select tSNP tagging LD blocks in each of the global collections and to facilitate imputation from the density of the SNP array. In brief, for each chromosome, the algorithm iteratively selected the most informative tSNPs passing all filters  to be the first element of the ordered sequence L, and select s_i.We define The algorithm is then:q = 0.9,m = 10 defines the first iteration of tSNP selection.For example, the above applied with To guard against possible loss of imputation accuracy due to SNP assays failing to provide reliable genotype calls, a level of redundancy was included in the tSNP sets for wheat and barley. Specifically, three tSNPs were chosen when the number of SNPs tagged was ≥ 50 and two tSNPs were selected when the number of SNPs tagged was ≥ 20. Single tSNP were selected when they tagged at least 10 SNPs. Some SNP sets could not be tagged because no probe passed all the filters; in this case, we ran the algorithm on the remaining sets allowing for SNP passing relaxed filters . In addition, tSNPs were selected to tag genomic regions that had sparse SNP coverage but high LD; i.e., tagging < 10 SNP within windows larger than 500 Kb in wheat and 1Mb in barley. Finally, SNPs were selected in regions still lacking SNPs after the previous steps.To ensure broader applicability of the SNP array in research and breeding, the content included SNP selected to specifically interlink germplasm resources, such as the 19,778 domesticated barley accessions with GBS genotypes described by The wheat and barley global diversity sets were used as reference haplotypes for imputation. For wheat, accessions clustering with the synthetic derivatives in a PCA analysis were excluded. For barley, only samples with < 20% missing data were used. In both species, missing data were filled in using Beagle 4.1  and phas5  between SNPs called in both genotypes being compared, while concordance was measured as the fraction of SNPs in agreement with those called in both genotypes being compared.To assess the accuracy of imputation into globally diverse germplasm, 100-fold cross validation was performed. A random subset of 100 wheat (or 10 barley) lines had their true genotypes masked, leaving only the tSNP. The remaining lines were then used as the reference population with Minimac3 software  to imputP-value of 1×10–5 was used as the significant threshold for declaring a marker-trait association.Genome-wide association studies were performed using the GCTA software  using a Samples were assayed following the protocol for Infinium XT bead chip technology . SNP clustering and allele calling was performed using GenomeStudio Polyploid software  using the Illumina-supplied wheat or barley SNP manifest file. The custom genotype calling pipeline described by 6 and ggplot2 , we define its tag SNP (or tSNP) set as the set of SNPs tagged by this SNP at r2 ≥ 0.9. This metric provides a measure of equivalence as well as a natural ranking of SNP by their informativeness, as defined by the size of their tSNP set. We assume that the relationship is symmetrical; i.e., if SNP A is in the tSNP set of SNP B, then SNP B should be in the tSNP set of SNP A. The original set of SNPs are then filtered using technology and application-specific criteria  while maintaining connectivity to SNPs that fail the filters via the tSNP that pass the filters.The central idea of the design concept is to exploit LD using the To design a genotyping array that has broader applicability in research and breeding, the SNPs should be discovered in diverse germplasm to avoid ascertainment bias (since LD is population-dependent) and with sufficient density to produce large tSNP sets. The latter helps ensure that at least one SNP in a tSNP set will pass all the design filters in most instances. Here, we used a globally diverse set of barley landrace accessions and a globally diverse set of wheat accessions that included landraces, varieties, novel trait donors, and historical breeding lines . For arrA novel selection algorithm  is then used to select SNPs that maximize LD capture, while minimizing the number of SNPs assayed on the array, using only SNPs that pass the design filters.The design concept can be applied to any animal or plant species. In addition to this set of SNPs, utility in research and breeding can be further enhanced by including context-relevant SNPs, such as trait-linked markers and markers that link germplasm resources across different genotyping technologies. The approach used to design the Wheat Barley 40K SNP array is summarized in https://dataverse.harvard.edu/dataverse/WheatBarley40k_v1.Filtering for a minimum minor allele frequency (MAF) of 1% and maximum missing rate of 40% using the 8,869,370 wheat SNP published in r2 ≥ 0.9, a total of 1.07M wheat and 413,508 barley high quality SNPs were singletons; i.e., they had no SNP within 1Mb up and downstream with r2 ≥ 0.9. These SNPs were either genuine singletons or categorized as singletons due to the absence of additional SNPs within the surrounding 2Mb region. As singleton SNPs can only be tagged directly, which is not feasible on a low-density array, these SNPs were not considered further for inclusion on the array.Based on LD values of r2 ≥ 0.9, 0.7 and 0.5, respectively. Of the 119,633 non-singleton barley SNPs passing all filters, the selection algorithm identified 7,316 tSNPs which tagged a total of 150,096, 294,659, and 390,844 SNPs at r2 ≥ 0.9, 0.7, and 0.5, respectively. At the genome level, the rate of return per tSNP was surprisingly similar for wheat and barley and plateaued at about 15,000 tSNP at r2 ≥ 0.9  and tagging SNP sets untaggable with the strictly filtered SNP; and SNP to tag genomic regions that had sparse SNP coverage but high LD; i.e., tagging < 10 SNPs within windows larger than 500 Kb in wheat and 1 Mb in barley. The latter SNPs are expected to support increased imputation density in these regions as higher density SNP datasets become available in the future. The wheat tSNPs tagged a total of 394,034, 636,641, and 758,452 SNPs at  another . The disr2 ≥ 0.50, 0.70, and 0.90, in the globally diverse wheat and barley germplasm. This was achieved by randomly selecting 100 wheat (or 10 barley) lines and masking their true genotypes to leave only the tSNPs. Using the remaining lines as the reference population, the masked genotypes for each randomly selected line were imputed to the density of one of the target SNP sets. Accuracy was determined from the correlation squared and concordance between the imputed and actual genotypes for each wheat or barley line averaged over the occurrences of that sample within the 100 iterations.Cross validation (100-fold) was used to assess the accuracy for imputing from the tSNPs on the array to the sets of SNPs tagged at r2 ≥ 0.90 and the lowest for those tagged at r2 ≥ 0.50 (r2 decreased), with reduced accuracy most evident in the bottom 50 accessions   .r2 ≥ 0.90, compared to those tagged at r2 ≥ 0.50  was observed between the average correlation squared  for the set of SNPs tagged at 2 ≥ 0.50 . As obseenotypes . The redTo confirm that the selected tSNPs were useful for detecting marker-trait associations, we performed GWAS using phenotype data for awned status (scored as a presence-absence trait) in 355 wheat accessions and defining row type (scored as two- or six-rowed) in 121 barley accessions and the selected tSNPs before and after imputation. The results were compared with GWAS performed using the same phenotypic data and exome SNP genotypes . SignifiThe final array design comprised 34,481 tSNPs and two additional categories of context-specific SNPs , domesticated emmer (T. turgidum ssp. dicoccocum), and durum (T. turgidum) landraces, and durum cultivars; (2) subgroup-specific private SNPs that showed a MAF ≥ 0.1 in one of the subgroups and were either monomorphic or showed a MAF < 0.05 in the other subgroups; (3) subgroup-specific high MAF SNPs that were present at ≥ 0.3 MAF in any one of the subgroups; and (4) neutral SNPs that did not show any signatures of selection, were polymorphic in all subgroups and showed an overall MAF of ≥ 0.4. The ability of these SNPs to reliably differentiate the tetraploid species subgroups as efficiently as the Infinium wheat 90K array is shown in The first context-specific category included 2,609 SNPs from the Infinium wheat 90K SNP array  that werAe. tauschii (D-genome) diversity present in backcross synthetic derivatives that originated from crosses involving 100 primary synthetic parents, which were selected for phenotypic and genetic diversity among approximately 400 primary synthetics developed at CIMMYT and imported to Australia in 2001. Each of the 100 primary synthetic parents was derived from a different Ae. tauschii accession. SNPs tagging diversity in Ae. tauschii were selected to provide high genome coverage in the D-genome  D-genome diversity from Ae. tauschii that showed a MAF ≥ 0.1 in the synthetic derivative collection and MAF ≤ 0.1 in the globally diverse wheat collection. The ability of these SNPs to reliably differentiate synthetic wheat from common wheat as efficiently as the Infinium wheat 90K array is shown in The second category included 1,206 exome SNPs tagging D-genome . They weThe final category included linked SNPs for key breeding traits and SNPs linking major germplasm resources genotyped with different technologies. In total, 457 wheat and 178 barley SNPs corresponded to published trait-linked markers as well as 109 SNPs associated with agronomically important genes reported in published GWAS studies  Supplem. AnotherA limitation of hybridization-based genotyping arrays is that their oligonucleotide probes hybridize both to the targeted locus and its homologs and paralogs, if present . ConsequFor the purpose of evaluating the performance of the array, the wheat and barley diversity populations were used to define cluster positions for SNP genotype calling. The vast majority (98%) of the 39,654 SNP assays on the array produced scorable cluster patterns when hybridized with a barley or wheat sample; about 91%  of the barley and 83%  of the wheat SNP assays could be reliably scored as single-copy biallelic markers, with the REF and ALT clusters having theta values close to 0 and 1 in GenomeStudio SNP plots . While tThe concordance between called and actual genotypes was exceptionally high for both wheat and barley. The average genotype concordance and correlation squared were 99.5 and 98.1%, respectively, in wheat when heterozygous genotype calls were excluded, and 97.6 and 95.7%, respectively, when heterozygous calls were included. Similarly, 99.8% concordance and 99.2% correlation squared were observed in barley when heterozygous calls were excluded, and 98.2 and 97.2% were observed with heterozygous calls included. The average missing data rates were 4.8 and 3.8% in wheat and barley, respectively.The design process specifically aimed to select species-specific SNP probes and thus it should be theoretically possible to jointly hybridize a wheat and barley sample to the same bead chip array  without the loss of genotype calling accuracy. Cross-hybridization between species is expected to confound genotype calling accuracy by creating shifts in SNP cluster positions and/or complex clustering patterns that cannot be easily scored.To evaluate the assay performance of a dual hybridization, samples from the InterGrain commercial barley and wheat breeding programs were used to define cluster positions and call SNP genotypes for 576 dual hybridization assays. The same samples were also assayed in single sample hybridization assays to enable genotype calling accuracy between dual and single hybridization assays to be directly compared.Most of the barley and wheat SNPs in dual hybridization assays produced scorable cluster patterns. Shifts in cluster positions were observed, which indicated either that some oligonucleotide probes showed a degree of cross-species hybridization or that deviation from the standard amount of sample DNA (200 ng per sample) recommended for the bead chip assay affected signal-to-noise. Through empirical testing, we found that the quantity of genomic DNA per sample was a major factor causing shifts in cluster position (data not shown) and could be minimized by adjusting the input DNA for each sample to match the ratio of the genome size for each species; e.g., 200 ng barley DNA and 600 ng wheat DNA; the bread wheat genome is about three times larger than that of barley.For the purpose of assessing genotype calling accuracy for dual hybridization assays, only SNPs that revealed polymorphism among the 576 wheat and barley samples assayed were considered. Of the 9,826 barley and 9,118 wheat SNPs showing polymorphism, the vast majority were easily scored as biallelic markers and had good cluster separation, indicating that oligonucleotide probe cross-species hybridization was minimal . The aveHigh-throughput, low-cost and flexible genotyping platforms are required for both research and breeding applications. Compared to GBS and PCR-based marker systems, array-based genotyping platforms are highly commercialized and highly customizable, both for the number of markers and the samples assayed. They also have low genotype error and missing data rates compared to GBS technologies . ConsequThe key difference between the Infinium Wheat Barley 40K SNP array and previously reported array-based genotyping assays is a paradigm shift in the logic underpinning its design. To date, commonly used low-density genotyping arrays are comprised of the most scorable and informative markers from higher density arrays. For example, the Infinium Wheat 15K SNP array  and AxioThe design implemented in the Infinium Wheat Barley 40K SNP array is based on the hugely expanded genotypic and genomic resources now available for wheat and barley. By using these resources, we were able to identify species-specific single-copy tSNPs that capture a large proportion of the haplotypic diversity in globally diverse germplasm, and are highly scorable for accurate genotype calling, minimize ascertainment bias, and enable accurate imputation to high SNP density. In the case of wheat, this included the use of 2.04M SNPs identified from exome sequence data of 1,041 accessions selected to maximally capture genetic diversity among a global collection of 6,087 accessions genotyped using the Infinium 90K SNP array . The glor2 has been used in many tSNP algorithms as it is a major determinant of imputation accuracy and has a simple inverse relationship with the sample size required to detect associations in GWAS . This aspect of the array design was confirmed by performing GWAS for awned status in wheat and row type in barley  when imputing the set of SNPs tagged at r2 ≥ 0.9  in both wheat and barley. Importantly, imputation accuracy was also high for the set of SNPs tagged at r2 ≥ 0.5  diversity represented in 100 primary synthetic wheats, where each primary synthetic was derived from a different Ae. tauschii accession. Collectively, these SNPs provide broad utility ranging from the differentiation and genetic characterization of tetraploid and synthetic wheat (as well as other secondary and tertiary gene pools of wheat) to the tracking of introgressed genomic segments during breeding. Also included are SNPs that directly link to the Infinium 90K , the manufacturer of the Infinium bead chip technology.In conclusion, we have described a novel approach applicable to any animal or plant species for designing cost-effective imputation-enabled SNP genotyping arrays that have broad applicability in research and industry applications  and support the hybridization of multiple samples to the same array. The utility of this design approach was demonstrated through its implementation to develop a new Infinium Wheat Barley 40K SNP array. In addition to supporting broad utility in research and breeding, this array can be used as a resource to connect genetic and genomic datasets generated across germplasm pools and time. The array is further supported by the publicly available web-tool PRJEB8044 and PRJEB31218, respectively. The filtered set of exome genotype calls for accessions and SNP underpinning the LD analysis and tag SNP selection for wheat (10.7910/DVN/5LVYI1) and barley (10.7910/DVN/CUPAXD) as well as the D-genome synthetic derivative-enriched SNP matrix (10.7910/DVN/0QEASF) are available through Dataverse at https://dataverse.harvard.edu/dataverse/WheatBarley40k_v1. Information about the status of each SNP, including tag SNP set ID and whether the SNP passed design filters, is included in the INFO column. Illumina 90k iSelect genotypes for the accessions used to select tetraploid-specific content is available at https://figshare.com/articles/dataset/Durum_Wheat_cv_Svevo_annotation/6984035 (Exome data used from /6984035 .RP performed LD analysis. GK-G selected tagging SNP, performed imputation analyses, and produced the final designs. KF and DW performed exome and whole genome sequencing, Infinium Wheat Barley 40K assays, and genotype calling. JT performed sequence alignments and genotype calling. HR, JG, AR, DMo, and DMu selected non-tagging SNPs and provided wheat and barley germplasm. TW, HD, JT, and MH conceived the project. GK-G and MH wrote the manuscript. All authors contributed to the article and approved the submitted version.HR, JG, AR, DMo, DMu, and TW were employed by InterGrain. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.