NPEPL1, EDN3, GNAS, C20orf85, VAPB, BMP7, TUBB1, ELMO2, DDX27, and NCOA5, which are associated with dermalhyperpigmentation.Facial pigmentation is an important economic trait ofchickens, especially for laying hens, which will affect the carcassappearance of eliminated layers. Therefore, identifying the genomic regionsand exploring the function of this region that contributes to understanding thevariation of skin color traits is significant for breeding. In the study,291 pure-line Xinyang blue-shelled laying hens were selected, of which 75 weredark-faced chickens and 216 were white-faced chickens. The population wassequenced and typed by GBS genotyping technology. The obtained high-qualitySNPs and pigmentation phenotypes were analyzed by a genome-wide associationstudy (GWAS) and a  Commercialized poultry varieties mostly have white or yellow skintraits. Some Chinese indigenous or Silkie chicken breeds have a black skinphenotype. These skin color traits are defined as quality traits andprimarily controlled by genetic factors. It is one of the representativephenotypes of hypermelanization, which is caused by the deposition ofnumerous melanin particles in dermal and visceral tissue and known asfibromelanosis (FM) . Melanocytes are the major cell typesproduced by birds and mammals to display body color. Melanocyte precursorcells, melanoblasts, are produced by the nerve crest and other progenitorcells in the early stage of embryogenesis . Thought tooriginate in China and described by Marco Polo in the 13th century duringhis explorations of Asia, the poultry breeds with FM are believed to havebeen established well before the 13th century due to references in writingsof unusual fowl in ancient Chinese . In 1911, researchers reportedthat the black trait in chicken is autosomal dominant . The physical location of FM was identified by a genome-wide single-nucleotide polymorphism (SNP)-traitassociation analysis . Studies have shown that an invertedreplication and junction of two genomic regions which are separated by morethan 400 kb in wild-type individuals is the causal FM mutation. The mutationis formed by the inversion and replication of two genomic fragments Dup1 andDup2 of 129 and 172 kb, respectively. The FM mutation has a dose effecton the regulation of black-skinned traits; the degree of blackness of theskin of homozygous FM mutants is significantly higher than that ofheterozygous and wild-type homozygotes .Chinese black-boned chicken breeds include Silkie, Dongxiang, Emei Black,Wumeng blacked-boned chicken, and other indigenous or hybrid breeds. Theseblack-boned chicken breeds are characterized by the intensity of the blackpigmentation in the skin, cockscombs, muscles, bones, and visceral organs,and it can be readily seen in the trachea, pericardium, blood vessels,sheaths of muscles and nerves, gonads, mesenteries of the gut, andperiosteum of bone . In particular, the Silkie is one of the mostwell-known breeds, and geneticists have researched this breed extensively asa pigmentation model. However, there are relatively few studies on theexcavation and identification of pigmentation functional genes in indigenousblue-shelled chicken breeds. Recent advances in next-generation sequencing have facilitated genome-wide SNP identification and SNPcharacterization. The advent of next-generation sequencing (NGS) has reduced the cost of genomesequencing to a level where genotyping by sequencing (GBS) is nowconsidered a powerful tool for inquiring into a large number of genomicvariations (SNPs). A large number of candidate genes related to differenteconomic traits of livestock and poultry have been identified by thismethod, such as the coat color of sheep , the fatty acidbiosynthesis and litter traits of pigs , and the bodyweight of chicken . The combined analysis of genome-wide associationstudy (GWAS) and selectionsignatures can effectively reveal agronomic traits of crops  and livestock economic traits . With advances in genomicstools, this study aims to identify SNP loci associated with dermalhyperpigmentation and useful SNP markers to track these loci; it also aims to determine newgenetics donors through signatures of selection and associated loci, whichwill provide a theoretical basis for the selection of blue-shelled hens andthe deposition of skin melanin.22.1Animals used in this study were approved by the Ethics and Animal WelfareCommittee of Shanghai Academy of Agricultural Sciences and performedaccording to the guidelines established by “the instructive notions withrespect to caring for laboratory animals” issued by the Ministry of Scienceand Technology of the People's Republic of China (no. (2006)398). This studywas conducted using 291 pure-line Xinyang blue-shelled layers  and 216 white-faced ) from Shanghai Poultry BreedingCo., Ltd., China. The original group of Xinyang blue-shelled layers comesfrom the local blue-shelled layers collected in Dongxiang County, JiangxiProvince. It is currently used as the parent line of the three-line matchingfor commercial egg production. The selected chicken population was from thesame batch, with pedigree, single-cage feeding, and consistent management.Pulmonary venous blood collection and extraction of genomic DNA occurred at36 weeks of age.2.2In the construction of the GBS library, the genome was digested byrestriction endonucleases, and each sample was amplified by a bar-codeinterface. Then the samples were mixed, and the required fragments wereselected to construct the library. Paired-end 150 sequencing was performedusing an Illumina HiSeq sequencing platform.2.3After the construction of the library, the initial quantification wascarried out with Qubit 2.0, and the library was diluted to 1 ng/2.4After the qualified library inspection, Illumina HiSeq PE150 was sequencedby pooling different libraries according to the effective concentration andquantity of target data. Efficient high-quality sequencing data were alignedto the reference genome by BWA  software (parameter:mem-t4-k32-M).2.5SNPs mainly refer to DNA sequence polymorphisms caused by variation in asingle nucleotide at the genome level and include conversions andtransversions of a single base. We used software such as SAMtools for group SNP detection. Bayesian models were used to detect polymorphicloci in the population. High-quality SNPs were obtained using ANNOVAR  software for population SNP annotations. ANNOVAR  is anefficient software tool that uses the latest information to functionallyannotate genetic variants detected in multiple genomes.2.6PCA only deals with autosomal data with the number of individuals https://cnsgenomics.com/software/gcta/#Download, last access: 13 December 2020) software calculates featurevectors and feature values, and R software draws PCA distribution maps.In Eq. (1), 2.7X is the indicator matrix of the fixedeffect, and Z is theindicator matrix of the SNP, and W is theindicator matrix of the random effect, and In the process of GWAS analysis, the main factors causing false associationare individual relationships and population stratification. Therefore, amixed linear model was used to analyze the correlation of traits. Populationgenetic structure was used as a fixed effect, and an individual geneticrelationship was used as a random effect to correct the effects ofpopulation structure and individual genetic relationships:We considered dark and white face of chicken as case/control groups toobtain Wright's 2.8http://metascape.org/gp/index.html#/main/step1, last access: 13 December 2020).The gene contents in the candidate regions were annotated according to theoutliers tested by 33.1The total raw base sequence obtained after sequencing was 267.433 Gb, andthe amount of individual sequencing was from 542.663 to 2079.192 Mb, withan average of 919.014 Mb per sample. The data obtained at this stagecontained a certain amount of interference information, which will causeinterference to subsequent analysis and need to be removed. According tothe filtration conditions, strict quality control was obtained, and 267.402 Gb high-quality clean bases were obtained, with an average of 918.910 Mb perindividual. The minimum sequencing of the individual was 542.510 Mb, and themaximum sequencing was 20795.560 Mb. The minimum value of Q20 was 93.29 %,indicating that the mass score of 93.29 % is greater than or equal to Q20(error rate is less than 1 %), and the minimum value of Q30 is 85 %,indicating that the quality score of more than 85 % of bases is greaterthan Q30 (accurate). The rate was equal to 99.9 %, the sequencing qualitywas high, the GC distribution was normal, and the average value was41.75 % (Supplement Table S1). All the samples used in the GBS libraryin this study were not contaminated, and the database was successfullyconstructed.3.2On comparing the 458 304 SNPs in this experiment with the chicken dbSNPlibrary, the proportion of known and newly discovered SNPs was 88.9 % and10.7 %, respectively. Because there is only chromosomal information in thereference dbSNP library, the information on the scaffold is missing.Therefore, a very small fraction (0.4 %) is on the scaffold .Except for the SNPs distributed in the scaffold, the distribution of theremaining SNPs on each chromosome was analyzed. Most of the SNPs weredistributed on chromosomes 1–5, while a small number were distributed onchromosomes 16, 32, W, and MT linkage groups .3.3PCA analysis of 458 304 independent SNPs revealed that the genetic distanceof pure lines of dark- and white-faced Xinyang blue shell layers was close,and there was no obvious segregation, which was consistent with the breedingsituation of the whole population .3.4EDN3 and BMP7 (Table 1). The most significant SNPis at position 11 205 031 on chromosome 20. The results of detectionTo explore the genetic mechanism of face color in Xinyang blue-shelledchickens, we conducted a GWAS with the univariate method for face colorphenotype. A total of 130 genome-wide significant SNPs located on chromosome 20 were successfully identified for the traits . The associated SNPs located between regions 10.7 and 11.6 Mb were entirely attributable to a chromosomal region . If SNPs located on the linkagegroup are not considered, the high-quality SNPs identified in this study aremainly located on chromosomes 1 to 5, and the number of SNPs on chromosomes6 to 10 is average, whereas a minimum number were found on chromosomes 16,32, and W. This is the same as in a previous study, because chromosomes 1–5are larger than the other chromosomes . After annotation, most ofthe SNPs are located in the intergenic and intron regions, and a very smallnumber of SNPs are located in the exon functional region, indicating thatonly a few variant sites have an effect on protein translation. This resultis the same as that reported in previous studies . In addition,10.7 % of high-quality SNPs were found to be different from those in thedbSNP library, which cannot be achieved by SNP chips.4.2NPEPL1, EDN3, GNAS, andSTX16 genes. Previous GWAS studies revealed that GNAS, BMP7, and STX16 genes may be associatedwith hyperpigmentation of the visceral peritoneum (HVP) . Themolecular function of NPEPL1 is enriched to GO:0030145 (manganese ion binding),and the protein function is metallopeptidases. Endocrine process(GO:0050886) involves the secretion of or response to endocrinehormones. An endocrine hormone is a hormone released into the circulatorysystem. CTSZ, EDN3, and GNAS are involved this process. In poultry, hyperpigmentation isdetermined by a dominant autosomal gene conditionally termed Fm/EDN3(fibromelanosis) interacting with the sex-linked recessive wild typeidEDN3 gene as the mainfactor provoking fibromelanosis in Silkie chickens .The most significant genomic regions were identified, including EDN3 gene.EDN3 is a gene with an acknowledged role in enhancing the proliferation ofmelanoblasts and melanocytic regulation . Endothelins are endothelium-derivedvasoactive peptides involved in a variety of biological functions, and theactive form of this protein is a 21 amino acid peptide processed from theprecursor protein . The active peptide is a ligand forendothelin receptor type B (EDNRB) . The interaction thisendothelin with EDNRB is essential for development of neural crest-derived celllineages, such as melanocytes and enteric neurons . Mutationsin this gene and EDNRB have been associated with Hirschsprung disease (HSCR) and Waardenburg syndrome (WS) . The gene mayalso be related to pigmentation of sheep skin . Recently, a combined analysis of genomic and transcriptomic datasuggests that the candidate gene related to the black-bone trait, EDN3, mightinteract with the upstream ncRNA LOC101747896 to generate black skin color duringmelanogenesis . The extremely significant SNP in this region canexplain more than 90 % of whether the face of the Xinyang blue-shelledlayer is dark or white. Research shows Cemani and Silkie have 2–4times larger copy numbers than non-Fm chickens . Such resultshave also been verified in two local chicken breeds in Sweden .The size of these areas is over 100 kb and code for EDN3, BMP7, TUBB1, and SLMO2. BMP7 plays acrucial role in regulate embryonic size , renal function, anddevelopment .The chickens used in the present study have been selected for over 19generations and subjected to unified selection for egg quality and yield,and black face accounts for about a quarter of the population. In thepresent study, we also found some extremely significant SNP loci in the Fmlocus region on chromosome 20. These SNPs were annotated, and regions relatedto dermal hyperpigmentation phenotypes were located, including the EDN3 and BMP7 is associated with hyperpigmentation in Dongxiang chicken comb . The results also showed that there was a significant correlationbetween melanin deposition and egg production, which suggests that thelaying number of black comb layers is significantly higher than that of redcomb chicken. Our research is also enriched in the GO:2000241 (regulation ofreproductive process), involving BMP7, AURKA, RTF2, EDN3, and SPO11 genes. However, we counted thenumber of laying eggs 129 d after 26 weeks of age, which show that therewas no statistically significant effect between pigmentation and eggproduction, although white chickens have three more eggs on average thandark layers (Table S6). The red and dark comb of Dongxiang layers were twodistinct lines, obviously different from our research population. However,the gene regions mapped are consistent with our results, from which it can befurther inferred that dermal hyperpigmentation as a quality trait isdetermined by a few major genes. It has been established that the Fmmutation is positively correlated with the duplication of a segment thatcontains the EDN3 gene . The located gene regions of this studyare consistent with the research, but the specific SNP sites are different.This suggests that the genetic mechanisms involved in different varieties orparts of chicken carcass  are alsodifferent and may be related to the adopted research methods. In thefuture, we need to further verify the gene expression in different tissuesof chicken.Recent studies have shown that a genomic region on chromosome 20 involving5EDN3 gene, which is essential for development ofmelanocytes. It is also verified that a large number of effective molecularmarkers can be obtained by GBS technology in chicken, which can be used tocorrelate important economic traits of chicken and thus lay the foundationfor maker-assisted selection or genome selection.In this study, two analysis methods (GWAS and 10.5194/aab-63-483-2020-supplementTable S1 contains sequencing reads, alignment statistics, andmean genome-wide coverage of each sample. Table S2 contains GWAS results of dermalhyperpigmentation in Xinyang blue-shelled pure-line layers. Table S3 contains thedetection FST result of dermal hyperpigmentation in Xinyang blue-shelledpure-line layers. Table S4 contains the genes of enrichment analysis. Table S5 contains theresults of GO analysis in Metascape. Table S6 contains the number of eggs laid in129 d of Xinyang green-shell layer at 26 weeks of age. The supplement related to this article is available online at: https://doi.org/10.5194/aab-63-483-2020-supplement.


Many studies exclude loci that exhibit linkage disequilibrium (LD); however, high LD can signal reduced recombination around genomic features such as chromosome inversions or sex-determining regions. Chromosome inversions and sex-determining regions are often involved in adaptation, allowing for the inheritance of co-adapted gene complexes and for the resolution of sexually antagonistic selection through sex-specific partitioning of genetic variants. Genomic features such as these can escape detection when loci with LD are removed; in addition, failing to account for these features can introduce bias to analyses. We examined patterns of LD using network analysis to identify an overlapping chromosome inversion and sex-determining region in chum salmon. The signal of the inversion was strong enough to show up as false population substructure when the entire dataset was analyzed, while the effect of the sex-determining region on population structure was only obvious after restricting analysis to the sex chromosome. Understanding the extent and geographic distribution of inversions is now a critically important part of genetic analyses of natural populations. Our results highlight the importance of analyzing and understanding patterns of LD in genomic dataset and the perils of excluding or ignoring loci exhibiting LD. Blindly excluding loci in LD would have prevented detection of the sex-determining region and chromosome inversion while failing to understand the genomic features leading to high-LD could have resulted in false interpretations of population structure. Eda gene that influences armor plating in stickleback   e.g.,  is increChromosome inversions have been associated with widely divergent adaptive variation ranging from social behavior  from western Alaska. Chum salmon exhibit an anadromous life history where individuals emerge in freshwater and then migrate to the ocean to mature before returning to freshwater streams to spawn. Populations often exhibit life-history variation in the timing of return migration. We used patterns of LD combined with network analysis to discover a chromosomal inversion in chum salmon that contains the sex-determining region. The inversion exhibits spatial variation in frequency throughout western Alaska and includes putatively adaptive genes associated with life-history variation in other salmonids.Here, we describe the detection and genetic architecture of multiple genomic features in chum salmon  using RAD sequencing . These cSbfI enzyme using a modification of the improved RADseq library preparation protocol developed in STACKS v1.47  using bowtie2 v2.2.1  to identify groups of linked SNPs, hereafter referred to as ‘sets’. This was done to determine if multiple patterns of LD were present within a single genomic region. For network analysis, SNP pairs with an r2 < 0.3 were removed. SNPs that were in LD with fewer than 3 other SNPs were also excluded to reduce the number of SNPs that were linked only by close physical proximity. Following network analysis, chromosomal positions of SNPs in LD sets were examined to determine if they could be attributed to genomic structures. Finally, patterns of variation for SNPs within LD sets were visualized using PCA.Genomic features were identified by examining LD between markers. Pairwise LD was estimated between SNPs using the r-squared method in Two putative, co-occurring genomic features were identified from the RADseq data (see results): a chromosome inversion and a sex-determining region.bowtie2; custom perl scripts were used to obtain flanking sequence for primer design. Primers were designed using BatchPrimer3 (GT-seq assays were developed for 39 loci diagnostic for the putative inversion and 21 loci diagnostic for the putative sex-determining region. Multiple loci for each genomic feature were included to provide a control for genotyping error and because some loci would be routinely lost during panel optimization (described below). Filters were applied prior to and during primer design to remove loci that were likely to amplify off-target sequence with GT-seq following the methods of GTscore . All GT-seq loci were used for evaluating sample quality even though loci developed for the separate project are not included in this study. Samples were evaluated for quality based on a minimum 90% genotype rate and visualization of allele scatter plots.An expanded sample of individuals was genotyped using the GT-seq panel to better characterize genomic structure across the region. This included additional individuals from the ascertainment collections as well as additional collections from Norton Sound, and the Yukon, Kuskokwim, and Nushagak rivers . Populathttps://doi.org/10.25387/g3.11903409.Raw RADseq is available in NCBI BioProject PRJNA611968. Raw GT-seq data are available in NCBI BioProject PRJNA609360.The authors affirm that all data necessary for confirming the conclusions of this article are represented fully within the article and its tables and figures. Supplemental material available at figshare: STACKS initially outputted 222,668 SNPs within 94,002 RAD tags. A total of 30,006 SNPs within 22,693 RAD tags were retained after applying all filters (Table S1). Of these loci, 13,015 SNPs within 10,821 RAD tags were aligned to the rainbow trout genome. After all filtering steps, 267 individuals were retained for SNP discovery.O. mykiss chromosome 28. Network analysis identified three distinct sets of loci that exhibited high LD  were plotted by SNP set and position to better visualize genotype patterns ; this plLoci in set 2 exhibited a more complex pattern that is consistent with a sex-determining region. Putative males in the top half of GTscore are in Table S3. A total of 43 individuals were removed for failing quality control: 19 samples had <90% genotype rate and an additional eight had broad allelic scatter due to reduced read depth , five individuals showed elevated heterozygosity and indistinct allelic clusters suggesting contamination , one individual showed four clusters consistent with triploidy , two individuals showed five clusters consistent with tetraploidy , and one individual had no heterozygous genotypes and was likely a different species of salmon .Development of the GT-seq panel involved filtering steps during primer design and panel optimization. After evaluation and optimization, the final GT-seq panel contained 22 SNPs diagnostic for a putative inversion and 18 SNPs diagnostic for a putative sex-determining region. Primer sequences for final loci are located in Table S2 and bioinformatic probes used for genotyping in Inversion type and sex were assigned by clustering samples with PCA ; this PCSimilar to the RADseq data alone, within the full dataset the inversion was found in every collection but showed regional variation in frequency. Inversion frequency ranged from 2 to 22% and was most common in the Lower Yukon collections followed by the Kuskokwim River collections . OutsideAnopheles sp.) (vs. residency in rainbow trout (Oncorhynchus mykiss)  (Genomic structures including inversions and sex-determining regions provide important contributions to adaptive variation and population structure (uttatus) . InhibitPopulation substructure shaped by the inversion and sex-determining region was only determined through a combination of LD and network analysis. Both the inversion and sex-determining region exhibited LD spanning the same genomic region, which would be difficult to ascertain manually. Network analysis and community detection was a simple method to automate detection of different groups of linked markers that contributed to this overall pattern of LD. Previous work has shown that a combination of LD and network analyses facilitates detection of genomic features even in the absence of reference genome (INV, XINVXINV, XY, XINVY. This agrees with the observed pattern of 5 sample clusters on the individual PCA (INV. Levels of LD for sex-associated loci varied depending on the X-chromosome type, suggesting different levels of divergence between the Y-chromosome and the ancestral and inverted X-chromosomes.The extended LD combined with the specific genotype patterns observed for high-LD loci suggests a genomic inversion that overlaps with the sex-determining region in chum salmon. The genotype patterns also suggest that the inversion arose on the X chromosome. An inversion arising on the X chromosome should result in 5 different chromosomal combinations: XX, XXdual PCA . If the Salmonids exhibit sex-specific recombination patterns where females recombine across the full length of chromosomes while recombination in males is restricted to telomeric regions (vs. resident life history in rainbow trout (Greb1L gene. This gene has been associated with variation in the timing of migration return in both Chinook salmon and rainbow trout (An inversion on the X chromosome containing the sex-determining region has interesting evolutionary implications. Females can exhibit all three genotypes of the inversion, while males can be homozygous for the ancestral form or heterozygous for the inversion but never homozygous for the inversion. Inversions can lead to fixation of genetic variants on alternate chromosomes and facilitate adaptive divergence (reviewed in i.e., Chum salmon exhibit two ecotypes that vary in season of migration into freshwater: summer migrants and fall migrants. In the Yukon River, summer chum salmon enter the river between early June and early July and spawn primarily in tributaries in the lower 700 km of the river. Fall chum salmon, the less-abundant ecotype, enter in mid-July to late August and, unlike the majority the chum salmon populations throughout the range, undertake long migrations with populations extending to the headwaters of Yukon River over 2800 km upriver (sdY (sdY has been identified on three different chromosomes (sdY gene in Atlantic salmon. Interestingly, the homeolog of LG15 is orthologous to the sex chromosome in coho salmon, lake whitefish, and sockeye salmon (sdY exon loss (sdY (Sex in salmon is genetically controlled  and is goss (sdY . DespiteExamining genome-wide patterns of LD is an important tool for evolutionary analysis. Many studies explicitly exclude loci with LD; these may be missing important patterns of genomic variation. We identified a co-locating inversion and sex-determining region in chum salmon by performing network analysis on patterns of LD. The signal of these features was strong enough to drive PCA of the full dataset, resulting in false population structure. Attempting to identify and account for genomic features should be standard practice in genome-scale datasets.


Citrobacter, Enterobacter, Escherichia, and Klebsiella.Accurately identifying single-nucleotide polymorphisms (SNPs) from bacterial sequencing data is an essential requirement for using genomics to track transmission and predict important phenotypes such as antimicrobial resistance. However, most previous performance evaluations of SNP calling have been restricted to eukaryotic (human) data. Additionally, bacterial SNP calling requires choosing an appropriate reference genome to align reads to, which, together with the bioinformatic pipeline, affects the accuracy and completeness of a set of SNP calls obtained. This study evaluates the performance of 209 SNP-calling pipelines using a combination of simulated data from 254 strains of 10 clinically common bacteria and real data from environmentally sourced and genomically diverse isolates within the genera Escherichia coli but less dominant for clonal species such as Mycobacterium tuberculosis.We evaluated the performance of 209 SNP-calling pipelines, aligning reads to genomes of the same or a divergent strain. Irrespective of pipeline, a principal determinant of reliable SNP calling was reference genome selection. Across multiple taxa, there was a strong inverse relationship between pipeline sensitivity and precision, and the Mash distance (a proxy for average nucleotide divergence) between reads and reference genome. The effect was especially pronounced for diverse, recombinogenic bacteria such as The accuracy of SNP calling for a given species is compromised by increasing intra-species diversity. When reads were aligned to the same genome from which they were sequenced, among the highest-performing pipelines was Novoalign/GATK. By contrast, when reads were aligned to particularly divergent genomes, the highest-performing pipelines often used the aligners NextGenMap or SMALT, and/or the variant callers LoFreq, mpileup, or Strelka. Accurately identifying single-nucleotide polymorphisms (SNPs) from bacterial DNA is essential for monitoring outbreaks as in , 2) and  and 2]) The output from different aligner/caller combinations is often poorly concordant. For example, up to 5% of SNPs are uniquely called by 1 of 5 different pipelines  with eveCaenorhabditis elegans  * 3 replicates * 254 genomes * 41 pipelines). The number of reads simulated from each species and the performance statistics for each pipeline—the number of true positives (TP), false positives (FP), and false negatives (FN), precision, recall, F-score, and total number of errors  per million sequenced bases—are given in Median F-scores were >0.99 for all but 4 aligner/callers, with small interquartile ranges (∼0.005), although outliers were nevertheless notable Fig. , suggestTable The evaluation of performance across all species showed that Novoalign/GATK had the highest median F-score (0.994), lowest sum of ranks 10), the lowest number of errors per million sequenced bases (0.944), and the largest absolute number of TP calls  . This overall reduction in performance with increased divergence was more strongly driven by reductions in recall  rather than precision because there was a particularly strong correlation between distance and recall .Although across the full range of genomes, many pipelines show comparable performance Fig. , there wThree commonly used pipelines—BWA-mem/Freebayes, BWA-mem/GATK, and Novoalign/GATK—were among the highest performers when the reference genome is also the source of the reads Table . HoweverN. gonorrhoeae but poorly for S. dysenteriae , derived from livestock farms, sewage, and rivers, and cultures of 2 reference strains , for which closed hybrid de novo assemblies were generated using both Illumina paired-end short reads and Nanopore long reads  and Freebayes ) already incorporating a method of haplotype assembly ,  CleanSam006525), ) becausembly see .Each pipeline produces a VCF as its final output. As with a previous evaluation , all VCFDifferent variant callers populate their output VCFs with different contextual information. Before evaluating the performance of each pipeline, all regularized VCFs were subject to minimal parsing to retain only high-confidence variants. This is because many tools record variant sites even if they have a low probability of variation, under the reasonable expectation of parsing. Some tools (including Snippy and SNVer) apply their own internal set of VCF filtering criteria, giving the user the option of a “raw” or “filtered” VCF; in such cases, we retain the filtered VCF as the default recommendation. Where possible,  filter criteria were applied as previously used by, and empirically selected for, COMPASS , an analFrom these filtered VCFs, evaluation metrics were calculated as detailed below.Enterobacter spp., 4 Klebsiella spp., 4 Citrobacter spp., and 4 Escherichia coli, with species identified using matrix-assisted laser desorption ionization (MALDI) time-of-flight mass spectrometry, plus subcultures of stocks of 2 reference strains, K. pneumoniae subsp. pneumoniae MGH 78,578 and E. coli CFT073. Additional predictions were made using both the protein- and nucleotide-level classification tools Kaiju v1.6.1 [RRID:SCR_005484) [Parallel sets of 150-bp Illumina HiSeq 4000 paired-end short reads and ONT long reads were obtained from 16 environmentally sourced samples from the REHAB project , as detu v1.6.1  and Krak_005484) , respect_005484) ; >20 mil_005484) , supplem_005484) ) and “E”_005484) ; >100 mi_005484) , createdRRID:SCR_015880) [Hybrid assemblies were produced using methods detailed in  and brie_015880)  with def_015880)  with defin silico into a genome, with reads containing these SNPs then simulated from it. With this dataset, however, there are no SNPs within each genome: we have only the short reads  and the genome assembled from them (with which there is an expectation of near-perfect read mapping).In the simulated datasets, SNPs are introduced To evaluate pipeline performance when the reads are aligned to a divergent genome, reference genomes were selected as representative of the predicted species, with distances between the 2 calculated using Mash v2.1  and spanSamples, source locations, MALDI ID scores, and associated species predictions are detailed in To allow both the replication and expansion of this evaluation using real sequencing data, a complete archive is available as N. gonorrhoeae differ by up to one-third; see For each pipeline, we calculated the absolute number of TP , FP , and FN SNP calls . We did not calculate true-negative calls for 2 reasons. First, to do so requires a VCF containing calls for all sites, a function offered by some variant callers (such as mpileup) but not all. Second, when aligning reads to a divergent genome, a disproportionately large number of reference sites will be excluded, particularly in more diverse species  of each pipeline as TP/(TP + FP), recall (sensitivity) as TP/(TP + FN), miss rate as FN/(TP + FN), and total number of errors (FP + FN) per million sequenced bases. We did not calculate specificity because this depends on true-negative calls. We also calculated the F-score (as in ), which in silico SNPs and any inter-strain variants. To allow a comparable evaluation of pipelines in this circumstance, inter-strain calls  are discarded and not explicitly considered either TP, FP, or FN. While the set of true SNPs when aligning to a divergent strain will be smaller than that when aligned to the same strain , this will not affect proportion-based evaluation metrics, such as F-score.We note that when SNPs are called after aligning reads from 1 strain to that of a divergent strain, the SNP-calling pipeline will call positions for both the truth set of strain-specific U tests, with results interpreted using the non-parametric effect size estimator Cliff delta [Differences between distributions are assessed by Mann-Whitney ff delta , 62, estff delta . The Cliff delta . Convershttp://dx.doi.org/10.5287/bodleian:AmNXrjYN8).All data analysed during this study are included in this published article and its supplementary information files. The simulated datasets generated during this study—comprising the SNP-containing genomes, log files of the SNPs introduced into each genome, and VCFs of strain–to–representative genome SNP calls—are available in https://www.ncbi.nlm.nih.gov/bioproject/PRJNA422511), with associated hybrid assemblies available via FigShare [Raw sequencing data and assemblies from the REHAB project, described in , are avaFigShare .https://tinyurl.com/v4p6vol). This archive comprises 18 sets of paired-end reads and assemblies, the associated indexed reference genomes, SNP call truth sets, VCFs, and a suite of Perl scripts. These scripts are also available via https://github.com/oxfordmmm/GenomicDiversityPaper. Snapshots of these data and code are also available from the GigaScience GigaDB repository [A complete archive to facilitate both the replication and expansion of this evaluation using the real (REHAB project) sequencing data is available as pository .Project name: Genomic diversity affects the accuracy of bacterial SNP calling pipelineshttps://github.com/oxfordmmm/GenomicDiversityPaperProject home page: Operating system(s): Platform-independentProgramming language: Perl (v5.22.1)https://tinyurl.com/v4p6vol)Other requirements: Third-party software prerequisites are detailed in documentation provided with License: GNU GPLSupplementary Table 1. Sources of softwareSupplementary Table 2. Genomes into which SNPs were introduced in silico, and various measures of distance between each strain's genome and the representative genome of that speciesSupplementary Table 3. Summary statistics of SNP-calling pipelines after aligning simulated reads to the same reference genome as their originSupplementary Table 4. Ranked performance of SNP-calling pipelines after aligning simulated reads to the same reference genome as their originSupplementary Table 5. Genome size diversity within 5 clinically common bacterial speciesSupplementary Table 6. Summary statistics of SNP-calling pipelines after aligning simulated reads to a reference genome differing from their originSupplementary Table 7. Ranked performance of SNP-calling pipelines after aligning simulated reads to reference genome differing from their originSupplementary Table 8. Environmentally sourced/reference gram-negative isolates and associated representative genomes.Supplementary Table 9. Summary statistics of SNP-calling pipelines after aligning real reads to a reference genome differing from their originSupplementary Table 10. Ranked performance of SNP-calling pipelines after aligning real reads to reference genome differing from their originSupplementary Table 11. Proportion of strain-specific in silico SNPs detected in whole-genome alignments between the strain genome and a representative genomeSupplementary Table 12. VCF filtering parameters, as used by BCFtoolsSupplementary Table 13. Summary statistics of SNP-calling pipelines after aligning both simulated error-free and error-containing reads to the same reference genome as their originSupplementary Table 14. Summary statistics of SNP-calling pipelines after aligning both simulated error-free and error-containing reads to a reference genome differing from their originSupplementary Table 15. Summary statistics of SNP-calling pipelines after aligning simulated error-free reads to a reference genome differing from their origin, both with and without local indel realignmentSupplementary Table 16. Summary statistics of E. coli SNP-calling pipelines after aligning simulated error-free reads to a reference genome differing from their origin, both with and without VCF regularizationSupplementary Table 17. Summary statistics of E. coli SNP-calling pipelines after aligning simulated error-free reads to a reference genome differing from their origin, at 5-, 10-, 25- and 50-fold depths of coverageSupplementary Figure 1. Reduced performance of SNP-calling pipelines with increasing genetic distance between the reads and the reference genome . The median F-score across a set of 41 pipelines, per strain, decreases as the distance between the strain and the reference genome increases . Each point indicates the genome of 1 strain per species (n = 254 strains). Points are coloured by the species of each strain (n = 10 species). Summary statistics for each pipeline are given in Supplementary Table 6, performance ranks in Supplementary Table 7, and the genetic distance between strains in Supplementary Table 2. Quantitatively similar results are seen if assaying distance as the Mash distance, which is based on the proportion of k-mers shared between genomes  with increasing genetic distance between the reads and the reference genome (assayed as Mash distance). The median sensitivity  across a set of 41 pipelines, per strain, increases as the distance between the strain and the reference genome increases . Each point indicates the genome of 1 strain per species (n = 254 strains). Points are coloured by the species of each strain (n = 10 species). Summary statistics for each pipeline are given in Supplementary Table 6, performance ranks in Supplementary Table 7, and the genetic distance between strains in Supplementary Table 2.Supplementary Figure 3. Total number of SNPs it is possible to call should reads from 1 strain be aligned to a representative genome of that species. Strong correlation between the total number of SNPs introduced in silico into 1 genome and the maximum number of SNPs it is possible to call assuming reads from the former are aligned to a representative genome of that species (which will not necessarily contain the same complement of genes). Each point represents the genome of 1 strain, with genomes detailed in Supplementary Table 2. The line y = x is shown in red.Supplementary Figure 4. Head-to-head performance comparison of all pipelines relative to Snippy, on the basis of F-score, using simulated data. This figure directly compares the performance, using simulated data, of 40 pipelines relative to Snippy. Each point indicates the median F-score for the genome of 1 strain per species (n = 254 strains). Data for Snippy are plotted on the x-axis, and for the named pipeline on the y-axis. Raw data for this figure are given in Supplementary Table 6. Text in the top left of each panel is an interpretation of the difference between each pair of distributions, obtained using the R package “effsize,” which applies the non-parametric effect size estimator Cliff delta to the results of a Mann-Whitney U test.Supplementary Figure 5. Selection of E. coli isolates by manual review of dendrogram topology. There are numerous usable complete genomes for E. coli. For the SNP-calling evaluation, a subset of isolates was selected (indicated in red boxes) so as to maximize the diversity of clades represented. To do so, an all-against-all distance matrix for each genome was created using the “triangle” component of Mash v2.1, with a dendrogram constructed using the neighbour-joining method implemented in MEGA v7.0.14. Sources for the selected genomes are given in Supplementary Table 2.Supplementary Figure 6. Selection of K. pneumoniae isolates by manual review of dendrogram topology. There are numerous usable complete genomes for K. pneumoniae. For the SNP-calling evaluation, a subset of isolates was selected (indicated in red boxes) so as to maximize the diversity of clades represented. To do so, an all-against-all distance matrix for each genome was created using the “triangle” component of Mash v2.1, with a dendrogram constructed using the neighbour-joining method implemented in MEGA v7.0.14. Sources for the selected genomes are given in Supplementary Table 2.Supplementary Figure 7. Selection of L. monocytogenes isolates by manual review of dendrogram topology. There are numerous usable complete genomes for L. monocytogenes. For the SNP-calling evaluation, a subset of isolates was selected (indicated in red boxes) so as to maximize the diversity of clades represented. To do so, an all-against-all distance matrix for each genome was created using the “triangle” component of Mash v2.1, with a dendrogram constructed using the neighbour-joining method implemented in MEGA v7.0.14. Sources for the selected genomes are given in Supplementary Table 2.Supplementary Figure 8. Selection of S. enterica isolates by manual review of dendrogram topology. There are numerous usable complete genomes for S. enterica. For the SNP-calling evaluation, a subset of isolates was selected (indicated in red boxes) so as to maximize the diversity of clades represented. To do so, an all-against-all distance matrix for each genome was created using the “triangle” component of Mash v2.1, with a dendrogram constructed using the neighbour-joining method implemented in MEGA v7.0.14. Sources for the selected genomes are given in Supplementary Table 2.Supplementary Figure 9. Selection of S. aureus isolates by manual review of dendrogram topology. There are numerous usable complete genomes for S. aureus. For the SNP-calling evaluation, a subset of isolates was selected (indicated in red boxes) so as to maximize the diversity of clades represented. To do so, an all-against-all distance matrix for each genome was created using the “triangle” component of Mash v2.1, with a dendrogram constructed using the neighbour-joining method implemented in MEGA v7.0.14. Sources for the selected genomes are given in Supplementary Table 2.Supplementary Dataset 1. Simulated datasets for evaluating bacterial SNP-calling pipelines. This archive contains the set of 254 SNP-containing genomes, VCFs containing the nucmer and Parsnp strain–to–representative genome SNP calls, and the final truth sets of SNPs used for evaluation.Supplementary Dataset 2. Real sequencing datasets for evaluating bacterial SNP-calling pipelines. This is a complete archive to facilitate both the replication and expansion of this evaluation using real (REHAB project) sequencing data. It comprises 18 sets of paired-end reads and assemblies, the associated indexed reference genomes, SNP call truth sets, VCFs, and a suite of Perl scripts.BWA: Burrows-Wheeler Aligner; COMPASS: Complete Pathogen Sequencing Solution; EMBL: European Molecular Biology Laboratory; FN: false negative; FP: false positive; GASSST: Global Alignment Short Sequence Search Tool; GATK: Genome Analysis Toolkit; GFF: General Feature Format; MALDI: matrix-assisted laser desorption ionization; NCBI: National Center for Biotechnology Information; NHS: National Health Service; ONT: Oxford Nanopore Technologies; SNAP: Semi-HMM-based Nucleic Acid Parser; SNP: single-nucleotide polymorphism; TP: true positive; VCF: variant call format.The authors declare that they have no competing interests.giaa007_GIGA-D-19-00189_Original_SubmissionClick here for additional data file.giaa007_GIGA-D-19-00189_Revision_1Click here for additional data file.giaa007_GIGA-D-19-00189_Revision_2Click here for additional data file.giaa007_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa007_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa007_Reviewer_1_Report_Original_SubmissionJason Sahl -- 7/15/2019 ReviewedClick here for additional data file.giaa007_Reviewer_1_Report_Revision_1Jason Sahl -- 12/17/2019 ReviewedClick here for additional data file.giaa007_Reviewer_2_Report_Original_SubmissionC. Titus Brown -- 9/29/2019 ReviewedClick here for additional data file.giaa007_Supplement_FilesClick here for additional data file.


Background: Whole-genome studies of vine cultivars have brought novel knowledge about the diversity, geographical relatedness, historical origin and dissemination, phenotype associations and genetic markers. Method: We applied SOM (self-organizing maps) portrayal, a neural network-based machine learning method, to re-analyze the genome-wide Single Nucleotide Polymorphism (SNP) data of nearly eight hundred grapevine cultivars. The method generates genome-specific data landscapes. Their topology reflects the geographical distribution of cultivars, indicates paths of cultivar dissemination in history and genome-phenotype associations about grape utilization. Results: The landscape of vine genomes resembles the geographic map of the Mediterranean world, reflecting two major dissemination paths from South Caucasus along a northern route via Balkan towards Western Europe and along a southern route via Palestine and Maghreb towards Iberian Peninsula. The Mediterranean and Black Sea, as well as the Pyrenees, constitute barriers for genetic exchange. On the coarsest level of stratification, cultivars divide into three major groups: Western Europe and Italian grapes, Iberian grapes and vine cultivars from Near East and Maghreb regions. Genetic landmarks were associated with agronomic traits, referring to their utilization as table and wine grapes. Pseudotime analysis describes the dissemination of grapevines in an East to West direction in different waves of cultivation. Conclusion: In analogy to the tasks of the wine waiter in gastronomy, the sommelier, our ‘SOMmelier’-approach supports understanding the diversity of grapevine genomes in the context of their geographic and historical background, using SOM portrayal. It offers an option to supplement vine cultivar passports by genome fingerprint portraits. Grapevine (Vitis Vinifera) is one of the oldest of the cultivated plants for which living progenitors still exist. The broad geographic area of the wild grapes and a bewildering number of different forms expanding from Europe to Asia and Caucasus continue to intrigued researchers, with a crucial question about the origin and domestication of the grape. Archaeological and historical studies suggested that the cultivation of the domesticated grape (V. vinifera L. subsp. sativa) started about 10,000 to 8000 years ago, from its putative wild ancestor (V. vinifera L. subsp. sylvestris), and the primary center of domestication was located between the Near East [http://www.oiv.int/). Different authors evidenced the presence of secondary domestication centers, where spontaneous hybridizations among wild plants and cultivated forms or targeted selection, created the pattern of the modern Western European cultivars [Grapes are not only tasty, but also one of the most economically and culturally important crops. They are used for both winemaking and fresh (‘table’) consumption. Grape is unique, not only because it is a major global perennial crop, but also because of its historical and cultural connections with the development of humans. According to the OIV  the surface area of the world vineyard is estimated at 7.4 million hectares, with a production of 76 million tons of fresh grapes and 260 million hectoliters of wine  and wild (V. Vinifera L. subsp. sylvestris) grapes suggest at minimum two separate domestication events, one derived from the Transcaucasian wild grape and another one in Western Europe, where cultivars experienced introgression from local wild grape [In the last years, whole-genome studies of vine cultivars using genotyping and sequencing technologies have added novel knowledge about the diversity, geographical relatedness, historical origin, phenotype associations, genetic markers and distribution paths of the vine ,9,10,11.ld grape ,7. Assocld grape . The furHere, we apply SOM (self-organizing maps) portrayal , a neuraStrengths of the applied method are dimension reduction and visualization capabilities . ParticuThis study aims at applying SOM-portrayal to the genetics of vine, in order to generate SNP-landscape of grape genotypes, to relate its topology to the geographical distribution, to possible paths of cultivar distribution, to selected genetic markers, and link them to grape utilization. In gastronomy, the sommelier is the wine ‘waiter’ (or ‘waitress’), who recommends combining wine with food based on traditional knowledge about the character of wine varieties, their taste and geographic origin, and thus to support the decision-making of guests for a successful dinner. In analogy, our ‘SOMmelier’-approach aims at supporting understanding genetic diversity and relatedness of grapevine genomes in the context of their geographic and historical background by application of SOM portrayal.Grape cultivars data: grapevine genetic SNP- and phenotype data were taken from [https://urgi.versailles.inra.fr/Species/Vitis/Data-Sequences/Genotyping-data repository.ken from . The datHuman population data: For a comparison of genomic relatedness between worldwide geographic strata, we analyzed genome-wide microarray SNP data (Illumina 650Y arrays) from the Human Genome Diversity Project (HGDP) , as descOur analysis concept is based on the following main ingredients: (i) transformation of SNP data into excess minor allele frequencies (eMAF), which increases sensitivity for subtle differences between the genomes of the vine accessions; (ii) dimension reduction of the more than 10,000 SNP data into a matrix of 2500 so-called ‘meta-SNPs’, using unsupervised clustering and their visualization in terms of individual accession portraits using SOM machine learning. (iii) further reduction of dimension by segmentation of the SNP-portraits into about one-to-two dozen so-called spot clusters of correlated SNPs, which serve as fingerprint features of the vine genomes; (iv) diversity analysis of accessions using different approaches . Each of the approaches used enables discovering different aspects of the mutual relatedness between the cultivars. (v) So-called pseudotime analysis as a special type of diversity analysis to analyze genetic similarities in terms of multibranched trees with possible impact for temporal diversification of vine cultivars; (vi) phenotype associations to map information of vine utilization on the SNP landscapes.In order to further process the data, we coded the genotype of each SNP using a trinary code, with ‘0′ for homozygous major alleles (AA), ‘1′ for heterozygous alleles (Aa and aA), and ‘2′ for homozygous minor alleles (aa).ij = Nij/N , where Nij is the number of SNPs with the respective allele in the data set. The minor allele frequency of a SNP is MAF = 2 paa + 1 (paA + pAa) with paa < pAA. With the SNP-code introduced above one finds for its mean value averaged over all cases, <SNP-code> = pAA * 0 + (pAa + paA) * 1 + paa * 2 = 2 MAF, i.e., the mean SNP-code of a SNP equals twice its MAF. For further data processing, we define a SNP-score by centralizing the SNP-code of each SNP with respect to its mean value, SNP-score = SNP-code – 2 MAF. One obtains SNP-score = −2 MAF for the major allelic SNPs, SNP-score = 1 – 2 MAF for heterozygous SNPs and SNP-score = 2 (1 – MAF) for the minor allelic SNPs.First, let us consider one SNP in the data set of N cases, e.g., of N =783 cultivar samples. The fractions of the three genotypes of this SNP are defined as pSecond, let us consider a group of n correlated SNPs in the data set, and calculate the mean of their SNP-score in one selected sample. In the next subsection, we show that such a group of SNPs is given, e.g., by a meta-SNP, or a ‘spot-module’ as obtained below in SOM-analysis. Analogous consideration as above deliver the result for the group-averaged SNP-score, <SNP-score> = 2 (MAF - <MAF>) where <…> here denotes group averaging referring to one selected sample. Hence, the group-averaged SNP-score estimates the deviation of the mean MAF of the considered SNPs, in a certain sample from their mean in the considered population. Accordingly, it can be understood as an excess MAF-value (eMAF), where positive SNP-scores mean higher frequencies than in the population while negative values refer to reduced frequencies. The centralization of each feature (here the SNP-code of each SNP) with respect to its mean value averaged over all samples is applied as a standard preprocessing step in SOM analysis. Features space of centralized values is more sensitive to subtle differences between samples than the feature space of non-centralized values.Euclidean distance between the SNP- profiles as a similarity measure. It ensures that SNPs with similar profiles cluster together in the same or in closely located meta-SNPs. Each meta-SNP profile can be interpreted as the mean profile averaged over all SNP profiles of the respective meta-SNP cluster. For each cultivar accession, the meta-SNP score obtained provides the excess MAF . It is defined as the difference between the mean minor allele frequency (MAF of meta-SNP) of all SNPs collected in this meta-SNP in this particular sample, minus the mean MAF of these SNPs averaged over all samples. The eMAF values of each cultivar accession are visualized by arranging them into a two-dimensional M = 50 × 50 grid, and by using a red to blue color-code for maximum to minimum eMAF-values in each of the grid images. These images ‘portray’ the genetic landscape of each accession studied in units of the eMAF SNP-score. We used SOM as implemented in the “oposSOM” R package [SOM-portrayal of SNP data was performed as described previously . In shor package . Cultiva package  for deta package  to extraCategorical phenotypes such as the utilization of grapes for table or wine usage were associated with the SOM SNP-landscape by performing ANOVA  of SNP-metagenes of the sub-collection of cultivars of a certain phenotype  and coloring the meta-SNP pixels in the SOM according to the obtained p-value (in units of -log(p)), from red (high) to blue (low). Phenotype correlation maps were obtained by calculating the point biserial correlation between the eMAF profile of each metagene and the respective phenotype profile, which is given, e.g., for ‘table vine utilization’ by categorical values ‘0′ (no table usage) and ‘1′ (table usage). The metagenes of the map were then colored between red (maximum correlation) and blue (minimum correlation). Point serial correlation de facto provides difference portraits between cultivars of the respective phenotype and all others, e.g., between the mean portrait of cultivars of wine utilization, minus the mean portraits of cultivars of non-wine utilization.Accession diversity analysis was performed based on meta-SNPs using principal component analysis (PCA), similarity net and minimum spanning tree plots, based on correlation metrics between the individual SOM-portraits as implemented in oposSOM . t-SNE  of correlated meta-SNPs in the chosen cultivar . AccordiThe inspection of the individual accession portraits  reveals The spot-profiles show the mean values of the eMAF-scores averaged over all meta-SNPs included in the respective spot among the cultivars and, particularly, their region-specific alterations C. LargesNext, we analyzed the distribution of selected SNPs across the map. Laucou et al.  identifiThe chromosome-wise distribution of SNPs shows local enrichment in most cases . Overall, we find an association between vine utilization and specific areas of the SOM, which reflects an East-to-West gradient with preferential table usage in the East and wine usage in the West in agreement with . Note thTo increase geographic resolution, we calculated country-wise SNP-landscapes using ANOVA -portrayal A. The ANIn order to evaluate the relatedness between the individual SNP-portraits of cultivars, we calculated similarity nets. They are based on Pearson′s-correlation coefficients between the individual SOM-portraits and arrange them in a way that highly correlated cultivars were connected by lines and locate in the close mutual distance B, left. In the next step, we analyzed SNP-portraits and their diversity on the level of individual cultivars. An inspection of their gallery  indicateTo further evaluate the relatedness between the cultivars, we calculated a minimum spanning tree (MST), which, in contrast to the correlation net discussed above, connects only most similar cultivars and, in consequence, generates ‘paths’ of closest similarities C. The MSNote, that the structure of the vine-MST is completely different from the MST of human genomes of worldwide populations taken from the human genome project. This MST of human genomes possesses an almost linear backbone, with only a few side branches. It reflects the ‘out of Africa’ basic migration history of modern humans, which initially proceeded mostly along one major path. Hence, MST presentation seems to reflect the mechanisms and paths of genome dissemination. In summary, diversity analysis on single cultivar level reveals a marked intermixing between the different geographic regions where, however, cultivars from the South-Caucasus and Near East from the trunk for diverging branches of vines are typical for IBER (and ITAP), WCEU, MFEA and BALK (and RUUK) regions, respectively.For a more detailed study of possible paths of the distribution of grapevine, we applied so-called ‘pseudotime’ (PT) analysis. It arranges cultivars according to their mutual genetic similarity along possible multi-branched paths, using a diffusion-law based algorithm . The obtThe consecutive alterations in feature space observed were supported by PT-profiles of selected spots B. The eMThe consideration of grape utilization along the different PT-paths clearly reveals the enrichment of table vines in branches 0 and 4, while wine cultivars strongly enrich in the branches 1–3 at later PTs C, middleIn summary, multibranched PT-analysis resolves the ‘transverse’ distribution of cultivars among geographic regions also in ‘longitudinal’ PT-dimension, by sorting them according to mutual similarities of their genomic landscapes. We find the accumulation of grapes used for wine production at later PTs especially in Europe, which suggests the diversification of wine cultivars at later times compared with table grapes.We applied SOM artificial neural network-based machine learning to genome-wide SNP data of 783 grapevine cultivars, in order to visualize and to analyze the landscape of grapevine genomes, in terms of topological features such as ‘mountains’ and ‘valleys’, referring to positive and negative eMAF values, respectively .We complemented this genetic feature (SNP-) landscape by grapevine accession-similarity plots. Each of them visualizes different aspects of the mutual relatedness of cultivars. Principal component plots distribute accessions in (eMAF) distance-scale. It results in local overcrowding of cultivars with similar genomes, e.g., from Near East, Caucasus and Middle Asia while Western European and Iberian cultivars separate into virtually different data clouds of large variability between sample genomes. PCA requires separate projection-plots to cover the essential properties of similarity space. The (correlation) similarity net projects multidimensional sample space into two dimensions. It reveals major routes of grape distribution around the Mediterranean Sea and the genetic barrier between Iberian and Western European cultivars, evident also in the feature landscape. In contrast, the minimum spanning tree (MST) generates path-like structures. For worldwide human genomes, the MST reflects roughly the ‘out-of-Africa’ migration history, in terms of a virtually linear path starting from Sub-Saharan Africa and ending in East Asia, with side branches towards Europe, America and Oceania. Interestingly, the grapevine MST splits into four mean branches, enriching West European, Iberian, Balkan and Middle East grapes, and with cultivars from South Caucasus and Fertile Crescent  at their central crossroad. A detailed comparison of individual (ANOVA-) portraits further supports this result, using the feature landscapes in a country-wise resolution.Overall, our genomic landscape and the different sample similarity plots are consistent with the historical knowledge and previous genetic findings of grapevine domestication and distribution ,10. AccoV. vinifera ssp. sylvestris), particularly in Western Europe [Hence, grape growing and winemaking in classical time disseminated from ‘Iberia-to-Iberia’, starting in or near the ancient kingdom Iberia in Middle Georgia in South Caucasus in the East and ending at the Iberian Peninsula in the west. The combined action of selection, breeding, admixture and migration have shaped the cultivated grape diversity. Substantial genetic diversity has been maintained, subsequent to domestication derived from Transcaucasian Wild Grape, possibly due to several events of introgression from local wild vine Silvestris-varieties, Wild grapes  analysis. This method has been originally developed for describing cell differentiation using single-cell transcriptomic data ,32, and Our ‘SOMmelier’ approach visualizes essential aspects of grapevine genomes related to geographic distribution, paths of dissemination and vine utilization. ‘SOMmelier’ portrays vine genomes in terms of individualized images. They are intuitive, meaning that they don’t need specialized genetic knowledge for interpretation. Together with vine genome landscapes described here, we propose to use individual genomic portraits as an option to supplement vine cultivar passports as fingerprint characteristics of their genomes. Such fingerprint portraits consider virtually the whole diversity of the vine. Note also that we here discuss accession portraits mainly on the level of geographic regions as a sort of worked example to support the interpretation of genomes of individual accession portraits. Our study also demonstrates that bioinformatics methods proven before in analytic tasks on different omics realms, mostly transcriptomics, but also epigenomics, proteomics and human genomics, provide reasonable results if applied to vine cultivars as an example of plant genomes. Our approach thus extends the methods toolbox for plant genetics by providing novel approaches which complement established ones. Their pros and cons should be evaluated in future applications. The ‘SOMelier ‘method opens the opportunity to process larger genotype data, obtained by, e.g., whole genome sequencing and/or increased number of cultivars included.


The genomic diversity of the domestic dog is an invaluable resource for advancing understanding of mammalian biology, evolutionary biology, morphologic variation, and behavior. There are approximately 350 recognized breeds in the world today, many established through hybridization and selection followed by intense breeding programs aimed at retaining or enhancing specific traits. As a result, many breeds suffer from an excess of particular diseases, one of many factors leading to the recent trend of “designer breed” development, i.e. crossing purebred dogs from existing breeds in the hope that offspring will be enriched for desired traits and characteristics of the parental breeds. We used a dense panel of 150,106 SNPs to analyze the population structure of the Australian labradoodle (ALBD), to understand how such breeds are developed. Haplotype and admixture analyses show that breeds other than the poodle (POOD) and Labrador retriever (LAB) contributed to ALBD formation, but that the breed is, at the genetic level, predominantly POOD, with all small and large varieties contributing to its construction. Allele frequency analysis reveals that the breed is enhanced for variants associated with a poodle-like coat, which is perceived by breeders to have an association with hypoallergenicity. We observed little enhancement for LAB-specific alleles. This study provides a blueprint for understanding how dog breeds are formed, highlighting the limited scope of desired traits in defining new breeds. Due to the selective breeding practices used to create modern breeds, dogs suffer from an unacceptable excess of disease, one of several reasons behind the recent trend of creating “designer breeds.” Such populations result from crosses between established breeds, with a goal of producing dogs that lack or have a reduced incidence of unfavorable disorders or phenotypes, yet retain desirable traits from parental breeds. One such example is the Australian labradoodle which was initiated from Labrador retriever and standard poodle crosses 31 years ago in order to produce dogs with a reduced tendency to elicit an allergic reaction but that retained the desirable characteristics of a service dog. In this study we have used nuclear DNA markers to determine the genetic history of the Australian labradoodle and develop an understanding of how breeds stabilize traits given particular breeding strategies. Our data shows that despite the major contributions from poodle and Labrador retriever, additional breeds contributed to the modern Australian labradoodle. Today’s Australian labradoodle is largely poodle with an excess of poodle alleles related to coat type. This study demonstrates that changes in very few genes can define a new breed and demonstrates how breeds can form in a small number of generations. Approximately 350 domestic dog breeds are recognized by organizations such as the American Kennel Club (AKC)  or the FBreeding programs needed to generate and maintain specific traits often require crossing closely related individuals, hence, some domestic breeds have an excess of specific diseases , many ofhttps://alaa-labradoodles.com). Today, the name labradoodle describes two populations. The first are F1 hybrids (LBD), which are simply the first-generation product of any LAB and SPOO cross. The Australian labradoodle breed (ALBD), which originated in 1989, is distinct from such hybrids. While also starting as a LAB and SPOO cross, it has since been propagated by planned crosses to establish a true-breeding population, meeting an established set of standards. With an original goal of producing a low-shedding service dog, offspring of the planned crosses are known today as Australian labradoodles, and fanciers seek to have it recognized by an international registry as a unique breed.One of the most popular of the designer breeds is the labradoodle, a cross between the Labrador retriever (LAB) and standard poodle (SPOO)   decay in multi-generational ALBD versus LBD provides insight as to the stability of a quickly developed breed.https://alaa-labradoodles.com). Of note, no IWSP was present in the pedigrees of ALBD we tested. The three spaniel breeds clearly separate from the LAB in PC1, as well as from the POOD and each other in PC2. There is overlap between the ALBD and POOD varieties but not with the distantly positioned LAB. These results provide the first indication that the ALBD retains more POOD ancestry than LBD, and both the SPOO and smaller POOD varieties contributed to the breed.We initially examined the relationship of the ALBD and LBD to six putative parental breeds using Principal Components Analysis (PCA) . The firWe next estimated admixture in the ALBD and related breeds using a cluster-based ancestry method that applies different models of population structure to the data . DividinTo further understand the genetic makeup of the LBD and ALBD identity-by-descent (IBD) haplotype sharing between 161 previously published breeds and nine wild canids versus LBD and ALBD was calculated, and two plots were drawn by subsetting LBD  and ALBDAs a complement to the above studies, we conducted a TreeMix analysis  Fig 5)..Fig 5). To better understand the placement of LBD with LAB, we generated a separate tree using the six established breeds but not the LBD or ALBD  .. We obsth century [We next examined inbreeding in the ALBD and associated breeds . Of the  century , 21, hav-7) with genome-wide significant differences in allele frequency distributed across all chromosomes (r-spondin 2 (RSPO2), keratin 71 (KRT71), a lncRNA upstream of adrenoceptro beta 1, ADRB1 (ADRB1-AU1), and c10orf118 [tudor domain containing 1 (TDRD1), which plays a role in embryogenesis and gametogenesis [rab11 family interacting protein 2 (RAB11FIP2), which encodes a GTP binding protein [To compare allelic variation between the ALBD and its parental breeds we performed three genome-wide comparisons using PLINK , 23 Fig. For eacomosomes . Peak SN10orf118 –27. The 10orf118 . Other gogenesis  and rab1 protein .−16–10−18) are present in the regions containing RSPO2, ADRB1-AU1, c10orf118, TDRD1, RAB11FIP2, and angiopoietin 1 (ANGPT1). The first five genes were also observed in the POOD vs. LAB comparison, while the last plays a role in vascular development and has been associated with hypoxia adaptation in grey wolves . The third analysis compared ALBD to the POOD. In this case, we observed only five SNPs in total passing the Bonferroni threshold, with p-values of 10−7–10−8 , TBD1 domain family member 5 (TBC1D5), and multiple PDZ domain crumbs cell polarity complex component (MPDZ). Among these, CACNG4 is perhaps the most interesting because of its role in the oxytocin signaling pathway.We next performed an allelic comparison of 21 ALBDs versus 28 LABs and observed 3,181 SNPs that crossed the Bonferroni threshold, demonstrating significant differences between the ALBD and LAB allele frequencies . The fivy wolves . The thi0−7–10−8 . HaplotyRSPO2 and ADRB1-AU1, where the ALBD shared common haplotypes with POOD.The large number of significant SNPs observed when comparing POOD to LAB and ALBD to LAB, versus the small number observed when comparing the ALBD and POOD, indicates a much stronger contribution of POOD varieties than the LAB to the ALBD. When examining haplotypes around the lead SNPs we were unable to identify common haplotypes between the ALBD and either parent breed except in regions surrounding 2 = 0.25) and least in the ALBD (mean r2 = 0.18). Among purebred dogs, the lowest LD at one Mb was observed in the TPOO (mean r2 = 0.20), which is very similar to the LD decay observed by Boyko et al. in a study of 59 domestic breeds [RSPO2 and ADRB1-AU1 regions. Unsurprisingly, LD in the LBD was higher than in the ALBD, as the former comprises the complete genomes of two distinct breeds while the latter reflects some generations of recombination.We next measured the extent of LD in ALBD in comparison to LAB, POOD, and LBD . At one fibroblast growth factor 5, FGF5), curl (KRT71), and furnishings, the latter of which describes the presence or absence of moustache and eyebrows (RSPO2) [ADRB1 (ADRB1-AU1) that is associated with the presence or absence of an undercoat [Since haplotypes across genes associated with coat variation are primarily shared by the ALBD and POOD, and absent in the LAB, we genotyped presumed functional variants in genes associated with coat in the POOD, including coat length ( (RSPO2) , 32, 33.ndercoat  which weRSPO2 with furnishings [As we have previously reported an association between a 167 bp indel in the 3' untranslated region of nishings , we direnishings . These rrs851828354) that is located in the first exon of FGF5. The variant causes a cysteine to phenylalanine (ENSCAFP00000013040.3:p.Cys95Phe) change in a highly conserved region of the gene [Fur length was examined by genotyping a strongly associated SNP  alteration. Our dataset revealed that 24 of 30 POOD and eight of 21 ALBD were homozygous for the derived (curl) allele, and all 24 LABs were homozygous for the ancestral (non-curl) allele. We genotyped a second variant located in the seventh exon of the same gene (chr.1266_1273delinsACA), which causes a frameshift/stop loss, changing the encoded KRT71 protein sequence  in curly coated retrievers [KRT71 within a cluster of keratin genes, and the long LD observed across the dog genome, the latter is a formal possibility.We also genotyped two mutations in the POOD . The firtrievers , 33. No ADRB1-AU1 [Finally, we genotyped two SNPs (chr28.g.24860187C>T and chr28.g.24870184G>A) within DRB1-AU1 . While tDRB1-AU1 , we obseDRB1-AU1 , althougDRB1-AU1 . Double To determine if genes affecting the coat are enriched in the SNPs showing significant allele frequency differences between ALBD and LAB, we performed an intersection analysis between the 3,181 SNPs with significant allele frequency differences between ALBD and LAB versus 146,925 SNPs with no significant difference. This revealed 24 SNPs within 250kbp of 11 genes associated with coat color and 69 SNPs within the same distance of 11 coat growth and texture genes. We compared this to 1,164 and 1,161 SNPs that appear in the coat color and growth/texture regions, respectively, but show no significant allele frequency differences. The Fisher’s exact p-value for an enrichment of coat growth and texture genes was <0.00001, with an odds ratio of 2.78 (95% CI 2.18–3.6). In contrast, the Fisher’s exact p-value for coat color genes was >0.05 with an odds ratio of 0.95 (95% CI 0.63–1.4).https://alaa-labradoodles.com).Designer dogs have become popular in recent years reflecting increased owner demand for pets that meet their wishes for specific traits, often related to personability, body size, or hoped-for low allergenicity, while retaining favored traits from familiar breeds. Most designer dogs represent first generation crosses, and there is generally little motivation to develop a hybrid into a true breed as subsequent generations, often produced by crossing closely related individuals, may reveal undesirable, recessively segregating traits. The exception is the ALBD, which began with crosses between LAB and SPOO in the 1980s. The original goal was to create a service dog with high biddability that was suitable for households with allergies or asthma, although whether the breed is in fact hypoallergenic, itself a poorly defined word, is a topic of debate. The resulting dogs are smart, friendly, and widely employed as assistance and therapy dogs. The ALBD is not yet recognized by any international registry, but breeding programs and breed standards are defined by the Australian Labradoodle Association of America ; the LAB; ECKR and ACKR; and IWSP . Based oFinally, the above conclusions are supported by our haplotype analysis which utilized two distinct datasets: the LBD hybrid versus 161 breeds which we described previously , and theThe rapid LD decay observed in ALBD combined with the low inbreeding coefficients reflect the high genetic diversity of their genome. This observation is also explained by the very young nature of this “breed in formation,” which also provides an explanation for why no particular haplotype structure is observed in genes associated with coat type. Allelic frequency analysis was used to identify loci shared by POOD and ALBD versus LAB. Our control comparison of POOD versus LAB identified thousands of SNPs with significant differences in allele frequency. That the LAB versus ALBD comparison also yielded thousands of significant SNPs, while the ALBD versus POOD yielded only five indicates that ALBD are more similar to POOD than to LAB and likely reflects breeding programs which favor reintroduction of POOD over multiple generations.https://alaa-labradoodles.com). This variation could reflect the inclusion of all three POOD varieties into ALBD breeding programs, as demonstrated by our admixture and haplotype analyses. However, no SNPs associated with body size loci were among the top five most significant in this analysis. For instance, the IGF1 locus, which accounts for about 15% of variation in body size in domestic breeds [nd in our allelic comparison study, suggesting that the smaller varieties of POOD are not, at present, highly favored in breeding programs.There are a wide range of body sizes in the ALBD which meet the breed standard. These are assessed as height at the withers (shoulder) and weight. Miniature varieties are 14–16 inches in height and weigh 15–25 lbs; medium ALBD are 17–20 inches and weigh 30–45 lbs; the standard size ALBD are 21–24 inches tall and weigh 50–65 lbs  and agouti signaling protein (ASIP) ranked 2,738th and 2,904th, respectively, in the allele frequency comparison, indicating some skewing of coat color alleles in the ALBD versus parental breeds. This is consistent with the breed standard, as the Australian Labradoodle Association of America accepts 14 coat colors including variants of cream, apricot, silver, red, chalk, and black; it also reflects the degree of coat color variation observed in the POOD but not the LAB.Finally, we genotyped a mutation in r length , 34. We RSPO2, ADRB1-AU1, FGF5, and c10orf118, do not exclude the possibility of other genes, such as those controlling shedding and inherent immune factors, playing a role in the reduced allergic response to the ALBD that has been reported.Low shedding, tight coats, and lack of a double coat are perceived as key traits contributing to the perceived “hypoallergenic” nature of established breeds like the Bichon Frisé, Portuguese water dog, and POOD. Indeed, POOD have been the breed of choice for the creation of many designer crosses such as the yorkipoo, peekaboo, lhasapoo and pomapoo. The intellect of the POOD combined with a desirable coat and ease of trainability are all traits which make the POOD a desirable inclusion in a hybrid. Many people with dog allergies do not have an allergic response when in proximity to breeds that share coat traits with the POOD, including the Portuguese water dog and Bichon Frisé. We note, though, that while there is some anecdotal evidence, a rigorous survey regarding the allergenicity of designer breeds has not, to our knowledge, been done. Our results, highlighting the coat and skin genes CACNG4, MPDZ, and TBC1D5 were evident in our comparison of ALBD versus POOD  Animal Care and Use Committee as per protocol GFS-05-. Dog owners provide written consent for sample collection.Published genotype data from ten LAB; 10 each of MPOO, TPOO and SPOO; and 10 each of ACKR, ECKR, and IWSP were used in these analyses . Breeds DNA from 21 ALBD, nine females and 12 males, and eight LBD, the latter of which were first generation LAB/POOD crosses, was provided by Optigen, LLC . The pedigrees provided for the ALBD are sufficiently complex that naming generations is difficult. Thus, for this ALBD analysis, generations were numbered back to the most recent purebred individual in the pedigree, up to four generations. Dogs with more extensive pedigrees were termed 4+. The “multigenerational” descriptor indicates the complex nature of the ALBD pedigrees. Three ALBD were of uncertain generation but were clearly ≥4 generations. To our knowledge, 19 ALBD were unrelated at the grandparent level. Of the remaining, there was one set of siblings and a second set of dogs who share one grandparent.All DNA samples were genotyped on the Illumina CanineHD SNP chip . The SNP dataset for LAB, SPOO, MPOO/TPOO, ACKR, IWSP and ECKR was previously described , and hadPrincipal components (PCs) were calculated using FlashPCA software from PLINK formatted bed files containing 39 chromosomes . We sougth percentile boundary calculated from all haplotype pairs. The proportion of genomic contribution by the LAB to the ALBD and LBD was calculated by extracting average haplotype sharing of all pairs of LBD or ALBD with LAB and dividing that number by the combined average haplotype sharing of all pairs of LBD or ALBD with ACKR and POOD. The inbreeding coefficient was calculated using the ‘—het’ function of VCFtools v.0.14 [IBD haplotype sharing analysis and phasing was performed with BEAGLE v4.1 using variant call format (VCF) files generated with PLINK v1.9 , which ps v.0.14 . R was us v.0.14 .To further evaluate admixture, a maximum likelihood tree was constructed using TreeMix v.1.12 . The treassoc function in PLINKv1.9. Three comparisons were as follows: POOD versus LAB, ALBD versus LAB, and ALBD versus POOD. Allele frequency comparisons were done using the 28 LABS described above. We utilize a balanced population of 20 POOD, 10 SPOO and five each of MPOO and TPOO. This included five female and five male SPOO, three female and two male MPOO, and two female and three male TPOO. Haploview [Allele frequency comparison analyses were conducted using the—aploview  was used2 and decay plots were made using PopLDdecay [2 between all pairs of SNPs (unphased), using PopLDdecay, where both SNPs had a MAF ≥15% and ≤10% missing data. We used a break point of 100,000 bp and -bin2 flag of 10,000 bp to smooth the lines. To compare LD between breeds, a random set of six individuals from each breed were selected. For ALBD, six dogs, all representative of the multigenerational nature of the designer breed, were selected.Pairwise LD was summarized using the genotype correlation coefficient rpLDdecay . For allFGF5, RSPO2, KRT71, and ADRB1-AU1 [FGF5, KRT71, ADRB1-AU1) or gel band size discrimination (RSPO2). Using manufacturer protocols, a touchdown polymerase chain reaction (PCR) protocol with AmpliTaq Gold  was used to amplify DNA fragments. The resultant PCR products were purified using Exosap , Sanger sequenced using BigDye Terminator v3.1 , and analyzed on an ABI 3730xl DNA analyzer . Genome positions of all primer sequences are provided in , melanophilin (MLPH), MC1R, defensin beta 103A (DEFB103/CBD103), silver locus protein homolog (PMEL/SILV), MITF, solute carrier family 45 member 2 (SLC45A2), proteasome subunit beta type-7 (PSMB7), solute carrier family 2 member 9 (SLC2A9), KIT ligand (KITLG)) while the second comprised coat growth and texture genes , ADRB1-AU1, forkhead box I3 (FOXI3), serine/threonine-protein kinase 3 (SGK3), fibroblast growth factors 3 and 4  FGF5 and oral cancer overexpressed 1 (ORAOV1)). To allow for LD, we increased the total length of each gene by 250kb at both ends. Individual bedfiles were created for gene and SNP loci and the overlapping positions were identified using the intersect function in bedtools v2.29.2. The Fischer’s exact test p-value was calculated by online Fischer test calculator (https://www.socscistatistics.com/tests/fisher/default2.aspx) and the odds ratio was calculated using the online tool .To determine if genes affecting the coat are enriched in SNPs showing significant allele frequency differences between ALBD and LAB, we made a 2x2 contingency table from the two sets of SNPSs; 3,181 with significant allele frequency differences and 146,925 showing no significant differences. We first counted the number of SNPs that fell within known genes affecting coat traits. The first set comprised coat color genes Click here for additional data file.S2 FigTree for six established breeds including ALBD and LBD was constructed with TreeMix. Breed abbreviations correspond to (TIF)Click here for additional data file.S3 FigLine graph of CV error values for each ancestry models denoted by K.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableSummary of breed abbreviations corresponding to (DOCX)Click here for additional data file.