Vitis vinifera cultivars and we obtained a data set with no missing values for a total of 10207 SNPs and 783 different genotypes. The average inter-SNP spacing was ~47 kbp, the mean minor allele frequency (MAF) was 0.23 and the genetic diversity in the sample was high (He = 0.32). Fourteen SNPs, chosen from those with the highest MAF values, were sufficient to identify each genotype in the sample. Parentage analysis revealed 118 full parentages and 490 parent-offspring duos, thus confirming the close pedigree relationships within the cultivated grapevine. Structure analyses also confirmed the main divisions due to an eastern-western gradient and human usage (table vs. wine). Using a multivariate approach, we refined the structure and identified a total of eight clusters. Both the genetic diversity  and linkage disequilibrium  varied between clusters. Despite the short span LD, we also identified some non-recombining haplotype blocks that may complicate association mapping. Finally, we performed a genome-wide association study that confirmed previous works and also identified new regions for important performance traits such as acidity. Taken together, all the results contribute to a better knowledge of the genetics of the cultivated grapevine.Grapevine is a very important crop species that is mainly cultivated worldwide for fruits, wine and juice. Identification of the genetic bases of performance traits through association mapping studies requires a precise knowledge of the available diversity and how this diversity is structured and varies across the whole genome. An 18k SNP genotyping array was evaluated on a panel of  Vitis vinifera subsp. vinifera, was domesticated from the dioecious taxon V. vinifera subsp. sylvestris [Grape is nowadays a crop of major economic importance in the world: in 2015, around 7.5 Mha were under cultivation and produced 75.7 Mt of fruits, 56% being processed for wine, juice and spirits and 44% used for fresh or dried raisin consumption [lvestris , 9 gave lvestris  resultinlvestris , 11, 12.lvestris .proles”  derived from two pools of V. vinifera subsp. sylvestris (typica and aberrans). In the last decades, the development of molecular markers allowed characterizing both the genetic diversity and structure in large samples of V. vinifera cultivars [Assessing the genetic diversity and structure of cultivated grape has been very useful to reconstruct the domestication history of grapevine , 18 and ultivars , 24. Allultivars  and alloultivars . Most ofultivars , 20.V. vinifera cultivars has also been widely investigated since the 1990’s through parentage studies using nSSRs [vs. second-degree relationships [The origin of specific ng nSSRs , 16. Morng nSSRs  or withong nSSRs  nSSRs mang nSSRs , 26, 27 ng nSSRs –30. Resung nSSRs ). For king nSSRs , 31 and Vitis genotyping array containing 18K SNPs was developed during the European project GrapeReseq (https://urgi.versailles.inra.fr/Projects/Achieved-projects/GrapeReSeq) to enable diversity, structure and genome wide association (GWA) studies in grapevine [V. vinifera subsp. vinifera (syn. sativa) selected in four repositories of three GrapeReseq Consortium partners .A rapevine . This arrapevine  and the rapevine , 30. At In the present study, we used this large genotyping data i) to determine a minimal set of SNP markers useful for cultivar identification ii) to precise their parentage relationships iii) to refine our understanding of the genetic diversity structure of the cultivated compartment iv) to obtain an overall estimate of the linkage disequilibrium decay at chromosome level and v) to perform tests of association mapping for performance traits previously measured for this sample.Vitis vinifera were analyzed with 18K SNPs. They represented 800 distinct genotypes, among which 17 with over 50% missing data. The analyses were thus performed on 783 unique genotypes , lack of polymorphism (23%) and weak amplification (3.5%). Twenty-three percent of the SNPs were mapped in exons and among them, 36% were non-synonymous. The SNP map  revealedThe cumulated probabilities of identity (PI) calculated with FAMOZ indicated 14 SNPs as a minimum number to distinguish all 783 genotypes . These mParentage analysis on 783 cultivars using 10207 SNPs enabled validating 118 full parentages . LOD scoThe analysis also validated 490 parent-offspring duos  among whWhile FAMOZ uses exclusion probabilities for parent-offspring detection, KING-robust uses a combination of identity-by-state and kinship information. We thus compared the lists of parent-offspring relationships obtained with FAMOZ and KING-robust. KING-robust validated all the parent-offspring (PO) trios and duos detected by FAMOZ.nd degree relationship, because Müller-Thurgau is a progeny of Riesling and Madeleine Royale, with both Gouais blanc and Pinot noir  as grand-parents. Because of these discrepancies, we decided not to publish the FS list proposed by KING-robust.We also tested whether KING-robust was able to correctly identify putative full-sib (FS) pairs. Full-sib pairs were called by KING-robust when IBS0 was comprised between 0.002 and 0.25 and K was within the class range for first degree. Under these assumptions, KING-robust identified 805 putative full-sib pairs, involving 419 cultivars, 313 of which were already listed as POs. We checked this list of proposed FS using both the above results for PO duos and previous SSRs data . We founnd and 3rd degree relatives were not considered as separate categories, but rather as indicators of generic lower degree relationships to create a conservative list of “unrelated” genotypes. The lists of unrelated cultivars at 1st, 2nd and 3rd degree, including 259, 78 and 45 cultivars respectively, are presented in Given the mixed results for the FS category, the KING-robust results for 2Preliminary STRUCTURE runs showed that the best model for partitioning the genetic structure of the sample was the admixed model with non-correlated allele frequencies and no prior geographic information. The STRUCTURE analysis, and in particular the similarity among runs, indicated that the best subdivision of the whole dataset (783 genotypes) was obtained at K = 4 . Using aFor the K-means clustering step in the Discriminant Analysis of Principal Components (DAPC), we retained 800 principal components (PCs) that allowed recovering 100% of the cumulated variance. The Bayesian Information Criterion (BIC) score sharply decreased to K = 4, more slowly to K = 8 and then increased beyond this value . We thusClusters varied in size from 20 to 141 genotypes  and diffC1 mainly included wine cultivars from Western and Central Europe (WCEUR) and the Iberian Peninsula (IBER).C2 was also composed of wine cultivars from Western and Central Europe (WCEUR) and Iberian Peninsula regions (IBER) but with a notable proportion of wine cultivars from the Italian Peninsula (ITAP).C3 included predominantly wine cultivars and few table grape from the Iberian Peninsula.C4 had a high percentage of table cultivars from diverse Western European regions and contained most of the new breeding table cultivars assigned to the New World group (NEWO).C5 contained cultivars from very diverse regions but mostly table cultivars from Eastern regions.C6 included a low number of wine cultivars from the Eastern Mediterranean and Caucasus regions .C7 was centered on the Balkan region but also included cultivars from Western and Eastern regions, most of them being wine grape cultivars.C8 included a large proportion of Iberian cultivars but also cultivars from WCEUR, BALK and ITAP regions and around one third of all cultivars from Maghreb.The DAPC grouping was highly consistent with the results obtained using STRUCTURE for the 233 non-admixed genotypes . The genVisual inspection of the scatterplots of the DAPC analysis showed that some clusters such as C3, C6 and C4 were more easily distinguishable than others . The difWe performed additional analyses to evaluate the robustness of the clustering obtained with DAPC. First, in order to reduce the first degree of kinship in the sample, the offspring in each trio was discarded, resulting in a new file with 665 genotypes. The decrease in size was different for the eight clusters, evidencing different levels of kinship within each one of them. Cluster C6 retained the same size , some clusters were only weakly affected , while others were more severely affected . K-means clustering with the same settings than above identified seven clusters as the most probable subdivision. Prior groups from the two analyses were compared in a contingency table. Both arrangements were mostly congruent, with the majority of genotypes in the eight clusters still being grouped together in one of the seven clusters . Second,Within-cluster genetic diversity  was evaluated in each DAPC subgroups. LD range varied between 28.8 kbp and 58.2 kbp according to subgroup . The lonGenome-wide association analysis (GWA) using the MLM model, corrected for both structure and kinship, provided interesting association results for some performance traits at the standard Bonferroni p level . We deteIn Vitis 18K genotyping array, allowed us to explore several characteristics in a large sample of grape genetic resources. The accessions studied in this work originated from four major European repositories and represent a large number of cultivars that have already been studied using either morphological traits or microsatellite markers [The availability of a new molecular tool, the  markers , 37, 38.−17) to discriminate 200 cultivars. In the present work, we found that 14 SNPs only are sufficient to distinguish the 783 genotypes representing most of the cultivated compartment diversity. The probability of identity obtained here was higher than that calculated with 9 SSR markers  similar to that obtained with the six SSRs included in the OIV reference set. The size and composition of minimal SNP sets depend both on the genetic diversity and the relatedness in the genotyped sample. However, using the same 18K SNP array, a minimal set of 12 SNPs has been found by Mercati et al. [In grapevine, a 9-SSR genotyping system has been established as a reference tool, to identify thousands of cultivars in Europe and neighboring countries . However4 10−11, ). In add4 10−11,  estimatei et al.  to distihttp://www.vivc.de/index.php) are needed.Although microsatellite markers are most adapted for cultivar identification , several research groups are currently using the 48-SNP set previously defined for that purpose , 40, 42.Several confounding factors may complicate parentage studies in grape: (i) genetic structure, (ii) generation overlapping due to vegetative propagation and use of the same cultivar in several breeding generations , (iii) sMost of the 118 parents-offspring trios had already been detected in previous studies mainly based on SSRs markers, but it was achieved here with higher LOD scores. The ten new trios detected (mostly for Spanish cultivars) corresponded to accessions not included in previous parentage studies . Finally, 10207 SNPs invalidated only 5% of the trios based on 20 nSSRs. We therefore argue that using around 30 nSSR loci, as recommended by Cipriani et al.  would prThe situation is different for parent-offspring duos (PO) with 19% of putative half-kinships based on 20 nSSR data finally invalidated with 10207 SNPs. Thus, our results confirmed other studies  on the und and 3rd degree relationship categories. While the kinship estimations  can be trusted, the exact family relationship was shown to be sometimes roughly estimated when the pair had two common ancestors instead of being FS. The kinship coefficient distribution allowed us to identify new unrelated cultivars (These other degrees of relationship could not be addressed using FAMOZ and we used KING-robust that however provided mixed results for full-sibs (FS), 2ultivars . The absThese parentage results together with diversity analysis were of interest both for elucidating origin and relationships of grape cultivars, and for GWAS.Population structure was detected within the cultivated compartment of grapevine as in previous studies , 18, 20.V. vinifera. In addition, we detected some original groups and increased resolution for others. First, a group (C6) containing mostly Georgian cultivars has been identified in Eastern Europe. Adding cultivars genotyped using the same SNP array [We therefore completed the STRUCTURE analysis with a multivariate approach that combined K-means clustering for detecting clusters and discriminant analysis to summarize differentiation between clusters . Using NP array  allowed NP array . AnotherNP array  using STNP array . The othV. vinifera may partly explain the original structure identified here in a large cultivated sample. The eight DAPC clusters indicated a complex domestication history with more secondary domestication events. The genome-wide diversity study published by Myles et al. [sylvestris. This latter point has been also suggested by De Andres et al. [V. vinifera cultivated compartment is due to separate contributions of diverse wild populations as previously suggested [The domestication process of s et al.  confirmes et al.  and poins et al.  to explas et al. . It is tuggested , 50. ThiThe analysis of genetic diversity at the DAPC subgroup level showed more diversity in central grape growing regions, and less in peripheral ones. Some genotypes with a higher inbreeding rate than expected could be found among Eastern and also modern cultivars, most probably due to the use of family-related cultivars as parents.However, the most striking result was the overall heterozygous excess found in spite of the widespread family structure within the 783 studied genotypes, which were connected respectively 67% and approx. 92% by first- and second-degree family relationships. While it could be expected that such a family structure had produced an overall heterozygote deficit (inbreeding), a heterozygote excess was observed on average both at the subpopulation and at the whole population level. This finding supports the hypothesis, recently proposed by Zhou et al.  of a higV. vinifera subsp. sylvestris [This hypothesis could explain both the weak genetic bottleneck in grape  and the lvestris , 24, 50.lvestris –58 and vr2 = 0.2 at a distance varying between 29 and 58 kbp among the DAPC subgroups. This rapid decay is comparable to earlier findings in grapevine [The linkage disequilibrium between adjacent SNPs showed a rapid decay, on average dropping to less than rapevine , 25, 44.On the other hand, we also found large variation among chromosome regions and SNP pairs, with, in some cases, LD extending over regions as long as 2 Mbp, a pattern that can be partly explained by a combination of breeding, selection and vegetative reproduction. The occasionally long non-recombining regions may prevent the genetic fine mapping of a trait by association genetics, if the genes determining the trait are located in these regions.GWA analysis provided interesting, and in one case unprecedented, association results for some performance traits. The SNPs associated with Muscat flavor co-localized with one region, and in particular with the VvDXS gene, already described by several previous publications –62. The GWA identified a locus for seedlessness on chromosome 6, partially co-localized with one of the QTLs identified by Costantini et al. , but it A large number of SNPs were associated with the berry skin color trait, spanning a surprisingly large 10 Mbp region around the VvMyb gene cluster . There aAssociation mapping revealed one SNP linked to cluster weight on chromosome 13, not coinciding with any previously described QTL for this trait. In previous works based on bi-parental crosses , 68, cluSimilar reasons may lay behind the lack of signal for phenology: we found only one SNP weakly associated with budburst-to-veraison time, while no association was found for other phenological traits. In this case too, previous works highlighted many different loci putatively linked to phenology, over many chromosomes, with little or no consensus among publications , 69–73. p<5.00E-11, r2 = 0.15, 7 SNPs over 4.4 Mbp). However, our analysis failed to detect any association with must acidity, a fact probably linked to the measurement methodology of must acidity. In fact, according to must storage conditions , the quantity of tartaric precipitation may vary greatly, impacting the measure of titratable acidity. This phenomenon is probably less critical in wine, where tartaric precipitation is expected to be homogeneously completed in the tanks.For the first time to our knowledge, we found on chromosome 2 an association between SNPs and wine acidity, with a quite high statistical support  and the ability of the techniques to provide genetic profiles at the lowest cost. In a more general way, the success of single nucleotide uses will depend on the understanding of the genotype-phenotype correlations, especially for complex traits governed by polygenic architecture, genotype environment interactions and low heritability.The currently available large number of markers spread all over the whole genome opens new prospects for a better understanding of http://www.vivc.de/index.php).The work presented here contributes to enlarge the existing database and makes it possible to propose a large number of additional SNPs markers for genetic questions and a sound basis for the expansion of a database similar to the VIVC database for SSRs , according to the manufacturer’s instructions except that 1% polyvinylpyrrolidone  and 1% β-mercapto-ethanol were added to the AP1 buffer. DNA was quantified with Quant-it Picogreen dsDNA Assay Kits .Vitis genotypes [V. vinifera subsp vinifera, four V. vinifera subsp sylvestris, three V. cinerea, three V. berlandieri, four V. aestivalis, three V. labrusca and five Muscadinia rotundifolia genotypes. A pipeline termed MAPHiTS  was used to map the short reads on the PN40024 reference genome and to detect polymorphisms. From over 4.3M SNPs initially detected, 18K SNPs were chosen according to several criteria: 1) no variation along 60 bp in both the 3’ and 5’ directions (Illumina specificities) 2) SNPs in regions involved in structural variations and repetitions were filtered out 3) the remaining SNPs were then selected based on their even physical repartition along the 12X.V0 version of the genome  together with their minor allele frequency (MAF) (http://urgi.versailles.inra.fr/Species/Vitis/GrapeReSeq_Illumina_20K).Within the GrapeReSeq project, SNP discovery was carried out using low-coverage, full sequencing of a panel of domesticated and wild enotypes . An averVitis genotyping chip raw data using GenomeStudio Data Analysis v2011.1 . The sequenced reference accession PN40024 was included as genotype control for allele standardization. In this process, we discarded SNPs that were monomorphic, with low amplification, more than 3 clusters, or missing data, as well as chloroplast SNPs. We obtained a data matrix of 10207 SNPs and 783 unique individuals with no missing data among our initial sample of 945 cultivars.This dataset is available at https://search.datacite.org/works/10.15454/1.4861359557068474E12 after registration at https://urgi.versailles.inra.fr/Species/Vitis/Data-Sequences/Genotyping-data.Genotype data were scored and validated from the GrapeReSeq 18K http://www.gnuplot.info/). Theoretical Illumina SNP positions were recalculated by re-mapping the flanking regions (2x60 bp) with NCBI/BLAST® v2.2.31 (https://blast.ncbi.nlm.nih.gov/Blast.cgi) against the whole PN40024 reference genome sequence .A genetic map was drawn using a homemade script in C to automate scaling and Gnuplot for graphical view  were calculated on the selected SNPs using GenomeStudio v2011.1 . AMaCAID R program  was usedFirst-degree parentage (i.e. parents-offspring trio and parent-offspring duo relationships) analysis and probability of identity (PI) were calculated using FAMOZ software . Due to http://www.vivc.de/index.php. The confirmation of already known pedigrees enabled us to empirically determine a LOD score threshold value for validation. A small mismatch rate (mismatches / Number of loci < 0.0009) is considered consistent with the intrinsic error rate of the Illumina chip technology, so that individual pairs with less than 9 mismatches over >10000 SNPs are still considered potential parent-offspring pairs if all other SNPs and the LOD-score support this hypothesis.Parentage results based on SNPs were compared to parentage analysis performed with 20 nSSRs available on 701 cultivars according to Lacombe et al.  and prevnd and 3rd degree relationships, however, given the existence in grape of several confounding factors , we used this option to generate a conservative list of unrelated cultivars, i.e. cultivars that have no parents within the dataset. In addition, KING-robust is based on different exclusion algorithm and initial assumptions than FAMOZ.Among first degree relationships, both parent-offspring duos and possible full-sib pairs were explored using the approach of Manichaikul et al. , based o4 burn-in and 5x104 samplings, as the analysis converged quickly. Indications of the most probable level of population subdivision were obtained using both Evanno’s delta-K [The genetic structure of the 783 cultivated grape samples was explored with the STRUCTURE software 2.3.4 . In a pr delta-K  and plot delta-K .To refine the sample’s genetic structure, we also examined the data  using the discriminant analysis of principal components (DAPC) implemented in the Adegenet package ver. 2.0.1 , 82 withTo assess the global differentiation and pairwise differentiation between clusters we used the R package mmod ver. 1.3.1  using thTo further characterize the cultivars belonging to each cluster inferred from the DAPC analysis we built contingency tables for counts and percentages (R base package) using several cross-classifying factors available in the passport data  and the group assignment based on STRUCTURE analysis.het option of the PLINK software  with the function Hs and to test differences in He between groups using the function Hs.test. Individual inbreeding coefficients were calculated using the—ion 1.9, ). The deion 1.9, ) and 100ion 1.9, ).r2VS measure of Mangin et al. [r2VS = 0.2. Haplotype non-recombining blocks as defined by Gabriel et al. [blocks in PLINK. The calculation was done both on the whole panel of 783 cultivars, and on each of the 8 subgroups resulting from the DAPC analysis. The resulting blocks were plotted on chromosomes using KaryoploteR R script [Linkage disequilibrium (LD) was explored using the n et al. , correctn et al. ). We focn et al. . LD extel et al. , were caR script .Genome-wide association analysis (GWA) was carried out using the weighted-MLM model in Tassel . TS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableA—Parents-offspring trios invalidated. B—Parent-offspring duos invalidated.(XLSX)Click here for additional data file.S4 TableA small mismatch rate (mismatches / Number of loci < 0.0009) is considered consistent with the intrinsic error rate of the Illumina chip technology, so that individual pairs with less than 9 mismatches over >10000 SNPs are still considered potential parent-offspring pairs if all other SNPs and the LOD-score support this hypothesis. The same reasoning applies to the IBS0 column.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S1 FigH’ is the coefficient of similarity among runs, %runs stands for the number of runs that could be clustered under the same solution. The best K level in our analysis was 4, the last point before a drop of the stability of the model.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigFor this analysis 800 principal components were kept and the number of starting points was set at 400. The chosen number of clusters was K = 8.(PDF)Click here for additional data file.S4 FigThe genetic clusters were identified among 783 individuals by K-means and discriminant analysis of principal components (DAPC) based on 10207 SNPs. Scatter plots show a) 1–2 DA components, b) 1–3 DA components, c) 1–4 DA components, d) 2–3 DA components, e) 2–4 DA components and f) 3–4 DA components. The contribution percentages are 38.2%, 18.0%, 16.0% and 9.0% for the four first DA components.(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.


SNP panels that uniquely identify an individual are useful for genetic and forensic research. Previously recommended SNP panels are based on DNA profiles and mostly contain intragenic SNPs. With the increasing interest in RNA expression profiles, we aimed for establishing a SNP panel for both DNA and RNA-based genotyping.− 20 when assuming no family relations and 1.2 × 10− 10 when accounting for the presence of full sibs. The ability of the SNP panel to uniquely identify individuals on DNA and RNA level was validated in an independent population dataset. The panel is applicable to individuals from European descent, with slightly lower power in non-Europeans. Whereas most of the genes containing the 50 SNPs are expressed in various tissues, our SNP panel needs optimization for other tissues than blood.To determine a small set of SNPs with maximally discriminative power, genotype calls were obtained from DNA and blood-derived RNA sequencing data belonging to healthy, geographically dispersed, Dutch individuals. SNPs were selected based on different criteria like genotype call rate, minor allele frequency, Hardy–Weinberg equilibrium and linkage disequilibrium. A panel of 50 SNPs was sufficient to identify an individual uniquely: the probability of identity was 6.9 × 10This first DNA/RNA SNP panel will be useful to identify sample mix-ups in biomedical research and for assigning DNA and RNA stains in crime scenes to unique individuals.The online version of this article (10.1186/s12864-018-4482-7) contains supplementary material, which is available to authorized users. Over the past years, DNA profiles have found increasing use in the identification of human samples: they are ideal for sample tracking in biomedical studies and forensic investigations. In recent years, joint analysis of DNA and RNA has proven to be valuable: 1) Forensic investigations where RNA profiles may complement DNA profiles, and may be used to establish the tissue origin of the specimen –5; 2) PoSingle nucleotide polymorphisms (SNPs) and other genetic markers like mitochondrial haplotypes, Y chromosomal markers and short tandem repeats (STRs) are all used for individual identification. Mitochondrial DNA (mtDNA) is found in both females and males but it is inherited only through the mother, which makes it impossible to differentiate between mothers and offspring , 6. Y chSNPs are defined here as single nucleotide substitutions that occur in more than 1 % of the general population. SNP assays can be used for multiple types of genetics studies. A recent review by Kayser and de Knijff provides an overview of recent advances in the use of SNPs for forensic investigation . Many stTo address the limitations of current IISNP panels, we present a small and powerful IISNP panel. When turned into a multiplex assay, this panel can be exploited for unequivocal identification of individuals in both DNA and RNA specimens in forensic investigations. Moreover the panel can be used to identify sample mix-ups in human gene expression studies, which have been demonstrated to severely affect the power of such studies [PMID: 21653519].Several Dutch biobanks contributed to sample collection of Dutch ancestry (with parents born in the Netherlands) within the Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL), established as a national node of the European BBMRI infrastructure in the Netherlands . Our DNAThe Biobank-based Integrative Omics Studies Consortium (BIOS Consortium) is also part of BBMRI-NL. Its aim is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch biobanks. The BIOS Consortium applies a functional genomics approach that integrates genome-wide genetic data with data on the epigenome and transcriptome. The RNA data were derived from individuals from seven Dutch biobanks participating in the BIOS Consortium . Globin RNAs were removed from whole blood and the polyA fraction of the remaining RNA was subjected to RNA sequencing using HiSeq2000 sequencers and were analyzed as described by . RNA-SeqOur strategy and criteria are shown in Fig. The selected SNPs were compared with previously published panels such as SNPforID 52-plex, 75 Chinese SNPs, 30 Korean SNPs and 92 IISNPs , 34, 35.Observed heterozygosity and expected heterozygosity were estimated based on genotype counts and allele frequencies, respectively. So, deviation from HWE was determined by comparing the expected and observed number of individuals with each possible genotype using Fisher exact test. The p-values of the HW tests were corrected for multiple testing using the method developed by Benjamin and Hochberg [Hochberg , impleme2 was calculated for all unique pairwise combinations of the common SNPs between DNA-Seq and RNA-Seq data. The LD values were used to determine whether there was any evidence for significant linkage among the markers. In addition, heterozygosity, fixation index  / He (where He is expected heterozygosity and Ho is observed heterozygosity). It may range from − 1 to + 1. Values close to zero are expected under random mating, while substantial positive values indicate inbreeding or undetected null alleles. Negative values indicate excess of heterozygosity, due to negative assortative mating, or selection for heterozygotes.), polymorphism, Hardy–Weinberg equilibrium and probability of identity , were checked using the Excel add-in GenAlEx [To evaluate statistical independence of the SNPs, r GenAlEx , 38.http://biopet-docs.readthedocs.io/en/latest/pipelines/gentrap/) was used to obtain genotype calls for 562 RNA-Seq samples. Further, for imputed GWAS samples, genotyping using the Human Core Exome chip was performed according the manufacturers protocol . Then, quality control was demonstrated using following settings: a cut-off of 99% for genotyping call rate, gentrain and cluster score < 0.6 and 0.4, respectively, and p-value cut-off for Hardy-Weinberg equilibrium was set at 10− 4. Consequently, imputation was done using SHAPEIT (v2.r644) and IMPUTE (v2.3.2). These two files were merged into one multi-sample VCF file that contained genotype information of total 3990 samples. All genomics coordinates specified by those two SNP lists were included in this VCF file. To compare these two SNP panels, we first calculated the pair-wise allele concordance scores for all 3990 by 3990 sample pairs by examining only the genomics coordinates specified in the 50 SNP panel. The allele concordance score (ranging from 0 to 1) for each sample pair was defined as the ratio between the number of identical alleles and the total number of alleles  through all 50 SNPs coordinates. In addition, the identification of the best matching GWAS samples for each RNA-Seq sample was examined with a minimal allele concordance score of 0.8. Multiple best GWAS sample hits for one RNA-Seq sample was possible as there have been repeated GWAS measurements performed on the same person. Then we performed the same steps using the long 2622 SNP panel to identify the best matching GWAS samples for each RNA-Seq sample.The performance of the 50 SNP panel was examined by two strategies. First, the ability of the 50 SNP panel to identify sample mix-ups was checked in the second batch of samples from the BIOS project with 1357 independent samples, not used for the panel identification, using the same genotype calling methods as described in the first phase of the project. In the second strategy, the SNP panel was compared with a much larger set of 2622 SNPs. The 2622 SNP panel was selected as follows: 1) All ExAC v0.3 biallelic SNPs were selected ; 2) overIn previous studies, most of the recommended individual identification SNP panels were generated based on DNA profiles and they mostly contain intragenic SNPs. Therefore, there is no efficient panel with informative SNPs which can be used for both DNA and RNA. To overcome these limitations, in this study both DNA and RNA-based genotype calls were used to find a small set of SNPs that can be used for identification of individuals. DNA-based genotype calls  and RNA-based genotype calls were made on 2115 individuals from four Dutch biobanks. Reliable RNA-based genotype calls were obtained for 507,975 SNP positions across four cohorts .To find the most informative SNPs, we applied a number of filtering steps: details on the different filters applied and number of SNPs remaining can be found in the Method section, Fig. NPs Fig. . SNP posted Fig. . We remoVariant consequences for each position were extracted from Ensembl. As expected, most SNPs are located in exonic regions, because we selected SNPs which can be consistently detected in both RNA and DNA data. Twenty-four SNPs are located in the 3′-UTR, 22 SNPs in the coding region , 3 SNPs in the 5′-UTR and 1 SNP in an intron . The correlation of MAF between RNA and DNA genotype calls was much higher for 50 selected SNPs (r2 = 0.81) than all confidently called SNP genotypes in both datasets (r2 = 0.6)  with nearly equal frequency between expected heterozygosity and observed heterozygosity of 50 selected SNPs, suggesting that there was no large bias in detection of these SNPs.  using the Excel add-in GenAlEx , 38. The− 7 for unrelated individuals and PI-sibs is 4.4 × 10− 4). For our final list of 50 SNPs, the PI was 6.9 × 10− 20 and PI-sibs were 1.2 × 10− 10 . Figure To investigate the utility of our 50 SNP panel in other than the Dutch population, AAF of 50 SNPs were compared with the AAF in 1000 Genomes, ExAC and Go.ESP Fig. .Fig. 5a Even in non-European populations, such as the South Asian and American populations, most of the 50 SNPs had MAF > 0.2 (AAF between 0.2 and 0.8) . Observing intronic SNPs among RNA-based calls analysis is common because of the presence of pre-mRNA, incomplete splicing or intron retention.There is no overlap between Dutch selected IISNPs and the established IISNP panels , 23, 35.The 50 SNP panel showed better identification performance when it was compared with another panel containing 2622 SNPs Table . There wRNA profiles can complement DNA profiles in research projects and forensic applications. In forensics, despite the presumed low stability of RNA, RNA profiles could not only identify individuals but also provide information about the type of tissues found at the crime scene, wound age determination, determination of the post-mortem interval and the functional status of cells as well as organs , 52–59. MNDA (MIM # 159553), SELL (MIM # 153240), CSF3R (MIM # 138971), IFITM2 (MIM # 605578), FPR1 (MIM # 136537), CXCR2 (MIM # 146928), GNLY (MIM # 188855), FCN1 (MIM # 601252)) was rather specific for whole blood, as their expression levels were near to zero in other tissues. Further, IFI30 (MIM # 604664), contained the lowest gene expression in different tissues. As 17 SNPs are sufficient to identify an individual uniquely in the Dutch population it is assumed that our SNP panel could be effective for identification of individuals in RNA from different tissues than blood. However, when testing our panel on another SNP-chip genotype and RNA-Seq dataset, consisting of 36 samples from brain tissue, it appeared that many of the 50 SNPs had insufficient coverage for reliable RNA-based genotype calling in brain. This indicates that our 50 SNP panel needs optimization to be used for other tissues than blood and/or high sequencing depth.To address whether the SNP panel could also be used on RNA samples from tissues other than blood, the expression level of genes in which the SNPs are located was surveyed using GTEx portal . While 2− 15 [− 21 for the European population [− 20 and 1.2 × 10− 10 in unrelated and related individuals, respectively, similar to the 52-plex SNP assay, far more discriminative than the 13 CODIS markers [Our SNP panel compared favorably to previously published panels in terms of discrimination power (probability of identity (PI)), even for closely related individuals. The FBI (USA) has selected 13 STR loci to serve as a panel for forensic investigations . With this set of loci, the probability of a match between the profiles of two unrelated persons in a randomly mating population of Caucasian Americans PI is 2.97 × 10− 15 . Also, tpulation . The PI  markers  and simi markers –63 .We developed a first SNP panel based on both DNA and RNA data for the Dutch population. This panel contains 50 informative SNPs with high heterozygosity, low PI and close MAF and AAF frequencies in DNA and RNA. It will be useful for efficient sample identification/tagging in large biomedical, association, and epidemiologic studies, and for developing forensic profiling and kinship assays. Our panel will be useful for other European populations and can be considered in conjunction with other panels to develop a global IISNP panel with more markers.Additional file 1: Table S1.Four biobanks (RNA-Seq) with number of different SNP positions and number of samples. (DOC 33 kb)Additional file 2: Table S2.Characteristics of 50 selected SNPs. (CSV 15 kb)Additional file 3: Figure S1.Distribution of MAF calculated from DNA and RNA data. (DOC 58 kb)Additional file 4: Figure S1.Plot of the expected heterozygosity (x-axis) and observed heterozygosity (y-axis) for the 50 SNPs in the panel. Pearson correlation is 0.98. (DOC 54 kb)Additional file 5: Figure S3.Individual heterozygosity across loci for each sample determined by GenAlEx software. (DOC 46 kb)Additional file 6: Figure S4.The fixation index values of 50 selected SNPs. (DOC 54 kb)Additional file 7: Figure S5.The average of population genetic parameters for 50 selected SNPs. (DOC 46 kb)


Gut microbes play a critical role in human health and disease, and researchers have begun to characterize their genomes, the so-called gut metagenome. Thus far, metagenomics studies have focused on genus- or species-level composition and microbial gene sets, while strain-level composition and single-nucleotide polymorphism (SNP) have been overlooked. The gut metagenomes of type 2 diabetes (T2D) patients have been found to be enriched with butyrate-producing bacteria and sulfate reduction functions. However, it is not known whether the gut metagenomes of T2D patients have characteristic strain patterns or SNP distributions.Bacteroides coprocola, whose relative abundance did not differ between the groups, had a characteristic distribution of SNPs in the T2D patient group. We identified 65 genes, all in B. coprocola, that had remarkably different enrichment of SNPs. The first and sixth ranked genes encode glycosyl hydrolases (GenBank accession EDU99824.1 and EDV02301.1). Interestingly, alpha-glucosidase, which is also a glycosyl hydrolase located in the intestine, is an important drug target of T2D. These results suggest that different strains of B. coprocola may have different roles in human gut and a specific set of B. coprocola strains are correlated with T2D.We downloaded public gut metagenome datasets from 170 T2D patients and 174 healthy controls and performed a systematic comparative analysis of their metagenome SNPs. We found that The online version of this article (doi:10.1186/s40168-017-0232-3) contains supplementary material, which is available to authorized users. Human gut microbiota are critical to human health and have been related to various disease conditions, such as obesity , diabeteA number of metagenomics studies have compared the relative abundance of bacterial species or pathway enrichment between patients and healthy controls (HCs). However, little work has been done to elucidate strain-level variations or single-nucleotide polymorphisms (SNPs) in the metagenome. Because even slight nucleotide variations can alter the pathogenic behavior and antibiotic resistance of bacteria –11, analRegarding strain-level variation in the microbial metagenome, Schloissnig and colleagues proposed a workflow for analyzing metagenomics datasets at the strain level and described the genomic variation landscape of human gut microbial genomes . SubsequFirmicutes and class Clostridia cells in the microbiome is significantly reduced in T2D f) acrossWe first determined a set of bacterial reference genomes for samples included in this study. Metaphlan2, which is based on approximately 17,000 reference genomes , was empClean reads were aligned to the whole reference set with Burrows-Wheeler Aligner-maximal exact match (BWA-MEM)  in defaup value 0.05, min-avg-qual 15,” respectively. SNPs detected by both the tools were selected. SNPs were further filtered with the requirements of a ≥0.5 mutated allele (relative to that in the reference genome) frequency, ≥4× supporting reads for the variant, and strand bias of sequencing bases less than tenfold in both BCFtools and VarScan2. Namely, when genome sites with heterozygosity were found in a sample, the major allele was used for the SNP analysis.Two tools, BCFtools  and VarSR2 = 0.997). To examine whether intra-sample variations affect the result, SNPs with a >0.8 MuAF were selected for a parallel SNP analysis.To address the heterozygosity or intra-sample variations, mutated allele frequencies (MuAFs) of variant sites were obtained for analysis under polyclonal scenario. The MuAFs of sites were defined as the mean values of outputs by BCFtools and VarScan2, which were highly correlated (Gene ontology (GO) annotations of each genome were downloaded from Uniprot . SNPs weB. coprocola , genome regions with >20% samples not having valid coverage (≥10× depth) were discarded. If ≤20% of the samples had invalid coverage in a region, the bases in that region in those samples were labeled as “N.” Then, the nucleotides at SNP sites from the samples were extracted to generate a pileup file. Phylogenetic trees were constructed based on the whole-genome level aligned SNPs by using randomized axelerated maximum likelihood (RAxML) v8.2.9 (100 bootstrap replicates), with GTR model of nucleotide substitution, γ-distributed rates among sites, and Felsenstein correction for ascertainment bias [Based on the reference genome of ent bias , 28. Theent bias .The nucleotide sequences of genes were obtained by aligning reads to corresponding full-length genes of the reference genome. For a given gene, gene regions were discarded if with >20% samples not having valid coverage ≥10× depth) and the coverages of full-length genes are listed in Additional file 0× depth K = 2.” The clusters generated by AP clustering were visualized by using Cytoscape v3.5.0 [For a given samples, the sites of bacterial genome and gene with a >0.2 MuAFs were selected. For all samples, we obtained a matrix with the column denoting site positions and the rows denoting samples. If there was no variation or MuAFs <0.2, the MuAFs were set to 0. Then, hierarchical clustering and affinity propagation (AP) clustering  were appe v3.5.0 . For eacq value with Storey and Tibshirani’s method  was applied for multiple testing correction [Relative abundance of species and genome/gene densities were compared between the T2D group and control group with the Mann-Whitney test. Fisher’s exact test was performed to test for bias of SNP sites and sample enrichment inferred from the phylogenetic tree and clusters by AP clustering. Hypergeometric test was applied for one-tailed test for enrichment of biased SNP sites at gene level. The rrection  to identFirmicutes, class Clostridia, and butyrate-producing bacteria , we identified a total of 356 bacterial species  for only one of the 20 species, namely, Bacteroides coprocola . However, the mean relative abundance of B. coprocola for the T2D group (9.10 ± 7.09%) was similar to that for the control group  in the samples with sufficient reads. Moreover, B. coprocola was prevalent and identified in 31.98% (110/344) of all the samples, ranked the top 24th among the 356 species.We selected 20 bacterial species with sufficiently supporting NGS reads in a sufficient number of samples  not in B. coprocola was from Faecalibacterium cf. prausnitzii. With the reference strain of B. coprocola DSM 17136, we observed that generally there are more SNPs in the HC group compared to those in the T2D group at the gene level  and identified 65 B. coprocola genes with significant enrichment of biased SNPs against the 1300 genes as a background . Phylogenetic trees constructed based on the nucleotide sequences of the 65 genes are shown together with their associated gene SNP distributions; the top two genes with the most differentiated SNP distribution are shown in Fig. p < 0.05). The most biased gene encodes a glycosyl hydrolase (GenBank accession EDU99824.1) with a biased SNP ratio of 0.59, and the second encodes a response regulator receiver domain protein (GenBank accession EDV02303.1) with a ratio of 0.48. Both of these genes had a biased distribution of T2D versus HC samples in the two tree clusters . For example, 88.89% (16 of 18) and 93.75% (15 of 16) of cluster 1 for the genes EDU99824.1 and EDV02303.1 were from HCs, respectively. Meanwhile, T2D samples were enriched preferentially in the second cluster (18 of 20 for EDU99824.1 and 18 of 19 for EDV02303.1). We further found that 9.08% of the SNPs in EDU99824.1 and 26.46% of the SNPs in EDV02303.1 were non-synonymous, and non-synonymous SNPs were differentially distributed between the two clusters . Similar to genome-level analysis under polyclonal scenario, the samples were clustered by MuAFs of each gene. The results also indicated the enrichment of T2D samples in one of the clusters  encode glycosyl hydrolases. Glycosyl hydrolases of bacteria play vital roles in degradation of cellulose and starch and hence generate sugars. Interestingly, alpha-glucosidase, which is also a glycosyl hydrolase located in the brush border of the small intestine, is an important drug target of T2D. Therefore, it is possible that EDU99824.1 and EDV02301.1 in T2D-related strain and control strain may have different glycosyl hydrolase properties, which is worthwhile to be investigated in the future.The present analysis of a Chinese metagenomics dataset revealed that the gut microbiota of T2D patients and HC individuals had different SNP distributions. The gut microbe species B. coprocola was previously reported to have high SNP density in the gut microbiota [B. coprocola strains may be associated with T2D and further suggest that strain-level bacterial colonization of the gut and the potential restorative influence of probiotic supplements should be investigated in T2D therapeutic research.crobiota . But theAs is shown, intra-sample variations affect very slightly on our results. However, assuming one strain type per sample may not be general, and intra-sample variations should not be overlooked in strain-level analysis of metagenomics.


Human genetic diversity in Europe has been extensively studied using uniparentally inherited sequences  and the Y chromosome), which reveal very different patterns indicating sex-specific demographic histories. The X chromosome, haploid in males and inherited twice as often from mothers as from fathers, could provide insights into past female behaviours, but has not been extensively investigated. Here, we use HapMap single-nucleotide polymorphism data to identify genome-wide segments of the X chromosome in which recombination is historically absent and mutations are likely to be the only source of genetic variation, referring to these as phylogeographically informative haplotypes on autosomes and X chromosome (PHAXs). Three such sequences on the X chromosome spanning a total of ~49 kb were resequenced in 240 males from Europe, the Middle East and Africa at an average coverage of 181 ×. These PHAXs were confirmed to be essentially non-recombining across European samples. All three loci show highly homogeneous patterns across Europe and are highly differentiated from the African sample. Star-like structures of European-specific haplotypes in median-joining networks indicate past population expansions. Bayesian skyline plots and time-to-most-recent-common-ancestor estimates suggest expansions pre-dating the Neolithic transition, a finding that is more compatible with data on mtDNA than the Y chromosome, and with the female bias of X-chromosomal inheritance. This study demonstrates the potential of the use of X-chromosomal haplotype blocks, and the utility of the accurate ascertainment of rare variants for inferring human demographic history. Autosomal sequence data from ancient remains have demonstrated discontinuity between Palaeolithic hunter-gatherers and Neolithic farmers,12 that has yet to be fully exploited. It is inherited twice as frequently from mothers as from fathers and therefore contains a record biased towards past female behaviours. In males, it is haploid, so sequencing in males provides unambiguous phasing of haplotypes, including those bearing rare variants. Finally, it shows high average levels of linkage disequilibrium (LD) because most of its length is exempt from crossover in male meiosis. It should therefore be possible to identify segments of the X chromosome that have histories of little or no recombination, to determine their sequences unambiguously in modern male samples using NGS methods and to use demographic reconstruction to infer female-biased histories. Moreover, recent work has focused on the X chromosome to derive sex-biased admixture models with constant ongoing admixture,13 and segments of the X chromosome showing little or no recombination would be suitable markers for these approaches as well.The properties allowing MSY and mtDNA to provide useful insights into the past are their haploidy and lack of recombination, which permit demographic reconstruction from haplotypes, and their uniparental inheritance, which provides a sex-specific aspect to the demographic inferences. In this light, the X chromosome represents a potentially useful additional source of information22 and have demonstrated excess gene diversity in African compared with non-African populations, consistent with observations from the autosomes.23 Some of these studies have chosen segments in which recombination is expected to be low,21 allowing the relatively simple construction of gene trees, while others have not used low recombination rate as a selection criterion, and have yielded haplotypes presenting evidence for multiple recombination events.17 To the authors' knowledge, a population-based study of X-chromosomal sequence diversity in Europe has not yet been undertaken.A number of resequencing studies of X-chromosomal loci have been carried out previously. These have generally surveyed segments of 1–10 kb in global samples,Here, we select three segments of the X chromosome that show no historical recombination in HapMap data, and use a population resequencing approach to analyse these in multiple European population samples, as well as single Middle Eastern and African population samples. Our findings show that the non-recombining nature of these segments persist, the histories of these X haplotypes in Europe are dominated by Palaeolithic expansions and suggest that larger-scale investigation of the X chromosome will provide further useful insights into the European past.11 An additional 13 unrelated male samples  collection in Utah, USA, with ancestry from Northern and Western Europe (CEU), the Yoruba from Ibadan, Nigeria (YRI), Han Chinese in Beijing, China (CHB) and Japanese in Tokyo, Japan (JPT). Haplotypes were inferred using PHASE26 and measures of LD were downloaded from the HapMap website, release 16c.Phylogeographically informative haplotypes on autosomes and X chromosome (PHAXs) were originally defined using publicly available single-nucleotide polymorphism (SNP) data from the HapMap project,Historically non-recombining regions were identified on both autosomes and X chromosome as non-overlapping series of at least three adjacent SNPs where each pair had a |D'| value of 1 in each of the three samples CEU, YRI and JPT+CHB, and for which only three of the four possible two-allele haplotypes were observed in the entire sample set, including the ancestral haplotypes (inferred from chimpanzee data), whether or not these were themselves observed. See 27 for convenience of ancestral state determination. PHAXs passing these filters were sorted by the number of haplotypes defined by SNPs in the CEU sample,25 given that the focus of our study was on European populations. We recognise that choice on the basis of diverse haplotypes may represent a source of bias, and will address this in a future study by analysing a larger random set.Candidate PHAXs for resequencing were chosen to be: (i) free of genes; (ii) separated from the nearest known or predicted gene by at least one recombination hotspot; (iii) lacking in segmental duplications, for ease of sequence interpretation; and (iv) possessing an ortholog in the chimpanzee genome,The top three X-chromosomal PHAXs were divided into 10 ~5-kb amplicons (including short overlaps) for resequencing. In all, ~10 ng of genomic DNA was used for amplification of the three chosen PHAXs via each of 10 amplicons, through polymerase chain reaction (PCR). Amplicons were quantified on 0.8% (w/v) agarose gels, and pooled at equimolar concentrations per sample. For each sample, 100 ng of amplified pooled DNA was used for library preparation, via the Ion Xpress Plus gDNA Fragment Library Preparation kit . Size selection was done using Agencourt AMPure XP beads . Ion Xpress barcodes were used to tag individual libraries, which were quantified using the 2100 Bioanalyzer  and the Agilent High Sensitivity DNA Kit. An equimolar pool of libraries was fractionated on a 2% (w/v) NuSieve 3:1 agarose gel. Final size selection and clean-up were performed with the Zymoclean Gel DNA Recovery Kit . Template preparation was carried out using the Ion Xpress Template 200 Kit (Thermo Fisher Scientific), before sequencing on an Ion Torrent PGM platform with 200-bp reads using the Ion PGM Sequencing 200 Kit (Thermo Fisher Scientific) and the Ion 316 chip v1.28 and picard v1.94 (http://picard.sourceforge.net/), respectively. All sites were called using SAMtools 0.1.1929 and filtering was done with in-house scripts. A total of 49 070 bp were called, including 419 raw variants from 240 samples. Following filtering, 297 variants and 238 samples were retained.Reads were mapped to the human reference sequence (hg19) using TMAP software implemented in the Ion Alignment plugin 3.2.1 . Local realignment and duplicate read marking were carried out with the Genome Analysis Tool Kit (GATK)In silico validation was done using Complete Genomics whole-genome sequence data  and Illumina sequence-capture data30 using only shared called sites in the PHAXs sequenced here (220 samples). Based on the complete genomics comparison, the false-positive rate was 0.0005% and false-negative rate 0, whereas via the Illumina comparison the false-positive rate was 0 and the false-negative rate 0.00003%. Further details on filtering and data analysis are reported in the 31 Tajima's D32 and Fu's Fs33 were calculated for each PHAX per population using Arlequin v3.5.34 Genetic differentiation between populations was measured with the molecular index ϕst,35 computed with Arlequin v3.5.34Haplotype diversity,36 implemented in Network 4.6 (http://www.fluxus-engineering.com/sharenet.htm). Ancestral state for each site was defined by comparison with the chimpanzee reference sequence.Relationships between different haplotypes were displayed in median-joining networks,37 Markov chain Monte Carlo samples were based on 200 000 000 generations, logging every 10 000 steps, with the first 20 000 000 generations discarded as burn-in. Traces were evaluated using Tracer v 1.6 (http://beast.bio.ed.ac.uk/software/tracer/). A piecewise linear skyline model with 10 groups was used with a Hasegawa, Kishino and Yano substitution model38 and a strict clock with a mean substitution rate of 6.59 × 10−10 mutations/nucleotide/year (details in 39Bayesian Skyline Plot (BSP) analyses were performed using BEAST v 1.8.0.41 using Network 4.6 (http://www.fluxusengineering.com/sharenet.htm). This analysis was performed on individual PHAXs with a scaled mutation rate of 41 171, 304 525 and 263 309 years per mutation for PHAX 5574, 3115 and 8913, respectively. These estimates were based on the mutation rate (6.59 × 10−10 mutations/nucleotide/year) and the number of nucleotides for each PHAX .TMRCA estimation for specific haplotype clusters was performed using the rho statistic,We used genome-wide HapMap data and LD analysis  to identFor the purposes of this study, we applied additional filters to the set of PHAXs identified on the X chromosome in order to obtain a set of informative, independent and putatively neutrally evolving markers. The top three regions were selected, spanning ~49 kb in total .in silico by comparison with published whole-genome sequences (http://www.completegenomics.com/public-data/69-genomes/), and with a subset of the same samples and target regions sequenced using Illumina technology.30 The high coverage and high threshold for variant calling led, respectively, to very low false-negative and false-positive rates . SNP density varies among the three PHAXs and the difference is marginally significant  suggesting different evolutionary histories for these loci. This was also supported by the significant pairwise differences between the distributions of haplotype diversity of the three PHAXs .We used Ion Torrent sequencing to assess the genetic diversity of the three X-chromosomal PHAXs in a total of 240 men from Europe, the Middle East and Africa . Mean cove rates . We asceve rates . Fifty-eve rates , that isPopulation structure was investigated by performing a principal components analysis (PCA) based on haplotype frequencies within populations. Overall, the first two PCs combined together explain a total of 32% and 61% of variance for the three PHAXs. When all populations were analysed together, the YRI sample was consistently separated from non-African populations in all three cases . To bettHaplotype frequencies were plotted per population according to their geographic locations . For all36 for each PHAX  were grouped together based on the low genetic distances suggested by the ϕst matrix and the absence of population structure indicated by the PCAs. The BSPs show an increase in effective population size of more than one order of magnitude , consistTime to most recent common ancestor (TMRCA) was calculated for specific haplotype clusters for each PHAX. Clusters were chosen based on the structures of networks – nodes of specific interest  and prominent ‘star-like' structures . Mean esOf the ‘odd couple' of the human sex chromosomes, the Y chromosome has received most attention in population genetics to date, because of its male specificity and the consequences for its mutation processes of its unusual state of constitutional haploidy. But the X chromosome is arguably just as interesting and strange, showing only one copy in males, female-biased mutation processes and the unique phenomenon of X inactivation.The X chromosome also shows reduced crossover activity compared with autosomes, because the non-pseudoautosomal majority (98%) of the chromosome is recombinationally active in only one sex – females. We therefore expect it to contain relatively long segments that show limited historical recombination activity, and can be treated as simple haplotypes in evolutionary studies. The fact that males are haploid for the X chromosome means that studying such segments in males will provide reliably phased haplotypes, even when variants within these are very rare in the population. Here, we have investigated this idea by targeting three candidate X-chromosomal segments based on SNP data that indicate no evidence of historical recombination in the four HapMap Phase I populations. We identified such segments genome wide, and have designated them PHAXs.High-coverage resequencing of the three PHAXs here, which cover 49 kb in total, reveals 297 SNPs in a sample of 240 males belonging to 12 populations, 11 of which are European or Middle Eastern. Almost 58% of SNPs are high-quality singletons suggesting that a resequencing approach is crucial for an accurate ascertainment of rare variants, which would have been lost with other techniques such as SNP arrays. Low-frequency sites provide vital information to finely assess and reconstruct recent demographic history. Network analysis shows that the absence of recombination generally persists in this independent sample in which variants are well ascertained. Two haplotypes showing evidence for recombination are found among the 20 males in the YRI population, whereas among 220 European and Middle Eastern chromosomes, one haplotype shows a single variant that requires recurrent mutation or recombination as an explanation. When additional available high-coverage sequence data from 13 samples sequenced by Complete Genomics are added to our data set, this pattern persists. HapMap Phase I data therefore seem a reliable source of information about non-recombining regions for evolutionary studies.vs non-African diversity.23 This can be seen in summary statistics ; in this, they are more consistent with the maternally inherited mtDNA, which shows expansion ~15–20 KYA4 and Ust'-Ishim (~45 KYA)42 from the Mesolithic and Palaeolithic, respectively ectively . Both maEach of the small number of PHAXs we have studied here is independently inherited, yet together they present a reasonably consistent picture of prehistory. The X chromosome contains a large number of additional PHAXs , and resequencing more would be desirable. In a much larger sample of PHAXs, we may expect to find a broader distribution of behaviours, including possible recent expansion haplotypes reflecting the Bronze Age migrations. Statistical approaches designed for multilocus data, incorporating sequence data from ancient DNA, would maximise the insights these loci can provide about the past, and in particular that of females.


TheBrassica napusIllumina array provides genome-wide markers linked to the available genome sequence, a significant tool for genetic analyses of the allotetraploidB. napusand its progenitor diploid genomes.Brassica napus. A stringent selection process employing the short probe sequence for each SNP assay was used to limit the majority of the selected markers to those represented a minimum number of times across the highly replicated genome. As a result approximately 60 % of the SNP assays display genome-specificity, resolving as three clearly separated clusters  when tested with a diverse range of B. napus material. This genome specificity was supported by the analysis of the diploid ancestors of B. napus, whereby 26,504 and 29,720 markers were scorable in B. oleracea and B. rapa, respectively. Forty-four percent of the assayed loci on the array were genetically mapped in a single doubled-haploid B. napus population allowing alignment of their physical and genetic coordinates. Although strong conservation of the two positions was shown, at least 3 % of the loci were genetically mapped to a homoeologous position compared to their presumed physical position in the respective genome, underlying the importance of genetic corroboration of locus identity. In addition, the alignments identified multiple rearrangements between the diploid and tetraploid Brassica genomes. Although mostly attributed to genome assembly errors, some are likely evidence of rearrangements that occurred since the hybridisation of the progenitor genomes in the B. napus nucleus. Based on estimates for linkage disequilibrium decay, the array is a valuable tool for genetic fine mapping and genome-wide association studies in B. napus and its progenitor genomes.A high-density single nucleotide polymorphism (SNP) Illumina Infinium array, containing 52,157 markers, was developed for the allotetraploid The online version of this article (doi:10.1007/s00122-016-2746-7) contains supplementary material, which is available to authorized users. Brassica napus is an economically important oilseed crop that is primarily grown to extract the healthy edible oil from the seed, but it is now also grown as a renewable feedstock for biodiesel. In addition, there are vegetable types of the species that have been bred for both human and animal consumption. It is a temperate crop widely grown in both the Northern and Southern hemispheres due to available genotypic variation for flowering time and response to photoperiod. It is believed that B. napus emerged from a small number of hybridisation events between the diploid progenitors Brassica oleracea (C genome) and Brassica rapa (A genome)  UN  that pronome) UN . The proty Dixon .B. napus and its diploid relatives in both the public and private domains that contribute to developing higher value crops with improved yields  across plant genomes has made them highly desirable for marker development  Illumina InfiniumPseudo-genome sequences of the diploid A and C genomes  were combined into a single reference sequence set for mapping. Sequence reads from each genotype were aligned independently using the CLC Genomics Server v3.6. Default parameters for the mapping algorithm were used except for the mapping identity parameter which was increased to 98 % to facilitate resolution of homoeologous sequence reads. Mapped reads were interrogated for sequence variation using the CLC Genomics Server v3.6 variant discovery algorithm. A minimum depth of coverage of 3× for 454 and 8× for Illumina data was required for SNP calling. Mapping data and variant calls were exported from CLC in the form of SAM alignment files and tab-delimited text files, respectively. Data from these files were combined using a custom Perl script to determine a missing, reference, or variant call in each genotype at each covered position of the genome.Combined SNP results were filtered using custom Perl scripts and eliminated based on the following criteria: (1) SNP positions without suitable flanking sequence (60 bp on at least one side of the SNP with no variation); (2) SNP positions with more than two variations within the surveyed genotypes; (3) SNP positions with high levels of heterozygous calls, biased allele ratio, or missing data; (4) Illumina Assay Design Tool (ADT) score less than 0.6; (5) SNP positions where the variation was the result of a transversion.Probe sequences for all filtered SNPs were obtained from Illumina and then aligned to the reference sequences using the open source alignment tool BLAT with default parameters Kent . These aB. napus was generated at AAFC through analysis of 437 genotypes and at TraitGenetics through the analysis of 432 genotypes. The cluster files for B. oleracea and B. rapa were generated with 129 and 121 samples, respectively. In both laboratories, DNA was extracted from young leaf tissue of greenhouse grown plants using a cetyltrimethylammonium bromide (CTAB)-based method (Murray and Thompson The cluster file for DNA from 124 lines of a doubled-haploid (DH) population  was hybridised to the Brassica 60 K Infinium array, and allele calls were made using the newly generated cluster file. The genetic linkage map was generated using the MSTmap software package  data from genomic material and Illumina HiSeq (417.85 Gb) data from both genomic and transcriptome sources were generated for an additional 13 B. napus, four B. oleracea, and three B. rapa genotypes (Supplementary Table 1).A set of 54,866 SNP assays, previously identified and tested on the Illumina platform, were provided from a number of different sources  of the filtered SNP assays had probe sequences that mapped to multiple regions of the Brassica genome. The final SNP list submitted for Illumina bead design contained 15,141 previously tested SNP loci, 32,294 newly designed SNP loci that matched the pseudo-genome uniquely, and 11,029 SNP loci that matched twice. Once synthesized, 52,157 SNP markers on the Brassica 60 K array passed bead representation and decoding quality metrics, including 1213 A/T or C/G SNPs, which are represented by Infinium I bead types that require two beads per assay.B. napus genotypes of both annual and biennial types, from diverse origins, ten F1 lines, and a subset of lines from two DH mapping populations. Independently at TraitGenetics, a second data set was generated that consisted of 432 mostly winter-type B. napus genotypes, including 67 hybrids, 88 F2, and 20 resynthesized B. napus lines. The two data sets were analysed independently and the resultant cluster files compared. After filtering out 173 SNP from the cluster file that displayed low intensity across the majority of the samples, 51,984 SNP remained. Based on the genotypes tested at AAFC, 1678 loci were monomorphic and for the genotypes tested at TraitGenetics, 2444 markers were monomorphic. Due to the strong sequence similarity between the A and C genomes of B. napus, it was anticipated that some of the SNP loci would display cluster patterns reflecting co-hybridisation of homoeologous loci. In such instances, when both homoeologous loci are polymorphic, the resultant SNP patterns are not automatically resolvable, generally these result in four-to-five clusters, and the Illumina software will identify exceptionally high numbers of heterozygotes  for each SNP locus. The cluster file is applied to intensity data to automatically call the genotypes for experimental samples, thus allowing easy comparison of data across labs Fig. a. At AAFB. napus cluster file .Diploid samples included in the initial analysis indicated that a subset of the loci could not be scored accurately in the diploids using the ile Fig. a–c. To fB. rapa .The physical positions of the assayed loci in the diploid A and C genomes were determined during the SNP calling process, based on unique read mapping to the reference sequence set, consisting of the A genome of mes Fig. . It was B. napus and an established B. napus line was used (Supplementary Tables 5 and 6). Based on informative recombination events, these loci were placed in 1310 bins across the 19 linkage groups and covered a length of 1815 cM  of the SNP loci, the highly polymorphic SG DH population derived from a cross between a resynthesized B. napus genome. In general, there was good correspondence between the two, with 20,138 of the 21,766 (93 %) SNP loci genetically mapping to the position expected based on sequence alignment. In addition, 3 % of loci that were physically mapped to one position were genetically mapped to the homoeologous region of the genome  were found to be anchored to the wrong chromosome relative to the two B. napus genome sequences (Supplementary Table 3). This is likely due to the relatively low marker density that was used to anchor the B. rapa genome assembly . The physical and genetic positions of the SNP loci have been imported into a web tool that visualizes these alignments (http://aafc-aac.usask.ca/Bn60). Particular regions of the genome can be selected to identify potentially useful SNP loci in any region of interest.Only markers that were positioned both genetically and physically could be definitively positioned on the ome Fig. . The rempes Fig. . HoweverB. napus genome becomes evident when carrying out genetic mapping even for relatively simple quality-based traits, where multiple loci control their expression. To dissect and follow genetically, complex traits within Brassica breeding programs robust sequence-based markers are required. The high-throughput genotyping array described here offers an excellent platform for facilitating such analyses and allows ready access to a set of well-characterised markers.The recent release of three Brassica crop genome sequences has provided opportunities for the development and application of new breeding tools. The complexity of the B. napus genome produced genome-specific three cluster patterns. Approximately 58 % of all assays produced clear genome-specific genotype calls for a wide range of B. napus genotypes, including F1 individuals. This is a marked improvement over similar arrays designed for hexaploid and tetraploid wheat where not more than 25.5 % of the SNP assays produced clear genome-specific cluster patterns  of the assays on the Brassica array included a number of steps to limit the impact of genome duplication and allopolyploidy on the resultant design. Reference mapping of short-read sequences to the constituent diploid genomes was optimised to prevent matching to homoeologous regions, sequence variation was avoided in the immediate proximity of the target SNP, and regions where multiple alleles were identified were excluded, all limiting the calling of paralogous SNPs. The final step of remapping the oligonucleotide probe for each SNP assay back to the reference genomes and selecting those with the lowest copy number further facilitated the design of the array, since 81 % of the SNPs that matched only once in the B. napus cultivars originating from multiple continents and covering the range of annual and biennial types, the SNP assays proved to be highly polymorphic, with only 3.5 % monomorphic loci identified indicating the value of the selected SNPs. This was also confirmed independently in a recently published work that used the array to assess diversity within a collection of predominantly Asian B. napus lines  of the loci mapped in one DH population. Due to the design process, each of the SNP loci was physically anchored to a specific base-pair position in one or other of the diploid progenitor genomes (Supplementary Table 3). On extending this analyses to B. napus, however, the alignment of only the isolated short SNP regions to the genome sequence introduced a level of ambiguity, with some SNPs being equally likely to align, or in the case of the assays, hybridise to the A or C genome of B. napus. In addition, due to the prevalent homoeologous recombination events which have occurred during the evolution of B. napus   of such intervals being localized to the C genome. This could reflect differing levels of genetic variation between the sub-genomes of B. napus, as observed by others  Table . The genion Fig. . AlignmeB. napus. Due to the natural and breeding bottlenecks created in modern Brassica germplasm much emphasis is now placed on capturing the wider allelic diversity within the species gene pool . SNP reports and SAM files were then combined to generate an SNP summary table (b). This table is filtered to remove SNPs according to user-defined criteria (c) resulting in a final set available for array design (d). (PDF 120 kb)Table 1List of genotypes and available sequence data used for SNP discovery (PDF 47 kb)Table 2B. napus, B. oleracea and B. rapa (PDF 9064 kb)Scoring metrics for SNP loci at TraitGenetics in Table 3Brassica napus genome based on BLAT analyses. SNP ID in B. napus genome is based on chromosome (or scaffold) name followed by base-pair position in the associated pseudo-molecule or scaffold. (PDF 6437 kb)Physical position of each SNP locus in the Table 4B. napus genes. (PDF 1071 kb)List of SNP loci which fall within annotated Table 5Brassica napus based on SG DH population (DH12075 x PSA12). (PDF 26859 kb)Complete scoring matrix for genetic map for Table 6Brassica napus based on SG DH population compressed into genetic bins according to recombination events. (PDF 1685 kb)Genetic map for Below is the link to the electronic supplementary material.