Runs of Homozygosity (ROH) are genomic regions where identical haplotypes are inherited from each parent. Since their first detection due to technological advances in the late 1990s, ROHs have been shedding light on human population history and deciphering the genetic basis of monogenic and complex traits and diseases. ROH studies have predominantly exploited SNP array data, but are gradually moving to whole genome sequence (WGS) data as it becomes available. WGS data, covering more genetic variability, can add value to ROH studies, but require additional considerations during analysis.Using SNP array and low coverage WGS data from 1885 individuals from 20 world populations, our aims were to compare ROH from the two datasets and to establish software conditions to get comparable results, thus providing guidelines for combining disparate datasets in joint ROH analyses. By allowing heterozygous SNPs per window, using the PLINK homozygosity function and non-parametric analysis, we were able to obtain non-significant differences in number ROH, mean ROH size and total sum of ROH between data sets using the different technologies for almost all populations.By allowing 3 heterozygous SNPs per ROH when dealing with WGS low coverage data, it is possible to establish meaningful comparisons between data using SNP array and WGS low coverage technologies.The online version of this article (10.1186/s12864-018-4489-0) contains supplementary material, which is available to authorized users. Runs of Homozygosity (ROH) are contiguous regions of the genome where an individual is homozygous across all sites. ROH arise when two copies of an ancestral haplotype are brought together in an individual. Consequently, that haplotype would be autozygous, i.e. homozygous by descent. ROH were first discovered using genome-wide microsatellite scans in the mid 1990s –5.Soon after the first ROH study using short tandem repeat polymorphisms (STRPs) was released, the first SNP arrays started to become available. During those first years, using arrays with densities of 40 K and 120 K SNPs, ROH were discovered to be ubiquitous across all human populations . HoweverROH have been a subject of study for understanding human population structures and disease genetics –13. The The first aim of this article is, therefore, to compare the outcomes and general conclusions drawn for array-based data and low coverage (3-6×) whole genome sequence data from the same groups of individuals. The second is to obtain appropriate parameters of ROH calling that allow meaningful comparison between ROH obtained from both technologies.There are two major methods for identifying ROH: observational genotype-counting algorithms  and mode3M2, was designed to deal with this type of data [PLINK, Germline and Beagle software have been used to find ROH in array and WGS data; yet, the HMM model approach is also used with Whole Exome Sequence (WES) data as an alternative to discover SNP variants and small to medium length ROH , 38. How of data .Accurate ROH calling requires high density SNP genome-wide scan data. A number of factors influence the quality of ROH calling, including the marker density, their distribution across the genome, the quality of the genotype calling/error rates and minor allele frequency. Currently ROH studies have been carried out using genome-wide scan data overwhelmingly from SNP arrays , 12, 40,A WGS approach, on the other hand, assays every variant so all accessible bases can now be genotyped and more than several million variants, from the most common to the most private can be obtained for each individual , 43. For9 nucleotide positions). On average, for all the populations analyzed, the WGS low coverage data had 6.3 times more heterozygous SNPs  compared to the array   as a measurement of the empirically observed number of heterozygous SNPs found in ROHs in population P when allow h heterozygous SNPs per window . This figure shows that for most of the populations the ep produced by allowing a single heterozygous SNP per window in array data is equivalent to allowing 4 to 5 heterozygous SNPs in WGS data. A few populations deviated from this observation: TSI , ASW (0.185 vs 0.122), ACB (0.185 vs 0.138), YRI (0.13 vs 0.114), BAG (0.161 vs 0.133) and ZUL (0.136 vs 0.105). These differences are provoked by differences in the mean number of SNPs per ROH as it can be seen in Additional file Due to its better performance in comparison to other software available, its efficiency execution on data from large consortia and the fact that is the software most used when searching for ROH we use PLINK 1.9 to develop this study. PLINK, by allowing a flexible number of heterozygous SNPs per window , already takes into account possible calling errors that may wrongly break a long ROH. By allowing this heterozygous SNP, the software produces an error that depends on the number of SNP (in homozygous state) per ROH. Figure p values; blue = not significant) in Figs. P-values are presented in Additional file According to both Table Once we established that the best PLINK condition to obtain comparable results is to allow 3 heterozygous SNPs per window when dealing with WGS low coverage, we compared the mean sum of ROH in both technologies for different ROH length categories Fig. . This isRuns of homozygosity are an excellent tool to delve into the exploration of different aspects of human genetics. Large genomic datasets, using array and whole genome sequence data, are now becoming available and offer the researcher a unique opportunity to better understand the influence of ROH on complex diseases architecture and demographic history.Ideally, WGS deep coverage would be the best option to study ROH, since genotype calling will be robust for low MAFs and ROH of virtually any size would be detected. However, two major issues prevent the use this technology. First, the lack of WGS deep coverage data for population studies and secondly, the extreme computational expense of analyzing this type of data using current software. Unlike deep coverage, low coverage WGS data is more abundant and affordable, and the computational effort of obtaining ROH is less computationally intensive. The only drawback of using this data is the calling error associated with it. By comparing ROH obtained from array data, we demonstrate in this article that this problem can be mitigated by allowing 3 heterozygous SNPs per window using PLINK software to obtain ROH longer than 1 Mb. In all populations, the highest correlation was achieved when allowing 3 to 4 heterozygous SNPs per window Fig. . RegardiInterestingly, four populations from East and South Asia did not conform to the patterns observed in the other populations; in fact for the Dai and Han populations from China , Kinh population from Vietnam (KHV) and the Japanese population (JPT), it was not possible to obtain the same mean number and total sum of ROH between array and WGS data. This may be explained by population structure, but perhaps the inferior performance of the Infinium Omni 2.5–8 Bead chip in Asian populations  is the mWGS data present the ability to identify shorter ROH Fig. , howeverThis study provides evidence-based guidelines for the combined analysis of array and low coverage WGS data when studying ROH to investigate population history and to detect associations with complex diseases and traits.We demonstrate that, even though there are differences between populations around the world, is possible to get equivalent results between WGS low coverage and SNP array technologies by allowing 3 heterozygous SNP per window when dealing with WGS low coverage data.n = 99), GBR , IBS , TSI  and CEU (Utah residents with European ancestry = 99). From America: ASW , ACB , PUR , PEL , CLM  and MXL . From Asia: CDX , CHB , CHS , JPT  and KHV . From Africa: YRI  and LWK . The AVGP includes 2185 samples from 16 African populations; we use WGS data for two: 100 Zulu from South Africa and 100 Baganda from Uganda, where genotype data from the Omni 2.5–8 SNP array and WGS data at 4× coverage are available. Only SNPs of the 22 autosomes were included in this analysis. For each population, data from both array genotyping and WGS were filtered to remove SNP with minor allele frequencies lower than 0.05 and those that divert from H-W proportions with p < 0.001. This filtering limits the effects of ascertainment bias caused by the small number of individuals in the SNP discovery panel, in the case of the array, and the calling errors associated with a low depth coverage of whole genome sequence data.Individuals with both genome-wide SNP genotypic data and WGS low coverage data from the 1000 Genomes Project – Phase 3 (KGP) , 47 and Identification and Characterization of ROH:homozyg-snp 50. Minimum number of SNPs that a ROH is required to havehomozyg-kb 300. Length in Kb of the sliding windowhmozyg-density 50. Required minimum density to consider a ROH (1 SNP in 50Kb)homozyg-gap 1000. Length in Kb between two SNPs in order to be considered in two different segments.homozyg-window-snp 50. Number of SNPs that the sliding window must havehomozyg-window-het (1 to 5). Number of heterozygous SNP allowed in a windowhomozyg-window-missing 5. Number of missing calls allowed in a windowhomozyg-window-threshold 0.05. Proportion of overlapping windows that must be called homozygous to define a given SNP as in a “homozygous” segment.We used PLINK v1.9 to identify ROH. The following conditions were used:−-homozyg-window-het 1). This is especially relevant in dealing with WGS low coverage data and therefore we were testing the equivalence between ROH obtained from array genotyping and WGS data.The minimum length of a ROH was set to 300 kb. PLINK allows the setting of different variable number of heterozygous SNPs per window, with a default value of 1 heterozygous genotype per window, in order to tolerate genotyping calling errors  as a measure of the empirically observed actual number of heterozygous SNPs found in population P when we allow h heterozygous SNPs.R be the set of ROHs with length in the range [x,y) in population P when allowing h heterozygous SNP.Let R| be the number of ROHs of length in the range [x,y) in population P when allowing h heterozygoteLet |epxy = arithmetic mean of the actual number of heterozygous found in all ROHs in R found in the population under studyLet Finally,The effect of allowing different numbers of heterozygous SNPs per ROH can be evaluated in different ways. We define We sum over  є {[1,1.5),[1.5,5),[5,10),[10,∞)}.This observed number of heterozygous SNPs differs from the parameter used for detecting ROHs depending on the population and technology platform characteristics.For comparison purposes three variables per population were defined. Mean number of ROH as the mean number of ROH longer than 1 Mb. Mean ROH size as the mean size of ROH longer than 1 Mb. Total sum of ROH as the mean total sum of ROH longer than 1 Mb. Considering just ROH longer than 1 Mb allows the selection of only the ROH arising from identity by descent and to remove any LD effects. Data distributions were illustrated using violin plots. This plot combines a box plot with a kernel density plot, where the interval width is obtained by the rule of thumb. The violin shows a colored kernel density trace with the interquartile range as a black line and median as a white dot. This representation is especially relevant when dealing with data or variables that show skewed distributions and is a good means of comparison between populations, when dealing with asymmetric distributions where the median is more informative than the mean. Statistical comparisons between mean number of ROH, mean ROH size and mean total sum of ROH for different populations, technologies and PLINK conditions were performed by Pearson’s correlation and Mann-Whitney-Wilcoxon non-parametric test (MWW). All the exploratory and statistical analyses were performed using R.Additional file 1: Table S1.ep values for different populations P and allowed heterozygous SNP. (DOCX 22 kb)Mean number of SNP (in homozygous state) per ROH in array data with 1 heterozygous SNP per ROH and WGS data with 1 to 5 heterozygous SNPs per ROH. Additional file 2: Table S2.Pearson correlations (y-axis) of number of ROH, mean ROH size and mean sum of ROH between array data with 1 heterozygous allowed per RHO and WGS with 1 to 5 heterozygous SNP allowed per ROH (x-axis). (DOCX 28 kb)Additional file 3: Table S3.Means and standard deviations of number of ROH, ROH size and total sum of ROH for different populations, technologies and allowed heterozygous SNPs per ROH (DOCX 35 kb)Additional file 4: Fig. S1.p-values) and Mann-Whitney-Wilcoxon non- parametrical test p vales between array data with 1 heterozygous SNP per ROH and WGS with 1 to 5 heterozygous SNPs per ROH. (JPEG 1011 kb)Pearson correlations (with


Beta vulgaris L.) genome as potential candidates for improving resistance against different diseases. In the present study, Ion Torrent sequencing technology was used to identify mutations in 21 RGAs. The DNA samples of ninety-six individuals from six sea beets (Beta vulgaris L. subsp. maritima) and six sugar beet pollinators  were used for the discovery of single-nucleotide polymorphisms (SNPs). Target amplicons of about 200 bp in length were designed with the Ion AmpliSeq Designer system in order to cover the DNA sequences of the RGAs. The number of SNPs ranged from 0 in four individuals to 278 in the pollinator R740 (which is resistant to rhizomania infection). Among different groups of beets, cytoplasmic male sterile lines had the highest number of SNPs (132) whereas the lowest number of SNPs belonged to O-types (95). The principal coordinates analysis (PCoA) showed that the polymorphisms inside the gene Bv8_184910_pkon (including the CCCTCC sequence) can effectively differentiate wild from cultivated beets, pointing at a possible mutation associated to rhizomania resistance that originated directly from cultivated beets. This is unlike other resistance sources that are introgressed from wild beets. This gene belongs to the receptor-like kinase (RLK) class of RGAs, and is associated to a hypothetical protein. In conclusion, this first report of using Ion Torrent sequencing technology in beet germplasm suggests that the identified sequence CCCTCC can be used in marker-assisted programs to differentiate wild from domestic beets and to identify other unknown disease resistance genes in beet.Resistance gene analogs (RGAs) were searched bioinformatically in the sugar beet ( Heterodera schachtii Schm.) [Sugar beet is an important crop that supplies around 20% of the sugar consumed worldwide. It is cultivated in over 50 countries . The croi Schm.) . The onli Schm.) ,3.Beta vulgaris L. ssp. maritima (L.) Arcang.), the wild progenitor of the domesticated sugar beet, provides useful sources of resistance to many diseases [Rz1) was identified in the WB258 sea beet population from the Adriatic coast of Italy [pro-1Hs1 is the only resistance gene cloned from sugar beet [The introgression of resistance genes from wild relatives to sugar beet cultivars remains fundamentally important. The sea beet  can be used to analyze variations inside the sequence of genes with typical RGA features. SNPs present advantages compared to other genetic marker types. SNPs are abundant, stable, co-dominant, amenable to an automated method of detection, accurate, and relatively inexpensive. Sugar beet has an estimated genome size of about 730 Mbp, and a 567 Mbp reference sequence with 26,923 annotated protein-coding genes has recently been published . The recB. maritima) population to demonstrate the potential of wild germplasms for sugar beet improvement. The results of the same study indicated that access to the DNA sequence of resistance gene Rz2 opens the path to improve rhizomonia resistance. Grimmer et al. [The Sanger sequencing method, introduced in 1977, allowed the identification of the sequence of genes and frequency of mutations inside genes . HoweverMolecular marker technologies greatly facilitate introgression of disease resistance traits into sugar beet breeding programs. Ion Torrent Ampliseq designer is a new technology with the ability to resequence genes and search for associations based on the Disease Research Area database . It has B. maritima). The seeds from this germplasm were produced in 2012 at the University of Padova, Italy. The seeds were germinated in soil pots with daily watering. Ten days after planting, seedlings were collected for DNA isolation.Ninety-six individual samples of beet were chosen in the present study. Samples were derived from 5 beet groups comprising cytoplasmic male sterile lines, hybrid varieties, O-types, pollinators, and wild-type accessions  in a BioSprint 96 workstation (Qiagen) following the manufacturer’s instructions. Leaf samples were ground using a Qiagen TissueLyser (Qiagen). Briefly, 20 mg of leaf tissue was placed into 2 mL tubes and 300 μL of RLT buffer (Qiagen) was added to each sample. One stainless steel 5 mm bead was used for each sample. Samples were then homogenized for 10 min at 30 Hz. Samples were centrifuged at 6000 g for 5 min and supernatant loaded into a 96-well plate with 200 μL isopropanol and 20 μL magnetic beads suspension (Qiagen). The beads were transferred consecutively into four other plates each with a premix, followed by a 4-min binding step and one bead collection step. The first plate was loaded with RPW buffer (guanidine thiocyanate buffer under patent protection). The second and third plates were loaded with 500 μL 96% ethanol. The fourth plate was loaded with 500 μL of 0.02% (v/v) using polyoxyethylene sorbitol anhydride monolaurate (Tween 20). DNA was eluted with 200 μL sterile water After isolation, DNA was assayed for concentration and purity by microfluidic gel electrophoresis with the Agilent 2200 TapeStation system . The average DNA yield was 50 ng μLwww.ampliseq.com) was used to generate target amplicons (about 200 bp in length) covering the RGAs sequences. The multiplexed amplicons were then used to generate barcoded libraries using the Ion AmpliSeq Library Kit 2.0 and the Ion Xpress barcoded adapters  to allow for discrimination between samples within a NGS run. Amplified libraries were quantified following the manufacturer’s recommendations. Barcoded libraries were combined to a final concentration of 7 pM, to achieve optimal yield of clonal templated Ion Sphere Particles (ISPs) (Life Technologies), for emulsion PCR (emPCR) and further ISP enrichment following the manufacturer’s recommendations. Sequencing was performed on 318 chips run on the Ion Torrent PGM and analyzed with the Torrent Suite v4.0.2 Software (Life Technologies). Quantification of prepared libraries was conducted by quantitative PCR using the Ion Library Quantification Kit (Life Technologies). Samples were run on the Ion Torrent PGM System (Life Technologies) as described by the manufacturer. The sequences were mapped against the sugar beet reference genome (RefBeet v. 1.2) and checked for known and novel mutations using Torrent Suite Software. The mutation positions identified were used for primer design and SNP Ampliseq assays.Sequences of the known RGAs were obtained from Hunger et al. . The IonBv8_184910_pkon in wild and cultivated plants was carried out using Clustal W with default parameters [Alignment of the sequences against the reference sugar beet genome and base calling were performed using the Torrent Suite software. The identification of variants was performed by the Ion Torrent Variant Caller plugin software. SNP or multiple-nucleotide polymorphism (MNP) frequencies were calculated by dividing the number of mutations by the length of each RGA gene in bp. To analyze and visualize the clustering and differentiation of wild and cultivated beets, a principal coordinates analysis (PCoA) was performed on the SNPs data. PCoA was based on genetic distances calculated from SNP markers. PCoA was performed using ad hoc scripts and the R package GenABEL, version 2.12.2 . Four sarameters . The nonrameters .Bv8_184910_pkon, 55 were SNPs and only 1 was a MNP. Bv3_063740_usyf located inside the Bvchr3.sca010 scaffold had the second highest number of identified SNPs and MNPs (127 mutations). The gene Bv3_063740_usyf showed the highest mutation frequency (2.91% for SNPs and 0.28% for MNPs) followed by Bv7_171340_mgxu (1.58% for SNPs and 0.19% for MNPs). Ion AmpliSeq was used to identify SNP within the sequence of twenty-one RGAs in individual DNA samples from wild and cultivated beets. The number of SNPs detected in 21 RGAs for each of the 96 individual samples is reported in Samples 8, 40 and 71 had the highest number of unique mutations (each with three unique SNPs) .N = 21, df = 4, p-value = 0.024) indicated that the mutations were distributed among five beet groups and the groups of beets were significantly different. The value of Kendall’s coefficient of concordance (0.13) suggests that the ranking (by number of polymorphisms) of RGA loci is not random with respect to beet groups. Rz2 gene for resistant to infection with rhizomonia) had the highest number of unique mutations (three unique SNPs). No mutation was identified in a number of RGA genes; ID 2, 3, 4, 13, 14, 16, 18, 19, 20 and 21 (Bv3_063740_usyf which lies in the scaffold Bvchr3.sca010 on chromosome 3 had the highest number of SNPs within the wild beet group. The gene Bv3_063740_usyf showed the largest difference in SNP numbers between O-types and pollinators. Coefficient of variation (CV%) of the number of mutations between four types of beet plants varied from 0–60.0%  had the highest number of SNPs followed by wild beets (117) whereas the fewest SNPs (95) were found in the O-type class (Bv8_184910_pkon gene were used (including the CCCTCC sequence) to calculate genetic distances were wild and cultivated beets clearly differentiated. Bv8_184910_pkon SNP-based genetic distances. Sequence alignment of the Bv8_184910_pkon gene in wild and cultivated beets is reported in The results of Friedman’s Analysis of Variance (ANOVA)  showed a density rate (variation at a specific position in the DNA sequence) of >1.0%. When the SNP data within the 21 RGAs were used to calculate distances between samples, no clear differentiation between wild and cultivated beets was observed from PCoA, save for one case. Only polymorphisms inside the RGA gene Bv8_184910_pkon could effectively differentiate wild from cultivated beet samples. These results suggest that most sources of resistance to rhizomania and nematodes in cultivated sugar beets have indeed been introgressed from B. maritima strains, hence they show no relevant genetic differences between the domesticated and wild germplasm. On the other hand, polymorphisms inside the Bv8_184910_pkon gene seem to be associated with a mutation which confers disease resistance. That mutation seems to have originated directly in cultivated beets in response to selective pressure related to rhizomania and nematode infections in field conditions. The Bv8_184910_pkon gene belongs to the receptor-like kinase (RLK) class of RGAs and its database annotation indicates that it relates to a hypothetical protein. A hypothetical protein is a protein whose existence has been predicted, but for which there is no experimental evidence that it is expressed in vivo. In silico methods can be used to predict hypothetical protein functions. The study of Beseli et al. [Cercospora nicotianae hypothetical proteins in cercosporin resistance. Their results indicated that the 71cR gene, encoding a hypothetical protein, was upregulated in C. nicotianae in response to cercosporin toxicity, and that the expression of this gene in the cercosporin-sensitive fungus Neurospora crassa can impart cercosporin resistance. RLKs are pattern recognition receptors (PRRs) that mediate pathogen-/microbe-associated molecular pattern (PAMP/MAMP) triggered immunity (PTI/MTI) to allow the recognition of a broad range of pathogens [Xa21 in rice encodes an RLK involved in resistance to a bacterial disease caused by Xanthomonas oryzae (Xoo) [R genes in beet plants. They could also help to characterize the genetic structure and geographic distribution of the beet. In the present study, we identified a large number of allelic modifications in RGAs that may be related to important adaptive functions in sugar beet. These modifications appear to be potential targets for subsequent association studies aimed to identify SNP markers linked to disease resistance in sugar beet. SNP markers can be designed from RGAs around a target disease gene to construct an RGA genetic map for the specific target region. Such mapped, genome-wide RGAs and linked SNP markers are valuable tools to develop high-density R-gene genetic maps, target R-genes, co-localize QTLs, design diagnostic markers of R-genes for fine mapping, clone R-genes, and breed for resistance. In this study, we used the Ion Ampliseq approach to identify variants for 21 RGAs in 96 samples from five beet groups. This technology has been widely used in disease diagnosis in medical sciences , but ouri et al.  characteae (Xoo) . In anotae (Xoo)  found thBv8_184910_pkon, responsible for differentiation of the wild from cultivated beets, can be used for rapid and convenient scanning of a large number of beet germplasms for identification of disease resistance in a time- and cost-effective assay. As most of the resistance genes share limited conserved domains, CCCTCC sequence information can be exploited to identify and clone unknown RGAs in wild beet plants. In conclusion, the results of non-parametric tests confirmed that there is untapped variation in the wild materials. Further, they show that the sequence CCCTCC inside the gene


MiSNPDb, available at http://webtom.cabgrid.res.in/mangosnps/ is based on 3-tier architecture, developed using PHP, MySQL and Javascript. This web genomic resources can be of immense use in the development of high density linkage map, QTL discovery, varietal differentiation, traceability, genome finishing and SNP chip development for future GWAS in genomic selection program. We report here world’s first web-based genomic resources for genetic improvement and germplasm management of mango.Mango is one of the most important fruits of tropical ecological region of the world, well known for its nutritive value, aroma and taste. Its world production is >45MT worth >200 billion US dollars. Genomic resources are required for improvement in productivity and management of mango germplasm. There is no web-based genomic resources available for mango. Hence rapid and cost-effective high throughput putative marker discovery is required to develop such resources. RAD-based marker discovery can cater this urgent need till whole genome sequence of mango becomes available. Using a panel of 84 mango varieties, a total of 28.6 Gb data was generated by ddRAD-Seq approach on Illumina HiSeq 2000 platform. A total of 1.25 million SNPs were discovered. Phylogenetic tree using 749 common SNPs across these varieties revealed three major lineages which was compared with geographical locations. A web genomic resources  Mangifera indica L.) a member of order Sapindales, family Anacardiaceae, is one of the popular fruit crops of tropical and sub-tropical ecological regions of the world. Especially in Asia, it is also referred as ‘King of fruits’ of the tropical world1. Its fruit is globally known for the strong aroma, coloration, delicious taste and a high nutritive value due to high content of vitamin C, β carotene and minerals2. Apart from being used as a fruit, it is also used in various processed forms like pickles, chutney, jelly, vegetable dishes and mango shake. It ranks as the fifth most produced fruit crop worldwide after banana, grapes, apples and oranges. India ranks first in the world in mango production with an annual production of 18–19 MT from an area of 2.31 MHa, which constitutes 40% of the total world production (>45MT worth >200 billion US dollars) (FAOSTAT-2014). India has exported more than 40000 ton mangoes majorly to UAE, UK, Saudi Arabia and the USA, which was of worth 50 million US dollar in 2013–14.Mango 6 and abiotic  factors had been major limiting factors in sustainable productivity of mango7. Though mango holds a huge economic aspect but owing to lack of genomic resources, genomic-based trait improvement and management has been one of the major impediments in mango productivity.10 but its whole genome sequence is yet to be completed. Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic markers and their high abundance in the genome makes them the ideal markers to study the inheritance of genomic regions13. Limited genic region SNP from transcriptome data of mango tissues have been reported14 but the whole genome-based bulk SNP discovery is yet to be reported. Genic region SNPs has been tried for varietal differentiation but with very limited success15. SSR marker-based approach has also been reported with limited success for example out of 41 only 5 varieties16, out of 36 only 7 varieties17 could be differentiated. These studies indicate that existing markers are not good enough for differentiation of mango varieties and there is need to discover more number of markers for variety differentiation and mapping18. An attempt has been made to develop genomic resources using NGS technology, but these resources are neither accessible to global community, nor it covers different varieties representing gene pool19.Mango is an allotetraploid (2n = 40) fruit tree with a small genome size of 450Mb20. RAD-Seq can be applied to study the population genetics of a species with no or very less sequence data and has several advantages over other methods for SNP discovery. Such approach reduces the complexity of the genome by sub-sampling only at the sites identified by restriction enzymes21. It reduces the investment drastically22. Till whole genome sequencing of Mango genome is completed such RAD-based bulk SNP discovery approach is most cost-effective and readily feasible alternative. Such SNP can directly be used for phylogeography and varietal differentiation. Similar studies have been done very successfully in various crops like cotton23 and sunflower24. Besides this such SNP data has further immense use and relevance in increasing marker density in linkage mapping programme. Even such approach has increased the marker density in specific variety of rice by utilizing available ref-sequence25. High heterozygosity in the plant is a big challenge in terms of whole genome sequencing-based SNP/polymorphism discovery.  The RAD-seq technique can be used for rapid marker discovery and genotyping in crops for highly heterozygous and outbreeding species like mango where de novo genome assembly is challenging26.RAD-Seq (restriction site associated DNA sequencing) approach can identify thousands of SNPs distributed randomly across the genome27. Unless markers from all important varieties of mango are not discovered such studies cannot be completed. Molecular markers are required for traceability of mango fruit and its product to address the issues of adulteration28. An allelic database of mango for varietal differentiation has been reported29 where limited markers are not able to differentiate varieties. Earlier reported molecular marker information are not in the form of web genomic resources to be used as a research tool for mapping, QTL studies and genome finishing. Moreover,  mango being fruit crop of relatively much longer life cycle (2–4 years), the DUS test statutory norms of varietal identification having two trials at two locations (http://www.plantauthority.gov.in/pdf/mango.pdf) becomes further challenging and cumbersome. The available genomic resources in mango are very scarce as only 107768 nucleotide sequences of mango are available in NCBI Genbank . Thus for a crop like mango, more holistic genomic resources are required to accelerate the DNA-based varietal differentiation, phylogeographic studies, increase in marker density for subsequent QTL and gene discovery.Attempts of geographical and varietal delineation of mango varieties with their genetic relationship have been made with RAPD and ISSR markers but there is no clear report covering the entire geographical locations of IndiaPresent work aims at ddRAD-based SNP mining from 84 varieties of mango along with the development of its web-based genomic resources. It also aims at phylogeographic studies of these mango varieties along with its evolutionary relationship.viz., Ratoul and Suvarnarekha, respectively. Conversely, in the data set, highest  and lowest  SNP density was observed in Suvarnarekha and Ratoul varieties, respectively. Overall SNP density over entire genome was estimated to be 357 bp. All these data have been populated in MiSNPDb to develop genomic resources. Only 749 SNPs were found common to all 84 varieties, which were subsequently used for diversity analysis and phylogeography studies. With increase in number of varieties for SNP discovery, we found decrease in number of common SNPs. For example, comparison of 10, 20, 50, 70 varieties, we got 10634, 7253, 2062 and 1343 SNPs, respectively  low quality reads were removed. A total of 1258705 SNPs were obtained from these RAD sequences representing 84 varieties of mango. RAD genomic data coverage of each variety are shown in Supplementary Table 32. Such use of RAD based SNP in 429 wheat variety differentiation using just 43 SNPs has been reported33.Magnitude of reduction in common SNP may not affect the phylogenetic tree, varietal differentiation, admixture and pedigree analysis. In crop like cassava, even 300 SNP markers have been found adequate enough for these kind of differentiationobs), the average nucleotide diversity (π), and the average Wright’s inbreeding coefficient (FIS).SNP-based diversity analysis of the 84 varieties of mango was done along with construction of phylogenetic tree for geographical relatedness using common RAD loci. The major allele frequency varied from 0.5 to 0.994 while minor allele frequency ranged from 0.006 to 0.5. The observed heterozygosity had a range of 0.0579 to 0.2649 with a mean of 0.1246 and expected heterozygosity varied from 0.029 to 0.1324 with a mean of 0.0623. The observed homozygosity was from 0.7351 to 0.9421 with a mean of 0.8753 and expected homozygosity varied from 0.8676 to 0.971 with a mean of 0.9376. The nucleotide diversity (π) had a range of 0.0579 to 0.2649 with a mean of 0.1246. The summary statistics for all the population of mango are summarized in Supplementary Table 34. The Ti/Tv ratio approximately nears the Ts/Tv estimated in non-long terminal repeat (Non-LTR) retrotransposon sequences in crops like maize (3.9), medicago (3.6), lotus (2.5)35, peach 34 and in another case of peach genotype (3.098)36. This high transition/transversion ratio of 4.312 in this current study may be an indication of low genetic divergence as it has been noted that lower is the genetic divergence, higher is the Ti/Tv ratio37. The Ti/Tv ratio is commonly used for ‘phylogentic’ tree construction, genetic divergence time estimation and to better the mechanisms of evolution at molecular level39. The number of SNP per variety of mango has been shown in Table A total of transition type SNPs  having A/G 427968) and C/T (429349) type, accounting for 34.00% and 34.11%, respectively were detected. The other four types were transversion type , observed as C/G (83914), G/T (100260), A/C (98811) and A/T (118403) with percentage varying between 6.66% and 9.40%  in geographical distribution.The phylogenetic tree of 84 varieties of mango was constructed using PHyML in order to classify the varieties according to their geographical relatedness. The phylogenetic tree constructed from the phylip file of these common RAD loci are shown in Fig. 30.As our study reveals distinct genetic differentiation in consonance with geographical distribution, thus these SNPs can be used in varietal differentiation of indigenous mango varieties along with traceability of mango produce/products. Hybrid/exotic varieties 13) taken under present study could not be assigned to any of these major lineage/group over three geographical zones. This is obviously expected due to attribute of a hybrid having been originated from two parental varieties. This unassigned group also includes Indian mango varieties having exotic in origin/exotic parental variety  but there is no web-based genomic resources having molecular markers with genome information. The SNP data sets generated in the present study was compiled and stored in a database (MiSNPDb), which is the first portal available with basic information of genomic information. In the homepage of the web portal, selection of radio button ‘variety’ displays total number of SNPs discovered in a particular variety. On clicking ‘SNP ID’ which is having assigned a serial number of every SNP across varieties (from 1 to 1.258705 Million), it will enlist varieties having very same SNP along with type of haplotype with respect to varieties. Our database also displays the stack depth for every SNP so that user can select SNP at desired threshold of stack depth to maneuver over confidence limit required in SNP array assay development. An advance search option has also been provided to customize the need of users based on haplotype, common SNPs in selected varieties, the threshold of depth coverage  of mango genome which are mapped using only genic region 1054 SNPs, thus representing very limited part of the genome14. Such use has been reported in other crop like Jujube (Ziziphus species) where 42784 putative high-quality SNP were linked to genetic map reducing the average marker interval of very drastically in tune of 0.34 cM making consistency with 12 linkage group i.e. haploid chromosome number45. Similar mapping and QTL discovery using RAD-based SNPs have been reported in barley as well46. In case of crop sesame, more number of RAD-based SNP has been added in the existing linkage map, to create ultra-dense genetic map or ‘improved SNP map’. This approach has discovered more number of QTLs associated with economically relevant traits with identification of candidate genes in sesame47.Our genomic resources of RAD data based SNPs can be used in making high-density linkage map of mango. First high density linkage map of mango has been reported by crossing two varieties 48. Such RAD-based SNPs have been successfully used in improvement of de novo genome assembly46.RAD data-based genomic resources of SNP markers has enormous application in genome assembly especially to assign scaffolds in process of genome finishing24. MAF information is directly relevant to selection of SNPs for array development in genomic selection program. Mango being highly heterozygous fruit crop with higher genome complexity27, thus RAD-based SNP can reduce the magnitude of this issue as this approach is covering on an average 10% of whole genome randomly over specific orthologous regions49. Further, such resources have been used in trait specific mapping in very same sunflower50. RAD-based SNPs have been successfully used in eggplant for increasing the marker density and development of SNP chip/array51.These mango SNPs can be used in the development of throughput SNP arrays (SNP chip) after using standard filtering criteria. Such approach and criteria have been reported in other crop like sunfloweri.e., 15) and limited varieties (41) differentiating Iranian varieties originated from India and Pakistan16. Since such differentiation among these disputed varieties requires genotyping of few selected SNPs, thus present MiSNPDb can be of immense utility as immediately available genomic resources. Respective location of SNPs displayed in our database along with the DNA fragments can directly be used in development of SNP allelic discrimination chemistry or screening assay like FRET, beacon, scorpion or multiplexing.Mango fruit traceability studies have been reported with a limited number of markers  without compromising the efficacy of genomic selection in future. Such approach even reduces the cost of genotyping. Use of RAD SNPs over existing ref-sequence has been very successfully used to enrich variety specific genomic resources of crop like rice with rapidity and cost effectiveness along with reduced computational complexitiesMiSNPDb. 749 SNPs were common to all these 84 varieties, which were used for diversity analysis and phylogeography studies. MiSNPDb has customized advance search options for haplotypes, depth and the varieties having common SNPs. The average genome coverage was 8.36% and average SNP depth was found to be 9.815. These resources can be used in mapping, QTL discovery, varietal differentiation, traceability, genome finishing and SNP array development in genetic improvement program and management of mango germplasm. Our study reveals that diversity of both soil and tropical ecology have contributed to evolution and differentiation of Indian mango varieties having a unique appearance, taste and aroma in different geographical regions.From a total of 84 mango varieties, 1.25 M SNPs using 171.80786 M RAD sequences are reported and catalogued in web genomic resource 58. One μg DNA from each sample was digested with Sphl and Mluc1 restriction enzyme and purified using AMPure XP Beads . Using T4 DNA ligase ligation of barcoded P1 and P2 adaptors was done and size selection of the product was done after 2% agarose gel electrophoresis. PCR amplification was done to enrich and add the Illumina specific adapters and flow cell annealing sequence, final quality check was done on bio analyzer. Sequencing was performed using Illumina TrueSeq chemistry (2X100nt) on Illumina HiSeq 2000 platform.High-molecular weight DNA from 91 leaf tissue samples was extracted and prepared for sequencing according to the modified version of RAD sequencing is known as Double Digestion Restriction site Associated DNA sequencing (ddRAD-Seq)59. The number of sequences before quality trimming were 177395296 with a total of 17898214358 bases. In order to remove low quality sequence data, the data was quality trimmed using the fastq_quality_trimmer command of the FASTX-Toolkit. The minimum phred-score of 30 and minimum sequence length of 30 base pairs was used as a threshold for quality trimming.The ddRAD-Seq data of 84 varieties of mango were preprocessed using the FASTX-ToolkitDe novo SNP mining was done by tool STACKS60 software version 1.29 using processed paired end RAD-Seq data of 84 varieties of mango. The denovo_map.pl perl wrapper script of the tool was used having three components viz., ustacks, cstacks and sstacks using the standard parameters. Population genetics statistics and transition and transversion ratio were calculated using in house perl script.Genetic diversity in the 84 varieties of mango was estimated using major and minor allele frequency, observed and expected heterozygosity as well as observed and expected homozygosity, and nucleotide diversity (π) at each locus. These parameters were calculated using the population program of the stacks pipeline. The population genetic statistics were generated using the population program of stacks pipeline using the common SNPs found in all the 84 varieties. Following the SNP mining and population genetic statistics, phylogenetic tree was drawn using the phylip file generated by the population program of the STACKS based on all the samples.denovo_map.pl and population program which can give the results in vcf format and phylip format. The SNPs common in 84 varieties of mango varieties were retrieved from the vcf file constructed by the stacks software. The number of common SNPs is 749 out of 1192 SNPs. The phylip tree of the common SNPs was constructed by PHyML software version 3.061 using BioNJ (neighbor joining) algorithm. The phylogenetic tree constructed by the PHyML software was visualized using the software Figtree version 1.4.062.The SNPs from 84 varieties of mango mined using stacks software using the MiSNPdb (Mangifera indica SNP database), a relational database with three-tier architecture was developed using PHP, MySQL and Javascript. These three tiers are client, middle and database tier. Various web pages are developed for database browsing. The queries by users are placed in client tier. Database includes tables for SNPs from different mango varieties. In order to fetch the data from database and execute the query fired by the user, middle tier has a role in server side scripting using PHP. The database is freely accessible at the http://webtom.cabgrid.res.in/mangosnps/.Supplementary Tables


Analyzing the geographical distribution of favorable haplotypes (FHs) among these lines revealed that two favorable haplotype frequencies (FHFs) were higher in accessions from low-latitude regions than in accessions from high-latitude regions. However, the genetic diversity of lines from the low-latitude regions was lower than the diversity of lines from the high-latitude regions in China. Furthermore, the FHFs differed among cultivars developed during different breeding periods. These results indicate that FHs have undergone artificial selection during upland cotton breeding in recent decades in China and provide a foundation for the further improvement of fiber quality traits.Fiber quality is one of the most important agronomic traits of cotton, and understanding the genetic basis of its target traits will accelerate improvements to cotton fiber quality. In this study, a panel comprising 355 upland cotton accessions was used to perform genome-wide association studies (GWASs) of five fiber quality traits in four environments. A total of 16, 10 and 7 SNPs were associated with fiber length (FL), fiber strength (FS) and fiber uniformity (FU), respectively, based on the mixed linear model (MLM). Most importantly, two major genomic regions (MGR1 and MGR2) on chromosome D Gossypium spp.) is an important crop that provides natural textile fiber and oilseed for human consumption. The cultivated types of cotton include two diploids, G. herbaceum L. (2n = 2x = 26) and G. arboreum L. (2n = 2x = 26), and two tetraploids, G. hirsutum L. (2n = 4x = 52) and G. barbadense L. (2n = 4x = 52). G. hirsutum is the most widely cultivated tetraploid cotton species and accounts for 90% of annual worldwide cotton production. G. hirsutum (upland cotton) is thought to have originated by hybridization between a maternal Old World “A” genome taxon resembling G. herbaceum or G. arboreum and paternal New World “D” genome taxon resembling G. raimondii2Cotton  marker map not only provides a resource for QTL linkage mapping and GWASs in cotton but also facilitates the detection of genetic changes associated with cotton domestication and improvement. Domesticated crops have undergone strong human-mediated selection aimed at developing high-yield, superior-quality and stress-tolerant cultivars that are adapted to diverse environmental conditions and agricultural practices222728Despite being the largest cotton-growing nation, China does not domesticate upland cotton. Most upland cotton cultivars planted in China were derived from several germplasm sources, such as Deltapine (DPL), Stoneville (STV), Foster and King, all of which were introduced from the USAA panel comprising 355 upland cotton accessions was established, and phenotype identification was conducted to study the distribution of five fiber quality traits: fiber length (FL), fiber uniformity (FU), fiber micronaire (FM), fiber elongation (FE) and fiber strength (FS). The diagrams revealed broad variation and a normal distribution without any significant skewness and kurtosis for the five fiber quality traits under four different conditions . Among tThe SLAF-seq approach was used to genotype the natural population as described in a previous studyTo represent the genetic, geographic and morphological diversity of Chinese upland cotton, the population included 331 cultivars and new strains gathered from multiple geographic regions across China . The pait7:25767969-25768030, Dt7:25931988-25999761 and Dt7:27425475-27437213) on chromosome Dt7 and a single region (At9:31687000-31778023) on chromosome At9 showed marker-trait associations. Four SNP loci within these associations  reached genome-wide significance after a Bonferroni correction for multiple testing (−log10 (p) ≥ 6.21), and these loci explained 10.10%, 9.31%, 9.18% and 8.95% of the phenotypic variation in FL, respectively  on chromosome Dt7 showed significant marker-trait associations with −log10 (p) values as high as 6.24, which explained 8.60% of the total observed variation in FS (10 (p) values. Synthetically, the SNP locus Dt7: 27437213 was significantly associated with FL and FS. Most importantly, all nine called SNP markers of the 66.77 kb region (Dt7:25931988-25999761) demonstrated associations with FL.GWASs were conducted for five fiber quality traits using the best linear unbiased predictions (BLUPs) of individual performance over four environments in an MLM, which accounts for both population structure and familial relatedness (PCA + K). A total of 16, 10 and 7 SNPs were associated with FL, FS and FU, respectively, whereas no SNPs were associated with FM and FE. For FL, three genomic regions  > 6.21). The four SNPs were distributed in two LD blocks. Although the four significantly associated SNPs were contained in a smaller region of 1.51 megabase pairs (Mbp), they were distributed in two separate genomic regions distinguished by LD block analysis (t7:25931988-25999761) consisted of nine SNP loci associated with FL on chromosome Dt7 and was detected by GWAS. The nine SNP alleles were A/G, C/T, C/T, A/C, A/G, C/T, C/G, C/T and A/G, respectively. We observed a close linkage relation among the nine SNP loci associated with FL. The haplotype (AA-TT-CC-AA-AA-TT-GG-TT-AA) that included 188 lines was deemed the favorable haplotype (FH) because the mean FL (28.99 mm) of the haplotype was significantly higher than the mean FL (27.86 mm) of the other corresponding haplotype , which included 74 lines. The mean FL of the remaining 93 lines was 28.53 mm; in these lines, the number of lines containing 1–8 copies of the favored alleles was 6, 7, 9, 4, 4, 3, 8 and 12, respectively, and 40 lines included the haplotype (AG-CT-CT-AC-AG-CT-CG-CT-AG)  have been annotated within the 66.77 kbp region of MGR1. CotAD_22823 and CotAD_22825 lack a definite annotation concerning their biological function. CotAD_22823 contains two conserved domains of unknown function (DUF4013 and DUF3816), whereas CotAD_22824 has a B3 binding domain, suggesting that it could be a member of the AP2/B3-like transcription factor family. Interestingly, the first and the second peak SNPs (rsDt7:25931998 and rsDt7:25932026) that were significantly associated with FL were positioned within one of the introns of CotAD_22823.To identify putative candidate genes in the neighboring regions of the SNP loci associated with FL and FS, we further determined LD blocks harboring four significant SNPs  . In addiG-CT-AG) . The afot7: 27425475-27437213) on chromosome Dt7 includes three SNP alleles. Of the three SNPs, two (rsDt7:27436981 and rsDt7:27437213) had significant associations with both FL and FS, and their alleles were A/G and C/T, respectively. The FL and FS value of accessions with FH (GG-CC) in MGR2 were higher on average than those of accessions with UFH (AA-TT) (t7:27437213) associated with FS and its adjacent SNP locus (Dt7:27436981) was distributed in the internal sequence of the gene CotAD_35088, and these results suggest that CotAD_35088 is a candidate gene controlling FL and FS. CotAD_35088 possesses a domain called a pentatricopeptide repeat (PPR) motif. The PPR protein gene family is distributed widely among terrestrial plants and has been shown to play an important role in plant development, organelle biogenesis, and cytoplasmic male sterility restoration.The second major genomic region of 11.74 kb  . Similar (AA-TT) . Interest4, and two SNP loci associated with target traits were detected ; YZR group (51 accessions from the Yangtze River region in China); NW group (98 accessions from the northwest inland region in China); LN group (20 accessions from Liaoning province in China); and USA group (20 accessions from the Texas cotton region of the United States of America). We analyzed the geographical distribution of two FHs among these upland cotton accessions, and heterozygous accessions were excluded from further analyses. Although the YR, YZR and USA lines had nearly the same level of FHF and showed little genetic differentiation, distinct FH distributions among the five different-source groups were found in MGR1 and MGR2 . For insdetected . The resdetected .Because of the FHF differences among the five geographic populations, the SNPs within the MGR1 and MGR2 regions may represent targets of artificial selection. To identify further potential selective sweeps of two FHs, a total of 77 (YR) and 79 (NW) source-identified and incubation-time-clear varieties were selected, and the FHF differentiation between MGR1 and MGR2 was scanned among varieties during six different breeding periods. We found that the FHFs of MGR1 and MGR2 differed among cultivars developed during different breeding periods. For cultivars developed before 2000, the FHFs did not exhibit obvious differences between YR and NW. However, the YR FHFs were much higher than those of NW for cultivars developed after 2000 because the FHFs resulted in a significant increase in YR and an evident reduction in NW between 2001 and 2005 . Afterwat7 were as high as 0.3730 and 0.3927, respectively, for all lines. However, the SNPs located within the MGR1 and MGR2 presented lower genetic diversity  and UFHs (0.3890). In the MGR2, the genetic diversity of the accessions that included FHs exhibited an average value of 0.0966, and the diversity of accessions that included UFHs had a mean of 0.1407. Furthermore, we analyzed the difference in genetic diversity among eleven SNPs within the MGR1 and MGR2 for the five geographical areas and found that the genetic diversity of the YR and YZR lines were lower than that of the NW accessions  were distributed within the sequences of two genes (CotAD_22823 and CotAD_35088). These findings suggest that two major genomic regions and candidate genes for targeted traits represent effective targets for improving fiber quality in future cotton breeding.In previous studies, a large number of association analyses of fiber quality traits and SSR markers in upland cotton have been reported131436http://www.cottongen.org). The physical locations of these SSR primer sequences were mapped to the reference genome sequencet7  > 2.0 were detected  value of 3.05 associated with FL. Although a comparison of the new GWAS with QTLs identified in previous studies was performed, it is very difficult to compare different QTLs for FL and FS in various populations, particularly in a changing environment. The vast majority of QTLs based on SSR markers for fiber quality traits have been mapped by crossing populations between G. hirsutum and G. barbadense, whereas the SNP loci associated with FL and FS were identified via GWAS in a natural population of G. hirsutum in the present study. Furthermore, only a rough draft of the upland cotton reference genome sequence is available, hindering highly accurate physical location of each marker. Hence, it was not possible to precisely integrate all the SSR and SNP markers into the reference genome sequence, and many of the QTLs differed between our study and previous studies.To fully understand the behavior of complex traits, the new GWAS must be compared with previous linkage and association studies. In previous QTL mapping studies, 557 SSR markers  containiquencet7 . One SSRand MGR2 . These fdetected . For exat7 in the other upland cotton reference genome is D11, and Dt7 corresponds to C21 in the linkage groups. The chromosomal position of each associated SNP locus was also determined for a second reference genome (t7)10335G. hirsutum and G. barbadense in recent decades. Therefore, it is important to develop additional markers for use in future studies.Two sets of upland cotton (TM-1) reference genome sequences39e genome . A meta-To identify and access the allelic variations affecting crop phenotypes, it is important to comprehensively evaluate and characterize large-scale representative genetic resources. In recent years, numerous studies have used a large number of germplasms and performed selective sweeps related to the domestication and improvement of crop traits. For example, the coincidence of salt-affected soils and salt-tolerant haplotypes in soybean indicates that these alleles are likely to be a major selection factor determining the distribution and utilization of soybean, particularly in saline soils26ST values are commonly used in the identification of genomic targets of artificial selection49Drosophila simulans has provided insight into common selective pressures and responsesComposite likelihood ratios (CLRs) and FThe detection of genome-wide genetic diversity and the identification of candidate genes that contribute to the domestication and improvement of target traits are essential for breeding superior varieties46A total of 355 upland cotton accessions  obtainedRsa I and Hae III, New England Biolabs, NE, USA) were used for library preparation. Paired-end sequencing (80 bp at each end) was performed on an Illumina HiSeq 2500 system  according to the manufacturer’s recommendations. The GATK and SAMtools packages were used for SNP calling, and BWA software was used to map the raw paired-end reads onto the reference genome (Gossypium hirsutum v 1.0)A total of 81,675 SNP markers were used for the subsequent analysis. SNP genotyping was performed using an SLAF-seq approachhttp://r-forge.r-project.org/projects/maptools/) and is shown in The geographic location of each upland cotton accession was obtained from the CRI-CAAS database. A map of the geographic positions of 331 accessions gathered from China was generated using the R software package ‘maptools’  values of five fiber quality traits in four environments were estimated using the R software package ‘lme4’58The phenotypic value of each haplotype was estimated through the average phenotypic value over accessions for each type of SNP locus associated with the target trait. The FHs were subsequently identified according to the breeding objective of each target trait. Box plots of the relative phenotypic values were generated using R software. The FHFs of the SNP loci associated with FL and FS were calculated via statistical methods using R software.Accession codes: The sequence read data from the SLAF-seq analysis of 355 sequenced upland cotton lines have been submitted and are available at the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/ bioproject/PRJNA314284/SRP071133) under the accession number PRJNA314284.How to cite this article: Su, J. et al. Two genomic regions associated with fiber quality traits in Chinese upland cotton under apparent breeding selection. Sci. Rep.6, 38496; doi: 10.1038/srep38496 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


These results suggest that both the structure and number of ACP domains are important for PUFA productivity.In some microorganisms, polyunsaturated fatty acids (PUFAs) are biosynthesized by PUFA synthases characterized by tandem acyl carrier proteins (ACPs) in subunit A. These ACPs were previously shown to be important for PUFA productivity. In this study, we examined their function in more detail. PUFA productivities increased depending on the number of ACPs without profile changes in each subunit A of eukaryotic and prokaryotic PUFA synthases. We also constructed derivative enzymes from subunit A with 5 × ACPs. Enzymes possessing one inactive ACP at any position produced ~30% PUFAs compared with the parental enzyme but unexpectedly had ~250% productivity compared with subunit A with 4 × ACPs. Enzymes constructed by replacing the 3 Polyunsaturated fatty acids PUFAs,  includin123Fishes and fish oils have traditionally been the sole PUFA sources. However, there is concern over the availability of PUFAs because of the unstable supply from marine resources and increasing demand. Therefore, alternative and sustainable sources of PUFAs are required. To this end, fermentative processes have been developed using microorganisms such as microalgae, fungi, and engineered yeasts for the production of DHA, ARA, and EPA, respectively56PUFAs are biosynthesized by two pathways, the aerobic desaturase/elongase pathway and the anaerobic PUFA synthase pathway. In the former pathway, which operates in plants, fungi, microalgae, and bacteria, specific desaturases and elongases catalyse individual desaturation and elongation steps to synthesize PUFAs from oleic acid C18:1 ω9):1 ω98910910Shewanella oneidensisPhotobacterium profundumMoritella marinaAureispira marinaSchizochytrium sp.cis double bond formation via dehydration and isomerization in fatty acid biosynthesis. Therefore, these domains perhaps participate in the introduction of cis configurations in PUFA biosynthesisEscherichia coli was successfully achieved121516et al. examined the biological role of these ACP domains using this system and PKSs with site-directed mutagenized ACP domains. They showed that PUFA productivity decreased depending on the number of inactivated ACP domainsPUFA synthase genes have been identified as clusters not only in marine microorganisms such as Modular and iterative type I PKSs with tandem-repeated ACP domains have also been identified and their biological roles have been studied. First, one of the double ACPs in a PKS responsible for naphthopyrone biosynthesis was shown to be enough to produce naphthopyrone, although the effect of inactivated ACPs on productivity was not reportedThese results suggest that the number of multi-tandem ACP domains in PUFA synthases/PKSs and polyketide productivity have a close relationship. However, only disruption methods have been employed in all preceding studies and there have been no reports using a theoretically reverse approach; that is, increasing the number of active ACP domains. In this paper, we constructed PUFA synthase derivatives with more active ACP domains than the native type and examined the effects on productivity. Finally, we succeeded in drastically enhancing PUFA productivity more than 16-fold for EPA and 1.8-fold for DHA. To the best of our knowledge, this is the first example of enhancement of PUFA productivity by enzymatic engineering with the huge enzyme complex PUFA synthase.Schizochytrium sp. on PUFA synthase activity. PUFA synthase activity was evaluated on the basis of PUFA productivity of recombinant E. coli expressing PUFA biosynthetic genes because PUFA synthases are huge enzyme complexes and hence it is difficult to prepare them as recombinants for in vitro assay. Metz et al. succeeded in producing PUFAs in recombinant E. coli harbouring the orfABC genes from Schizochytrium sp. and phosphopantetheinyl transferase gene, hetI, from Nostoc sp.orfABC and hetI genes were independently cloned into different and compatible expression vectors to construct pET-orfA, pCDF-orfB, pCOLA-orfC, and pSTV-hetI, respectively , was disrupted (E. coli BLR(DE3)∆fadE harbouring all the plasmids was cultured in terrific broth medium, the products were analysed by GC/MS. As shown in −1 OD−1, respectively, showing that the system constructed in this study worked well under the experimental conditions we employed (We first investigated the effect of increasing and decreasing the number of ACP domains in OrfA of ectively . To previsrupted . After Eemployed .orfA derivative plasmids with different numbers of ACP domains. Each of the ACP domains is highly conserved and separated by conserved and repeated regions with Ala and Pro rich sequences (orfA with more ACP domains (10× and 11×) were higher than those of the native plasmid, pET-orfA . These results indicated that the number of ACP domains in OrfA just controls productivity, and that the tandem ACP domain is a key factor controlling PUFA productivity.We then constructed pET-equences . This arequences . Using tSopfaABCDE, of Shewanella oneidensisSopfaA (with 4 × ACP domains), C, and D genes were independently cloned into three expression vectors as shown in the SopfaB and E genes were inserted into different multi-cloning sites in a pACYCDuet derivative to express them independently. The plasmids were introduced into E. coli BLR(DE3)∆fadE and the products were analysed by GC/MS. EPA and DPAω3 were successfully detected  and eicosatetraenoic acid  was also produced as a minor product. Because these PUFAs are not produced by the original strain, S. oneidensis, their production was caused by the heterologous expression in E. coli. Thus, the SopfaABCDE genes were also shown to work under the experimental conditions employed.To examine whether the functions of ACP domains demonstrated above are a common feature in other PUFA synthases, we next carried out the same experiment with the EPA synthase genes, detected  with yieectively . HoweverSopfaA genes (orfA derivatives (E. coli BLR(DE3)∆fadE carrying the SopfaA derivative genes together with the SopfaBCDE were analysed as described above. As shown in SopfaA derivatives and the native gene were observed  (We constructed with Ala . The PUFectively . The pro4 × ACP)  This resSopfaA5 derivatives. The inactivated ACP domain located at the third position of SopfaA5-3M was replaced with another inactive ACP domain of PUFA synthase from Moritella marina (a DHA producer), Photobacterium profundum (an EPA producer), Schizochytrium sp., or Aureispira marina (an ARA producer). Their ACP domains show 70%, 71%, 49% and 48% identities to that of SoPfaA ∆fadE together with the SopfaBCDE genes and PUFA productivity was examined. All the enzymes produced similar amounts of PUFAs but the productivities varied slightly depending on the similarity between the native and replaced ACP domains , B, C, D, and E, in E. coli although the productivity was quite lowSopfaA for EPA biosynthesis. Because the substitution of AmpfaA with SopfaA in the previously constructed plasmid was technically difficult, we constructed plasmids with the same expression vectors and strategies used for EPA production in this study , which has the same number of ACP domains as AmpfaA (4×). Moreover, SopfaA6 and SopfaA9, which had 6× and 9 × ACP domains, respectively, enhanced the productivity depending on the number of ACP domains (2.8-fold for 6× and 5.5-fold for 9× compared with SopfaA), and also enhanced the productivities of γ-linoleic acid  and 4,7,10-hexadecatrienoic acid  flux, utilization of inhibiters of the acyl-exchange reaction between phosphatidylcholine and acyl-CoA substrates, and prevention of PUFA degradation by β-oxidation were shown to be effective for titre improvement625Schizochytrium sp. and prokaryotic PUFA synthase of S. oneidensis. However, the PUFA product profiles of the engineered enzymes were the same as those of the native type. Taking the reaction mechanisms of these PUFA synthases together, we speculated that tandem ACP domains would allow for simultaneous access of other enzyme domains and enhance productivity. To test this hypothesis, we constructed five mutants (SoPfaA5-1M to SoPfaA5-5M) in which the first to fifth active Ser residues of the ACP domain were mutated to Ala, respectively. All the mutants produced 20–37% PUFAs compared with the parental enzyme (SoPfaA5). However, the amounts were unexpectedly higher than that of SoPfaA4 possessing four active ACP domains , DPAω3 (C22:5 ω3), DPAω6 (C22:5 ω6), EPA (C20:5 ω3), ETA (C20:4 ω3), vaccenic acid (C18:1 ω7), palmitic acid (C16:0), GLA (C18:3 ω6), SDA (C18:4 ω3), and other chemicals were purchased from Sigma-Aldrich Japan K.K.  or Cayman Chemical Company . Primers were purchased from FASMAC Co. Ltd. . Heptadecanoic acid (C17:0) was obtained from Tokyo Chemical Industry Co. Ltd. . Enzymes and kits for DNA manipulation were purchased from Takara Bio Inc.  or New England Biolabs Japan Inc. . PCR reactions were carried out using a GeneAmp PCR System 9700 thermal cycler  with Tks Gflex DNA polymerase (Takara Bio). General genetic manipulations of E. coli XL1-Blue  was routinely used for plasmid construction. For PUFA production, a β-oxidation deficient mutant, E. coli BLR(DE3)∆fadE, was constructed with E. coli BLR(DE3)  and an E. coli gene deletion kit .The strains used in this study are summarized in −1, respectively, if necessary.The media used were LB broth medium  and terrific broth (TB) medium . For growth on plates, 1.5% agar was added into the media. Ampicillin (Ap), chloramphenicol (Cm), kanamycin (Km), and streptomycin (Sm) were added to the media at concentrations of 100, 30, 25, and 20 mg LSchizochytrium sp. , Shewanella oneidensis MR-1 , and Aureispira marina . The amplified fragments were digested with appropriate restriction enzymes and inserted into the corresponding restriction sites of the expression vectors. Detailed processes for plasmid construction are described in the hetI; accession number L22883) gene of Nostoc sp. PCC 7120 (ATCC 27893) was constructedMmpfaA of Moritella marina MP-1  and PppfaA of Photobacterium profundum SS9  were used.The plasmids used in this study are summarized in E. coli BLR(DE3)∆fadE was used as a host. The PUFA biosynthetic gene sets were co-introduced into the host with the corresponding phosphopantetheinyl transferase gene . The transformants were cultured at 30 °C in TB broth medium for 24 h, and then 1 mL of the broth was inoculated into 200-mL baffled flasks containing 20 mL of TB medium and 1 mM IPTG. After cultivation for 48 h at 20 °C with agitation (230 rpm), 5 mL of the culture broth were collected and centrifuged. Total lipids were extracted from the pelleted cells following Bligh and Dyer−1; injection temperature, 250 °C; column temperature, 150 °C (5 min) −250 °C (2 °C min−1) −250 °C (15 min); ion source temperature, 250 °C; detection, scan mode (m/z 50 to 500) for qualitative analysis and selected ion mode (m/z 79) for quantitative analysis. PUFA products were identified by comparing mass spectra of the products with those of authentic ones and by utilizing National Institute of Standards and Technology (NIST) mass spectral library. Heptadecanoic acid was used as an internal standard. To determine the double bond positions of the PUFAs, pyrrolidide derivatives of fatty acid methyl esters were preparedTo prevent degradation of the synthesized PUFAs, How to cite this article: Hayashi, S. et al. Enhanced production of polyunsaturated fatty acids by enzyme engineering of tandem acyl carrier proteins. Sci. Rep.6, 35441; doi: 10.1038/srep35441 (2016).


Furthermore, a study of epistasis, aiming to detect statistical interactions between loci with respect to their association with transcription levels has provided a list of 30 associated interacting SNP pairs, of which 13 are present in the GAW19 family GWAS and expression data. We show for this set of results, using the program GEMMA  to account for family relatedness, that there is evidence of replication within the real GAW19 data. Two individual SNP pairs reach significance, and the set of remaining results give a combined p value of 0.017 that at least 1 of these remaining SNP pairs interacts to influence an expression phenotype.We investigate the possible replication of “known” associated single-nucleotide polymorphisms (SNPs) with blood pressure and expression phenotypes. Previous studies have provided a list of 95 SNPs thought to be associated with blood pressure phenotypes, of which 44 were present in the Genetic Analysis Workshop 19 (GAW19) family-imputed genome-wide association studies (GWAS) data and 4 in the GAW19 unrelateds sequence data. Using only the real (not simulated) GAW19 data, we show through the use of statistical tests that account for family relatedness, using FaST-LMM , that none of our candidate SNPs yields a significant  Previous studies using very large data sets have provided a list of single-nucleotide polymorphisms (SNPs) believed to be associated with blood pressure and expression phenotypes. We attempt to replicate these SNPs in the Genetic Analysis Workshop 19 (GAW19) family genome-wide association studies (GWAS) data set and GAW19 sequence data, which may indicate the feasibility of finding novel SNPs in the GAW19 data sets.The GAW19 family GWAS data  on the GA meta-analysis study conducted by Tragante et al.  of 87,73pi is the p value of the ith SNP tested from n candidate SNPs. Candidate SNPs for each phenotype were considered together giving overall p values for DBP, MAP, PP, SBP, and HTN. The null hypothesis is formed by assuming that none of the SNPs are associated with the phenotype in question. A set of p values can be generated by sampling U, where the correlations  of nearby SNPs (<2 Mb) are accounted for [p value is then given by the proportion of simulated test statistics greater than the observed test statistic, from 500,000 replicates generated under the null hypothesis. An alternative method to control the false discovery rate for correlated test statistics is given by Yekutieli and Benjamini [The 44 candidate SNPs were tested individually using FaST-LMM  with the realized relationship matrix (RRM) option to adjust for relatedness between individuals. To examine the overall association of a set of SNPs we defined a statistic inspired by Dudbridge and Koeleman’s rank truncated product statistic [http://biostats.usc.edu/Quanto.html), assuming that the individuals are unrelated, thus providing upper limits for the power. Parameter estimates and minor allele frequencies were taken from Tragante et al. [Estimates of the power to detect association, at significance level 0.05, for each of the tested SNPs with the appropriate phenotypes were calculated using the program Quanto , together with 1 covariate on HTN medication. PLINK was used to calculate (CTSC) had 2 gene probes.A recent study by Hemani et al. , motivatp values using the likelihood ratio test.To test for SNP–SNP interactions while allowing for family relatedness, GEMMA  was used with an estimated kinship matrix. GEMMA does not have an interaction option but it does allow covariates, which were used to encode SNP data through use of 2 linear mixed models. The first model encoded 3 variables: the number of minor alleles for each SNP and the intercept. The second model encoded an extra variable given by the product of the number of minor alleles of the 2 SNPs, thus imposing an additive × additive interaction model. The maximum likelihood estimates for each model were used to evaluate p value for all 14 interaction tests was calculated using the method previously described for single SNP association analyses in the GAW19 family GWAS data, using 10 million replicates.An overall p value of 0.05/75 = 0.00067 for a family-wise error rate (FWER) of 0.05. Figure p values of 0.34, 0.17, 0.052, 0.25, and 0.53 for DBP, MAP, PP, SBP, and HTN respectively. This appears to provide some very weak evidence of association for PP; that is, at least 1 of the tested SNPs is significantly associated with PP.Table Upper limits of the power to detect the tested SNP associations, at significance level 0.05, gave estimates ranging from approximately 0.080 to 0.30.p value (0.05/12 = 0.0042) for a FWER of 0.05.Table p values below the Bonferroni corrected p value of 0.0036 for a FWER of 0.05: rs4284750 and rs873870 on chromosome 19 interacted to influence gene expression at ATP13A1; and rs9979356 and rs3761385 on chromosome 21 interacted to influence gene expression at CSTB. The p values are generally lower than expected with a median of 0.223. Furthermore, the overall p value for the 14 tests, as shown in Fig. −6, and with the 2 significant SNP pairs removed the remaining 12 tests give an overall p value of 0.017, which provides reasonable evidence that at least 1 of these SNP pairs also interacts to influence the corresponding gene expression measurement.Table p value 0.05) expected to be less than 0.080 to 0.30; perhaps it is not too unexpected that no associations were replicated. The sample size of the unrelateds sequence data, 1943 individuals, was greater, but nonetheless did not replicate any previously observed associations. Although the low sample sizes are the most obvious reason for the nonreplication, there may also be more subtle reasons for the nonreplication, such as the relatedness and ethnicity of the samples used. The quality and accuracy of the measured phenotypes may also be relevant, in particular whether individuals took HTN medicine or not.The candidate SNPs and blood pressure phenotypes investigated here were previously detected in large meta-analyses or other replicated studies, giving considerable confidence that these SNPs are in fact genuinely associated. However, the sample size in the GAW19 family GWAS data consists of only 954 related individuals, giving power  can be flawed when the SNPs are close to one another [The SNP–SNP interactions previously shown to be associated with transcription did, however, show some evidence of replication, with 2 SNP pairs showing significant evidence of association and the remaining SNP pairs giving an overall  another .There was no evidence of replication using the GAW19 data for previously found SNP associations with blood pressure phenotypes, possibly because of the low sample size. However, there was some evidence of replication for SNP–SNP interactions associated with transcription. This may be the result of a greater power to detect associations with transcription than with more distantly related phenotypes.


Vitis vinifera subsp. sativa and V. vinifera subsp. sylvestris are expected to provide a better estimate of the valuable genetic diversity still present in grapevine, and help to reconstruct the evolutionary history of a major crop worldwide. To this aim, the increase of molecular marker density across the grapevine genome is fundamental. Here we describe the SNP discovery in a grapevine germplasm collection of 51 cultivars and 44 wild accessions through a novel protocol of restriction-site associated DNA (RAD) sequencing. By resequencing 1.1% of the grapevine genome at a high coverage, we recovered 34K BamHI unique restriction sites, of which 6.8% were absent in the ‘PN40024’ reference genome. Moreover, we identified 37,748 single nucleotide polymorphisms (SNPs), 93% of which belonged to the 19 assembled chromosomes with an average of 1.8K SNPs per chromosome. Nearly half of the SNPs fell in genic regions mostly assigned to the functional categories of metabolism and regulation, whereas some nonsynonymous variants were identified in genes related with the detection and response to environmental stimuli. SNP validation was carried-out, showing the ability of RAD-seq to accurately determine genotypes in a highly heterozygous species. To test the usefulness of our SNP panel, the main diversity statistics were evaluated, highlighting how the wild grapevine retained less genetic variability than the cultivated form. Furthermore, the analysis of Linkage Disequilibrium (LD) in the two subspecies separately revealed how the LD decays faster within the domesticated grapevine compared to its wild relative. Being the first application of RAD-seq in a diverse grapevine germplasm collection, our approach holds great promise for exploiting the genetic resources available in one of the most economically important fruit crops.Whole-genome comparisons of  At the same time, huge progress has been achieved for high throughput SNP genotyping thanks to the introduction of array-based technologies, able to screen several thousand SNPs per assay [V. vinifera cultivars and wild Vitis species. The second high throughput SNP array (Illumina Vitis18KSNP array) was produced in grapevine as part of the GrapeReSeq Consortium [The introduction of molecular markers in plant breeding has enabled remarkable advances in agricultural production thanks to the discovery of genes associated to major agronomic traits, the study of species diversity and evolution, and the characterization of plant genetic resources . During er assay . SNP arrer assay . Myles eer assay  designednsortium . Many exnsortium –8.Restriction-site Associated DNA sequencing (RAD-seq) based on rare-cutter restriction enzymes (6–8 bp recognition site) for sequencing short DNA fragments surrounding a particular recognition site throughout the genome [Erysiphe necator) resistance within a segregating population of V. rupestris x V. vinifera ‘Chardonnay’, finally mapping 35,8% of the 47K SNPs identified. Actually, one of the major drawbacks reported for GBS is the high rate of missing data which is currently faced by imputation programs such as LinKImpute [Several methods that combine genome-wide SNP discovery and SNP genotyping are nowadays available. They rely on the use of restriction enzymes in order to reduce the portion of the genome to be sequenced. The number and type of restriction enzyme used as well as the amount of digested DNA, the multiplexing capabilities and the final depth of SNPs coverage vary between the different protocols of genome-wide SNP discovery. One of these approaches is the e genome . This mee genome  adapted e genome –12. The e genome . Recentle genome –15. Sevee genome , double e genome  and 2b-Re genome  methods.e genome  used a fe genome  to invesnKImpute  and BeagnKImpute .de novo assembly and iterative mapping onto the ‘PN40024’ reference genome, identifying over two million single-base differences with the latter. At the same time, Di Genova et al. [V. vinifera ‘PN40024’ reference genome. More recently Corso et al. [sylvestris, which is believed to be the wild ancestor of present cultivars [V. vinifera as well as the genetic relationships between sativa and sylvestris genotypes have been mostly evaluated using a small number of microsatellite and SNP markers [sativa cultivars retain a higher level of heterozygosity than the sylvestris accessions [V. vinifera, with a decay of LD at ~10 kb inter-SNP distances [sativa and sylvestris [sylvestris and sativa individuals would enable to perform whole-genome comparisons of the level of genetic diversity and LD between the two V. vinifera subspecies, as well as to characterize their genetic relationship and to reveal the evolutionary events occurred during the long history of viticulture [The reference genome sequence of grapevine has been available since 2007  with a ta et al.  by sequeo et al.  resequenultivars –28. To d markers –30. It hcessions –34. In aistances –37. In plvestris –36 or sllvestris . An expaiculture .V. vinifera through a novel protocol of RAD sequencing based on the 5500 SOLiD™ System. The 37K identified variants were annotated in order to weight their effect by type and region, and used to assess the genetic variation within the cultivated grapevine and its wild form. This proof-of-concept study showed that the RAD-seq is able to unlock valuable genetic diversity hidden in wild relatives of grape, and to provide new patterns of the LD decay in grapevine germplasm collections.In this study we describe the SNP discovery carried out for the first time in a diverse set of cultivated and wild forms of V. v. spp. sativa) and 44 wild-type (V. v. spp. sylvestris) female grapevines was sorted at the FEM grape repository (ITA362), located in San Michele all'Adige, Italy (sativa accessions were chosen within a genetic core collection (G-110) that retains 100% of SSR and SNP loci diversity present in the source collection [sylvestris accessions of the same repository previously clustered through a hierarchical STRUCTURE analysis [A germplasm collection of 51 cultivated  separately that present a different number of recognition sites on the grapevine reference genome. The number of restriction sites recovered by each RAD-seq library at different coverage thresholds  were previously constructed with genomic DNA from PN40024 using three restriction enzymes  and then annealed in a thermocycler according to the following conditions: 95°C for 3 min, ramp down to 4°C by 1°C/30 secs; 4°C hold. The second adapter type was the standard barcoded adaptor used for 5500 SOLiD Fragment libraries and has a 10 bp barcode sequence. The different oligonucleotide sequences of the standard barcoded adapters are available on the Fragment Library Preparation 5500 Series SOLiD™ Systems User Guide [Two types of adapters were used. The common 5500 Series SOLiDm strand . The seqer Guide . Both biBamHI-High Fidelity  enzyme for 1h at 37°C in 25 μL volumes containing 1X NEB CutSmart Buffer and 5U of BamHI (™) and 1U T4 DNA ligase (Invitrogen™) were added to the digestion products, and samples were incubated at 16°C overnight. The ligation products were purified using one volume of Agencourt AMPure XP beads (Beckman Coulter) according to the manufacturer’s instructions and solubilized in 50 μL of 1X Low TE . DNA fragments were random sheared with a Covaris S220 Focused-ultrasonicator in 130 μL microTUBEs AFA Fiber Snap-Cap following the manufacturer’s protocol for Target BP Peak of 200 bp. Afterwards the samples were vacuum concentrated to a final volume of 20 μL. Next 10 μL of Dynabeads® MyOne™ Streptavidin C1 (10 μg/μL), previously washed three times with 50 μL of 2X Binding and Washing (B&W) Buffer , were added to each sample and resuspended in 20 μL of 2X B&W. Samples were incubated for 30 min at room temperature in rotation in order to capture the biotinylated fragments. Biotinylated coated beads of each sample were separated with a magnet for 2–3 min, collecting the supernatant in a clean tube to estimate the DNA recovery rate through a Qubit® 2.0 Fluorometer . The biotinilated coated beads were first washed with 50 μL of 1X B&W buffer and later with 50 μL of Buffer EB (Qiagen), and then resuspended in 20 μL of Buffer EB. Next 25 μL of NEBNext® End Repair Module (New England Biolabs) master mix, containing 5 μl of NEBNext End Repair Reaction Buffer (10X) and 2.5 μl of NEBNext End Repair Enzyme Mix , were added to the biotinylated beads. The End Repair mix was incubated for 15 min at room temperature in rotation. After the End Repair Enzymes inactivation at 75°C for 20 min, 50 μl of ligation master mix, containing 4 pmols of the blunt barcoded P2 adapters, 1X T4 DNA ligase reaction buffer and 10U T4 DNA ligase (Invitrogen™), were added to the biotinylated samples and incubated 1h at room temperature in rotation. The biotinylated fragments from each library were amplified in 50 μl volumes containing 25 ng DNA fragments, 1X GoTaq® Green Master Mix (Promega) and 25 pmol each of the following primers: Library PCR Primer 1, 5′ -CCACTACGCCTCCGCTTTCCTCTCTATG-3′ and Library PCR Primer 2, 5′ -CTGCCCCGGGTTCCTCATTCT-3′ [™ System  pooling the libraries and running them in two different flow-cell lanes using the Exact Call Chemistry module (ECC).DNA samples (500 ng) were digested with of BamHI . Next 30ATTCT-3′ . The ampBamHI cut and corresponding to T12320 in color space format. Reads 75 bp long obtained from SOLiD sequencing were inspected for the presence of the T12320 sequence at their starting point. When there were no color errors or one color sequencing error at the beginning, the read starting sequence was replaced with the full color space BamHI restriction site (T102320). Reads with more than one color error in their starting sequence were discarded.Reads were expected to start with the 5’-GATCC-3’ sequence released by Pre-processed reads in color space were mapped on the reference 12X grape genome , the mitBayesian genotyper implemented in GATK that assigned genotype at each site as the genotype with the greatest posterior probability  v3.2–2  was applbability . SNPs frVCFtools . PearsonVCFtools .V.v. sativa variety, that showed an uncommon low level of genetic variation at microsatellite loci, was also included during Sanger sequencing in order to test the ability of RAD-seq markers to capture undisclosed genetic diversity. The products of Sanger sequencing were run on the 96-capillary 3730xl DNA analyzer (Applied Biosystems®). STADEN package v2.0.0 [50 fragments were selected to validate 183 SNPs with Sanger sequencing . PCR prie v2.0.0  was usedThe grapevine population investigated in this study had previously been genotyped with the commercial GrapeReseq Illumina Vitis20KSNP chip . The InfHO) and expected (HE) heterozygosity, and the fixation index , through the R package “diveRsity” [sativa and sylvestris subgroups separately by using Plink v1.9 software [r2 estimate of correlation between genotypes was used [r2 in sequential bins of 10 Kb against physical position.Samples with a missing rate > 0.5 and SNPs with a missing rate > 0.2 were filtered out. Genotype imputation was performed to fill in the missing data using LinkImpute v1.1.1 software, which is based on a k-nearest neighbor genotype imputation method (LD-kNNi) designed to work with unordered markers . AfterwaveRsity” . The numveRsity” . Finallysoftware . The clawas used . LD decaBamHI as candidate restriction enzyme to construct RAD-seq libraries. Indeed, it showed almost a constant and high number of recovered RE sites at different levels of coverage, compared to the other two REs used to test the technical performance of the novel RAD-seq protocol , cleaving just after the first 5'-guanine on each strand. It leaves five base-long sticky ends (GATC-C) whose sequences are equal in color space format to T12320. As shown in BamHI restriction site in color space format (T102320), yielding finally 485M correct reads (76 bp).We selected protocol . RAD-seqproduced  with an Pre-processed reads were aligned onto the reference 12X grape genome including mtDNA and cpDNA sequences in order to reduce the rate of multiple alignments . 60.3% uIn silico digestion of the grapevine reference genome with BamHI identified 60,733 putative restriction sites with an average distance of 7.9 Kb. We recovered a total of 34K unique restriction sites with at least ten alignments, 93.2% of which were predicted and 6.8% were absent in the reference genome  showed differences in read depth between the two adjacent RAD loci. Indeed, those RE sites presented high depth  in more than 80% of the samples at either upstream or downstream RAD loci. The correlation between read depth and the logarithm of restriction fragment length for 69,525 unique RAD loci covered by at least one read was very small . We observed a slightly higher correlation  for RAD loci from restriction fragments shorter than 10kb . The correlation between read depth and the logarithm of restriction fragment length was not significant  for RAD loci coming from restriction fragments above 10kb in length .r = 0.68; p-value = 0.0001). We split the reference genome in 985 bins of 500 kb and the number of SNPs per each bin was determined. Thirty-five SNPs were present on average per bin. While three bins showed zero variants, 655 bins had 10 to 50 SNPs, 83 bins had < 10 SNPs and 244 bins had 51 to 104 SNPs.Variants on unique high quality alignments were called using UnifiedGenotyper module of Genome Analysis Toolkit (GATK) program . We idenAccording to the grape gene annotation v2.1 more than half of the SNPs fell in intergenic regions. 18,121 SNPs belonged to 6,634 grapevine predicted genes of which 1,680 presented 2,557 nonsynonymous polymorphisms . We looksativa and three sylvestris accessions already used to construct the RAD-seq libraries, and the Caucasian cultivar “Mgaloblishvili N.” Targeted SNPs included 123 transitions and 60 transversions, which were found at 10X coverage in at least 50 libraries. Out of 148 confirmed SNPs, 43.9% perfectly agreed with the RAD-seq data in all the resequenced samples, while 51.3% showed from one to three different genotypes  than expected (HE). Finally, we tested the extent of LD in the two subspecies, carrying out a pairwise analysis between all SNPs with a MAF >5%. A slower LD decay was observed within the sylvestris subset, where the classical correlation coefficient r2 reached values below 0.2 within 20kb, compared to the sativa subgroup, where the LD (r2) decayed below 0.2 within 10 kb  and the wild form (V. v. subsp sylvestris), which is supposed to be the ancestor of present varieties [sativa and sylvestris is still controversial [sativa and sylvestris accessions will make it possible to characterize the relatedness between wild and cultivated grapes at genomic level and to deeply explore the natural genetic variation still preserved within wild grapevine populations [V. vinifera reference genome Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableThe SNPs included in the PCR-products are indicated between squared brackets.(XLSX)Click here for additional data file.S4 TableUnfitted genotypes between RAD and Sanger assays are marked in red, with the Sanger genetic profile indicated in brackets. Missing genotypes are represented as “0 0”.(XLSX)Click here for additional data file.S1 File(PDF)Click here for additional data file.