Kuchinoshima-Ushi have been isolated in a small island and their lineage has been intensely protected, it has been assumed to date that numerous and valuable genomic variations are conserved in this cattle breed.Because the Japanese native cattle Kuchinoshima-Ushi is clearly distinct from European domestic breeds of cattle.In this study, we evaluated genetic features of this breed, including single nucleotide polymorphism (SNP) information, by whole-genome sequencing using a Genome Analyzer II. A total of 64.2 Gb of sequence was generated, of which 86% of the obtained reads were successfully mapped to the reference sequence (Btau 4.0) with BWA. On an average, 93% of the genome was covered by the reads and the number of mapped reads corresponded to 15.8-fold coverage across the covered region. From these data, we identified 6.3 million SNPs, of which more than 5.5 million (87%) were found to be new. Out of the SNPs annotated in the bovine sequence assembly, 20,432 were found in protein-coding regions containing 11,713 nonsynonymous SNPs in 4,643 genes. Furthermore, phylogenetic analysis using sequence data from 10 genes (more than 10 kbp) showed that Kuchinoshima-Ushi population and research on functions of SNP-containing genes, which would aid in understanding the molecular basis underlying phenotypic variation of economically important traits in cattle and in improving intrinsic defects in domestic cattle breeds.These results provide a framework for further genetic studies in the  Kuchinoshima-Ushi has long been bred on Kuchinoshima Island in the Tokara Archipelago of Kagoshima Prefecture, Japan , wide breast, narrow waist, and horns. They have variable coat colours, including black, black with white spots, and brown. This phenotype is probably due to a population bottleneck caused by long-term isolation on this island. Similarities between ancient Japanese cattle described in historical records and Kuchinoshima-Ushi suggest that they have retained some features of ancient native cattle  contributed more than 23,000 single nucleotide polymorphisms (SNPs) to the bovine SNP database (dbSNP) by next-generation sequencing using a dairy breed (Holstein) and seven major breeds of beef cattle  and identified SNPs excluding ambiguous bases and those mapping to multiple locations unless providing more than 5 kbp flanking sequences were submitted to the single nucleotide polymorphism database (dbSNP) at NCBI under the handle TUANGRC. In addition to submitting data to the standard repositories, the positions of the SNPs for The SNPs in gene regions were annotated using the RefSeq and GenBank gene sets . We found 1,004,496 SNPs in introns, 17,684 in untranslated regions (UTRs), 801 SNPs in splice-sites, and 20,432 coding SNPs leading to 11,713 nonsynonymous nucleotide substitutions Figure . The perKuchinoshima-Ushi data set, nsSNPs were detected in a total of 4,643 genes, which are listed in Additional file Kuchinoshima-Ushi population  were extracted and compared with the list of nsSNP-containing genes in our analysis of Kuchinoshima-Ushi.We also examined whether nsSNP-containing genes were reportedly associated with economically important traits such as meat/milk production, growth rate, and domestication in other breeds of cattle. In order to extract information from studies reporting relationships between SNPs/genes and traits, we performed a PubMed search using economical traits such as growth rate or meat production as the query and limited the search to studies of http://www.animalgenome.org/cgi-bin/QTLdb/BT/index); for example, body growth-associated genes such as genes for growth hormone (GH), growth hormone receptor (GHR), and leptin receptor (LEPR) [PRLR) and caseins (CSN); and genes encoding immune-related proteins in milk, such as toll-like receptors (TLRs) [Of the 4,643 nsSNP-containing genes, 708 matched to genes that have been reported to be potentially associated with economically important traits such as meat/milk production, growth rate, and domestication in other cattle breeds -22 (the r (LEPR) -20; lacts (TLRs) ,22.Kuchinoshima-Ushi have already been reported to be associated with phenotypes in other cattle breeds  was reported to be associated with palmitoleic acid (C16:1) content in intramuscular fat in Japanese Black cattle [UTS2R) was associated with skeletal muscle fat accumulation in Wagyu × Limousin population [CAPN1) was associated with meat tenderness in Aberdeen Angus-cross cattle [NOD2/CARD15) gene were associated with the estimated breeding values for somatic cell score in Canadian Holstein cattle [LEP and LEPR genes were associated with fat content in carcass meat of other cattle breeds including Nellore, Holstein-Friesian, Angus, Charolais, Hereford, and Simmental [Recently, associations between SNPs and phenotypes have been reported in many studies, including cattle ,24. Somes Figure . For exak cattle . A mutatpulation  and a mus cattle . The nsSn cattle . Identifimmental ,29,30.Kuchinoshima-Ushi breed are required to validate the identified nsSNPs and to examine their association with relevant phenotypic traits in this population. Also, most nsSNPs identified in Kuchinoshima-Ushi have yet to be reported, nor have their functions or associations been investigated; new nsSNPs identified in this study will be valuable for future research.Nevertheless, population genetic studies in the Kuchinoshima-Ushi and other breeds of cattle, we carried out a phylogenetic analysis. The resultant maximum likelihood tree of bovine-related species is shown in Figure Bubalus bubalis) and swamp buffalo . Two subtribes, namely, Bovina and Bubalina, formed a monophyletic group with high maximum likelihood (ML) bootstrap (BS) and Bayesian posterior probability (PP) support , respectively. In the Bovina clade, Hereford, Holstein, Tuli, and Kuchinoshima-Ushi comprised a monophyletic group with high statistical support , and Kuchinoshima-Ushi was the sister lineage of all other breeds of Bos taurus cattle with relatively high statistical support . Among species of wild cattle that have never been subjected to selective breeding, such as Banteng and Gaur, Kuchinoshima-Ushi was the nearest species of domesticated cattle. These data also suggest that Kuchinoshima-Ushi would be an appropriate breed for investigating adaptations associated with the domestication of wild cattle. When the sequence data of Japanese domestic breed of cattle such as Japanese Black will be available, the phylogenetic position of "Japanese native cattle" should be revealed more clearly.To understand the genetic relationships between Kuchinoshima-Ushi, which have retained their phenotype over time without being affected genetically by European domestic breeds, were used for whole-genome resequencing with a next-generation sequencer. We identified 6.3 million SNPs, of which more than 5.5 million (87%) were found to be new. Among the identified SNPs, some nonsynonymous SNPs were found in genes that have been reported as candidate genes for QTL affecting economically important traits in other cattle breeds. The results of phylogenetic analysis showed the phylogenetic position of Kuchinoshima-Ushi. In the current status of domestic cattle, repeated selection for specific traits such as weight gain as well as milk and meat production has progressed, but problems have emerged with unconsidered traits such as reproduction and disease resistance. Our results provide a framework for future investigations aiming at the understanding of gene functions and identifying the molecular basis underlying phenotypic variation of economically important traits in domestic cattle breeds. It would contribute to the development of a new, more efficient breeding system preferentially for East Asian domestic cattle breeds including Japanese one. Furthermore, genome information for Kuchinoshima-Ushi is expected to be important not only in revealing the genetic structure of a geographically isolated breed originating from ancient, native Japanese cattle but also in identifying genetic traits that distinguish domestic breeds from original, undomesticated species.In the present study, Japanese native cattle Kuchinoshima-Ushi (#8915)  Figure  that weiBlood sampling was carried out according to the 'Regulations for Animal Experiments in Nagoya University' and the 'Guidelines for the Care and Use of Laboratory Animals', as specified by the Tokyo University of Agriculture.® Kit for Sequencing, Invitrogen Corp., Carlsbad, CA, USA) and 96 clones were sequenced by sanger sequencing. We found that all sequences were unique, and duplicates were not detected in the analyzed sample. BLAST search in the bovine genome sequence database using these sequences revealed high (90%) similarities to respective bovine sequences. DNA concentration was measured using UV-1800 UV-VIS Spectrophotometer . The genomic DNA library was diluted to 10 nM, and a 2 μl aliquot was used to generate clusters on the Illumina Cluster Station using the Paired-End Cluster Generation Kit v2 (Illumina) and sequenced on the GAII with the SBS 36-cycle Sequencing Kit v3 following the manufacturer's instructions  as 75-bp and 36-bp reads .Genomic DNA was extracted from blood using standard phenol/chloroform extraction methods . A genomftp://hgdownload.cse.ucsc.edu/goldenPath/bosTau4/chromosomes/ as a reference source. In this study, sequence scaffolds not yet assigned to specific chromosomes were excluded and no repeat masker was applied to the assembly.For alignment and annotation of the sequence reads, we used the bovine genome assembly Btau 4.0 Image analysis and ELAND alignment were performed with Illumina's Pipeline Analysis software ver. 1.4. Sequences passing through the standard Illumina GA pipeline filters  were retained for further analysis.http://bio-bwa.sourceforge.net/) [For short-read alignment and consensus assembly, we used a recently developed algorithm, BWA ver. 0.5.0  . The BWAftp://hgdownload.cse.ucsc.edu/goldenPath/bosTau4/database).To call SNPs, we used SAMtools  and applKuchinoshima-Ushi were previously determined and published  for the phylogenetic analysis . However, in some read pairs, only one was mapped (red column). Additionally, some read pairs were mapped, but the distances or directions were not adequate (green columns). Length of the chromosomes is shown in the yellow line. High number of reads mapped to BTA13 of the assembly without repeat masking (left column) was removed in the repeat-masked assembly (right column).Click here for fileLength of the regions that are covered by reads for each chromosome. Length of the regions that are covered by reads for each chromosome. The length of chromosomes is shown in the blue columns, and that of the regions covered by reads is shown in the red columns. The percentage of the regions that are covered by reads in each chromosome is indicated by the green line. On an average, 93% of the genome is covered by reads.Click here for fileThe number of identified SNPs and indels for each chromosome. SNPs are shown in the blue columns, and indels are shown in the red columns. Length of chromosomes is indicated by the green line.Click here for fileSNP distribution on each chromosome. SNP density (SNPs per 1 kbp) is plotted by physical position. Relative length of the chromosomes was correlated with the length of each chromosome without repeat regions.Click here for fileDistribution of the size of indels. We identified 284,007 insertions  and 345,249 deletions .Click here for fileThe list of genes containing nsSNPs. The list of genes containing nsSNPs along with the accession numbers (ss number).Click here for fileGO terms which were over-represented in nsSNP-containing genes.Click here for fileThe list of genes containing nsSNPs that were reported as trait-associated in other cattle breeds.Click here for fileThe number of SNPs and indels with various filters. Detected SNPs and indels were filtered with additional filters and the number of homozygous and heterozygous SNPs and indels was compared. Parameters for the filters were (1) Depth: the number of reads mapped to the SNP sites, (2) SNPs: the number of reads calling SNP at the SNP site, and (3) Mutation: the cutoff value of percent aligned reads calling the SNP per total mapped reads at the SNP sites. Cut off value was 30% in all filters. In the table, "%" means the reduced percentage of the number of SNPs/indels compared with basic parameters .Click here for fileList of species examined in this study.Click here for fileSummary of the genes sequenced for the phylogenetic reconstruction. List of the genes and their accession numbers which were used for the phylogenetic analysis.Click here for file


Rapid and consistent genotyping is an important requirement for cultivar identification in many crop species. Among them grapevine cultivars have been the subject of multiple studies given the large number of synonyms and homonyms generated during many centuries of vegetative multiplication and exchange. Simple sequence repeat (SSR) markers have been preferred until now because of their high level of polymorphism, their codominant nature and their high profile repeatability. However, the rapid application of partial or complete genome sequencing approaches is identifying thousands of single nucleotide polymorphisms (SNP) that can be very useful for such purposes. Although SNP markers are bi-allelic, and therefore not as polymorphic as microsatellites, the high number of loci that can be multiplexed and the possibilities of automation as well as their highly repeatable results under any analytical procedure make them the future markers of choice for any type of genetic identification.We analyzed over 300 SNP in the genome of grapevine using a re-sequencing strategy in a selection of 11 genotypes. Among the identified polymorphisms, we selected 48 SNP spread across all grapevine chromosomes with allele frequencies balanced enough as to provide sufficient information content for genetic identification in grapevine allowing for good genotyping success rate. Marker stability was tested in repeated analyses of a selected group of cultivars obtained worldwide to demonstrate their usefulness in genetic identification.Vitis vinifera L.) genotyping. Any previous problems derived from microsatellite allele confusion between labs or the need to run reference cultivars to identify allele sizes disappear using this type of marker. Furthermore, because SNP markers are bi-allelic, allele identification and genotype naming are extremely simple and genotypes obtained with different equipments and by different laboratories are always fully comparable.We have selected a set of 48 stable SNP markers with a high discrimination power and a uniform genome distribution (2-3 markers/chromosome), which is proposed as a standard set for grapevine ( Vitis vinifera L.) is one of the most valuable horticultural crops in the world. Many of the widely cultivated varieties are very ancient genotypes that have been vegetatively multiplied for centuries and spread worldwide. In many places the same genotypes were re-named leading to synonyms (different names for the same variety) as well as homonyms (different varieties identified under the same name). Currently, there is a large but imprecise number of grapevine varieties in the world : This nWhen genetic identification is taken into account, two goals have to be fulfilled: i) the availability of a large enough number of polymorphic markers; and ii) the existence of public genotype databases allowing for comparisons with previously characterized genotypes. Markers should provide a high discrimination power and yield reproducible genotype data among different laboratories and detection platforms as well as over time. Markers should also be stable, meaning that they produce consistent and repeatable results after repeated propagation of the varieties. This is especially important in the case of grapevine where many varieties have been under cultivation for centuries, and some molecular markers have been shown not to be fully stable in certain old varieties, due to somatic mutation . In addiGrapevine genotyping is currently based on microsatellite markers or simple sequence repeats (SSR), which have been very useful not only for genetic identification  but alsoIn recent years, numerous sequencing projects have generated an abundance of sequence information and nucleotide polymorphisms. These belong to two basic types: single nucleotide polymorphisms (SNP) and insertions-deletions of different lengths (INDEL). Among them, SNP markers have the advantage that they are mostly bi-allelic and are very frequent in genomes. Although SNP polymorphism information content (PIC) is lower than that of SSR markers, tens, hundreds or even thousands SNP can be easily used when required. SNP are highly reproducible among laboratories and detection techniques, since the different alleles are not distinguished on the basis of their size but on the basis of the nucleotide present at a given position. All these features and their unlimited availability are making SNP the markers of choice for the development of identification panels in many animal and plant species -12.In this work, we characterized the genetic features of 332 SNP to select a panel of 48 markers suitable for cultivar identification in grapevine. We show here that the panel has a similar discrimination power as a set of 15 SSR markers and can represent a very robust genetic identification system, problem-free of allele miscalling among laboratories or detection technologies. We also demonstrate that markers have a very low genotyping error rate, a low rate of appearance of new mutations when compared to SSR, and are amenable for easy storage in genotype databases. Given the state of revision and integration of genetic resources in grapevine, our SNP panel may become a rapid tool for genetic identification and genotype calling in the crop.Identification of SNP markers in the grapevine genome was carried out based on a re-sequencing strategy in a selected sample of grapevine genotypes as previously described . The samGenotyping of four grapevine segregating progeny populations with the seven SNPlex™ sets allowed us to genetically map most of the 238 polymorphic SNP, which were heterozygous in one or both parents in at least one of the progeny populations  allowing for identifying the relative positions of markers not segregating in the same progeny population  showed significant discrepancies between physical and genetic distances with the surrounding markers leading to differences in marker order for this region . However, marker order on the genetic map aligns with marker order in the version of the genomic sequence  in which both SNP are separated by 4.4 Mb as well as with marker order in the Pinot Noir sequence http://genomics.research.iasma.it/gb2/gbrowse/grape/.Marker order was generally conserved between physical and genetic maps, although discrepancies were found on chromosomes 1, 3, 10, 12 involving differences of up to 7.6 Mb and 12 cM. In addition, small local marker inversions, involving < 1.5 Mb and < 6 cM distances, were observed for chromosomes 1, 2, 4, 5, 6, 7, 8, 13 and 19 .Stability of the 48 SNP markers was evaluated through the analysis of the genotypes obtained for an average of 85 plants for each 15 cultivars  was obtained for 990 plants corresponding to an average of 66 plants per variety with a range from 54 to 86 plants Table , Table 3Regarding the stability analysis, 99.4% of all the genotyped plants showed the genotype expected for the cultivar. Only three SNP showed a different genotype in plants of the same cultivar: SNP1119_176 and SNP581_114 (in one 'Ohanes' plant), and SNP1347_100 (in one 'Flame Seedless' plant). To determine if these variations were due to mutations (lack of stability) or genotyping errors, the analyses were repeated using the same DNA extraction as well as independent DNA extractions for each plant. The results indicate that all discrepancies corresponded to genotyping errors. In summary, no mutation could be found in the 58251 individual SNP genotypes established for the 15 varieties studied and, therefore, the SNP marker set could be considered highly stable.A total of 200 grapevine accessions were genotyped with the 48 SNP set including a sample from each of the varieties studied in the stability analysis. Some of the accessions resulted in identical genotypes but these results always agreed with the expectations; since they corresponded either to synonymous cultivars or sports . Sports are not expected to differ from their initial cultivar by using molecular markers. This was confirmed for several sports: 'Chasselas Apyrene', a seedless sport, did not differ from 'Chasselas Blanc'. Within the Pinot group, 'Pinot Blanc' showed an identical genotype for the 48 SNP set to 'Pinot Noir' and also 'Pinot Meunier', a genetic chimera , showed Another one-allele difference was observed when genotypes obtained in this study were compared with those obtained for the same varieties in the stability analysis (see above) but, while in the case of 'Pinot Gris' the difference was consistent and could be considered a genetic mutation, in the later cases they were shown to be due to genotyping errors. The difference was observed in 5 varieties for the SNP1119_176 Table . In all -17. This value is much smaller than that obtained with the 6 SSR markers approved as descriptors by the International Organisation of Vine and Wine (OIV) in the analysis of 57 unique Spanish genotypes  and wit10-7 [-9 ), or of -9 [-12, ). In con2, [-22, ), or wit22, [-27 ). These http://www.upov.org/en/publications/conventions/1991/act1991.htm). The key concept for establishing distinctness is the minimum distance between varieties, which is currently established on a species by species basis, using morphological descriptors. In recent years, some efforts have been directed to incorporate molecular markers [The task of cultivar characterization is often related to legal issues. Of utmost importance is that in the technical test any variety has to overcome the authorization to be cultivated in many countries and that distinctness is the most important issue to be established in such tests: a variety is considered distinct if it can be clearly distinguished from all the varieties of common knowledge  Convention, 1991;  markers . In the  markers  as well  markers . The sam markers . Hence cFrom the data, a very clear border exists between the highest intra-varietal variability (including here the sports) with 1 different allele and the lowest inter-varietal distance of 9 different alleles. Thus, there should not be any difficulty in establishing a minimum distance between 2 and 9 alleles for the 48 SNP set and it is large enough as to be considered conclusive for establishing distinctness in grapevine cultivars (excluding that of sports). Still a more extensive diversity study would be needed to find a more reliable minimum distance, since it could be shorter in full siblings derived from closely related progenitors as those used in current table grape breeding.The Mendelian genetic inheritance of these 48 SNP markers has been confirmed in several previously described mapping populations. This feature also permits the genetic examination of pedigrees and parent/offspring relationships. Using the selected 48 SNP set, the total exclusion probability of paternity found for the set of 151 cultivars was high (0.9997) but the number of markers is far too small for a reliable pedigree analysis. Logarithm of odds (LOD) scores obtained for several trios ranged from 17 to 23, which are not large enough to reach final conclusions.A set of 48 single nucleotide polymorphisms (SNP) have been selected well distributed throughout the grapevine genome and tested for genetic identification purposes. The selected markers have proven to be highly stable and repeatable and also have a high discriminating power for grapevine cultivars. SNP data do not require any allele binning and allows for direct databasing and direct comparison of data arising from different laboratories. All these characteristics make our set of markers very suitable for the building of a worldwide publicly available genotype database for grapevine cultivars.Three different cultivar sample sets and four segregating populations were used in this study. For the determination of genetic parameters concerning the 332 SNP markers under study a sample of 300 accessions including 91 wild accessions as well as wine- and table- grape cultivars  were used: Dominga × Autumn Seedless , MonastrVitis vinifera species. Leaf material from a total of 1277 plants belonging to those cultivars was collected in 154 different plots in 7 different countries  . Usefulhttp://www.vitaceae.org/index.php/ recommendations.SNP genomic locations were determined based on both genetic and physical information. Genetic positions were established using four mapping populations following a two-stage strategy. First, SNP markers were positioned on the consensus framework map developed for each cross using microsatellite markers. Molecular marker and linkage analyses were carried out according to Cabezas et al. 2006  using a http://www.genoscope.cns.fr/externe/GenomeBrowser/Vitis/. Location of markers involved in important discrepancies between genetic and physical positions was also checked on the Pinot noir genomic sequence http://genomics.research.iasma.it/gb2/gbrowse/grape/[Physical positions of SNP markers were determined by Blat searching for their adjacent sequences on the 12× grapevine genomic sequence of the near homozygous Pinot line PN40024 [se/grape/.Over 48 SNP markers were selected from the previously developed 332 according to their genotyping success rate, MAF as well as their genetic and physical positions. The last step of selection of the set for genetic identification was based on the technical requirements needed for the design of a plex for the SNPlex™ platform.Experimental design of the stability test for the selected 48 SNP set included the analysis of 85 plants from 10 different plots (on the average) for each of 15 varieties. Plots had been planted in different years and locations in 7 different countries , expected heterozygosity (He) and probability of identity (PI) were calculated using the IDENTITY 1.0 tool [Genotyping and genetic parameters were estimated from these tests. For each SNP the rate of genotyping success was calculated after excluding DNA samples that failed in the amplification of all SNP. Genotyping error was calculated based on the results obtained in different analyses: by genotyping different DNA extractions of the same plant; by genotyping different plants belonging to the same cultivar; or by studying known sports of a given genotype such as those of the Pinot family. Genetic parameters were estimated on non-redundant genotypes. Minor allele frequency (MAF), observed heterozygosity (H1.0 tool  and the 1.0 tool . Pedigre1.0 tool . LOD sco1.0 tool .o: Observed heterozygosity; He: Expected heterozygosity; IGGP: International Grapevine Genome Program; INDEL: Insertion-deletion; LOD: Logarithm of odds; MAF: Minor allele frequency; Mb: Megabase; NCBI: National Center for Biotechnology Information; OIV: International Organisation of Vine and Wine; PI: Probability of identity; PIC: Polymorphism information content; SNP: Single nucleotide polymorphism; SSR: Simple sequence repeat; UPOV: International Union for the Protection of New Varieties of Plants.cM: centimorgan; HJAC carried out the physical and genetic mapping of the SNP, participated in the SNP selection and drafted part of the manuscript. JI carried out the stability and genetic diversity analyses and drafted part of the manuscript. DL played a part in the selection of SNP selection and characterization. MDV participated in the stability analyses. GB, VR, IC and LRG contributed in the genotyping of cultivars and progenies. AMJ participated in the SNP selection. JC generated and maintained most of the progenies. MRT assisted in the SNP selection and helped draft the manuscript. JMZ conceived the study, partook in its design and coordination and helped draft the manuscript. All authors read and approved the final manuscript.Supplementary Tables S1 to S5. Table S1: Plant samples analyzed. Table S2: Plant samples used for the stability studies of the 48 SNP set. Table S3: Basic information on the 238 SNP analyzed. Table S4: Genetic maps features. Table S5: Number of progenies with heterozygous markers in at least one progenitor.Click here for file


In this study, we employed a reduced representation sequencing approach called Restriction Enzyme Site Comparative Analysis (RESCAN) to simultaneously identify and genotype single nucleotide polymorphisms (SNPs) in forty-five rice cultivars representing the majority of the 100 year-old breeding history in California. Over 20,000 putative SNPs were detected relative to the Nipponbare reference genome which enabled the identification and analysis of inheritance of pedigree haplotypes. Haplotype blocks distinguishing modern California cultivars from each other and from the ancestral short grain temperate japonica cultivars were easily identified. Reduced representation sequencing methods such as RESCAN are a valuable alternative to SNP chip genotyping and low coverage whole genome sequencing.Very low polymorphism in the germplasm typically used by breeding programs poses a significant bottleneck with regards to molecular breeding and the exploitation of breeding materials for quantitative trait analyses. California rice cultivars, derived from a very small base of  Oryza sativa L.), the sequencing of the temperate japonica cultivar Nipponbare indica 93-11, Recent advances in genomics have made single nucleotide polymorphisms (SNPs) the marker of choice for genome-wide genetic analyses. Since SNPs occur at much higher density than other markers such as microsatellites, they are particularly useful in distinguishing closely related individuals. In rice  have been developed with more diverse germplasm as reflected in the pedigrees  and most of the modern cultivars can be traced back through their pedigrees to 23 ancestral introductions edigrees .ArabidopsisGiven the extremely narrow germplasm base of California cultivars, particularly the short and medium grain types, we sought to employ a genotyping by sequencing approach to evaluate forty-five cultivars released from 1917–2006. We employed a reduced representation sequencing method called Restriction Enzyme Site Comparative Analysis (RESCAN), which has been previously used in rice and in silico digestion of Nipponbare reference genome in silico digestion of the Nipponbare reference genome. While both fractionation methods significantly enriched the fragments in the target size range, the electrophoresis fractionation method produced a greater effect than the SPRI-based protocol . A total of ∼240 M reads (∼12 Gb raw sequence reads) were obtained from the two runs (150 M reads for gel electrophoresis and 94 M reads for SPRI). After de-multiplexing according to unique barcodes for each genotype and filtering bad sequences, a combined total of 208 M reads (138 M reads for gel electrophoresis and 70 M for SPRI) were obtained . CombineIn order to assess the SNP scoring error rate, CAPS markers were designed from SNPs identified from RESCAN  and usedtropical japonica (long grain) and the fewest SNPs (10.2 SNPs/sequenced 100 kb) were found in temperate japonica short grains while 15.6 SNPs/sequenced 100 kb were found in temperate japonica medium grains. These results are consistent with the pedigrees of the cultivars. More diverse germplasm has been incorporated in the breeding of the long grain California cultivars with contributions from the southern U.S. rice belt breeding programs which primarily focus on long grain types. On the other hand, the short and medium grain California cultivars share more common ancestors and the lower density of SNPs in the short grain cultivars likely reflects the contributions of Japanese rice germplasm in the breeding of these types.Since more SNPs were found in cultivars with greater sequence coverage  the frequency of SNPs in each cultivar was compared by using the total SNPs per sequenced 100 kb . Colusa Some SNPs were only found in certain cultivars while others were common to most of the California cultivars. About 9% of the SNPs that were detected were found in only one cultivar while no SNP was present in all 45 cultivars. In particular, L-201 (32%), A-201 (22%) and Calmati-201 (23%) contained a high proportion of unique SNPs. In order to compare the type of SNPs in California cultivars, we grouped SNPs into three categories: rare SNPs found in less than 15 cultivars, medium frequency SNPs found in between 16–30 cultivars and common SNPs found in more than 30 cultivars. The composition of each group in California cultivars were presented in High-density haplotype maps were generated based on the SNP data . Highly japonica subgroups of rice  clustered closely regardless of their grain size. Principal component analysis (PCA) also indicated most of the short grain temperate japonica accessions were separated from others while the medium grain temperate japonicas clustered closely  clustered together.For phylogenetic analysis, 4,469 SNPs were selected which were scored in 23 or more of the cultivars. Missing data for this subset of SNPs ranged from 1.5% to 85.5% with a mean of 31%±26% SD. Two distinct groups are observed in the resulting neighbor-joining tree, which is consistent with the two  closely . With thtemperate and tropical japonica subgroups was observed starting at K = 2. However, short and medium grain groups were indistinguishable even at the highest K values. The likelihood value decreased continuously and the most significant change was observed between K = 4 and K = 5 (Fst) values between major subgroups were 0.85, the differences within the subgroup were very small (less than 0.01) suggesting that California cultivars can be divided into two major groups.Population structure analysis was conducted using the STUCTURE program from K (number of fixed subgroups or clusters) values ranging from 1 to 10. Separation of the nd K = 5 . Populatnd K = 5 . While tsd-1 introgression from Calrose 76, which is presumably responsible for the short stature of M7, could not be detected. However, an introgression from CS-M3 on chromosome 5 for the glabrous locus (gl-1) was found in M7. Of the four cultivars, M-102 exhibited the highest proportion of SNPs that appeared to originate from one parent with 11.6% SNPs shared with M-201 and 11.5% shared with M-101. M-102 also had the highest percentage of unexplained SNPs at ∼2%. The SNP data indicated that M-102 has the Calrose 76 sd-1 allele from M-101  which was consistent with previous analysis of the sd-1 alleles present in California cultivars sd-1 allele from S-301 parent Four of the California cultivars were derived from crosses between two other cultivars . S6 was NlaIII fragments based on in silico digestion of the Nipponbare genome. By raising the size range to 300–400 bp , 500–600 bp , or 700–800 bp , a significant reduction in target sequences and therefore greater sequence coverage can be achieved. It should also be noted that improvements in Illumina HiSeq2000 sequencing have increased the numbers of reads to approximately 2-fold (currently >300 million reads per lane). In this study, individuals with ∼2.5–3 M reads had ∼50% or fewer missing data. By employing a 300–400 bp size fractionation window, the current Illumina HisSeq2000 output should provide sufficient coverage of a multiplex of 96 individuals per lane for a comparable level of missing data. At these levels of multiplexing, RESCAN may be applied cost-effectively to linkage analysis of single genes and QTLs as well as genome-wide association studies and genomic selection As with other genotyping by sequencing methods temperate japonica cultivars. Our results were consistent with the pedigree information and we were able to examine the origin of various haplotype blocks defined by the RESCAN SNPs as well as to identify regions that may have undergone selection for rice production in the Mediterranean climate of California. In addition to defining introgressions in the cultivars examined, the density of SNPs detected by RESCAN in this study was sufficient for single gene and QTL mapping. Like other genotyping by sequencing approaches, the RESCAN method is amenable to commonly employed strategies to increase marker density for applications such as genome-wide association studies and genomic selection.The RESCAN method facilitated the rapid, cost-effective discovery and genotyping of thousands of SNPs in very closely related California To facilitate the identification of SNPs, a reduced representation sequencing strategy called Restriction Enzyme Site Comparative Analysis RESCAN,  was emplForty-five California rice cultivars were used in this study . Seeds waaacaAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAG-3′; adB2_NlaIII_01_aaaca: 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCTtgtttCATG-3′. NlaIII site in bold and 5-bp barcode underlined). Sequences of barcodes used in this study are shown in RESCAN libraries were constructed as described previously https://github.com/ucdavis-bioinformatics). The quality was checked at each step using the Quick Read Quality Control R package (http://www.bioconductor.org/packages/2.10/bioc/html/qrqc.html). Individual libraries (i.e. cultivars) were identified by their unique barcode. The reads were then aligned to the O. sativa 'Nipponbare' genome using BWA http://www.broadinstitute.org/igv/) for the samples.Illumina single-end 50 bp sequence reads were trimmed for adapter contamination and for quality using Scythe and Sickle (Specific primers  were deshttp://www.powermarker.net). The MEGA5 program Genetic distance was calculated based on the neighbor-joining method using the C.S. Chord distance Figure S1Distribution of SNPs on the annotated gene structures.(DOCX)Click here for additional data file.Figure S2Chromosomal distribution of SNPs found in 45 California cultivars.(DOCX)Click here for additional data file.Figure S3Population structure analysis using the STRUCTURE program.(DOCX)Click here for additional data file.Table S1List of California rice cultivars.(DOCX)Click here for additional data file.Table S2Multiplexing barcodes and number of raw sequence reads.(DOCX)Click here for additional data file.Table S3List of the 4,469 SNPs used for the phylogenetic analysis.(XLSX)Click here for additional data file.Table S4Primer sequences for CAPS marker.(DOCX)Click here for additional data file.Table S5Comparison of genotype results between RESCAN and CAPS marker.(DOCX)Click here for additional data file.


The GBS approach presented here provides a powerful method of developing high-density markers in species without a sequenced genome while providing valuable tools for anchoring and ordering physical maps and whole-genome shotgun sequence. Development of the sequenced reference genome(s) will in turn increase the utility of GBS data enabling physical mapping of genes and haplotype imputation of missing data. Finally, as a result of low per-sample costs, GBS will have broad application in genomics-assisted plant breeding programs.Advancements in next-generation sequencing technology have enabled whole genome re-sequencing in many species providing unprecedented discovery and characterization of molecular polymorphisms. There are limitations, however, to next-generation sequencing approaches for species with large complex genomes such as barley and wheat. Genotyping-by-sequencing (GBS) has been developed as a tool for association studies and genomics-assisted breeding in a range of species including those with complex genomes. GBS uses restriction enzymes for targeted complexity reduction followed by multiplex sequencing to produce high-quality polymorphism data at a relatively low per sample cost. Here we present a GBS approach for species that currently lack a reference genome sequence. We developed a novel two-enzyme GBS protocol and genotyped bi-parental barley and wheat populations to develop a genetically anchored reference map of identified SNPs and tags. We were able to map over 34,000 SNPs and 240,000 tags onto the Oregon Wolfe Barley reference map, and 20,000 SNPs and 367,000 tags on the Synthetic W9784×Opata85 (SynOpDH) wheat reference map. To further evaluate GBS in wheat, we also constructed a  Aegilops tauschii, was recently completed using next-generation sequencing (NGS), marking a step forward for SNP markers in large and complex genomes The development of molecular markers and genomic resources in barley and wheat has always been a formidable task due the massive, complex, and, in the case of wheat, polyploid genomes The use of genome complexity reduction combined with multiplex sequencing was first demonstrated through restriction-site associated DNA (RAD) tagging RAD genotyping was recently applied in barley to identify 530 SNP markers, construct a genetic linkage map and map quantitative trait loci PstI (CTGCAG), was chosen based on previous success in complexity reduction in large genomes MspI (CCGG), has a more common recognition site. Barcoded forward adapters were designed with the PstI restriction overhang while the reverse adapter matches the MspI overhang. To eliminate amplification of the more common MspI-MspI fragments, we designed the common reverse adapter as a Y-adapter. The Y-adapter contains an exact match (but no complement) to the reverse primer used in PCR amplification. During the first cycle of PCR, amplification proceeds only from primer annealing to the forward adapter. Binding sites for the reverse primer are only created during the first round of PCR by extension from forward primers on the other end of the same fragment . (see details in We employed a two-enzyme restriction digest to generate a library consisting of DNA fragments with a forward adapter and reverse adapter on opposite ends of every fragment. The combination of a rare-cutting enzyme and a second common-cutting enzyme will produce a digest largely consisting of 1) fragments with a rare cut-site and a common cut-site or 2) fragments with two common cut-sites. A rare-cutting restriction enzyme, fragment . This dePstI GBS libraries, we designed a set of DNA barcodes ranging in length from 4 bp to 9 bp that balanced the base composition of the GBS library with the overhang generated by PstI restriction digest  population consists of 82 double haploid (DH) lines and is a morphologically diverse population segregating for a number of distinct traits PstI cut-site were kept. Tags were defined as unique sequences within the data set and collapsed by lines.We constructed two 48-plex libraries from the OWB population and four 48-plex libraries from the SynOpDH population consisting of a single sample of each DH line and replicated samples of each parent. The libraries were sequenced on Illumina GAII or Illumina HiSeq2000. From the (unfiltered qseq) Illumina data, sequences were assigned to individual samples using the barcode sequence and trimmed to 64 bp for faster processing. Only sequences that had an exact match to a barcode followed by the expected sequence of 5 nucleotides remaining from a To identify SNPs in the populations, all pairs of tags were evaluated for a one or two base-pair difference. Bi-allelic SNPs were identified by querying the filtered tags for pairs of sequences which were 1) identical except for one or two nucleotide(s), 2) present in >20% of the individuals and 3) passed a Fisher Exact test for independence. We examined independence at each possible pair to avoid paralogous SNPs which would presumably segregate independently. In contrast, allelic SNPs should be mutually exclusive in inbred lines. These tags were then designated bi-allelic SNPs with two alleles and missing data. If a SNP call was heterozygous, presumably due to sequencing errors, this call was set to missing data.Genetic Mapping of Tags and SNPs: GBS SNPs were identified from sequence tags that were identical save for one or two base pairs and that passed other filtering steps. We used reference genetic maps for OWB and SynOpDH to first add biallelic GBS SNPs and then sequence tags (treated as dominant markers) to the maps. This enabled full utilization of data sets with large amounts of missing data. The reference map for OWB consists of 2,382 markers, primarily SNPs and DArT (Diversity Arrays Technology Pty Ltd) Using this approach in the OWB population, we first mapped 9,545 SNPs with less than 20% missing data . The OWBWe observed a high density of GBS markers in the likely centromeric regions of each chromosome . A similEven at the high marker density provided with GBS, we also observed several regions on the OWB map without any markers. Most notable was a region of 14 recombination bins on chromosome 2 where there was not a single informative marker. In other words, that region contains an interval for which the flanking markers recombined in 15 different lines. Given the number of lines evaluated (82), this indicates that the markers are about 18 cM apart. Yet there were no polymorphic markers in that interval enabling us to observe where recombination occurred for each line and suggesting a region of identity-by-descent (IBD) between the two parents.de novo using only the GBS SNP markers. Presumably, due to a larger genome and lower sequence coverage of the GBS tags in the wheat population, we only observed 1,771 putative SNPs that had less than 20% missing data. We selected a set of 1,491 SNP markers where there were low levels of missing data and the SNP call was present in the parents and of the opposite allele. We then used AntMap to construct a genetic linkage map of the 21 wheat chromosomes In the wheat SynOpDH population marker density on the reference map was initially lower. We therefore first explored development of a genetic linkage map 1 plants. The presence of this deletion among some but not all of the DH progeny indicates heterogeneity for the deletion in the original parental seed stocks of the synthetic parent. We also observed the deletion in an additional DH line that was derived from a different F1 than the F1 plants for the first set. Based on other DH progeny, this F1 plant should have carried the full 2D chromosome, indicating the DH line originated from a different F1 than indicated by the pedigree record.In the SynOpDH population we observed a deletion on Chr. 2D with the GBS data . This dePstI as a rare-cutting restriction enzyme we first developed a set of GBS barcodes compatible with the PstI restriction site and overhang. Using this algorithm we also designed a set of 384-barcodes for PstI that are being used for subsequent experiments  contextual ordering of reference sequence will enable better SNP calling in the GBS data-sets and haplotypic imputation of any missing data.The GBS data enabled observations of several genomic features in the barley and wheat populations. Analysis of the OWB GBS data highlighted structural features such as the probable genetic position of the centromere for each chromosome. This was evidenced by a high number of GBS markers falling into one or a few recombination bins. This trend was also evident in the OWB SNP marker map In the OWB population we also observed a region of approximately 18 cM that did not have any informative markers. The lack of informative markers in a region this large likely indicates a region of identity-by-descent (IBD) between the two parents. The OWB parents were created through introgressions of dominant and recessive alleles into related recurrent parents 1 plants. Such deletions will cause map distortion on a marker platform such as DArT where markers and dominant and scored as presence or absence. Further cytological investigation of these DH lines and the original parental stocks can confirm the presence and size of the deletion.Through development of GBS maps in the SynOpDH population, we were able to observe a heterogeneous deletion presumably present in the synthetic parental stock. This deletion was present in some but not all of the DH progeny and was found within lines derived from a common set of Fde novo marker discovery and removal of ascertainment bias in new germplasm will make this the genotyping platform of choice in the future.The development of GBS methods and high-density genetic maps represents an important advance in the genomics tools available for these cereal crops that currently lack a reference genome sequence. Further, our cost per sample was less than half the cost of other whole-genome genotyping platforms presenting an attractive option for genomic selection applications in breeding programs where cost per sample is critical. Recent increases in the data-output of the next-generation sequencing machines have further reduced per sample cost by generating the same amount of data per sample using a 96-plex library. As sequencing output continues to increase at a rapid pace, GBS clearly becomes more and more attractive. Furthermore, the advantages of GBS such as Plant Material: Two community resource double-haploid (DH) populations were used for genetic mapping. In barley, the Oregon Wolfe Barley (OWB) DH population was developed from a cross between morphologically diverse genotypes that are differentiated by a number of dominant morphological markers www.barleyworld.org). In wheat, we used 147 lines from the Synthetic W9784×Opata M85 DH population Adapters: A set of 48 barcoded adapters with a PstI overhang was designed using a custom script in Java (sourceforge.net/projects/tassel/). The barcodes were designed with the following criteria 1) each barcode must be 2 or more bp different from all other barcodes, 2) barcodes can not contain a run of more than 2 of the same nucleotide, 3) barcodes can not contain or recreate (when ligated) the PstI or MspI restriction site. The full-set of barcodes was designed to optimize the uniformity of each nucleotide at each position. Nucleotide uniformity was accomplished by designing barcodes of different lengths (4 bp to 9 bp) and selecting nucleotides in the barcode that balanced bases in the restriction site AAGTGA_topA01_5′ – gatctacactctttccctacacgacgctcttccgatctAAGTGAtgca – 3′AAGTGA_botA01_5′ – TCACTTagatcggaagagcgtcgtgtagggaaagagtgtagatc – 3′PstI is included in PstI is included in The full list of barcoded adapters for MspI-MspI fragments and adapter-dimers formed by self-ligation of adapter fragments. During the PCR amplification the reverse primer is identical to the Y-tail of the common adapter and can only anneal if the complimentary strand has first been synthesized from the other end of the fragment containing Adapter 1 . The adapters were adjusted to a uniform concentration of 0.1 uM.Restriction Digest: Genomic DNA (200 ng) was digested in 20 ul reaction volume of NEB Buffer 4 with 8 U of HF-PstI (High-Fidelity) and 8 U of MspI . The digest was conducted at 37 C for 2 h and then 65 C for 20 min to inactivate the enzymes.Ligation: The ligation reaction was completed in the same tube/plate as the digestion, again using NEB Buffer 4 with the addition of ATP. For wheat and barley, 0.1 pmol of the respective Adapter 1 (0.1 pmol for 200 ng of genomic DNA) and 15 pmol of the common Y-adapter were added to the samples. A master mix of NEB Buffer 4 , ATP , and 200 U T4 ligase (NEB T4 DNA Ligase #M0202) were added to each sample. The ligation was completed at 22 C for 2 h and the ligase was inactivated prior to pooling the samples by holding at 65 C for 20 min.Multiplexing and Amplification: Ligated samples were pooled and PCR-amplified in a single tube, producing a single library from 48 samples, which was sequenced on a single lane of Illumina GAII or HiSeq2000. The libraries were amplified for 18 cycles consisting of 95 (30 sec), 62 C (30 sec), 68 C (30 sec).Sequencing: Two 48-plex libraries from the OWB population and four 48-plex libraries from the SynOpDH population were constructed for this study. To construct full libraries, the parents and a few of the DH lines were replicated. The barley libraries were each sequenced on one lane of Illumina GAII and one lane of Illumina HiSeq2000. The first two wheat libraries were sequenced on Illumina GAII and the second two libraries were sequenced on Illumina HiSeq2000. All barley and wheat sequences were submitted to the National Center for Biotechnology Information (NCBI) Short Read Archive (study # SRP009867.1).Processing of Illumina Raw Data: From the (unfiltered qseq) Illumina data, sequences were assigned to individual samples using the barcode sequence and trimmed to 64 bp using a custom script in Java . Only sequences that had an exact match to a barcode followed by the expected sequence of 5 nucleotides remaining from a PstI cut-site were kept. The full set of reads was then examined for unique tags that were present in more than five different lines. The tags were then collapsed into a matrix of presence/absence for each sample. This matrix was then used for mapping the tags as dominant markers and internal referencing for SNP calling.Genetic Mapping of SNPs and Tags: For bin mapping we assumed that marker order in the reference map was correct. The recombination events present within the progeny lines defined the attainable mapping resolution. We define bins such that, for all markers within a bin, the alleles received by a line should have originated from the same parent. This definition also gives the bin's genotype for that line. Thus, if a recombination between a pair of ordered markers occurs in a single line this recombination defines the boundary between two bins. If a recombination between a pair of markers occurs in two lines, those two recombination events define both left and right boundaries of a bin. For those two lines, however, the genotype of that bin is unknown. That is, it is unknown whether the recombination present in a line forms the left boundary of the bin (in which case the bin genotype for that line would be that of the right flanking marker) or if it forms the right boundary of the bin (in which case the bin genotype for that line would be that of the left flanking marker). Similarly, if the same recombination occurs in three lines, two bins are defined, and their genotype is unknown in all three lines. For lines with no recombination between the flanking markers, the bin genotype can be inferred because there is no change in the parent of origin in successive bins. A bin for which the genotype of all lines is known we considered “resolved.” Equivalently, one or more markers will be definitively placed in this bin. The algorithm placed a GBS SNP into a bin if the SNP allele matched the bin genotype for all lines where the bin genotype was known. Unknown bin genotypes result in a GBS SNP being consistent with a range of bins. Once an initial range was determined, it was narrowed by counting the number of recombinations from the last resolved bin, as illustrated In the case of missing data in the bin map due to unresolved recombination bins, GBS markers were placed into exact bins in two steps. Referring to For GBS SNPs with missing data in lines with informative recombinations  the GBS Mapping the SNP markers with low levels of missing data resulted in an improved genetic bin map. This improved bin map was then used to map all GBS tags as presence/absence dominant markers. A binomial test was used to determine the maximum likelihood bin for each tag Text S1Genotyping-by-sequencing protocol for PstI-MspI.(PDF)Click here for additional data file.Table S1GBS barcodes sequences optimized for 48-plex with PstI.(XLSX)Click here for additional data file.Table S2Design of 384 barcodes optimized for PstI.(XLSX)Click here for additional data file.Dataset S1GBS SNPs mapped in the Oregon Wolfe Barley Population.(TXT)Click here for additional data file.Dataset S2Genetic linkage map of Synthetic W9784×Opata M85 constructed using GBS SNP markers and AntMap algorithm.(XLSX)Click here for additional data file.Dataset S3GBS SNPs mapped in the Synthetic W9784×Opata M85 DH population.(TXT)Click here for additional data file.Dataset S4Barcodes and samples used for GBS of Oregon Wolfe Barley DH and Synthetic W9784×Opata M85 DH populations. Additional files of GBS tags mapped as dominant markers in the OWB and SynOpDH populations are available from the corresponding author on request.(XLSX)Click here for additional data file.


Brucella. The genus is genetically monomorphic, requiring extensive genotyping to differentiate isolates. We utilized two different genotyping strategies to characterize isolates. First, we developed a microarray-based assay based on 1000 single nucleotide polymorphisms (SNPs) that were identified from whole genome comparisons of two B. abortus isolates , one B. melitensis, and one B. suis. We then genotyped a diverse collection of 85 Brucella strains at these SNP loci and generated a phylogenetic tree of relationships. Second, we developed a selective primer-extension assay system using capillary electrophoresis that targeted 17 high value SNPs across 8 major branches of the phylogeny and determined their genotypes in a large collection ( n = 340) of diverse isolates.Brucellosis is a worldwide disease of mammals caused by Alphaproteobacteria in the genus B. abortus, B. melitensis, and B. suis, differentiating B. melitensis and B. suis into two clades each. Brucella abortus was divided into four major clades. Our capillary-based SNP genotyping confirmed all major branches from the microarray assay and assigned all samples to defined lineages. Isolates from these lineages and closely related isolates, among the most commonly encountered lineages worldwide, can now be quickly and easily identified and genetically characterized.Our 1000 SNP microarray readily distinguished Brucella that can be used for rapid assignment into major groups below the species level in the three main Brucella species. Our assays represent SNP genotyping approaches that can reliably determine the evolutionary relationships of bacterial isolates without the need for whole genome sequencing of all isolates.We have identified clade-specific SNPs in  Brucella are Gram-negative bacteria and the causative agent of brucellosis in domesticated animals, wildlife, and humans. Although the bacteria exhibit relatively strong host preference, separating the various Brucella species has proven extremely difficult due to minimal genetic differentiation [Brucella species: B. abortus (bovine), B. melitensis (caprine and ovine), B. suis (porcine), B. canis (canine), B. neotomae (desert woodrat), and B. ovis (ovine) [B. ceti in dolphins, porpoises, and whales and B. pinnipedialis in seals) [B. microti in the common vole  [Brucella species undoubtedly exist within known and novel hosts [ntiation . Based ontiation . However (ovine) -5. Severn seals)  and an aarvalis) . Other Bel hosts -11.Brucella genomes has made genotyping a challenge. A promising approach that is capable of being incorporated into high-throughput assays is the use of single nucleotide polymorphisms (SNPs). Comparisons of Brucella genomes have revealed hundreds of SNPs that distinguish various strains [Brucella only a small fraction of the nucleotides are polymorphic, suggesting that once rare polymorphisms are discovered, methods other than whole genome sequencing are more efficient for most purposes.The limited genetic differentiation and conservation within  strains -14. Alth strains  is rapidB. abortusB. melitensis, and B. suis to discover SNPs. We created a MIP assay to genotype 85 diverse samples and to discover canonical SNPs [Brucella species, strains, or isolates. We then created SNP-specific assays that use a Capillary electrophoresis Universal-tailed Mismatch Amplification mutation assay (CUMA) approach for major branch points in the phylogeny and screened them against a large and diverse collection of isolates ( n = 340). Finally, we compared these results to 28 Brucella whole genomes in silico to place our genotyping into context with all major biovars and isolates.Molecular Inversion Probe (MIP) assays are an efficient and relatively inexpensive method of interrogating thousands of SNPs in large numbers of samples . Althougcal SNPs  that defin silico determination of their SNP alleles. The full MIP tree with all 777 loci and 85 samples, excluding the whole genomes in the comparisons, is also given , where no SNP was determined at a particular locus for a sample. Comparisons to SNPs from 28 whole genomes yielded 735 SNPs in our phylogenetic analysis Figure , allowinB. abortus, B. melitensis, and B. suis; the three prominent Brucella species  group biovar 3 with biovar 2 isolates. Based on these data, the assay for branch H appears to be specific to B. melitensis biovar 1. The two clades in B. suis, denoted by branches I and J, included all isolates of in the species except for biovar 5, which was distantly related to other members of this species. Some isolates from B. suis are more closely related to B. canis isolates (branch J) than to other B. suis isolates (branch I), indicating that B. suis is a paraphyletic species. Of the SNPs with complete genotyping data, at least 31 SNPs on branch I separate B. suis 1330 and related isolates from B. canis and related B. canis and B. suis isolates. However, no SNPs uniquely identified B. canis. Brucella abortus was even more differentiated, and can be divided into at least four distinct clades. Samples from B. abortus biovar 1, which contains the two SNP discovery strains, plus the type strain for biovar 2 (strain 86/8/59), make up the majority of samples and diversity within the B. abortus clade. All were found on branch E, which was further divided into branches A-D. Samples from the other B. abortus biovars are more distantly related and form distinct branches. As expected, the other species in the assay, including B. neotomae, B. ceti, B. pinnipedialis, and B. ovis were poorly distinguished from each other. Missing data for SNP loci caused the differences in branch lengths that are seen between Figure Our MIP assay distinguished s Figure . A totalB. abortus 2308, B. abortus 2308 + S19, B. melitensis 16 M, B. melitensis biovar 1, and B. suis 1330. From our diverse isolate collection we had the following distribution of calls for the branches, with the most derived call taking precedence over more ancestral calls: A = 1, B = 23, C = 8, D = 22, E = 7, F = 0, G = 15, H = 91, I = 33, J = 17, no derived call  = 25, no call for any assay = 7, ancestral within B. abortus = 12, ancestral within B. melitensis = 68, ancestral within B. suis = 11.CUMA assays verified the SNP alleles for all 85 of the samples run in the MIP assay. In addition, the 17 SNPs from the CUMA assays allowed for placement of a larger panel of 340 isolates within the MIP phylogeny  fall onto the B/C branch. This would suggest that either biovar 1 is paraphyletic or there have been issues with biovar determination.Branch E is particularly interesting in terms of eviously . Upon clB. suis/ B. canis, also share a SNP allele with B. abortus 9–941. Taken at face value, this would suggest homoplasy at this locus. Yet, in our CUMA assays B. abortus 9–941 did not group with B. suis, likely indicating sequencing error.SNP-based approaches also enable assessment of errors in genome sequences. Whole genome comparisons of the region associated with SNP10621, which were intended to target branch J in B. abortus simply because two strains were part of the discovery panel. Furthermore, although isolates on a branch will be grouped by the SNPs they share (or do not share), additional structure exists in the “true” phylogeny that is not apparent in the genotype tree. Branch lengths are also highly affected by the SNP discovery process. Species that are basal within this phylogeny, such as B. cetiB. pinnipedialisB. ovis, and B. neotomae have short branch lengths merely because these genomes were not part of SNP discovery. It must also be noted that B. suis biovar 5 is part of this basal group. SNPs that should group it with the rest of the B. suis clade were not present in our MIP assay, which is not surprising since this branch is extremely short, even with whole genome analysis [JTF unpubl. data, [Brucella species, nor did we expect it because genomes from these groups were not a part of SNP discovery.Finding nucleotide polymorphisms that differentiate clades, species, or isolates is dependent on the genomes used for SNP discovery. In general, one will only find those SNPs that exist among the genomic samples used in the comparisons and novel SNPs will remain undiscovered . This dil. data, . We did Brucella strains and these genomes should provide a broad basis for future genotyping efforts, with canonical SNPs developed for each of the important isolates and clades. Future genotyping efforts should include SNPs from all of the recognized species and biovars. Comparative work using some of these genomes has already been fruitful, demonstrating the emergence of the marine Brucella from within the terrestrial Brucella and showing a methodology for whole genome analysis [Whole genome resequencing at the Broad Institute of MIT/Harvard recently generated genomes for over 100 additional analysis .Brucella and other bacteria.A trade-off exists in current genotyping efforts between throughput and genomic sampling. Does one aim for a maximum amount of potentially informative loci through approaches such as whole genome sequencing but having to sacrifice the number of isolates that can be evaluated? Or does one aim for more complete sampling of large numbers of isolates but with a limited set of loci using individual SNP assays such as CUMA? Of course the ultimate answer depends on your research interest or clinical application as well as the amount of resources at hand. MIP assays provide phylogenetic resolution for an intermediate number of samples and intermediate number of SNPs. Nonetheless, MIP assays, or any assays based on previously discovered SNPs, will always have their inference limited by the genomes used in SNP discovery . MIP assBrucella: B. abortus, B. melitensis, and B. suis. The first genotyping approach, using Molecular Inversion Probes, divided the species into its three respective groups and allowed for finer scale genetic resolution. Notably, this resolution occurred almost entirely within the lineages of the four strains that were used for SNP discovery: B. abortus 2308, B. abortus 9–941, B. melitensis 16 M, and B. suis 1330. This is to be expected since the choice of genomes for SNP discovery has a pervasive effect on the phylogenetic patterns that can be determined. We followed the MIP assay with development of Capillary electrophoresis Universal-tailed Mismatch Amplification mutation assays that targeted major branch points in the MIP phylogeny. We then genotyped a large and diverse collection of isolates. The main result is the development of fine scale genotyping assays that target among the most important and widespread lineages of Brucella. Moreover, these and closely related isolates can be easily and quickly distinguished from all other Brucella isolates.We developed and evaluated two different SNP-based genotyping systems for three well studied species of Despite the era of whole genome sequencing being upon us, SNP-based genotyping and other targeted assays will remain relevant. Sequencing technology is advancing rapidly and costs per genome are quickly diminishing such that whole genome genotyping is the future of phylogenetics, forensics, and diagnostics. In fact, whole genome genotyping will soon be cost competitive with most other genotyping strategies and will have the advantage of capturing nearly all of the genetic variation with no issues of discovery bias. Nonetheless, targeted assays will remain a viable option for such goals as rapidly and easily characterizing large strain collections, clinical samples, and samples containing only trace amounts of DNA. Concerted efforts must be made to incorporate data from earlier genotyping strategies into genomic databases so this wealth of genetic information is not lost in the rush to sequence everything.Brucella genomes that were available at the time of MIP development: B. melitensis 16 M [B. suis 1330 [B. abortus 2308 [B. abortus 9–941 [SNPs were selected by comparisons of the four sis 16 M , B. suistus 2308  and B. aus 9–941 . SNPs frus 9–941 . Determius 9–941 . QualityThe MIP workflow is relatively straightforward: 1) SNPs are first discovered using comparisons of whole genomes or particular regions of interest within sequenced genomes; 2) a series of assays are created with primers targeting each SNP; 3) amplification products are generated in a single multiplexed PCR; 4) amplicons specific to each SNP for each sample are hybridized to a universal tag microarray; 5) each SNP is fluorescently labeled based on the corresponding nucleotide of the sample and is then visualized on the microarray.Francisella tularensis SNPs [Burkholderia pseudomallei SNPs (Keim unpubl. data) were run concurrently on the same chip, which reduced the cost of the assays for each group. MIP technology involves a specific probe that binds to flanking sequence surrounding a SNP site. Due to the orientation of the oligonucleotide sequence, the probe anneals as an inverted loop and a single base gap is created at the SNP site. The base at the SNP site is then added in one of four reactions involving unlabeled nucleotides. After ligation and exonuclease steps, the probe released from the sequence is amplified with PCR using universal primers specific for a portion of all probes. Only those probes where the SNP base has been added are successfully amplified. For a full description of the MIP methodology, see Hardenbol et al. [Brucella isolates for at least 80% of SNP alleles at a locus. Among these SNPs, 56 were monomorphic, leaving a final set of 777 phylogenetically informative loci. This dataset contained only 4% missing data, which were given an allele of N in phylogenetic analyses. To allow this dataset to be directly comparable to SNPs from whole genome analyses, we then did an in silico comparison of 28 whole genome sequences of Brucella from GenBank , which is one of the available forms of the MIP assay. In this assay, all 1000 SNPs were assessed in a single multiplex reaction for each sample. Assays containing ~3000 sis SNPs  and ~100l et al. . TypicalBrucella DNA samples on the MIP assay from a diverse isolate collection that included B. abortus (33), B. melitensis (30), B. suis (11), B. canis (6), B. neotomae (1), B. ovis (1), B. ceti (1), and B. pinnipedialis (2). The 85 samples tested are indicated , used only in the CUMA assays (detailed below), is from a portion of our DNA collection, which came from a variety of sources . The 83/13 isolate came from an Australian rodent and data suggest that it is related to the traditional Brucella spp. [Brucella. Using the phylogeny developed from the MIP assay to determine groups, we employed clade-specific SNPs using CUMA [2, 0.2 mM dNTPs , 0.2 U iTaq DNA Polymerase , and molecular grade water . Reactions were multiplexed in two different PCR reactions and fragments were run in a single capillary injection on an ABI 3100 Genetic Analyzer . Thermocycler conditions were as follows: hot start of 95°C for 5 min, followed by 40 cycles of 30 s at 95°C, 30 s at 60°C and 30 s at 72°C, with a final extension at 72°C for 5 min. Melting temperatures for the primers ranged from 57.1 to 67.0°C (mean 61.7°C).We created a matrix of SNP alleles for all SNP positions and formatted the data as one concatenated sequence for each sample. We analyzed this sequence in PAUP* using a heuristic search with the maximum parsimony algorithm, simple sequence addition and TBR branch swapping . We rootlla spp.  but likeing CUMA , followiing CUMA ,33. Brien = 340), which included all recognized biovars and type strains (except B. microti and B. suis biovars 3 and 5), against 17 SNP assays for 10 branches. For each sample we determined if the SNP allele for each locus was ancestral or derived on the corresponding branch and then verified where the sample was placed on the tree. When possible, we selected two SNPs from each of the major branches. We generated amplicons for the SNP regions in four PCR reactions for each of the two multiplex PCRs and then pooled the PCR product in one capillary injection. If the CUMA assay failed any locus in multiplex reactions, we reran that locus in singleplex, which generally allowed for determination of the SNP allele. Samples with singleplex failure largely appeared to be of poor DNA quality since there were typically failures across several different CUMA assays . (PDF 284 kb).Click here for fileList of BrucellaDNA samples tested with CUMA. DNA samples came from the following institutions, Louisiana State University (LSU), California Department of Health Services (CDHS), U.S. Armed Forces Institute of Pathology (AFIP), Alaska Public Health Laboratory (APHL), Brigham Young University (BYU), U.S. Centers for Disease Control (CDC), USDA-National Animal Disease Center (NADC), and the Arizona Department of Health Services (ADHS). Samples with a species name in the branch column were genotyped as that species using assays in  but gave all ancestral SNP alleles in our assays. Assays for B. abortus in blue B. melitensis in pink, and B. suis/canis in green, which correspond to the branches in Figure Click here for fileList of 28 whole genomes used forin silicocomparisons to SNP alleles from MIP assay. (PDF 62 kb).Click here for fileList ofBrucellaisolates used in 17 CUMA assays, including isolate name, species, and biovar when known or applicable and the SNP allele for each assay. (PDF 44 kb).Click here for file


G matrix), genetic variance explained and GE were investigated using the genotype data of about 40,000 SNPs and two statistical models.Japanese Black cattle are a beef breed whose meat is well known to excel in meat quality, especially in marbling, and whose effective population size is relatively low in Japan. Unlike dairy cattle, the accuracy of genomic evaluation (GE) for carcass traits in beef cattle, including this breed, has been poorly studied. For carcass weight and marbling score in the breed, as well as the extent of whole genome linkage disequilibrium (LD), the effects of equally-spaced single nucleotide polymorphisms (SNPs) density on genomic relationship matrix (r2) at ranges 0–0.1, 0.1–0.2, 0.2–0.5 and 0.5–1 Mb were 0.22, 0.13, 0.10 and 0.08, respectively, and 25.7, 13.9, 10.4 and 6.4% of the r2 values exceeded 0.3, respectively. While about 90% of the genetic variance for carcass weight estimated using all available SNPs was explained using 4,000–6,000 SNPs, the corresponding percentage for marbling score was consistently lower. With the conventional linear model incorporating the G matrix, correlation between the genomic estimated breeding values (GEBVs) obtained using 4,000 SNPs and all available SNPs was 0.99 for carcass weight and 0.98 for marbling score, with an underestimation of the former GEBVs, especially for marbling score.Using all pairs of two adjacent SNPs in the whole SNP set, the means of LD (The Japanese Black is likely to be in a breed group with a relatively high extent of whole genome LD. The results indicated that the degree of marbling is controlled by only QTLs with relatively small effects, compared with carcass weight, and that using at least 4,000 equally-spaced SNPs, there is a possibility of ranking animals genetically for these carcass traits in this breed. We have provided important basic information on the relationships between SNP marker density and genetic variance explained and accuracy and bias of GEBVs obtained. However, as the size of the dataset available in this study was limited, all of the available animals were used in the estimation analyses. In the analyses, the number of animals available (about 900) was well exceeded by the number of SNPs in most settings of SNP selection and use. Thus, we note that the genetic variance explained and the accuracy of GEBVs obtained in the current study may be somewhat inflated, relative to those values obtained using many independent validation animals. Therefore, further research is needed to confirm the current findings, especially from the perspective of prediction, and accumulating a much larger volume of relevant data.G matrix, the degree of genetic variance explained and accuracy of GEBVs for carcass weight and marbling score, which are representative traits for carcass quantity and meat quality, respectively. Our study revealed that the Japanese Black is likely to be in a breed group with a relatively high extent of whole genome LD, and that the degree of marbling is controlled by only QTLs with relatively small effects, compared with carcass weight. The possibility of effective GE with at least 4,000 equally-spaced SNPs was suggested for these traits.To our knowledge, this is the first study reporting the level of whole genome LD in Japanese Black cattle, using about 40,000 genome-wide SNPs, as well as the effects of their equally-spaced subsets on the elements of the Animal care and use was according to the protocol approved by the Shirakawa Institute of Animal Genetics Animal Care and Use Committee, Nishigo, Japan (ACUCH21-1).Cold carcass weights and marbling scores of 872 Japanese Black fattened steers, whose ages ranged between 15.3–43.0 months, were used for the current analyses. These records were collected from 2000–2009 at two large meat markets in Japan, namely Tokyo Metropolitan Central Wholesale Market and Osaka Municipal South Port Wholesale Market. Marbling score is the degree of marbling, ranging from null (1) to very abundant (12), assessed on the ribeye of the carcass dissected at the sixth and the seventh rib section, according to the Japan carcass grading standards . The disp > 0.001) and had position information. As a few percent of genotype data was missing, missing genotype filling was conducted using “Beagle 3.3.2” package [DNA samples were extracted from perirenal adipose tissues. Sample DNA was quantified and genotyped using the BovineSNP50 BeadChip (hereafter referred to as the 50K chip). The 50K assay contains 54,001 SNPs with an average probe spacing of 51.5 kb and a median spacing of 37.3 kb. A total of 38,502 SNPs were included in the statistical analyses, based on the following criteria: MAF and genotype call rates were larger than 0.01 and 0.95, respectively, were in Hardy-Weinberg equilibrium :y is the vector of records, b is the vector of fixed discrete effects of market and year at slaughter and the continuous effects of the linear and quadratic covariates of month of age at slaughter, g is the vector of additive genetic effects being assumed to follow G, respectively, e is the vector of residuals assumed to follow I, respectively, and X is incidence matrix.where G matrix was constructed according to [Using the SNP genotype data, the rding to  by:M is the matrix whose row elements include the number of minor alleles in each animal at each SNP locus, P is the matrix whose row elements contain the MAF at each SNP locus, pi is the MAF at the ith SNP locus, and n is the number of SNPs used.where G matrices were constructed and employed by selecting from 100 to 30,000 equally-spaced SNPs in number or using all available SNPs. To make the G matrices always positive definite, 10-4I was added to G in construction. We note that pedigree information for the animals, consequently the A matrix, was not available in this study.In this study, 12 different r2) of the alleles at two loci for all pairs of two adjacent SNPs on all chromosomes [d) between two adjacent SNPs were also calculated. In addition, correlations between the diagonal, upper triangular, and all the elements of a given G matrix, and the corresponding elements of the matrix constructed using all available SNPs, were examined .For each of the 12 sets of SNPs, including the set of all available SNPs, the extent of LD was measured by the squared correlation , and multivariate normal distributions were employed as priors for the additive genetic effects. As prior distributions for All the parameters in model 1 were estimated via the Bayesian framework using Gibbs sampling in “BLR” package under R environment ,42. A flsoftware , was useQTLs: Quantitative trait loci; GE: Genomic evaluation; GS: Genomic selection; SNPs: Single nucleotide polymorphisms; LD: Linkage disequilibrium; BLUP: Best linear unbiased prediction; G matrix: Genomic relationship matrix; A matrix: Additive relationship matrix; GEBV: Genomic estimated breeding value; MAF: Minor allele frequencies; AI: Artificial insemination; GWAS: Genome-wide association study.The authors declare that they have no competing interests.HI, HM and SO conceived of the study. SO carried out the analyses and drafted the manuscript. HI and HM contributed in writing and improving the manuscript. YT, TW, SN and YS participated in the design of the study. All authors read and approved the final manuscript.


Advances in high-throughput sequencing, genotyping, and characterization of haplotype diversity are consistently generating vast amounts of genomic data. Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation . In the In line with the increasing interest in the genetic variation analysis and annotation, on July 14th, 2012 [e.g. a new way to efficiently analyze multi-GWAS data [The topics covered in the SIG presentations focused primarily on annotating the phenotypic traits associated with the specific SNPs - everything from annotating associated protein function changes -10 to scWAS data . Finallyhttp://snpsig.biofold.org).We are currently preparing for the next edition of the SNP-SIG meeting to be held in the context of the ISMB 2013, Berlin, Germany. Further information about the SNP-SIG 2013 is available on our web site (