Food security is an issue that has come under renewed scrutiny amidst concerns that substantial yield increases in cereal crops are required to feed the world’s booming population. Wheat is of fundamental importance in this regard being one of the three most important crops for both human consumption and livestock feed; however, increase in crop yields have not kept pace with the demands of a growing world population. In order to address this issue, plant breeders require new molecular tools to help them identify genes for important agronomic traits that can be introduced into elite varieties. Studies of the genome using next-generation sequencing enable the identification of molecular markers such as single nucleotide polymorphisms that may be used by breeders to identify and follow genes when breeding new varieties. The development and application of next-generation sequencing technologies has made the characterisation of SNP markers in wheat relatively cheap and straightforward. There is a growing need for the widespread dissemination of this information to plant breeders.Triticum aestivum) that will assist plant breeders and scientists to select the most appropriate markers for marker assisted selection. CerealsDB includes a database which currently contains in excess of 100,000 putative varietal SNPs, of which several thousand have been experimentally validated. In addition, CerealsDB contains databases for DArT markers and EST sequences, and links to a draft genome sequence for the wheat variety Chinese Spring.CerealsDB is an online resource containing a range of genomic datasets for wheat (CerealsDB is an open access website that is rapidly becoming an invaluable resource within the wheat research and plant breeding communities. The issue of food security  has become a focus of concern for governments around the world and has moved steadily up the political agenda. The human population is expected to reach nine billion by 2050, and it has been estimated that cereal production will need to increase by 50% by this time if we are to meet the challenge of feeding ourselves and the animals upon which we are dependent . The cerSingle nucleotide polymorphisms (SNPs), which are single base pair changes in the DNA sequence, are the most common form of sequence variation between individuals of the same species. Because of their frequency in the genome, SNPs have become the marker of choice for marker assisted selection (MAS) of agronomic traits of importance such as disease and drought resistance . In ordeThe development of NGS platforms and high-throughput genotyping methods has made it possible to rapidly characterise and validate thousands of putative SNP markers in wheat at relatively low cost ,8. A numWe have greatly extended the functionality of CerealsDB, which was originally constructed to store a dataset of 26,382 EST sequences , to inclCerealsDB was implemented using a MySQL relational database management system (RDMS) database (version 5.051b ) running on a MacOSX server and hosted using the Apache web server (version 2.2) with Perl and PHP scripts used for all data retrieval and output. MySQL was selected for the database, as it is one of the leading open source industrial strength RDMS, and matches the quality and performance of other major proprietary databases. MySQL also finds advantage in multi-layered server design with independent modules and fast execution process. The Apache HTTP server was adopted due to its reliability, relative ease of configuration and wide language interface support system.CerealsDB is a relational collection of entities (tables) with appropriate attributes (fields) to define the wheat SNPs. An entity-relationship (ER) model for the SNP database is displayed in Figure The ‘Location’ table consists of 3,813 entries and contains information with regard to chromosomal location and position in centimorgans (cM) of all the SNPs that have been mapped. The ‘Primers’ table, which has 6,337 records, contains the KASPar primer sequences (two forward and one reverse) for all the SNPs that have been experimentally assayed. The ‘Haplotype’ table includes 6,337 records and contains the SNP calls for all the validated SNPs against a panel of 101 wheat varieties. The ‘blast_url2’ table holds 37,837 entries that are linked to the contig table by the `NewContigName` attribute in a 1:1 identifying relationship. This table contains all hits reported when a BLASTX similarity search was performed for each contig versus the NCBI ‘embryophyta plant’ protein database . InclusiThe ‘Homo_and_var_SNPs’  and ‘VarietalSNPS’ tables  are linked to one another by a 1:1 non-identifying relationship and are unlinked to any of the other tables. The ‘Homo_and_var_SNPs’ table contains information derived from a particular experiment designed to identify both homoeologous and varietal SNPs in eight wheat varieties : a homoeologous SNP is a sequence variation which exists between the different genomes  which make up the hexaploid wheat genome and a varietal SNP exists between wheat varieties and enables the differentiation of two varieties based upon that particular SNP location. The table, which contains 511,439 entries, contains a primary key which uniquely identifies the SNP and contig from which it has been derived, and the SNP calls, depth and value, for each locus for all eight varieties. This information has been published in Winfield et al. .The ‘VarietalSNPS’ table contains information solely on those putative SNPs that have been classified as varietal; that is, SNPs that clearly distinguish one wheat variety from another. These varietal SNPs are of particular interest to breeders. This table is used principally for database searches used to populate the summary tables at the head of several web pages.There are three stand-alone tables that are not linked to any other table in the database. The ‘WheatVariety’ table contains 101 records, one entry for each variety studied, and includes information on pedigree, winter/spring habit, and the original plant breeder for the 101 wheat varieties studied so far. The Images table (21 records) contains the URL to images of SNP maps of the 21 wheat chromosomes, and the table ‘IUPAC_codes’ holds 36 entries for translation of ambiguity codes.The CerealsDB site has been extensively redesigned using cascading style-sheets (CSS) to standardise the format of the site, and improve the presentation, to provide a more intuitive browsing experience. Links are provided to the WheatBP educational resource and to other related sites that provide additional background information on wheat genomics.SNP data was included from previous studies  as well The KASPar SNP database currently consists of data on 111,442 SNPs derived from 8 wheat varieties of which 99,945 are varietal SNPs  . Of these SNPs 4,986 have been validated (by screening against 101 varieties) and 3,813 mapped to a specific chromosomal location. Validated SNP entries contain information on location within a reference contig and primer sequences used for validation.http://www.k-state.edu/wgrc/Germplasm/Deletions/delindex.html) to assign DArT markers to chromosome arm specific bins. Of the 420 deletion lines held at the WGGRC, 74 lines were selected because they were the lines used in the NSF Wheat EST Genomics Project designed to assign wheat ESTs to chromosome bins. These lines possess homologous terminal deletions in single chromosome arms. These deletion lines (developed in the wheat variety Chinese Spring) provide powerful tools for the physical mapping of wheat chromosomes giving us the ability to map markers not only to specific homoeologous chromosomes but to assign them to bins of around 28 Mb [CerealsDB includes the original wheat EST database, containing 26,382 expressed sequence tags that were generated from hexaploid winter wheat (var. Mercia) cDNA and previously described by Wilson et al. . These wnd 28 Mb  on speciThe CerealsDB website contains a range of datasets that are accessible via the menu bar and include the KASPar SNP database, a BLAST search page to query the Wheat genomic sequence, a download page for the draft wheat genome, a wheat EST database, a search page for wheat DArT data and a page describing the mapping of DArT markers using wheat deletion lines.The KASPar SNP database allows SNP name and contig queries. Some queries (e.g. SNP name) are exact. Sequence queries are BLAST similarity based . Some ofThe interface is designed to be simple and provide quick access for plant breeders who may not possess an in depth knowledge of genetics. Chromosome information is accessed via a clickable image (ideogram). These are arrayed on the screen according to the chromosome and sub-genome to which they belong. The initial page for the KASPar SNP database contains statistics on the SNPs within the database and also links to bar graphs showing the distribution across the 21 wheat chromosomes of all mapped SNPs for the mapping populations Avalon x Cadenza and Rialto x Savannah. In addition, there are also graphs showing the relationship between the expected number of mapped SNPs to each of the three genomes , based on their relative lengths, and the actual number mapped.There are four main query roots for the KASPar SNP database:1) “Select primers”; this can provide information on the primers for individual SNPs or for all the SNPs on a particular chromosome. Primers for SNPs on each chromosome can be selected by clicking on the relevant chromosome image of the ideogram, or by entering a specific SNP ID. Alternatively, all SNPs located on that particular chromosome can be downloaded as an Excel spreadsheet. Scrolling down the page reveals a map of the chromosome and the SNPs that are currently mapped to it.2) “Obtain haplotypes”; select for information on the SNP alleles in the 101 wheat varieties against which the SNPs have as yet been validated. Clicking the relevant chromosome image of the ideogram will take the user to information on that particular chromosome. Alternatively, haplotypes can be selected for particular varieties on specific sub-genomes and chromosomes. Once again, results can be downloaded as an Excel spreadsheet.3) “Blast contigs”; allows the user to input a nucleotide sequence and perform similarity searches (using BLAST) against the Bristol contigs. If the search identifies a contig and it contains SNPs, then the user can display the positions of each SNP within that particular contig.4) “Contig information”; facilitates the search for information about the contig on which a SNP is found by providing a SNP ID, or the contig name to see whether it contains any SNPs. If a SNP is found, the output displays the contig sequence, SNP position, information on mapping and any associated BLAST annotation . A field is provided to search for contigs based on a specific term, for example ‘disease resistance’ or ‘cold’, and will return all contigs that contain this term in the contigs’ associated BLASTX report.The draft assembly of the gene-rich regions of the Chinese Spring genome, or the raw sequence reads can be searched using BLAST and a drop down field is available for setting the e-value cut-off.Wheat ESTs can be searched by gene name, similarity search (via BLAST) or westdb cloneID. The wheat DArT markers can be searched by marker name or wheat line and the facility is available to download the DArT datasets in Excel format. Mapped DArT markers can also be viewed on their respective chromosome by clicking the appropriate chromosome image of the ideogram.Finally, there is a Help/FAQ page for users, with answers to potential questions about the data in the database and other features on the website.Substantial enhancement has been made to the original CerealsDB site through the inclusion of databases for SNP and DArT markers as well as other genomic resources. In order to make CerealsDB comprehensible to non-scientists and to retain its relevance to scientific specialists, version 2.0 has improved both functionality and the user-friendliness of the web interface. As the SNP database grows in size and the number of KASPar validated SNPs increases, we expect traffic to the CerealsDB site to grow. The website has already attracted considerable interest from the plant-breeding and research communities and has recorded, on average, more than 5,600 unique visits per month over the past year . Our laboratory is actively developing and validating SNP markers, and these are regularly added to the database. Future developments planned for the CerealsDB site include the linking of SNP data with phenotypic data and the integration of the KASPar SNP database with other cereal genomics databases. We aim to make CerealsDB an important, freely available resource by releasing all data into the public domain without restrictions. This will ensure that wheat breeders across the world have the data they require to create new varieties of wheat that will be needed to help feed the world beyond 2050.http://www.cerealsdb.uk.net/CerealsDB/Documents/DOC_CerealsDB.php. The SNP database is publicly and freely accessible, requiring no registration and with no restrictions on use.CerealsDB can be accessed online from the following URL : It is recommended that one of the following browsers is used: Mozilla Firefox 3 on Linux, Mac OSX or Windows, Internet Explorer 8 on Windows, Safari 4 on Mac OsX or Windows, Chrome on Linux or Windows.BC, Bristol contig; BLAST, Basic local alignment search tool; BS, Bristol SNP; CSS, Cascading style sheets; DArT, Diversity array technology; ER, Entity-relationship; EST, Expressed sequence tag; FAO, Food and Agriculture Organisation of the United Nations; FAQ, Facts and questions; HTTP, Hypertext transfer protocol; ID, Identification; MAS, Marker-assisted selection; NCBI, National Center for Biotechnology Information; NGS, Next-generation sequencing; Perl, Practical extraction and report language; PHP, Hypertext pre-processor; RDMS, Relational database management system; SNP, Single nucleotide polymorphism; URL, Uniform resource locator.The authors declare that they have no competing interests.PAW and MOW wrote the manuscript. MOW, GLAB & PAW built CerealsDB version 2.0, AMA and AB carried out the SNP validation experiments, JC carried out NGS sequencing and KJE conceived of the study, participated in its design and coordination, and helped to draft the manuscript. All authors have read and approved the final manuscript.


Pseudotsuga menziesii) extends over a wide range of contrasting environmental conditions, reflecting substantial local adaptation. For this reason, it is an interesting model species to study plant adaptation and the effects of global climate change such as increased temperatures and significant periods of drought on individual trees and the forest landscape in general. However, genomic data and tools for studying genetic variation in natural populations to understand the genetic and physiological mechanisms of adaptation are currently missing for Douglas-fir. This study represents a first step towards characterizing the Douglas-fir transcriptome based on 454 sequencing of twelve cDNA libraries. The libraries were constructed from needle and wood tissue of coastal and interior provenances subjected to drought stress experiments.Douglas-fir  was assembled. Functional annotation by BLAST searches and Gene Ontology mapping showed that the composition of functional classes is very similar to other plant transcriptomes and demonstrated that a large fraction of the Douglas-fir transcriptome is tagged by the PUTs. Based on evolutionary conservation, we identified about 1,000 candidate genes related to drought stress. A total number of 187,653 single nucleotide polymorphisms (SNPs) were detected by three SNP detection tools. However, only 27,688 SNPs were identified by all three methods, indicating that SNP detection depends on the particular method used. The two alleles of about 60% of the 27,688 SNPs are segregating simultaneously in both coastal and interior provenances, which indicates a high proportion of ancestral shared polymorphisms or a high level of gene flow between these two ecologically and phenotypically different varieties.We established a catalogue of PUTs and large SNP database for Douglas-fir. Both will serve as a useful resource for the further characterization of the genome and transcriptome of Douglas-fir and for the analysis of genetic variation using genotyping or resequencing methods. Pseudotsuga menziesii (Mirb.) Franco) is an ecologically highly variable species that occurs in two main varieties in North America. The natural range of the coastal or green Douglas-fir (Pseudotsuga menziesii var. menziesii) extends over 2,200 km from the Pacific Northwestern fog belt and the adjacent summer-dry Coastal Range and Cascade mountains to the drier coastland of Southern California. The interior or blue Douglas-fir (Pseudotsuga menziesii var. glauca) is distributed over more than 4,500 km along the dry continental climates of the montane to the subalpine Rocky Mountains from Alberta to Colorado with isolated populations reaching into Mexico. Douglas-fir grows from sea level on Vancouver Island up to 3,000 m altitude in the southern Rocky Mountains[Douglas-fir  expects increasing summer temperatures and decreasing precipitation in Central Europe in the coming years. A trendArabidopsis thaliana[Pinus contorta, Picea glauca and Pinus taeda[Because of the large genome size of Douglas-firs , tra thaliana-27, and nus taeda,28,29.Douglas-firs, like other forest trees, have a high level of genetic diversity,31. For The purpose of this study was to establish a catalogue of Douglas-fir putative unique transcripts (PUTs) enriched for drought stressed genes and to identify genetic polymorphisms as resource for further analysis such as resequencing projects, association studies, and gene expression profiling.r = 0.4972, P < 0.0001; Additional fileThe sequencing of twelve cDNA libraries resulted in 3,619,544 reads with an average length of 338 bp. After preprocessing, the number of reads decreased to 2,957,373. Read numbers were not equally distributed among libraries  with an average sequence length of 564.6 bp  were hit in the nr database. The largest number of hits was against Picea sitchensis sequences, followed by Vitis vinifera  were divided and clustered according to the origin of their reads Figure. About oss Table. 134 of nr, the ara, and the picea databases (Tablecms group (e.g. 58.11% against nr) and the least number of hits in the m group (14.13% against nr). However, there is a highly significant correlation between the average length of isotigs and percent BLAST hits . Hence, the differences in the proportion of BLAST hits among classes of isotigs are not a result of differential evolutionary conservation, but of the amount of sequence data in each class.To test this hypothesis, we determined the proportion of significant BLASTX hits within each group of isotigs against the es Table. Most hinr database, 5,378 SNPs were classified as synonymous and 4,129 as non-synonymous.SNP detection was performed with three different programs, GSMapper, ssahaSNP, and bwa/SAMtools, to minimize the number of false positives. PUTs obtained from the assembly served as reference. The programs detected 57,691 (Newbler), 155,269 (ssahaSNP), and 85,346 (bwa/SAMtools) SNPs, resulting in a total number of 187,653 different SNPs. However, only 27,688 SNPs were detected by all three tools  has now a major impact on the genome-wide analysis of transcriptomes in non-model species-27. To aMelitaea cinxia[Eucalyptus grandis[Pandinus imperator[Pinus contorta[Pinus contorta [94.44% of all reads were assembled into isotigs during assembly, which is a high proportion compared to similar 454 transcriptome assemblies. For example, 88% of reads were assembled in ea cinxia and Eucas grandis, 78% in imperator, and 48% contorta. One cau contorta. Neverthscripts), but larscripts), 108,297scripts)). It shoscripts), and theThe average length of transcripts is 564.60 bp  provide a standardized set of terms to describe genes and gene products consistently in different species and databases. GO terms Figure suggestsc, m, or s. On average, they are shorter than isotigs containing reads from at least two different treatments. The isotigs of the m and s groups, but also the ms group, were considered to be top candidates for drought stress tolerance or resistance. However, since most treatment-specific isotigs consist of only two or three reads that originated from a total of twelve cDNA libraries, we reasoned that the power of a statistical model to detect significant differences is low. Therefore, we compared the extent of evolutionary conservation between groups as judged by the proportion of significant BLAST hits. This analysis is based on the notion that widely expressed genes are under stronger selective constraint than treatment-specific genes[cms group isotigs are more conserved than isotigs from the c, m, and s groups. Since the libraries were normalized and cDNA levels do not represent true expression levels, we restricted our analysis to presence-absence patterns. The differences in the fractions of BLASTX hits in single treatment groups and the cms group suggested that genes expressed in all three treatments are more conserved. However, if groups are corrected for the total amount of sequence data, cms group isotigs are not more conserved than treatment-specific isotigs, because the main determinant for a BLAST hit is isotig length which is longer in cms isotigs (TableDividing PUTs consisting of multiple reads (i.e. the isotigs) by the origin of their reads is a simple, but useful method to identify potential treatment-specific sequences. About 50% of isotigs consist of reads from the single treatment groups ic genes,44. Undecms group and only 134 candidates are part of the m, s, and ms groups. This reflects that the response to drought seems to be mainly facilitated through changes in gene expression levels via up- or down-regulation. The small set of 134 PUTs exclusively induced by drought stress appears to contribute to a specific drought response, but this needs to be further verified because their expression pattern may reflect a sampling artifact. Even though the function of those PUTs may not be conserved across large evolutionary distances, the identified PUTs serve as top candidates for further analysis of sequence and expression variation in comparisons of differentially adapted  Douglas-fir provenances.In addition to testing the general hypothesis that treatment-specific genes are less conserved than widely-expressed genes, we also parsed BLASTX results for drought stress related keywords to find potential drought stress related PUTs. We expect that the 1,503 transcripts with a BLASTX hit containing one of the keywords are probably involved in the Douglas-firs response to drought  SNPs were obtained, which is only about half of the number detected with gsMapper, which identified the lowest number of SNPs . Since the numbers of called SNPs differed highly between SNP detection tools, our results indicate that SNP calling from next generation sequencing data depend to a high degree on the software tools used. Therefore, results should be interpreted with caution, if relying on a single SNP detection approach only. To our knowledge there are no systematic studies yet that compared the accuracy of different SNP callers with next-generation sequencing data.A comparison of the SNP density (SNPs per kb) of the most reliable SNPs with published data shows that the former is an underestimate of the true level of sequence variation in Douglas-fir. The SNP density is 1 SNP for every 2,530 bp, whereas other studies estimated an average SNP density from 1 SNP per 43 bp to 1 SNP per 112 bp using Sanger sequencing protocols. The reaversus 473 SNPs in the i/i group). Although these results are consistent with earlier studies on the genetic diversity of Douglas-fir varieties[Nevertheless, our sequence data make a significant contribution to the number of Douglas-fir SNPs available for further applications. Until now, only around 1,300 SNPs have been identified in Douglas-fir,45. If oarieties,47, theyΠ can be calculated from 454 data[Different numbers of reads can be accounted for by using methods for population genetic inference developed for next-generation sequencing that account for differences in read numbers from individuals or pools of individuals in estimating allele frequencies and population parameters,49. Howe454 data, but as Arabidopsis thaliana and Picea sitchensis. By analyzing sequence variation in the PUTs we detected thousands of new SNPs. Furthermore, we identified drought stress specific candidate sequences. Taken together these data represent a useful resource for the next steps in the characterization of the Douglas-fir genome and transcriptome and the association of genetic variation with phenotypic traits such as adaptation to different ecogeographic environments.In this study we established a catalogue of Douglas-fir putative unique transcripts (PUTs) enriched for drought stress induced genes. Although the real magnitude of the transcriptome is yet unknown, we estimate that the majority of the transcriptome has been tagged by the PUT set presented here. This is based on the results of the functional annotation and the comparison of the GO term distributions with those of 1.5 year old Douglas-fir seedlings were obtained from tree nurseries in British Colombia (Canada), Washington, Colorado, and New Mexico (USA) and grown in the greenhouse in a mixture of soil:perlit:sand (50:25:25). All seedlings were fertilized with Osmocote Exact Hi End 5-6m . Potted seedlings were watered every second day. Drought stress experiments started after two month of growth in the greenhouse, when visual inspection of the seedlings indicated a well developed root system. For the experiments, seedlings were randomly assigned to one of three different treatments: (1) control seedlings kept under well watered conditions, (2) mildly water stressed seedlings  and (3) severely water stressed seedlings . Water stress was imposed by withholding watering until a desired water potential had been reached. Water pFrozen needles or sections of the stem were homogenized using mortars and pistils chilled with liquid nitrogen until a fine powder was obtained. Total RNA was extracted from 300 mg aliquots of the frozen powder using the CTAB method. IsolateAliquots of the RNA from several seedlings and several provenances were then combined for synthesis into a total of twelve pooled RNA samples. Six of these pooled samples represented a subset of coastal and six samples represented a subset of interior Douglas-fir. Each of these two subsets included two sets of pooled RNA samples from either needle tissue or from wood tissue. Finally, each of these tissue specific sets consisted of one pooled RNA sample from control, mildly water stressed or severely water stressed seedlings Table.Normalized cDNA libraries were generated by Evrogen LAB . Starting from 0.3 Âµg of total RNA double-stranded cDNA was synthesized using SMART Oligo II oligonucleotides and CDS primers 30– 3’). Amplifi454 sequencing of the normalized cDNA libraries was carried out by Seq-IT  using a Genome Analyzer FLX with 454 titanium chemistry . Prior to sequencing, each cDNA library was first fragmented. Fragments were tagged with multiplex identifiers (MIDs) to allow library identification of the reads obtained from parallel sequencing of the libraries on the Genome Analyzer FLX. In total three titanium runs were performed, with 1.5 runs analyzing the needle libraries, and 1.5 runs analyzing the wood libraries. The proprietary genome analyzer software was used for the first preprocessing of sequence reads including the assignment of quality scores to generate.sff-files for further processing and assembly of the data.The resulting.sff-files were extracted with the sff_extract tool. All seq-cdna and -urt options[Sequences were assembled with Newbler v2.6 using default parameters supplemented by the  options,59. NewbAll twelve libraries were assembled together. Based on the assembly, we created a set of PUTs. We first searched for false positive singletons, i.e. reads that were marked as singletons although they matched nearly perfect to an existing isotig. For this purpose, all reads marked as singletons were mapped to the isotigs of the assembly using ssaha2 with defSNPs were identified with GSMapper, ssahaSNGSMapper v2.6 was run with default parameters for cDNA libraries. We constructed a.sff-file including all assembled reads of the assembly and all singleton reads that were mapped to the isotigs with ssaha2 . All reads of that.sff-file were mapped against the reference sequences with GSMapper. The resulting file was parsed for SNPs using a custom script.ssahaSNP v2.5.3 does not handle duplicate reads internally like GSMapper. Therefore, duplicate sequences were removed from the set of all assembled reads and all false positive singletons using cd-hit-454 v3.1.2. A.fastqbwasw option of bwa was used. The reads were mapped against the sequences of the PUTs. SAMtools was applied to convert the resulting.sam-file to a sorted.bam-file and to call the variants in that.bam-file. The resulting SNPs were again parsed to report only those SNPs with at least three reads confirming the variant and at least three reads confirming the reference nucleotide. The final set of potentially most reliable SNPs was obtained by combining the results of the three approaches and extracting those SNPs that were detected by all three programs.The third approach combined two tools, bwa v0.5.9 for mapping and SAMtools v0.1.16 for variant calling. The same.fastq-file that was used for ssahaSNP was used as input for bwa. As recommended for 454 reads, the nr database (see below). All high-scoring segment pairs (HSP) of the top hit of each query were considered. Using the information of the BLAST results, we examined whether a SNP was in a coding or non-coding region of a gene. For SNPs in coding regions, we determined the amino acid at the corresponding position with the reference nucleotide as well as with the variant nucleotide to call synonymous or non-synonymous SNPs.Synonymous and non-synonymous polymorphisms were detected using the results of the BLASTX search of the assembly against NCBI’s non-redundant According to the criteria for SNP detection, a transcript was covered by at least six reads at each SNP position and at least three reads had to confirm each allele of a SNP. Each of the two alleles of a biallelic SNP can therefore include reads from coastal or interior varieties only, or from both varieties, resulting in nine combinations, which are summarized in TablePicea sitchensis protein sequences downloaded from the NCBI data repository  and onedatabase). The pinr database were used as input for Blast2GO v2.4.9[P. sitchensis and A. thaliana used in the picea and ara databases. A BLASTP (v2.2.25+) search with an e-value of e−5 against NCBI’s non-redundant protein sequences was done before running Blast2GO. We did not annotate these two data sets with InterProScan, but with Annex and GO-Slim. The results of functional annotation of PUTs were compared to the results of the functional annotation of P. sitchensis and A. thaliana.Results of the BLASTX search of PUTs against O v2.4.9. Blast2GO v2.4.9 . The isotigs in the c, m, and s groups were assumed to contain most likely treatment- specific sequences, as they contained isotigs composed of only reads of one treatment. Therefore, we expected to find drought stress related sequences mainly in the m and s, but also in the ms groups. For the second approach, the BLASTX results were searched for specific keywords to identify candidate genes previously assigned to drought, water stress, or other stress related pathways . PUTs, annotated SNPs, and Blast2GO results will be available from http://www.treeversity.org. BLASTX results and Python scripts used for the analysis are available upon request.The sequence reads were submitted to the ENA Sequence Read Archive (SRA) under study accession number ERP001358 .Click here for fileNumber of identical BLASTX hits of different combination of groups after the keyword search. This table lists the number of identical BLASTX hits of different combination of groups after the keyword search. If combinations of sets are not listed, there were no equal BLASTX hits.Click here for fileBLASTX keyword search results. This file lists in a tab separated style for each BLASTX keyword search hit the following informations: keyword, isotig id, isotig group, hit id, hit definition, e-value. If there were more than one hit per keyword and isotig, only the best hit  is listed.Click here for fileSchematic example of Newbler output. Schematic example of contigs, isotigs, and isogroups produced by Newbler.Click here for fileComposition of the cDNA libraries in detail. Two tables describe the cDNA libraries and the provenances in detail.Click here for file


Bos taurus autosome (BTA) 4∶46–56 Mb and a diagnostic test based on the 6 microsatellite (MS) markers is commercially available, neither the causative gene nor mutation has been identified; therefore misdiagnosis can occur due to recombination between the diagnostic MS markers and the causative mutation. Analysis of 34,980 BTA 4 SNPs genotypes derived from the Illumina BovineHD assay for 20 Brown Swiss Weaver carriers and 49 homozygous normal bulls refined the Weaver locus to 48–53 Mb. Genotyping of 153 SNPs, identified from whole genome sequencing of 10 normal and 10 carrier animals, across a validation set of 841 animals resulted in the identification of 41 diagnostic SNPs that were concordant with the disease. Except for one intergenic SNP all are associated with genes expressed in nervous tissues: 37 distal to NRCAM, one non-synonymous (serine to asparagine) in PNPLA8, one synonymous and one non-synonymous (lysine to glutamic acid) in CTTNBP2. Haplotype and imputation analyses of 7,458 Brown Swiss animals with Illumina BovineSNP50 data and the 41 diagnostic SNPs resulted in the identification of only one haplotype concordant with the Weaver phenotype. Use of this haplotype and the diagnostic SNPs more accurately identifies Weaver carriers in both Brown Swiss purebred and influenced herds.Bovine Progressive Degenerative Myeloencephalopathy (Weaver Syndrome) is a recessive neurological disease that has been observed in the Brown Swiss cattle breed since the 1970’s in North America and Europe. Bilateral hind leg weakness and ataxia appear in afflicted animals at 6 to 18 months of age, and slowly progresses to total loss of hind limb control by 3 to 4 years of age. While Weaver has previously been mapped to  Bovine progressive degenerative myeloencephalopathy (Weaver Syndrome) is a neurodegenerative recessive genetic disorder that has been reported in Brown Swiss pure and crossbred cattle in the USA Bos taurus autosome (BTA) 4 by Georges et al. TGLA116 (58.21 Mbp) was identified as a diagnostic marker due to its close linkage (estimated 3% recombination rate) with the locus containing the Weaver allele. The locus was later refined to a 10 Mbp window between markers BMS2646 and MAF50  Weaver Syndrome was mapped to th century to an approximate peak of 6% in USA and 5% in Austrian Brown Swiss herds. While the MS test did help identify carrier bulls before their use as breeding stock, the Weaver allele is still present in modern herds with an allele frequency of 2.6% in USA and 3.5% in Austria Cases of Weaver Syndrome affected cattle presumably have been occurring since the 1950’s A major concern of Brown Swiss Associations is that the Weaver allele frequency is slowly increasing in some herds and breeds, such as Carora, who have used Brown Swiss genetics to improve milk production A testing population was assembled from cryopreserved semen from 70 Brown Swiss sires and hair roots on 3 dams obtained from the Cooperative Dairy DNA Repository (CDDR) and from the BSUSA. Genomic DNA was isolated by proteinase K digestion followed by Phenol:Chloroform:Isoamyl alcohol extraction, and ethanol precipitation According to BSUSA, 52 of the animals are normal and 21 are Weaver carriers. The disease status of 6 normal and 2 carrier animals was determined via a commercial MS test. The remaining 22 carrier animals were determined via progeny testing . At the Seventy-one animals from the testing population  were genotyped using the BovineHD assay , which interrogates 777,962 evenly spaced SNPs Genotypes were filtered for SNP call rate ≥0.90 and autosomal placement on the UMD3.1 assembly nominal).A GWAS was performed using the testing population (N = 71) and filtered BovineHD SNP using reported Weaver phenotypes with SVS (SNP & Variation Suite) v7.6.2 . As the testing population contained no affected animals, the data was analyzed as a case/control association test of Weaver carrier versus normal individuals. Pedigree information was included in the analysis. P values were converted to –log10, 6 lanes were used for the normal pool and 5 lanes for the Weaver pool. The Weaver library was also sequenced as a 80 bp single read using a GAIIx (Illumina). Sequences were aligned to the UMD3.1 assembly using the Burrows-Wheeler Alignment (BWA) Sequence data from SNP Discovery Animals for the Illumina HDSNP assay development (data unpublished) representing 8 breeds  were also aligned using BWA and GATK for BTA 4.Sequence variations identified within the refined Weaver locus identified by the GWAS analysis , were filtered for minor allele frequency (MAF) ≥ 0.30 in the Weaver pool and≤0.20 in the Normal pool, and≥6× coverage in the Weaver pool. Stricter filtering was not used to allow for sequencing and alignment errors and to account for potential mis-phenotyped animals in the normal pool. Alleles that were observed in any of the SNP discovery breeds were removed as were ones located in repeat regions . The annotation of the 153 remaining Weaver candidate variations, all SNP, were estimated using SnpEff version 1.0301 A verification population (N = 742) was assembled with 2 confirmed Weaver cases, 26 progeny confirmed carriers, 66 MS test identified carriers, 29 MS test identified normal, and 573 assumed normal animals. Fifty-four animals from the testing population with available DNA were included in the verification population. This population was further increased with 4 Angus, 26 Carora, 4 Holstein, 4 Jersey, 3 Hereford, and 3 Senepol animals derived from other research projects at the Bovine Functional Genomics Laboratory (BFGL). While the verification population is mainly comprised of animals of US descent, 208 Brown Swiss were of Italian descent including the confirmed affected animals which were provided by the Associazione Nazionale Allevatori bovini della Razza Bruna (ANARB). Weaver phenotype status was provided by BSUSA and ANARB.Weaver affected animals were diagnosed by Italian veterinarians and confirmed via the MS diagnostic test. Animal BS0158 was born 2/15/1999 and diagnosed at 130 days of age, while BS0083 was born 11/16/1999 and diagnosed at 110 days of age. Both had symptoms of weaving walk because of poor control, particularly of rear limbs, and problems in standing up.All animals’ DNA was obtained via extraction of cryopreserved semen or hair roots using either the method described above, PrepSEQ Nucleic Acid Extraction Kit , Genomix DNA Extraction Kit , or Chelex 100 The Weaver affected animal (BS0158), 4 Holstein, 4 Jersey, and 3 Senepol animals were separately HDSNP genotyped at USDA-ARS-BFGL. The second Weaver affected animal (BS0083) had <300 ng of DNA available, therefore it was not HDSNP genotyped. Samples were processed according to the manufacturer’s recommendations and were scanned using a BeadStation 500GX (Illumina) with high-density upgrade. Genotypes were called using BeadStudio v.3 software (Illumina) and filtered for SNP call rate ≥0.90 and autosomal placement on the UMD3.1 assembly.CNVs were detected using PennCNV as previously described Multiplex genotyping assays for the 153 Weaver candidate SNPs were designed for the MassARRAY analyzer  and genotypes from the verification population at GeneSeek  were loaded into SVS. Illumina BovineSNP50 http://genome.ucsc.edu/util.html), was used to map the predicted TFBSs to the UMD3.1 assembly.Two algorithms were used to detect candidate TFBSs within the Weaver locus. The first, MatInspector An initial genotype frequency analysis of the targeted SNPs revealed 4 Italian Brown Swiss female animals  who were homozygous for the same allele as the known Weaver affected animals for >99% of the targeted SNPs. Further analysis on these 4 animals revealed that they were killed at 6, 15, 17, and 26 months of age, respectively, and that all had a progeny confirmed Weaver carrier ancestor on both their maternal and paternal lineage in≤4 generations. Based on these observations, consistent with non-reported Weaver affected animals, they were not considered for further analysis.The Carora breed was formed in the 1930’s by using USA and European Brown Swiss semen on Venezuela Criollo cattle The genotypes of the 153 candidate Weaver SNPs were analyzed for their segregation pattern and allele frequency within the different phenotypic groups. The ‘Weaver’ allele for each SNP was determined according to which allele was homozygous in the 2 Weaver affected animals. SNPs were filtered on the following criteria: 1) heterozygous in the affected animals, 2) homozygous in progeny confirmed Weaver carrier animals, 3) homozygous Weaver allele in non affected animals, and 4) Weaver allele frequency (WAF) ≥0.06  who had Weaver candidate SNPs and 50K genotypes in the AIPL Brown Swiss North American (BSNA) database were used to determine Weaver haplotype. Animals were filtered on call rate≥0.9 for the targeted SNPs. The resulting 573 animals had their filtered targeted SNPs (N = 141) added to their 50 k genotypes. The BSNA database also contained 6,362 additional animals with 50K genotypes, 468 with GoldenGate Bovine3K Haplotypes were obtained with version 2 of the Fortran program findhap.f90 One of the Holstein negative control animals (HO1) was homozygous for 32 concurrent Weaver diagnostic SNPs . The comNext it was determined which animals have the highest relationship with HO1 to trace its non-Holstein ancestry. The percent conflict method, similar to parentage validation, was used to calculate the percentage of opposite homozygous SNP genotypes between HO1 and all genotyped animals HO1 was commercially used for artificial insemination and has 718 recorded progeny. To investigate the prevalence of the Holstein haplotype from this bull containing the SNP haplotype for Weaver, all genotyped Holsteins in the AIPL database were imputed using methods by VanRaden et al. GWAS analyses on both the testing and validation populations resulted in a reduction of the Weaver locus from 46–56 Mb to 48–53 Mb on BTA 4 . MultiplNRCAM and PNPLA8 .Whole genome sequencing of the two Brown Swiss pools and alignment to the UMD3.1 assembly resulted in an average of 21X coverage for pool 1 and 22X for pool 2 across the Weaver locus as redefined by the GWAS results. Within the locus 50,814 variations were identified, of which there were 4,570 insertions, 4,137 deletions, and 42,107 SNPs. These were filtered down to 153 SNPs unique to Brown Swiss and most were determined to be intergenic by annotation analysis . Four SNFifteen SNPs were present in dbSNP , but thePNPLA8 (serine to asparagine), 50858538 a synonymous in CTTNBP2, and 50929556 a non-synonymous (lysine to glutamic acid) in CTTNBP2.Filtering on genotype and allele frequencies in the Weaver affected and non-affected groups left 41 SNPs . Thirty-NRCAM and PNPLA8 and then homozygous normal for the remaining 4 SNPs  that was homozygous normal for 5 SNPs, heterozygous for next 32 SNPs between g 4 SNPs . AnalysiNRCAM was typically heterozygous for the remaining 4 diagnostic SNPs  were called unknown as they were not fully heterozygous or homozygous for all 41 SNPs. The same assignment was made for the Carora and of them 21 were called normal and 5 carrier .From the haplotype analysis of 7,458 Brown Swiss with 50K or imputed 50K data 563 haplotypes were identified, of which only one, haplotype #2, was identified as the Weaver carrier haplotype . ApplicaA low (0.03) haplotype frequency (data not shown) was identified in the Holstein breed for animals containing an identical haplotype or crossover to HO1, who was heterozygous for the majority of the diagnostic SNPs. With such a low frequency, the opportunity for a carrier-by-carrier mating is very low, and may explain why the disease has not been documented in the Holstein breed.SOD1, TDP-43, FUS, VCP, and PFN1 , PNPLA8 (patatin-like phospholipase domain containing 8) and CTTNBP2 (cortactin binding protein2).The refined Weaver locus (48–53 Mb) contains 17 genes with predicted associations with and PFN1 . MutatioNRCAM encodes for neuronal adhesion molecule, an ankyrin-binding protein that is involved in neuron-neuron adhesion and promotes directional signaling during axonal cone growth (www.uniprot.org). NRCAM is engaged in such biological processes as axonal fasciculation, cell-cell adhesion, central nervous system development, clustering of voltage-gated sodium channels, neuron migration, positive regulation of neuron differentiation, regulation of axon extension, and synaptogenesis. It also may play a role in the molecular assembly of the nodes of Ranvier. NRCAM is linked with different recognition processes and signal transduction pathways regulating cell differentiation, proliferation, or migration NRCAM promotes directional signaling during nervous system development in several different regions as the spinal cord, the visual system, and the cerebellum NRCAM is associated with mammalian phenotypes for abnormal axon morphology, locomotor behavior/coordination, motor coordination/ balance, nervous system electrophysiology, neuron morphology, voluntary movement, nerve conduction, neurite morphology, ataxia, paralysis, and reduced nerve conduction velocity  and human  differs by >219 Kb. Alignment of the 2 genome annotations shows that NRCAM has 7 exons, not present in the bovine annotation, which narrows the gap between NRCAM  and PNPLA8  to <20 Kb based on the human annotation, versus a 252 Kb gap on the bovine annotation . Of the 37 diagnostic SNPs that lie between the bovine annotations of NRCAM and PNPLA8, one is in bovine EST DR115601, two overlay non-bovine EST that align to the region, and five affect predicted TFBS , the intergenic SNP 50481151, or the four SNPs which failed to produce genotypes  are the causative mutation for Weaver. But the gene expression patterns, biological processes affected, associated phenotypes, and density of target SNP strongly suggest that one of the 37 SNPs distal to NRCAM or the non-synonymous SNP in PNPLA8 (49878773), affects the gene in a manner that ultimately results in Weaver Syndrome in homozygous animals.Weaver is a neurological disease and while nal cord . This stA low (0.03) haplotype frequency was identified the Holstein breed for animals containing an identical haplotype or crossover to HO1, who was heterozygous for the majority of the diagnostic SNPs. If HO1 is a Weaver carrier, then the low haplotype frequency and therefore very low random opportunity for a carrier-by-carrier mating may explain why the disease has not been documented in the Holstein breed. If HO1 is a non-carrier, then this haplotype represents a small level of Brown Swiss introgression in Holstein and also further reduces the Weaver SNP haplotype. HO1 was born in 1976 and has had 718 registered progeny with the youngest born in 2008. Unfortunately none of his offspring have SNP genotypes in the AIPL database.While a single conclusive causal allele was not identified in this study we were able to identify a reduced list of potential causative SNPs. The combined analysis of targeted SNP genotypes and imputed haplotypes resulted in the identification of 285 new Brown Swiss and 5 Carora Weaver carriers. Selective breeding of animals like BS0310, BS0146, and BS0418 who are heterozygous for some of the 41 diagnostic SNPs and homozygous for others to known carrier animals would allow for a further reduction of potential diagnostic SNP based upon which homozygous SNP are present in affected animals. While use of the diagnostic SNPs and haplotypes reported here will be beneficial to identify Weaver carriers in both Brown Swiss pure and crossbred animals, continuing research needs to take place to identify the true causative Weaver mutation.Regardless of which SNP is the causative mutation, the imputation of the diagnostic SNPs for animals with 50K genotypes and their haplotype analysis resulted in >7,500 animals having their Weaver phenotype determined. At a modest $30 cost of using a commercial lab to extract DNA and genotype the Weaver diagnostic SNPs this represents a combined $225,000 cost saving for the owners of these animals. An added benefit is that this diagnostic SNP imputation and haplotype analysis can be performed on future animals with low-density SNP genotypes that can be imputed accurately up to 50K, until genotyping platforms are able to economically add the diagnostic SNP reported here or the causative allele(s) is determined by further research.Table S1Target SNP interactions and filter information.(XLSX)Click here for additional data file.Table S2140 SNP Weaver Haplotype.(XLSX)Click here for additional data file.Table S3Target SNP genotpyes and pedigree information.(XLSX)Click here for additional data file.Table S4Predicted effect of filtered sequence variations.(XLSX)Click here for additional data file.Table S5Weaver 50K+Target SNP Haplotypes.(XLSX)Click here for additional data file.


As the more recent next-generation sequencing (NGS) technologies provide longer read sequences, the use of sequencing datasets for complete haplotype phasing is fast becoming a reality, allowing haplotype reconstruction of a single sequenced genome. Nearly all previous haplotype reconstruction studies have focused on diploid genomes and are rarely scalable to genomes with higher ploidy. Yet computational investigations into polyploid genomes carry great importance, impacting plant, yeast and fish genomics, as well as the studies of the evolution of modern-day eukaryotes and (epi)genetic interactions between copies of genes. In this paper, we describe a novel maximum-likelihood estimation framework, HapTree, for polyploid haplotype assembly of an individual genome using NGS read datasets. We evaluate the performance of HapTree on simulated polyploid sequencing read data modeled after Illumina sequencing technologies. For triploid and higher ploidy genomes, we demonstrate that HapTree substantially improves haplotype assembly accuracy and efficiency over the state-of-the-art; moreover, HapTree is the first scalable polyplotyping method for higher ploidy. As a proof of concept, we also test our method on real sequencing data from NA12878 (1000 Genomes Project) and evaluate the quality of assembled haplotypes with respect to trio-based diplotype annotation as the ground truth. The results indicate that HapTree significantly improves the switch accuracy within phased haplotype blocks as compared to existing haplotype assembly methods, while producing comparable minimum error correction (MEC) values. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5. While human and other eukaryotic genomes typically contain two copies of every chromosome, plants, yeast and fish such as salmon can have strictly more than two copies of each chromosome. By running standard genotype calling tools, it is possible to accurately identify the number of “wild type” and “mutant” alleles  for each single-nucleotide polymorphism (SNP) site. However, in the case of two heterozygous SNP sites, genotype calling tools cannot determine whether “mutant” alleles from different SNP loci are on the same or different chromosomes. While the former would be healthy, in many cases the latter can cause loss of function; it is therefore necessary to identify the phase—the copies of a chromosome on which the mutant alleles occur—in addition to the genotype. This necessitates efficient algorithms to obtain accurate and comprehensive phase information directly from the next-generation-sequencing read data in higher ploidy species. We introduce an efficient statistical method for this task and show that our method significantly outperforms previous ones, in both accuracy and speed, for phasing triploid and higher ploidy genomes. Our method performs well on human diploid genomes as well, as demonstrated by our improved phasing of the well known NA12878 (1000 Genomes Project). This Methods article is associated with RECOMB 2014.phasing) —the copies of a chromosome on which the mutant alleles occur—in addition to the genotype  for each single-nucleotide polymorphism (SNP) site. However, in the case of two heterozygous SNP sites, genotype calling tools cannot determine whether “mutant” alleles from different SNP loci are on the same or different chromosomes (i.e. compound heterozygote). While the former would be healthy, in many cases the latter can cause loss of function; it is therefore necessary to identify the phase  Unlike diploid genomes, computational identification of common chromosomal variants in polyploid genomes using sequencing data has received little attention, except in the pioneering work of Aguiar & Istrail Diploid phasing methods focus on a given list of heterozygous variants that are guaranteed to contain a single reference allele, as well as an alternative allele . In contrast, in the polyploid phasing problem, there is no such guarantee of a single type of heterozygous SNP. Each heterozygous locus for a In this paper, we introduce a maximum-likelihood formulation of the polyploid full haplotype reconstruction problem and present a haplotype assembly algorithm, HapTree, which concurrently performs SNP-pair phasing and full haplotype assembly based on a probabilistic framework. We observe that, on simulated polyploid data, HapTree substantially improves the phasing capabilities and performance of any existing program. Because real polyploid data is hard to come by, we also evaluate HapTree on real human diploid data and find that, when compared to the more accurate trio-based data as the ground truth The HapTree pipeline is designed to perform phasing and full haplotype assembly of a single genome. The key component of HapTree is a relative likelihood function which measures the concordance between the aligned read data and a given haplotype phase under a probabilistic model that also accounts for possible sequencing errors. To identify a phasing solution of maximal likelihood, HapTree finds a collection of high-likelihood solutions for phases of the first maximum likelihood, haplotype based on the read data available. Theoretically, one could enumerate all possible haplotypes, compute the likelihood of each being the true haplotype (using formulas described below), and choose the most likely one; in most cases this approach is intractable as there are exponentially many possible haplotypes. HapTree therefore has a variety of ways of trimming down the solution set from all possible haplotypes to a much smaller set of more likely solutions, making the problem tractable. It does so by taking an inductive approach, generating a collection of likely phasing solutions for the first two SNPs in the genome, and then extending those to phasing solutions of the first three SNPs, and those to the first four SNPs, and so on. When extending any particular solution, HapTree chooses (based on computing likelihoods) how the alleles of the newly added SNP may be assigned to chromosomes; it includes only those assignments that are sufficiently likely. Additionally, if HapTree finds after extending all solutions to include the next SNP that there are too many likely solutions, it throws the worst (least likely) solutions away. Upon including all SNPs to be phased, HapTree randomly chooses a solution of maximum likelihood from amongst the solutions it has found.Broadly speaking, HapTree aims to discover the best, or http://groups.csail.mit.edu/cb/haptree/An implementation of our method, HapTree, is available for download at: Discussion.We describe below the problem of sequence-based polyploid haplotype assembly and provide basic technical notation that will be useful for describing our method. We assume for now that each SNP locus to be phased is bi-allelic . We further assume that for each SNP locus contains a SNP We denote the sequence of observed nucleotides of a fragment simply as a “read” (independent from single/paired-end reads and sub-reads of a strobe read structure). The set of all reads is denoted as Read Graph, Upon the set of SNP loci k-ploidy phase of vector set and we think of each vector as a row vector.A We can build a phase by selecting a permutation of the alleles present for each SNP locus For example, let The sample tetraploid genome featured above on the right has a genotype vector: We formulate the haplotype reconstruction problem as identifying the most likely phase(s) given the read data relative likelihood measure (RL) asAs As for parallel bias) and For real diploid data, there will never be duplicate vectors. To model Finally, we consider The goal of our haplotype reconstruction problem is to find the vector set(s) maximizing the product semi-reads of a SNP locus sub-reads of a subset To properly describe our method we must first define the Semi-reads. To form the set of semi-reads of {1∶1, 2∶1, 3∶1, 4∶1} {3∶1, 4∶1, 5∶0, 6∶0} {4∶0, 5∶1, 6∶1} {4∶0, 5∶1, 6∶1, 7∶0} {5∶0, 6∶0, 7∶1} {5∶1, 6∶1, 7∶0}The corresponding semi-reads for each SNP locus would be:Sub-reads. The sub-reads of Our main approach to solving the single individual polyploid haplotype assembly problem is by finding highly probable solutions on We first describe how to extend an existing a haplotype assembly This computation is similar to those done above in branching a collection of haplotypes Here we define For a collection of haplotypes Since The goal of PRUNE score vector error. In higher ploidy cases, at any SNP locus, it is possible for no chromosomes in a proposed phase to require a switch or anywhere from vector error of a proposed phase (with respect to the true phase) is defined by the minimum number of segments on all chromosomes for which a switch must occur; for the diploid case this score is exactly twice the switch error. One may also think of the vector error as the minimum number of segments a proposed phase and the true phase have in common, less the ploidy. Even for triploid genomes, the vector error is more discriminative than switch error. Consider the following example in When a true phase is available, there are a variety ways to evaluate how accurate any predicted phase is. A widely used measure in diploid phasing is switch error, which is calculated as the number of positions where the two chromosomes of a proposed phase must be switched in order to agree with the true phase. For polyploid phasing, we generalize switch error to In We assessed the effectiveness of our RL score by comparison to MEC score on simulated data. To do so, we simulated reads with error rate for all  . It is ancreases . In addincreases .The results demonstrate that the higher the ploidy, the better the relative likelihood (RL) score performs in comparison to MEC score for phasing a pair of SNPs . In factwww.brown.edu/Research/Istrail_Lab/hapcompass.php), to our knowledge the only other existing program that directly addresses polyploid haplotype assembly, over multiple depth coverage values and component sizes for triploid and tetraploid simulated genomes. We simulated triploid and tetraploid genomes with different block lengths , different coverages , SNP positions, and SNP densities. Throughout the simulations for both the triploid and tetraploid cases, our EXTEND module is run with threshold To evaluate the phasing capabilities of HapTree, we compare it with HapCompass For the triploid case, we observed that HapTree finds a perfect solution at a rate independent of the number of SNPs used in the simulation; in contrast, HapCompass declines in performance the larger the block size . While bFor tetraploid simulations, HapTree significantly outperforms HapCompass with block length of 10 SNP loci . For larWe varied the allele error rates ; and second, that HapTree's inference algorithm is more accurate than the approximation algorithm used by HapCompass.The primary reasons for HapTree's superior performance are, first, that HapTree's relative likelihood is more effective than HapCompass's MEC score . We fix the read-end length (read_len) to be 150. The fragment length (frag_len) is normally distributed with a mean of 550 and standard deviation of 30, but with min and max lengths of 500 and 600 respectively. The insert length (insert_len) is determined by the fragment length and read-end length, that is, insert_len  =  frag_len - 2read_len. Once we know the start and fragment length, we must choose from which chromosome to read; we do so uniformly from the To simulate a genome, we fix a ploidy For any genome, to generate a read set with We have presented a scalable algorithm, HapTree, for polyplotyping using NGS sequencing data and a new metric for measuring accuracy in this context. We have described an efficient algorithm to identify phases that maximize our RL metric, a relative likelihood function which measures the quality of a given phase according to the read dataset. We have demonstrated the advantages of such a likelihood formulation over the existing MEC score in phasing both polyploid and diploid genomes. HapTree not only substantially improves the efficiency and phasing accuracy of the state-of-the-art in polyploid phasing, but also produces more accurate phased haplotype blocks for diploid genomes, as compared to HapCUT, which is designed for diploid phasing by MEC score optimization. Our results indicate that HapTree can be used in phasing individual triploid and tetraploid genomes, as well as improving phasing of real diploid genomes. HapTree also easily scales to genomes of higher ploidy.Our algorithm can be easily extended to phase data with multi-allelic SNPs and with unknown genotype information as well. With unknown genotype information and multi-allelic SNPs, instead of A future application of HapTree is genotype imputation, which can predict missing genotype from phasing results. As polyploid sequencing data becomes available, HapTree will be useful for the investigation of the role of heterozygosity in plant, fish, and other species. Moreover, accurate individual phases of diploid haplotypes can be assembled without the use of pedigree or population information.A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5 [19].


We present GStream, a method that combines genome-wide SNP and CNV genotyping in the Illumina microarray platform with unprecedented accuracy. This new method outperforms previous well-established SNP genotyping software. More importantly, the CNV calling algorithm of GStream dramatically improves the results obtained by previous state-of-the-art methods and yields an accuracy that is close to that obtained by purely CNV-oriented technologies like Comparative Genomic Hybridization (CGH). We demonstrate the superior performance of GStream using microarray data generated from HapMap samples. Using the reference CNV calls generated by the 1000 Genomes Project (1KGP) and well-known studies on whole genome CNV characterization based either on CGH or genotyping microarray technologies, we show that GStream can increase the number of reliably detected variants up to 25% compared to previously developed methods. Furthermore, the increased genome coverage provided by GStream allows the discovery of CNVs in close linkage disequilibrium with SNPs, previously associated with disease risk in published Genome-Wide Association Studies (GWAS). These results could provide important insights into the biological mechanism underlying the detected disease risk association. With GStream, large-scale GWAS will not only benefit from the combined genotyping of SNPs and CNVs at an unprecedented accuracy, but will also take advantage of the computational efficiency of the method. Over the last years, Genome-Wide Association Studies (GWAS) using microarray-based technology have played an important role in the identification of common genetic variations and their relationship with disease susceptibility In addition to SNPs, Copy Number Variants (CNVs) have shown to play an important role in disease susceptibility The development of the two mentioned technologies (CGH arrays and genotyping microarrays) for high throughput CNV screening has prompted the inclusion of CNVs in GWAS studies In this study we present GStream, a method for SNP and CNV/CNP genotyping that is tailored to GWAS objectives. GStream integrates a substantially improved version of our previous CNV calling software CNstream In addition to presenting this new method and demonstrating its superior performance over reference datasets, we have also performed different relational analyses concerning previously known risk loci. Using GStream we have been able to identify, for the first time, several CNVs in strong linkage disequilibrium (LD) with risk-associated SNPs We first introduce the Illumina BeadChip microarrays and describe the algorithms for SNP and CNV genotyping. Next, we provide information about the datasets used in this study and the implemented metrics for evaluating SNP and CNV genotyping accuracy. Finally, we describe the methods used for the CNV association studies that we have run using the GWAS catalog Illumina BeadChips have been largely used in large-scale genome-wide association studies and are based on the Infinium assay technology Before identifying the clusters corresponding to the AA, AB and BB genotypes at each probe, raw intensity data of each probe must be normalized in order to equalize the overall sample intensity distribution at each channel . This stOnce the intensities from both A and B channels have been normalized, GStream proceeds to identify the clusters corresponding to each SNP genotype . Developing an accurate SNP genotyping method is crucial not only for SNP analysis itself, but also because GStream CNV genotyping method uses this information to improve the accuracy of the CNV call. GStream applies the following procedure to assign a SNP genotype to each sample at each marker:Channel A and channel B intensities from the analyzed marker are transformed to B allele frequencies (BAF) and absolute intensities Absolute intensities are used to detect samples without any allele (homozygous deletions) which are characterized by very low intensities at both channels. In order to compute the absolute intensity threshold between homozygous deletion samples and the other samples, the absolute intensities are sorted and then differences between each pair of consecutive intensities are computed. If high intensity differences are found within the expected threshold range , the zero-threshold is fixed to the corresponding intensities .The BAF probability density function (PDF) is estimated by computing the scaled histogram of all the sample BAF values. Peaks over this PDF corresponding to genotype clusters will be identified sequentially starting by the peak generated by the major allele frequency homozygote cluster. The algorithm establishes a minimum separation between peaks in order to assign them to different genotype clusters and it stops when three peaks have been found or when no more peaks are found. Once genotype peaks have been found, genotype limits are computed by finding the PDF minimum between each consecutive pair of centres . These lIf the number of genotype peaks identified is less than three, each genotype cluster is re-analyzed with a better resolution (i.e. increasing the number of histogram bins to estimate the BAF PDF) in order to identify sub-clusters corresponding to different genotypes. This procedure avoids common errors seen in others algorithms where, for example, genotypes of SNPs with highly discordant sensitivities at each channel are incorrectly assigned.Finally, a global genotyping quality score and an individual score for each sample genotype are computed . The gloBoth genotypes and quality control measurements for each sample are extracted by GStream to the output files. This information is also required by the CNV genotyping method, which is based both on the normalized channel intensities and the SNP genotype information. Further algorithm details are given in CNV identification and genotyping is one of the principal contributions of GStream to the current state-of-the-art microarray genotyping methodology, clearly outperforming previous approaches. Although this method has been based on our previous CNstream method GStream uses normalized intensities and SNP genotypes computed in the SNP genotyping stage to identify the presence of deletions and amplifications characterized by variable clustering patterns on the intensity data (i.e. high frequency CNVs) or by slight deviations from the diploid distribution (i.e. low frequency CNVs).One of the improvements incorporated in the algorithm is that each SNP genotype cluster is independently analyzed taking only into account the intensity channel that carries valuable information. This way, the CNV algorithm is divided in four parallel steps :Analysis of channel A intensities from the samples previously genotyped as AA homozygotes.Analysis of channel B intensities from the samples previously genotyped as BB homozygotes.Analysis of channel A intensities from the samples previously genotyped as heterozygotes (i.e. AB).Analysis of channel B intensities from the samples previously genotyped as heterozygotes (i.e. AB).As well as dividing the analysis in four independent steps, the algorithm is based on the following assumptions:Homozygous deletions  are previously detected during the SNP genotyping stage.Due to the technical limitations of genotyping microarrays, the intensity measurements show a saturation effect when amplifications are found. For this reason, intensity clustering patterns corresponding to amplifications are very rare and hard to detect unless they span multiple probes Samples categorized as homozygote samples (i.e. AA and BB) can correspond to hemizygous deletions (i.e. A and B) or amplifications (i.e. AA+ and BB+). Due to the saturation effect the algorithm does not stratify amplifications by the number of allele copies.Samples characterized as heterozygotes (i.e. AB) can have two or more copies . The total number of copies can be inferred by independently computing the number of copies of each allele and then adding the results for each sample.Below we describe the procedure for determining the CNV genotypes from the set of channel intensities of each one of the four analysis steps.For each SNP genotype, the algorithm starts identifying clusters over the channel intensities that carry the corresponding allele information . Due to the mentioned saturation effects, it is very uncommon to observe more than two intensity clusters in microarray data and, for this reason, only two models will be fitted to the intensity data: a one- and a two-component Gaussian mixture model (GMM). The first one will be fitted using the mean and the variance of all the intensities while the second one will be fitted using the Expectation-Maximization algorithm If the two-component model has been selected, a copy number category will be assigned to each one of the two components. As no prior knowledge is available to assign the two components either to a deletion pattern (i.e. CN = 1 and CN = 2) or to an amplification pattern (i.e. CN = 2 and CN = 3), a disambiguation method is necessary. GStream bases the component labelling both on the relative weight of each component  and on the presence of homozygous deletions . When thi a score S between 0 and 3 depending on its copy number posterior probabilities (Outlier identification is intended to capture low frequency CNVs that are not captured by a two-component GMM and is based on identifying samples showing high or low deviations from the intensity distributions defined by the selected model. CNV scoring assigns to each sample bilities . At the bilities . Additiohttp://www.ncbi.nlm.nih.gov/geo/) In order to evaluate and compare the performance of GStream SNP and CNV genotyping methods we have used raw Illumina microarray data from HapMap samples available at the Gene Expression Omnibus . For this study, we downloaded the genotypes corresponding to the samples having available microarray data and used them as the golden standard. SNPs used for performance evaluation were chosen in order to fulfil three criteria: (i) to have available Build37 mapping information, (ii) to be present both in the analyzed microarray platform and in the golden standard HapMap dataset, and (iii) to have concordant reference alleles both in the microarray and in the golden standard annotations  GenoSNP Copy number evaluation was performed at two levels: Evaluation of the GStream ability to detect structural variation obtained from the 1000 Genomes Project ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release). From all the variants included in the 1KGP release we chose those that corresponded to structural variations (i.e. CNVs) and we filtered out variants with a MAF lower than 2% within all the populations. The resulting set included 2,531 structural variants (SV) with their respective calls over N = 353 unrelated HapMap samples. From these 2,531 loci, 1,956 are covered by HumanOmni1-Quad markers and GStream calls were available for 149 out of the 353 1KGP samples, which jointly formed the final evaluation set.In order to test the ability of GStream to detect copy number variation, we have used the HumanOmni1-Quad GStream calls and a golden standard dataset from a public release of the 1KGP. HumanOmni1-Quad platform was chosen due to its highest coverage and resolution which allowed an evaluation over a major number of loci. The golden standard dataset consisted of the last variant call files that have been released by the 1KGP  and without (i.e. Human1M) a specific design to cover CNV.The genome-wide association study over CNV markers was performed at the population level aiming to identify CNVs significantly associated to specific populations and comparing the association statistics with those obtained from golden standard datasets.The CNV algorithms used are described below:PennCNV QuantiSNP is also one of the well-known methods for CNV analysis over Illumina microarrays. It is based on an Objective Bayes Hidden-Markov Model that is used to set certain hyperparameters in the HMM priors  in any population test  were performed using the calls from the three golden standard datasets. Loci that either were not covered by the microarray platform or did not obtain significant associations  In order to identify putative causal CNVs we have analyzed the LD patterns between all the trait-associated SNPs reported by the catalog of published genome-wide association studies .Correlation coefficient rDistance between the CNV marker and the nearest gene not greater than 100 kb or CNV marker spanning binding transcription factor regions as defined by the Transcription Factor ChIP-seq track on the UCSC browser From the 7,571 trait-associated SNPs, 382 were paired with one or more CNV markers fulfilling these conditions. A final set of 333 SNP-CNV pairs was obtained after filtering out repeated associations of SNPs with the same trait by different GWAS studies.http://www.omim.org) In this second approach, we examined the CNV variants called by GStream spanning genes known to be involved in disease based on the OMIM database  only those located less than 15 kb away from an OMIM gene and with at least two more CNV markers covering this gene were selected .2>0.7) spanning the same OMIM gene. After applying this filter we obtained a final set of 212 CNV loci spanning OMIM genes.We defined CNV loci as sets of three or more nearer CNV markers (i.e. distance between them not greater than 5 kb) in high LD  were compared with their respective GStream calls in order to measure the ability to detect this type of variation using GStream on microarray data. For each SV , we computed the CNV genotyping accuracy by finding the maximum LD measurement between its golden standard calls and the GStream calls over the HumanOmni1-Quad markers covering the region. The results showed a high correlation between GStream and 1KGP calls: 75.7% of the SVs were captured by GStream with an r2>0.8, 18.3% with an r2<0.8 and only 6.0% were not detected by GStream , obtaining CNV calls with an rIn order to evaluate the power to detect CNV associations we have performed a pairwise association study between three HapMap populations using golden standard data from three reference studies 10P-value ratio between the association values obtained by the calls of each method and those obtained from the golden standard calls. Under this metric, ratios near 1 represent a good performance for the method since the obtained association P-value is very similar to the golden standard. A performance summary statistic was computed as the percentage of −log10P-value ratios giving values between 0.9 and 1.1 over all the associated CNV loci.The main metric used to test CNV methods was the −logFirstly, the obtained results showed a high performance decrease in the detection of CNV associations when comparing HumanOmni1-Quad results with Human1M-Duo . This loP-value obtained by the golden standard calls  as the P-value ranges decreased (i.e. greater evidence of association). Interquartile ranges obtained by the other methods were at least twice the obtained by GStream and increased as the P-value ranges decreased, losing performance when comparing higher associated loci. Poorer ratio medians were also obtained when using the other methods.HumanOmni1-Quad results show that GStream is able to precisely capture an average of 23.6% more associated loci than the other methods  and thatrd calls . GStream10P-value ratio distributions by their respective golden standard association P-values, ratio medians were only maintained near one when using GStream  has been associated to Rheumatoid Arthritis (RA) in a previous GWAS  using a Japanese cohort (1247 RA cases and 1486 controls) PADI4 intron (length  = 800 bp) in high LD with rs2240335 both on CEU (r2 = 0.82) and CHB+JPT (r2 = 0.95) HapMap populations.A synonymous exonic SNP rs2240335 (TMEM18 gene) has been associated to body mass index in previous GWAS  2 = 1 both on CEU and CHB+JPT populations) with this SNP.An intergenic SNP rs2867125  Levels of glycated hemoglobin have been associated to the MAEA gene has been associated  with type 2 diabetes Intronic SNP rs6815464 on HCG9 gene) has been previously associated with Vitiligo  HCG9 gene found in high LD with this SNP.5′UTR SNP rs6904029 (HLA-DPA1 gene) has been recently associated to chronic Hepatitis B  on a Japanese cohort HLA-DPA1 exons has been identified to be highly correlated with this SNP both on the CHB+JPT and the YRI populations.Intronic SNP rs3077 (MLIP gene) has been identified spanning 5 kb MLIP intron. This SNP has been associated with liver enzyme levels  A deletion highly correlated with intronic SNP rs9296736 (ZAN gene) has been associated to red blood cell count  on a GWAS exploring erythrocyte phenotypes 2 = 0.87) and the CHB+JPT (r2 = 0.91) populations.Intronic SNP rs2075671 (ZNF490 gene), modestly associated with bipolar disorder  ZNF490 intron.Finally, the 3′UTR SNP rs7247513  deletion has been identified by GStream within eight microarray probes spanning two gene exons and two gene introns . Since mutations within this gene have been previously associated to uric acid concentration SLC2A9 gene) is predominantly expressed in the kidney and expression association signals reported by Doring et al. DAZL (OMIM 601486) deletion was identified over 4 microarray probes spanning 5.8 kb . This deletion could affect multiple gene exons resulting in a drastic functional modification. Previous studies MBL2 gene. MBL2 mutations and the consequent Mannose Binding Lectin deficit have been previously associated with cystic fibrosis MBL2 gene expression and subsequently involve a Mannose Binding Lectin deficit whose association has also been demonstrated.Furthermore, as CNV are known to span from hundreds of bases to multiple kilobases, it is interesting to analyze not only those that correlate previously associated SNPs, but also those that overlap coding sequences of genes that have been previously associated to human disease (i.e. OMIM genes). The results from this analysis include several known CNV associated loci, as those spanning In a time of rapidly evolving technologies and where Next-Generation Sequencing is becoming available for the study of common diseases, microarray-based technologies are still a commonly used strategy to identify the genetic basis of human traits. First, they allow the analysis of large sample collections at an affordable cost and, second, they have anincreasing global genome coverage, expanding their analysis scope to rarer variants. Therefore, accurate genotyping methods are basic to discover new associated loci that can be then further studied in more detail using Next-Generation Sequencing. The tool that we present in this study, GStream, provides an unprecedented accuracy when analyzing GWAS data from previous and recent Illumina microarray platforms. Furthermore, our software tool implementation allows large-scale GWAS projects to be analyzed in a very short time, providing both SNP and CNV in a single analysis. With these results, we encourage researchers conducting GWAS on these genotyping platforms to use GStream in order to leverage the power of their SNP and CNV loci association analyses.Figure S1Example of raw intensity normalization. The intensity distribution of candidate homozygote samples (i.e. AA) across its specific allele channel (i.e. channel A) is plotted together with its centroid scaling value as computed by Peiffer et al.(PDF)Click here for additional data file.Figure S2Example of how zero-threshold is computed. (A) BAF and absolute intensities of an example marker where some homozygous deletion samples with low intensity values can be observed. (B) Absolute intensities are sorted and differences between consecutive sorted intensities normalized to one. The observed peak over these differences points to the intensity value that will be set as threshold.(PDF)Click here for additional data file.Figure S3CNV labelling and scoring. (A). Category disambiguation when the two-component model is selected. The leftmost graph shows a case where the higher intensity component (blue) is more frequent and it is assigned to the diploid state while the lower intensity component (red) is assigned to the deletion state. This assignment is due to the fact that high frequency amplifications are very uncommon and undetectable with this technology. The centre graph shows a case where the higher intensity component is less frequent and homozygous deletion samples have been found (magenta). In this case, the higher component (blue) is assigned to the diploid state and the lower component (red) to the deletion state fulfilling the expected Hardy-Weinberg equilibrium frequencies. Finally, the rightmost graph shows the last case where the higher intensity component is less frequent and no homozygous deletion samples have been found. In this case the higher component is assigned to the amplification state and the lower component to the diploid state. (B) Posterior probability of each copy number depending on the score assigned by GStream: From 0 to 0.5 samples can be categorized as homozygous deletion, from 0.5 to 1.5 as hemizygous deletion, from 1.5 to 2.5 as diploid and from 2.5 to 3 as amplification.(PDF)Click here for additional data file.Figure S4Genotyping performance. (A) Genotyping performance depending on the drop rate, where calls dropped from the accuracy analysis were selected according to the genotype call quality score. (B) Genotyping performance depending on the SNP minor allele frequency. (C) Genotyping accuracy of GStream at different sample sizes  compared to the accuracies obtained by GenoSNP and M3 with the highest sample size (N = 225).(PDF)Click here for additional data file.Figure S5Microarray coverage density. Coverage density of each microarray platform over the CNV regions defined by each reference study. There are major differences between the first Infinum HD platforms (Human610-Quad and Human1M-Duo) and their succesors including specific CNV coverage (Human660W-Quad and HumanOmni1-Quad). Both Human610-Quad and Human1M-Duo have a mean number of ∼10 markers covering CNV regions, while Human660W was designed with a highest coverage (∼20 markers/region) for almost 50% of the regions. Finally, HumanOmni1-Quad increased the global coverage to ∼20 markers/region.(PDF)Click here for additional data file.Figure S6Missed associations. Percentage of associations  that were not captured by the different methods tested .(PDF)Click here for additional data file.Figure S7HumanOmni1-Quad P-value distributions. Distributions of the P-value association ratios depending on the golden standard dataset used for evaluation (i.e. represented by different colours) and on the P-value range obtained over the golden standard calls .(PDF)Click here for additional data file.Figure S81M-Duo P-value distributions. Distributions of the P-value association ratios depending on the golden standard dataset used for evaluation (i.e. represented by different colours) and on the P-value range obtained over the golden standard calls .(PDF)Click here for additional data file.Figure S9Previously reported CNV associations detected by LD analysis between GStream CNV genotypes and trait-associated SNPs. (A) LCE gene cluster deletion associated with Psoriasis risk. (B) NEGR1 deletion associated with body mass index. (C) IRGM deletion associated with Chrohn's disease. (D) ARMS2 deletion associated with age-related macular degeneration. (E) GPRC5B upstream deletion associated with body mass index.(PDF)Click here for additional data file.Figure S10Interesting CNV associations detected by LD analysis between GStream CNV genotypes and trait-associated SNPs. (A) PADI4 gene deletion associated with Rheumatoid Arthritis. (B) TMEM18 downstream deletion associated with body mass index. (C) 3′-deletion of gene ABCB11 associated with glycated hemoglobin levels. (D) MAEA gene intron deletion associated with type 2 diabetes. (E) HCG9 deletion associated with Vitiligo. (F) HLA-DPA1 deletion associated with Hepatitis B. (G) MLIP intron deletion associated with liver enzyme levels. (H) ZAN gene deletion associated with red blood cell count. (I) ZNF490 intron deletion associated with bipolar disorder.(PDF)Click here for additional data file.Figure S11GStream detected CNP loci spanning disease-related genes (OMIM) where CNVs have been previously associated with disease. (A) CNP spanning CFHR1 and CFHR3 previously associated to age-related macular degeneration. (B) Deletion of GHR exon 3 that has been previously associated with increased responsiveness to growth hormone and Laron dwarfism. (C) Detected SMN gene deletion previously associated with spinal muscular atrophy. (D) PKHD1 deletion associated with polycystic kidney. (E) CCL3L1/CCL3L3 deletion previously associated with susceptibility to HIV/AIDS.(PDF)Click here for additional data file.Figure S12GStream calls across consecutive markers spanning the same CNV loci. These 6 microarray probes cover the same CNV loci but show very different CNV intensity patterns. GStream is completely adapted to these types of variations and its calling procedure is able to obtain very concordant calls when analyzing probes spanning the same CNV.(PDF)Click here for additional data file.Table S1CNV markers in high LD with trait-associated SNPs reported in the GWAS catalog.(XLSX)Click here for additional data file.Table S2Set of 149 CNV consistent loci spanning OMIM genes.(XLSX)Click here for additional data file.Text S1GStream algorithm details.(PDF)Click here for additional data file.