DATABASE Open Access

solQTL a tool for QTL analysis visualization andlinking to genomes at SGN databaseIsaak Y Tecle1 Naama Menda1 Robert M Buels1 Esther van der Knaap2 Lukas A Mueller1

Abstract

Background A common approach to understanding the genetic basis of complex traits is through identificationof associated quantitative trait loci (QTL) Fine mapping QTLs requires several generations of backcrosses andanalysis of large populations which is time-consuming and costly effort Furthermore as entire genomes are beingsequenced and an increasing amount of genetic and expression data are being generated a challenge remainslinking phenotypic variation to the underlying genomic variation To identify candidate genes and understand themolecular basis underlying the phenotypic variation of traits bioinformatic approaches are needed to exploitinformation such as genetic map expression and whole genome sequence data of organisms in biologicaldatabases

Description The Sol Genomics Network (SGN httpsolgenomicsnet) is a primary repository for phenotypicgenetic genomic expression and metabolic data for the Solanaceae family and other related Asterids species andhouses a variety of bioinformatics tools SGN has implemented a new approach to QTL data organization storageanalysis and cross-links with other relevant data in internal and external databases The new QTL module solQTLhttpsolgenomicsnetqtl employs a user-friendly web interface for uploading raw phenotype and genotype datato the database RQTL mapping software for on-the-fly QTL analysis and algorithms for online visualization andcross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and GenomeBrowser Here we describe the development of the solQTL module and demonstrate its application

Conclusions solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN performQTL analysis and dynamically cross-link to relevant genetic expression and genome annotations Exploration andsynthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypicvariation and markers more closely linked to QTLs solQTL is freely available on SGN and can be used in private orpublic mode

BackgroundMany economically important traits are quantitativelyinherited influenced by the environment and controlledby many genes of small and large effect [1-4] A power-ful approach to understanding the genetic basis of suchcomplex traits is through identification of quantitativetrait loci (QTL) associated with the trait [156] OnceQTLs are identified generally confirmation and finemapping of the QTLs is required followed by map-based cloning or a candidate gene approach that inturn necessitates further functional analysis of the

candidate genes through expression profiling geneticcomplementation andor insertional mutagenesis[2378] Fine mapping of QTL requires mendelizationof the locus [9] and large populations This effectivelymeans several generations of backcrosses to createnearly isogenic lines (NILs) which is a costly and timeconsuming effort [1011] Furthermore dissecting thequantitative trait nucleotide (QTN) variation and under-standing the molecular and physiological basis underly-ing the phenotypic variation remains a great challenge[312] A step forward in identifying candidate genes anddissecting the QTN(s) would be to develop tools to har-ness the genetic resources as well as expression andwhole genome sequence data of organisms in biologicaldatabases through bioinformatics approaches [311-13]

Correspondence lam87cornelledu1Boyce Thompson Institute for Plant Research Tower Rd Ithaca NY 14853USAFull list of author information is available at the end of the article

Tecle et al BMC Bioinformatics 2010 11525httpwwwbiomedcentralcom1471-210511525

copy 2010 Tecle et al licensee BioMed Central Ltd This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (httpcreativecommonsorglicensesby20) which permits unrestricted use distribution and reproduction inany medium provided the original work is properly cited

The Sol Genomics Network (SGN httpsolgenomicsnet [14]) is a repository for phenotypic genetic geno-mic expression and metabolic data for the Solanaceaeand other related Asterids families and houses a varietyof bioinformatic tools SGN has added a new QTL mod-ule solQTL httpsolgenomicsnetqtl to its repertoireof bioinformatics resources The solQTL module pre-sents a new approach to QTL data organization storageand analysis Additionally the module allows dynamiccross-linking with other relevant data including to theannotated genome sequence expression data andgenetic maps found in internal and external databasesThe solQTL module employs a user-friendly web inter-face for uploading raw phenotype and genotype data RQTL mapping software (httpwwwrqtlorg[15]) anadd-on package to the R statistical software for on-the-fly QTL analysis and algorithms to cross-referenceQTLs to relevant datasetsIn primary mapping populations QTL regions may

span up to 30 cM [12] containing thousands of genes[316] However some studies have revealed that QTLsand genes of quantitative traits can be identified fromwithin 0 to 19 cM for major QTLs which explain gt25of the phenotypic variation (eg fw22 [17] ovate [18]in tomato) and 0 to 3 cM for small QTLs (lt25) (Hd1Hd2 and Hd3 in rice [19]) from the peak marker [10]Price (2006) in his review article implied that searchesfor candidate genes of small QTLs can be done within 1- 2 cM on either side of the mean QTL position in aprimary QTL population especially when fine mappingis problematic but datasets from multiple studies can beused However it is known that genes or QTNs thataffect the QTL may be tens of kilobases (kbs) awayfrom the mean QTL position or in genes with unrelatedor regulatory functions [131220] Nonetheless explora-tion and synthesis of genetic genomic and expressiondata on corresponding regions around the peak QTLand within region delimited by flanking markers is use-ful and can yield data leading to identification of candi-date genes for the QTL of interestSGN as a primary data and bioinformatics center for

the Solanaceae has the database structure informationand comparative analysis tools at hand to facilitate thecross-referencing of QTLs with other internal and exter-nal datasets The SGN database houses the tomato gen-ome sequence Solanum lycopersicum cv Heinz 1706including annotations provided by the InternationalTomato Genome Annotation Group (ITAG) [21] SGNalso hosts whole genome sequences of S tuberosum cvDM1-3 516R44 (currently BAC-based sequencing only)and S pimpinellifolium cv LA1589 With the on-goingefforts to sequence 100 more Solanaceae species (SOL-100 project [22]) and the SNP database under develop-ment at SGN (unpublished) we envision that anchoring

QTLs to the genomes will open additional windows infacilitating the elucidation of the molecular basis ofcomplex traits and may lead in identification of QTLmarkers for efficient marker-assisted breedingCurrently major plant model organism databases such

as Gramene (httpwwwgrameneorg[2324] MaizeGDB(httpwwwmaizegdborg[25] and animal databasessuch as Animal QTLdb (httpwwwanimalgenomeorg[2627]) rely on in-house curators or the research com-munity to curate QTL mapping outputs from publishedliterature A problem with QTL data is that historicallya large part of the original data has been lost as the rawdata (phenotypic and genotypic scores of the popula-tions) were not recorded in the publications It is there-fore impossible to evaluate many of these describedQTLs independently and retroactively The solQTLmodule is unique in that (1) it uses a user-friendlyguided step-wise web interface to allow researchers toupload their raw phenotype and genotype data (2)researchers can choose the statistical parameters for theon-the-fly real-time QTL mapping (3) users can opt tomake their raw and analyzed data either private or pub-lic and (4) as growing number of journals are requiringauthors to deposit their data in public databases prior topublication of their work solQTL could facilitate thisfor the Solanaceae research community if it was made arequirement for QTL data Importantly this systemstores all the primary data for the identification of aQTL such that the data can be re-evaluated in thefutureHere we describe the development of the solQTL tool

and demonstrate its application

Construction and contentData submissionThe researchers upload their raw phenotype and geno-type data into the solQTL database and select the sta-tistical parameters for the QTL mapping andvisualization A step-by-step user-friendly web inter-face (Additional file 1) allows users to upload theirQTL population details such as name description andparents trait data (description definition and units ofmeasurement) phenotypic and genotypic data sepa-rately in a tab-delimited files To allow cross-referen-cing QTL markers to relevant datasets in SGNmarkers used in the mapping population must exist inthe SGN marker database Users are prompted to setthe statistical parameters they would like to applyfor the QTL analysis The statistical options availableat this stage are described in the lsquoQTL Statistical Ana-lysisrsquo section below Users have the option to save theirdata in the database for either public or private useThe public option allows all users to see the datasetwhereas the private option only lets the data owner

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and SGN curators access the data For private data thepopulation name description and contact informationof the data owner are publicly accessibleBuilt-in validation algorithms check for mandatory

fields that need to be filled in and verify data formattingfor the statistical parameters the consistency of theselection of the parameters is also validated For exam-ple the combination of QTL mapping method and QTLgenotype probability estimation method selected ischecked Appropriate informative messages are displayedto the user when a required data field is empty or filledwith incompatible information To assist users in theuse of the tool a link to the guideline document is avail-able at each step in the uploading and analysis processhttpsolgenomicsnetqtlguidepl

Database contentCurrently the solQTL database holds raw QTL data forthree F2 intercross tomato QTL populations [28-30]submitted by Esther van der Knaap of Ohio State Uni-versity For each population 18 - 46 fruit morphologyrelated traits are evaluated for QTLs

QTL statistical analysisThe solQTL module uses RQTL for the QTL mappinganalysis Among the QTL mapping functions presentlyimplemented using the RQTL in the solQTL toolinclude a one-dimension single-QTL scan model alongwith the single marker analysis and interval mappingmethods With the single-QTL model the genome isscanned at a self-determined genome size one at a timefor the presence of a QTL [31] Genome scan sizes of 0(marker regression) 1 25 5 or 10 cM can be selectedUsers can choose between QTL mapping methods suchas standard interval mapping (Maximum Likelihoodwith EM algorithm [3233]) Haley-Knott regression [34]and Multiple Imputation [35] For the Multiple Imputa-tion QTL mapping method and the QTL genotypeprobability estimation using Simulation one can selecteither 5 10 15 or 20 imputations to run The underly-ing algorithm for calculating the QTL genotype prob-abilities and for dealing with missing or partially missinggenotype data is hidden Markov model (HMM) technol-ogy [36] A permutation test [37] for determining theLOD threshold significance level is also implementedUsers have the option to run 100 or 1000 permutationtests or none at all QTL confidence interval (flankingmarkers) is determined based on a 95 Bayesian Cred-ible Interval method with the interval expanded to thenearest markers [3538] Based on the highest LODscore in the linkage group a peak marker for a QTL isidentified using RQTLrsquos lsquofindmarkerrsquo method When amarker is not located at the peak QTL the nearest pos-sible marker is identified [15]

Currently solQTL uses genetic map data from a prioricomputation and thus users need to provide linkagemap data with the genotype data file as indicated indocumentation provided with the tool httpsolge-nomicsnetqtlguidepl The linkage map plotting andvisualization however is done using Perl modules devel-oped both in-house and from the open sources commu-nity via the Comprehensive Perl Archive Network(CPAN httpcpanorg)At this stage the solQTL is available only for back-

cross and F2 intercross diploid populations for traitswith continuous phenotypic variation As the primaryfocus of solQTL is to link the predicted QTLs to anno-tated whole genome sequences dense reference geneticmaps and other relevant expression data at SGN usingtools such as the SGN Comparative Map Viewer [39]and genome browser (GBrowse) the tool is designed foruse at SGN and hence applicable and most useful fororganisms that are supported at SGN

Software developmentSGN currently uses PostgreSQL httpwwwpostgresqlorg version 83 for the back-end database The websiteis implemented in Perl httpwwwperlorg version5100 In-house and externally developed Perl modulesare used for organizing formatting and communicatingdata to R for the statistical analysis the visualization ofthe QTL mapping output on the browser and thecross-linking of QTLs to relevant datasets on SGN A108-56 version of the RQTL (httpwwwrqtlorg [15])is implemented on a 271 version of the R statisticalsoftware (httpwwwr-projectorg [4041])

UtilityQTL mapping and visualizationOnce a user successfully uploads the prerequisite data inthe supported format and sets the statistical parametersthe respective QTL population detail page is displayed(Additional file 2) For the purpose of demonstrating theQTL analysis output visualization and cross-referencingto relevant genetic and genomic datasets with solQTLwe use raw data from the F2 population of Solanumlycopersicum cv Howard German x S pimpinellifoliumaccession LA1589 httpsolgenomicsnetphenomepopulationplpopulation_id=12 and specifically the traitldquoFruit Areardquo [29] The statistical parameters used for theparticular QTL mapping output are in the correspond-ing legend However it should be noted that validationof the predicted QTL for the trait is beyond the scopeof this work On the population webpage the name anddescription of the population is displayed along with alist of the traits phenotyped with the correspondingdescriptive statistics Also on the same page are anypublications that document the QTL data if available

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and links to download the public raw phenotype andgenotypic data of the QTL populationThe QTL analysis is performed dynamically on a trait-

by-trait basis when the user visits the trait of interest onthe QTL populationrsquos webpage On the QTL popula-tionrsquos page under the QTL(s) heading clicking thegraph icon corresponding to the trait of interest initiatesthe QTL mapping analysis The analysis output is dis-played on a new trait page where one can view its QTLanalysis output (Figure 1) and frequency distribution ofthe phenotype data for the trait QTL mapping locationsfrom the output of the QTL analysis are viewable forthe whole genome on a per-linkage group basis Thedetails of the analysis are displayed in a legend includinggenome-wide LOD threshold significance level if theuser opted for permutation test and probability levelsize of genome scan and method used for the calcula-tion of the QTL genotype probabilities QTL mappingmethod and QTL model used Clicking a chromosomewith a QTL after visually determining the chromosomewith a significant QTL relative to the LOD thresholdvalue in the legend takes the user to a QTL detail page(Figure 2)

Linking QTLs to the genome and genetic mapsA crucial feature of the solQTL tool is that it links theputative QTL regions to relevant genetic and genomic

data in the SGN database In this subsection we describethe options for linking the predicted QTLs with otherdatasets both at SGN and at external databases usingbioinformatic tools such as the SGN Comparative Map[39] Viewer and GBrowse [42] toolsOn the QTL detail page (Figure 2) for each QTL the

peak and flanking markers with their correspondingpositions on the respective genetic map for the popula-tion are shown The 95 QTL confidence interval islinked to the corresponding linkage group page whereone can view all the markers in the linkage group withthe QTL segment zoomed out On the linkage groupwebpage users can perform comparative map analysisbetween the QTL region of interest and physical andgenetic maps from several Solanaceae species in thedatabase using the SGN Comparative Map Viewer aslong as the QTL flanking markers are mapped to theother maps Marker-dense genetic maps available inSGN for comparative analysis include the S lycopersi-cum LA925 x S pennellii LA716 F22000 map [43]which contains more than 2500 Restriction FragmentLength Polymorphism (RFLP) Cleaved Amplified Poly-morphic Sequences (CAPS) and Conserved OrthologSet (COS) markers (Figure 3) The SGN ComparativeMap Viewer is described in [39] and additional helpand documentation for this tool can be found athttpsolgenomicsnethelpcviewpl

Figure 1 Genome-wide QTL mapping output An example of genome-wide QTL mapping output from solQTL for a trait called lsquoFruit Arearsquo(see supplemental figure 2) and legend detailing the analysis parameters (Source httpsolgenomicsnetphenomepopulation_indlsplpopulation_id=12ampcvterm_id=39945) Clicking a linkage group with a significant QTL (eg Chr 3) leads to the QTL detail page (see Figure 2)

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From the QTL detail page the markers are linked togenomic regions visible in SGNrsquos installation ofGBrowse provided that there is a match where one canview the corresponding genomic sequences and annota-tions such as gene mRNA protein and CDS modelsESTs and unigene alignments and experimentallycharacterized genes from tomato or other organisms(Figure 4) Documentation using GBowse can be foundin httpsolgenomicsnetgbrowsestaticgeneral_helphtml and [4244]The peak and flanking markers are also cross-referenced

to their respective SGN detail pages where one can find

among other data the primer sequences map positions onany other genetic maps and matching BAC clones ForCOS markers one can also find matching ArabidopsisBAC clones and protein hits from NCBI httpwwwncbinlmnihgov

solQTL tool and data accessibilitysolQTL is accessible at httpsolgenomicsnetqtl orfrom the lsquosearchrsquo and lsquotoolsrsquo pull down menus on theSGN toolbar located at the top of every SGN pageFrom the solQTL database frontpage users interested insubmitting new QTL data and in performing QTL

Figure 2 A QTL detail page An example QTL detail page eg for putative lsquoFruit Arearsquo QTL (see figure 1) showing the analysis details links todetail pages of QTL flanking and peak markers the QTLrsquos linkage group page and Comparative Map Viewer page (see Figure 3) and genomepositions of the markers (see Figure 4)

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mapping and linking to relevant datasets in SGN canfollow the self-guiding lsquoSubmit new QTL datarsquo linkFrom the same solQTL front page users intending toquery the database for QTLs for traits of interest canalso (1) browse the alphabetically indexed list of traits(2) use the search box and query the database usingrelevant keywords or (3) browse through the list ofQTL populations for traits of interest In the cases ofoption 1 and 2 the user is directed to the traitrsquos detailpage where among other data QTL populations assayedfor the trait are listed Following the link to the popula-tion of interest initiates solQTLrsquos on-the-fly QTL map-ping and generates the QTL detail page for the trait ofinterest in that QTL population In the case of option 3users can select a trait of their interest evaluated in anyof the populations and run the QTL mapping analysisThe population genetic map can be accessed either by

following a genetic map link at the population or QTLdetail pages or by browsing for it from the lsquomapsrsquo pulldown menu on any SGN page

DiscussionBeyond QTL comparative analysis and identification ofcandidate genesLinking QTLs to corresponding regions in genomes andtheir annotations is pursued by several biological data-bases [2427] Most databases store pre-computed QTL

coordinates extracted from published literature and aremostly populated manually by in-house curators In con-trast SGN has implemented a new approach that dyna-mically links phenotypes to genomes by implementingthe RQTL mapping software [15] and thus allowingresearchers to upload their raw phenotype and genotypeQTL data to the SGN database and set the statisticalparameters for the analysis using a user-friendly webinterface To our knowledge none of these features areavailable at other databasesRQTL is a robust and widely used (with about 450

citations as of this publication GoogleScholar) opensource QTL mapping software We have implementedits analytical functions such as single marker analysisand interval mapping methods (maximum likelihood[3233] Haley-Knott regression [34] and Multiple Impu-tation [35]) for diploid F2 intercross and backcrosspopulations solQTL is publicly and freely available andSGN maintains and upgrades the software so that thelatest versions are available to run on SGN web serversIn this sense from the users perspective the data arestored and analyzed in the lsquocloudrsquo as such internet-based services are sometimes referred to although inthis case the lsquocloudrsquo is currently limited to the SGNcluster infrastructure The user-friendly web interfacefor uploading raw data and setting the statistical para-meters combined with the on the-fly QTL analysis by

Figure 3 Comparative genetic analysis of a predicted QTL with other genetic maps Panel (A) shows an example linkage map with azoomed out QTL segment generated after clicking the linkage group link on the detail page of the QTL of interest (see Figure 2) (B) shows acomparison of the QTL shown on (A) to the Tomato-EXPEN 2000 (F22000) consensus genetic map The zoomed out region generated aftermanually feeding the Comparative Map Viewer with the QTL marker positions on the F22000 map instead of their genetic positions in theTomato-EXIMP 2008 map displays greater number of markers from the reference map within the shared genetic segment between the twomaps

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users in private or public mode further reinforces theldquocommunity annotationrdquo philosophy SGN has pioneeredfor its locus and phenotype databases [45]SGN is a clade-oriented database with a computa-

tional infrastructure and capacity to store and makeaccessible data in a comparative manner for the Sola-naceae family and related Asterids It is a primary repo-sitory for genetic phenotype genome and expressiondata for the Solanaceae family [14] These datasets aredeeply and extensively cross-referenced As a result pro-vided that the QTL flanking andor peak markers existwith their sequences in the SGN marker database thesolQTL tool links dynamically predicted QTLs to corre-sponding relevant datasets and builds upon the con-stantly growing networking of emerging datasets in SGNand external databasesPredicted QTLs are seamlessly cross-linked to the

SGN Comparative Map Viewer [39] and SGNrsquos instanceof GBrowse [42] The comparative map viewer enablescomparative genetic analysis between QTLs of interestand corresponding regions in genetic and physical mapsof the same or different Solanaceae species and Arabi-dopsis Corresponding regions in other maps for exam-ple the tomato F22000 map [43] with more dense and

informative markers such as COS help identify moretightly linked genetic markers and possibly matchinggenomic clones or regions QTLs are currently linked tothe GBrowse on a marker-by-marker basis TheGBrowse is also searchable using other genetic featuressuch as unigenes which might enable investigators toinfer some missing links from genetic markers to thegenome We anticipate in the near future further pro-gress in the assembly of the genomes SGN hosts andsearch features under development will allow searchingfor genomic regions matching an entire QTL region Atthis stage once a genome region matches a flanking orpeak QTL marker one can zoom inout (bi-direction-ally) or scroll the genomic segment one-directionallyto explore genomic annotations such as gene mRNACDS and protein models unigenes ESTs and experi-mentally characterized loci Currently SGNrsquos GBrowsedatabase houses the Solanum lycopersicum cv Heinz1706 genome sequence and annotations by ITAG [21]More genomes are in the pipeline to be sequenced andannotated andor added to SGNrsquos GBrowse and cross-referenced to solQTLThe solQTL tool allows users to use non-controlled

vocabularies trait names However users are highly

Figure 4 Linking predicted QTLs to an annotated genome An example of a 20 kbp segment on the tomato genome matching a QTLmarker (eg TO581 peak marker for a putative lsquoFruit Arearsquo QTL generated after following a link from the QTL detail page (see Figure 2)) Withinthe 20 kbp of the marker are annotations including gene and protein models with their GO annotations ESTs cDNAs and markers

Tecle et al BMC Bioinformatics 2010 11525httpwwwbiomedcentralcom1471-210511525

Page 7 of 9

encouraged to use controlled vocabularies for their traitnames to facilitate cross-study comparisons for QTLs ofthe same traits and to fully exploit expression and phe-notype data annotated with controlled vocabulary suchas the Gene Ontology (GO) [46] and Plant Ontology(PO) [47] terms SGN in collaboration with the Solana-ceae breeders continues to develop and maintain a con-trolled vocabulary for Solanaceae traits calledSolanaceae Phenotype Ontology accessible at httpsol-genomicsnettoolsontoLooking toward a longer time horizon the SOL-100

project has an ambitious plan to sequence and makepublicly available genomes of 100 or more Solanaceaespecies [22] A single nucleotide polymorphism (SNP)database and viewer is also under development at SGN(unpublished) As these projects bear fruit solQTL willbe a window linking phenotypes to this rich genomicdata and analytical tools We envision this integrationand analysis of a wide variety of data in SGN and cross-referenced external databases will have an importantrole to play in identification of candidate genes forfurther functional analysis and gene cloning identifica-tion of more closely linked QTL markers for marker-assisted breeding and understanding the molecular andphysiological underlying phenotypic variation for traitsof interest

Conclusion and future plansAs genome sequencing becomes less costly the numberof organisms with sequenced and annotated genomeswill increase Linking the emerging wealth of genomedata to phenotypes in order to help elucidate the mole-cular basis for phenotypic variation of quantitative traitswill be a major challenge We believe tools such assolQTL will help facilitate the dynamic linking of phe-notypes to genomes for the Solanceae speciesAt this stage solQTL implements a limited number of

QTL mapping capabilities of the RQTL software In thenear future we plan to implement the full capacity ofthe RQTL tool such as genetic map estimation two-dimensional two-QTL scan QTL mapping for out-crossing and recombinant inbred lines and account forcovariates such as environment We plan to extend itsanalysis capabilities to support multiple QTL mappingQTL effects calculation and interactive statistical analy-sis where users can predict QTLs using several combi-nations of statistical parametersWe also plan to implement tools to allow meta-QTL

analysis across multiple QTL populations To enablecommunication between QTL databases and access tothe raw and analyzed QTL data at SGN programmati-cally we will generate QTL output in XML format asspecified by the emerging standards of Minimum Infor-mation for QTLs and Association Studies (MIQAS) [48]

Availability and requirementsAll SGN source code and database schemas are opensource and available for download at httpgithubcomsolgenomics Raw phenotype and genotype data can alsobe freely downloaded from the SGN web page of theQTL population of interest provided that the owner hasreleased the data for the public use solQTL works withall web browsers and good internet connection

Additional material

Additional file 1 Web interface for uploading raw QTL data andstatistical parameters Web interface for step-wise uploading of QTLpopulation details list of traits phenotype data genotype data andstatistical parameters The user is prompted for the next step aftersuccessfully uploading data in the preceding step httpsolgenomicsnetphenomeqtl_formpl

Additional file 2 QTL population detail page A webpage displaying aQTL populationrsquos description list of traits evaluated for QTLs and theircorresponding phenotype data descriptive statistics and links to the QTLanalysis page Clicking on the graph icon initiates the on-the-fly QTLmapping of the trait (eg for lsquoFruit Arearsquo see Figure 1) Also on the samepage are functions for downloading the phenotype and genotype dataof the population and links to the genetic map and publication of thepopulation (source httpsolgenomicsnetpopulationplpopulation_id=12) Note the population genetic map shown is aconsensus map that included linkage map data from this population

AcknowledgementsThe project was funded by the USDA CSREES For part of the duration ofthis project the first author of this manuscript has been a visiting scientist atWageningen University Plant Breeding and Genetics Department TheNetherlands Many thanks to Dr Richard Finkers and his lab for hosting thefirst author

Author details1Boyce Thompson Institute for Plant Research Tower Rd Ithaca NY 14853USA 2Department of Horticulture and Crop Science The Ohio StateUniversityOARDC Wooster OH 44691 USA

Authorsrsquo contributionsIYT designed the workflow implemented the RQTL code and wrote mostof the code and part of the solQTL database schema or implementedmodules contributed by NM RMB LAM and CPAN authors NM RMB andLAM contributed in code writing database schema development anddiscussion LAM oversaw the development of the project EVDK contributedthe QTL data The manuscript was drafted by IYT and all authors reviewedand contributed up to the final version

Received 21 July 2010 Accepted 21 October 2010Published 21 October 2010

References1 Fridman E Pleban T Zamir D A recombination hotspot delimits a wild-

species quantitative trait locus for tomato sugar content to 484 bpwithin an invertase gene Proc Natl Acad Sci USA 2000 97(9)4718-4723

2 Causse M Duffe P Gomez MC Buret M Damidaux R Zamir D Gur AChevalier C Lemaire-Chamley M Rothan C A genetic map of candidategenes and QTLs involved in tomato fruit size and composition J Exp Bot2004 55(403)1671-1685

3 Pflieger S Lefebvre V Causse M The candidate gene approach in plantgenetics a review Molecular Breeding 2001 7275-291

4 Zou F QTL mapping in intercross and backcross populations MethodsMol Biol 2009 573157-173

Tecle et al BMC Bioinformatics 2010 11525httpwwwbiomedcentralcom1471-210511525

Page 8 of 9

users in private or public mode further reinforces theldquocommunity annotationrdquo philosophy SGN has pioneeredfor its locus and phenotype databases [45]SGN is a clade-oriented database with a computa-

tional infrastructure and capacity to store and makeaccessible data in a comparative manner for the Sola-naceae family and related Asterids It is a primary repo-sitory for genetic phenotype genome and expressiondata for the Solanaceae family [14] These datasets aredeeply and extensively cross-referenced As a result pro-vided that the QTL flanking andor peak markers existwith their sequences in the SGN marker database thesolQTL tool links dynamically predicted QTLs to corre-sponding relevant datasets and builds upon the con-stantly growing networking of emerging datasets in SGNand external databasesPredicted QTLs are seamlessly cross-linked to the

SGN Comparative Map Viewer [39] and SGNrsquos instanceof GBrowse [42] The comparative map viewer enablescomparative genetic analysis between QTLs of interestand corresponding regions in genetic and physical mapsof the same or different Solanaceae species and Arabi-dopsis Corresponding regions in other maps for exam-ple the tomato F22000 map [43] with more dense and

informative markers such as COS help identify moretightly linked genetic markers and possibly matchinggenomic clones or regions QTLs are currently linked tothe GBrowse on a marker-by-marker basis TheGBrowse is also searchable using other genetic featuressuch as unigenes which might enable investigators toinfer some missing links from genetic markers to thegenome We anticipate in the near future further pro-gress in the assembly of the genomes SGN hosts andsearch features under development will allow searchingfor genomic regions matching an entire QTL region Atthis stage once a genome region matches a flanking orpeak QTL marker one can zoom inout (bi-direction-ally) or scroll the genomic segment one-directionallyto explore genomic annotations such as gene mRNACDS and protein models unigenes ESTs and experi-mentally characterized loci Currently SGNrsquos GBrowsedatabase houses the Solanum lycopersicum cv Heinz1706 genome sequence and annotations by ITAG [21]More genomes are in the pipeline to be sequenced andannotated andor added to SGNrsquos GBrowse and cross-referenced to solQTLThe solQTL tool allows users to use non-controlled

vocabularies trait names However users are highly

Figure 4 Linking predicted QTLs to an annotated genome An example of a 20 kbp segment on the tomato genome matching a QTLmarker (eg TO581 peak marker for a putative lsquoFruit Arearsquo QTL generated after following a link from the QTL detail page (see Figure 2)) Withinthe 20 kbp of the marker are annotations including gene and protein models with their GO annotations ESTs cDNAs and markers

Tecle et al BMC Bioinformatics 2010 11525httpwwwbiomedcentralcom1471-210511525

Page 7 of 9

encouraged to use controlled vocabularies for their traitnames to facilitate cross-study comparisons for QTLs ofthe same traits and to fully exploit expression and phe-notype data annotated with controlled vocabulary suchas the Gene Ontology (GO) [46] and Plant Ontology(PO) [47] terms SGN in collaboration with the Solana-ceae breeders continues to develop and maintain a con-trolled vocabulary for Solanaceae traits calledSolanaceae Phenotype Ontology accessible at httpsol-genomicsnettoolsontoLooking toward a longer time horizon the SOL-100

project has an ambitious plan to sequence and makepublicly available genomes of 100 or more Solanaceaespecies [22] A single nucleotide polymorphism (SNP)database and viewer is also under development at SGN(unpublished) As these projects bear fruit solQTL willbe a window linking phenotypes to this rich genomicdata and analytical tools We envision this integrationand analysis of a wide variety of data in SGN and cross-referenced external databases will have an importantrole to play in identification of candidate genes forfurther functional analysis and gene cloning identifica-tion of more closely linked QTL markers for marker-assisted breeding and understanding the molecular andphysiological underlying phenotypic variation for traitsof interest

Conclusion and future plansAs genome sequencing becomes less costly the numberof organisms with sequenced and annotated genomeswill increase Linking the emerging wealth of genomedata to phenotypes in order to help elucidate the mole-cular basis for phenotypic variation of quantitative traitswill be a major challenge We believe tools such assolQTL will help facilitate the dynamic linking of phe-notypes to genomes for the Solanceae speciesAt this stage solQTL implements a limited number of

QTL mapping capabilities of the RQTL software In thenear future we plan to implement the full capacity ofthe RQTL tool such as genetic map estimation two-dimensional two-QTL scan QTL mapping for out-crossing and recombinant inbred lines and account forcovariates such as environment We plan to extend itsanalysis capabilities to support multiple QTL mappingQTL effects calculation and interactive statistical analy-sis where users can predict QTLs using several combi-nations of statistical parametersWe also plan to implement tools to allow meta-QTL

analysis across multiple QTL populations To enablecommunication between QTL databases and access tothe raw and analyzed QTL data at SGN programmati-cally we will generate QTL output in XML format asspecified by the emerging standards of Minimum Infor-mation for QTLs and Association Studies (MIQAS) [48]

Availability and requirementsAll SGN source code and database schemas are opensource and available for download at httpgithubcomsolgenomics Raw phenotype and genotype data can alsobe freely downloaded from the SGN web page of theQTL population of interest provided that the owner hasreleased the data for the public use solQTL works withall web browsers and good internet connection

Additional material

Additional file 1 Web interface for uploading raw QTL data andstatistical parameters Web interface for step-wise uploading of QTLpopulation details list of traits phenotype data genotype data andstatistical parameters The user is prompted for the next step aftersuccessfully uploading data in the preceding step httpsolgenomicsnetphenomeqtl_formpl

Additional file 2 QTL population detail page A webpage displaying aQTL populationrsquos description list of traits evaluated for QTLs and theircorresponding phenotype data descriptive statistics and links to the QTLanalysis page Clicking on the graph icon initiates the on-the-fly QTLmapping of the trait (eg for lsquoFruit Arearsquo see Figure 1) Also on the samepage are functions for downloading the phenotype and genotype dataof the population and links to the genetic map and publication of thepopulation (source httpsolgenomicsnetpopulationplpopulation_id=12) Note the population genetic map shown is aconsensus map that included linkage map data from this population

AcknowledgementsThe project was funded by the USDA CSREES For part of the duration ofthis project the first author of this manuscript has been a visiting scientist atWageningen University Plant Breeding and Genetics Department TheNetherlands Many thanks to Dr Richard Finkers and his lab for hosting thefirst author

Author details1Boyce Thompson Institute for Plant Research Tower Rd Ithaca NY 14853USA 2Department of Horticulture and Crop Science The Ohio StateUniversityOARDC Wooster OH 44691 USA

Authorsrsquo contributionsIYT designed the workflow implemented the RQTL code and wrote mostof the code and part of the solQTL database schema or implementedmodules contributed by NM RMB LAM and CPAN authors NM RMB andLAM contributed in code writing database schema development anddiscussion LAM oversaw the development of the project EVDK contributedthe QTL data The manuscript was drafted by IYT and all authors reviewedand contributed up to the final version

Received 21 July 2010 Accepted 21 October 2010Published 21 October 2010

References1 Fridman E Pleban T Zamir D A recombination hotspot delimits a wild-

species quantitative trait locus for tomato sugar content to 484 bpwithin an invertase gene Proc Natl Acad Sci USA 2000 97(9)4718-4723

2 Causse M Duffe P Gomez MC Buret M Damidaux R Zamir D Gur AChevalier C Lemaire-Chamley M Rothan C A genetic map of candidategenes and QTLs involved in tomato fruit size and composition J Exp Bot2004 55(403)1671-1685

3 Pflieger S Lefebvre V Causse M The candidate gene approach in plantgenetics a review Molecular Breeding 2001 7275-291

4 Zou F QTL mapping in intercross and backcross populations MethodsMol Biol 2009 573157-173

Tecle et al BMC Bioinformatics 2010 11525httpwwwbiomedcentralcom1471-210511525

Page 8 of 9

encouraged to use controlled vocabularies for their traitnames to facilitate cross-study comparisons for QTLs ofthe same traits and to fully exploit expression and phe-notype data annotated with controlled vocabulary suchas the Gene Ontology (GO) [46] and Plant Ontology(PO) [47] terms SGN in collaboration with the Solana-ceae breeders continues to develop and maintain a con-trolled vocabulary for Solanaceae traits calledSolanaceae Phenotype Ontology accessible at httpsol-genomicsnettoolsontoLooking toward a longer time horizon the SOL-100

project has an ambitious plan to sequence and makepublicly available genomes of 100 or more Solanaceaespecies [22] A single nucleotide polymorphism (SNP)database and viewer is also under development at SGN(unpublished) As these projects bear fruit solQTL willbe a window linking phenotypes to this rich genomicdata and analytical tools We envision this integrationand analysis of a wide variety of data in SGN and cross-referenced external databases will have an importantrole to play in identification of candidate genes forfurther functional analysis and gene cloning identifica-tion of more closely linked QTL markers for marker-assisted breeding and understanding the molecular andphysiological underlying phenotypic variation for traitsof interest

Conclusion and future plansAs genome sequencing becomes less costly the numberof organisms with sequenced and annotated genomeswill increase Linking the emerging wealth of genomedata to phenotypes in order to help elucidate the mole-cular basis for phenotypic variation of quantitative traitswill be a major challenge We believe tools such assolQTL will help facilitate the dynamic linking of phe-notypes to genomes for the Solanceae speciesAt this stage solQTL implements a limited number of

QTL mapping capabilities of the RQTL software In thenear future we plan to implement the full capacity ofthe RQTL tool such as genetic map estimation two-dimensional two-QTL scan QTL mapping for out-crossing and recombinant inbred lines and account forcovariates such as environment We plan to extend itsanalysis capabilities to support multiple QTL mappingQTL effects calculation and interactive statistical analy-sis where users can predict QTLs using several combi-nations of statistical parametersWe also plan to implement tools to allow meta-QTL

analysis across multiple QTL populations To enablecommunication between QTL databases and access tothe raw and analyzed QTL data at SGN programmati-cally we will generate QTL output in XML format asspecified by the emerging standards of Minimum Infor-mation for QTLs and Association Studies (MIQAS) [48]

Availability and requirementsAll SGN source code and database schemas are opensource and available for download at httpgithubcomsolgenomics Raw phenotype and genotype data can alsobe freely downloaded from the SGN web page of theQTL population of interest provided that the owner hasreleased the data for the public use solQTL works withall web browsers and good internet connection

Additional material

Additional file 1 Web interface for uploading raw QTL data andstatistical parameters Web interface for step-wise uploading of QTLpopulation details list of traits phenotype data genotype data andstatistical parameters The user is prompted for the next step aftersuccessfully uploading data in the preceding step httpsolgenomicsnetphenomeqtl_formpl

Additional file 2 QTL population detail page A webpage displaying aQTL populationrsquos description list of traits evaluated for QTLs and theircorresponding phenotype data descriptive statistics and links to the QTLanalysis page Clicking on the graph icon initiates the on-the-fly QTLmapping of the trait (eg for lsquoFruit Arearsquo see Figure 1) Also on the samepage are functions for downloading the phenotype and genotype dataof the population and links to the genetic map and publication of thepopulation (source httpsolgenomicsnetpopulationplpopulation_id=12) Note the population genetic map shown is aconsensus map that included linkage map data from this population

AcknowledgementsThe project was funded by the USDA CSREES For part of the duration ofthis project the first author of this manuscript has been a visiting scientist atWageningen University Plant Breeding and Genetics Department TheNetherlands Many thanks to Dr Richard Finkers and his lab for hosting thefirst author

Author details1Boyce Thompson Institute for Plant Research Tower Rd Ithaca NY 14853USA 2Department of Horticulture and Crop Science The Ohio StateUniversityOARDC Wooster OH 44691 USA

Authorsrsquo contributionsIYT designed the workflow implemented the RQTL code and wrote mostof the code and part of the solQTL database schema or implementedmodules contributed by NM RMB LAM and CPAN authors NM RMB andLAM contributed in code writing database schema development anddiscussion LAM oversaw the development of the project EVDK contributedthe QTL data The manuscript was drafted by IYT and all authors reviewedand contributed up to the final version

Received 21 July 2010 Accepted 21 October 2010Published 21 October 2010

References1 Fridman E Pleban T Zamir D A recombination hotspot delimits a wild-

species quantitative trait locus for tomato sugar content to 484 bpwithin an invertase gene Proc Natl Acad Sci USA 2000 97(9)4718-4723

2 Causse M Duffe P Gomez MC Buret M Damidaux R Zamir D Gur AChevalier C Lemaire-Chamley M Rothan C A genetic map of candidategenes and QTLs involved in tomato fruit size and composition J Exp Bot2004 55(403)1671-1685

3 Pflieger S Lefebvre V Causse M The candidate gene approach in plantgenetics a review Molecular Breeding 2001 7275-291

4 Zou F QTL mapping in intercross and backcross populations MethodsMol Biol 2009 573157-173

Tecle et al BMC Bioinformatics 2010 11525httpwwwbiomedcentralcom1471-210511525

Page 8 of 9

5 Schauer N Semel Y Roessner U Gur A Balbo I Carrari F Pleban T Perez-Melis A Bruedigam C Kopka J Willmitzer L Zamir D Fernie ARComprehensive metabolic profiling and phenotyping of interspecificintrogression lines for tomato improvement Nat Biotechnol 200624(4)447-454

6 Alpert KB Tanksley SD High-resolution mapping and isolation of a yeastartificial chromosome contig containing fw22 a major fruit weightquantitative trait locus in tomato Proc Natl Acad Sci USA 199693(26)15503-15507

7 Xu X Martin B Comstock JP Vision TJ Tauer CG Zhao B Pausch RCKnapp S Fine mapping a QTL for carbon isotope composition in tomatoTheor Appl Genet 2008 117(2)221-233

8 Eshed Y Zamir D An introgression line population of Lycopersiconpennellii in the cultivated tomato enables the identification and finemapping of yield associated QTL Genetics 1995 1411147-1162

9 Alonso-Blanco C Koornneef M Naturally occurring variation inArabidopsis an underexploited resource for plant genetics Trends PlantSci 2000 5(1)22-29

10 Price AH Believe it or not QTLs are accurate Trends Plant Sci 200611(5)213-216

11 Bermudez L Urias U Milstein D Kamenetzky L Asis R Fernie AR VanSluys MA Carrari F Rossi M A candidate gene survey of quantitative traitloci affecting chemical composition in tomato fruit J Exp Bot 200859(10)2875-2890

12 Flint J Mott R Finding the molecular basis of quantitative traitssuccesses and pitfalls Nat Rev Genet 2001 2(6)437-445

13 Sharma A Zhang L Nino-Liu D Ashrafi H Foolad MR A Solanumlycopersicum x Solanum pimpinellifolium Linkage Map of TomatoDisplaying Genomic Locations of R-Genes RGAs and CandidateResistanceDefense-Response ESTs Int J Plant Genomics 20082008926090

14 Mueller LA Solow TH Taylor N Skwarecki B Buels R Binns J Lin CWright MH Ahrens R Wang Y Herbst EV Keyder ER Menda N Zamir DTanksley SD The SOL Genomics Network a comparative resource forSolanaceae biology and beyond Plant Physiol 2005 138(3)1310-1317

15 Broman KW Wu H Sen S Churchill GA Rqtl QTL mapping inexperimental crosses Bioinformatics 2003 19(7)889-890

16 Arcade A Labourdette A Falque M Mangin B Chardon F Charcosset AJoets J BioMercator integrating genetic maps and QTL towardsdiscovery of candidate genes Bioinformatics 2004 20(14)2324-2326

17 Frary A Nesbitt TC Grandillo S Knaap E Cong B Liu J Meller J Elber RAlpert KB Tanksley SD Fw22 a Quantitative Trait Locus Key to theEvolution of Tomato Fruit Size Science 2000 289(5476)85-88

18 Liu J Van Eck J Cong B Tanksley SD A new class of regulatory genesunderlying the cause of pear-shaped tomato fruit Proc Natl Acad Sci USA2002 99(20)13302-13306

19 Yamamoto T Kuboki Y Lin SY Sasaki T Yano M Fine mapping ofquantitative trait loci Hd-1 Hd-2 and Hd-3 controlling heading date ofrice as single Mendelian factors TAG 1998 97(1-2)37-44

20 Tanksley SD Young ND Paterson AH Bonierbale MW RFLP Mapping inPlant Breeding New Tools for an Old Science Nat Biotechnol 19897257-264

21 Mueller LA Lankhorst RK Tanksley SD Giovannoni JJ A Snapshot of theEmerging Tomato Genome Sequence The Plant Genome 2009 278-92

22 Klein Lankhorst R Sol 100 Sol Newsletter March 2010 261-323 Liang C Jaiswal P Hebbard C Avraham S Buckler ES Casstevens T

Hurwitz B McCouch S Ni J Pujar A Ravenscroft D Ren L Spooner WTecle I Thomason J Tung CW Wei X Yap I Youens-Clark K Ware DStein L Gramene a growing plant comparative genomics resourceNucleic Acids Res 2008 36(Database issue)D947-53

24 Ni J Pujar A Youens-Clark K Yap I Jaiswal P Tecle I Tung CW Ren LSpooner W Wei X Avraham S Ware D Stein L McCouch S Gramene QTLdatabase development content and applications Database (Oxford)2009 2009bap005

25 Lawrence CJ Harper LC Schaeffer ML Sen TZ Seigfried TE Campbell DAMaizeGDB The Maize Model Organism Database for Basic Translationaland Applied Research Int J Plant Genomics 2008 2008496957

26 Hu ZL Fritz ER Reecy JM AnimalQTLdb a livestock QTL database tool setfor positional QTL information mining and beyond Nucleic Acids Res2007 35(Database issue)D604-9

27 Hu ZL Reecy JM Animal QTLdb beyond a repository A public platformfor QTL comparisons and integration with diverse types of structuralgenomic information Mamm Genome 2007 18(1)1-4

28 Gonzalo MJ van der Knaap E A comparative analysis into the geneticbases of morphology in tomato varieties exhibiting elongated fruitshape Theor Appl Genet 2008 116(5)647-656

29 Brewer MT Moyseenko JB Monforte AJ van der Knaap E Morphologicalvariation in tomato a comprehensive study of quantitative trait locicontrolling fruit shape and development J Exp Bot 2007 58(6)1339-1349

30 van der Knaap E Tanksley SD The making of a bell pepper-shapedtomato fruit identification of loci controlling fruit morphology in YellowStuffer tomato Theor Appl Genet 2003 107(1)139-147

31 Broman KW Sen S Single-QTL Analysis In A Guide to QTL mapping with RQtl Statistics for Biology and Health Edited by Broman KW Sen S Springer200975-133

32 Dempster AP Laird NM Rubin DB Maximum likelihood from incompletedata via the EM algorithm J Roy Statist Soc 1977 39(B)1-38

33 Lander ES Botstein D Mapping mendelian factors underlyingquantitative traits using RFLP linkage maps Genetics 1989 121(1)185-199

34 Haley CS Knott SA A simple regression method for mappingquantitative trait loci in line crosses using flanking markers Heredity1992 69(4)315-324

35 Sen S Churchill GA A statistical framework for quantitative traitmapping Genetics 2001 159(1)371-87

36 Baum LE Petrie G Weiss N A maximization technique occurring in thestatistical analysis of probabilistic functions of Markov chains Ann MathStat 1970 41164-171

37 Churchill GA Doerge RW Empirical threshold values for quantitative traitmapping Genetics 1994 138(3)963-971

38 Manichaikul A Dupuis J Sen S Broman KW Poor performance ofbootstrap confidence intervals for the location of a quantitative traitlocus Genetics 2006 174(1)481-489

39 Mueller LA Mills AA Skwarecki B Buels RM Menda N Tanksley SD TheSGN comparative map viewer Bioinformatics 2008 24(3)422-423

40 R Development Core Team R A language and environment for statisticalcomputing 2008 271

41 Zhao JH Tan Q Integrated analysis of genetic data with R HumGenomics 2006 2(4)258-265

42 Stein LD Mungall C Shu S Caudy M Mangone M Day A Nickerson EStajich JE Harris TW Arva A Lewis S The generic genome browser abuilding block for a model organism system database Genome Res 200212(10)1599-1610

43 Fulton TM Van der Hoeven R Eannetta NT Tanksley SD Identificationanalysis and utilization of conserved ortholog set markers forcomparative genomics in higher plants Plant Cell 2002 14(7)1457-1467

44 Donlin MJ Using the Generic Genome Browser (GBrowse) Curr ProtocBioinformatics 2009 Chapter 9 Unit 99

45 Menda N Buels RM Tecle I Mueller LA A community-based annotationframework for linking solanaceae genomes with phenomes Plant Physiol2008 147(4)1788-1799

46 Ashburner M Ball CA Blake JA Botstein D Butler H Cherry JM Davis APDolinski K Dwight SS Eppig JT Harris MA Hill DP Issel-Tarver L Kasarskis ALewis S Matese JC Richardson JE Ringwald M Rubin GM Sherlock G Geneontology tool for the unification of biology The Gene OntologyConsortium Nat Genet 2000 25(1)25-29

47 Avraham S Tung CW Ilic K Jaiswal P Kellogg EA McCouch S Pujar AReiser L Rhee SY Sachs MM Schaeffer M Stein L Stevens P Vincent LZapata F Ware D The Plant Ontology Database a community resourcefor plant structure and developmental stages controlled vocabulary andannotations Nucleic Acids Res 2008 36(Database issue)D449-54

48 The Minimum Information for QTLs and Association Studies (MIQAS)[httpmiqassourceforgenet]

doi1011861471-2105-11-525Cite this article as Tecle et al solQTL a tool for QTL analysisvisualization and linking to genomes at SGN database BMCBioinformatics 2010 11525

Tecle et al BMC Bioinformatics 2010 11525httpwwwbiomedcentralcom1471-210511525

Page 9 of 9