VaRank: a simple and powerful tool for ranking genetic variants

20 Pages·2015·0.72 MB·English

VaRank: a simple and powerful tool for
ranking genetic variants
Ve´roniqueGeoffroy1,2,Ce´cilePizot2,ClaireRedin2,Ame´liePiton2,3,
NasimVasli2,CorinneStoetzel1,Andre´ Blavier4,JocelynLaporte2 and
JeanMuller2,3,5
1LaboratoiredeGe´ne´tiqueme´dicale,UMR SINSERMU1112,IGMA,Faculte´deMe´decine
FMTS,Universite´deStrasbourg,Strasbourg,France
2IGBMC,CNRSUMR7104/INSERMU964/Universite´deStrasbourg,IllkirchCedex,France
3LaboratoiredeDiagnosticGe´ne´tique,HoˆpitauxUniversitairesdeStrasbourg,StrasbourgCedex,
France
4InteractiveBiosoftware,Rouen,France
5LaboratoireICUBE,UMRCNRS7357,LBGI,Universite´deStrasbourg,Strasbourg,France
ABSTRACT
Background. Most genetic disorders are caused by single nucleotide variations
(SNVs) or small insertion/deletions (indels). High throughput sequencing has
broadenedthecatalogueofhumanvariation,includingcommonpolymorphisms,
rarevariationsordiseasecausingmutations.However,identifyingonevariation
amonghundredsorthousandsofothersisstillacomplextaskforbiologists,geneti-
cistsandclinicians.
Results.WehavedevelopedVaRank,acommand-linetoolfortherankingofgenetic
variantsdetectedbyhigh-throughputsequencing.VaRankscoresandprioritizes
variantsannotatedeitherbyAlamutBatchorSnpEff.Abarcodeallowsusersto
quicklyviewthepresence/absenceofvariants(withhomozygote/heterozygotestatus)
inanalyzedsamples.VaRanksupportsthecommonlyusedVCFinputformatfor
variantsanalysisthusallowingittobeeasilyintegratedintoNGSbioinformatics
analysispipelines.VaRankhasbeensuccessfullyappliedtodisease-geneidentifica-
tionaswellastomoleculardiagnosticssetupforseveralhundredpatients.
Conclusions. VaRank is implemented in Tcl/Tk, a scripting language which is
Submitted17October2014 platform-independentbuthasbeentestedonlyonUnixenvironment.Thesource
Accepted 5February2015 codeisavailableundertheGNUGPL,andtogetherwithsampledataanddetailed
Published 3March2015
documentationcanbedownloadedfromhttp://www.lbgi.fr/VaRank/.
Correspondingauthor
JeanMuller,[email protected]
Academiceditor Subjects Bioinformatics,Genetics,Genomics
GerardLazo Keywords Nextgenerationsequencing,Variantranking,Humangenetics,Moleculardiagnostic,
Mutationdetection,Annotation,Software,Barcode
AdditionalInformationand
Declarationscanbefoundon
page16
INTRODUCTION
DOI10.7717/peerj.796
Inrecentyears,highthroughputsequencinghasgeneratedthousandsofnewgenomes
Copyright
fromvariousspeciesacrossthetreeoflifeandmillionsofgeneticvariants.Especially
2015 Geoffroyetal.
inthefieldofhuman genetics,targetedorwholeexomeandgenomesequencing are
Distributedunder
becomingstandardassays(Ngetal.,2010;Ngetal.,2009;Saundersetal.,2012)toidentify
CreativeCommonsCC-BY4.0
causalsingle-nucleotidevariations(SNVs)aswellasshortinsertions/deletions(indels)
OPENACCESS
HowtocitethisarticleGeoffroyetal.(2015),VaRank:asimpleandpowerfultoolforrankinggeneticvariants.PeerJ3:e796;
DOI10.7717/peerj.796
Figure1 HighthroughputsequencingdataanalysisworkflowandVaRankpositioning.
inpatientswithMendeliandiseases,orvariantsassociatedtoincreaseddiseaserisk(Cirulli
&Goldstein,2010;Manolioetal.,2009).
Theclassicaldataworkflowinnextgenerationsequencingincludesseveralbioinfor-
maticsstepsfromtherawsequencingdataanalysiswhichtransformsthesignalfrom
the sequencers (e.g., fluorescence, pH...) to raw sequences that are further aligned
to the reference genome. Sequence differences from the reference genome (variants)
arethendetectedaimingatidentifyingcausalmutation(Fig.1).Althoughsequencing
limitationsareovercomewithincreasinginstrumentcapacity(Glenn,2011andhttp://
www.molecularecologist.com/next-gen-fieldguide-2014/),thedevelopmentofbioinfor-
maticssolutionsforvariantprioritizationremainsagreatchallenge.Thefocusonhigh
throughputsequencingresultedinthedevelopmentofavarietyoftools,protocolsandap-
plicationsincludingvariantfilteringandranking(forareviewseeBaoetal.,2014).Recent
approachesincludetheuseofadditionaldatasuchashaploinsufficiencypredictionand
phenotypeinformation(Sifrimetal.,2013),crossspeciesphenotypeinformation(Robin-
sonetal.,2014)orinteractiondata(Smedleyetal.,2014)toenhancetheanalysis.However,
molecularbiologistsstillrequiresimpletoolsinthevariantfilteringandrankingprocessto
identifycausalmutationsamongalargepoolofrarevariantsexistingineachindividual.
HereweproposeanewsimpleandpowerfultoolnamedVaRank(http://www.lbgi.
fr/VaRank)forhumanvariantrankingwhichprovidesacomprehensiveworkflowfor
annotating and ranking SNVs and indels. Four modules create the strength of this
workflow(Fig.2):(i)Dataintegrationwithvariantcallqualitysummary,tofilterout
falsepositivecalls,dependingonthesequencingtechnologyandtheanalysispipeline;(ii)
Variantannotationtointegrategeneticandpredictiveinformation(functionalimpact,
putativeeffectsinproteincodingregions,populationfrequency,phenotypicfeatures...)
from different sources, using HGVS nomenclature (Taschner & den Dunnen, 2011);
(iii) Presence/absence of variants (with homozygote/heterozygote status) within all
samplesrepresentedinabarcode,tosearchforrecurrencebetweenfamiliesorgroupof
individualsand(iv)Prioritization,toscoreandrankvariantsaccordingtotheirpredicted
pathogenicstatus.
VaRankcansubstantiallyreducethenumberofpotentialcausalvariantstobemanually
inspectedforfurtherstudiesandincreasetheefficiencyofsequenceanalysisforresearchers
andclinicians.VaRankhasbeenalreadysuccessfullyappliedinthefieldofhumangenetics,
firstinresearchincludingidentificationofnewgenesresponsibleforrarehumandisease
andsecondindiagnosticstoidentifymutationinknownhumandiseaseimplicatedgenes.
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 2/20
Figure 2 VaRank’s workflow. The work flow is separated into 4 major steps, (i) Sequencing data
from a single or from multiple VCF files are integrated including variant call quality summary, (ii)
Annotation of each variant including genetic and predictive information (functional impact, putative
effectsinproteincodingregions,populationfrequency,phenotypicfeatures...)fromdifferentsources.
TheannotationcaneitherbedonebyAlamutBatchorSnpEff.(iii)Presence/absenceofvariants(with
homozygote/heterozygotestatus)withinallsamplesrepresentedinabarcode,and(iv)Prioritization,to
scoreandrankvariantsaccordingtotheirpredictedpathogenicstatus.Thefinaloutputfilesareavailable
foreachsamples.
InthisworkwedescribeVaRankasatoolandhowitisimplementedbutalsopresenthow
itwasusedinseveralrealdatasets.
IMPLEMENTATION
VaRankiswritteninTcl/TkandrunsonallUnixplatformswithastandardTcl/Tk8.5
installationandoneofthecompatibleannotationengines(AlamutBatch,acommercial
softwaredevelopedbyInteractiveBiosoftware,Rouen,France,orSnpEff(Cingolanietal.,
2012)).PolyPhen-2(Adzhubeietal.,2010)canalsobeinstalledlocallyandresultscanbe
automaticallyintegratedintoVaRank.
Input and output
To run VaRank, the user specifies the input files and chosen options with a single
command.Incaseofwrongcommands,VaRankwillprintadescriptionoftheoptions
anddefaultssettings.
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 3/20
VaRankreadsSNVandindelvariantdescriptionsfromaVCFfile(variantcallformat
Daneceketal.,2011),thereferenceformatforgenomicvariants.TheVCFfilecanbegzip
compressed.TheprogramcantakeanycombinationofasingleVCFfilewithmultiple
patientsand/ormultipleVCFfileswithsinglepatient’sdataasinput(Fig.2).
Each variant is checked for consistency (genotype, depth of coverage, variant call
especiallyforindels)andVaRankprintswarningsifappropriate(i.e.,patientsredundancy
based on the sample identifiers in the VCFs), information from each analysis step
(includingstatusandrunningtime),aswellasstatisticsonthesubmitteddatasuchas
totalnumberofvariantsandnumberofpatients.
TheoutputfromVaRankispresentedinaneasily-accessibletab-separatedfilethatcan
beopenedinanyspreadsheetprogram.Outputfilesreportonevariantperline.Never-
theless,twotypesofrankingsareprovided:onepresentingeachvariantindependently,
orderedfrommostlikelypathogenictoleastlikelypathogenic(fileswith“byVar”suffix),
andanotherrankinggenesfrommostlikelycausativetoleastlikelycausative(fileswith
“byGene”suffix).Forthelatter,eachgeneisscoredalongtwocriteria:(i)basedonits
homozygousmostpathogenicvariant,or(ii)basedonitsfirsttwoheterozygousmost
pathogenicvariants.Inordertomakesurethatnovariantsareoverlookedbytheuser(by
onlydisplayingthemostpathogenicvariants)allothergenevariantsarealsoreported.
The“byVar”fileismoreappropriateforanalyzingpatient’sdataunderthedominant
orpseudominanthypothesis,whilethe“byGene”fileismoreappropriateforrecessive
diseases(especiallyforcompoundheterozygouscases).
Eachoftheseoutputfilesisalsoavailableintwoversions:onecontainsallsubmitted
variants(“AllVariants”files)whiletheotheroneisprefiltered(“filteredVariants”files).The
defaultfiltersremovevariants:(i)calledwithareaddepth<=10,(ii)withasupporting
readcount<=10,(iii)witharatioofsupportingreads<=15%,(iv)withavalidated
status annotation in the dbSNP (Sherry et al., 2001) database (based on at least two
supportingevidences)thatarenotpathogenic(basedontheClinicalSignificancefield),
and(v)withanallelefrequency>1%(extractedfromthedbSNPdatabaseortheExome
VariantServer).Alltheseparameterscanbemodifiedbytheuser.
Finally,ashortreportofcounts(homozygous,heterozygousandtotalcounts)foreach
′ ′
ofthevariantcategories(5 and3 UTR,upstream,downstream,frameshift,in-frame,
nonsense,splicesite,startloss,stoploss,missense,synonymous,intronic,notannotated)
isgeneratedforeachsampleandforthewholesubmitteddataset.
Variant annotation
Theannotationofvariantsisperformedbytheannotationengine(Fig.2).Itiscomposed
ofseveralpartsincludingthemainannotationsoftware,whichcanbeeitherAlamut
Batch or SnpEff, the barcode annotation (see Fig. 3 and corresponding section) and
optionallyPolyPhen-2predictions.Asanexample,AlamutBatchcollectsamongothers
(Table2):thegenesymbol,theOMIMID,thetranscriptID(i.e.,RefSeq),theprotein
IDs (RefSeq and UniProt), the HGVS nomenclature (genomic, cDNA and proteic),
informationfrompublicvariationdatabasessuchasdbSNPandtheExomeVariantServer
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 4/20
Figure3 Barcode.(A)ThebarcoderepresentstheSNV’szygositystatusinanorderedlistofsamples.
Samplesthatarehomozygousforthereferenceallelearerepresentedusing“0,”heterozygousvariants
arerepresentedusing“1”andhomozygousvariantsarerepresentedwith“2.”(B)Selectedannotations
fromtheVaRankoutputrepresenting3variantsfromasinglepatient.Thebarcodegivesanoverview
of the presence/absence of one variant in all other patients analyzed. The family barcode gives a user
orderedviewofthepresence/absenceofonevariantinaselectionofpatients.Togetherwiththis,the
totalcountsofallelesaregiveninthelast4columns.(C)Exampleofpedigreesandbarcodesthatcanbe
specificallyusedinfamilyanalysessuchastrioexomesequencing.Ontheleft,homozygousmutations
inaconsanguineousfamilycouldbehighlightedbythe“121”barcodeindicatinghomozygousvariants
(“2”)intheprobandinheritedfromheterozygousparents(“1”).Ontherightdenovovariantsinthe
probandcouldbehighlightedwiththeproposedbarcode“010.”
(EVS, http://evs.gs.washington.edu/EVS/) and predicted effects at both nucleotide
and protein levels. When available, known mutations are highlighted by extracting
either reported SNVs/indels flagged as “probably-pathogenic”/“pathogenic” in the
field“Clinicalsignificance”introducedsincedbSNP134orusingtheHGMDdatabase
(Stensonetal.,2014).
Thechoiceoftranscriptisacriticaltaskthatcanleadtomisannotation(McCarthyet
al.,2014).Toavoidunderestimatingvarianteffects,theyareannotatedonalltranscripts
available(i.e.,onevariantcanbeeitherintronicorexonicdependingontheisoform)and
themostpathogeniceffectisretained.GiventhatVaRankiscompatiblewith2annotation
softwaretherearesmalldifferences.UsingAlamutBatch,eachvariantisscoredforeach
transcript.Bydefaultwereportthelongesttranscriptforeachgeneexceptifanyvariation
ismorepathogenicinanothertranscript.InthecaseofSnpEff,theannotationsarealready
sortedfromthemosttotheleastpathogenic.
Inordertofurtherenrichtheannotationforeachvariantandeachgene,VaRankcan
integrate(usingtheoption-extann)externalannotationsprovidedbytheuserasatab
separatedfile.Onecouldforinstanceassociateprivateexpressiondataortransmission
modeforeachgeneofinterest.
Annotatingandanalyzingseveralindividualstogethercanbeverycomputationally
effectivesincemostofthevariantsarecommonpolymorphisms.Asanexample,looking
at180patientssequencedfor217genes,atotalof204,625variationscouldbeidentified
where only 9,378 were non redundant. In this case, the separate annotation of each
patient’s variant set would have required ∼20× times the computational cost of the
combined analysis. Although the total number of non-redundant variants does not
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 5/20
Figure4 Distributionofvariantsin180patientsfor217genes.Thegraylinerepresentsthedistribution
ofthenumberofvariantsidentifiedineachsampleinacohortof180patientssequencedfor217genes.
Thedarklinerepresentsthecumulativenumberofnon-redundant(NR)variantsinthesamedatasetdue
toeachnewsampleadded.
plateau,eachnewsampleaddsonlyaverylimitednumberofnewvariantstotheanalysis
(Fig.4).
Variant ranking
Theobservedvariants(SNVs/indels)canbecharacterizedatdifferentlevels(DNA,RNA
andproteinlevels)thatVaRankaimsatsummarizingintoasinglescore.Thisscoreis
thenusedtorankvariantsbasedontheirpredictedpathogenicityandthusaccelerates
identificationofrelevantonesbybiologists.Theaimofthisscoreisnottoprovideyet
anotherscoretoassessthepathogenicityofeachvariantbutarationaletopresentthemost
relevantvariantsaccordingtothebiologistcommonuseandinterpretationrules.Thus,
therelativeweightsofeachscorecomponentsweredeterminedexperimentallytobest
separatecategories.VaRankusesthevariationtype(i.e.,substitution,deletion,insertion,
duplication)andthecodingeffecttoscore.TheVaRankscoringiscategorizedfromthe
mostlikelytothelesslikelypathogenicstateasfollows(scoreinparenthesis):known
mutation(110),nonsense(100),frameshift(100),startloss(80),stoploss(80),missense
(50),in-frame(40)andsynonymouscoding(10)(Table1).
InadditionwhenusingAlamutBatch,eachvariantisassessedforanypotentialeffecton
thenearestsplicesite.FollowingtheguidelinesfromHoudayeretal.(2012)andourown
tests(datanotshown),weselectedthreeassessmentprograms:MaxEntScan(Yeo&Burge,
2004),NNSplice(Reeseetal.,1997)andSpliceSiteFinder(basedonShapiro&Senapathy,
1987).Avariantisconsideredtoaffectsplicingwhenatleasttwooutofthethreeprograms
indicateasignificantscorechangebetweenthewildtypeandthemutatedsequences
(respectively−10%,−5%and−15%).AVaRankscoreisthenattributeddependingonthe
followingthreesplicesitecategories(scoreintoparenthesis):(i)essentialsplicesite:two
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 6/20
Table1 Scoringschemedescription.Scoresinboldreflectscorevaluesaftertheadjustmentscoreisapplied.
Variantcategory Optionname VaRankscore Definitions
Knownmutation S Known 110 KnownmutationasannotatedbyHGMDand/ordbSNP(rsClinical-
Significance=“pathogenic/probable-pathogenic”).
Nonsense S Nonsensea 100,105 Asingle-basesubstitutioninDNAresultinginaSTOPcodon
(TGA,TAAorTAG).
Frameshift S Fs 100 Exonicinsertion/deletionofanon-multipleof3bpresultingoftenina
prematurestopinthereadingframeofthegene.
Essentialsplicesite S EssentialSplicea 90,95 Variationinoneofthecanonicalsplicesitesresultinginasignificant
effectonsplicing.
Startloss S StartLossa 80,85 Variationleadingtothelossoftheinitiationcodon(Met).
Stoploss S StopLossa 80,85 VariationleadingtothelossoftheSTOPcodon.
Intron-exonboundary S CloseSplicea 70,75 Variationoutsideofthecanonicalsplicesites(donorsiteis−3to+6,
acceptorsite−12to+2).
Missense S Missensea,b 50,55,60,65 Asingle-basesubstitutioninDNAnotresultinginachangeinthe
aminoacid.
Indelin-frame S Inframe 40 Exonicinsertion/deletionofamultipleof3bp.
Deepintron-exonboundary S DeepSplicea 25,30 Intronicvariationresultinginasignificanteffectonsplicing.
Synonymouscoding S Synonymousa 10,15 Asingle-basesubstitutioninDNAnotresultinginachangeinthe
aminoacid.
Notes.
aEachvariantscoreisadjusted(+5)ifhighconservationatthegenomiclevelisobserved(phastConscutoff>0.95).
bMissensescoresareadjusted(+5)foreachdeleteriousprediction(SIFTand/orPPH2).
firstintronicbasesup-ordownstreamtheexon(90),(ii)intron-exonboundary:donor
sitefrom−3to+6,acceptorsitefrom−12to+2(70)and(iii)deepintronicchanges(25).
ItshouldbenotedthataSNVaffectingthefirstorlastbaseofanexoncaneitherhavea
codingeffectasamissenseoraneffectonsplicing.VaRankreportsthemostpathogenicof
thesetwopossibleeffects.
If appropriate the variant score is further adjusted using additional information
as nucleotide-level conservation (phastCons Siepel et al., 2005) and protein-level
pathogenicitypredictions(SIFTandPolyPhen-2)(Adzhubeietal.,2010;Kumar,Henikoff
&Ng,2009)thatareusedtocomputeanadjustmentscore(0or+5)tobeaddedtothe
relevantcategory(Table1).
Barcode
Comparingvariationsofseveralindividuals,relatedand/orunrelated,affectedornot,
hasproventobeaveryeffectivestrategyfordistinguishingpolymorphismsfromvariants
causingorincreasingthelikelihoodofdisease(Ngetal.,2010;Ngetal.,2009).Inorder
totakeadvantageofthis,VaRankintroducesabarcodethatallowsaquickoverviewof
thepresence/absencestatusofeachvariantwithinallsamplesandtheirzygositystatus
(“0”representinghomozygouswildtype,“1”heterozygousand“2”homozygousforthe
variant)(Fig.3A).InFig.3B,threevariantsarereportedforonespecificpatientoutof
acohortof32samplesanalyzedtogether.Forexample,thethirdvariationc.601G>A
inTTC21Bisheterozygousforthispatient.Inlightofthepresentedbarcode,onecan
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 7/20
Table2 SummarydescriptionoftheannotationsprovidedbyVaRankusingAlamutBatch.
Columnname Annotation
VariantID Variantidentifier[#chr] [genomicposition] [RefBase] [VarBase]
Gene Genesymbol
®
omimId OMIM id
TranscriptID RefSeqtranscriptid
TranscriptLength Lengthoftranscript(fullcDNAlength)
Chr Chromosomeofvariant
Start Startpositionofvariant
End Endpositionofvariant
Ref Nucleotidesequenceinthereferencegenome(restrictedto50bp)
Mut Alternatenucleotidesequence(restrictedto50bp)
Uniprot Uniprot
protein Proteinid(NCBI)
posAA Aminoacidposition
wtAA 1 Referencecodon
varAA 1 Alternatecodon
Phred QUAL QUAL:ThePhredscaledprobabilitythataREF/ALTpolymorphismexistsatthissitegivensequencing
data.BecausethePhredscaleis−10*log(1−p),avalueof10indicatesa1in10chanceoferror,while
a100indicatesa1in10ˆ10chance.ThesevaluescangrowverylargewhenalargeamountofNGSdata
isusedforvariantcalling.
HomHet Homozygoteorheterozygotestatus
TotalReadDepth Totalnumberofreadscoveringtheposition
VarReadDepth Numberofreadssupportingthevariant
%Reads variation Percentofreadssupportingvariantoverthosesupportingreferencesequence/base
VarType VariantType(substitution,deletion,insertion,duplication,delins)
CodingEffect VariantCodingeffect(synonymous,missense,nonsense,in-frame,frameshift,startloss,stoploss)
VarLocation Variantlocation(upstream,5’UTR,exon,intron,3’UTR,downstream)
Exon Exon(nearestexonifintronicvariant)
Intron Intron
gNomen Genomic-levelnomenclature
cNomen cDNA-levelnomenclature
pNomen Protein-levelnomenclature
rsID dbSNPvariation
rsValidation dbSNPvalidatedstatus
rsClinicalSignificance dbSNPvariationclinicalsignificance
rsAncestralAllele dbSNPancestralallele
rsHeterozygosity dbSNPvariationaverageheterozygosity
rsMAF dbSNPvariationglobalMinorAllele
rsMAFAllele dbSNPvariationglobalminorallele
rsMAFCount dbSNPvariationsamplesize
1000g AF 1,000genomesglobalallelefrequency
1000g AFR AF 1,000genomesallelefrequencyinAfricanpopulation
1000g SAS AF 1,000genomesallelefrequencyinSouthAsianpopulation
1000g EAS AF 1,000genomesallelefrequencyinEastAsianpopulation
(continuedonnextpage)
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 8/20
Table2(continued)
Columnname Annotation
1000g EUR AF 1,000genomesallelefrequencyinEuropeanpopulation
espRefEACount ESPreferenceallelecountinEuropeanAmericanpopulation
espRefAACount ESPreferenceallelecountinAfricanAmericanpopulation
espRefAllCount ESPreferenceallelecountinallpopulation
espAltEACount ESPalternateallelecountinEuropeanAmericanpopulation
espAltAACount ESPalternateallelecountinAfricanAmericanpopulation
espAltAllCount ESPalternateallelecountinallpopulation
espEAMAF MinorallelefrequencyinEuropeanAmericanpopulation
espAAMAF MinorallelefrequencyinAfricanAmericanpopulation
espAllMAF Minorallelefrequencyinallpopulation
espAvgReadDepth AveragesamplereadDepth
deltaMESscore(%) %differencebetweenthesplicescoreofvariantwiththescoreofthereferencebase
wtMEScore WTseq.MaxEntScanscore
varMEScore Variantseq.MaxEntScanscore
deltaSSFscore(%) %differencebetweenthesplicescoreofvariantwiththescoreofthereferencebase
wtSSFScore WTseq.SpliceSiteFinderscore
varSSFScore Variantseq.SpliceSiteFinderscore
deltaNNSscore(%) %differencebetweenthesplicescoreofvariantwiththescoreofthereferencebase
wtNNSScore WTseq.NNSPLICEscore
varNNSScore Variantseq.NNSPLICEscore
DistNearestSS DistancetoNearestsplicesite
NearestSS Nearestsplicesite
localSpliceEffect Splicingeffectinvariationvicinity(NewdonorSite,NewAcceptorSite,CrypticDonorStrongly
Activated,CrypticDonorWeaklyActivated,CrypticAcceptorStronglyActivated,CrypticAcceptor
WeaklyActivated)
SiftPred SIFTprediction
SiftWeight SIFTscorerangesfrom0to1.Theaminoacidsubstitutionispredicteddamagingisthescoreis
<=0.05,andtoleratedifthescoreis>0.05.
SiftMedian SIFTmedianrangesfrom0to4.32.Thisisusedtomeasurethediversityofthesequencesusedfor
prediction.Awarningwilloccurifthisisgreaterthan3.25becausethisindicatesthattheprediction
wasbasedoncloselyrelatedsequences.Thenumbershouldbebetween2.75and3.5
PPH2pred PolyPhen-2predictionusingHumVarmodelareeither“neutral,possiblydamaging,probablydamag-
ing”or“neutral,deleterious”dependingontheannotationengine.
phyloP phyloP
PhastCons PhastConsscore
GranthamDist Granthamdistance
VaRank VarScore PrioritizationscoreaccordingtoVaRank
AnnotationAnalysis YesorNoindicatesifthevariationcouldannotatedbyanyannotationengine
Avg TotalDepth Totalreaddepthaverageatthevariantpositionforallsamplesanalyzedthathavethevariation
SD TotalDepth StandarddeviationassociatedwithAvg TotalDepth
Count TotalDepth Numberofsamplesconsideredfortheaveragetotalreaddepth
Avg SNVDepth Variationreaddepthaverageatthevariantpositionforallsamplesanalyzedthathavethevariation
SD SNVDepth StandarddeviationassociatedwithAvg SNVDepth
Count SNVDepth NumberofsamplesconsideredfortheaverageSNVreaddepth
(continuedonnextpage)
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Table2(continued)
Columnname Annotation
familyBarcode Homozygoteorheterozygotestatusforthesampleofinterestanditsassociatedsamples
Barcode Homozygoteorheterozygotestatusforallsampleanalyzedtogether(Hom:2;Het:1;Samplenameis
givenatthefirstlineofthefile:##Barcode)
Hom Count Numberofhomozygoteoverallsamplesanalyzedtogether
Het Count Numberofheterozygoteoverallsamplesanalyzedtogether
Allele Count Numberofallelessupportingthevariant
Sample Count Totalnumberofsamples
immediatelynoticethatthevariantisalsopresentin28othersamplesfromthecohort,of
whichintotal12arehomozygousand17heterozygous.
Inordertoallowinheritanceanalysis,asecondbarcode(thefamilybarcode)represent-
ingonlyuserselectedsamplescanbedefined.Asanexampleintrioexomesequencing,
wehaverepresentedtwotypicalpedigrees(Figs.3Band3C),oneconsanguineousfamily
ontheleftandonesporadiccaseontheright.Inthecaseofconsanguinity,homozygous
mutationsareoftenthecauseofthediseaseinthefamily.Thiscouldbehighlightedby
selectingthe“121”barcodeindicatinghomozygousvariants(“2”)intheprobandinherited
fromheterozygousparents(“1”).Inthesporadiccase,severalhypothesescouldbetested
includingadenovovariantwhichcouldbehighlightedusingthebarcode“010.”
Togetherwiththebarcode,simplecountsontheindividuals(homozygous,heterozy-
gousandtotalalleliccounts)arealsoavailableandcaneasilybeusedtofurtherfilter
variants. Indeed, in rare diseases such as the Bardet-Biedl syndrome (BBS, OMIM#
209900),mutationsareoftenprivate(i.e.,onemutationfoundonlyinonefamily)(Muller
etal.,2010)meaningthattheirfrequencyinthepopulationisverylow.Countscanbeused
toestimatethefrequencyofaknownvariantintheusercohortandaddsignificantvalue
tovariantsnotyetreportedinanypublicvariantdatabasebutforwhichafrequencycanbe
estimatedbasedontheuser’scohort.Asanexample,lookingat2,888nonredundantSNVs
observedin107patientswithmoderatetosevereintellectualdisability,979didnothave
anyfrequencyinformationinthedbSNPandEVSdatabases.Suchinformationcouldbe
directlyretrievedfromtheVaRankoutput.
Theobservedfrequencyofvariantsinpublicdatabasesbutalsoinprivatecohortscan
beapowerfulfilteringstrategy.Usingthesamedata(Fig.3B),variantc.7911dupinALMS1
ispresentonlyonceinthecohortofpatientsatthehomozygousstateandisverylikelythe
diseasecausingmutationinthispatient.
RESULTS AND DISCUSSION
VaRankwassuccessfullyappliedinvarioushumangeneticsstudiesbothindiagnosticsand
research.Intotal,morethan800patientsfromseveraldatasetsofincreasingcomplexity
including the Cockayne syndrome (10 genes tested, manuscript in preparation),
Bardet-Biedlsyndrome(30genes(Redinetal.,2012)),ataxias(60genes),leucodystrophies
(70genes,manuscriptinpreparation),congenitalmyopathies(142genes(Vaslietal.,
2012) and 275 genes, manuscript in preparation), intellectual disability (217 genes,
Geoffroyetal.(2015),PeerJ,DOI10.7717/peerj.796 10/20