Download Epistatic networks jointly influence phenotypes related to

bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Epistaticnetworksjointlyinfluencephenotypesrelatedtometabolicdiseaseandgene
expressioninDiversityOutbredmice
AnnaL.Tyler,BoJi,DanielM.Gatti,StevenC.Munger,GaryA.Churchill,KarenL.Svenson,GregoryW.
Carter*
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
*Correspondingauthor:GregoryW.Carter
TheJacksonLaboratory,600MainSt,BarHarbor,ME,USA
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Abstract
Multiplegeneticandenvironmentalfactorscontributetometabolicdisease,witheffectsthatrange
acrossmolecular,organ,andwhole-organismlevels.Dissectingthismulti-scalecomplexityrequires
systemsgeneticsapproachestoinferpolygenicnetworksthatinfluencegeneexpression,serum
biomarkers,andphysiologicalmeasures.Inrecentyears,multi-parentmodelorganismcrosses,such
astheDiversityOutbred(DO)mice,haveemergedasapowerfulplatformforsuchsystems
approaches.TheDOmiceharborextensivephenotypicandgeneticdiversity,allowingfordetection
ofmultiplequantitativetraitloci(QTL)andtheirinteractionsathighgenomicresolution.Inthis
study,weused474DOmicetomodelgeneticinteractionsinfluencinghepatictranscriptome
expressionandphysiologicaltraitsrelatedtometabolicdisease.Bodycomposition,serum
biomarker,andlivertranscriptomedatafrommicefedeitherahigh-fatorstandardchowdietwere
combinedandsimultaneouslymodeled.Modulesofco-expressedtranscriptswereidentifiedwith
weightedgeneco-expressionnetworkanalysis,withsummarymodulephenotypesrepresenting
coordinatedtranscriptionalprogramslinkedtospecificbiologicalfunctions.Wethenusedthe
CombinedAnalysisofPleiotropyandEpistasis(CAPE)tosimultaneouslydetectdirectedepistatic
interactionsbetweenhaplotype-specificQTLfortranscriptmodulesandphysiologicalphenotypes.
Bycombininginformationacrossmultiplephenotypiclevels,weidentifiednetworksofQTLwith
numerousinteractionsthatrevealhowgeneticarchitectureaffectsmetabolictraitsatmultiplescales.
Specifically,thesenetworksmodelhowgeneregulatoryprogramsfromdifferentinbredfounder
strainsinfluencemorecomplexphysiologicaltraits.Byconnectingthreelevelsoftheorganismal
hierarchy–geneticvariation,transcriptabundance,andphysiology–werevealedadetailedpicture
ofgeneticinteractionsinfluencingcomplextraitsthroughdifferentialgeneexpression.
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Introduction
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Traitsrelevanttometabolicdisease,suchasobesity,andbloodlipidprofiles,havecomplexgenetic
architecture(Schork1997).Manygeneticfactorscontributetothesetraitsandpotentiallyinteractto
influencemultipletraitssimultaneously.Identifyingthesegenesandtheirinteractionswillplaya
criticalroleinpredictingindividualsusceptibilitytometabolicdiseaseandprioritizingdrugtargets
fortargetedtreatments(MooreandWilliams2009).However,despiteavailabilityoflarge-scale
genotypeandphenotypedatainmultiplehumanpopulations,littleisknownaboutthegenetic
architectureofmetabolicdisease-relatedtraits.
Thereareanumberofchallengesassociatedwithmappingthegeneticarchitectureofcomplextraits
inhumanpopulations.IncontrasttoMendeliantraits,inwhichasinglegeneticvariantisresponsible
forthevastmajorityofphenotypicvariation,complextraitsareinfluencedbymanyvariantswith
smalleffects,whicharedifficulttodetect.Largevariationinenvironmentalexposuresbetween
individualscaneasilyoverwhelmsmallgeneticeffects,compoundingtheproblem.Human
populations,moreover,haveintricatepopulationstructure(Rosenbergetal.2002)whichcancause
spuriousassociationsingeneticmappingexperiments(Pritchardetal.2000).Detectinggenetic
interactions,orepistasis,inhumansraisesadditionalchallenges.Epistaticinteractionstendtobe
weakerthanmaineffectsandcangenerateadditivegeneticvariance(HuangandMackay2016),and
variationinallelefrequenciesbetweenpopulationsmakesreplicationoftrueinteractionsbetween
populationsdifficult(Greeneetal.2009).
Highlydiversemulti-parentpopulations,suchastheDiversityOutbred(DO)mice(Svensonetal.
2012)[@Gatti]offerapowerfulalternativetohumanpopulationsformappingthegenetic
architectureofcomplextraits.Asanoutbredpopulation,theDOmicearepotentiallyabettermodel
ofhumanpopulationsthaninbredmice.BecausetheDOfoundersincludedthreestrainsrecently
derivedfromwildmice,thepopulationcontainsextensiveallelicvariationthatisevenlydistributed
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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acrossthegenome(Philipetal.2011;Svensonetal.2012;Loganetal.2013).Thisdensityof
polymorphismsallowsmuchmoreextensivemappingthancanbedoneintypicalcrossesbetween
inbredstrains,whichcansharelargeregionsofidenticalsequence(Yangetal.2011).Furthermore,
thebreedingparadigmintheDOisdesignedtomaintainallelicdiversity,reducelinkage
disequilibrium,andgenerateminimalpopulationstructure(Svensonetal.2012;Chesleretal.2016).
Thusvariationinallelefrequencydoesnotconfounddetectionofvarianteffectsorepistasisasit
doesinhumanpopulations,andeffectscanbemappedtorelativelynarrowgenomicloci,whichwill
enhancethediscoveryofgeneticinfluencesonphenotype.
Alargenumberoftraits,includingmanyclinicallyrelevanttraits,havebeenmeasuredinDOmice
(Svensonetal.2012;Bogueetal.2015)[and@Gatti].Whileheritable,fewofthesetraitshavea
singleQTLofexceptionaleffect[@Gatti].TheDOmicethusprovideanidealplatformfor
investigatingthegeneticarchitectureofcomplextraits.Theirphenotypicdiversitycombinedwith
extensivegeneticvariationthatisevenlydistributedandhighlyrecombinedfacilitatesdetectionof
bothgeneticmaineffectsandinteractionsinfluencingmanyclinicallyrelevanttraits.
Inthisstudyweusecombinedanalysisofpleiotropyandepistasis(CAPE){tyler2013cape}to
investigatethegeneticarchitectureofmultiplecomplextraitsrelatedtometabolicdiseasein474
maleandfemaleDOmicefedeitherahigh-fatorstandardchowdiet.Specifically,weanalyzed
epistasisinfluencingfatmass,leanmass,andcirculatinglevelsofcholesterol,triglycerides,and
leptin,aswellasthreegeneexpressionphenotypes.CAPEisanapproachthatcombinesinformation
acrossmultiplephenotypestoinferdirectedgeneticinteractions.Itinfersasinglemodelformultiple
quantitativetraits,andleveragesstatisticalpowerfrommultiplephenotypestoenhancethe
detectionofQTLandtheirinteractions.Withthisapproach,werecentlyanalyzedthegenetic
architectureofbodycompositionandbonedensityinawell-poweredF2mouseintercross(Tyleret
al.2016)thatrevealedalargenetworkofweakinteractionsthatgenerallyreducedphenotypic
variationacrossthepopulation.Hereweapplytheprinciplesofthisanalysistoinvestigatethe
contributionsofwithin-strainandbetween-strainepistaticinteractionsintheDO,augmentedby
interactiverolesofsexandhigh-fatdietinthenetwork.
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Results
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Transcriptswithtransgeneticeffectsclusterintofunctionallyenrichedmodules
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Becausewewereinterestedingeneticinteractionsthatinfluenceexpressiontraits,whichmust
includeatleastonetranseffect,wefirstfilteredthelivertranscriptometo3635transcriptsthatwere
influencedby
transgeneticloci(Methods).Weperformedweightedgenecorrelationnetwork
analysis(WGCNA)(LangfelderandHorvath2008)onthesetranscriptsandobtained11distinct
modules.UsingtheDatabaseforAnnotation,VisualizationandIntegratedDiscovery(DAVID)(Huang
etal.2009a;b)wefoundthatthreeofthesemoduleshadsignificantlyenrichedfunctional
annotations(Benjamini-adjustedp≤0.05):(1)cellularmetabolicprocess(MetabolismModule)(p
=6.3x10-17),(2)oxidationreductionprocess(RedoxModule)(p=7.7x10-7),and(3)immune
response(ImmuneModule)(p=5.2x10-15)(Table1).Weusedthemoduleeigengenesfromthese
modulesasphenotypesforCAPEanalysis(Methods)(Ghazalpouretal.2006;Philipetal.2014).We
refertothemhereafterbytheirfunctionalannotations.
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PleiotropicQTLinfluencephysiologicalandexpressiontraits
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Wecombinedthemoduleeigengenesdescribedabovewithfivephysiologicaltraits:leantissuemass,
fattissuemass,aswellascholesterol,leptin,andtriglyceridelevels.Fatmasswaslog-transformedto
reproduceamorelinearrelationshipwithleanmass(Forbes1987).Thesetraitsweremodestly
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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correlated(Figure1),implyingthatsomegeneticfactorsmaybesharedamongthetraits,while
othersmaybedistinct.Todeterminewhetherthetraitsherewereinfluencedbothbypleiotropicloci
andlocispecifictoindividualtraits,weperformedlinearregressiontoassociatethehaplotypeat
eachlocuswitheachofoureightphenotypes(Methods).Acrossalltraits,onlyoneQTLfor
cholesterolondistalChr1reachedgenome-widesignificance(permutation-basedp<0.05).
However,thereweremultiplelociwhereindividualhaplotypeshadsubstantialeffectsthat
potentiallycontributetopolygenicetiology(Figure2).Insomecases,asinglehaplotypehadan
apparenteffectonasinglephenotype.Forexample,apositiveeffectoftheNZOhaplotypeon
cholesterolcanbeseenondistalChr11(Figure2).Likewise,theA/Jhaplotypeatanearbylocushad
apositiveeffectonleptinlevels(Figure2).Otherlociwerepleiotropic.TheCASThaplotypeatathird
locusonchromosome11hadnegativeeffectsonfatmass,cholesterol,leptin,triglycerides,andthe
ImmuneModule(Figure2).ThiseffectwassharedtoalesserextentbythePWKhaplotypeinfat
mass,leptin,andtriglyceridelevels.Thiscomplexpatternofeffectssuggestsacomplexunderlying
geneticarchitecture.Thehaplotypeeffectsthatarecommonacrossmultiplephenotypesmay
representacommongeneticfactorinfluencingmultipletraits.Wecombinedthesecommonsignalsto
gaininformationaboutindividualloci.Haplotypesthatinfluenceasinglephenotype,forexamplethe
NZOhaplotypeeffectoncholesterol,providenon-redundantinformationthatcanbeusedtoidentify
geneticfactorswithspecificphenotypiceffects.
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Singularvaluedecompositionconcentratesfunctionalgeneticeffects
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Wedecomposedthetraitmatrixusingsingularvaluedecompositiontoobtaineigentraits(ETs)
(Figure3A).InouranalysisweusedthefirstthreeETs,whichcaptured88.3%oftheoverall
variance.ETsrecombinecovaryingelementsofthemeasuredtraits,andpotentiallyconcentrate
functionallyrelatedeffects.Forexample,leptin,cholesterol,andfatmass,alongwiththeRedoxand
ImmuneModules,wereaveragedinET2.ThisETappearstocapturetheCAST/PWKeffectonChr11
notedearlytoinfluencemultipletraits.(Figure3B).
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Anepistaticnetworkinvolvingallhaplotypesinfluencesphysiologicalandexpressiontraits
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Becausethereweremoremarkersgenotypedthancouldbetestedexhaustivelyinpairs,weuseda
subsetofhaplotypeswiththegreatesteffect-sizesinallthreeETs(seeMethods).Thehaplotypewith
greateststandardizedeffectfromeachpotentialQTLpeakwasretainedandthepeakwasfurther
sampledtokeep10%ofmarkerswithinit.Thisprocessyieldedatotalof515markersrepresenting
allsevenhaplotypeson17chromosomes(TheC57BL/6Jhaplotypewasexcludedbecauseweusedit
asthereferencestrain).Becausemarkerselectionwasbasedoneffectsize,thehaplotypeswere
unevenlyrepresented(Figure5A).A/Jwasthemosthighlyrepresentedhaplotypewith100markers
oneightchromosomes,andNODwastheleastrepresentedwith32.WSBalleleswerethemost
widelydistributed,beingselectedfrom12differentchromosomes.WeranCAPEonthesemarkers
andthefirstthreeETstofindanepistaticnetworkbetweenloci(Methods).
Theresultingnetworkconsistedof89interactionsamong49lociandtwocovariates(Figure4).All
haplotypesparticipatedinatleastoneinteraction(Figure5A).WSBhaplotypeswereinvolvedinthe
largestnumberofinteractions(32),whileNZOparticipatedinthefewest(8).Thenumberoftotal
interactionseachhaplotypeparticipatedindidnotcorrelatewithitsrepresentationinthe515
markersselectedfortheCAPEpipeline(Figure5A)(p=0.1).Thefinalepistaticnetworkwas
directed,meaningthatinteractionsmodelasourcemarkerthatactsonatargetmarker,andwecan
thusmeasurethenumberoftimeseachhaplotypewasthesourceofaninteractionorthetargetofan
interaction.Themajorityofhaplotypeswereroughlyevenlyrepresentedasbothsourcesandtargets.
However,the129haplotypewasatargetofinteractionsaboutfourtimesmorefrequentlythanit
wasasource,whiletheNZOhaplotypewasasourceabouttwiceasmanytimesasitwasatarget
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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(Figure5A).Thecovariates,sexanddietwerebothmuchmorefrequentlysourcesofinteractions
thantheyweretargets(Figure5A).
Theinteractionsbetweenhaplotypesweremostoftenbetweenstrainsratherthanwithin-strain
(Figure5B).Inter-straininteractionswereconcentratedamongthe129,WSB,NZOandA/J
haplotypes,whichareallintheMusmusculusdomesticussubspecies.CAST,M.musculuscastaneus,
interactedwitheachoftheotherstrainsrelativelyevenly,whilePWK,M.musculusmusculus,wasthe
mostisolatedstrain,anddidnotinteractatallwiththeNZOorNODhaplotypes.Theonlyhaplotype
withmultipleintra-straininteractionswasWSB.Thismaybeduetothewidesamplingofthe
selectedWSBallelesfrom12differentchromosomesresultinginmoreuniquelociwithpotentialfor
interactingwitheachother.
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Sexinteractedwithallfounderhaplotypes
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Sexsignificantlyaffectedallphysiologicaltraitsexceptleptinlevels.Thiseffectwaspositiveforall
phenotypesmeaningthatmaleshadhigherlogfatmass(males1.9g,females:1.7g,p=5.7x10-2),
leanmass(males:25.1g,females:18.3g,p<2x10-16),cholesterol(males110.4mg/dl,females:93.8
mg/dl,p=4.3x10-10),andtriglycerides(males:156.0mg/dl,females:115.0mg/dl,p=7.6x10-14).All
expressionmodulesweresignificantlylowerinmales(allp<2x10-16).Sexalsoparticipatedin
interactionswithgeneticloci.Themajorityofgeneticinteractionswithsex(12of15)involveda
suppressionofalleleeffectsbysex,indicatingthatthealleleshadlargereffectsinfemalesthanin
males.Allelesfromallfounderstrainswereaffected.Onelocus,theCASTalleleonChr11,enhanced
theeffectsofsex.Thealleleoverallhadnegativeeffectsonleptin,cholesterol,andleanmass,butin
males,thesemeasureswerehigherinthepresenceofthisallelethanexpectedfromtheadditive
model.Therewasalsoasinglelocus,theWSBalleleonChr17,thatsuppressedtheeffectsofsex,
indicatingthatmalescarryingthisallelehadlowerthanexpectedleanmass,fatmass,etc.For
example,boththisalleleandsexhadpositiveeffectsoncholesterol,butcholesterollevelsinmale
micecarryingthisWSBallelewerelowerthanexpectedfromtheadditivemodel.Finally,phenotypic
effectsofthemalesexwereenhancedbythehigh-fatdiet,suggestingthatmalesweremore
susceptibletotheeffectsofthehigh-fatdiet.
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Dietinteractedwithasubsetofparentalhaplotypes
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Dietsignificantlyincreasedlogfatmass(chow:1.6g,HF:2.1g,p<2x10-16),cholesterol(chow:85.8
mg/dl,HF:119.1mg/dl,p<2x10-16),andleptin(chow:7.7mg/dl,HF:19.7mg/dl,p<2x10-16)and
significantlydecreasedtriglyceridelevels(chow:146.7mg/dl,HF:124.3mg/dl,p=1x10-4).Italso
significantlydecreasedallexpressionmodules(allp<0.001).Similartosex,themajorityofgenetic
interactionswithdiet(fiveofseven)werethoseinwhichhigh-fatdietsuppressedgeneticeffects.
Thatis,thealleleshadgreaterphenotypiceffectinchow-fedmicethanmiceonthehigh-fatdiet.
Therewasonelocus,theCASTalleleonChr2,thatenhancedtheeffectsofdiet,indicatingthat
animalscarryingthisalleleweremoresusceptibletotheeffectsofthehigh-fatdiet.Theeffectsofdiet
werealsoenhancedbysex,asmentionedabove,indicatingthatmalesinthispopulationweremore
susceptibletotheeffectsofthehigh-fatdietthanfemales.
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Networkmotifshadbothredundantandsynergisticeffectsonphenotypes
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Tobetterunderstandtheoverallinfluenceofgeneticinteractionsontraits,weperformedanetwork
motifanalysisasdescribedin(Tyleretal.2016).Networkmotifsarecomposedofoneinteraction
betweentwoloci,eachofwhichhasamaineffectononephenotype(Figure6A).Theinteractioncan
eitherbesuppressingorenhancing,andthetwomaineffectscandrivethephenotypeeitherinthe
samedirection(coherent)orinopposingdirections(incoherent).Hereweinvestigatedtheeffectsof
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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networkmotifsontraitsintheDOandcompareourresultstoourpreviousresultsfromresultsfrom
anF2intercrossbetweeninbredstrainsinTyleretal.(2016)(Tyleretal.2016).
Onlyenhancing-incoherentandsuppressing-coherentmotifswerepresentintheDOepistatic
network(Figure6B).Theyinvolvedallparentalhaplotypesandwerepredominantlyinteractions
betweenhaplotypesfromdifferentparents(enhancing-incoherent:72%differentparental
haplotypes,suppressing-coherent:96%differentparentalhaplotypes).Incontrasttotheintercross,
theenhancing-incoherentmotifswerenotpredominantlybalancing,buttendedtodrivetraitsaway
fromthepopulationmean.Thevastmajorityofthesemotifs(92%)hadadestabilizingeffecton
phenotype,and80%drovethephenotypepastanyadditiveprediction(Figure7).Asubstantial
fractionofthesuppressing-coherentmotifs(25%)werenon-redundant,meaningtheypushed
phenotypesfartherfromthepopulationmeanthanpredictedbytheadditivemodel(Figure7).
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Discussion
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Traitsassociatedwithmetabolicdisease,suchascholesterollevels,bodyfatmass,andtriglyceride
levelshavecomplexgeneticarchitecture.Mappinggenesinfluencingthesetraitswillhelpidentify
mechanisticfactorsinfluencingthemand,togetherwithmolecularbiomarkers,mayultimately
providetargetsfortherapies.Mappingcomplexgeneticeffects,however,ischallenging,especiallyin
humanpopulationsinwhichenvironmentalfactorsandpopulationstructurecanoverwhelmweak
geneticeffects.Miceofferanexcellentalternativeaspre-clinicalmodelorganismsinwhichtodissect
complextraitsmechanistically.However,themajorityofinbredstrainsusedinmedicalresearchare
closelyrelatedtoeachother.Thehavelimitedphenotypicdiversityandlargegeneticblindspotsdue
toalackofgeneticvariantsbetweenthem.TheDOmiceprovideapowerfulalternativeplatformfor
fine-mappingcomplextraits.Theyharborimmensegeneticandphenotypicdiversity,andhave
minimalpopulationstructure,therebyallowingmuchmoredetailedassessmentsofcomplexgenetic
architectureinfluencingcomplextraits.ThegeneticdiversityintheDOdoescreateitsownissues,
however,inthatlargegeneticeffectscanbedifficulttofind.Usingstandardmappingmethods,we
andothershaveshownthatmosttraitsareinfluencedbymanyQTLwithsmalleffects(@gatti),and
fewQTLrisetogenome-widesignificance.HereweusedCombinedAnalysisofPleiotropyand
Epistasis(CAPE)tocombinemulti-dimensionalphenotypeinformationandtestforgenetic
interactionsinfluencingasuiteofrelatedtraits.Wefoundnumerousindividualeffectsandan
epistaticinteractionnetworkinfluencingbothphysiologicalandexpressiontraits.Theinteraction
effects,whichtendedtobeweak,identifiedgeneticelementsthatpotentiallyinfluencethetraitsand
informedonthegeneralgeneticarchitectureofthesetraits.
Thetwofactorswiththelargestinfluenceonmostphenotypesweresexanddiet.Sexinfluencedall
traitsexceptserumleptinlevels.Inournetwork,sexalsointeractedwith14geneticloci.Although
multiplesex-specificQTLhavebeenmappedinhumans(Weissetal.2006;Oberetal.2008),the
studiesareoftenoflowpowerandfewindividualresultshavebeenreplicated(Oberetal.2008).The
DOmiceofferapowerfulplatformtoinvestigatetheroleofsexincomplextraitsinmammalian
systems.Inourstudy,themajorityofthegeneticinteractionswithsexwereasuppressionofallele
effectsinmales.Theseallelesmayhelpidentifyimportantriskandprotectiveallelesformetabolic
diseaseinfemales.Forexample,the129alleleatalocusonChr19hadpositiveeffectsonthe
MetabolismExpressionModuleandtriglyceridelevels,suggestingthatthislocuscontainsagenethat
increasestriglyceridesthroughgeneexpressiondifferencesinmetabolicpathways.Theeffectsofthe
129alleleweresuppressedbysex,indicatingthatithadalargereffectinfemalesthanmales.
CombiningthealleleandinteractioninformationfromtheCAPEnetwork,wecangeneratea
hypothesisaboutthecausalgeneinthislocus.Therearesixgenesknowntoinfluencetriglyceridesin
theChr19.4locusandoneofthese,Sorbs1,hasacis129-specificeffectincreasingSorbs1expression
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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(Figure8A).Sorbs1isfurthermoreexpressedmorehighlyinfemales(p=0.002)(Figure8B),andis
significantlycorrelatedwithtriglyceridelevelsintheDOmice(r2=0.17,p<2x10-16).Previouswork
hasshownthatmicewithhomozygousdeletionsofthisgenehavereducedtriglyceridelevels
(Lesniewskietal.2007).Increasedexpressionduetothegain-of-function129alleleisconsistent
withincreasedtriglyceridesincarriers,andthereforethe129alleleofthisgenemayincreaseriskfor
elevatedtriglyceridelevelsinfemalemice.
Inadditiontosex,dietisanimportantfactorindeterminingriskofmetabolicdiseaseandtheir
relatedphenotypes.Thehigh-fatdietinourstudyhadasubstantialimpactonalltraitsexceptthe
MetabolismModule.High-fatdietenhancedtheeffectsofsexindicatingthatmalesintheDO
populationweremoresusceptibletotheeffectsofthedietthanfemales.Ithasbeenshownthat
inbredmaleB6micegainmoreweightandhavehigherbloodlipidprofileswhengivenahigh-fatdiet
(Hwangetal.2010).AndalthoughnotrepresentedintheDO,maleBALB/cAmicehavealsobeen
showntobemoresusceptiblethanfemalestoweightgainandhepaticlipidaccumulation(Nishikawa
etal.2007).Dietinteractedwithanumberofgeneticloci,andlikesex,mostlysuppressedtheeffects
oftheseloci,indicatingthatthealleleshadalargereffectinanimalsfedstandardchow.Multiple
studieshaveshowninteractionsbetweengenesanddietininfluencingfactorsrelatedtotraits
associatedwithmetabolicdisease(forreviewsee(Ordovas2006)).TheresolutionintheDOgenome
combinedwithinformationaboutgeneticinteractionswillhelpspeedidentificationofgenes
interactingwithdietandhelpelucidatehowhigh-fat,high-sucrosedietsleadtoobesityand
metabolicdisease,aswellashowhealthydietshelppreventtheseconditions.
Inadditiontotheinteractionswithsexanddiet,geneticlocialsointeractedwitheachotherto
influencephenotypesinnetworkmotifs.InapreviousstudyofanF2intercross(Tyleretal.2016),we
foundthatsuppressing-coherentandenhancing-incoherentmotifsweresignificantlyenrichedinthe
epistaticnetwork.InthisF2population,bothtypesofmotifstendedtohavemoderatingeffectson
phenotypes.
Thesuppressing-coherentnetworkmotifstendedtoreflectredundancy,whiletheenhancingincoherentinteractionshadabalancingphenotypiceffectdrivingphenotypestowardinbredstrain
means(Tyleretal.2016).Animalshomozygousforoneparentalalleleatbothinteractinglocihad
lessextremephenotypesthanthosewithamixofparentalallelesatthetwoloci(Tyleretal.2016).
Similartoourpreviousfindings,networkmotifsintheDOwerepredominantlyenhancingincoherentorsuppressing-coherent(Figure6B).However,incontrasttotheintercross,the
enhancing-incoherentmotifsfrequentlydrovetraitsfartherfromthepopulationmeanthan
predictedbytheadditivemodel.Themajorityofthesuppressing-coherentmotifshadredundant
effects,i.e.thetwolocihadlessthanadditiveeffects,butasubstantialfraction(36%)also
destabilizedphenotypes,drivingthemawayfromthepopulationmean.
Thisphenotypicdestabilizationislikelyduetothedifferenceinalleliccombinationsbetweenthe
multi-parentDOmiceandaclassicintercrossdesign.Inanintercrossallinteractionsbydefinition
arebetweenallelesfromasinglenon-referenceparent,whereasinteractionsintheDOweremost
frequentlybetweenallelesfromdifferentparentalancestries.Inbothdesigns,eachoftheparental
strainshasdevelopeditsownuniquesetofallelestomaintainquantitativetraitsatstrain
homeostasis.Inanintercross,accumulationofallelesfromasingleparentalstrainmay
combinatoriallyachievehomeostaticphenotypesforthatparent.Bycontrast,intheDOthemixingof
parentalallelesmayinsteaddestabilizephenotypesbydrivingthemtoextremesandcreatingthe
immensephenotypicdiversityseeninthispopulation.Furthermore,thatweseemoredestabilizing
interactionsamongtheenhancing-incoherentmotifsmayimplysomethingaboutmolecularpathway
structure.Wehypothesizethatsuppressing-coherentmotifsrepresentinteractionsbetweengenes
withinasinglepathway,whileenhancing-incoherentmotifsrepresentinteractionsbetweengenesin
different,butfunctionallyrelatedpathways.Thisisconsistentwithearlierworkonperturbationsof
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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fruitflysignalingpathways(Hornetal.2011;Carter2013).Thepatternsofstabilizingand
destabilizingmotifsinourstudysuggeststhatrecombiningparentalalleleswithinpathwaysiswell
toleratedandoftenredundant,whilerecombinationbetweenrelatedpathwaysmorefrequently
destabilizesphenotypes.
AlthoughthegeneticdiversityintheDOallowsrelativelyfinemapping,wecannotdefinitively
identifywhichgenesintheselociareresponsibleforthephenotypiceffects.Wecan,however,
combinetheinformationinepistaticinteractionswithestimatedfunctionalinteractionstogenerate
hypothesesaboutcausalgenes.Forexample,wefoundaninteractionbetweentheA/Jhaplotypeon
Chr9locus2(Chr9.2:5Mbto36Mb)andtheCASThaplotypeonChr2locus2(Chr2.2:123Mbto
133Mb)thatinfluencedtheImmuneModule.EachlocushadanegativemaineffectontheImmune
Module,andtheircombinedeffectwasredundantwiththeeffectoftheChr2.2locus(Figure9A).
Thispatternofeffectsindicatesaredundantinteractionandthepossibilitythatthecausalgeneson
thetwolocioperateinthesamepathway.Tofurtherinvestigatethispremise,weidentifiedallthe
genesinthetworegionsthathadstrain-specificpolymorphisms(A/JonChr9.2andCASTonChr
2.2),andfilteredthesetoincludegenesthathadbeenpreviouslyannotatedtothemammalian
phenotype(MP)term“immunephenotype”(seeMethods).WethenusedIntegrativeMulti-species
Prediction(IMP)(Wongetal.2015)toidentifythemostlikelyamongthesegenestointeract
functionally.ThisfilteringprocessidentifiedCasp4onChr9.2andIl1bonChr2.2asthemostlikely
genesinthesetwolocitointeract.IntheIMPnetwork,thetwogenesinteracteddirectlyinanetwork
functionallyenrichedforcytokineproductionandsecretion(p=4.3x10-12)(Motenkoetal.2015)
(Figure9B).InsupportofthehypothesisthatCasp4andIl1binteract,bothtranscriptsarecorrelated
withtheImmuneModule(Figure9C,Casp4:r2=0.48,p=2.6x10-28,Il1b:r2=0.49,p=1x10-30),and
witheachother(r2=0.32,p=7.4x10-13)(Figure8C).Casp4,alsoknownasCasp-11,isamemberof
thecysteine-asparticacidproteasefamilyandisessentialforIL1Bsecretion.Micewithhomozygous
mutationsofCasp4havedecreasedlevelsofcirculatingIL1B(Wangetal.1998).ThatCasp4is
directlyinvolvedinIL1Bsecretionisconsistentwiththeredundantgeneticinteractionweobserved
betweenChr9.2andChr2.2intheCAPEnetwork.Redundantinteractionsarehypothesizedtooccur
betweenvariantsencodinggeneswithinasinglepathway(AveryandWasserman1992;Lehner
2011).Eachvarianthasasimilareffectonthepathway,butbecausethepathwaycanonlybe
disruptedonce,thecombinationofthetwovariantsdidnothaveafurthereffectdespitebeingfrom
differentparentalstrains.Suchcombinatorial,polygeniccandidategeneswererevealedbyour
geneticinteractionanalysisthatidentifiedredundantgeneticeffects.
Elsewhereinthenetwork,wehypothesizethatgenesinteractinginenhancing-incoherentnetwork
motifsfunctionindistinctpathwaysthatneverthelessinfluenceeachother.Inadditiontothe
redundantinteractionabove,weprioritizedinteractinggenesinasecondinteractionbetweenthe
sameA/JhaplotypeonChr9.2anotherQTLonChr2.Thissecondlocus,Chr2locus4(Chr2.4:165
Mbto171Mb)representedaneffectoftheNODhaplotypeanddidnotoverlaptheCASTQTL(123
Mbto133Mb)thatalsointeractedwiththeChr9.2A/JQTL.ThisQTLthusrepresentsadistinct
interaction.TheA/JChr9.2andtheNODChr2.4lociinfluencedtheImmuneExpressionModulein
oppositedirections,andtogether,theydrovethetraittobeslightlymorenegativethanpredictedby
theadditivemodel(Figure10A).Followingthesamegeneselectionpipelinedescribedabove,we
identifiedCasp4againfortheChr9.2A/Jlocus,andSrcasalikelyinteractingpartnerintheChr2.4
NODlocus(Figure10B).Transcriptsforbothgenesaresignificantlycorrelatedwiththeimmune
expressionmodule(Casp4:r=0.47,p=6.3x10-28;Src:r=0.47,p=3.7x10-27)andwitheachother(r=
0.21,p=3.2x10-6)(Figure10C).IntheIMPnetworkCasp4andSrcoccupytwolobesofaconnected
graph,indicatingthattheyarelessdirectlyfunctionallyrelatedthanCasp4andIl1b.TheCasp4sideof
thenetworkisenrichedforgenesinvolvedininflammasomepathways(p=2.9x10-6)(Motenkoetal.
2015),whiletheSrcsideofthenetworkisenrichedforEGFRsignaling(p=2.7x10-4)(Motenkoetal.
2015).TheIL-1andEGFfamiliesofproteinsareupregulatedinhumankeratinocytesduringwound
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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healingandinpsoriasis,andtheyhavebeenshowntointeractsynergisticallyinupregulating
transcriptsinvolvedinantimicrobialdefenses(Johnstonetal.2011).Conversely,inhibitingEGFR
signalinginkeratinocytesreducestheirIL-1secretioninresponsetoStaphylococcusaureusinfection
(Simanskietal.2016).Insum,theseobservationssuggestthattheA/JalleleofCasp4andtheNOD
alleleofSrcmayinteracttoinfluenceimmune-relatedexpressioninmice.
OuranalysisofgeneticinteractionsinDOmicehasrevealedanumberofinterestingfeaturesofthe
geneticarchitectureofcomplextraitsrelatedtometabolicdisease.First,wedetectednumerous
significantgeneticinteractionsinfluencingbothphysiologicalandexpressiontraitsinanoutbred
population.Althoughtheseeffectsweresmallrelativetothemaineffectsweidentified,wewereable
todetectthembycombininginformationacrossmultiplephenotypes.Theinteractionsprimarily
involvedallelesfromdifferentparentalhaplotypes.Thispatternindicatesthatmulti-parent
populationsmaybemorepowerfulplatformsthanstandardintercrossesfordetectingepistasisdue
totheincreasedgeneticdiversity.Interactionsinanintercrossarebydefinitionbetweenallelesfrom
thesameparentalstrain,butintheDOinteractionswithinstrainhaplotypesarerelativelyrare.With
theadditionalallelicvariationintheDO,moregeneticcombinationswithdiversephenotypiceffects
arepresent.Second,wefoundthatnetworkstructureofgeneticinteractionsinoutbredmiceis
distinctfromthenetworkstructurewefoundpreviouslyinamouseintercross.Intheintercross
interactionsdescribedbynetworkmotifspredominantlyreducevariationintraits,drivingthem
towardtheparentalstrainmean.Incontrast,theenhancing-incoherentmotifsintheoutbredmice
tendedtodrivetraitsawayfromthepopulationmean.Theextremetraitsweremostfrequently
causedbyinteractionsbetweenallelefromdifferentparentalhaplotypes.Extremephenotypesupon
recombinationofallelesintheDOmayhavethebenefitofmakingepistasisinoutbredpopulations
easiertodetectthanepistasisinintercrossesbetweentwoinbredstrains.Finally,weshowedthatwe
canusegeneticinteractionsasinformationtoprioritizecandidategenesingenomicregions.
Interactionsbetweentwolociimplyafunctionalrelationshipbetweenelementsencodedinthetwo
loci.Bycombininginformationabouthaplotype-specificgeneticinteractionswithgenomicfunctional
data,liketheIMPnetwork,wecangenerateplausiblehypothesesregardingcausalgenes.The
hypothesesgeneratedbythismethodinthisstudyweresupportedbyexpressiondatanotusedin
thehypothesisgeneration.Togethertheseresultsspeaktothevalueofmulti-parentoutbred
populationsinthedissectionofthegeneticarchitectureofclinicallyrelevantcomplextraits.
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Methods
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Mice
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MicewereobtainedfromTheJacksonLaboratory(BarHarbor,ME)asdescribedin(Svensonetal.
2012)and@Gatti.Theanimalswerenon-siblingDOmicerangingfromgeneration4to11,andmales
andfemaleswererepresentedequally.AllanimalprocedureswereapprovedbytheAnimalCareand
UseCommitteeatTheJacksonLaboratory(AnimalUseSummary#06006).Micewerehousein
same-sexcageswithfiveanimalspercageasdescribedin(Svensonetal.2012)[email protected]
hadfreeaccesstoeitherstandardrodentchow(6%fatbyweight,LabDiet5K52,LabDiet,Scott
Distributing,Hudson,NH)orahigh-fat,high-sucrosediet(HFD)(EnvigoTekladTD.08811,Envigo,
Madison,WI)forthedurationofthestudyprotocol(26weeks).CaloriccontentoftheHFDwas45%
fat,40%carbohydratesand15%protein.Dietswereassignedrandomly.
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Phenotype Measurements
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Phenotypesweremeasuredasdescribedin(Svensonetal.2012)[email protected]
weeksofage,bloodwascollectedretro-orbitallyafteradministrationoflocalanesthetic.Cholesterol
andtriglyceridesweremeasuredusingtheBeckmanSynchronDXC600ProClinicalchemistry
analyzer.Leptinwasmeasuredinnon-fastedplasmapreparedaspreviouslydescribed(Svensonet
al.2012).LevelswereanalyzedusingtheMesoScaleDiscoveryelectrochemiluminescentsystem
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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accordingtothemanufacturer'srecommendedprotocol(MesoScaleDiagnostics,Rockville,MD).
Bodycomposition(leanmassandtotalmass)weremeasuredatage12weeksusingdualX-ray
absorptiometry(DEXA)usingaLunarPIXImusdensitometer(GEMedicalSystems).Fatmasswas
calculatedaslog(totalmass-leanmass).Measurementswereperformedattwotimepoints.All
measurementsinthisstudyweretakenfromthefirsttimepoint.
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Genetic analysis
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Genotypingwasperformedontailbiopsiesasdescribedin(Svensonetal.2012)usingtheMouse
UniversalGenotypingArray(MUGA).Asubsetoftheanimals(293)weregenotypedonthe
Megamuga(GeneSeek,Lincoln,NE).Theintensitiesfromthearrayswereusedtoinferthehaplotype
blocksineachDOgenomeusingahiddenMarkovmodel(HMM)(Gattietal.2014b).
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Merging Haplotype Reconstructions from Different Methods
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Genotypes were measured with the MUGA (7,854 markers), Megamuga (77,642 markers) and by
GBRS, which is a set of software tools that uses RNA-Seq data to reconstruct individual sample
genomes in multiparental population (MPP) (@Gatti, and http://https://github.com/churchilllab/gbrs).Tomergediplotypeprobabilitiesfromallsources,weinterpolatedmarkersonanevenly
spaced64,000markergrid(0.0238cMbetweenmarkers).
Transcriptomeprofiling
Transcriptome-wideexpressionlevelsweremeasuredasdescribedin(Chicketal.2016),(Mungeret
al.2014)and@Gatti.TotalliverRNAwasisolatedfromeachmouseandsequencedusingsingle-end
RNA-Seq(Mungeretal.2014).Transcriptswerealignedtostrain-specificgenomesfromtheDO
founders(Chicketal.2016).Weusedanexpectationmaximizationalgorithm(EMASE,
https://github.com/churchill-lab/emase)toestimatereadcounts.Readcountsineachsamplewere
normalizedusingupper-quantilenormalizationandarankZtransformationwasappliedacross
samples.
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Filteringtranscriptsfortranseffects
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Wewereinterestedinmappingeffectstotranscriptsthatwereinfluencedbydistant(trans)genetic
loci.Todeterminewhichtranscriptshadtransloci,wefirstusedDOQTL(Gattietal.2014a)tomap
QTLforalltranscriptsexpressedinatleast50samples(26,875transcripts).DOQTLeffectsusing
founderallelehaplotypeprobabilitiescalculatedasdescribedin(Gattietal.2014b).Inaddition,we
usedsex,dietandbatchasadditivecovariatesandusedhierarchicallinearmodelstocorrectfor
geneticrelatedness(Kangetal.2008).
Fromthismappingweidentifiedcis-eQTLsfortranscripts,whichwedefinedasasuggestiveeQTL
(LOD>=7.4)within2Mbpoftheencodinggene’stranscriptionstartsite.Foreachtranscript,we
regressedouttheeffectsofthecis-eQTL(Pierceetal.2014)andre-mappedQTLusingDOQTL.We
identified3635trans-eQTLsdefinedasaQTL(LOD>=7.4)onachromosomeotherthanthe
transcriptsencodinggeneoratleast10Mbawayonthesamechromosome.Additionally,forthe
followingclusteringanalysis,weusedtheresidualexpressionbyremovingtheeffectsofcishaplotypeandbatcheffectvialinearregression.TheprocedureisoutlinedinSupplementaryFigure
1.
WeightedGeneCo-expressionNetworkAnalysis
Co-expressiongenemoduleswereobtainedbyclusteringtrans-actingtranscriptsusingtheWGCNA
packageinR(LangfelderandHorvath2008;undefinedauthor2016).WGCNAcomputestheabsolute
valueofPearsoncorrelationforallgenepairsandgeneratesanadjacencymatrixbyraisingthe
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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correlationmatrixtoauser-definedpower.Wesetthepowertosixtoachieveanetworkwithscalefreedegreedistribution.Toconstructthemodulenetwork,WGCNAuseshierarchicalclusteringto
produceadendrogramofgenes.Individualbranchesofthedendrogramrepresentmodules,which
areclustersofhighlyco-expressedgenes.Themoduleswithsimilarexpressionprofilescanbe
mergedbasedontheircorrelation.Wesettheminimummodulesizeto30andtheminimumheight
formergingto0.25(correspondingtoaPearsoncorrelationof0.75)toobtainrelativelylargeand
distinctmodules.Thefirstprincipalcomponentforeachmodule(termedeigengenesinWGCNA)is
usedtorepresentthesummaryco-expressionpatternforeachmodule.Theseeigengenesare
hereafterreferredtoasmodulephenotypesforCAPEanalysis.Eachmodulewasassessedfor
functionalenrichmentusingtheDAVIDdatabase(Huangetal.2009a;b).TheGOenrichment
significancethresholdforallgeneontologyenrichmentanalyseswasp≤0.05,withBenjamini
correctionformultiplecomparisons.
Combinedanalysisofpleiotropyandepistasis
Combinedanalysisofpleiotropyandepistasis(CAPE)isamethodforderivinggeneticinteraction
networksofgeneticvariantsthatinfluencemultiplephenotypes{tyler2013cape}.Theopen-sourceR
packageofcapewasadapted(below)touseforDOmicewithextensiontomultipleallelesinour
analysis.
WebeganouranalysisbyregressingoutbreedinggenerationoutofeachtraitandapplyingarankZ
transformationtoeachphysiologicaltrait.Thesewerecombinedwiththethreemoduleeigengenes
representingsignificantlyenrichedmodulesfromWGCNA(seeabove).Wethenperformedsingular
valuedecomposition(SVD)onthetraitmatrixtoobtaineightorthogonaleigentraits(ET’s).TheET’s
combinecommonsignalsacrossalltraits.Inthisanalysis,weusedthefirstthreeETs,whichcaptured
88.3%ofthevariationinthetraits.Wethenperformedlinearregressiontoassociateeachmarker
witheachET.
Foreachmarkerweusedaseven-statemodeltoestimatetheeffectofthefounderhaplotypeson
eachtrait.WeusetheB6alleleasthereference,andthusB6allelesarenotexplicitlyincludedinour
finalresults.Wealsoincludedtwocovariates,sex(female:0,male:1)anddiet(chow:0,high-fat:1).
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Theindex𝑖isfrom1tonumberofsamplesand𝑗 isfrom1tonumberofET’s.
Pi,aistheprobabilityofeachalleleaatthelocus,andxc,iisthepresenceorabsenceofeachcovariate.
Weusedtheresultsofthesingle-locusregressiontoselectmarkersforthelocus-pairregressions.
Variantselectionforpairwiseregression
Becausethereweremoremarkersgenotypedthancouldbetestedinapairwiseregression,we
selectedasubsetofvariantsbasedonstandardizedeffectsize.Weselectedindividualhaplotypes(for
exampletheA/Jhaplotypeatmarker1)suchthathaplotypesfrommultiplefounderstrainsand
multiplechromosomeswouldberepresentedinthelocus-pairregression.Todothis,wepickedan
arbitrarythresholdandidentifiedhaplotypepeaksineffectsizethatroseabovethisthreshold.We
pickedthemarkerwiththelargesteffectsizeinthispeakandsampled10%oftheremaining
markersinthepeakuniformlyatrandom.Weprogressivelyloweredthethresholduntilwehad
sampledapproximately500individualvariants.(SupplementaryTable2)Thefinalnumberof
variantsselectedwas515,representingallhaplotypesacross17chromosomes.
Pairwiseregression
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Weexpressthefullmodelfortwovariantslabeled1and2as:
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Theindex𝑖isfrom1tonumberofsamplesandjisfrom1tonumberofET’s.
Pi,aistheprobabilityofeachalleleaatthelocus,andxc,iisthepresenceorabsenceofeachcovariate.
EijistheETforsample𝑖.P1,iandP2,iaretheprobabilitiesofthealleleateachoftwovariantsfor
samplei.P1,iP2,iistheinteractionoftwovariants,𝛽! and 𝛽! aretheeffectsoftwovariantsontheET𝑗,
and𝛽!" istheinteractioncoefficient.
Foreachmarkerpair,theregressioncoefficientsacrossallET’swerereparametrizedtoobtaintwo
newparameters(𝛿! and𝛿! ).The𝛿termsareindependentofphenotypeandcanbedefinedasthe
degreetowhichonevariantinfluencestheeffectoftheotheronthephenotypes.𝛿! representsthe
inferredgeneticactivityofthefirstvariantwhenthesecondvariantispresent.Anegative𝛿
coefficientindicatesonevariantsuppressinganother.Forexample,anegative𝛿! indicatesthat
variant1suppressestheeffectofvariant2onthatphenotype.The𝛿termsarecomputedintermsof
coefficientsfrompairwiseregressionasfollows:
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Next,the 𝛿 termsaretranslatedintodirectedvariables𝑚!" and𝑚!" ,whichdescribevariant-tovariantinfluencesthatfitallphenotypesviaindirectassociations.Theterm𝑚!" and𝑚!" aredirect
influencesofonevariantontheother,withnegativeinfluencesindicatingsuppressionandpositive
influencesindicatingenhancement.Theterms𝑚!" and𝑚!" aredefinedintermsof𝛿! and𝛿! :
!
!
𝑚!" = !!!! ,𝑚!" = !!!! 536
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!
!
Errorsareestimatedthroughstandardleast-squaresregressionandasecond-orderTaylor
expansionontheregressionparameters(Carteretal.2012).Wedefinedtheabsolutevalueofthe
ratioofanestimatedcoefficientanditsstandarderror(|β/se|)asthestandardizedeffecttoevaluate
themaineffectsofthevariantsonthephenotypesandtheinteractiveeffectsofthevariants.The
significancethresholdofthestandardizedeffectisdeterminedbasedongenotypepermutationtest
andadjustedformultipletesting.Toavoidfalsepositivesduetolinkagedisequilibrium(LD),we
excludedvariantpairswithPearson’scorrelationcoefficientabove0.5inthepairwiseregression.
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Permutationtesting
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Permutationtestingwasconductedtogeneratenulldistributionsofmparameters.Foreach
permutation,weshuffledtheETsrelativetogenotypes.Wethenperformedasinglelocusscanand
selectedthetop~500markersforapairwisemarkerscanasdescribedabove.Werepeatedthis
processuntil500,000markerpairsweretested.Wecombinedpermutationsacrossmarkerpairsto
generateasinglenulldistribution(Tyleretal.2014).Empiricalpvaluesforeachmodelparameter
werecalculatedandcorrectedusingfalsediscoveryrate(FDR){benjamini1995controlling}.
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Grouping linked markers
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Finalresultsarereportedforlinkageblocksratherthanindividualmarkers.Theblockswere
determinedasdescribedin(Tyleretal.2016).Briefly,foreachhaplotype,weusedthecorrelation
matrixbetweenvariantsasanadjacencymatrixtoconstructaweightednetwork,andusedthefast
greedycommunitydetectionalgorithminR/igraphtoestimateboundariesbetweenblocksofsimilar
markers(CsardiandNepusz2006).
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Phenotypic Effects of Motifs
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Foreachmotifintheepistaticnetwork,weexaminedthephenotypiceffectsofeachoftheindividual
lociaswellastheinteractioneffect.Foreachindividuallocus,wedividedtheanimalsintotwobins:
thosecarryingthealternateallele(e.g.atleastheterozygousfortheA/Jalleleatlocus1),andall
others.Wecalculatedthemeantraitvalueacrossalltraitsforbothgroups,anddefinedthemain
effectofthealleleasthedifferencebetweenthegroups.Thepredictedadditiveeffectwasthesumof
thetwomaineffects.Tocalculatetheactualeffectoftheinteraction,webinnedtheanimalsintotwo
groups:thosecarryingthealternatealleleatbothloci(e.g.atleastheterozygousfortheA/Jalleleat
locus1andatleastheterozygousfortheNODalleleatlocus2),andallothers.
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Prioritization of genes in interacting loci
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Weusedafunction-orientedmethodtogeneratehypothesesaboutwhichgenesininteracting
regionsmightbecontributingtotheepistaticeffectsinferredbyCAPE.Wefocusedontwo
interactionsthatinfluencedtheImmuneModule,themoduleeigengenefromthegenemodule
enrichedforimmunefunction.BothinteractionsinvolvedtheA/JhaplotypefromaregiononChr9.
ThisregioninteractedwiththeNODhaplotypeonChr2andtheCASThaplotypeonChr2to
influencetheImmuneModule.WefirstusedbiomaRtfoundallproteincodinggenesintheregionby
findingallgenesintheeffectsizepeakcreatedbythehaplotype(Durincketal.2005;2009).Weused
theRpackageSNPTools(Gatti)toquerytheSangerSNPdatabase(Keaneetal.2011;Yalcinetal.
2011)tofindgenesharboringvariantsprivatetothestrainofinterest.Thus,wefoundallprivateA/J
variantsintheregiondefinedbytheA/JeffectonChr9,andallvariantsprivatetoNODandCASTon
theChr2regionsdefinedbythesehaplotypeeffectsrespectively.
Becausethemaineffectsoftheseregionswererelatedtotheimmunemodule,wefurtherfilteredthe
genesineachregiontogenesannotatedtotheMousePhenotype(MP)Ontology(Smithetal.2005)
term“immunephenotype.”Wethenlookedforthemostprobablefunctionalinteractionsbetween
thegroupsofgenesfromeachchromosomalregionusingIntegrativeMulti-speciesPrediction(IMP)
(Wongetal.2015).IMPisaBayesiannetworkbuiltthroughintegrationofgeneexpressiondata,
protein-proteininteractiondata,geneontologyannotationsandotherdata.Itpredictsthelikelihood
thatpairsofgenesinteractfunctionallyinmultiplemodelorganismsandhumans.WeusedIMPto
findthehighestlikelihoodconnectedcomponentthatcontainedatleastonegenefromeach
chromosomalregionparticipatingintheepistaticinteraction.Weselectedthegenepairwiththe
highestlikelihoodofinteractingfunctionallyasourtopcandidategenepairfortheinteraction.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Figures
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Table1.FunctionalenrichmentforthreegeneexpressionmodulesfoundbyWGCNA.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Figure1. Correlationplotsforallphenotypesusedinthisstudy. Traits tend to be modestly correlated with each other. Physiological
traits and expression traits are positively correlated within their groups, but negatively correlated between groups. Malesareshown
asgreentrianglesandfemalesarebluesquares.Darkershadeindicateshigh-fatdiet(HF).
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Figure2.EffectsizesofeachstrainhaplotypeonChr11onfivetraits:leanmass,logfatmass,cholesterol,triglycerides,andthe
metabolismexpressionmodule.Individualhaplotypeshavedistincteffectsontraits.TheCASThaplotypeondistalChr11has
pleiotropiceffectsonalltraits(greenboxes).TheNZOandA/Jhaplotypeshaveindividualeffectsoncholesterol(bluebox)andleptin
(yellowbox)respectively.
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Figure3.Eigentrait(ET)selectionfromdecompositionoftraits.A)Traitsweredecomposedbysingularvaluedecomposition(SVD)to
orthogonalETs.ThegraybarsshowtheproportionofthetotalvariancecapturedbyeachET,andtheheatmapshowsrelative
contributionsofeachtraittoeachET.B)HaplotypeeffectsforChr11onthefirstthreeETs.
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Figure4.Thefinallocusinteractionnetwork.Maineffectsareshowningrayconcentriccircles.Significantmaineffectsarecoloredfor
thehaplotypethathadthesignificanteffects.Positive(brown)andnegative(blue)effectsareonlyshownforSexandDiet.Interactions
areshownasarrowsbetweenchromosomalregionsandarecoloredtoindicateanenhancingeffect(brown)orasuppressingeffect
(blue).
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Figure5.Tabulationofalleleparticipationinepistaticinteractions.A)Thenumberoftimeseachhaplotypewasthesourceofan
interactionorthetargetofaninteraction,andthetotalnumberofinteractionseachhaplotypeparticipatedin.Rowsaresortedbytotal
numberofinteractions.Thefinaltwocolumnsindicatehowmanymarkersweretestedinthepairwisemarkertestsforeach
haplotype,andhowmanychromosomesthesemarkerswerefoundon.Darkerbluehighlightingindicateshighercounts.B)Adetailed
countoftheinteractionseachhaplotypeparticipatedinwitheachotherhaplotypeandeachcovariate(SexandDiet).Darkerblue
squaresrepresenthighercounts.Countsof0arerepresentedbydashesforvisualizationpurposes.
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Figure6.NetworkMotifsA)Cartoonsdepictingfourtypesofnetworkmotif.Eachmotifconsistsoftwomarkersinteractingtoinfluence
onephenotype.Themarkerscaneitherhavethesame(coherent)ordifferent(incoherent)maineffect.Theinteractionbetweenthem
canbeeitherenhancingorsuppressing.B)Countsofeachdifferentmotiftypeforeachphenotype.Darkershadesofblueindicate
highercounts.
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Figure7.Phenotypiceffectsofenhancing-incoherent(left)andsuppressing-coherent(right)networkmotifs.“Main1”and“Main2”
showtheaveragedeviationfrompopulationmeaninnormalizedphenotypeforanimalscarryingthealternatealleleatmarker1and
marker2inthemotifrespectively.Marker1andmarker2aresortedsuchthatmarker1alwayshasthesmaller(morenegative)effect.
“Additive”showsthepredictedadditiveeffectgiventheMain1andMain2effects.“Actual”showstheactualdeviationfromthe
populationmeanofanimalscarryingthealternatealleleatbothmarker1andmarker2inthemotifs.Linesaredrawntoconnectdots
fromindividualmotifs.Bluelinesindicatemotifsthatbringphenotypesclosertothepopulationmeanthanpredictedbytheadditive
model.Brownlinesindicatemotifsthatdrivethephenotypefartherfromthepopulationmeanthanpredictedbytheadditivemodel.
Redlinesindicateasubsetofmotifsthatcreatemoreextremephenotypesthanpredictedbyanyadditivemodel.
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Figure8.Evidencesupportingaroleofthe129alleleofSorbs1increasingtriglyceridelevelsthroughincreasedtranscription.A)LOD
score(top)andhaplotypecoefficients(bottom)forexpressionofSorbs1.TheverticalblacklinemarksthepositionofSorbs1inthe
genomeonChr19.B)ExpressionofSorbs1inmaleandfemaleDOmice(a.u.=arbitraryunits)..C)Correlationbetweentriglyceride
levelsandSorbs1expression(r=1.7,p<2x10-16).Femalemiceareshowninblue,andmalesareshowningreen.
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Figure9.Geneprioritizationininteractingloci.A)EffectsofaninteractionbetweenChr9locus2(Chr9.2)andChr2locus2(Chr2.2).
TheA/JhaplotypeonChr9.2andtheCASThaplotypeonChr2.2haveindividualnegativeeffectsontheImmuneModule.Together,
theyhavethesameeffectastheCASTalleleonChr2.2,indicatingaredundantinteraction.Errorbarsshowstandarderror.B)The
transcriptsofCasp4,onChr9,andIl1b,onChr2,arebothcorrelatedwiththeImmuneModule.Thetranscriptsarealsocorrelatedwith
eachother.C)ThefunctionalconnectionsbetweenCasp4andIl1bfromtheIMPnetwork.Thetwogenesarepredictedtointeract
functionallywithhighconfidence.
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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Figure10.Geneprioritizationininteractingloci.A)EffectsofaninteractionbetweenChr9locus2(Chr9.2)andChr2locus2(Chr2.4).
TheA/JhaplotypeonChr9.2hasanegativeeffectontheImmuneModuleandtheNODhaplotypeonChr2.4hasapositiveeffectonthe
ImmuneModule.Together,theyhaveaneffectsimilartothatoftheA/JalleleonChr9.2.Errorbarsshowstandarderror.B)The
transcriptsofCasp4,onChr9,andSrc,onChr2,arebothcorrelatedwiththeImmuneModule.Thetranscriptsarealsocorrelatedwith
eachother.C)FunctionalconnectionsbetweenSrcandCasp4fromtheIMPnetwork.Thetwogenesarepredictedtointeract
functionallybyoperatinginrelated,butdistinctpathways.
bioRxiv preprint first posted online Jan. 5, 2017; doi: http://dx.doi.org/10.1101/098681. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
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