1

Dateofrevision:11February2016

Wordcount(text):3837

Geneticevidenceforcausalrelationshipsbetweenmaternalobesity-related

traitsandbirthweight

JessicaTyrrell,PhD1,2;RebeccaC.Richmond,PhD3,4,12;TomM.Palmer,PhD5,6*;BjarkeFeenstra,

PhD7;JananiRangarajan,MS8;SarahMetrustry,MSc9;AlanaCavadino,MSc10,11;LaviniaPaternoster,

PhD12;LorenL.Armstrong,PhD13;N.ManekaG.DeSilva,PhD12;AndrewR.Wood,PhD1;Momoko

Horikoshi,MD,PhD14,15;FrankGeller,MSc7;RonnyMyhre,PhD16;JonathanP.Bradfield,BS17;Eskil

Kreiner-Møller,MD18;VilleHuikari,MSc19;JodieN.Painter,PhD20;Jouke-JanHottenga,PhD21,22;

CatherineAllard,BSc23,24;DianeJ.Berry,PhD11;LuigiBouchard,PhD,MBA24-26;ShiktaDas,PhD27;

DavidM.Evans,PhD3,12,28;HakonHakonarson,MD,PhD17,29,30;M.GeoffreyHayes,PhD13;Jani

Heikkinen,MSc31;AlbertHofman,PhD32;BridgetKnight,PhD1;PenelopeA.Lind,PhD20;MarkI.

McCarthy,MD,PhD14,15,33;GeorgeMcMahon,PhD3;SarahE.Medland,PhD20;MadsMelbyeMD,

DMSc7,34;AndrewP.Morris,PhD15,35;MichaelNodzenski,MS8;ChristophReichetzeder,MD36,37;

SusanM.Ring,PhD3,12;SylvainSebert,PhD19,38;VerenaSengpiel,PhD39;ThorkildI.A.Sørensen,

MD12,40,41;GonnekeWillemsen,PhD21,22;EcoJ.C.deGeus,PhD21,22;NicholasG.Martin,PhD20;Tim

D.Spector,MD9;ChristinePower,PhD11;Marjo-RiittaJärvelin,MD,PhD19,38,42-44;HansBisgaard,MD,

DMSci18;StruanF.A.Grant,PhD17,29,30;EllenA.Nohr,PhD45;VincentW.Jaddoe,PhD4,32,46;Bo

Jacobsson,MD,PhD16,39;JeffreyC.Murray,MD47;BertholdHocher,MD,PhD36,48;AndrewT.

Hattersley,DM1;DeniseM.Scholtens,PhD8;GeorgeDaveySmith,DSc3,12;Marie-FranceHivert,

MD49-51;JanineF.Felix,PhD4,32,46;ElinaHyppönen,PhD11,52,53;WilliamL.Lowe,Jr.,MD13;TimothyM.

Frayling,PhD1*;DebbieA.Lawlor,PhD3,12*;andRachelM.Freathy,PhD1,12*fortheEarlyGrowth

Genetics(EGG)Consortium

1. InstituteofBiomedicalandClinicalScience,UniversityofExeterMedicalSchool,RoyalDevonand

ExeterHospital,BarrackRoad,Exeter,EX25DW,UK.2. EuropeanCentreforEnvironmentandHumanHealth,UniversityofExeter,TheKnowledgeSpa,

Truro,TR13HD.3. SchoolofSocialandCommunityMedicine,UniversityofBristol,OakfieldHouse,OakfieldGrove,

Bristol,BS82BN,UK.4. TheGenerationRStudyGroup,ErasmusMC,UniversityMedicalCenterRotterdam,P.O.Box2040,

3000CA,Rotterdam,theNetherlands.5. DivisionofHealthSciences,WarwickMedicalSchool,UniversityofWarwick,Coventry,UK.6. DepartmentofMathematicsandStatistics,LancasterUniversity,Lancaster,UK.7. DepartmentofEpidemiologyResearch,StatensSerumInstitut,Copenhagen,Denmark.

2

8. DepartmentofPreventiveMedicine,NorthwesternUniversityFeinbergSchoolofMedicine.9. DepartmentofTwinResearch,King'sCollegeLondon,St.Thomas'Hospital,London,UK.10. CentreforEnvironmentalandPreventiveMedicine,WolfsonInstituteofPreventiveMedicine,

BartsandtheLondonSchoolofMedicineandDentistry,QueenMaryUniversityofLondon.11. Population,PolicyandPractice,UCLInstituteofChildHealth,UniversityCollegeLondon,UK.12. MedicalResearchCouncilIntegrativeEpidemiologyUnitattheUniversityofBristol,UK.13. DivisionofEndocrinology,MetabolismandMolecularMedicine,NorthwesternUniversity

FeinbergSchoolofMedicine14. OxfordCentreforDiabetes,EndocrinologyandMetabolism,UniversityofOxford,UK.15. WellcomeTrustCentreforHumanGenetics,UniversityofOxford,Oxford,UK.16. DepartmentofGenesandEnvironment,DivisionofEpidemiology,NorwegianInstituteofPublic

Health,Oslo,Norway.17. CenterforAppliedGenomics,TheChildren’sHospitalofPhiladelphia,Philadelphia,Pennsylvania,

USA.18. CopenhagenProspectiveStudiesonAsthmainChildhood(COPSAC),FacultyofHealthSciences,

UniversityofCopenhagen,Copenhagen,Denmark&DanishPediatricAsthmaCenter,CopenhagenUniversityHospital,Gentofte,Denmark.

19. InstituteofHealthSciences,UniversityofOulu,Oulu,Finland20. QIMRBerghoferMedicalResearchInstitute,LockedBag2000,RoyalBrisbaneHospital,Herston,

Qld4029,Australia.21. EMGOInstituteforHealthandCareResearch,VUUniversityMedicalCenter,Amsterdam,The

Netherlands.22. DepartmentofBiologicalPsychology,VUUniversityAmsterdam,VanderBoechorststraat1,1081

BTAmsterdam,TheNetherlands.23. DepartmentofMathematics,UniversitedeSherbrooke,QC,Canada.24. CentrederechercheduCentreHospitalierUniversitairedeSherbrooke,Sherbrooke,QC,Canada.25. ECOGENE-21andLipidClinic,ChicoutimiHospital,Saguenay,QC,Canada.26. DepartmentofBiochemistry,UniversitédeSherbrooke,Sherbrooke,QC,Canada.27. DepartmentofPrimaryCareandPublicHealth,ImperialCollegeLondon.28. UniversityofQueenslandDiamantinaInstitute,TranslationalResearchInstitute,Brisbane,

Queensland,Australia.29. DivisionofHumanGenetics,TheChildren’sHospitalofPhiladelphia,Philadelphia,Pennsylvania,

USA.30. DepartmentofPediatrics,PerelmanSchoolofMedicine,UniversityofPennsylvania,Philadelphia,

Pennsylvania,USA.31. FIMMInstituteforMolecularMedicineFinland,HelsinkiUniversityHelsinki,FI-00014,Finland.32. DepartmentofEpidemiology,ErasmusMC,UniversityMedicalCenterRotterdam,P.O.Box2040,

3000CA,Rotterdam,theNetherlands.33. OxfordNationalInstituteforHealthResearch(NIHR)BiomedicalResearchCentre,Churchill

Hospital,Oxford,UK.34. DepartmentofMedicine,StanfordUniversitySchoolofMedicine,Stanford,California,USA.35. DepartmentofBiostatistics,UniversityofLiverpool,LiverpoolL693GA,UK.36. InstituteofNutritionalScience,UniversityofPotsdam,Germany37. CenterforCardiovascularResearch/Charité,Berlin,Germany.38. DepartmentofEpidemiologyandBiostatistics,SchoolofPublicHealth,MedicalResearch

Council-HealthProtectionAgencyCentreforEnvironmentandHealth,FacultyofMedicine,ImperialCollegeLondon,UK

39. DepartmentofObstetricsandGynecology,SahlgrenskaAcademy,SahgrenskaUniversityHospital,Gothenburg,Sweden.

40. InstituteofPreventiveMedicine,BispebjergandFrederiksbergUniversityHospital,CapitalRegion,Copenhagen,Denmark

41. NovoNordiskFoundationCenterforBasicMetabolicResearchandDepartmentofPublicHealth,FacultyofHealthandMedicalSciences,UniversityofCopenhagen,Copenhagen,Denmark

42. DepartmentofChildrenandYoungPeopleandFamilies,NationalInstituteforHealthandWelfare,Aapistie1,Box310,FI-90101Oulu,Finland.

43. BiocenterOulu,UniversityofOulu,Oulu,Finland.

3

44. UnitofPrimaryCare,OuluUniversityHospital,Kajaanintie50,P.O.Box20,FI-90220Oulu,90029OYS,Finland.

45. ResearchUnitofObstetrics&Gynecology,InstituteofClinicalResearch,UniversityofSouthernDenmark,Odense,Denmark.

46. DepartmentofPediatrics,ErasmusMC,UniversityMedicalCenterRotterdam,P.O.Box2040,3000CA,Rotterdam,theNetherlands.

47. DepartmentofPediatrics,UniversityofIowa,IowaCity,Iowa,USA.48. TheFirstAffiliatedHospitalofJinanUniversity,Guangzhou,510630,China.49. DepartmentofPopulationMedicine,HarvardPilgrimHealthCareInstitute,HarvardMedical

School,Boston,MA.50. DiabetesCenter,MassachussettsGeneralHospital,Boston,MA.51. DepartmentofMedicine,UniversitedeSherbrooke,QC,Canada.52. CentreforPopulationHealthResearch,SchoolofHealthSciences,andSansomInstitute,

UniversityofSouthAustralia,Adelaide,Australia.53. SouthAustralianHealthandMedicalResearchInstitute,Adelaide,Australia.

*Theseauthorsjointlydirectedthiswork

Correspondingauthors:Dr.RachelM.FreathyUniversityofExeterMedicalSchoolRoyalDevonandExeterHospitalBarrackRoadExeter,UKTel:+44(0)1392408238Email:r.freathy@ex.ac.ukProf.DebbieA.LawlorMRCIntegrativeEpidemiologyUnitattheUniversityofBristolOakfieldHouse,OakfieldRoad,Bristol,UKTel:+44(0)1173310096Email:d.a.lawlor@bristol.ac.ukProf.TimothyM.FraylingUniversityofExeterMedicalSchoolRoyalDevonandExeterHospitalBarrackRoadExeter,UKTel:+44(0)1392408256Email:t.m.frayling@ex.ac.uk

4

Structuredabstract

Importance:Neonatesborntooverweight/obesewomenarelargerandathigherriskofbirth

complications.Manymaternalobesity-relatedtraitsareobservationallyassociatedwithbirth

weight,butthecausalnatureoftheseassociationsisuncertain.

Objective:Totestforgeneticevidenceofcausalassociationsofmaternalbodymassindex(BMI)and

relatedtraitswithbirthweight.

Design,SettingandParticipants:WeusedMendelianrandomizationtotestwhethermaternalBMI

andobesity-relatedtraitsarecausallyrelatedtooffspringbirthweight.Mendelianrandomization

makesuseofthefactthatgenotypesarerandomlydeterminedatconceptionandarethusnot

confoundedbynon-geneticfactors.Datawereanalysedon30,487womenfrom18studies.

ParticipantswereofEuropeanancestryfrompopulation-orcommunity-basedstudieslocatedin

Europe,NorthAmericaorAustraliaandparticipatingintheEarlyGrowthGenetics(EGG)

Consortium.Live,term,singletonoffspringbornbetween1929and2013wereincluded.Wetested

associationsbetweenageneticscoreof30BMI-associatedsinglenucleotidepolymorphisms(SNPs)

and(i)maternalBMIand(ii)birthweight,toestimatethecausalrelationshipbetweenBMIandbirth

weight.Analyseswererepeatedforotherobesity-relatedtraits.

Exposures:GeneticscoresforBMI,fastingglucoselevel,type2diabetes,systolicbloodpressure

(SBP),triglyceridelevel,HDL-cholesterollevel,vitaminDstatusandadiponectinlevel.

MainOutcome(s)andMeasure(s):Offspringbirthweightmeasuredbytrainedstudypersonnel(n=2

studies),frommedicalrecords(n=10studies)orfrommaternalreport(n=6studies).

Results:Amongthe30,487newbornsthemeanbirthweightinthevariouscohortsrangedfrom

3325gto3679g.ThegeneticscoreforBMIwasassociatedwitha2g(95%CI:0,3g)higheroffspring

birthweightpermaternalBMI-raisingallele(P=0.008).Thematernalgeneticscoresforfasting

glucoseandSBPwerealsoassociatedwithbirthweightwitheffectsizesof8g(95%CI:6,10g)per

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glucose-raisingallele(P=7x10-14)and-4g(95%CI:-6,-2g)perSBP-raisingallele(P=1x10-5),

respectively.A1standarddeviation(1SD≈4kg/m2)geneticallyhighermaternalBMIwasassociated

witha55g(95%CI:17,93g)higherbirthweight.A1-SDgeneticallyhighermaternalfastingglucose

(≈0.4mmol/L)orSBP(10mmHg)wereassociatedwitha114g(95%CI:80,147g)higheror-208g(95%

CI:-394,-21g)lowerbirthweight,respectively.ForBMIandfastingglucosethesegenetic

associationswereconsistentwiththeobservationalassociations,butforSBP,thegeneticand

observationalassociationswereinoppositedirections.

ConclusionsandRelevance:InthisMendelianrandomizationstudyofmorethan30,000women

withsingletonoffspringfrom18studies,geneticallyelevatedmaternalBMIandbloodglucoselevels

werepotentiallycausallyassociatedwithhigheroffspringbirthweight,whereasgeneticallyelevated

maternalsystolicbloodpressurewasshowntobepotentiallycausallyrelatedtolowerbirthweight.

Ifreplicated,thesefindingsmayhaveimplicationsforcounselingandmanagingpregnanciestoavoid

adverseweight-relatedbirthoutcomes.

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Introduction

Neonatesborntooverweightorobesewomenaremorelikelytobelargeforgestationalage.1The

precisemechanismsunderlyingthisassociationandtheextenttowhichconfoundingfactors

contributearepoorlyunderstood.Itisimportanttounderstandwhichmaternaltraitsarecausally

associatedwithbirthweightbecausethismay(i)facilitatetargeteddevelopmentofinterventionsto

betestedinrandomizedcontrolledtrials,and(ii)enableclear,evidence-basedrecommendationsin

pregnancy.

Maternaloverweightandobesityarekeyriskfactorsforgestationaldiabetes.2Evenintheabsence

ofdiabetes,obesewomenhavehigherglucoselevelsthannormalweightwomen,despitea

controlleddiet.3Theassociationbetweengestationaldiabetesandhigherbirthweightiswell

documented4,andmaternalglucoselevelsbelowthosediagnosticofdiabetesalsoshowstrong

associationswithbirthweight.5

Thefetusofanoverweightorobesewomanmaybeexposedtotheconsequencesofhigher

maternaltriglyceridelevelsandbloodpressure,lowerlevelsofHDL-cholesterol(HDLc)and

adiponectinandlowervitaminDstatus1,6,7(Box1).Thesematernalobesity-relatedtraitshavebeen

variablyassociatedwithbirthweightinobservationalstudies:highertriglyceridesandlowerHDLc

withhigherbirthweight8,9;higherbloodpressurewithlowerbirthweight10;lowervitaminDstatus

withlowerbirthweight11;andloweradiponectinwithhigherbirthweight12.However,associations

arenotalwaysconsistentlyobservedandmaybeconfounded,forexamplebymaternal

socioeconomicstatusandassociatedbehaviourssuchassmokinganddiet.Furthermore,thehigh

inter-correlationofobesity-relatedtraitscomplicatesdeterminationofcausalrelationshipsinan

observationalsetting.

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MaternalgenotypesmaybeusedinaMendelianrandomization13,14approachtoprovideevidenceof

apotentialcausalassociationbetweenmaternaltraitsandbirthoutcomes(Figure1).Mendelian

randomizationisanalogoustoarandomizedcontrolledtrial:genotypes,whicharerandomly

allocatedatconception,arelargelyfreefromconfoundingandcanbeusedtoestimatethepossible

causaleffectsofmaternaltraits.Inthisstudy,geneticvariantswereselectedtocalculategenetic

scoresrepresentingmaternalBMIandeachof7obesity-relatedmaternaltraits.Thepotentialcausal

relationshipbetweenmaternalBMIandeachrelatedtraitwasestimatedbytestingassociations

betweenmaternalgeneticriskscoresandoffspringbirthweights.

8

Methods

Studyparticipants

Singlenucleotidepolymorphism(SNP)genotypedatawereusedfromatotalof30,487womenfrom

18population-orcommunity-basedstudieslocatedinEurope,NorthAmericaorAustralia.Thebirth

weightofonechildpermotherwasincluded(seeeTable1forfulldetailsofparticipant

characteristicsandeTable2forgenotypinginformation).Birthweightwasmeasuredbytrained

studypersonnel(n=2studies),frommedicalrecords(n=10studies)orfrommaternalreport(n=6

studies).Theoffspringyearsofbirthwerefrom1929to2013.Multiplebirths,stillbirths,congenital

anomalies,birthsbefore37weeksgestationandindividualsofnon-Europeanancestrywere

excluded.Informedconsentwasobtainedfromallparticipants,andstudyprotocolswereapproved

bythelocalregionalorinstitutionalethicscommittees.

Selectionofmaternalobesity-relatedtraitsandSNPs

InadditiontoBMI,traitswereselectedthatareassociatedwithmaternalobesityandmayaffect

fetalgrowththroughtheintrauterineenvironment.Theireffectsweremodelledinthedirections

hypothesisedbytheirrelationshipstomaternalBMI(Box1)

SNPsknowntoberobustlyassociated(P<5x10-8)withBMIandeachobesity-relatedtraitwere

selected.FulldetailsoftheselectedSNPsareprovidedineTable3.SNPsassociatedwith(i)fasting

glucoseand(ii)type2diabeteswereusedtorepresentmaternalglycemia.TheType2diabetesSNPs

wereconsideredtorepresentexposuretomaternaldiabetesinpregnancy,includinggestational

diabetes,givenoverlapbetweentype2andgestationaldiabetesgeneticsusceptibilityvariants.15For

bloodpressure,SNPswereselectedthatareprimarilyassociatedwithsystolicbloodpressure(SBP),

thoughallalsoshowstrongevidenceofassociationwithdiastolicbloodpressure.ForvitaminD

status,twoSNPswithhypothesisedrolesinvitaminDsynthesiswereusedtorepresent25(OH)D

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levels(anindicatorofoverallvitaminDstatus),aspreviouslyrecommended.16,17Furtherdetailsof

SNPselectionareprovidedintheeMethods.

Aweightedgeneticscorewascalculatedforeachmaternaltrait(seeeMethodsforfulldetails).Very

fewoftheselectedSNPshavebeentestedinpregnancy.Geneticscoreswerevalidatedby

confirmingthateachwasassociatedwithitsrespectivematernaltrait,measuredduringpregnancy

(withtheexceptionofBMI,forwhichthepre-pregnancyvaluewasused).Maternalpre-pregnancy

BMIwasavailablefromregistrydata(N=2studies)orcalculatedfromself-reportedweightand

height(N=3studies).IntheAvonLongitudinalStudyofParentsandChildren(ALSPAC)study,the

self-reportwasvalidatedwithaclinicmeasure18.Detailsoftraitsmeasuredinpregnancyandtheir

sourcesaregivenineTable4.Ineachavailablestudy,linearregressionofthematernaltrait(e.g.

BMI)againstthegeneticscorewasperformed,adjustingformaternalage.Toconfirmthat

associationsbetweeneachgeneticscoreanditsrespectivematernaltraitweresimilarinthesame

individualsduringandafterpregnancy,availabledatawereusedfromtwolongitudinalstudies(the

AvonLongitudinalStudyofParentsandChildren[ALSPAC]andtheExeterFamilyStudyofChildhood

Health[EFSOCH]).TocheckthatthestrategyforSNPselectionhadresultedingeneticscoresthat

werespecifictoeachmaternaltrait,theassociationwastestedbetweeneachofthe8geneticscores

andtheothermaternaltraits,andindicatorsofmaternalsocio-economicstatusandsmoking.

TestingthehypothesisthatmaternalBMIandobesity-relatedtraitsareassociatedwithbirth

weightthroughtheintra-uterineenvironment.

ForBMIandeachrelatedmaternaltrait,twoMendelianrandomizationapproacheswereusedto

testthehypothesis.First,associationsweretestedbetweengeneticscoresrepresentingmaternal

traitsandoffspringbirthweightusingthemaximumnumberofparticipants(i.e.foreachtrait,those

withgeneticscoreandoffspringbirthweightdataavailable,irrespectiveofwhethertheyhadthe

maternaltraitmeasured).Anassociationofthegeneticscorewithbirthweightwouldsupporta

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possiblecausalrelationshipbetweenthetrait(e.g.pre-pregnancyBMI)andbirthweight,butwould

notprovideinformationonthesizeofthatassociation.Second,weperformedanalysesinthosewith

themeasuredtraitthatenabledanestimateofthesizeofapossiblecausalrelationship.The

analysestookintoaccounttheassociationbetweeneachgeneticscoreandthematernaltraitit

represented(e.g.BMI),inadditiontotheassociationbetweenthesamegeneticscoreandbirth

weight.Thesetworesultswereusedtocalculateanassociationbetweenthematernaltrait(e.g.

BMI)andbirthweightthatwasfreefromconfounding.Thissecondapproachmeasuresthe

relationshipbetweenvariationinmaternalBMI(orBMI-relatedtrait)andbirthweightthatis

attributableonlytogeneticfactors(seeFigure1foranexplanationofthemethod).Foreach

approachmeta-analysiswasusedtocombinedatafromindividualstudies(seeeMethods).

Usingthefirstapproach,weinvestigatedtheassociationbetweeneachgeneticscoreand(i)birth

weightand(ii)ponderalindex(anindexofneonatalleanness,measuredinkg/m3).Withineach

study,birthweightorponderalindexZ-scoreswereregressedagainsteachmaternalgeneticscore,

adjustedforoffspringsexandgestationalage.Analysesusingthetype2diabetesgeneticscorewere

repeatedafterexcludingparticipantswithpre-existingandgestationaldiabetes.Analysesusingthe

SBPgeneticscorewererepeatedafterexcludingparticipantswithpre-eclampsiaandexistingor

gestationalhypertension.

Thegeneticestimateoftheassociationbetweeneachmaternaltraitandbirthweight/ponderal

indexfromthesecondapproachwascomparedwiththecorrespondingobservationalassociation.

Toobtaintheobservationalestimateslinearregressionwasperformedusingbirthweightor

ponderalindexasthedependentvariable,andeachof7maternaltraitsasindependentvariables,

adjustingforsexandgestationalage.Therewasinsufficientinformationonmaternaltype2diabetes

prevalence,soitwasnotpossibletoestimatethecausalrelationshipforthattrait.Fulldetailsofthe

analysisareprovidedintheeMethods.

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EstimatinghowmuchoftheassociationbetweenmaternalBMIandbirthweightismediatedby

fastingglucose

Availabledatawereusedtoestimatetheapproximatecausalrelationshipbetweena1SD(≈4kg/m2)

highermaternalBMIand(i)fastingglucoseand(ii)SBP.Usingeachofthoseestimates,theresultsof

theMendelianrandomizationanalyseswererescaledtorepresenttheeffectsoffastingglucoseand

SBPthatcouldbedirectlycomparedwiththecausalrelationshipbetweena1SDhigherBMIand

birthweight(seeeMethodsforadetaileddescriptionofthemethod).

Correctingfordirectfetalgenotypeeffects

Genotypesofmaternal-fetalpairswereavailableinupto8studies(N=upto11,494).Analyseswere

repeatedincludingthefetalgenotypeateachSNPinthemodel,tocorrectforpotentialconfounding

causedbydirecteffectsofthefetalgenotype.

12

Results

Thecharacteristicsofincludedparticipantsfromthe18contributingstudiesareshowninTable1.

Amongthe30,487newbornsthemeanbirthweightrangedfrom3325gto3679g.Themeanpre-

pregnancyBMIwasavailablein11studiesandrangedfrom22.78kg/m2to24.83kg/m2.Themean

maternalageatdelivery,availablein16studies,rangedfrom24.5yearsto31.5years.

Therewasevidenceofassociationbetweeneachgeneticscoreanditscorrespondingmaternaltrait

measuredinpregnancy(P≤0.003;Table2).ForBMI,fastingglucoseandSBP,datafrommultiple

studiesweremeta-analysed,withsimilareffectestimatesbetweenstudiesforBMIandfasting

glucose(Phet>0.05)andevidenceofheterogeneityforSBP(Phet=0.04).Theeffectsizesofassociations

betweenmaternaltraitsandtheirrespectivegeneticscoreswereverysimilarwhencomparedinthe

sameindividualsduringandoutsidepregnancy,withtheexceptionoftheSBPgeneticscorewhich

hadaweakereffectduringpregnancy(eTable5).Therewasnoevidenceofassociationbetweenany

geneticscoreandpotentiallyconfoundingvariables.Noindividualgeneticscorewasassociatedwith

anyoftheothermaternaltraits,exceptforthegeneticscoreforBMI,whichwaspositively

associatedwithSBP(P<0.003Bonferroni-correctedfor15tests;eTable6).

GeneticevidenceforapossiblecausalassociationbetweenhighermaternalBMIandhigherbirth

weight

ThematernalBMIgeneticscorewasassociatedwithhigherbirthweight(Table3)andponderal

index(eTable7)withsimilareffectsizesbeforeandafteradjustingforpossibleeffectsoffetal

genotype.Usingthegeneticscoretoquantifythepossiblecausalassociation,a1SDgenetically

highermaternalBMI(equivalentto4kg/m2)wasassociatedwitha55g(95%CI:17,93)higher

offspringbirthweight.Afteradjustingforfetalgenotype,theestimatedeffectwas104g(95%CI:32,

176)(Table4).TheseMendelianrandomizationcausalestimatesweresimilartotheobservational

13

associationof62g(95%CI:56,70)per1SD(4kg/m2)highermaternalBMI(Figure2).Similarresults

wereobtainedforponderalindex(eTable8andeFigure1).

Geneticevidenceforapossiblecausalassociationbetweenhighermaternalfastingglucoseand

higherbirthweight,butnoassociationwithmaternallipidsoradiponectin

Thematernalfastingglucoseandtype2diabetesgeneticscoreswereassociatedwithhigherbirth

weight(Table3)andponderalindex(eTable7)withsimilareffectsizeestimatesbeforeandafter

adjustingforfetalgenotype,andbeforeandafterexcludingpre-existingandgestationaldiabetes.

Usingthegeneticscoretoestimatethepossiblecausalrelationship,a1SD(0.4mmol/L)genetically

highermaternalglucosewasassociatedwitha114g(95%CI:80,147)higherbirthweight.After

adjustingforfetalgenotype,theassociationwas145g(95%CI:91,199)(Table4).Thesegenetic

estimatesweresimilartotheobservationalassociationof92g(95%CI:80,104)per1SD(0.4mmol/L)

highermaternalglucose(Figure2).Similarresultswereobtainedforponderalindex(eTable8and

eFigure1).

Thematernaltriglyceridegeneticscorewasnotassociatedwithoffspringbirthweight(Table3)or

ponderalindex(eTable7).Usingthegeneticscoretoestimatethepossiblecausalrelationship,a

geneticallyhighermaternaltriglyceridelevelwasnotassociatedwithoffspringbirthweightandthe

95%confidenceintervalsaroundthegeneticestimateexcludedtheobservationalassociation

betweenmaternaltriglyceridesandbirthweight(P=0.007testingdifferencebetweengeneticand

observationalassociation;Table4;Figure2).Likewise,thegeneticestimateofthepossibleeffectof

maternaladiponectinlevelsonoffspringbirthweightwasdifferentfromtheobservational

association(P=0.002).ThegeneticscoreforHDLcwasnotassociatedwithbirthweightorponderal

indexandtheanalysiswasconsistentwithnocausalrelationship,howeverthiscouldnotbe

distinguishedfromthenegativeobservationalassociationbetweenmaternalHDLcandbirthweight.

14

Geneticevidenceforapossiblecausalassociationbetweenhighersystolicbloodpressureand

lowerbirthweight

ThematernalSBPgeneticscorewasassociatedwithlowerbirthweight(Table3)andponderalindex

(eTable7)withsimilareffectsizeestimatesbeforeandafteradjustingforfetalgenotype,andbefore

andafterexcludingmaternalpre-eclampsiaandhypertension.Usingthegeneticscoretoestimate

thepossiblecausalrelationship,a1SD(10mmHg)geneticallyhighermaternalSBPwasassociated

witha-208g(95%CI;-394,-21)loweroffspringbirthweight.Afteradjustingforfetalgenotype,the

estimatedeffectwas-151g(95%CI:-390,89)(Table4).Thegeneticestimateoftheassociation

betweenmaternalSBPandbirthweightinthefullsampleofwomenwasintheoppositedirectionto

theobservationalassociation(P=0.01fordifferencebetweengeneticandobservationalassociations;

Table4;Figure2).Similarresultswereobtainedforponderalindex(eTable8andeFigure1).

ThematernalgeneticscoreforlowervitaminDstatuswasassociatedwithlowerbirthweight

(P=0.03;Table3).However,theestimatedcausalrelationshipwasnotsignificantlydifferentfrom

zero(theestimatedchangeinbirthweightfora10%geneticallylowermaternal25[OH]Dlevelwas-

26g(95%CI:-54,2);Table4,Figure2).

Associationsbetweenthegeneticscoresandbirthweightwereconsistentacrossstudies

Associationsbetweenmaternalgeneticscoresandoffspringbirthweightweresimilarbetween

studiesinthemeta-analysis(Table3;Phet>0.05).Wheredatawerecombinedfromobservational

analyses,theassociationsbetweenmaternalfastingglucoseorSBPandbirthweightweresimilar

(Phet>0.05),andtherewasevidenceofheterogeneityfortheBMI-birthweightobservational

association(Table4;Phet=0.03).

Exposureofthefetustohighermaternalfastingglucoseisunlikelytoexplainalloftheassociation

betweenhighermaternalBMIandhigheroffspringbirthweight

15

ToestimatehowmuchoftheassociationbetweenmaternalBMIandbirthweightmightbe

mediatedbyfastingglucose,theBMIandfastingglucosegeneticscoreswereused:a1SD(≈4

kg/m2)geneticallyhighermaternalBMIwasassociatedwitha0.34SD(≈0.14mmol/L)higher

maternalfastingglucose.FromtheMendelianrandomizationanalyses,1SD(≈0.4mmol/L)

geneticallyhighermaternalfastingglucosewasassociatedwitha114g(95%CI:80,147)higherbirth

weight.Consequently,itwaspredictedthata0.34SDhigherfastingglucosewouldbeassociated

witha114g×0.34=39g[95%CI:27,50]higherbirthweight.Thisapproximationisbroadlysimilarto

thetotalestimatedeffectofa1SDhigherBMIonbirthweight(55g[95%CI:17,93]).However,using

thesamemethodwiththeBMIandSBPgeneticscoresweestimatedthata1SDhighermaternalBMI

wouldbeassociatedwitha-40g[95%CI:-75,-4]lowerbirthweightviaitsassociationwithmaternal

SBP(eFigure2),whichwouldopposethepositiveassociationwithmaternalfastingglucose.

16

Discussion

ThisstudyprovidesevidenceforapossiblecausalassociationbetweenmaternalBMIandoffspring

birthweight.A4kg/m2geneticallyhighermaternalBMI(a1SDrise)wasassociatedwitha55g(95%

CI:17,93)higheroffspringbirthweight.Inaddition,a0.4mmol/l(1SD)geneticallyhighercirculating

maternalfastingglucosewasassociatedwitha114g(95%CI:80,147)higherbirthweight,whilea10

mmHggeneticallyhighermaternalSBPwasassociatedwitha-208g(95%CI:-394,-21)lowerbirth

weight.TheseresultsprovideevidencethatgeneticallyelevatedmaternalglucoseandSBPhave

directionallyoppositecausalassociationswithbirthweight.Theestimatedassociationsbetween

thesematernaltraitsandbirthweight(eitherincreasedorreduced)aresubstantialandofclinical

importance.Theysupporteffortstomaintainhealthygestationalglucoseandbloodpressurelevels

toensurehealthyfetalgrowth.ThepositiveassociationbetweenmaternalBMIandbirthweight

maybepartiallymediatedbytheeffectofhigherBMIoncirculatingmaternalfastingglucose.There

wasnoevidenceofassociationwithageneticscoreformaternaltriglycerides,whichhavealsobeen

hypothesisedtobeimportantcontributorstohigherbirthweightinoverweightorobesewomen.

Otherlipids,orspecificsubclassesoftriglycerides,mightbeimportantbutrequirefurtherstudy.

Theseresultsprovidegeneticevidenceofacausalassociationbetweenmaternalglycemiaandbirth

weightandponderalindex,eveninwomenwithnopre-existingorgestationaldiabetes,whichis

consistentwithpublishedobservationaldata.5Apossibleexplanationforthisfindingisthatwomen

withahighergeneticscorefortype2diabeteshaverelativelyhigherglucoselevelsinpregnancy,as

aresultofinadequatebetacellcompensationinresponsetogestationalinsulinresistance,19,20

leadingtoincreasedplacentalglucosetransferandfetalinsulinsecretion,21andconsequentlyhigher

birthweight.

Thesedatadidnotsupportacausalassociationbetweenmaternaltriglyceride,HDLcoradiponectin

levelsandbirthweightorponderalindex.Thegeneticassociationsbetweenmaternaltriglycerides

17

andadiponectinandbirthweightwerenull,incontrasttotheobservationalassociations,suggesting

thattheobservationalassociationsseenhere,andinotherpublishedstudies8,9,12,areconfounded.

TheMendelianrandomizationanalysisshowedthatthepositiveobservationalassociationbetween

SBPandbirthweightisconfounded,mostlikelybyBMI,whichisbothanimportantriskfactorfor

higherSBPinpregnancyandpositivelyassociatedwithbirthweight.1Usinggeneticvariantsthatare

independentofconfoundingbyBMI,itwasdemonstratedthatgeneticallyhighermaternalSBPis

associatedwithlowerbirthweight,evenafterexcludingpre-eclampsiaandhypertension.The

precisionofourestimateofthechangeinbirthweightper1SDinmaternalSBPcouldbeaffectedby

theheterogeneitybetweenstudiesinthegeneticscore-SBPassociation(P=0.04,I2=76.0%;Table2).

However,associationsbetweentheSBPgeneticscoreandbirthweightwereconsistentacrossall13

meta-analyzedstudies(P=0.14,I2=30.4%;Table3)andsupportiveofacausalassociationbetween

highermaternalSBPandlowerbirthweight.Thesefindingssupportobservationalassociations

betweenmaternalSBPandbirthweightthatwereadjustedforawiderangeofconfounders,22and

areconsistentwithlaboratoryandpopulationstudiessuggestingalinkbetweenhypertensive

disordersofpregnancyandimpairedfetalgrowthduetoplacentalpathology.23Thereareincreasing

concernsabouttheeffecttheobesityepidemicmighthaveonbirthsize,viagreatermaternalBMI.

However,thefocusofthatconcernhasbeenlargelyonincreasedbirthsizeasaresultofgreater

maternalglucoseandotherfetalnutrients.Ourfindingssuggestthatthereareopposingeffectsof

maternalbloodpressureandglucose.

PublishedMendelianrandomizationanalysesprovideevidencethathigherBMIiscausallyassociated

withlowervitaminDstatus,6andevidencefrommultipleobservationalstudiessuggeststhatlower

maternalvitaminDisassociatedwithlowerbirthweight.11,24OuranalysisofthevitaminDgenetic

scoreprovidedsomeevidencetosupportapossiblecausalassociationwithbirthweight,butthis

requiresfurtherexplorationinlargernumbersofpregnancies.

18

Socio-economicfactorsandrelatedbehaviourssuchassmokingarekeyconfoundersof

observationalassociationsbetweenmaternalBMI(orBMI-relatedtraits)andoffspringbirthweight,

sincetheyareassociatedwithbothvariables(seeeTable9forademonstrationoftheseassociations

intheALSPACstudy).Thegeneticscoresusedinouranalyseswerenotassociatedwithsocio-

economicfactorsorsmoking,andthisillustratesakeystrengthoftheMendelianrandomization

approach:sincegenotypesaredeterminedatconception,suchconfoundingisavoided.

Therearesomelimitationstothisstudy.Despiteattemptstomaximisespecificityofthegenetic

scores,wecannotfullyexcludethepossibilitythattheselectedgeneticvariantsactonmorethan

onematernaltrait.Althoughallavailableinformationwasused,therewaslimitedpowertodetect

associationsbetweenthegeneticscoresandothertraits.Forexample,theknownassociation

betweenBMI-associatedvariantsandtriglyceridelevelswasnotdetected.25Withthepotentialfor

high-throughputmetabolomicstudiesandagrowingpublicdatabaseofgeneticassociations,26-28

futurestudieswillimprovethespecificity(fordifferentlipidsub-fractions)ofselectedgenetic

variants.

Despitethelargesampleinthisstudy,statisticalpowertodetectcausalrelationshipswaslimitedfor

somematernaltraits(seeeMethodsandeTable10forpowercalculations).Thetotalsample

provided>99%powertodetectassociationsatP<0.05betweenbirthweightandgeneticscoressuch

asfastingglucoseandsystolicbloodpressurethatexplainatleast0.1%varianceinbirthweight.

However,largersamples(N>80,000)willbeneededtoconfidentlydetectorruleout(i)the

associationwithvitaminDstatussuggestedbyourdata,or(ii)smallerpositiveornegativecausal

associationsbetweenmaternaltriglycerides,HDLcoradiponectinandbirthweight.

19

Whileadjustingforthefetalgeneticscoreswasnecessarytoseparatematernaleffectsfromthe

directeffectsofgeneticvariantsinthefetus,thiscouldpotentiallyintroducebiasviaassociation

withpaternalgenotypes.AssortativematingforBMIcouldadditionallyresultinacorrelation

betweenmaternalandpaternalgenotypes,leadingtosimilarbias.However,afather’sgeneticscore

wouldonlyconfoundtheMendelianrandomizationestimatesifthefather’sphenotypewererelated

tobirthweight,andwefoundonlyveryweakassociationsoffathersBMIandrelatedtraitswith

offspringbirthweight(eTable11).Anotherpotentialbiascouldbeinducedbytheuseofthegenetic

scoreforSBP,whichwasderivedfromagenome-wideassociationstudyofbloodpressure

conditionalonBMI.SinceBMIisalsoassociatedwithbirthweight,thiscouldbiastheresults.

However,similarresultswereobtainedusinganalternativegeneticscorethatwasunadjustedfor

BMI(eMethods).

InMendelianrandomizationanalysis,aweakstatisticalassociationbetweenageneticscoreanda

maternaltrait(duetolowvarianceexplainedand/orsmallsamplesize)hasthepotentialtocause

weakinstrumentbiastowardstheobservationalresults.29Theproportionsofmaternaltraitvariance

explainedbythegeneticscoresaremodestinourstudy(Table2).However,thelargeoverallsample

sizeensuredthatthepossiblecausalassociationsidentifiedareunlikelytobeduetoweak

instrumentbias(seeeMethods).

OuranalysesassumethatmaternalBMIandrelatedtraitsarelinearlyassociatedwithoffspringbirth

weight.Wehavenottestedfornon-linearassociationswhich,inaMendelianrandomizationdesign,

wouldrequireverylargenumbers30.However,formaternalBMI,fastingglucoseandsystolicblood

pressure,thereisobservationalevidenceofsuchlinearassociationsacrossthedistribution,withno

evidenceofthresholdorcurvilinearassociations5,10,31.

20

Conclusions

InthisMendelianrandomizationstudyofmorethan30,000womenwithsingletonoffspringfrom18

studies,geneticallyelevatedmaternalBMIandbloodglucoselevelswerepotentiallycausally

associatedwithhigheroffspringbirthweight,whereasgeneticallyelevatedmaternalsystolicblood

pressurewasshowntobepotentiallycausallyrelatedtolowerbirthweight.Ifreplicated,these

findingsmayhaveimplicationsforcounselingandmanagingpregnanciestoavoidadverseweight-

relatedbirthoutcomes.

21

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24

ARTICLEINFORMATION

AuthorContributionsDr.FreathyandProfsLawlorandFraylinghadfullaccesstoallofthedataintheALSPAC,EFSOCHandHAPO(non-GWAS)studiesandaccesstosummarydatafromallcontributingstudiesandtakeresponsibilityfortheintegrityofthedataandaccuracyofthedataanalysis.Studyconceptanddesign:J.Tyrrell,D.A.Lawlor,T.M.Frayling&R.M.FreathyAnalysisandinterpretationofdata:J.Tyrrell,R.C.Richmond,S.Metrustry,B.Feenstra,A.Cavadino,E.Hyppönen,M-FHivert,J.F.Felix,W.L.Lowe,D.A.Lawlor,T.M.Frayling&R.M.FreathyDraftingmanuscript:J.Tyrrell,R.C.Richmond,S.Metrustry,B.Feenstra,A.Cavadino,E.Hyppönen,M-FHivert,J.F.Felix,W.L.Lowe,D.A.Lawlor,T.M.Frayling&R.M.Freathy

Criticalrevisionofmanuscriptforimportantintellectualcontent:Allauthors

Statisticalanalysis:N.M.G.DeSilva,A.R.Wood,G.McMahon,D.M.Evans,E.Kreiner-Møller,F.Geller,C.Allard,J-J.Hottenga,J.P.Bradfield,M.G.Hayes,D.M.Scholtens,L.L.Armstrong,J.Rangaraja,M.Horikoshi,M.I.McCarthy,M.Nodzenski,L.Paternoster,S.Das,V.Huikari,J.N.Painter,S.E.Medland,P.A.Lind,D.J.Berry,R.Myhre,V.Sengpiel,J.Tyrrell,R.C.Richmond,S.Metrustry,B.Feenstra,A.Cavadino,J.F.Felix,D.A.Lawlor&R.M.Freathy

Genotyping:D.M.Evans,S.M.Ring,J.C.Murray,L.Bouchard,J.F.Felix,J-J.Hottenga,H.Hakonarson,T.I.A.Sørensen,N.G.Martin,E.A.Nohr,T.D.Spector,S.F.Grant,D.A.Lawlor,T.M.Frayling&R.M.Freathy

Individualstudydesign:T.D.Spector,B.Jacobsson,C.Power,N.G.Martin,S.Serbert,T.I.A.Sørensen,M.Järvelin,B.Hocher,M.G.Hayes,W.L.Lowe,S.F.Grant,H.Hakonarson,J.P.Bradfield,E.J.C.deGeus,V.W.Jaddoe,A.Hofman,M.Melbye,J.C.Murray,H.Bisgaard,G.DaveySmith,A.T.Hattersley,E.Hyppönen,M-FHivert,J.F.Felix,W.L.Lowe,D.A.Lawlor,&T.M.Frayling

Phenotypingincontributingstudies:T.D.Spector,B.Jacobsson,C.Power,E.Hyppönen,N.G.Martin,T.I.A.Sørensen,E.A.Nohr,C.Reichetzeder,B.Hocher,S.F.Grant,H.Hakonarson,J.P.Bradfield,G.Willemsen,V.W.Jaddoe,M-FHivert,M.Melbye,F.Geller,B.Feenstra,E.Kreiner-Møller,H.Bisgaard,G.DaveySmith,S.M.Ring,D.A.Lawlor,B.Knight,A.T.Hattersley

25

ConflictsofInterestandFinancialDisclosuresNoconflictsofinterestwerereported.Funding/SupportFunding/supportofauthorsisasfollows(fundingdetailsforindividualstudiesarereportedintheSupplementaryMaterial).T.M.FandA.R.WaresupportedbytheEuropeanResearchCouncilgrant:323195SZ-24550371-GLUCOSEGENES-FP7-IDEAS-ERC;A.T.H.andM.I.M.areWellcomeTrustSeniorInvestigators;M.I.M.isanNIHRSeniorInvestigator.R.M.F.isaSirHenryDaleFellow(WellcomeTrustandRoyalSocietygrant:104150/Z/14/Z);J.T.isfundedbytheERDFandaDiabetesResearchandWellnessFoundationFellowship;RCRisfundedbytheWellcomeTrust4-yearstudentship(GrantCode:WT083431MF).D.A.L.,G.D-S.,D.M.E.andS.R.allworkinaUnitthatreceivesfundingfromtheUniversityofBristolandtheUKMedicalResearchCouncil(MC_UU_1201/1/5,MC_UU_1201/1andMC_UU_1201/4).D.A.L.issupportedbyawardsfromtheWellcomeTrust(WT094529MAandWT088806),USNationalInstituteofHealth(R01DK10324)andisanNIHRSeniorInvestigator(NF-SI-0611-10196).D.M.EandS.E.M.weresupportedbyAustralianResearchCouncilFutureFellowship(FT130101709andFT110100548).B.FissupportedbyanOakFoundationScholarship.L.B.isajuniorresearchscholarfromtheFondsdelarechercheensantéduQuébec(FRQS)andamemberoftheFRQS-fundedCentrederechercheduCHUS.M.F.H.isaFRQSresearchscholarsandwasawardedaClinicalScientistAwardbytheCanadianDiabetesAssociationandtheMaudMentenAwardfromtheInstituteofGenetics–CanadianInstituteofHealthResearch(CIHR).C.A.wasawardedtheCIHR-FrederickBantingandCharlesBestCanadaGraduateScholarships.V.W.J.issupportedbytheNetherlandsOrganizationforHealthResearchandDevelopment(ZonMw–VIDI016.136.361).A.P.M.isaWellcomeTrustSeniorResearchFellow(grantnumberWT098017).T.S.isholderofanERCAdvancedPrincipalInvestigatoraward.RoleoftheSponsorsThefundingagencieshadnoroleinthedesignandconductofthestudy;collection,management,analysisandinterpretationofdata;preparation,review,approvalofmanuscript;ordecisiontosubmitmanuscriptforpublication.PreviousPresentationsThisworkwaspresentedattheDiabetesUKAnnualProfessionalConference2014,5-7March,Liverpool,UK.AdditionalContributionsWeareextremelygratefultotheparticipantsandfamilieswhocontributedtoallofthestudiesandtheteamsofinvestigatorsinvolvedineachone.Theseincludeinterviewers,computerandlaboratorytechnicians,clericalworkers,researchscientists,volunteers,managers,receptionistsandnurses.Foradditionalstudy-specificacknowledgements,pleaseseeSupplementaryMaterial.ThisresearchhasbeenconductedusingtheUKBiobankresource.

26

FIGURE/BOXLEGENDS

Box1.Thematernalobesity-relatedtraitshypothesizedtocauseincreasedordecreasedfetalgrowth,

based on observational associations with birth weight: body mass index (BMI)1; fasting glucose5;

gestationalortype2diabetes32;triglycerides9;HDL-cholesterol8;systolicbloodpressure10;vitaminD

status(asindicatedby25-hydroxyvitaminD,25[OH]Dlevel)11;adiponectin12.

Figure1

PrincipleofMendelianrandomization:Ifamaternaltraitcausallyinfluencesoffspringbirthweight,

thenariskscoreofgeneticvariantsassociatedwiththattraitwillalsobeassociatedwithbirth

weight.Sincegenotypeisdeterminedatconception,itshouldnotbeassociatedwithfactorsthat

normallyconfoundtheassociationbetweenmaternaltraitsandbirthweight(e.g.socio-economic

status).Estimatesofthegeneticscore-maternalphenotypeassociation(w)andthegeneticscore-

birthweightassociation(x)maybeusedtoestimatetheassociationbetweenthematernaltrait

variationthatisduetogeneticscore,andbirthweight(y=x/w),whichisexpectedtobefreefrom

confounding.Iftheestimatedcausalrelationship,y,isdifferentfromtheobservationalassociation

betweenthemeasuredmaternalphenotypeandbirthweight,thiswouldsuggestthatthe

observationalassociationisconfounded(assumingthattheassumptionsoftheMendelian

randomizationanalysesarevalid).14Thedashedlineconnectingmaternaltraitwithfetalgrowthhas

noarrow,toindicatethatthecausalnatureoftheassociationisuncertain.Itisimportanttoadjust

forpossibledirecteffectsoffetalgenotype(z).

Figure2.Comparisonoftheobservationalwiththegeneticchangeinbirthweight(ingrams)fora1

standarddeviation(SD)changeineachmaternalobesity-relatedtrait.For25[OH]Dandadiponectin,

wepresentthechangeinbirthweightfora10%changeinmaternaltraitlevelbecausethese

variableswereloggedforanalysis.ThegeneticchangewasestimatedfromMendelian

randomizationanalysis,inwhichageneticscorewasusedtoestimatethepossiblecausal

27

relationshipbetweenthematernaltraitandbirthweight.Thegeneticestimateispresentedtwice:in

thesecondcaseitwasadjustedforfetalgenotypeusingasubsetofavailablestudies.Theerrorbars

representthe95%confidenceintervalsaroundtheeffectsizeestimates.Formaternalpre-

pregnancyBMIandfastingglucose,the95%confidenceintervalsforboththeobservationaland

geneticapproachesexcludethenull,suggestingpositivepossiblecausalrelationshipsbetween

maternalBMIandfastingglucoseandbirthweight.FormaternalSBP,theobservationalanalysis

suggestedaweakpositiveassociationwithbirthweight,whereasthegeneticanalysisshowed

evidenceofanegativepossiblecausalrelationship.Observationalanalysessuggestedthathigher

maternaltriglyceridelevels,lowermaternaladiponectinandlowermaternalHDL-cholesterollevels

wereassociatedwithhigherbirthweight,whilelowermaternalvitaminDstatuswasassociatedwith

lowerbirthweight,butnoneoftheseweresupportedbythegeneticanalyses.

28Table1.Keycharacteristicsofparticipantsbystudy(forfulldetails,seeeTable1)

Abbreviatedstudyname*

Country(samplesource)

Offspringyearsofbirth

Nwomenwithbirthweightofonechild

Noffspringwith

genotype

Meanmaternalageat

deliveryinyears(SD)

Meanmaternal

pre-pregnancyBMI(SD)in

kg/m2

Meanoffspring

birthweight(SD)ingrams

ALSPACmothers33 UK 1991-1992 7304 4913 28.5(4.8) 22.93(3.73) 3481(475)

BBCmothers34 Germany 2000-2004 1357 1357 30.1(5.4) 22.78(3.93) 3472(511)

B58C-WTCCC35 UK 1972-2000 855 NA 26.2(5.2) NA 3325(483)

B58C-T1DGC35 UK 1972-2000 836 NA 26.1(5.4) NA 3379(469)

CHOPmothers36 USA 1987-

present 312 NA NA NA 3440(562)

COPSAC-2000mothers37 Denmark 1998-2001 282 282 30.4(4.3) NA 3560(505)

DNBC-GOYA38 Denmark 1996-2002 1805 NA 29.2(4.2) 23.57(4.27) 3643(495)

DNBC-PTB-CONTROL39 Denmark 1987-2009 1649 975 29.9(4.2) 23.57(4.27) 3595(497)

EFSOCHmothers40 UK 2000-2004 746 332‡ 30.5(5.3) 24.07(4.42) 3512(480)

GEN-3Gmothers41 Canada 2010-2013 676 NA 28.4(4.4) 24.83(5.63) 3448(433)

GenerationRmothers42 TheNetherlands 2002-2006 3810 2196 31.2(4.5)† 23.12(3.92) 3528(494)

HAPOmothers(GWAS)5

UK,Canada,Australia 2000-2006 1380 1300 31.5(5.3)† 24.5(5.0) 3557(517)

HAPOmothers(non-

GWAS)5

USA,UK,Canada,Australia

2000-2006 3590 2318 30.4(5.4)† 24.63(5.33) 3526(463)

MoBamothers43 Norway 1999-2008 650 350 28.5(3.3) 23.93(3.94) 3679(430)

NFBC196644 Finland 1987-2001 2035 NA 26.5(3.7) NA 3525(461)

NTR45 TheNetherlands 1946-2003 706 NA 27.1(3.7) NA 3469(529)

QIMR46 Australia 1929-1990 892 NA Q24.5(4.0)R 22.79(5.13) 3344(532)

TwinsUK47,48 UK NA 1602 NA NA NA 3365(581)

*Expandedstudynames:ALSPAC,AvonLongitudinalStudyofParentsandChildren;BBC,BerlinBirthCohort;B58C-WTCCC,1958BritishBirthCohort-WellcomeTrustCaseControlConsortium;B58C-T1DGC,1958BritishBirthCohort-Type1DiabetesGeneticsConsortium;CHOP,Children’sHospitalOfPhiladelphia;DNBC-GOYA,DanishNationalBirthCohort-GeneticsofObesityinYoungAdultsstudy;DNBC-PTB-CONTROLS,DanishNationalBirthCohortPretermBirthstudyControls;EFSOCH,ExeterFamilyStudyOfChildhoodHealth;GEN-3G,GeneticsofGlycemicregulationinGestationandGrowth;HAPO,HyperglycemiaandAdversePregnancyOutcomestudy(GWAS,Genome-WideAssociationStudy);MoBa,theNorwegianMotherandBabyCohort;NFBC1966,theNorthernFinland1966BirthCohort;NTR,NetherlandsTwinRegistry;QIMR,QueenslandInstituteofMedicalResearch.†InGenerationR,maternalagewasrecorded,onaverage,at14.4weeksofgestation;inHAPO,maternalagewasrecorded,onaverage,at28weeksofgestation.‡FetalgenotypeinEFSOCHavailableonlyforthefastingglucosegeneticscore.NA,notavailable.

29Table2.Associationsbetweenmaternalgeneticscoresandmaternalobesity-relatedtraits

Maternalobesity-relatedtrait

NumberofSNPsusedtoconstructgeneticscore

ReferenceforprimaryGWAS

paperforeachsetofSNPs*

Estimateof%variancein

maternaltraitexplainedby

geneticscoreinpregnantwomen†

TotalNwomenwithtraitmeasured

duringpregnancy(exceptBMI,for

whichtheappropriate

measurementispre-pregnancy)

Nstudies

Estimatedchangeinmaternaltraitperaverageweighted

trait-raising/lowering‡allele(95%CI)

Pvalue

HeterogeneityPvalue;I2%,

whereresultsfrom>1studyweremeta-analysed

Higherpre-pregnancyBodyMassIndex

(BMI)30

Speliotesetal.,2010,NatGenet49

1.8%inALSPAC11,822 5 0.145(0.126,0.164)

kg/m2 <2x10-16 0.18;35.8

Higherfastingglucose§ 13

Dupuisetal.2010,NatGenet50

5%inEFSOCH5,402 3 0.029(0.025,0.032)

mmol/L <2x10-16 0.70;0

Higheroddsofgestationaldiabetesandexistingdiabetes(SNPsassociatedwith

type2diabetes)

55

Morrisetal.2012,NatGenet51

1.4%inALSPAC6,606

(54Cases,6,552controls)

1 Oddsratio:1.08(1.03,1.14) 0.003 -

Highertriglycerides 17 Teslovichetal.2010,Nature52

3%inEFSOCH 663 1 0.055(0.032,0.078)mmol/L 3x10-6 -

LowerHDL-cholesterol 4 Teslovichetal.2010,Nature52

3%inEFSOCH 733 1 -0.050(-0.072,-0.027)mmol/L 1x10-5 -

Highersystolicbloodpressure 33 Ehretetal.2010,

Nature531%inALSPAC 8,450 2 0.186(0.140,0.231)

mmHg <2x10-16 0.04;76.0

LowervitaminDstatus,ln[25(OH)D] 2(“Synthesis”score)¶

Vimaleswaranetal.2013,PloSMed6

0.2%inALSPAC4,767 1 -0.024(-0.039,-

0.009)onlogscale 0.002 -

Loweradiponectin,ln[adiponectin] 3

Yaghootkaretal.2013,Diabetes54

2%inHAPO1,376 1 -0.17(-0.23,-0.11)

onlogscale 1x10-8 -

*Thereferencesincolumn3indicatethepublishedgenome-wideassociationstudiesthatoriginallyidentifiedtheSNPsusedinthegeneticscores(studiesofnon-pregnantindividuals).†Toestimatethevarianceineachtraitexplainedbyitsrespectivegeneticscoreinpregnantwomen(column4),thelargestavailablestudywasused.Abbreviations:ALSPAC,AvonLongitudinalStudyofParentsandChildren33.EFSOCH,ExeterFamilyStudyOfChildhoodHealth40.HAPO,HyperglycemiaandAdversePregnancyOutcomestudy5.FurtherdetailsabouttheincludedstudiescanbefoundineTable4.‡Thedecisiontomodeltheassociationinrelationtothetrait-raisingortrait-loweringalleledependedontheknowndirectionofassociationofeachtraitwithhigherBMI(seeBox1).Column1specifieseachofthesedirectionsofassociation.§Removingtheonestudyinwhichthers10830963SNPwaspoorlyimputed(r2<0.8),weobtainedverysimilarresults(n=4026;effectsize=0.028(95%CI:0.024,0.032);P<2x10-16;Phet=0.46;I2=0%).

30¶ThetwoSNPsselectedforthevitaminDgeneticscorehaveahypothesizedroleinthesynthesisofvitaminD(asopposedtoitsmetabolism)andarerecommendedforuseinMendelianrandomizationstudies.16,17Levelsof25(OH)Dandadiponectinlevelswerelog-transformedtoachievenormalitybeforeanalyses.

31Table3.Associationsbetweenmaternalgeneticscoresandbirthweightofoffspring

Maternaltraitforwhichgeneticscorewas

constructedNstudies TotalN

women

Estimatedchangeinoffspringbirthweight(grams)permaternaltrait-

raising/lowering*allele(95%CI),tothenearest1

gram†

Pvalue

HeterogeneityPValue;I2%frommeta-analysis

Nstudieswithfetalgenotypes

TotalNoffspringwith

genotypesavailable

Estimatedchangeinbirthweight(grams)permaternaltrait-raising/loweringallele*(95%CI),tothenearest1gram†,adjustedforfetal

genotypes

Pvalue(adjustedforfetal

genotypes)

HeterogeneityPValue;I2%frommeta-analysis

(adjustedforfetal

genotypes)

Higherpre-pregnancyBMI 16 25,265 2(0,3) 0.008 0.84;0 7 10,964 4(1,6) 0.004 0.20;30.5

Higherfastingglucose 15 23,902 8(6,10) 7x10-14 0.11;33.3 8 11,493 11(7,14) 7x10-9 0.26;21.6

Higheroddsoftype2Diabetes 12 18,670 2(0,2) 0.06 0.22;23.1 5 7,769 4(2,6) 0.0004 0.81;0

Higheroddsoftype2Diabetes(excludingpre-existingandgestational

diabetes)

6 13,029 2(1,3) 0.02 0.92;0 4 6,210 4(1,6) 0.006 0.93;0

Highertriglycerides 15 24,985 -2(-4,0) 0.12 0.83;0 6 11,031 -2(-7,1) 0.21 0.86;0

LowerHDL-cholesterol 15 22,167 0(-3,3) 1 0.52;0 6 9,176 0(-5,5) 0.98 0.85;0

Highersystolicbloodpressure 13 20,062 -4(-6,-2) 1x10-5 0.14;30.4 5 7,790 -3(-6,0) 0.09 0.50;0

Highersystolicbloodpressure(excludingpre-

eclampsiaandhypertension)

7 13,271 -5(-7,-3) 6x10-6 0.18;32 4 5,488 -4(-8,0) 0.04 0.16;41.2

LowervitaminDstatus 18 30,340 -6(-12,0) 0.03 0.13;37.1 3 9,510 -14(-25,3) 0.01 0.77;0

Loweradiponectin 9 14,920 -2(-16,12) 0.76 0.90;0 5 7,820 7(-16,30) 0.55 0.71;0

*Thedecisiontomodeltheassociationinrelationtothetrait-raisingortrait-loweringalleledependedontheknowndirectionofassociationofeachtraitwithhigherBMI(seeBox1).Column1specifieseachofthesedirectionsofassociation.Resultsareperaverageweightedallele,adjustedforsexandgestationalage.†StandarddeviationofbirthweightaveragedoveranumberofEuropeanstudies(=484g)55wasusedtogeneratetheseestimatesfromz-scores.Weconsidereda2-tailedPvalueof<0.05toprovideevidenceagainstthenullhypothesis.

32

33Table4.Acomparisonoftheobservationalwiththegeneticassociationbetweeneachmaternaltraitandoffspringbirthweight

Maternaltrait Valueofa1SDchangeinthetraitwithunits

Study/ies*usedforobservationalestimates

[TotalNwomen]

Nwomenforobservationalestimates

Observationalestimateofthechangeinbirth

weight(g)per1SD(or10%†)changein

maternaltrait,adjustedforsexand

gestationalage(95%CI)

Geneticestimateofthechangeinbirthweight(g),adjusted

forsexandgestationalage,per1SD(or10%†)changeinmaternaltrait,unadjustedfor

fetalgenotype(95%CI)[NwomenasinTables

1and2]

Pvalue‡comparing

observationalwithgeneticbirth

weightassociations(unadjustedforfetalgenotype)

Geneticestimateofthechangeinbirthweight(g),adjusted

forsex,gestationalageandfetalgenotype,per1SD(or10%†)changeinmaternaltrait(95%CI)[N

offspringasinTables1and2]

Pvalue‡comparing

observationalwithgeneticbirth

weightassociations(adjustedfor

fetalgenotype)

Higherpre-pregnancyBMI 4kg/m2

ALSPACMothers,EFSOCHMothers,HAPO

Mothers11,969 62(56,70) 55(17,93) 0.70 104(32,176) 0.28

Higherfastingglucose 0.4mmol/L EFSOCHMothers,HAPO

Mothers 6,008 92(80,104) 114(80,147) 0.28 145(91,199) 0.09

Highertriglycerides 0.7mmol/L EFSOCHMothers 930 32(7,56) -24(-55,8) 0.007 -33(-86,20) 0.03

LowerHDL-cholesterol 0.5mmol/L EFSOCHMothers 927 30(3,58) 0(-33,34) 0.17 -1(-55,54) 0.32

Highersystolicbloodpressure 10mmHg ALSPACMothers,HAPO

Mothers 12,077 24(15,34) -208(-394,-21) 0.01 -151(-390,89) 0.14

LowervitaminDstatus 10%† ALSPACMothers 4,710 -4(-7,-2) -26(-54,2) 0.13 -56(-112,1) 0.07

Loweradiponectin 10%† HAPOMothers(GWASonly) 1,376 14(9,18) -1(-9,7) 0.002 4(-9,17) 0.19

*Heterogeneitystatisticsfromthemeta-analysesofobservationalassociationswere:Phet=0.03andI2=67.7%forBMI;Phet=0.09andI2=59.1%forfastingglucose;Phet=0.54andI2=0%forSBP.† For25[OH]Dandadiponectin,wepresenttheestimatedchangeinbirthweightper10%reductioninmaternaltraitlevelbecausethesevariableswereloggedforanalysis.‡P-values<0.05areconsideredtoindicateevidencethatthegeneticeffectsizeestimateisdifferentfromtheobservationalestimate,suggestingthattheobservationalestimateissubjecttoconfoundingorbias.ALSPAC:AvonLongitudinalStudyofParentsAndChildren33;EFSOCH:ExeterFamilyStudyofChildhoodHealth40;HAPO:HyperglycaemiaandAdversePregnancyOutcomes5;SDstandarddeviation

Pre−pregnancy BMI

Observational

Genetic

Genetic (adjusted for fetal genotype in subset)

Fasting glucoseO

bservationalG

eneticG

enetic (adjusted for fetal genotype in subset)

TriglyceridesO

bservationalG

eneticG

enetic (adjusted for fetal genotype in subset)

HDL cholesterolO

bservationalG

eneticG

enetic (adjusted for fetal genotype in subset)

Systolic blood pressureO

bservationalG

eneticG

enetic (adjusted for fetal genotype in subset)

25(OH)D

Observational

Genetic

Genetic (adjusted for fetal genotype in subset)

AdiponectinO

bservationalG

eneticG

enetic (adjusted for fetal genotype in subset)

ID Study

4 kg/m2 higher

0.4 mm

ol/L higher

0.7 mm

ol/L higher

0.5 mm

ol/L lower

10 mm

Hg higher

10% lower

10% lower

change in traitStandard deviation

316721581156115621351183195 studiesN

119692526510964

60082390211493

9302498511031

927221679176

12077200627790

4710303409510

1376149207820

women

N

0010964

0011493

0011031

009176

007790

009510

007820

genotypesoffspringN

4 kg/m2 higher

0.4 mm

ol/L higher

0.7 mm

ol/L higher

0.5 mm

ol/L lower

10 mm

Hg higher

10% lower

10% lower

change in traitStandard deviation

316721581156115621351183195 studiesN

0−400

−300−200

−1000

100200

300Estim

ated Difference in Body Weight (gram

s)

1

SUPPLEMENTARY MATERIAL

Genetic evidence for causal relationships between maternal obesity-related traits and birth weight

Overview of Supplementary Material

Content Page Description eMethods 2 Technical descriptions of the methods, including how we selected the

genetic variants and calculated the genetic scores, in addition to details of statistical analyses

eFigure 1 6 Comparison of the observational with the genetic change in ponderal index (in kg/m3) for a 1 standard deviation (SD) change in each maternal trait.

eFigure 2 7 Estimating how much of the possible causal effect of maternal BMI on birth weight is mediated by maternal fasting glucose.

eTable 1(a) 8 Basic characteristics of study participants and their offspring (studies 1-5)

eTable 1(b) 11 Basic characteristics of study participants and their offspring (studies 6-10)

eTable 1(c) 14 Basic characteristics of study participants and their offspring (studies 11-14)

eTable 1(d) 17 Basic characteristics of study participants and their offspring (studies 15-18)

eTable 2(a) 20 Genotyping information (studies 1-5) eTable 2(b) 22 Genotyping information (studies 6-10) eTable 2(c) 25 Genotyping information (studies 11-14) eTable 2(d) 27 Genotyping information (studies 15-18) eTable 3 29 Details of single nucleotide polymorphisms (SNPs) used to construct

the genetic scores eTable 4 35 Studies with maternal traits ascertained during pregnancy (or for

BMI, pre-pregnancy) and available for association analysis with genetic scores

eTable 5 36 Associations between maternal genetic scores and maternal traits during and post-pregnancy in the same individuals

eTable 6 37 Associations between each maternal genetic score and potentially confounding or mediating variables: one table per genetic score on each of pages 37-44: (a) BMI, (b) fasting glucose, (c) type 2 diabetes, (d) triglycerides, (e) HDL-cholesterol, (f) systolic blood pressure, (g) vitamin D status, (h) adiponectin

eTable 7 45 Associations between maternal genetic scores and ponderal index of offspring at birth

eTable 8 46 A comparison of the observational with the genetic association between each maternal trait and offspring ponderal index at birth

eTable 9(a) 47 Observational associations between offspring birth weight and maternal socio-economic status or maternal smoking in the ALSPAC study

eTable 9(b) 47 Observational associations between maternal BMI and maternal socio-economic status or maternal smoking in the ALSPAC study

eTable 10 48 Power calculations

eTable 11 49 Association between father’s phenotypes and offspring birth weight using data from the ALSPAC study

References 50 References supporting the eMethods and eTables Funding/support of individual studies

53 Funding/support acknowledgements by study, in alphabetical order

Individual study acknowledgements

55 Acknowledgements specific to certain individual contributing studies, arranged in alphabetical order

2

eMethods

Maximising specificity of genetic variants: excluding single nucleotide polymorphisms (SNPs) with effects on multiple traits (“pleiotropic” SNPs) It was important to ensure that each genetic score would enable us, as far as possible, to capture specifically the respective maternal trait. To identify SNPs with pleiotropic effects, we queried each of our initial 193 selected SNPs and all SNPs in linkage disequilibrium with these (r2>0.2) using the National Human Genome Research Institute (NHGRI) catalog of published genome-wide association studies (GWAS)1 and listed SNPs associated with other traits at P < 5x10-8. Starting with this list, we excluded SNPs whose location near a candidate gene and/or the strength of association with another trait suggested that the association with the maternal exposure of interest is almost certainly secondary to the other trait (e.g. exclusion of index SNPs at FTO and MC4R from the type 2 diabetes genetic score, since these are primarily associated with BMI and secondarily with type 2 diabetes via their effect on BMI). We additionally excluded SNPs from the list with strong evidence of effects on two or more traits that are potentially relevant to the maternal environment and birth weight. Details of SNPs in the final selected list are shown in eTable 3. We performed an updated search of the NHGRI catalog, while writing the research paper, to check for further pleiotropic associations identified for SNPs used in our analyses, which were published after our initial search. We performed sensitivity analyses excluding these additional SNPs to check that they did not alter our findings (results available from the authors on request). Maximising specificity of genetic variants: separating genetic scores for closely-related maternal traits Fasting glucose and type 2 diabetes share several genetic susceptibility variants, reflecting the overlap between these two phenotypes (eTable 3). We excluded from the type 2 diabetes genetic score the index SNPs at the two fasting glucose loci that explain the most variance in fasting glucose, but have relatively moderate effects on type 2 diabetes risk (MTNR1B and GCK). Likewise, we excluded the index SNP at the TCF7L2 locus from the fasting glucose genetic score as it has a proportionately much larger effect on type 2 diabetes risk. In this way, our fasting glucose genetic score would predominantly capture variation in maternal fasting glucose in the normal physiological range, while our type 2 diabetes genetic score would be more likely to capture pathologically-raised fasting and non-fasting maternal glucose levels. Maternal triglycerides and HDL-cholesterol also share associations with several genetic variants. We therefore attempted to make our genetic scores for these exposures as specific as possible. For HDL- cholesterol, we included only SNPs near genes associated with known Mendelian lipid disorders (see eTable 3)2. For triglyceride levels, SNPs were included in the genetic score if they were solely associated with triglyceride levels, or if their effect on triglyceride levels was at least three times greater than that of HDL-, LDL- or total cholesterol, based on effect sizes reported in 2. To facilitate these comparisons, the raw effect sizes in mg/dL were first converted to percentages of the mean of the corresponding lipid concentration. SNPs missing from studies When index SNPs were missing from individual studies, we used the SNP Annotation and Proxy Search tool, SNAP3 to identify suitable proxy SNPs (r2 > 0.8). If a study had fewer than 80% of the index or proxy SNPs required to generate a specific genetic score, it was excluded from the analysis. The one exception to this was the HAPO (non-GWAS) Study, for which only 6 of 17 triglyceride SNPs had been genotyped. We included this study, despite the missing SNPs, because the 6 genotyped SNPs included those with the largest effects on triglyceride levels, covering the majority of variation captured by the 17-SNP score. Imputation quality For each study with GWAS data, we examined the imputation quality (r2 4 or proper_info5) of SNPs selected for each score. We excluded four studies (B58C-WTCCC, NFBC1966, QIMR and TwinsUK) from analyses of the adiponectin genetic score due to imputation quality scores <0.8 for either 1 or 2 of the 3 SNPs in that score. In each of the remaining 7 genetic scores, a small number of included SNPs had imputation quality scores <0.8, but this only affected a median of 0 to 1 SNP per study, equivalent to a maximum of 6% of the SNPs comprising the score, so we did not exclude them. Finally, we identified that 3 individual SNPs were poorly imputed (r2<0.8) in multiple studies: rs10830963 (fasting glucose genetic score, 4 of 15 studies), rs11063069 (type 2 diabetes genetic score; 11 of 13 studies) and rs13238203 (triglycerides genetic score; 11 of 16 studies). To verify that these individual SNPs did not materially alter our results, we performed sensitivity analyses: (i) we repeated the meta-analyses of the fasting glucose genetic score excluding the 4 studies in which SNP rs10830963 was poorly imputed; (ii) we performed weighted meta-analyses of existing summary GWAS data6,

3

as described previously7 both including and excluding the rs11063069 and rs13238203 SNPs. Results of these analyses are available from the authors on request.

Calculation of maternal genetic scores We calculated a weighted genetic score for each maternal exposure to account for the fact that some SNPs have relatively larger effects than others. Formula 1 below describes the calculation, where w is the weight and SNP is the number of trait-raising or lowering alleles at that locus. The decision to model according to the trait-raising or lowering allele was informed by the known association between each maternal trait and BMI (Box 1). The weights used for each SNP were obtained from published GWAS of non-pregnant individuals, which either did not include any of the studies used in this paper or had at most 17% of participants overlapping. These weights and their sources, are summarised in eTable 3. Weightedscore = w/×SNP/ + w5×SNP5 + ⋯w7×SNP7(1) We rescaled each weighted genetic score (GS) to reflect the number of available SNPs using formula 2 as described in Lin et al 8

GS = WeightedScorexNumberofSNPsavailableSumofweightsofavailableSNPs (2)

Meta-analyses We meta-analysed data from all available studies to give an overall result from each side of the triangle (Figure 1): the genetic score-maternal exposure association; the genetic score-birth weight association; and the observational maternal exposure-birth weight association. We combined the regression coefficients and standard errors from individual study analyses by performing inverse variance meta-analyses with fixed effects as there was little evidence of between-study heterogeneity of effect size. All meta-analyses were performed using the user-written Stata command, metan.9 We estimated the percentage of total variation among study estimates due to between-study heterogeneity using Cochran’s Q test and the I2 statistic.10 To convert the overall results from birth weight and ponderal index Z-scores into grams and kgm-3 respectively, we multiplied the effect size and their upper and lower 95% confidence limits by a representative value of the standard deviation of birth weight (484g)11 or ponderal index (2.78 kgm-3; ALSPAC study). Mendelian randomization analysis We performed instrumental variable (IV) estimation using the ratio estimator 12. We estimated the effect of each maternal exposure on either birth weight or ponderal index by dividing the overall genetic score -birth weight or genetic score -ponderal index association by the overall genetic score -maternal exposure association. The standard error of these estimates was calculated using a Taylor series approximation 13: we used a 2nd order Taylor series expansion to obtain the variance of the IV estimate. We then made a normal distribution assumption by calculating the 95% confidence interval as follows: IV estimate -/+ 1.96*sqrt(variance of IV estimate from Taylor series expansion). We used a Z-test to test for a difference between the instrumental variable (genetic) and observational associations. The Z-score was calculated by estimating the covariance between the observational and instrumental variable (genetic) estimates using a bootstrapping procedure.We used the following formula for our Z-test: Z = (difference between IV and observational estimate)/sqrt(variance of difference between the estimates) where the variance of the difference between the estimates is given by: var(IV estimate) + var(observational estimate) - 2*cov(IV estimate, obs estimate) The covariance between the IV and observational estimates was estimated by nonparametric bootstrapping the IV and observational estimates using 20 replications (we chose a relatively small number of replications because we included meta-analyses with up to 18 studies). We then compared the Z-statistic with a standard normal distribution. Guarding against weak instrument bias Mendelian randomization studies may be susceptible to weak instrument bias. Bias is the difference between the estimated value of a parameter and its true value. Weak instrument bias occurs in the direction of the

4

confounded observational association if the instrument (i.e. the genetic score) is only weakly associated with the phenotype (i.e. the maternal trait).14 The strength of each instrument used in our study is a function of (i) the proportion of variance in the maternal trait explained by the genetic score and (ii) the sample size. Since the variance in each maternal trait explained by the genetic score was modest, we maximized the sample size (Table 2). The possible causal associations identified in our study are therefore unlikely to be due to weak instrument bias. Control for population stratification The presence of subpopulations, which differ in mean birth weight and have genetic variants present at different frequencies, can cause artificial associations between genotypes and birth weight. To ensure that the genetic associations we tested were not confounded in this way, we took the following steps: (i) we included only women of European ancestry; (ii) where necessary, analyses in the individual studies were adjusted for ancestry principal components; (iii) in those studies that had performed a genome-wide association study of birth weight, we checked the genomic control lambda values (ratio of median of the empirically observed distribution of the test statistic to the expected median), which suggested only minimal inflation: median lambda = 1.006 [inter-quartile range: 1.004-1.012]; (iv) we combined summary statistics from individual studies by inverse variance meta-analysis, thereby controlling for any population stratification between studies in the overall sample.

Sensitivity analyses The ascertainment of offspring birth weight or gestational age data varied among the individual studies, from measurement by trained study personnel, to ascertainment from medical records or birth registries, to self-report. To verify that our results were unaffected by the varying quality or availability of phenotypic data, we performed sensitivity meta-analyses of the associations between the 8 genetic scores and birth weight in up to 12 studies with best quality data (i.e. measured or medical record birth weight and gestational age available). Results of these analyses are available from the authors on request.

To verify that the SBP genetic score-birth weight associations were unaffected by using weights from the original GWAS15 which were adjusted for BMI, we performed a blood pressure GWAS in 127,698 individuals of British descent using the UK Biobank data. The UK Biobank recruited over 500,000 individuals aged 37-73 years (99.5% were between 40 and 69 years) in 2006-2010 from across the country16. Two blood pressure readings were taken approximately 5 minutes apart using an automated Omron blood pressure monitor. Two valid measurements were available for most participants, and the average was taken. Individuals were excluded if the two readings differed by more than 4.56SD, and blood pressure measurements more than 4.56SD away from the mean were excluded. We accounted for blood pressure medication use by adding 15 to the systolic blood pressure measure. Valid blood pressure measurements were available for 120,008 individuals. Blood pressure was adjusted for age, sex and centre location and then inverse normalized.The weights from the blood pressure GWAS in the UK Biobank were utilised to create a genetic risk score in the ALSPAC study (n=7,304). We investigated the correlation of the two blood pressure risk scores (r2=0.77) and performed Mendelian randomization. The results are available from the authors on request.

Estimating how much of the possible causal effect of BMI on birth weight is mediated by fasting glucose To begin to understand what proportion of the estimated causal effect of BMI on birth weight might be mediated by fasting glucose, we first estimated the causal effect of BMI on maternal fasting glucose. Using available studies (see eTable 6a), each additional allele of the BMI genetic score was associated with a 0.145 kg/m2 (95%CI: 0.126, 0.164) higher BMI and a 0.005 mmol/L (95%CI: 0.001, 0.009) higher fasting glucose. This is equivalent to 0.34 SD higher fasting glucose level per 1 SD higher genetically instrumented BMI. (To convert to SD units, we used BMI SD = 4 kg/m2 and fasting glucose SD = 0.4 mmol/l.) We then multiplied the genetic estimate and 95%CI for the effect of fasting glucose on birth weight (114g [95%CI: 80, 147g]) by 0.34 to represent the possible causal effect of fasting glucose on birth weight for every 1 SD higher maternal BMI. Since we found genetic evidence that systolic blood pressure (SBP) was causally associated with birth weight in the opposite direction and positively associated with BMI, we additionally estimated the causal effect of BMI on SBP. Each additional allele of the BMI genetic score was associated with a 0.07 mmHg (95%CI: 0.02, 0.11) higher SBP. This is equivalent to 0.19 SD higher SBP per 1 SD higher genetically instrumented BMI. (To convert to SD units, we used SBP SD = 10 mmHg.) We then multiplied the IV estimate and 95%CI for the effect of SBP on birth weight (-208g [95% CI: -394, -21]) by 0.19 to represent the causal effect of SBP on birth weight for every 1 SD higher maternal BMI. Power calculations

5

Using data available from the ALSPAC study, we estimated the variance explained in birth weight (BW) by each maternal genetic score as the difference in adjusted-R2 values between linear regression models (i) and (ii) as follows: (i) BW = sex + gestational_age (ii) BW = sex + gest_age + genetic_score We then used these values to estimate (a) the power available in our included sample to detect evidence of association between maternal genetic score and birth weight at P<0.05, and (b) the minimum sample size needed to detect association between maternal genetic score and birth weight at P<0.05 with 80% power. Power calculations were performed using Quanto v.1.2 (http://biostats.usc.edu/software).

6eFigure 1. Comparison of the observational with the genetic change in ponderal index (in kg/m3) for a 1 standard deviation (SD) change in each maternal trait. For 25[OH]D and adiponectin, we present the change in ponderal index for a 10% change in maternal trait level because these variables were logged for analysis. The genetic change was estimated from Mendelian randomization analysis, in which a genetic score was used to estimate the possible causal effect of the maternal trait on ponderal index. The genetic estimate is presented twice: in the second case it was adjusted for fetal genotype using a subset of the available studies. The error bars represent the 95% confidence intervals around the effect size estimates.

Pre-pregnancy BMIObservationalGeneticGenetic (adjusted for fetal genotype in subset)

Fasting glucoseObservationalGeneticGenetic (adjusted for fetal genotype in subset)

TriglyceridesObservationalGeneticGenetic (adjusted for fetal genotype in subset)

HDL cholesterolObservationalGeneticGenetic (adjusted for fetal genotype in subset)

Systolic blood pressureObservationalGeneticGenetic (adjusted for fetal genotype in subset)

25(OH)DObservationalGeneticGenetic (adjusted for fetal genotype in subset)

AdiponectinObservationalGeneticGenetic (adjusted for fetal genotype in subset)

IDStudy

4 kg/m2 higher

0.4 mmol/L higher

0.7 mmol/L higher

0.5 mmol/L lower

10 mmHg higher

10% lower

10% lower

change in traitStandard deviation

3107

2108

196

196

275

173

165

studiesN

9690177439628

4917178189902

857174409335

854155738207

9691135276821

3718140047292

1373115016851

womenN

009628

009902

009335

008207

006821

007292

006851

genotypesoffspringN

4 kg/m2 higher

0.4 mmol/L higher

0.7 mmol/L higher

0.5 mmol/L lower

10 mmHg higher

10% lower

10% lower

change in traitStandard deviation

3107

2108

196

196

275

173

165

studiesN

0-2 -1.5 -1 -.5 0 .5 1 1.5

Change in ponderal index (kg/m3)

7eFigure 2. Estimating how much of the estimated possible causal effect of maternal BMI on birth weight is mediated by maternal fasting glucose. The solid, horizontal arrow indicates our genetic estimate [95%CI] of the causal effect of BMI on birth weight. The dashed arrows on the left side show genetic causal estimates of a 1 SD (≈ 4kg/m2) higher maternal pre-pregnancy BMI on maternal fasting glucose (+0.34SD ≈ 0.14 mmol/L) and maternal systolic blood pressure in pregnancy (+0.19 SD ≈ 2 mmHg). The dashed arrows on the right show the scaled genetic causal estimates of these changes in fasting glucose and systolic blood pressure on birth weight. The effect of maternal BMI on birth weight via fasting glucose (+ 39g) is broadly similar to the total effect of maternal BMI on birth weight (+55g), but that effect is opposed by the birth weight lowering effect of SBP (-40g). Overall, this suggests that while maternal fasting glucose mediates part of the positive association between maternal BMI and birth weight, other BMI-related factors are likely to be involved. Abbreviations: BMI, body mass index; BW, birth weight; FPG, fasting plasma glucose; SBP, systolic blood pressure.

BMI

SBP

FPG

+55g[95%CI:17-93]BWper1SDhigherBMI BW

8eTable 1(a) Basic characteristics of study participants and their offspring (studies 1-5)

STUDY ALSPAC Mothers Berlin Birth Cohort

(BBC) Mothers

1958 British Birth Cohort or NCDS (B58C-WTCCC)

1958 British Birth Cohort or NCDS (B58C-T1DGC)

CHOP Mothers

STUDY INFORMATION

Ethnicity British/European descent European descent British/European descent

British/European descent European American

Country (Sample source) UK Germany UK UK United States of

America

Collection type (e.g. population-based) Population-Based Community-based Population-based Population-based Population-based

N women with birth weight of 1 child and genotypes for at least

one genetic score

7,304 1,357 855 836 312

Year(s) of birth of offspring April 1991 - Dec 1992 2000-2004 1972-2000 1972-2000 1987-present

Fetal genotype data available? (Y/N) Y Y N N Y

BIRTH WEIGHT

Method by which offspring birth weight

(and length, if available) were

collected

Obstetric records / measured by trained study personnel

Measured by trained personnel immediately

after birth

Maternal self-report (information from

questionnaires at age 33 and 42 years)

Maternal self-report (information from

questionnaires at age 33 and 42 years)

Questionnaire and EPIC medical

records (9.5% of questionnaire values were checked against

medical records: r=0.83).

GESTATIONAL AGE Method by which

gestational age was collected

By date of last menstrual period (LMP), paediatric

assessment, obstetric assessment, ultrasound

assessment.

Calculated from LMP and corrected by ultrasound, if the

difference was > 2 weeks

From maternal self-report at ages 33 and 42 years: a question inquiring if the child was born at term, or

alternatively how many weeks in advance or late.

From maternal self-report at ages 33 and 42 years: a question inquiring if the child was born at term, or

alternatively how many weeks in advance or late.

NA

9

ALSPAC Mothers Berlin Birth Cohort

(BBC) Mothers

1958 British Birth Cohort or NCDS (B58C-WTCCC)

1958 British Birth Cohort or NCDS (B58C-T1DGC)

CHOP Mothers

SUMMARY MATERNAL

CHARACTERISTICS, where available in the INCLUDED sample,

DURING PREGNANCY

(median (IQR) given where the trait

distribution deviates strongly from the

normal distribution

Maternal age at delivery, unless

otherwise stated [Mean (sd)], years

28.5 (4.8) 30.1 (5.4) 26.2 (5.2) 26.1 (5.4) NA

Maternal pre-pregnancy BMI [Mean

(sd)], kg/m2 22.93 (3.73) 22.78 (3.93) NA NA NA

Maternal pregnancy BMI [Mean (sd)],

kg/m2 26.63 (4.03) 28.39 (4.25) NA NA NA

Fasting glucose [Mean (sd)], mmol/L NA NA NA NA NA

Triglycerides [Mean (sd)], mmol/L NA NA NA NA NA

HDL-cholesterol [Mean (sd)], mmol/L NA NA NA NA NA

Blood pressure [Mean (sd)], mmHg 112.9 (7.5)/65.5 (4.8)

117.28 (10.85)/70.73 (7.56) This was

measured at the 3rd trimester.

NA NA NA

25-hydroxyvitamin D [Median (IQR)],

nmol/L 62.1 (43.6, 85.4) NA NA NA NA

Adiponectin [Mean (sd)], ug/mL NA NA NA NA NA

N (%) of mothers who smoked in pregnancy 1,278 (17.5%) 212 (15.6%) 325 (38.0%) 285 (34.1%) NA

Mean gestational week of collection of

maternal characteristics

28 28 Retrospective Retrospective NA

N (%) Parity ( primiparous births) 2,483 (34%) 727 (53.6%) 855 (100%) 836 (100%) NA

10

ALSPAC Mothers Berlin Birth Cohort

(BBC) Mothers

1958 British Birth Cohort or NCDS (B58C-WTCCC)

1958 British Birth Cohort or NCDS (B58C-T1DGC)

CHOP Mothers

SUMMARY OFFSPRING

CHARACTERISTICS (offspring of the

INCLUDED sample of mothers)

Birth weight [Mean (sd) ], grams 3481 (475) 3472 (511) 3325 (483) 3379 (469) 3440 (562)

Gestational age at delivery [Median

(IQR)], weeks 40 (39, 41) 40 (38, 40) 40 (40, 41) 40 (40, 41) NA

Birth length [Mean (sd)], cm 51 (2) 51.31 (2.50) NA NA NA

Ponderal index [Mean (sd)], kg/m3 26 (3) 25.68 (3.40) NA NA NA

REFERENCES

Reference - cohort

MOTHERS: Fraser, A. et al. (2013) Cohort Profile: the

Avon Longitudinal Study of Parents and Children:

ALSPAC mothers cohort. Int J Epidemiol. 42(1):97-110.17 CHILDREN: Boyd A et al. (2013). Cohort Profile: the 'children of the 90s'--the

index offspring of the Avon Longitudinal Study of

Parents and Children. Int J Epidemiol. 42(1):111-27.18

a) Schlemm et al., J Hypertens. 2010

Apr;28(4):732-919 b) Hocher et al., Pharmacogenet Genomics. 2009

Sep;19(9):710-8.,20 c) Pfab et al.,

Circulation. 2006 Oct 17;114(16):1687-9221

Power C, Elliott J. Cohort profile: 1958 British birth cohort

(National Child Development Study). Int J Epidemiol 2006;

35(1):34-4122

Power C, Elliott J. Cohort profile: 1958 British birth cohort

(National Child Development Study). Int J Epidemiol 2006;

35(1):34-4122,23

Zhao et al., 2009 Examination of type

2 diabetes loci implicates CDKAL1

as a birth weight gene. Diabetes

58(10): 2414-824

Study URL http://www.bristol.ac.uk/alspac/ a NA www.cls.ioe.ac.uk and

www.wtccc.org.uk www.cls.ioe.ac.uk NA

aPlease note that the study website contains searchable details of all data, available through: http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/ NA, not available Informed consent was obtained from all participants, and study protocols were approved by the local regional or institutional ethics committees (ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees) .

11eTable 1(b) Basic characteristics of study participants and their offspring (studies 6-10)

STUDY COPSAC-2000 Mothers DNBC-GOYA Random

Set DNBC-PTB-CONTROL

Mothers EFSOCH Mothers GEN-3G Mothers

STUDY INFORMATION

Ethnicity Danish/European descent Danish/European descent Danish/European descent British/European descent Canadian/European descent

Country (Sample source) Denmark Denmark Denmark UK Canada

Collection type (e.g. population-based)

High-risk asthma birth cohort Population baseda Population-based Community-based Population-based

N women with birth weight of 1 child and

genotypes for at least one genetic score

282 1,805 1,649 746 676

Year(s) of birth of offspring 1998 - 2001 1996-2002 1987-2009 2000-2004 2010-2013

Fetal genotype data available? (Y/N) Y N Y Y N

BIRTH WEIGHT

Method by which offspring birth weight

(and length, if available) were collected

Medical Records Obstetric data from medical birth register

Obstetric data from medical birth register

At birth using standard neonatal anthropometry

measures

Hospital electronic medical records

GESTATIONAL AGE Method by which

gestational age was collected

Medical Records

The National Birth Register, where gestational age is reported by doctors

and midwives at birth

Consensus algorithm for gestational age was developed based on

information from medical birth register, hospital

discharge register, LMP, LMP corrected for

menstrual cycle length, Expected date of delivery

(often based on ultrasound), and mother's selfreport of gestational

age.

Hospital records and study midwives

Hospital electronic medical records

12

COPSAC-2000 Mothers DNBC-GOYA Random

Set DNBC-PTB-CONTROL

Mothers EFSOCH Mothers GEN-3G Mothers

SUMMARY MATERNAL

CHARACTERISTICS, where available in the INCLUDED sample,

DURING PREGNANCY

(median (IQR) given where the trait

distribution deviates strongly from the

normal distribution

Maternal age at delivery, unless otherwise stated

[Mean (sd)], years 30.4 (4.3) 29.2 (4.2) 29.9 (4.2) 30.5 (5.3) 28.4 (4.4)

Maternal pre-pregnancy BMI [Mean (sd)], kg/m2 NA 23.57 (4.27) 23.57 (4.27) 24.07 (4.42) 24.83 (5.63)

Maternal pregnancy BMI [Mean (sd)], kg/m2 NA NA NA 28.01 (4.55) 27.98 (5.38)

Fasting glucose [Mean (sd)], mmol/L NA NA NA 4.35 (0.38) 4.20 (0.41)

Triglycerides [Mean (sd)], mmol/L NA NA NA 2.13 (0.73) 1.93 (0.64)

HDL-cholesterol [Mean (sd)], mmol/L NA NA NA 2.08 (0.46) 1.91 (0.43)

Blood pressure [Mean (sd)], mmHg NA NA NA NA 107.5 (9.2) / 67.6 (6.8)

25-hydroxyvitamin D [Median (IQR)], nmol/L NA NA NA NA 61.7 [50.1 ; 75.5] (at ~9

weeks)

Adiponectin [Mean (sd)], ug/mL NA NA NA NA 12.57 (4.72)

N (%) of mothers who smoked in pregnancy 36 (12.8%) 465 (25.8%) 294 (17.8%) 97 (13.0%) 59 (8.88%)

Mean gestational week of collection of maternal

characteristics NA NA NA 28 ~26

N (%) parity primiparous births) 178 (63.2%) 903 (50.0%) 510 (30.9%) 372 (49.8%) 321 (47.5%)

13

COPSAC-2000 Mothers DNBC-GOYA Random

Set DNBC-PTB-CONTROL

Mothers EFSOCH Mothers GEN-3G Mothers

SUMMARY OFFSPRING

CHARACTERISTICS (offspring of the

INCLUDED sample of mothers)

Birth weight [Mean (sd) ], grams 3560 (505) 3643 (495) 3595 (497) 3512 (480) 3448 (433)

Gestational age at delivery [Median (IQR)],

weeks 40 (39, 41) 40.3 (39.4 , 41.1) 40 (39, 40) 40 (37, 43) 39.7 [38.9, 40.4]

Birth length [Mean (sd)], cm 52.5 (2.2) 52.5 (2.2) 52 (2) 50 (2) 51.1 (2.1)

Ponderal index [Mean (sd)], kg/m3 24.6 (2.4) 25.0 (2.3) 25 (2) 28 (3) 25.9 (2.5)

REFERENCES Reference - cohort

Bisgaard H. The Copenhagen Prospective

Study on Asthma in Childhood (COPSAC): design, rationale, and baseline data from a

longitudinal birth cohort study. Ann Allergy Asthma Immunol

2004;93:381–389.25

Nohr et al. (2009) PLoS One. 4(12):e844426

Olsen, J., Melbye, M., Olsen, S. F. et al (2001).

The Danish National Birth Cohort-its background,

structure and aim. Scandinavian journal of

public health, 29(4), 300-307.27

Knight, B., Shields, B. M., Hattersley, A. T. (2006). The Exeter Family Study

of Childhood Health (EFSOCH): study protocol

and methodology28

Lacroix M et al. Diabetes Care 201329

Study URL www.copsac.com www.dnbc.dk www.dnbc.dk www.diabetesgenes.org NA aPregnant women recruited 1996-2002 as part of the Danish National Birth Cohort. A random (according to BMI) selection with genotype data are included in the current study. NA, not available Informed consent was obtained from all participants, and study protocols were approved by the local regional or institutional ethics committees

14eTable 1(c) Basic characteristics of study participants and their offspring (studies 11-14)

STUDY Generation R Mothers HAPO Mothers (GWAS) HAPO Mothers (nonGWAS) MoBa (Mothers)

STUDY INFORMATION

Ethnicity Dutch/European descent Northern European European descent Norwegian/European descent

Country (Sample source) The Netherlands UK, Canada, Australia USA, UK, Canada, Australia Norway

Collection type (e.g. population-based) Population-based Population-based Population-based Population based

N women with birth weight of 1 child and

genotypes for at least one genetic

score

3,810 1,380 3,590 650

Year(s) of birth of offspring 2002-2006 2000-2006 2000-2006 1999-2008

Fetal genotype data available? (Y/N) Y Y Y Y

BIRTH WEIGHT

Method by which offspring birth

weight (and length, if available) were

collected

Hospital records and community midwives Medical record abstraction Medical record abstraction From The Medical Birth

Registry of Norway

GESTATIONAL AGE Method by which

gestational age was collected

Hospital records and community midwives

Estimated according to last menstrual period or ultrasound gestational age

and estimated date of delivery or confinement

Estimated according to last menstrual period or ultrasound gestational age

and estimated date of delivery or confinement

Gestational age was obtained from ultrasound at

gestational week 17-19 of pregnancy.

15

Generation R Mothers HAPO Mothers (GWAS) HAPO Mothers (nonGWAS) MoBa (Mothers)

SUMMARY MATERNAL

CHARACTERISTICS, where available in the INCLUDED sample,

DURING PREGNANCY

(median (IQR) given where the trait

distribution deviates strongly from the

normal distribution

Maternal age at delivery, unless otherwise stated

[Mean (sd)], years

31.2 (4.5) [=Maternal age at intake, when average

gestational age = 14.4 weeks]

31.5(5.3) [=Maternal age at OGTT, when average gestaional age = 28

weeks]

30.4 (5.4) [=Maternal age at OGTT, when average gestaional age = 28

weeks] 28.5 (3.3)

Maternal pre-pregnancy BMI

[Mean (sd)], kg/m2 23.12 (3.92) 24.5 (5.0) 24.63 (5.33) 23.93 (3.94)

Maternal pregnancy BMI [Mean (sd)],

kg/m2 26.98 (4.04) 28.46 (4.82) 28.58 (5.25) 24.78 (3.80)

Fasting glucose [Mean (sd)], mmol/L NA 4.56 (0.37) 4.54 (0.37) NA

Triglycerides [Mean (sd)], mmol/L NA - - NA

HDL-cholesterol [Mean (sd)], mmol/L NA - - NA

Blood pressure [Mean (sd)], mmHg 120.0 (11.4) / 69.3 (9.2) 108.6 (9.9) / 71.4 (8.0) 108.3 (9.6) / 70.7 (8.1) 113.5 (11.8) / 68.5 (8.4)

25-hydroxyvitamin D [Median (IQR)],

nmol/L NA - - NA

Adiponectin [Mean (sd)], ug/mL NA 20.37(12.83) - NA

N (%) of mothers who smoked in

pregnancy 1,033 (27.1%) 186 (13.5%) 539 (15.0%) 53 (8.1%)

Mean gestational week of collection of

maternal characteristics

30 28.5 28.3 18

N (%) parity (primiparous births) 2236 (58.7%) 785 (56.9%) 1795 (50.0%) 298 (45.8%)

16

Generation R Mothers HAPO Mothers (GWAS) HAPO Mothers (nonGWAS) MoBa (Mothers)

SUMMARY OFFSPRING

CHARACTERISTICS (offspring of the

INCLUDED sample of mothers)

Birth weight [Mean (sd) ], grams 3528 (494) 3557 (517) 3526 (463) 3679 (430)

Gestational age at delivery [Median

(IQR)], weeks 40 (39, 41) 40 (39, 41) 40 (39, 41) 40.1 (39.3, 41.0)

Birth length [Mean (sd)], cm 51 (2) 50.6 (2.3) 50.8 (2.3) 50.6 (1.8)

Ponderal index [Mean (sd)], kg/m3 27 (3) 27.48 (3.19) 26.87 (2.86) 28.31 (2.53)

REFERENCES Reference - cohort

Jaddoe, van Duijn, Franco et al. 2012 Eur J Epidemiol.

27(9):739-56.23

Metzger et al., Hyperglycemia and adverse pregnancy outcomes. N Engl

J Med 2008; 358:1991–200230

Metzger et al., Hyperglycemia and adverse pregnancy outcomes. N Engl

J Med 2008; 358:1991–2002 30

Cohort profile: The

Norwegian Mother and Child Cohort Study (MoBa)

Per Magnus, Lorentz M Irgens,

Kjell Haug, Wenche Nystad, Rolv Skjærven,�

Camilla Stoltenberg and The Moba Study Group. Int. J. Epidemiol. (October

2006) 35 (5): 1146-1150.31 Rønningen KS, Paltiel L,

Meltzer HM et al. The biobank of the Norwegian Mother and Child Cohort

Study—A Resource for the next 100 years.Eur J

Epidemiol. 2006;21(8):619-25.32

Study URL www.generationr.nl http://www.hapo.northwestern.edu/index.html

http://www.hapo.northwestern.edu/index.html http://www.fhi.no/morogbarn

NA, not available Informed consent was obtained from all participants, and study protocols were approved by the local regional or institutional ethics committees

17eTable 1(d) Basic characteristics of study participants and their offspring (studies 15-18)

STUDY NFBC1966 NTR QIMR TWINSUK

STUDY INFORMATION

Ethnicity Finnish/European descent Dutch/European descent European descent European descent

Country (Sample source) Northern Finland, Provinces of Oulu and Lapland The Netherlands Australia UK

Collection type (e.g. population-based)

Prospective general population-based Population-based controls Population-based recruitment

of adult twins population based

N women with birth weight of 1 child and

genotypes for at least one genetic score

2,035 706 892 1,602

Year(s) of birth of offspring 1987-2001 1946 - 2003 1929-1990 NA

Fetal genotype data available? (Y/N) N N N N

BIRTH WEIGHT

Method by which offspring birth weight

(and length, if available) were collected

Birth Register Data

From longitudinal surveys by self-report/parental report. Birth weight determined as average of

all valid data points.

Self-report through questionnaire Questionnaire

GESTATIONAL AGE Method by which

gestational age was collected

Last menstrual period and Scans. Based on hospital records

From longitudinal surveys by self-report/parental report.

Gestational age determined as average of all valid data points.

NA Questionnaire

18

NFBC1966 NTR QIMR TWINSUK

SUMMARY MATERNAL

CHARACTERISTICS, where available in the INCLUDED sample,

DURING PREGNANCY

(median (IQR) given where the trait

distribution deviates strongly from the

normal distribution

Maternal age at delivery, unless otherwise stated

[Mean (sd)], years

26.5 (3.7) available for 2010 participants 27.1 (3.7) 24.5 (4.0) NA

Maternal pre-pregnancy BMI [Mean (sd)], kg/m2 NA NA 22.79 (5.13) NA

Maternal pregnancy BMI [Mean (sd)], kg/m2 NA NA NA NA

Fasting glucose [Mean (sd)], mmol/L NA NA NA NA

Triglycerides [Mean (sd)], mmol/L NA NA NA NA

HDL-cholesterol [Mean (sd)], mmol/L NA NA NA NA

Blood pressure [Mean (sd)], mmHg NA NA NA NA

25-hydroxyvitamin D [Median (IQR)], nmol/L NA NA NA NA

Adiponectin [Mean (sd)], ug/mL NA NA NA NA

N (%) of mothers who smoked in pregnancy NA NA NA NA

Mean gestational week of collection of maternal

characteristics NA NA NA NA

N (%) parity (primiparous births) NA 593 (84.0%) NA NA

19

NFBC1966 NTR QIMR TWINSUK

SUMMARY OFFSPRING

CHARACTERISTICS (offspring of the

INCLUDED sample of mothers)

Birth weight [Mean (sd) ], grams 3525 (461) 3469 (529) 3344 (532) 3365 (581)

Gestational age at delivery [Median (IQR)],

weeks 40 (39, 41) 40 (38, 42) NA NA

Birth length [Mean (sd)], cm 50.3 (2.0) NA NA NA

Ponderal index [Mean (sd)], kg/m3 27.61 (2.44) NA NA NA

REFERENCES Reference - cohort

Rantakallio P. Groups at risk in low birth weight infants and

perinatal mortality. Acta Paediatr Scand 1969;193(suppl 193):1–71;33 Järvelin M-R., Sovio U.,

King V., Laurén L., Xu B., McCarthy M., Hartikainen A-L.,

Laitinen J., Zitting P., Rantakallio P., Elliott P.: Early Life Factors and Blood Pressure at Age 31 Years in the 1966 Northern

Finland Birth Cohort. Hypertension 44:838-846, 2004.34

[1] Boomsma DI et al. Netherlands Twin Register: from

twins to twin families. Twin Research and Human Genetics. 2006, 9, 849-5735; [2] Willemsen et al. The Adult Netherlands Twin

Register: twenty-five years of survey and biological data

collection. Twin Research and Human Genetics, 2013, 16, 271-

8136.

Medland SE et al. (2009) Common variants in the

trichohyalin gene are associated with straight hair

in Europeans. American Journal of Human Genetics

85:750-755.37

[1] Moayyeri A, Hammond CJ, Hart DJ, et al., 2013, The UK Adult Twin Registry (TwinsUK Resource)., Twin Research and Human Genetics, Vol:16, ISSN:1832-4274, Pages:144-149 38 [2] Moayyeri A, Hammond CJ, Valdes AM, et al., 2013, Cohort Profile: TwinsUK and healthy ageing twin study., Int J Epidemiol, Vol:42, 0300-5771, Pages:76-85 39

Study URL http://www.oulu.fi/nfbc/ www.tweelingenregister.org http//www.genepi.qimr.edu.au/general/researchtopics.cgi www.twinsuk.co.uk

NA, not available Informed consent was obtained from all participants, and study protocols were approved by the local regional or institutional ethics committees

20eTable 2(a) Genotyping information (studies 1-5)

STUDY ALSPAC Mothers Berlin Birth Cohort

(BBC) Mothers

1958 British Birth Cohort or NCDS (B58C-WTCCC)

1958 British Birth Cohort or NCDS (B58C-T1DGC)

CHOP Mothers

MATERNAL GENOME- OR EXOME-WIDE GENOTYPING

Genotyping platform and SNP panel

Illumina Human660W-Quad BeadChip Human Exomechip ver1.1

Affymetrix Genome-wide Human SNP Array

6.0 Illumina 550K Infinium Illunina550,

Illumina610 Infinium

Genotyping centre Centre National de

Génotypage (CNG), Evry, France

Oxfor Centre for Diabete, Endocrinology and

Metabolism, University of Oxford, UK

Wellcome Trust Sanger Institute, Cambridge,

UK

JDRF/WT DIL Lab in Cambridge, UK

The Center for Applied Genomics, Children's

Hospital of Philadelphia, USA

N SNPs in QC'd dataset 526,688 NA 721,428 520,413 513,518

Imputation software / reference panel

MACH v.1.0.16 / HapMap Phase II NA Impute /HapMap Phase

II Impute /HapMap Phase

II Impute /HapMap Phase

II

N QC'd SNPs available for GWAS analysis 2,450,866 NA 2,543,926 2,451,644 2,546,219

Genomic control lambda from GWAS analysis of offspring birth weight

1.039 NA 0.984 1.007 NA

FETAL GENOME- OR EXOME-WIDE

GENOTYPING

Genotyping platform and SNP panel

Illumina HumanHap550 quad

array Human Exomechip ver1.1 NA NA Illunina550,

Illumina610 Infinium

Genotyping centre

Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and

LabCorp (Laboratory Corportation of

America) using support from 23andMe

Oxford Centre for Diabete, Endocrinology and

Metabolism, University of Oxford, UK

NA NA The Center for Applied Genomics

N SNPs in QC'd dataset 500,541 NA NA NA 513,518

Imputation software / reference panel

MACH v.1.0.16 / HapMap Phase II NA NA NA Impute /HapMap Phase

II

DATA ANALYSIS Analysis software Stata v.13 PLINK and R Stata, version 12 Stata, version 12 SNPtest and R

21

ALSPAC Mothers Berlin Birth Cohort

(BBC) Mothers

1958 British Birth Cohort or NCDS (B58C-WTCCC)

1958 British Birth Cohort or NCDS (B58C-T1DGC)

CHOP Mothers

REFERENCES Reference - MATERNAL genotyping

Evans DM et al. (2013) Genome-wide

association study identifies loci affecting blood copper, selenium

and zinc. Hum Mol Genet. 22(19):3998-

4006.40

NA

Sawcer S, Hellenthal G, Pirinen M, Spencer CC, Patsopoulos NA, et al.

(2011) Genetic risk and a primary role for cell-

mediated immune mechanisms in multiple sclerosis. Nature 476:

214–21941

Barrett JC, Clayton DG, Concannon P et al.

Genome-wide assocition study and meta-analysis

find that over 40 loci affect risk of type 1 diabetes. Nat. Genet

200942

NA

REFERENCES Reference -FETAL genotyping

Paternoster L. et al. (2012) Genome-wide association study of three-dimensional facial morphology

identifies a variant in PAX3 associated with nasion position. Am J

Hum Genet. 90(3):478-85.43

NA NA NA NA

22eTable 2(b) Genotyping information (studies 6-10)

STUDY COPSAC-2000 Mothers

DNBC-GOYA Random Set

DNBC-PTB-CONTROL Mothers EFSOCH Mothers GEN-3G Mothers

MATERNAL GENOME- OR EXOME-WIDE GENOTYPING

Genotyping platform and SNP panel Illumina 550K Illumina Human610-

Quad v1.0 Illumina Human 660W-quad

Bead Array Illumina Human

Exome Beadchip v1 NA

Genotyping centre Children's Hospital of

Philadelphia, Center for Applied Genomics

Centre National de Génotypage (CNG),

Evry, France

Center for Inherited Disease Research, Johns Hopkins

University, Baltimore, Maryland, USA

Centre National de Génotypage, France NA

N SNPs in QC'd dataset 486,373 545,349 518,097 234,763 NA

Imputation software / reference panel

MacH-minimac/Hapmap Phase II Mach 1.0 MaCH/HapMap Phase II NA NA

N QC'd SNPs available for GWAS

analysis NA 2,449,993 2,543,887

57 QC'd SNPs available for analysis

of genetic scores selected for the current

project

NA

Genomic control lambda from GWAS analysis of offspring

birth weight

NA 1.006 1.006 NA NA

MATERNAL CUSTOM

GENOTYPING

Genotyping centre and method NA NA NA

LGC Genomics (formerly

Kbiosciences); KASPar

Genome Quebec Innovation Centre

Call rate [Median (range)]; N SNPs

genotyped NA NA NA 0.953 [0.932, 0.999]

(N=16 SNPs) 0.99 [0.98 ; 1] (N=4

snps)

Did any SNPs deviate from HWE

(Bonferroni corrected P<0.05)? Y/N

NA NA NA N N

Duplicate concordance (%) NA NA NA >99% (approx. 10%) 100%

23

COPSAC-2000 Mothers

DNBC-GOYA Random Set

DNBC-PTB-CONTROL Mothers EFSOCH Mothers GEN-3G Mothers

FETAL GENOME- OR EXOME-WIDE GENOTYPING

Genotyping platform and SNP panel Illumina 550K NA Illumina Human 660W-quad

Bead Array NA NA

Genotyping centre Children's Hospital of

Philadelphia, Center for Applied Genomics

NA

Center for Inherited Disease Research, Johns Hopkins

University, Baltimore, Maryland, USA

NA NA

N SNPs in QC'd dataset 486,373 NA 514,382 NA NA

Imputation software / reference panel

MacH-minimac/Hapmap Phase II NA MaCH/HapMap Phase II NA NA

FETAL CUSTOM

GENOTYPING

Genotyping centre and method NA NA NA

LGC Genomics (formerly

Kbiosciences); KASPar NA

Call rate [Median (range)]; N SNPs

genotyped NA NA NA 0.926 [0.907, 0.934]

(N=13 SNPs) NA

Did any SNPs deviate from HWE

(Bonferroni corrected P<0.05)? Y/N

NA NA NA N NA

Duplicate concordance (%

duplicated genotypes) NA NA NA >99% (approx. 10%) NA

DATA ANALYSIS Analysis software R-project Stata R Stata v.13 R

24

COPSAC-2000 Mothers

DNBC-GOYA Random Set

DNBC-PTB-CONTROL Mothers EFSOCH Mothers GEN-3G Mothers

REFERENCES Reference -

MATERNAL genotyping

HakonarsonH,GrantSF,BradfieldJP,MarchandL,KimCE,GlessnerJT, Grabs

R, Casalunovo T, Taback SP, Frackelton EC, et al.

A genome- wide association study

identifies KIAA0350 as a type 1 diabetes gene.

Nature 2007;448:591–594.44

Paternoster et al. (2011) PLoS One. 6(9):e24303

Ryckman, K. K., Feenstra, B., Shaffer, J. R., et al

(2012). Replication of a genome-wide association study of birth weight in preterm neonates. The Journal of pediatrics,

160(1), 19-24.45

NA NA

REFERENCES Reference -FETAL genotyping

HakonarsonH,GrantSF,BradfieldJP,MarchandL,KimCE,GlessnerJT, Grabs

R, Casalunovo T, Taback SP, Frackelton EC, et al.

A genome- wide association study

identifies KIAA0350 as a type 1 diabetes gene.

Nature 2007;448:591–594.44

NA

Ryckman, K. K., Feenstra, B., Shaffer, J. R., et al

(2012). Replication of a genome-wide association study of birth weight in preterm neonates. The Journal of pediatrics,

160(1), 19-24.45

NA NA

25eTable 2(c) Genotyping information (studies 11-14)

STUDY Generation R Mothers HAPO Mothers (GWAS) HAPO Mothers (nonGWAS) MoBa (Mothers)

MATERNAL GENOME- OR EXOME-WIDE GENOTYPING

Genotyping platform and SNP panel NA Illumina Human 610 Quad

v1 B SNP array NA Illumina 660Wquad

Genotyping centre NA Broad Institute Center for Genotyping and Analysis

(CGA), USA NA The Norwegian Cancer

Hospital, Oslo

N SNPs in QC'd dataset NA 559,739 NA 432,270

Imputation software / reference panel NA Beagle / HapMap3 CEU &

TSI NA PLINK /HapMap Phase II

N QC'd SNPs available for GWAS analysis NA 1,968,447 NA NA

Genomic control lambda from GWAS analysis of offspring birth weight

NA 1.016 NA None

MATERNAL CUSTOM

GENOTYPING

Genotyping centre and method

LGC Genomics (formerly Kbiosciences); KASPar NA LGC Genomics (formerly

Kbiosciences); KASPar NA

Call rate [Median (range)]; N SNPs genotyped 99.3% (N=34 snps) NA 0.983 [0.977, 0.988]

(N=23 SNPs) NA

Did any SNPs deviate from HWE (Bonferroni corrected

P<0.05)? Y/N

Y: rs4836133 (P = 3x10-15; excluded) NA N NA

Duplicate concordance (% duplicated genotypes) 99.80% NA >=99% (min 4%) NA

26

Generation R Mothers HAPO Mothers (GWAS) HAPO Mothers (nonGWAS) MoBa (Mothers)

FETAL GENOME- OR EXOME-WIDE GENOTYPING

Genotyping platform and SNP panel Ilumina 610 Quad and 660W Illumina Human 610 Quad

v1 B SNP array NA Illumina 660Wquad

Genotyping centre

Human Genotyping Facility (HuGeF), Dept Internal

Medicine, Erasmus MC, The Netherlands

Broad Institute Center for Genotyping and Analysis

(CGA) NA The Norwegian Cancer

Hospital, Oslo

N SNPs in QC'd dataset 489,879 559,739 NA 432270

Imputation software / reference panel Minimac and MACH Beagle / HapMap3 CEU &

TSI NA PLINK /HapMap Phase II

FETAL CUSTOM

GENOTYPING

Genotyping centre and method NA NA LGC Genomics (formerly

Kbiosciences); KASPar NA

Call rate [Median (range)]; N SNPs genotyped NA NA 0.983 [0.977, 0.988]

(N=23 SNPs) NA

Did any SNPs deviate from HWE (Bonferroni corrected

P<0.05)? Y/N NA NA N NA

Duplicate concordance (%) NA NA >=99% (min 4%) NA

DATA ANALYSIS Analysis software Stata version 12 R 3.0.2 Stata v.13 IBM SPSS Statistics 20

REFERENCES Reference - MATERNAL genotyping NA

Hayes et al., Identification of HKDC1 and BACE2 as

genes influencing glycemic traits during pregnancy through genome-wide

association studies. Diabetes. 2013

Sep;62(9):3282-9146

NA NA

REFERENCES Reference -FETAL genotyping

Jaddoe, van Duijn, Franco et al. 2012 Eur J Epidemiol.

27(9):739-56.23

Urbanek et al., The chromosome 3q25 genomic

region is associated with measures of adiposity in

newborns in a multi-ethnic genome-wide association

study.Hum Mol Genet. 2013 Sep 1;22(17):3583-96.47

NA NA

27eTable 2(d) Genotyping information (studies 16-18)

STUDY NFBC1966 NTR QIMR TWINSUK

MATERNAL GENOME- OR EXOME-WIDE GENOTYPING

Genotyping platform and SNP panel

Illumina HumanCNV-370DUO Analysis BeadChip

Perlegen-Affymetrix, Affymetrix 6.0, Illumina 370K, 600K, 1M Omni

HumanCNV370-Quadv3 HumanHap3001,2,

HumanHap610Q, 1M-Duo and 1.2MDuo 1M

Genotyping centre Broad Institute

Perlegen Sciences Mountain View CF USA, Finnish

Genome Center Helsinki Finland, SNP technology

Platform Uppsala Sweden, Molecular Epidemiology Leiden The Netherlands, Translational Genomics

Research Institute Phoenix AZ USA, Institute of Human

Genetics LIFE & BRAIN Center Bonn Germany.

CIDR Sanger

N SNPs in QC'd dataset 324,896 312,214-814,708

323,093, reduced to a common set of 274,604 SNPs (across the QIMR

sample)

up to 874,733 SNPs

Imputation software / reference panel Impute version 2 / HapMap2 Impute 1.0 / Build 36r24

Hapmap 2 MACH/HapMap Phase II

IMPUTE software package (v2) 5 using two

referencepanels, P0 (HapMap2, rel 22,

combined CEU+YRI+ASN panels) and P1 (610k+, including combinedHumanHap610k and 1M reduced to 610k

SNP content).

N QC'd SNPs available for GWAS analysis 2,487,934 2385474 2,454,244 2,401,373

Genomic control lambda from GWAS analysis of offspring birth weight

1.020 1.002 1.012 1

28

NFBC1966 NTR QIMR TWINSUK

FETAL GENOME- OR EXOME-WIDE GENOTYPING

Genotyping platform and SNP panel NA

Perlegen-Affymetrix, Affymetrix 6.0, Illumina 370K, 600K, 1M Omni

NA NA

Genotyping centre NA

Perlegen Sciences Mountain View CF USA, Finnish

Genome Center Helsinki Finland, SNP technology

Platform Uppsala Sweden, Molecular Epidemiology Leiden The Netherlands, Translational Genomics

Research Institute Phoenix AZ USA, Institute of Human

Genetics LIFE & BRAIN Center Bonn Germany.

NA NA

N SNPs in QC'd dataset NA 312214-814708 NA NA

Imputation software / reference panel NA Impute 1.0 / Build 36r24

Hapmap 2 NA NA

DATA ANALYSIS Analysis software R 2.14.2 Stata R SNPTEST , Stata

REFERENCES Reference - MATERNAL genotyping

Sabatti et al. Genome-wide association analysis of

metabolic traits in a birth cohort from a founder population. Nat Genet.

2009;41:35-46 and Prokopenko et al. Variants

in MTNA1B influence fasting glucose levels. Nat

Genet 2009;41:77-81.48

[1] Willemsen et al. The Netherlands Twin Register

biobank: a resource for genetic epidemiological

studies. Twin Research and Human Genetics. 2010, 13,

231-45.36

Medland SE et al. (2009) Common variants in the

trichohyalin gene are associated with straight hair

in Europeans. American Journal of Human Genetics

85:750-755.37

Moayyeri A, Hammond CJ, Hart DJ, et al., 2013,

The UK Adult Twin Registry (TwinsUK

Resource)., Twin Research and Human Genetics,

Vol:16, ISSN:1832-4274, Pages:144-14938

29eTable 3. Details of single nucleotide polymorphisms (SNPs) used to construct the genetic scores

Trait SNP Nearest/nearby gene

Trait-raising allelea

Trait-lowering

allelea

Beta for weighting

genetic score

Units of beta and source (including any extra details)

Adiponectin rs17300539 ADIPOQ A G 0.330 Units: age- and sex-adjusted z-scores.

Source: Yaghootkar et al (2013) Diabetes 62(10):3589-9849

Adiponectin rs3774261 ADIPOQ A G 0.354

Adiponectin rs3821799 ADIPOQ C T 0.352

BMI rs10150332 NRXN3 C T 0.13

Units: kg/m2 increase Source: Speliotes et al., Nature Genetics

(2010)50

BMI rs10767664 BDNF A T 0.19 BMI rs10938397 GNPDA2 G A 0.18 BMI rs10968576 LRRN6C G A 0.11 BMI rs11847697 PRKD1 T C 0.17 BMI rs12444979 GPRC5B C T 0.17 BMI rs13078807 CADM2 G A 0.1 BMI rs1514175 TNNI3K A G 0.07 BMI rs1555543 PTBP2 C A 0.06 BMI rs1558902 FTO A T 0.39 BMI rs206936 NUDT3 G A 0.06 BMI rs2112347 FLJ35779 T G 0.1 BMI rs2241423 MAP2K5 G A 0.13 BMI rs2287019 QPCTL C T 0.15 BMI rs2815752 NEGR1 A G 0.13 BMI rs2867125 TMEM18 C T 0.31 BMI rs2890652 LRP1B C T 0.09 BMI rs29941 KCTD15 G A 0.06 BMI rs3810291 TMEM160 A G 0.09 BMI rs3817334 MTCH2 T C 0.06 BMI rs4771122 MTIF3 G A 0.09 BMI rs4836133 ZNF608 A C 0.07 BMI rs4929949 RPL27A C T 0.06 BMI rs543874 SEC16B G A 0.22 BMI rs571312 MC4R A C 0.23 BMI rs713586 RBJ C T 0.14 BMI rs7138803 FAIM2 A G 0.12 BMI rs887912 FANCL T C 0.1 BMI rs9816226 ETV5 T A 0.14 BMI rs987237 TFAP2B G A 0.13

30

Trait SNP Nearest/nearby gene

Trait-raising allelea

Trait-lowering

allelea

Beta for weighting

genetic score

Units of beta and source (including any extra details)

Blood pressure rs2932538 MOV10 G A 0.3884

Units: mmHg per BP-raising allele;

Sources: Ehret et al 2010 Nature15;

Johnson et al 2010 Hypertension51; Wain

et al 2010 Nat Genet.52

Blood pressure rs13082711 SLC4A7 C T 0.3151 Blood pressure rs419076 MECOM T C 0.4088

Blood pressure rs13139571 GUCY1A3-GUCY1B3 C A 0.3213

Blood pressure rs1173771 NPR3-C5orf23 G A 0.5041 Blood pressure rs11953630 EBF1 C T 0.4119 Blood pressure rs805303 BAT2-BAT5 G A 0.3756 Blood pressure rs4373814 CACNB2(5') C G 0.3726 Blood pressure rs932764 PLCE1 G A 0.4837 Blood pressure rs7129220 ADM A G 0.6186

Blood pressure rs633185 FLJ32810-TMEM133 C G 0.5647

Blood pressure rs2521501 FURIN-FES T A 0.6498 Blood pressure rs17608766 GOSR2 C T 0.5564 Blood pressure rs1327235 JAG1 G A 0.3404 Blood pressure rs6015450 GNAS-EDN3 G A 0.8964 Blood pressure rs17367504 MTHFR-NPPB A G 0.9031 Blood pressure rs3774372 ULK4 C T 0.0666 Blood pressure rs1458038 FGF5 T C 0.7057 Blood pressure rs1813353 CACNB2(3') T C 0.5686 Blood pressure rs4590817 C10orf107 G C 0.6457

Blood pressure rs11191548 CYP17A1-NT5C2 T C 1.0952

Blood pressure rs381815 PLEKHA7 T C 0.5747 Blood pressure rs17249754 ATP2B1 G A 0.9282 Blood pressure rs3184504 SH2B3 T C 0.5976 Blood pressure rs10850411 TBX5-TBX3 T C 0.3541 Blood pressure rs1378942 CYP1A1-ULK3 C A 0.6125 Blood pressure rs12940887 ZNF652 T C 0.3622 Blood pressure rs1801253 ADRB1 C G 0.57 Blood pressure rs13002573 FIGN A G 0.416 Blood pressure rs17477177 PIK3CG C T 0.552 Blood pressure rs1446468 FIGN C T 0.499 Blood pressure rs319690 MAP4 (intron) T C 0.423 Blood pressure rs2782980 ADRB1 C T 0.406

31

Trait SNP Nearest/nearby gene

Trait-raising allelea

Trait-lowering

allelea

Beta for weighting

genetic score

Units of beta and source (including any extra details)

Fasting Glucose rs560887 G6PC2 C T 0.075

Units: mmol/L per fasting glucose raising

allele Source: Dupuis et al.,

2010 Nature Genetics53

Fasting Glucose rs13266634 SLC30A8 C T 0.027 Fasting Glucose rs11708067 ADCY5 A G 0.027 Fasting Glucose rs10830963 MTNR1B G C 0.067

Fasting Glucose rs2191349 DGKB/TMEM195 T G 0.03

Fasting Glucose rs7944584 MADD A T 0.021 Fasting Glucose rs10885122 ADRA2A G T 0.022 Fasting Glucose rs11605924 CRY2 A C 0.015 Fasting Glucose rs340874 PROX1 C T 0.013 Fasting Glucose rs11920090 SLC2A2 T A 0.02 Fasting Glucose rs7034200 GLIS3 A C 0.018 Fasting Glucose rs11071657 C2CD4B A G 0.008 Fasting Glucose rs4607517 GCK A G 0.062

HDL-specificb rs1532085 LIPC A G 1.45

Units: mg/dL per HDL raising allele

Source: Teslovich et al., (2010) Nature

Genetics2

HDL-specific rs16942887 LCAT A G 1.27

HDL-specific rs1883025 ABCA1 C T 0.94

HDL-specific rs3764261 CETP A C 3.39

32

Trait SNP Nearest/nearby gene

Trait-raising allelea

Trait-lowering

allelea

Beta for weighting

genetic score

Units of beta and source (including any extra details)

Triglyceride main effectc rs10761731 JMJD1C A T 2.38

Units: mg/dL per triglyceride raising

allele Source: Teslovich et

al., (2010) Nature Genetics2

Triglyceride main effect rs10889353 DOCK7 A C 4.94

Triglyceride main effect rs11613352 LRP1 C T 2.7

Triglyceride main effect rs11649653 CTF1 C G 2.13

Triglyceride main effect rs13238203 TYW1B C T 7.91

Triglyceride main effect rs1495741 NAT2 G A 2.97

Triglyceride main effect rs2068888 CYP26A1 G A 2.28

Triglyceride main effect rs2412710 CAPN3 A G 7

Triglyceride main effect rs2929282 FRMD5 T A 5.13

Triglyceride main effect rs2954029 TRIB1 A T 5.64

Triglyceride main effect rs328 LPL C G 13.64

Triglyceride main effect rs442177 KLHL8 T G 2.25

Triglyceride main effect rs5756931 PLA2GS T C 1.54

Triglyceride main effect rs645040 MSL2L1 T G 2.22

Triglyceride main effect rs714052 BAZ1B A G 7.91

Triglyceride main effect rs964184 APOA1 G C 16.95

Triglyceride main effect rs9686661 MAP3K1 T C 2.57

33

Trait SNP Nearest/nearby gene

Trait-raising allelea

Trait-lowering

allelea

Beta for weighting

genetic score

Units of beta and source (including any extra details)

Type 2 diabetes rs10203174 THADA C T 0.131

Units: Natural logarithm of odds

ratio for type 2 diabetes per type 2 diabetes increasing

allele Source: Morris et al., (2012) Nature Genetics54

Type 2 diabetes rs10758593 GLIS3 A G 0.058 Type 2 diabetes rs10811661 CDKN2A/B T C 0.166 Type 2 diabetes rs10842994 KLHDC5 C T 0.095 Type 2 diabetes rs10923931 NOTCH2 T G 0.077 Type 2 diabetes rs11063069 CCND2 G A 0.077 Type 2 diabetes rs1111875 HHEX/IDE C T 0.104

Type 2 diabetes rs11257655 CDC123/CAMK1D T C 0.068

Type 2 diabetes rs11634397 ZFAND6 G A 0.049 Type 2 diabetes rs11717195 ADCY5 T C 0.104 Type 2 diabetes rs12242953 VPS26A G A 0.068 Type 2 diabetes rs12427353 HNF1A (TCF1) G C 0.077 Type 2 diabetes rs12497268 PSMD6 G C 0.030 Type 2 diabetes rs12571751 ZMIZ1 A G 0.077 Type 2 diabetes rs12899811 PRC1 G A 0.077 Type 2 diabetes rs1359790 SPRY2 G A 0.077 Type 2 diabetes rs1496653 UBE2E2 A G 0.086 Type 2 diabetes rs1552224 ARAP1 (CENTD2) A C 0.104 Type 2 diabetes rs163184 KCNQ1 G T 0.086 Type 2 diabetes rs16927668 PTPRD T C 0.039 Type 2 diabetes rs17168486 DGKB T C 0.104 Type 2 diabetes rs17301514 ST64GAL1 A G 0.049 Type 2 diabetes rs17791513 TLE4 A G 0.113 Type 2 diabetes rs17867832 GCC1 T G 0.086 Type 2 diabetes rs1801282 PPARG C G 0.122 Type 2 diabetes rs2007084 AP3S2 G A 0.020 Type 2 diabetes rs2075423 PROX1 G T 0.068 Type 2 diabetes rs2261181 HMGA2 T C 0.122 Type 2 diabetes rs2334499 DUSP8 T C 0.039 Type 2 diabetes rs243088 BCL11A T A 0.068 Type 2 diabetes rs2447090 SRR A G 0.039 Type 2 diabetes rs2796441 TLE1 G A 0.068 Type 2 diabetes rs3734621 KCNK16 C A 0.068 Type 2 diabetes rs3802177 SLC30A8 G A 0.131 Type 2 diabetes rs4299828 ZFAND3 A G 0.039 Type 2 diabetes rs4402960 IGF2BP2 T G 0.122 Type 2 diabetes rs4430796 HNF1B (TCF2) A G 0.095 Type 2 diabetes rs4458523 WFS1 G T 0.095 Type 2 diabetes rs4502156 C2CD4A T C 0.058 Type 2 diabetes rs459193 ANKRD55 G A 0.077 Type 2 diabetes rs4812829 HNF4A A G 0.058 Type 2 diabetes rs516946 ANK1 C T 0.086 Type 2 diabetes rs5215 KCNJ11 C T 0.068

34

Type 2 diabetes rs6819243 MAEA T C 0.068 Type 2 diabetes rs6878122 ZBED3 G A 0.095 Type 2 diabetes rs7177055 HMG20A A G 0.077 Type 2 diabetes rs7202877 BCAR1 T G 0.113 Type 2 diabetes rs7569522 RBMS1 A G 0.048 Type 2 diabetes rs7756992 CDKAL1 G A 0.157 Type 2 diabetes rs7845219 TP53INP1 T C 0.058 Type 2 diabetes rs7903146 TCF7L2 T C 0.329 Type 2 diabetes rs7955901 TSPAN8/LGR5 C T 0.068 Type 2 diabetes rs8108269 GIPR G T 0.068 Type 2 diabetes rs8182584 PEPD T G 0.039 Type 2 diabetes rs849135 JAZF1 G A 0.104

Trait SNP Nearest/nearby gene

Trait-raising allelea

Trait-lowering

allelea

Beta for weighting

genetic score

Units of beta and source (including any extra details)

Vitamin D - synthesis rs10741657 CYP2R1 A G 0.03 Units: nmol/L per vitamin D raising

allele Source: Wang et al.,

(2010) Lancet55 Vitamin D - synthesis rs12785878 DHCR7/NADSYN1 T G 0.05 a Based on the positive strand according to HapMap Phase 2

b HDL-specific SNPs were selected due to being near genes with known Mendelain lipid disorder

c Triglyceride main effect SNPs were included in the genetic score if they were solely associated with triglyceride levels, or if their effect on triglyceride levels was at least three times greater than that of HDL-, LDL- or total cholesterol (using Teslovich et al 2010, Nat Genet 2).

35eTable 4. Studies with maternal traits ascertained during pregnancy (or for BMI, pre-pregnancy) and available for association analysis with genetic scores

Trait Total N women

N Studie

s Study Name Time of

ascertainment Brief description of maternal phenotype ascertainment and reference, if available

BMI 11,822 5

ALSPAC Mothers Pre-pregnancy

Self-reported weight and height, weight validated with clinic measure (Lawlor et al., (2010)

Diabetologia 53: 89-97)56 DNBC-GOYA

Random Set Pre-pregnancy Registry data (Olsen et al., (2001) Scandinavian

Journal of Public Health 29:300-307)27

DNBC-PTB-CONTROL

Mothers Pre-pregnancy Registry data (Olsen et al., (2001) Scandinavian

Journal of Public Health 29:300-30727

EFSOCH Mothers Pre-pregnancy

Measured height 3 times and averaged, pre-pregnancy weight self reported (Knight et al., (2006)

Paediatric Perinal Epidemiology 20:172-179)28 HAPO

Mothers (GWAS)

Pre-pregnancy Height measured, pre-pregnancy weight self reported (Metzger et al., (2008) NEJM 358:1991-2002)30

Fasting glucose 5,402 3

EFSOCH Mothers

Gestational week 28

Fasting blood samples (10 hours fasting minimum) (Knight et al., (2006) Paediatric Perinal

Epidemiology 20:172-179)28 HAPO

Mothers (GWAS)

Gestational week 28 Fasting blood samples (Metzger et al., (2008) NEJM

358:1991-2002)30 HAPO Mothers (non-

GWAS)

Gestational week 28

Gestational or existing diabetes

6,827 1 ALSPAC Mothers

Gestational week 28

Questionnaire at recruitment about existing diabetes and history of gestational diabetes. Data abstracted

on gestational diabetes and glycosuria (Lawlor et al., (2010) Diabetologia 53: 89-97)56

Triglycerides 663 1 EFSOCH

Mothers Gestational

week 28

Fasting blood samples (10 hours fasting minimum) (Knight et al., (2006) Paediatric Perinal

Epidemiology 20:172-179)28

HDL 733 1 EFSOCH Mothers

Gestational week 28

Fasting blood samples (10 hours fasting minimum) (Knight et al., (2006) Paediatric Perinal

Epidemiology 20:172-179)28

Blood pressure 9,100 3

ALSPAC Mothers

Gestational week 28

Data abstracted from obstetric medical charts at various time points in pregnancy. Data for 28 weeks gestation predicted using fractional polynomials and spline multilevel models. (Macdonald-Wallis et al., (2011) Journal of Hypertension 29: 1703-1711)57

HAPO Mothers (GWAS)

Gestational week 28

Measured blood pressure (Metzger et al., (2008) NEJM 358:1991-2002)30

MoBa Mothersa

Gestational week 18

Self-reported blood pressure from medical card http://www.fhi.no/dokumenter/1f32a49514.pdf

Vitamin D status

(25(OH)D levels)

5,305 2 ALSPAC Mothers

Gestational week 28

Measured in non-fasting blood samples. Data for 28 weeks gestation predicted using fractional

polynomials and spline multilevel models. (Lawlor et al., (2013) Lancet 6736: 62203)58

GEN-3Gb Yes (9 weeks) Measured in blood samples

Adiponectin 1,376 1 HAPO

Mothers GWAS

Gestational week 28

Measured in fasting serum samples (Lowe et al., (2010) Journal Clinical Endocrinology and

Metabolism 95: 5427-5434)59 aBlood pressure was measured in the MoBa study and showed strong evidence of association with the genetic score (P=0.001), but since it was measured at 18 weeks of gestation, we chose not to meta-analyse with ALSPAC and HAPO data (measured at 28 weeks). bVitamin D status was measured in the GEN-3G study and showed strong evidence of association the genetic score (P<5x10-5), but since it was measured at 9 weeks of gestation, we chose not to meta-analyse with ALSPAC data above (measured at 28 weeks).

36eTable 5. Associations between maternal genetic scores and maternal traits during and post-pregnancy in the same individuals

Trait Study

N women with both pregnancy and post-

pregnancy data

available

Mean (SD) of trait

measured during

pregnancy

Mean (SD) age of

mother when pregnancy

measurement taken

Change in maternal trait

per trait-increasing allele (95% CI) during

pregnancy

P value (during

pregnancy)

Mean (SD) of trait

measured post-

pregnancy

Mean (SD) age of

mother when post-

pregnancy measurement

taken

Change in maternal trait

per trait-increasing allele (95% CI) post-

pregnancy

P value (post-

pregnancy)

BMI (kg/m2) ALSPAC 2,927 26.02 (3.55) 29.7 (4.4) 0.13 (0.09, 0.16) 7x10-14 26.54 (5.21) 48.0 (4.4) 0.14 (0.09, 0.19) 1x10-8

BMI (kg/m2) EFSOCH 456 27.55 (4.19) 31.5 (4.7) 0.21 (0.11, 0.32) 9x10-5 25.03 (4.60) 36.8 (4.9) 0.16 (0.05, 0.28) 0.006

Fasting glucose (mmol/L) EFSOCH 312 4.39 (0.38) 32.0 (4.4) 0.04 (0.02, 0.05) 1x10-6 4.60 (0.48) 37.1 (4.7) 0.04 (0.02, 0.06) 2x10-4

Triglycerides (mmol/L) EFSOCH 360 2.13 (0.70) 31.6 (4.7) 0.05 (0.03, 0.08) 2x10-4 0.91 (0.40) 36.9 (4.9) 0.03 (0.01, 0.05) 4x10-4

HDL-cholesterol (mmol/L) EFSOCH 408 2.10 (0.46) 31.5 (4.7) 0.02 (0.01, 0.03) 1x10-4 1.71 (0.42) 36.8 (4.9) 0.03 (0.02, 0.04) 7x10-9

SBP (mmHg) ALSPAC 2,930 112.3 (7.2) 29.6 (4.5) 0.17 (0.10, 0.24) 4x10-6 117.9 (12.2) 47.9 (4.4) 0.43 (0.31, 0.56) 2x10-12

SBP, systolic blood pressure

ALSPAC, Avon Longitudinal Study of Parents and Children17. EFSOCH, Exeter Family Study Of Childhood Health28.

37eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(a) BMI genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, EFSOCH, HAPO (GWAS), DNBC-GOYA-RANDOM, DNBC-PTB-CONTROLS

[0.18] 11,822 0.145 (0.126, 0.164) < 2x10-16

Waist-Hip Ratio - EFSOCH 438 0.001 (-0.001, 0.003) 0.18 Fasting glucose mmol/L HAPO, EFSOCH [0.14] 2,104 0.005 (0.001, 0.009) 0.026

Gestational/existing diabetes Odds ratio ALSPAC 6,827 1.04 (0.97, 1.12) 0.28 Triglycerides mmol/L EFSOCH 735 0.009 (-0.006, 0.023) 0.25

HDL-cholesterol mmol/L EFSOCH 732 -0.008 (-0.017, 0.002) 0.11 LDL-cholesterol mmol/L EFSOCH 727 -0.004 (-0.026, 0.019) 0.76

Systolic blood pressure mmHg ALSPAC, HAPO [0.08] 8,450 0.07 (0.02, 0.11) 0.003 Vitamin D, ln[25(OH)D] ALSPAC 4,767 0.002 (-0.001, 0.006) 0.25

Adiponectin log10(ug/ml) HAPO (GWAS) 1,376 -0.001 (-0.006, 0.000) 0.08 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO, EFSOCH [0.08] 9,212 1.00 (1.00, 1.01) 0.19 Highest educational qualification attaineda ALSPAC 6,855 -0.00 (-0.01, 0.01) 0.63

Occupational positionb ALSPAC 5,766 1.00 (0.99, 1.02) 0.67 Occupational positionc EFSOCH 612 0.015 (-0.003, 0.033) 0.11

Townsend deprivation scored EFSOCH 743 0.035 (-0.029, 0.100) 0.28 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

cNational Statistics Socio Economic Class Occuptation Code (3). Subjects grouped as 1=managerial & professional; 2=intermediate; 3=routine & manual

dTownsend deprivation score, a continuous variable based on UK postal code: 0=average; >0=more deprived; <0=more affluent

ALSPAC, Avon Longitudinal Study of Parents and Children17. DNBC-GOYA, Danish National Birth Cohort-Genetics of Obesity in Young Adults study26. DNBC-PTB-CONTROLS, Danish

National Birth Cohort Preterm Birth study Controls27. EFSOCH, Exeter Family Study Of Childhood Health28. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30

38eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(b) Fasting glucose genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, EFSOCH, HAPO (GWAS) [0.89] 8,232 0.007 (-0.025, 0.039) 0.68 Waist-Hip Ratio - EFSOCH 320 0.000 (-0.002, 0.003) 0.74 Fasting glucose mmol/L HAPO, EFSOCH [0.70] 5,402 0.029 (0.025, 0.032) < 2x10-16

Gestational/existing diabetes Odds ratio ALSPAC 6,827 1.06 (0.95, 1.17) 0.29 Triglycerides mmol/L EFSOCH 537 -0.007 (-0.029, 0.016) 0.57

HDL-cholesterol mmol/L EFSOCH 535 -0.004 (-0.018, 0.010) 0.57 LDL-cholesterol mmol/L EFSOCH 531 -0.014 (-0.049, 0.022) 0.45

Systolic blood pressure mmHg ALSPAC, HAPO (GWAS) [0.69] 8,450 0.038 (-0.026, 0.102) 0.25 Vitamin D, ln[25(OH)D] ALSPAC 4,767 0.003 (-0.003, 0.008) 0.34

Adiponectin log10(ug/ml) HAPO (GWAS) 1,376 -0.001 (-0.006, 0.004) 0.72 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO (GWAS), EFSOCH [0.49] 9,012 0.97 (0.99, 1.00) 0.32 Highest educational qualification attaineda ALSPAC 6,855 -0.01 (-0.02, 0.01) 0.24

Occupational positionb ALSPAC 5,766 1.01 (0.98, 1.03) 0.68

Occupational positionc EFSOCH 447 -0.018 (-0.046, 0.011) 0.22

Townsend deprivation scored EFSOCH 542 -0.050 (-0.151, 0.051) 0.33 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

cNational Statistics Socio Economic Class Occuptation Code (3). Subjects grouped as 1=managerial & professional; 2=intermediate; 3=routine & manual

dTownsend deprivation score, a continuous variable based on UK postal code: 0=average; >0=more deprived; <0=more affluent

ALSPAC, Avon Longitudinal Study of Parents and Children17. EFSOCH, Exeter Family Study Of Childhood Health28. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30

39eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(c) Type 2 diabetes genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, HAPO (GWAS) [0.09] 7,901 0.010 (-0.008, 0.028) 0.28 Waist-Hip Ratio NA Fasting glucose mmol/L HAPO (GWAS) 1,376 0.002 (-0.002, 0.006) 0.25

Gestational/existing diabetes Odds ratio ALSPAC 6,827 1.08 (1.03, 1.14) 0.003 Triglycerides NA - - - -

HDL-cholesterol NA - - - - LDL-cholesterol NA - - - -

Systolic blood pressure mmHg ALSPAC, HAPO (GWAS) [0.27] 8,450 0.037 (0.004, 0.071) 0.028 Vitamin D, ln[25(OH)D] ALSPAC 4,767 0.001 (-0.002, 0.004) 0.37

Adiponectin log10(ug/ml) HAPO (GWAS) 1,376 0.001 (-0.002, 0.003) 0.63 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO (GWAS) [0.24] 8,471 1.00 (1.00, 1.00) 0.55 Highest educational qualification attaineda ALSPAC 6,855 -0.01 (-0.01, 0.00) 0.10

Occupational positionb Odds ratio ALSPAC 5,766 1.00 (0.99, 1.01) 0.95 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

ALSPAC, Avon Longitudinal Study of Parents and Children17. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30

40eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(d) Triglycerides genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, EFSOCH, HAPO (GWAS) [0.06] 8,353 -0.007 (-0.041, 0.027) 0.70 Waist-Hip Ratio - EFSOCH 392 0.000 (-0.002, 0.003) 0.84 Fasting glucose mmol/L HAPO (GWAS), EFSOCH [0.07] 2,036 0.002 (-0.003, 0.008) 0.42

Gestational/existing diabetes Odds ratio ALSPAC 6,827 0.93 (0.83, 1.04) 0.19 Triglycerides mmol/L EFSOCH 663 0.055 (0.032, 0.078) 3x10-6

HDL-cholesterol mmol/L EFSOCH 660 -0.003 (-0.018, 0.012) 0.71 LDL-cholesterol mmol/L EFSOCH 656 0.003 (-0.033, 0.039) 0.88

Systolic blood pressure mmHg ALSPAC, HAPO (GWAS) [0.97] 8,450 0.002 (-0.065, 0.069) 0.96 Vitamin D, ln[25(OH)D] ALSPAC 4,767 0.003 (-0.003, 0.009) 0.27

Adiponectin log10(ug/ml) HAPO (GWAS) 1,376 0.000 (-0.004, 0.004) 0.93 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO (GWAS), EFSOCH [0.23] 9,142 1.00 (0.99, 1.01) 0.87 Highest educational qualification attaineda ALSPAC 6,855 0.00 (-0.01, 0.01) 0.78

Occupational positionb Odds ratio ALSPAC 5,766 1.01 (0.98, 1.04) 0.47

Occupational positionc EFSOCH 556 -0.004 (-0.034, 0.026) 0.81

Townsend deprivation scored EFSOCH 671 0.009 (-0.097, 0.115) 0.87 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

cNational Statistics Socio Economic Class Occuptation Code (3). Subjects grouped as 1=managerial & professional; 2=intermediate; 3=routine & manual

dTownsend deprivation score, a continuous variable based on UK postal code: 0=average; >0=more deprived; <0=more affluent

ALSPAC, Avon Longitudinal Study of Parents and Children17. EFSOCH, Exeter Family Study Of Childhood Health28. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30

41eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(e) HDL-cholesterol genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, EFSOCH, HAPO (GWAS) [0.10] 8,420 -0.048 (-0.097, 0.002) 0.06 Waist-Hip Ratio - EFSOCH 438 0.002 (-0.002, 0.005) 0.40 Fasting glucose mmol/L HAPO (GWAS), EFSOCH [0.48] 2,107 -0.002 (-0.011, 0.007) 0.70

Gestational/existing diabetes Odds ratio ALSPAC 6,827 0.83 (0.70, 0.99) 0.04 Triglycerides mmol/L EFSOCH 736 0.001 (-0.035, 0.037) 0.96

HDL-cholesterol mmol/L EFSOCH 733 0.050 (0.027, 0.072) 1x10-5 LDL-cholesterol mmol/L EFSOCH 728 -0.032 (-0.088, 0.024) 0.26

Systolic blood pressure mmHg ALSPAC, HAPO (GWAS) [0.77] 8,450 -0.013 (-0.112, 0.085) 0.79 Vitamin D, ln[25(OH)D] ALSPAC 4,767 -0.000 (-0.009, 0.008) 0.95

Adiponectin log10(ug/ml) HAPO (GWAS) 1,376 -0.001 (-0.007, 0.005) 0.81 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO (GWAS), EFSOCH [0.38] 9,215 1.00 (0.99, 1.00) 0.29 Highest educational qualification attaineda ALSPAC 6,855 0.01 (-0.01, 0.02) 0.55

Occupational positionb ALSPAC 5,766 1.00 (0.96, 1.04) 0.93

Occupational positionc EFSOCH 613 0.004 (-0.042, 0.049) 0.87

Townsend deprivation scored EFSOCH 744 0.081 (-0.081, 0.243) 0.33 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

cNational Statistics Socio Economic Class Occuptation Code (3). Subjects grouped as 1=managerial & professional; 2=intermediate; 3=routine & manual

dTownsend deprivation score, a continuous variable based on UK postal code: 0=average; >0=more deprived; <0=more affluent

ALSPAC, Avon Longitudinal Study of Parents and Children17. EFSOCH, Exeter Family Study Of Childhood Health28. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30

42eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(f) Systolic blood pressure genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, HAPO (GWAS) [0.78] 7,741 -0.011 (-0.030, 0.008) 0.27 Waist-Hip Ratio NA Fasting glucose mmol/L HAPO (GWAS) 1,376 0.002 (-0.003, 0.007) 0.45

Gestational/existing diabetes Odds ratio ALSPAC 6,827 0.98 (0.91, 1.05) 0.52 Triglycerides NA - - - -

HDL-cholesterol NA - - - - LDL-cholesterol NA - - - -

Systolic blood pressure mmHg ALSPAC, HAPO (GWAS) [0.04] 8,450 0.186 (0.140, 0.231) < 2x10-16 Vitamin D, ln[25(OH)D] ALSPAC 4,767 -0.001 (-0.005, 0.002) 0.45

Adiponectin log10(ug/ml) HAPO (GWAS) 1,376 0.001 (-0.002, 0.004) 0.46 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO (GWAS) [0.36] 8,471 1.00 (0.99, 1.00) 0.17 Highest educational qualification attaineda ALSPAC 6,855 0.00 (-0.00, 0.01) 0.32

Occupational positionb ALSPAC 5,766 0.99 (0.97, 1.01) 0.43 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

ALSPAC, Avon Longitudinal Study of Parents and Children17. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30

43eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(g) Vitamin D genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, EFSOCH, HAPO (GWAS) [0.86] 8,420 0.073 (-0.019, 0.164) 0.12 Waist-Hip Ratio - EFSOCH 438 -0.001 (-0.008, 0.005) 0.68 Fasting glucose mmol/L HAPO (GWAS), EFSOCH [0.96] 2,109 -0.001 (-0.019, 0.016) 0.96

Gestational/existing diabetes Odds ratio ALSPAC 6,827 1.19 (0.88, 1.62) 0.25 Triglycerides mmol/L EFSOCH 736 0.001 (-0.060, 0.061) 0.98

HDL-cholesterol mmol/L EFSOCH 733 -0.034 (-0.072, 0.005) 0.09 LDL-cholesterol mmol/L EFSOCH 728 0.048 (-0.046, 0.141) 0.32

Systolic blood pressure mmHg ALSPAC, HAPO (GWAS) [0.98] 8,454 -0.030 (-0.211, 0.151) 0.98 Vitamin D, ln[25(OH)D] ALSPAC 4,767 0.024 (0.009, 0.039) 0.002

Adiponectin log10(ug/ml) HAPO (GWAS) 1,339 -0.029 (-0.772, 0.713) 0.94 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO (GWAS), EFSOCH [0.83] 9,217 1.00 (0.95, 1.05) 0.98 Highest educational qualification attaineda ALSPAC 6,855 0.033 (0.001, 0.065) 0.05

Occupational positionb Odds ratio ALSPAC 5,766 0.95 (0.88, 1.02) 0.17

Occupational positionc EFSOCH 613 0.057 (-0.017, 0.130) 0.13 Townsend deprivation scored EFSOCH 744 -0.058 (-0.329, 0.213) 0.67

aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

cNational Statistics Socio Economic Class Occuptation Code (3). Subjects grouped as 1=managerial & professional; 2=intermediate; 3=routine & manual

dTownsend deprivation score, a continuous variable based on UK postal code: 0=average; >0=more deprived; <0=more affluent

ALSPAC, Avon Longitudinal Study of Parents and Children17. EFSOCH, Exeter Family Study Of Childhood Health28. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30

44eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

(h) Adiponectin genetic score

Outcome variable tested for association (measured or ascertained during pregnancy, except BMI and

WHR)

Units of outcome variable

Study(ies) [Phet from meta-analysis] Total N women

Estimated change in outcome variable per

trait-raising allele (95%CI)

P-value

Body Mass Index kg/m2 ALSPAC, HAPO (GWAS) [0.79] 7,741 -0.04 (-0.23, 0.14) 0.61 Waist-Hip Ratio NA - - - - Fasting glucose mmol/L HAPO 1,376 -0.01 (-0.06, 0.04) 0.69

Gestational/existing diabetes Odds ratio ALSPAC 6,827 1.57 (0.94, 2.64) 0.09 Triglycerides NA - - - -

HDL-cholesterol NA - - - - LDL-cholesterol NA - - - -

Systolic blood pressure mmHg ALSPAC, HAPO (GWAS) [0.66] 8,450 -0.047 (-0.389, 0.295) 0.79 Vitamin D, ln[25(OH)D] ALSPAC 4,767 -0.015 (-0.043, 0.012) 0.28

Adiponectin ln(ug/ml) HAPO (GWAS) 1,376 0.17 (0.11, 0.23) 1x10-8 Smoking (current smoker vs non-smoker) Odds ratio ALSPAC, HAPO (GWAS) [0.91] 8,471 0.97 (0.93, 1.02) 0.22 Highest educational qualification attaineda NA ALSPAC 6,855 -0.04 (-0.10, 0.02) 0.16

Occupational positionb Odds ratio ALSPAC 5,766 0.96 (0.84, 1.10) 0.57 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree

bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

ALSPAC, Avon Longitudinal Study of Parents and Children17. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30.

45eTable 7. Associations between maternal genetic scores and ponderal index of offspring at birth

Maternal exposure for which genetic

score was constructed

N Studies

Total N

women

Change in ponderal index z-

score per additional

maternal trait raising/lowering allele (95% CI)a

Equivalent change in PI (kgm-3) per allele (95% CI)b

P value

Heterogeneity P Value; I2 %

from meta-analysis

N Studies

with fetal

genotype

Total N offspring

with genotype

data

Change in PI z-score per additional maternal

trait raising /loweringa

allele (95% CI)c

Equivalent change in PI (kgm-3) per allele (95% CI)b

P value

Heterogeneity P Value (I2

%) from meta-analysis

Higher pre-pregnancy BMI 10 17,743 0.006 (0.002,

0.010) 0.02 (0.01,

0.03) 0.003 0.48; 0 7 9,628 0.006 (0.000, 0.012)

0.02 (0.00, 0.03) 0.05 0.38; 6.5

Higher fasting glucose 10 17,818 0.007 (0.001,

0.012) 0.02 (0.00,

0.03) 0.02 0.49; 0 8 9,902 0.014 (0.005, 0.022)

0.04 (0.01, 0.06) 0.001 0.78; 0

Higher odds of type 2 Diabetes 7 13,518 0.002 (-0.002,

0.005) 0.01 (-0.01,

0.01) 0.3 0.20; 29.6 5 6,800 0.005 (0.000, 0.011)

0.01 (0.00, 0.03) 0.05 0.11; 47.5

Higher odds of type 2 Diabetes

(excluding pre-existing and gestational diabetes)

6 11,653 0.002 (-0.001, 0.006)

0.01 (0.00, 0.02) 0.18 0.32; 14.9 4 5,330 0.008 (0.002,

0.014) 0.02 (0.01,

0.04) 0.01 0.38; 2.9

Higher triglycerides 9 17,440 0.000 (-0.006, 0.007)

0.00 (-0.02, 0.02) 0.89 0.11; 39.0 6 9,335 -0.004 (-

0.014, 0.005) -0.01 (-0.04,

0.01) 0.41 0.07; 51.3

Lower HDL-cholesterol 9 15,573 0.005 (-0.004,

0.014) 0.01 (-0.01,

0.04) 0.27 0.45; 0 6 8,207 -0.001 (-0.013, 0.012)

0.00 (-0.04, 0.03) 0.92 0.13; 41.4

Higher systolic blood pressure 7 13,527 -0.005 (-0.010, -

0.001) -0.01 (-0.03.

0.00) 0.03 0.74; 0 5 6,821 -0.003 (-0.011, 0.004)

-0.01 (-0.03, 0.01) 0.43 0.14; 42.3

Higher systolic blood pressure (excluding pre-eclampsia and hypertension)

6 10,770 -0.005 (-0.010, 0.000)

-0.01 (-0.03, 0.00) 0.07 0.60; 0 4 4,735 -0.005 (-

0.014, 0.004) -0.01 (-0.04,

0.01) 0.25 0.28; 22.2

Lower vitamin D status 7 14,004 -0.007 (-0.025,

0.011) -0.02 (-0.07,

0.03) 0.44 0.22; 27.9 3 7,292 -0.026 (-0.054, 0.002)

-0.07 (-0.15, 0.01) 0.07 0.04; 68.7

Lower adiponectin 6 11,501 0.017 (-0.020, 0.054)

0.05 (-0.06, 0.15) 0.37 0.83; 0 5 6,851 0.039 (-0.016,

0.094) 0.11 (-0.04,

0.26) 0.17 0.89; 0 aThe decision to model the association in relation to the trait-raising or trait-lowering allele depended on the known direction of association of each trait with higher BMI (see Box 1). Column 1 specifies each of these directions of association. Results are per average weighted allele, adjusted for sex and gestational age. bStandard deviation of ponderal index from ALSPAC study was used for these estimates (=2.78 kg/m3). cResults are per average weighted allele, adjusted for sex, gestational age and fetal genotype

46eTable 8. A comparison of the observational with the genetic association between each maternal trait and offspring ponderal index at birth

Maternal trait (value of 1 SD

with units)

Value of a 1 SD change in the trait with

units

Study/iesa used for observational estimates

[Total N women] N women

Observational estimate of the

change in ponderal index

(kg/m3) per 1 SD (or 10% b) change in

maternal trait, adjusted for sex and gestational

age (95%CI)

Genetic estimate of the change in ponderal index

(kg/m3), adjusted for sex and

gestational age, per 1 SD (or 10%

b) change in maternal trait, unadjusted for fetal genotype

(95%CI)

P value c comparing

observational with genetic

ponderal index associations

(unadjusted for fetal genotype)

Genetic estimate of the change in

ponderal index (kg/m3), adjusted for sex, gestational age and fetal genotype, per 1 SD (or 10% b) change in maternal

trait (95%CI)

P value c comparing

observational with genetic

ponderal index associations (adjusted for

fetal genotype)

Higher pre-pregnancy BMI 4 kg/m2

ALSPAC Mothers, EFSOCH Mothers,

HAPO Mothers 9,690 0.24 (0.19, 0.29) 0.45 (0.14, 0.75) 0.20 0.47 (0.14, 0.79) 0.21

Higher fasting glucose 0.4 mmol/L EFSOCH Mothers,

HAPO Mothers 4,917 0.31 (0.22, 0.39) 0.27 (0.05, 0.48) 0.72 0.53 (0.20, 0.87) 0.24

Higher triglycerides 0.7 mmol/L EFSOCH Mothers 857 0.15 (-0.03, 0.33) 0.02 (-0.21, 0.24) 0.35 -0.14 (-0.48, 0.20) 0.14

Lower HDL-cholesterol 0.5 mmol/L EFSOCH Mothers 854 0.12 (-0.08, 0.31) 0.14 (-0.11, 0.39) 0.91 -0.02 (-0.37, 0.34) 0.51

Higher Systolic blood pressure 10 mmHg ALSPAC Mothers,

HAPO Mothers 9,691 0.00 (-0.08, 0.06) -0.77 (-1.80, 0.25) 0.16 -0.46 (-1.95, 1.03) 0.56

Lower vitamin D status 10% b ALSPAC Mothers 3,718 -0.02 (-0.03, 0.00) -0.08 (-0.28, 0.13) 0.56 -0.29 (-0.65, 0.07) 0.14

Lower adiponectin 10% b HAPO Mothers (GWAS only) 1,373 0.05 (0.02, 0.08) 0.03 (-0.03, 0.09) 0.49 0.06 (-0.03, 0.15) 0.82

aHeterogeneity statistics from the meta-analyses of observational associations were: Phet = 0.35 and I2 = 9.1% for BMI; Phet = 0.23 and I2 =32.7% for fasting glucose; Phet = 0.67 and I2 = 0% for SBP. bFor 25[OH]D and adiponectin, we present the estimated change in ponderal index per 10% reduction in maternal trait level because these variables were logged for analysis. cP-values <0.05 are considered to indicate evidence that the genetic effect size estimate is different from the observational estimate, suggesting that the observational estimate is subject to confounding or bias. ALSPAC, Avon Longitudinal Study of Parents and Children17. EFSOCH, Exeter Family Study Of Childhood Health28. HAPO, Hyperglycemia and Adverse Pregnancy Outcome study30.

47

eTable 9 (a) Observational associations between offspring birth weight and maternal socio-economic status or maternal smoking in the ALSPAC study17

Maternal trait N women

Change in birth weight (g) per unit change in maternal

trait (95% CI)

P

Highest educational qualification attaineda 6,855 19 (10, 29) 0.00004 Occupational positionb 5,588 -34 (-68, 0) 0.06

Smoking (current smoker vs non-smoker) 7,021 -208 (-237, -179) 1x10-43 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V. (b) Observational associations between maternal BMI and maternal socio-economic status or maternal smoking in the ALSPAC study17

Maternal trait N women

Change in BMI (kgm-2) per unit change in

maternal trait (95%CI) P

Highest educational qualification attaineda 6,115 -0.35 (-0.42, -0.27) 9x10-20 Occupational positionb 5,128 0.38 (0.11, 0.65) 0.005

Smoking (current smoker vs non-smoker) 6,238 0.01 (-0.24, 0.26) 0.95 aSubjects grouped as: 1=CSE; 2=Vocational; 3=Ordinary Level; 4=Advanced level; 5=Degree bDerived from Office of Population Censuses & Surveys Standard Occupational Classification (1). Subjects dichotomized as: 0=I, II & III (non-manual); 1=III (manual), IV & V.

48

eTable 10. Power calculations

Maternal trait Total N

women

Adjusted-R2 from BW~GA

sex

Adjusted-R2 from BW~GA sex GS

Estimated proportion of

variance in birth weight explained by

maternal genetic score

Power available in our included sample to detect evidence of association between

maternal genetic score and birth

weight at P<0.05

Minimum sample size needed to detect association between

maternal genetic score and birth

weight at P<0.05 with 80% power

BMI 25265 0.1308 0.1312 0.0004 0.89 19618

Fasting glucose 23902 0.1308 0.1319 0.0011 1.00 7131

Type 2 diabetes 18670 0.1308 0.1312 0.0004 0.78 19618

Triglycerides 24985 0.1308 0.1307 -0.0001* 0.35 78485

HDL-cholesterol

22167 0.1308 0.1307 -0.0001* 0.32 78485

Systolic Blood Pressure

20062 0.1308 0.1324 0.0016 1.00 4902

25-hydroxy vitamin D

30340 0.1308 0.1309 0.0001 0.41 78485

Adiponectin 14920 0.1308 0.1307 -0.0001* 0.23 78485

*Where the estimated variance explained was negative, we assumed a value of 0.0001 for the calculations. BW, birth weight; GA, gestational age; GS, genetic score.

49eTable 11. Association between father’s phenotypes and offspring birth weight using data from the ALSPAC study17

Father’s phenotype N men Correlation coefficient (95% CI)

of father’s phenotype with offspring birth weight

BMI* 7491 0.04 (0.02, 0.06) BMI 1721 0.03 (-0.02, 0.07)

Systolic BP 1732 -0.03 (-0.07, 0.01) Glucose 1656 -0.01 (-0.06, 0.03)

Triglycerides 1656 -0.02 (-0.07, 0.02) HDLc 1656 0.02 (-0.03, 0.06)

* Based on paternal report of weight and height at the time that their partner was in early pregnancy; all other phenotypes were assessed at a clinic visit ~18-19 years after the child’s birth. Correlation coefficients of paternal phenotypes with offspring birth weight were all weak and mostly null (Pearson correlation coefficients all ≤ 0.04). The correlation between and offspring birth weight and father’s BMI, assessed when the mothers were pregnant, was similar to that between offspring birth weight and father’s BMI 18 years later, suggesting that the postnatal measures for other phenotypes are a reasonable approximation for them before/at the time of their partner’s pregnancy.

50

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53

Funding/support of individual studies

ALSPAC: The work undertaken in this paper was funded by the the US National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK10324) and the Wellcome Trust (WT088806), with the WT088806 grant also providing funds for completion of genome wide genotyping on the ALSPAC mothers (WT088806). ALSPAC offspring GWAS data was generated by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and LabCorp (Laboratory Corportation of America) using support from 23andMe. Additional maternal data were funded by the British Heart Foundation (SP/07/008/24066) and WellcomeTrust (WT087997). The UK Medical Research Council and Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. BBC: The Berlin Birth Cohort study was funded by the Deutsche Forschungsgemeinschaft (DFG), Else Kröner-Fresenius Foundation, Jackstädt-Foundation and a research grant of the University of Potsdam, Germany. Details of the study are provided in: Pharmacogenet Genomics. 2009;19(9):710-8.; Circulation. 2006 Oct 17;114(16):1687-92 and Lancet. 2000 Apr 8;355(9211):1241-2. We deeply acknowledge the contribution of the participating families. Replication genotyping was supported by ENGAGE Framework VII HEALTH-F4-2007- 201413 and Wellcome Trust 098381. 1958BC WTCCC: Statistical analyses were funded by the Academy of Finland (Project 24300796 and SALVE/PREVMEDSYN). DNA collection was funded by MRC grant G0000934 and cell-line creation by Wellcome Trust grant 068545/Z/02. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of investigators who contributed to generation of the data is available from the Wellcome Trust Case-Control Consortium website. Funding for the project was provided by the Wellcome Trust under the award 076113. Great Ormond Street Hospital/University College London, Institute of Child Health receives a proportion of funding from the Department of Health's National Institute for Health Research (NIHR) ('Biomedical Research Centres' funding).

1958BC T1DGC: Statistical analyses were funded by the Academy of Finland (Project 24300796 and SALVE/PREVMEDSYN). DNA collection was funded by MRC grant G0000934 and cell-line creation by Wellcome Trust grant 068545/Z/02. This research used resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development, and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. Great Ormond Street Hospital/University College London, Institute of Child Health receives a proportion of funding from the Department of Health's National Institute for Health Research (NIHR) ('Biomedical Research Centres' funding).

CHOP: This research was financially supported by an Institute Development Award from the Children’s Hospital of Philadelphia, a Research Development Award from the Cotswold Foundation and NIH grant R01 HD056465.

COPSAC- 2000: COPSAC is funded by private and public research funds listed on www.copsac.com. The Lundbeck Foundation; The Danish Strategic Research Council; the Pharmacy Foundation of 1991; the Danish Medical Research Council and The Danish Pediatric Asthma Centre provided the core support for COPSAC research center.

DNBC-GOYA: The genotyping for GOYA was funded by the Wellcome Trust (WT 084762). GOYA is a nested study within The Danish National Birth Cohort which was established with major funding from the Danish National Research Foundation. Additional support for this cohort has been obtained from the Pharmacy Foundation, the Egmont Foundation, The March of Dimes Birth Defects Foundation, the Augustinus Foundation, and the Health Foundation.

DNBC-PTB Controls: The Danish National Birth Cohort is a result of major grants from the Danish National Research Foundation, the Danish Pharmacists’ Fund, the Egmont Foundation, the March of Dimes Birth Defects Foundation, the Augustinus Foundation, and the Health Fund of the Danish Health Insurance Societies. The generation of GWAS genotype data for the Danish National Birth Cohort samples was carried out within the Gene Environment Association Studies (GENEVA) consortium with funding provided through the National Institutes of Health’s Genes, Environment, and Health Initiative (U01HG004423; U01HG004446; U01HG004438).

54

EFSOCH: The Exeter Family Study of Childhood Health (EFSOCH) was supported by South West NHS Research and Development, Exeter NHS Research and Development, the Darlington Trust and the Peninsula National Institute of Health Research (NIHR) Clinical Research Facility at the University of Exeter. The opinions given in this paper do not necessarily represent those of NIHR, the NHS or the Department of Health.

Gen3G: The Gen3G prospective cohort was supported by the Fonds de Recherche du Québec – Santé (FRQS – subvention Fonctionnement – Recherche Clinique - grant #20697) and by a Canadian Institute of Health Research (CIHR) Operating grant (Institute of Nutrition, Metabolism and Diabetes; MOP- 115071).

Generation R: The general design of Generation R Study is made possible by financial support from the Erasmus Medical Center, Rotterdam, the Erasmus University Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), the Netherlands Organisation for Scientific Research (NWO), the Ministry of Health, Welfare and Sport and the Ministry of Youth and Families. This research also received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013), project EarlyNutrition under grant agreement n°289346. VWJ received an additional grant from the Netherlands Organization for Health Research and Development (VIDI 016.136.361).

HAPO: This study was supported by National Institutes of Health (NIH) grants HD34242, HD34243, HG004415, DK097534 and DK099820

MoBa: This work was supported by grants from the Norwegian Research Council (FUGE 183220/S10, FRIMEDKLI-05 ES236011), Swedish Medical Society (SLS 2008- 21198), Jane and Dan Olsson Foundations and Swedish government grants to researchers in the public health service (ALFGBG-2863, ALFGBG-11522), and the European Community’s Seventh Framework Programme (FP7/2007-2013). The Norwegian Mother and Child Cohort Study was also supported by the Norwegian Ministry of Health and the Ministry of Education and Research, NIH/NIEHS (contract no N01-ES-75558), NIH/NINDS (grant no.1 UO1 NS 047537-01 and grant no.2 UO1 NS 047537-06A1), and the Norwegian Research Council/FUGE (grant no. 151918/S10).

NFBC1966: Academy of Finland (Grant no. 1114194, Grant no. 12926901), University Hospital Oulu and Unit of Primary Care, Biocenter, University of Oulu, and the European Commission (EURO-BLCS, Framework 5 award QLG1-CT-2000-01643 and EU FP7- EurHEALTHAgeing - European Research on Developmental, Birth and Genetic Determinants of Ageing. Grant no. 24000522). Medical Research Council Medical Research Council, UK (G0500539, G0600705, PrevMetSyn/SALVE, PS0476)

NTR: Funding was obtained from the Netherlands Organization for Scientific Research (NWO: MagW/ZonMW grants 904-61-090, 985-10-002,904-61-193,480-04-004, 400-05-717, Addiction-31160008 Middelgroot-911-09-032, Spinozapremie 56-464-14192), Center for Medical Systems Biology (CMSB, NWO Genomics), NBIC/BioAssist/RK(2008.024), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL), the VU University’s Institute for Health and Care Research (EMGO+ ) and Neuroscience Campus Amsterdam (NCA), the European Science Foundation (ESF, EU/QLRT-2001-01254), the European Community's Seventh Framework Program (FP7/2007-2013), ENGAGE (HEALTH-F4-2007-201413); the European Research Council (ERC Advanced, 230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), and the National Institutes of Health (NIH, R01D0042157-01A). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the US National Institutes of Health, and by grants from GAIN and the NIMH (MH081802).

QIMR: Supported by National Institutes of Health Grants AA07535, AA0758O, AA07728, AA10249, AA13320, AA13321, AA14041, AA11998, AA17688, DA012854, DA018267, DA018660, DA23668 and DA019951; by Grants from the Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498, and 628911); by Grants from the Australian Research Council (A7960034, A79906588, A79801419, DP0770096, DP0212016, and DP0343921); and by the 5th Framework Programme (FP-5) GenomEUtwin Project (QLG2-CT-2002-01254).

Twins UK: The study was funded by the Wellcome Trust; European Community’s Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR)- funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. SNP Genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH/CIDR.

55

Individual study acknowledgements:

ALSPAC: We are extremely grateful to all of the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, receptionists, managers and nurses.

CHOP: The authors thank the network of primary care clinicians and the patients and families for their contribution to this project and to clinical research facilitated by the Pediatric Research Consortium (PeRC) at The Children’s Hospital of Philadelphia. R. Chiavacci, E. Dabaghyan, A. (Hope) Thomas, K. Harden, A. Hill, C. Johnson-Honesty, C. Drummond, S. Harrison, F. Salley, C. Gibbons, K. Lilliston, C. Kim, E. Frackelton, F. Mentch, G. Otieno, K. Thomas, C. Hou, K. Thomas and M.L. Garris provided expert assistance with genotyping and/or data collection and management. The authors would also like to thank S. Kristinsson, L.A. Hermannsson and A. Krisbjörnsson of Raförninn ehf for extensive software design and contributions.

COPSAC: We gratefully express our gratitude to the children and families of the COPSAC2000 cohort study for all their support and commitment. We acknowledge and appreciate the unique efforts of the COPSAC research team.

Gen3G: Gen3G investigators would like to acknowledge the participants, the research staff from the endocrinology group at the Centre de Recherche clinique of the Centre Hospitalier de l’Université de Sherbrooke (CR-CHUS), the clinical and research staff of the Clinique de Prélèvement en Grossesse du CHUS, and the clinical staff from the CHUS Obstetric Department. The CR-CHUS is a FRQ-Sante affiliated research centre; recruitment and follow-up of Gen3G participants was possible based on the on-going support from the CR-CHUS.

Generation R: The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and Faculty of Social Sciences of the Erasmus University Rotterdam, the Municipal Health Service Rotterdam area, Rotterdam, the Rotterdam Homecare Foundation, Rotterdam and the Stichting Trombosedienst and Artsenlaboratorium Rijnmond (STAR), Rotterdam. We gratefully acknowledge the contribution of participating mothers, general practitioners, hospitals, midwives and pharmacies in Rotterdam. The generation and management of GWAS genotype data for the Generation R Study were done at the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, The Netherlands. We would like to thank Karol Estrada, Dr. Tobias A. Knoch, Anis Abuseiris, Luc V. de Zeeuw, and Rob de Graaf, for their help in creating GRIMP, BigGRID, MediGRID, and Services@MediGRID/D-Grid, (funded by the German Bundesministerium fuer Forschung und Technology; grants 01 AK 803 A-H, 01 IG 07015 G) for access to their grid computing resources. We thank Mila Jhamai, Manoushka Ganesh, Pascal Arp, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters for their help in creating, managing and QC of the GWAS database. Also, we thank Karol Estrada for his support in creation and analysis of imputed data.

HAPO: HAPO would like to acknowledge the participants and research personnel at the participating HAPO field centres.

MoBa: We are grateful to all the participating families in Norway who take part in this ongoing cohort study. Researchers interested in using MoBa data must obtain approval from the Scientific Management Committee of MoBa and from the Regional Committee for Medical and Health Research Ethics for access to data and biological material. Researchers are required to follow the terms of an Assistance Agreement containing a number of clauses designed to ensure protection of privacy and compliance with relevant laws.

QIMR: We thank Richard Parker, Soad Hancock, Judith Moir, Sally Rodda, Pieta-Maree Shertock, Heather Park, Jill Wood, Pam Barton, Fran Husband, Adele Somerville, Ann Eldridge, Marlene Grace, Kerrie McAloney, Anjali Henders, Lisa Bowdler, Alexandre Todorov, Steven Crooks, David Smyth, Harry Beeby, and Daniel Park. Finally, we thank the twins and their families for their participation.