Meta-analysis of exome array data identifies six novel genetic loci for lung function
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This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Meta-analysis of exome array data identifies six novel genetic loci for lung function © 2018 Jackson VE et al. Published version Jackson, Victoria E.; Latourelle, Jeanne C.; Wain, Louise V.; Smith, Albert V.; Grove, Megan L.; Bartz, Traci M.; Obeidat, Ma'en; Province, Michael A.; Gao, Wei; Qaiser, Beenish; Porteous, David J.; Cassano, Patricia A.; Ahluwalia, Tarunveer S.; Grarup, Niels; Li, Jin; Altmaier, Elisabeth; Marten, Jonathan; Harris, Sarah E.; Manichaikul, Ani; Pottinger, Tess D.; Li-Gao, Ruifang; Lind-Thomsen, Allan; Mahajan, Anubha; Lahousse, Lies; Imboden, Medea; Teumer, Alexander; Prins, Bram; Lyytikäinen, Leo-Pekka; Eiriksdottir, Gudny; Franceschini, Nora; Sitlani, Colleen M.; Brody, Jennifer A.; Bossé, Yohan; Timens, Wim; Kraja, Aldi; Loukola, Anu; Tang, Wenbo; Liu, Yongmei; Bork-Jensen, Jette; Justesen, Johanne M.; Linneberg, Allan; Lange, Leslie A.; Rawal, Rajesh; Karrasch, Stefan; Huffman, Jennifer E.; Smith, Blair H.; Davies, Gail; Burkart, Kristin M.; Mychaleckyj, Josyf C.; Bonten, Tobias N.; Enroth, Stefan; Lind, Lars; Brusselle, Guy G.; Kumar, Ashish; Stubbe, Beate; Kähönen, Mika; Wyss, Annah B.; Psaty, Bruce M.; Heckbert, Susan R.; Hao, Ke; Rantanen, Taina; Kritchevsky, Stephen B.; Lohman, Kurt; Skaaby, Tea; Pisinger, Charlotta; Hansen, Torben; Schulz, Holger; Polasek, Ozren; Campbell, Archie; Starr, John M.; Rich, Stephen S.; Mook-Kanamori, Dennis O.; Johansson, Åsa; Ingelsson, Erik; Uitterlinden, André G.; Weiss, Stefan; Raitakari, Olli T.; Gudnason, Vilmundur; North, Kari E.; Gharib, Sina A.; Sin, Don D.; Taylor, Kent D.; O'Connor, George T.; Kaprio, Jaakko; Harris, Tamara B.; Pederson, Oluf; Vestergaard, Henrik; Wilson, James G.; Strauch, Konstantin; Hayward, Caroline; Kerr, Shona; Deary, Ian J.; Barr, R. Graham; Mutsert, Renée de; Gyllensten, Ulf; Morris, Andrew P.; Ikram, M. Arfan; Probst-Hensch, Nicole; Gläser, Sven; Zeggini, Eleftheria; Lehtimäki, Terho; Strachan, David P.; Dupuis, Josée; Morrison, Alanna C.; Hall, Ian P.; Tobin, Martin D.; London, Stephanie J. Jackson, V. E., Latourelle, J. C., Wain, L. V., Smith, A. V., Grove, M. L., Bartz, T. M., Obeidat, M., Province, M. A., Gao, W., Qaiser, B., Porteous, D. J., Cassano, P. A., Ahluwalia, T. S., Grarup, N., Li, J., Altmaier, E., Marten, J., Harris, S. E., Manichaikul, A., . . . London, S. J. (2018). Metaanalysis of exome array data identifies six novel genetic loci for lung function. Wellcome Open Research, 3, Article 4. https://doi.org/10.12688/wellcomeopenres.12583.3 2018
RESEARCHARTICLE Meta-analysis of exome array data identifies six novel genetic loci for lung function[version 3; referees: 2 approved] VictoriaE.Jackson , JeanneC.Latourelle , LouiseV.Wain , AlbertV.Smith , MeganL.Grove , TraciM.Bartz , Ma'enObeidat , MichaelA.Province , WeiGao , BeenishQaiser , DavidJ.Porteous , PatriciaA.Cassano , TarunveerS.Ahluwalia , NielsGrarup , JinLi , ElisabethAltmaier , JonathanMarten , SarahE.Harris , AniManichaikul , TessD.Pottinger , RuifangLi-Gao , AllanLind-Thomsen , AnubhaMahajan , LiesLahousse , MedeaImboden , AlexanderTeumer , BramPrins , Leo-PekkaLyytikäinen , GudnyEiriksdottir , NoraFranceschini , ColleenM.Sitlani , JenniferA.Brody , YohanBossé , WimTimens , AldiKraja , AnuLoukola , WenboTang , YongmeiLiu , JetteBork-Jensen , JohanneM.Justesen , AllanLinneberg , LeslieA.Lange , RajeshRawal , StefanKarrasch , JenniferE.Huffman , BlairH.Smith , GailDavies , KristinM.Burkart , JosyfC.Mychaleckyj , TobiasN.Bonten , StefanEnroth , LarsLind , GuyG.Brusselle , AshishKumar , BeateStubbe , UnderstandingSocietyScientificGroup, MikaKähönen , AnnahB.Wyss , BruceM.Psaty , SusanR.Heckbert , KeHao , TainaRantanen , StephenB.Kritchevsky , KurtLohman , TeaSkaaby , CharlottaPisinger , TorbenHansen , HolgerSchulz , OzrenPolasek , ArchieCampbell , JohnM.Starr , StephenS.Rich , DennisO.Mook-Kanamori , ÅsaJohansson , ErikIngelsson , AndréG.Uitterlinden , StefanWeiss , OlliT.Raitakari , VilmundurGudnason , KariE.North , SinaA.Gharib , DonD.Sin , KentD.Taylor , GeorgeT.O'Connor , JaakkoKaprio , TamaraB.Harris , OlufPederson , HenrikVestergaard , JamesG.Wilson , KonstantinStrauch , CarolineHayward , ShonaKerr , IanJ.Deary , R.GrahamBarr , RenéedeMutsert , UlfGyllensten , AndrewP.Morris , 1 2 1,3 4,5 6 7 8 9 10 11 12 13,14 15,16 15 17 18 19 20,21 22 23,24 25 26 27 28,29 30,31 32 33 34,35 5 36 37 37 38 39,40 9 11 13,41 42 15 16 43-45 46 18 47,48 19 49 20,50 23 22 51,52 26 53 28,54,55 27,30,31,56 57 58,59 60 61,62 63 64,65 66 67 42 43 43 15 47,68 69 12 20,70 22 25,52 26 71,72 54,73 74,75 76,77 4,5 78 79 8,80 81 82,83 11,84,85 86 15 15,16 87 88,89 19 19 20,50 23,90 25 26 27,91 Page 1 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
R.GrahamBarr , RenéedeMutsert , UlfGyllensten , AndrewP.Morris , M.ArfanIkram , NicoleProbst-Hensch , SvenGläser , EleftheriaZeggini , TerhoLehtimäki , DavidP.Strachan , JoséeDupuis , AlannaC.Morrison , IanP.Hall , MartinD.Tobin , StephanieJ.London 60 DepartmentofHealthSciences,UniversityofLeicester,Leicester,UK DepartmentofNeurology,BostonUniversitySchoolofMedicine,Boston,MA,USA NationalInstituteforHealthResearch,LeicesterRespiratoryBiomedicalResearchUnit,GlenfieldHospital,Leicester,UK IcelandicHeartAssociation,201Kopavogur,Iceland UniversityofIceland,101Reykjavik,Iceland HumanGeneticsCenter,DepartmentofEpidemiology,HumanGenetics,andEnvironmentalSciences,SchoolofPublicHealth,The UniversityofTexasHealthScienceCenteratHouston,Houston,TX,77030,USA CardiovascularHealthResearchUnit,DepartmentsofMedicineandBiostatistics,UniversityofWashington,Seattle,WA,98101,USA TheUniversityofBritishColumbiaCentreforHeartLungInnovation,StPaul’sHospital,Vancouver,BC,Canada DepartmentofGenetics,WashingtonUniversitySchoolofMedicine,St.Louis,MO,USA DepartmentofBiostatistics,BostonUniversitySchoolofPublicHealth,Boston,MA,USA InstituteforMolecularMedicineFinland(FIMM),UniversityofHelsinki,FI-00014,Helsinki,Finland CentreforGenomic&ExperimentalMedicine,MRCInstituteofGenetics&MolecularMedicine,UniversityofEdinburgh,Edinburgh,EH4 2XU,UK DivisionofNutritionalSciences,CornellUniversity,Ithaca,NY,USA DepartmentofHealthcarePolicyandResearch,DivisionofBiostatisticsandEpidemiology,WeillCornellMedicalCollege,NewYorkCity, NY,USA NovoNordiskFoundationCenterforBasicMetabolicResearch,FacultyofHealthandMedicalSciences,UniversityofCopenhagen,2200 Copenhagen,Denmark StenoDiabetesCenterCopenhagen,Gentofte,2820,Denmark DepartmentofMedicine,DivisionofCardiovascularMedicine,StanfordUniversitySchoolofMedicine,PaloAlto,CA,USA ResearchUnitofMolecularEpidemiology,InstituteofEpidemiologyII,HelmholtzZentrumMünchen,GermanResearchCenterfor EnvironmentalHealth,85764Neuherberg,Germany MedicalResearchCouncilHumanGeneticsUnit,InstituteofGeneticsandMolecularMedicine,UniversityofEdinburgh,Edinburgh,EH4 2XU,UK CentreforCognitiveAgeingandCognitiveEpidemiology,UniversityofEdinburgh,Edinburgh,EH89JZ,UK CentreforGenomicandExperimentalMedicine,UniversityofEdinburgh,Edinburgh,EH42XU,UK CenterforPublicHealthGenomics,UniversityofVirginia,Charlottesville,VA,USA DepartmentofMedicine,CollegeofPhysiciansandSurgeons,ColumbiaUniversity,NewYork,NY,USA DepartmentofPreventiveMedicine-DivisionofHealthandBiomedicalInformatics,NorthwesternUniversity-FeinbergSchoolofMedicine, Chicago,IL,USA DepartmentofClinicalEpidemiology,LeidenUniversityMedicalCenter,Leiden,2333ZA,Netherlands DepartmentofImmunology,Genetics,andPathology,BiomedicalCenter,SciLifeLabUppsala,UppsalaUniversity,SE-75108Uppsala, Sweden WellcomeTrustCentreforHumanGenetics,UniversityofOxford,Oxford,UK RespiratoryMedicine,GhentUniversityHospital,Ghent,BE9000,Belgium Bioanalysis,GhentUniversity,Ghent,BE9000,Belgium SwissTropicalandPublicHealthInstitute,Basel,Switzerland UniversityofBasel,Basel,Switzerland InstituteforCommunityMedicine,UniversityMedicineGreifswald,17475Greifswald,Germany HumanGenetics,WellcomeTrustSangerInstitute,Hinxton,CB101SA,UK DepartmentofClinicalChemistry,FimlabLaboratories,Tampere33520,Finland DepartmentofClinicalChemistry,FacultyofMedicineandLifeSciences,UniversityofTampere,Tampere33014,Finland DepartmentofEpidemiology,GillingsSchoolofGlobalPublicHealth,UniversityofNorthCarolinaatChapelHill,NC27514,USA CardiovascularHealthResearchUnit,DepartmentofMedicine,UniversityofWashington,Seattle,WA,98101,USA InstitutuniversitairedecardiologieetdepneumologiedeQuébec,DepartmentofMolecularMedicine,LavalUniversity,Québec,Canada DepartmentofPathologyandMedicalBiology,UniversityMedicalCenterGroningen,UniversityofGroningen,NL9713GZ,Netherlands 23,90 25 26 27,91 54,92,93 30,31 57,94 33 34,35 95 10 6 96 1,3 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Page 2 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
DepartmentofPathologyandMedicalBiology,UniversityMedicalCenterGroningen,UniversityofGroningen,NL9713GZ,Netherlands GroningenResearchInstituteforAsthmaandCOPD,UniversityMedicalCenterGroningen,UniversityofGroningen,Groningen, Netherlands BoehringerIngelheim,Danbury,CT,USA WakeForestSchoolofMedicine,Winston-Salem,NorthCarolina,USA CentreforClinicalResearchandPrevention,BispebjergandFrederiksbergHospital,TheCapitalRegion,Copenhagen,Denmark DepartmentofClinicalExperimentalResearch,Rigshospitalet,2600Glostrup,Denmark DepartmentofClinicalMedicine,FacultyofHealthandMedicalSciences,UniversityofCopenhagen,2200Copenhagen,Denmark DepartmentofMedicine,DivisionofBioinformaticsandPersonalizedMedicine,UniversityofColoradoDenver,Aurora,CO,USA InstituteofEpidemiologyI,HelmholtzZentrumMünchen,GermanResearchCenterforEnvironmentalHealth,85764Neuherberg,Germany InstituteandOutpatientClinicforOccupational,SocialandEnvironmentalMedicine,Ludwig-Maximilians-Universität,Munich,Germany DivisionofPopulationHealthSciences,NinewellsHospitalandMedicalSchool,UniversityofDundee,Dundee,DD19SY,UK DepartmentofPsychology,UniversityofEdinburgh,Edinburgh,EH89JZ,UK DepartmentofPulmonology,LeidenUniversityMedicalCenter,Leiden,2333ZA,Netherlands DepartmentofPublicHealthandPrimaryCare,LeidenUniversityMedicalCenter,Leiden,2333ZA,Netherlands DepartmentofMedicalSciences,UppsalaUniversityHospital,Uppsala,Sweden Epidemiology,ErasmusMedicalCenter,Rotterdam,3000CA,Netherlands RespiratoryMedicine,ErasmusMedicalCenter,Rotterdam,3000CA,Netherlands InstituteofEnvironmentalMedicine,KarolinskaInstitutet,Stockholm,Sweden InternalMedicineB,UniversityMedicineGreifswald,Greifswald,17475,Germany DepartmentofClinicalPhysiology,TampereUniversityHospital,Tampere,33521,Finland DepartmentofClinicalPhysiology,FacultyofMedicineandLifeSciences,UniversityofTampere,Tampere,33014,Finland EpidemiologyBranch,NationalInstituteofEnvironmentalHealthSciences,NationalInstitutesofHealth,DeptofHealthandHuman Services,ResearchTrianglePark,NC,27709,USA CardiovascularHealthResearchUnit,DepartmentsofEpidemiology,MedicineandHealthServices,UniversityofWashington,Seattle,WA, 98101,USA KaiserPermanenteWashingtonHealthResearchInstitute,Seattle,WA,USA CardiovascularHealthResearchUnit,DepartmentofEpidemiology,UniversityofWashington,Seattle,WA,98101,USA DepartmentofGeneticsandGenomicSciences,IcahnSchoolofMedicineatMountSinai,NewYork,NY,10029-6574,USA IcahnInstituteofGenomicsandMultiscaleBiology,IcahnSchoolofMedicineatMountSinai,NewYork,NY,10029-6574,USA DepartmentofHealthSciences,UniversityofJyväskylä,Jyväskylä,Fl-40014,Finland StichtCenteronAging,WakeForestSchoolofMedicine,Winston-Salem,NC,USA ComprehensivePneumologyCenterMunich(CPC-M),MemberoftheGermanCenterforLungResearch,Munich,Germany FacultyofMedicine,UniversityofSplit,Split,Croatia AlzheimerScotlandResearchCentre,UniversityofEdinburgh,Edinburgh,EH89JZ,UK DepartmentofMedicalSciences,MolecularEpidemiologyandScienceforLifeLaboratory,UppsalaUniversity,Uppsala,Sweden DepartmentofMedicine,DivisionofCardiovascularMedicine,StanfordUniversitySchoolofMedicine,Stanford,CA,94305,USA InternalMedicine,ErasmusMedicalCenter,Rotterdam,3000CA,Netherlands InterfacultyInstituteforGeneticsandFunctionalGenomics,UniversityMedicineGreifswald,Greifswald,17475,Germany DZHK(GermanCentreforCardiovascularResearch),partnersite:Greifswald,Greifswald,Germany DepartmentofClinicalPhysiologyandNuclearMedicine,TurkuUniversityHospital,Turku,20521,Finland ResearchCentreofAppliedandPreventativeCardiovascularMedicine,UniversityofTurku,Turku,20014,Finland DepartmentofEpidemiologyandCarolinaCenterforGenomeScience,UniversityofNorthCarolina,ChapelHill,NC,27514,USA ComputationalMedicineCore,CenterforLungBiology,UWMedicineSleepCenter,DepartmentofMedicine,UniversityofWashington, Seattle,WA,98109,USA RespiratoryDivision,DepartmentofMedicine,UniversityofBritishColumbia,Vancouver,BC,Canada InstituteforTranslationalGenomicsandPopulationSciencesandDepartmentofPediatrics,LosAngelesBiomedicalResearchInstituteat Harbor-UCLAMedicalCenter,Torrance,CA,90502,USA PulmonaryCenter,DepartmentofMedicine,BostonUniversitySchoolofMedicine,Boston,MA,02118,USA NationalHeart,LungandBloodInstitute'sandBostonUniversity'sFraminghamHeartStudy,Framingham,MA,01702,USA DepartmentofHealth,UniversityofHelsinki,Helsinki,FI-00014,Finland DepartmentofPublicHealth,NationalInstituteforHealthandWelfare,Helsinki,FI-00271,Finland NationalInstituteonAging,NationalInstitutesofHealth,Bethesda,MD,20892,USA DepartmentofPhysiologyandBiophysics,UniversityofMississippiMedicalCenter,Jackson,MS,39216,USA InstituteofGeneticEpidemiology,HelmholtzZentrumMünchen,GermanResearchCenterforEnvironmentalHealth,Neuherberg,85764, 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 Page 3 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
Open Peer Review Discuss this article (0)Comments InstituteofGeneticEpidemiology,HelmholtzZentrumMünchen,GermanResearchCenterforEnvironmentalHealth,Neuherberg,85764, Germany ChairofGeneticEpidemiology,IBE,FacultyofMedicine,LMUMunich,Munich,81377,Germany DepartmentofEpidemiology,MailmanSchoolofPublicHealth,ColumbiaUniversity,NewYork,NY,10032,USA DepartmentofBiostatistics,UniversityofLiverpool,Liverpool,L693GL,UK Radiology,ErasmusMedicalCenter,Rotterdam,3000CA,Netherlands Neurology,ErasmusMedicalCenter,Rotterdam,3000CA,Netherlands DepartmentofInternalMedicine-PulmonaryDiseases,VivantesKlinikumSpandauBerlin,Berlin,13585,Germany PopulationHealthResearchInstitute,StGeorge's,UniversityofLondon,London,SW170RE,UK NIHRNottinghamBiomedicalResearchCentreandDivisionofRespiratoryMedicine,UniversityofNottingham,Nottingham,NG72UH,UK Abstract Over90regionsofthegenomehavebeenassociatedwithlungBackground: functiontodate,manyofwhichhavealsobeenimplicatedinchronic obstructivepulmonarydisease. Wecarriedoutmeta-analysesofexomearraydataandthreelungMethods: functionmeasures:forcedexpiratoryvolumeinonesecond(FEV ),forcedvital capacity(FVC)andtheratioofFEV toFVC(FEV /FVC).Theseanalysesby theSpiroMetaandCHARGEconsortiaincluded60,749individualsofEuropean ancestryfrom23studies,and7,721individualsofAfricanAncestryfrom5 studiesinthediscoverystage,withfollow-upinupto111,556independent individuals. Weidentifiedsignificant(P<2·8x10 )associationswithsixSNPs:aResults: nonsynonymousvariantin ,whichispredictedtobedamaging,threeRPAP1 intronicSNPs( and )andtwointergenicSNPsnearSEC24C, CASC17 UQCC1 to and Expressionquantitativetraitlocianalysesfoundevidence LY86 FGF10. forregulationofgeneexpressionatthreesignalsandimplicatedseveralgenes, including and .TYRO3 PLAU FurtherinterrogationoftheselocicouldprovidegreaterConclusions: understandingofthedeterminantsoflungfunctionandpulmonarydisease. Keywords Lungfunction,respiratory,exomearray,GWAS,COPD MartinD.Tobin( ),StephanieJ.London( )Corresponding authors: [email protected] [email protected] 88 89 90 91 92 93 94 95 96 Referee Status: InvitedReferees version 3 published 07Aug2018 version 2 published 21Jun2018 version 1 published 12Jan2018 1 2 report report report ,UniversityofRobin Beaumont Exeter,UK ,UniversityofRachel M. Freathy Exeter,UK 1 ,HospitalforSickChildren,Lisa Strug Canada ,TheHospitalforSickNaim Panjwani Children,Canada 2 12Jan2018, :4(doi: )First published: 3 10.12688/wellcomeopenres.12583.1 21Jun2018, :4(doi: )Second version: 3 10.12688/wellcomeopenres.12583.2 07Aug2018, :4(doi: )Latest published: 3 10.12688/wellcomeopenres.12583.3 v3 1 1 1 -7 Page 4 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
:FormalAnalysis,Writing–OriginalDraftPreparation; :FormalAnalysis,Writing–Review&Editing;Author roles: Jackson VE Latourelle JC :FormalAnalysis,Supervision,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing;Wain LV Smith AV Grove :DataCuration,Writing–Review&Editing; :FormalAnalysis,Writing–Review&Editing; :FormalAnalysis,Writing–ML Bartz TM Obeidat M Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :FormalAnalysis,Writing–Review&Province MA Gao W Editing; :FormalAnalysis,Writing–Review&Editing; :DataCuration; :DataCuration,FormalAnalysis,Qaiser B Porteous DJ Cassano PA Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Conceptualization,DataAhluwalia TS Grarup N Curation,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing; :FormalAnalysis,Writing–Li J Altmaier E Review&Editing; :FormalAnalysis,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing;Marten J Harris SE :DataCuration,FormalAnalysis,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Manichaikul A Pottinger TD Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Li-Gao R Lind-Thomsen A Review&Editing; :FormalAnalysis,Writing–Review&Editing; :Conceptualization,DataCuration,FormalAnalysis,Mahajan A Lahousse L Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing; :DataCuration,FormalAnalysis,Imboden M Teumer A Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing; :DataCuration,FormalAnalysis,Prins B Lyytikäinen LP Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :FormalAnalysis,Eiriksdottir G Franceschini N Writing–Review&Editing; :FormalAnalysis,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–ReviewSitlani CM Brody JA &Editing; :DataCuration,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataCuration,Bossé Y Timens W Kraja A FormalAnalysis,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataCuration,FormalAnalysis,Loukola A Tang W Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing; :Conceptualization,DataCuration,Liu Y Bork-Jensen J Writing–Review&Editing; :FormalAnalysis,Writing–Review&Editing; :Conceptualization,Writing–Review&Justesen JM Linneberg A Editing; :DataCuration,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataCuration,Lange LA Rawal R Karrasch S Writing–Review&Editing; :FormalAnalysis,Writing–Review&Editing; :DataCuration,Writing–Review&Editing;Huffman JE Smith BH :DataCuration,Writing–Review&Editing; :Conceptualization,Writing–Review&Editing; :DataDavies G Burkart KM Mychaleckyj JC Curation,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Bonten TN Enroth S Review&Editing; :DataCuration,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing;Lind L Brusselle GG :DataCuration,FormalAnalysis,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing;Kumar A Stubbe B :Conceptualization,DataCuration,Writing–Review&Editing; :Conceptualization,FormalAnalysis,Writing–Review&Kähönen M Wyss AB Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :Psaty BM Heckbert SR Hao K DataCuration,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Rantanen T Kritchevsky SB Conceptualization,DataCuration,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Editing; :Lohman K Skaaby T Conceptualization,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Pisinger C Hansen T Conceptualization,DataCuration,FormalAnalysis,Writing–Review&Editing; :Conceptualization,Writing–Review&Editing;Schulz H Polasek :Conceptualization,DataCuration,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataCuration,O Campbell A Starr JM Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Conceptualization,DataRich SS Mook-Kanamori DO Curation,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataCuration,Writing–Review&Johansson Å Ingelsson E Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Uitterlinden AG Weiss S Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Conceptualization,FormalAnalysis,Raitakari OT Gudnason V Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :Writing–Review&Editing; :DataCuration,North KE Gharib SA Sin DD Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataCuration,Writing–Review&Editing;Taylor KD O'Connor GT :Conceptualization,DataCuration,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing;Kaprio J Harris TB :DataCuration,FormalAnalysis,Writing–Review&Editing; :DataCuration,FormalAnalysis,Writing–Review&Pederson O Vestergaard H Editing; :DataCuration,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :Wilson JG Strauch K Hayward C Conceptualization,DataCuration,FormalAnalysis,Writing–Review&Editing; :DataCuration,Writing–Review&Editing; :DataKerr S Deary IJ Curation,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Conceptualization,Barr RG de Mutsert R DataCuration,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :DataCuration,Gyllensten U Morris AP FormalAnalysis,Writing–Review&Editing; :Conceptualization,Writing–Review&Editing; :Conceptualization,Ikram MA Probst-Hensch N DataCuration,FormalAnalysis,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Gläser S Zeggini E Conceptualization,Writing–Review&Editing; :Conceptualization,DataCuration,Writing–Review&Editing; :Lehtimäki T Strachan DP Conceptualization,DataCuration,Writing–Review&Editing; :FormalAnalysis,Supervision,Writing–Review&Editing; :Dupuis J Morrison AC FormalAnalysis,Writing–Review&Editing; :Conceptualization,FormalAnalysis,Supervision,Writing–Review&Editing; :Hall IP Tobin MD Conceptualization,FormalAnalysis,Supervision,Writing–Review&Editing; :Conceptualization,FormalAnalysis,Supervision,London SJ Writing–Review&Editing Nocompetinginterestsweredisclosed.Competing interests: MDThasbeensupportedbyMRCfellowshipsG0501942andG0902313.MDTandLVWaresupportedbytheMRCGrant information: (MR/N011317/1).IPHissupportedbytheMRC(G1000861).ALWandSJLaresupportedbytheIntramuralResearchProgramoftheNIH, NationalInstituteofEnvironmentalHealthSciences(ZIAES043012).WeacknowledgeuseofphenotypeandgenotypedatafromtheBritish1958 BirthCohortDNAcollection,fundedbytheMedicalResearanchCouncilgrantG0000934andtheWellcomeTrustgrant068545/Z/02.APMwasa WellcomeTrustSeniorFellowinBasicBiomedicalScience(grantnumberWT098017)andwasalsosupportedbyWellcomeTrustgrant WT064890.EIissupportedbytheSwedishResearchCouncil(2012-1397),KnutochAliceWallenbergFoundation(2013.0126)andtheSwedish Heart-LungFoundation(20140422).JKissupportedbyAcademyofFinlandCenterofExcellenceinComplexDiseaseGeneticsgrants213506, 129680andAcademyofFinlandgrants265240,263278.TheFinnishTwinCohortissupportedbytheWelcomeTrustSangerInstitute,UK.The LothianBirthCohortissupportedbyAgeUK(TheDisconnectedMindProject),theUKMedicalResearchCouncil(MR/K026992/1)andTheRoyal SocietyofEdinburgh.ÅJissupportedbytheSwedishSocietyforMedicalResearch(SSMF),TheKjellochMärtaBeijersFoundation,TheMarcus BorgströmFoundation,TheÅkeWibergfoundationandTheVleugelsFoundation.UGissupportedbySwedishMedicalResearchCouncilgrants K2007-66X-20270-01-3and2011-2354andEuropeanCommissionFP6(LSHG-CT-2006-01947).SHIPispartoftheCommunityMedicine ResearchnetoftheUniversityofGreifswald,Germany,whichisfundedbytheFederalMinistryofEducationandResearch,theMinistryofCultural Page 5 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
ResearchnetoftheUniversityofGreifswald,Germany,whichisfundedbytheFederalMinistryofEducationandResearch,theMinistryofCultural AffairsaswellastheSocialMinistryoftheFederalStateofMecklenburg-WestPomerania,andthenetwork‘GreifswaldApproachtoIndividualized Medicine(GANI_MED)’fundedbytheFederalMinistryofEducationandResearch,andtheGermanAsthmaandCOPDNetwork(COSYCONET) (grantno.01ZZ9603,01ZZ0103,01ZZ0403,03IS2061A,BMBF01GI0883).ExomeChipdatahavebeensupportedbytheFederalMinistryof EducationandResearch(grantno.03Z1CN22)andtheFederalStateofMecklenburg-WestPomerania.TheUniversityofGreifswaldisamember oftheCachéCampusprogramoftheInterSystemsGmbH.UKHLSissupportedbygrantsWT098051(WellcomeTrust)andES/H029745/1 (EconomicandSocialResearchCouncil).Y.B.holdsaCanadaResearchChairinGenomicsofHeartandLungDiseases.LiesLahousseisa PostdoctoralFellowoftheResearchFoundation-Flanders(FWOgrantG035014N).TheRotterdamStudyisfundedbyErasmusMedicalCenter andErasmusUniversity,Rotterdam,theNetherlandsOrganizationforScientificResearch(NOW),theNetherlandsOrganizationfortheHealth ResearchandDevelopment(ZonMw),theResearchInstituteforDiseasesintheElderly(RIDE),theMinistryofEducation,CultureandScience, theMinistryforHealth,WelfareandSports,theEuropeanCommission(DGXII),andtheMunicipalityofRotterdam.GenotypingintheRotterdam studywassupportedbyNetherlandsOrganizationforScientificResearch(NOWgrants175.010.2005.011;911-03-305012),theResearch InstituteforDiseasesintheElderly(RIDE2grants014-93-015)andNetherlandsGenomicsInitiative(NGI)/NetherlandsConsortiumforHealthy Aging(NCHAgrant050-060-810).MESA/MESASHAReissupportedbyHHS(HHSN268201500003I),NIH/NHLBI(contractsN01-HC-95159, N01-HC-95160,N01-HC-95161,N01-HC-95162,N01-HC-95163,N01-HC-95164,N01-HC-95165,N01-HC-95166,N01-HC-95167, N01-HC-95168,N01-HC-95169)andHIH/NCATS(contractsUL1-TR-000040,UL1-TR-001079,UL1-TR-001881,DK063491).MESASHAReis fundedbyNIH/NHLBIcontractN02-HL-64278,MESAAirisfundedbyUSEPA(RD831697)andMESASpirometryfundedbyNIH/NHLBI (R01-HL077612).SSRandBMParesupportedbyNIH/NHLBIgrantrarevariantsandNHLBItraitsindeeplyphenotypedcohorts(R01-HL120393). CardiovascularHealthStudy:ThisCHSresearchwassupportedbyNHLBIcontractsHHSN268201200036C,HHSN268200800007C, HHSN268201800001C,HHSN268200960009C,N01HC55222,N01HC85079,N01HC85080,N01HC85081,N01HC85082,N01HC85083, N01HC85086;andNHLBIgrantsU01HL080295,R01HL068986,R01HL087652,R01HL105756,R01HL103612,R01HL120393,and R01HL130114withadditionalcontributionfromtheNationalInstituteofNeurologicalDisordersandStroke(NINDS).Additionalsupportwas providedthroughR01AG023629andR01HL085251fromtheNationalInstituteonAging(NIA).Theprovisionofgenotypingdatawas suprovidedpportedinpartbytheNationalCenterforAdvancingTranslationalSciences,CTSIgrantUL1TR001881,andtheNationalInstituteof DiabetesandDigestiveandKidneyDiseaseDiabetesResearchCenter(DRC)grantDK063491totheSouthernCaliforniaDiabetesEndocrinology ResearchCenter.ThecontentissolelytheresponsibilityoftheauthorsanddoesnotnecessarilyrepresenttheofficialviewsoftheNational InstitutesofHealth.TheAtherosclerosisRiskinCommunities(ARIC)studyiscarriedoutasacollaborativestudysupportedbytheNationalHeart, Lung,andBloodInstitute(NHLBI)contracts(HHSN268201100005C,HHSN268201100006C,HHSN268201100007C,HHSN268201100008C, HHSN268201100009C,HHSN268201100010C,HHSN268201100011C,andHHSN268201100012C).Fundingsupportfor“BuildingonGWASfor NHLBI-diseases:theU.S.CHARGEconsortium”wasprovidedbytheNIHthroughtheAmericanRecoveryandReinvestmentActof2009(ARRA) (5RC2HL102419).DOMKreceivedfundingfromtheDutchScienceOrganisation(ZonMW-VENIGrant916.14.023).ThegenotypingintheNEO studywassupportedbytheCentreNationaldeGénotypage(Paris,France),headedbyJean-FrançoisDeleuze.TheNEOstudyissupportedby theparticipatingDepartments,theDivisionandtheBoardofDirectorsoftheLeidenUniversityMedicalCenter,andbytheLeidenUniversity, ResearchProfileAreaVascularandRegenerativeMedicine.SAPALDIAwassupportedbytheSwissNationalScienceFoundation(grantsno 33CS30-148470/1,33CSCO-134276/1,33CSCO-108796,,324730_135673,3247BO-104283,3247BO-104288,3247BO-104284,3247-065896, 3100-059302,3200-052720,3200-042532,4026-028099,PMPulDP3_129021/1,PMPDP3_141671/1),theFederalOfficefortheEnvironment,the FederalOfficeofPublicHealth,theFederalOfficeofRoadsandTransport,thecanton'sgovernmentofAargau,Basel-Stadt,Basel-Land,Geneva, Luzern,Ticino,Valais,andZürich,theSwissLungLeague,thecanton'sLungLeagueofBaselStadt/BaselLandschaft,Geneva,Ticino,Valais, GraubündenandZurich,StiftungehemalsBündnerHeilstätten,SUVA,FreiwilligeAkademischeGesellschaft,UBSWealthFoundation,Talecris BiotherapeuticsGmbH,AbbottDiagnostics,EuropeanCommission018996(GABRIEL),WellcomeTrustWT084703MA.TheNovoNordisk FoundationCenterforBasicMetabolicResearchisanindependentResearchCenterattheUniversityofCopenhagenpartiallyfundedbyan unrestricteddonationfromtheNovoNordiskFoundation(www.metabol.ku.dk).GenerationScotlandreceivedcoresupportfromtheChiefScientist OfficeoftheScottishGovernmentHealthDirectorates[CZD/16/6]andtheScottishFundingCouncil[HR03006].GenotypingoftheGS:SFHS sampleswascarriedoutbytheGeneticsCoreLaboratoryattheEdinburghClinicalResearchFacility,UniversityofEdinburgh,Scotlandandwas fundedbytheMedicalResearchCouncilUK..TheCroatiaKORCULAstudywassupportedbytheMinistryofScience,EducationandSportinthe RepublicofCroatia(108-1080315-0302).JD,JCL,WGandGTOCaresupportedbyNIH/NHLBIContractHHSN268201500001I.Genotyping, qualitycontrolandcallingoftheIlluminaHumanExomeBeadChipintheFraminghamHeartStudywassupportedbyfundingfromtheNational Heart,LungandBloodInstituteDivisionofIntramuralResearch(DanielLevyandChristopherJ.O’Donnell,PrincipleInvestigators).TheAGES studyissupportedbytheNIH(N01-AG012100),theIcelandParliament(Alþingi)andtheIcelandicHeartAssociation.HABCwassupportedbyNIA contractsN01AG62101,N01AG62103,andN01AG62106;NIAgrantR01-AG028050,andNINRgrantR01-NR012459andwassupportedinpart bytheIntramuralResearchProgramoftheNIH,NationalInstituteonAging.TheHABCgenome-wideassociationstudywasfundedbyNIAgrant 1R01AG032098-01A1andgenotypingserviceswereprovidedbytheCenterforInheritedDiseaseResearch(CIDR).CIDRisfullyfundedthrough afederalcontractfromtheNationalInstitutesofHealthtoTheJohnsHopkinsUniversity,contractnumberHHSN268200782096C.Wethankthe JacksonHeartStudy(JHS)participantsandstafffortheircontributionstothiswork.TheJHSissupportedbycontractsHHSN268201300046C, HHSN268201300047C,HHSN268201300048C,HHSN268201300049C,HHSN268201300050CfromtheNationalHeart,Lung,andBlood InstituteandtheNationalInstituteonMinorityHealthandHealthDisparities.JGWissupportedbyU54GM115428fromtheNationalInstituteof GeneralMedicalSciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ©2018JacksonVE .Thisisanopenaccessarticledistributedunderthetermsofthe ,Copyright: et al CreativeCommonsAttributionLicence whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.Theauthor(s)is/are employeesoftheUSGovernmentandthereforedomesticcopyrightprotectioninUSAdoesnotapplytothiswork.Theworkmaybeprotected underthecopyrightlawsofotherjurisdictionswhenusedinthosejurisdictions. JacksonVE,LatourelleJC,WainLV How to cite this article: et al. Meta-analysis of exome array data identifies six novel genetic loci for WellcomeOpenResearch2018, :4(doi: )lung function [version 3; referees: 2 approved] 3 10.12688/wellcomeopenres.12583.3 12Jan2018, :4(doi: )First published: 3 10.12688/wellcomeopenres.12583.1 Page 6 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
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Introduction Measures of lung function act as predictors of mortality and morbidity and form the basis for the diagnosis of several diseases, most notably chronic obstructive pulmonary disease (COPD), one of the leading causes of death globally1. Environmental factors, including smoking and exposure to air pollution play a significant role in lung function; however there has also been shown to be a genetic component, with estimates of the narrow sense heritability ranging between 39–66%2–5. Genomewide association studies (GWAS) of lung function have identified associations between single nucleotide polymorphisms (SNPs) and lung function at over 150 independent loci to date6–14. Associations have also been identified in GWAS of COPD15–19; however, the identification of disease associated SNPs has been restricted by limited sample sizes. Many signals first identified in powerful studies of quantitative lung function traits, have been found to be associated with risk of COPD, highlighting the potential clinical usefulness of comprehensive identification of lung function associated SNPs13. Low frequency (minor allele frequency (MAF) 1–5%) and rare (MAF<1%) variants have been largely underexplored by GWAS to date. Exome arrays have been designed to facilitate the investigation of these low frequency and rare variants, predominately within coding regions, in large sample sizes. Alongside a core content of rare coding SNPs, the exome array additionally includes common variation, including tags for previously identified GWAS hits, ancestry informative SNPs, a grid of markers for estimating identity by descent and a random selection of synonymous SNPs20. An earlier version of this article can be found on bioRxiv (https://doi.org/10.1101/164426) Results We carried out a meta-analysis of exome array data and three lung function measures: forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 to FVC (FEV1/FVC). These analyses included 68,470 individuals from the SpiroMeta and CHARGE consortia in a discovery analysis, with follow-up in an independent sample of up to 111,556 individuals. All studies are listed with their study-specific sample characteristics in Table 1, with full study descriptions, including details of spirometry and other measurements described in the Supplementary Note. The genotype calling procedures implemented by each study (Supplementary Table 1) and quality control of genotype data are described in the Supplementary Methods. We have undertaken both single variant analyses, and gene-based associations, which test for the joint effect of several rare variants in a gene (see Methods for details). Meta-analyses of single variant associations We first evaluated single variant associations between FEV1, FVC and FEV1/FVC and the 179,215 SNPs that passed study level quality control and were polymorphic in both consortia. These analyses identified 34 SNPs in regions not previously associated with lung function, showing association with at least one trait at overall P<10-5, and showing association with consistent direction and P<0·05 in both consortia (full results in Supplementary Table 2, quantile-quantile and Manhattan plots shown in Supplementary Figure 1). We followed up these SNP associations in a replication analysis comprising 3 studies with 111,556 individuals. Combining the results from the discovery and replication stages in a meta-analysis identified six SNPs in total that were independent to known signals and met the pre-defined significance threshold (P<2·8×10-7) overall in, or near to FGF10, LY86, SEC24C, RPAP1, CASC17 and UQCC1 (Table 2, Supplementary Figure 2). A SNP near to the CASC17 signal (rs11654749, r2=0·3 with rs1859962) has previously been associated with FEV1 in a genome-wide analysis of gene-smoking interactions, although this association was not replicated at the time21; the present analysis provides the first evidence for independent replication of this signal. A seventh signal was also identified in LCT (Table 2, Supplementary Figure 2); whilst this locus has not previously been implicated in lung function, this SNP is known to vary in frequency across European populations22, and we cannot rule out that this association is not an artefact of population structure. Our discovery analysis furthermore identified associations (P<10-5) in 25 regions previously associated with one or more of FEV1, FVC and FEV1/FVC (Supplementary Table 3). Generally, the observed effect of the SNPs at the novel signals were similar in ever and never smokers; the exception was rs1448044 near FGF10, which showed a significant association with FVC only in ever smokers in our discovery analysis (ever smokers P=1·49×10-6; never smokers P=0·695, Supplementary Table 4 and Supplementary Figure 3). In the replication analysis, however, this association was observed in both ever and never smokers (ever smokers P=3·14×10-5; never smokers P=1·40×10-4, Supplementary Table 5). For rs1200345 (RPAP1) and rs1859962 (CASC17), associations were most statistically significant in the analyses restricted to individuals of European Ancestry (Supplementary Table 4 and Supplementary Figure 3), as was the association with rs2322659 (LCT), giving further support that this association may be due to population stratification. Meta-analyses of gene-based associations We undertook Weighted Sum Tests (WST)23 and Sequence Kernel Association tests (SKAT)24 to assess the joint effects of multiple low frequency variants within genes on lung function traits. In our discovery analyses of all 68,470 individuals, we tested up to 14,380 genes that had at least two variants with MAF<5% and met the inclusion criteria (exonic or loss of function [LOF], see Methods for definitions) in both consortia. The SKAT analyses identified 16 genes associated (P<0·05 in both consortia and overall P<10-4) with FEV1, FVC or FEV1/FVC (Supplementary Table 6), whilst the WST analyses identified 12 genes Amendments from Version 2 We have added a further limitation to the discussion of the paper outlining a recently highlighted issue regarding the trait transformation undertaken in our replication analyses. We show through sensitivity analyses that our results are not affected by this issue (Supplementary Figure 4), but note that future studies should avoid such a transformation. See referee reports REVISED Page 8 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
lung function SNP, we deemed the SNP to represent an independent signal if it had r2<0·2 with the known SNP, and if it retained a P <10-5, when conditional analyses were carried out with the known SNP, or a genotyped proxy, using data from the SpiroMeta Consortium, or UK Biobank. Our primary meta-analysis included all individuals; we additionally carried out analyses in smoking subgroups (ever and never smokers), and in the subgroup of individuals of European ancestry only. For genes which contained at least 2 polymorphic SNPs in both consortia, we combined the results of the consortium level gene based tests using either z-score meta-analysis (for the WST analysis) or Fisher’s Method for combining P-values (in the case of SKAT). We identified genes of interest as those with P<0·05 observed in both consortia and an overall P<10-4, thresholds again chosen to limit both false positive and false negative findings. As in the analyses of single variant associations, our primary meta-analyses included all individuals, with secondary analyses undertaken in smoking and ancestry specific subgroups. Replication analyses: All SNP and gene-based associations were followed up for the trait with which they showed the most statistically significant association only. For associations identified through the smoking subgroup analyses, we followed up associations in the appropriate smoking strata; however, no ancestry stratified follow-up was undertaken as replication studies included only a sufficient number of individuals of European Ancestry. Single variant associations in UK Biobank were tested in ever smokers and never smokers separately, and stratified by genotyping array (UK BiLEVE array or UK Biobank array) using the score test as implemented in SNPTEST v2·5b452. Traits were adjusted for age, age2, height, sex, ten principal components and pack-years (ever smokers only), and the adjusted traits were inverse normally transformed. Correlations between principal components and transformed phenotypes may be introduced where adjustment is made prior to transformation. In this analysis, we found any introduced correlations to have no impact on the conclusion of our replication analyses; however future studies should apply transformation of phenotypes prior to covariate adjustment, to avoid this issue. For UKHLS, analyses were undertaken analogously to the SpiroMeta discovery studies using RAREMETALWORKER, while for NEO, analyses were undertaken in the same way as was done in the CHARGE discovery studies using SeqMeta. The single variant results from all replication studies were combined using sample size weighted Z-score meta-analysis. Subsequently, we combined the results from the discovery and replication stage analyses and we report SNPs with overall exome-wide significance of P<2·8×10-7 (Bonferroni corrected for the original 179,215 SNPs tested). We followed up genes of interest (P<10-4) using data from UK Biobank only. Summary statistics for UK Biobank were generated using RAREMETALWORKER, with gene-based tests then constructed using RAREMETAL. Finally, we combined the results from the discovery analysis with the replication results in an overall combined meta-analysis using either z-score metaanalysis (WST) or Fisher’s Method (SKAT). We declared genes with overall P<3·5×10-6 (Bonferroni corrected for 14,380 genes tested) in our combined meta-analysis to be statistically significant. For these statistically significant genes, we carried out additional analyses using the UK Biobank data in which we conditioned on the most significantly associated individual SNP within that gene, to determine whether this was a true gene-based signal, or whether the association could be ascribed to the single SNP (if the conditional P<0·01, then association was deemed to not be driven by the single SNP). Characterization of findings In order to gain further insight into the loci identified in our analyses of single variant associations, we assessed whether these regions were associated with gene expression levels in various tissues (FDR of 5%, or q-value<0·05), by querying a publically available blood eQTL database53 and the GTEx project54 for the sentinel SNPs, or any proxy (r2>0·8). We further assessed SNPs of interest (and proxies) within a lung eQTL resource based on non-tumour lung tissues of 1,111 individuals55–57. Descriptions of these resources and further details of the look-ups are provided in the Supplementary Methods. Moreover, all sentinel SNPs and proxies with r2>0.8 were annotated using ENSEMBL’s Variant Effect Predictor (VEP)58; potentially deleterious coding variants were identified as those annotated as ‘deleterious’ by SIFT59 or ‘probably damaging’ or ‘possibly damaging’ by PolyPhen-260. For all genes implicated through the expression data or functional annotation, we searched for evidence of protein expression in the respiratory system by querying the Human Protein Atlas61. Data availability Summary level results for all analyses are available on OSF: https://doi.org/10.17605/OSF.IO/NSDPJ62 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). This research has been conducted using the UK Biobank Resource. The genetic and phenotypic UK Biobank data are available upon application to the UK Biobank (https://www. ukbiobank.ac.uk/) to all registered health researchers. These data are from Understanding Society: The UK Household Longitudinal Study (UKHLS), which is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The data were collected by NatCen and the genome wide scan data were analysed by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website https://www.understandingsociety.ac.uk/. Author contributions Ordered alphabetically: ABW, AGE, AL, BMP, BS, CH, CP, DOMK, DPS, EZ, GGB, HS, IPH, JBJ, JK, KMB, LL, MAI, MAP, MDT, MK, NG, NMPH, OP, OTR, RdM, RGB, SBK, SG, SJL, SSR, TA, Page 15 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
TBH, TH, TL, TR, TS, UG contributed to study concept and designs. AC, AJ, A.Manichaikul, BHS, BMP, BS, CP, DJP, DPS, EI, GGB, GTOC, IJD, JBJ, JGW, JK, JMS, KS, LAL, LL, LL, MAP, MI, MK, NG, NMPH, OP, OTR, PAC, RdM, RGB, RR, SBK, SE, SEH, SG, SK, SK, TA, TBH, TDP, TL, TNB, TR, UG, WT, WT contributed to phenotype data acquisition and quality control. AGE, AJ, AK, AK, ALT, ALT, A.Manichaikul, APM, AT, BMP, BP, CH, DOMK, EI, GD, HV, IJD, JAB, JCM, JGW, JL, KDT, KEN, KL, L-PL, LAL, LL, MAP, MI, MLG, NMPH, OP, RGB, RLG, RR, SBK, SE, SEH, SRH, SSR, SW, TBH, TDP, TH, TL, YL contributed to genotype data acquisition and quality control. DDS, KH, WT, YB contribute to eQTL data acquisition and quality control. ABW, ACM, AK, AK, ALT, A.Mahajan, A.Manichaikul, APM, AT, BP, BQ, CH, CMS, EA, HV, IPH, JAB, JCL, JD, JEH, JL, JM, JMJ, KL, L-PL, LL, LVW, MDT, MI, MO, NF, NMPH, OP, PAC, RLG, SE, SEH, SJL, SW, TDP, TH, TMB, VEJ, WG, WT, YL contributed to data analysis. All authors contributed to writing and/or critical review of the manuscript. The ‘Understanding Society Scientific Group’ include the following: Understanding Society Scientific Group: Michaela Benzeval, Jonathan Burton, Nicholas Buck, Annette Jäckle, Meena Kumari, Heather Laurie, Peter Lynn, Stephen Pudney, Birgitta Rabe, Shamit Saggar, Noah Uhrig, Dieter Wolke. Competing interests No competing interests were disclosed. Grant information This article presents independent research funded partially by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. MDT has been supported by Medical Research Council (MRC) fellowships G0501942 and G0902313. MDT and LVW are supported by the MRC (MR/N011317/1). IPH is supported by the MRC (G1000861). ALW and SJL are supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (ZIA ES 043012). We acknowledge use of phenotype and genotype data from the British 1958 Birth Cohort DNA collection, funded by the MRC (G0000934) and the Wellcome Trust (068545). APM was a Wellcome Trust Senior Fellow in Basic Biomedical Science (098017) and was also supported by Wellcome Trust grant 064890. EI is supported by the Swedish Research Council (2012-1397), Knut och Alice Wallenberg Foundation (2013.0126) and the Swedish Heart-Lung Foundation (20140422). JK is supported by Academy of Finland Center of Excellence in Complex Disease Genetics (213506, 129680) and Academy of Finland (265240, 263278). The Finnish Twin Cohort is supported by the Welcome Trust Sanger Institute, UK. The Lothian Birth Cohort is supported by Age UK (The Disconnected Mind Project), the MRC (MR/K026992/1) and The Royal Society of Edinburgh. ÅJ is supported by the Swedish Society for Medical Research, The Kjell och Märta Beijers Foundation, The Marcus Borgström Foundation, The Åke Wiberg foundation and The Vleugels Foundation. UG is supported by Swedish Medical Research Council (K2007-66X-20270-01-3, 2011-2354) and European Commission FP6 (LSHG-CT-2006-01947). SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research, the Ministry of Cultural Affairs, as well as the Social Ministry of the Federal State of Mecklenburg- West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine’ funded by the Federal Ministry of Education and Research, and the German Asthma and COPD Network (01ZZ9603, 01ZZ0103, 01ZZ0403, 03IS2061A, BMBF 01GI0883). ExomeChip data have been supported by the Federal Ministry of Education and Research (03Z1CN22) and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. UKHLS is supported by the Wellcome Trust (098051) and Economic and Social Research Council (ES/ H029745/1). Y.B. holds a Canada Research Chair in Genomics of Heart and Lung Diseases. Lies Lahousse is a Postdoctoral Fellow of the Research Foundation - Flanders (G035014N). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for Scientific Research (NOW), the Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. Genotyping in the Rotterdam study was supported by NOW (175.010.2005.011, 911-03-305 012), RIDE2 (014-93-015) and Netherlands Genomics Initiative/Netherlands Consortium for Healthy Aging (050-060-810). MESA/MESA SHARe is supported by the US Department of Health and Human Services (HHS) (HHSN268201500003I), NIH/National Heart, Lung and Blood Institute (NHLBI; N01-HC-95159, N01-HC-95160, N01-HC- 95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01- HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169) and NIH/National Center for Advancing Translation Studies (NCATS) (UL1-TR-000040, UL1-TR-001079, UL1-TR-001881, DK063491). MESA SHARe is funded by NIH/NHLBI contract N02-HL-64278, MESA Air is funded by US Environmental Protection Agency (RD831697) and MESA Spirometry funded by NIH/NHLBI (R01-HL077612). SSR and BMP are supported by NIH/NHLBI grant rare variants and NHLBI traits in deeply phenotyped cohorts (R01-HL120393). The CHS research was supported by NHLBI (contracts: HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, HHSN268200960009C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; grants: U01HL080295, R01HL068986, R01HL087652, R01HL105756, R01HL103612, R01HL120393, R01HL130114), with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided through R01AG023629 and R01HL085251 from the National Institute on Aging (NIA). The provision of genotyping data was supported in part by the NCATS, CTSI (UL1TR001881), and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DK063491) to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the Page 16 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
authors and does not necessarily represent the official views of the National Institutes of Health. ARIC study is carried out as a collaborative study supported by the NHLBI (contracts: HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C). Funding support for “Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium” was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (5RC2HL102419). DOMK received funding from the Dutch Science Organisation (ZonMW-VENI Grant 916.14.023). The genotyping in the NEO study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-François Deleuze. The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. SAPALDIA was supported by the Swiss National Science Foundation (33CS30-148470/1, 33CSCO-134276/1, 33CSCO-108796, 324730_135673, 3247BO-104283, 3247BO-104288, 3247BO-104284, 3247- 065896, 3100-059302, 3200-052720, 3200-042532, 4026-028099, PMPDP3_129021/1, PMPDP3_141671/1), the Federal Office for the Environment, the Federal Office of Public Health, the Federal Office of Roads and Transport, the canton’s government of Aargau, Basel-Stadt, Basel-Land, Geneva, Luzern, Ticino, Valais, and Zürich, the Swiss Lung League, the Canton’s Lung League of Basel Stadt/Basel Landschaft, Geneva, Ticino, Valais, Graubünden and Zurich, Stiftung ehemals Bündner Heilstätten, SUVA, Freiwillige Akademische Gesellschaft, UBS Wealth Foundation, Talecris Biotherapeutics GmbH, Abbott Diagnostics, European Commission 018996 (GABRIEL), Wellcome Trust (084703). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland, and was funded by the MRC. The Croatia KORCULA study was supported by the Ministry of Science, Education and Sport in the Republic of Croatia (108-1080315-0302). JD, JCL, WG and GTOC are supported by NIH/NHLBI (HHSN268201500001I). Genotyping, quality control and calling of the Illumina HumanExome BeadChip in the Framingham Heart Study was supported by funding from the National Heart, Lung and Blood Institute Division of Intramural Research (Daniel Levy and Christopher J. O’Donnell, Principle Investigators). The AGES study is supported by the NIH (N01-AG012100), the Iceland Parliament (Alþingi) and the Icelandic Heart Association. HABC was supported by NIA (contracts: N01AG62101, N01AG62103, N01AG62106; grant: R01-AG028050), and NINR (grant R01- NR012459), and was supported in part by the Intramural Research Program of the NIA. The HABC genome-wide association study was funded by NIA (1R01AG032098- 01A1) and genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University (HHSN268200782096C). We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. JGW is supported by U54GM115428 from the National Institute of General Medical Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgements The authors would like to thank the staff at the Quebec Respiratory Health Network Tissue Bank for their valuable assistance with the lung eQTL dataset at Laval University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. A full list of principal CHS investigators and institutions can be found at https://CHS-NHLBI.org. The authors thank the staff and participants of the ARIC study for their important contributions. The authors of the NEO study thank all individuals who participated in the Netherlands Epidemiology in Obesity study, all participating general practitioners for inviting eligible participants and all research nurses for collection of the data. We thank the NEO study group, Pat van Beelen, Petra Noordijk and Ingeborg de Jonge for the coordination, lab and data management of the NEO study. SAPALDIA could not have been done without the help of the study participants, technical and administrative support and the medical teams and field workers at the local study sites. Local fieldworkers: Aarau: M Broglie, M Bünter, D Gashi; Basel: R Armbruster, T Damm, U Egermann, M Gut, L Maier, A Vögelin, L Walter; Davos: D Jud, N Lutz; Geneva: M Ares, M Bennour, B Galobardes, E Namer; Lugano: B Baumberger, S Boccia Soldati, E Gehrig-Van Essen, S Ronchetto; Montana: C Bonvin, C Burrus; Payerne: S Blanc, AV Ebinger, ML Fragnière, J Jordan; Wald: R Gimmi, N Kourkoulos, U Schafroth. Administrative staff: N Bauer, D Baehler, C Gabriel, R Gutknecht. SAPALDIA Team: Study directorate: NM Probst Hensch, T Rochat, N Künzli, C Schindler, JM Gaspoz; Scientific team: JC Barthélémy, W Berger, R Bettschart, A Bircher, G Bolognini, O Brändli, C Brombach, M Brutsche, L Burdet, M Frey, U Frey, MW Gerbase, D Gold, E de Groot, W Karrer, R Keller, B Knöpfli, B Martin, D Miedinger, U Neu, L Nicod, M Pons, F Roche, T Rothe, E Russi, P Schmid-Grendelmeyer, A Schmidt-Trucksäss, A Turk, J Schwartz, D. Stolz, P Straehl, JM Tschopp, A von Eckardstein, E Zemp Stutz; Scientific team at coordinating centers: M Adam, E Boes, PO Bridevaux, D Carballo, E Corradi, I Curjuric, J Dratva, A Di Pasquale, L Grize, D Keidel, S Kriemler, A Kumar, M Imboden, N Maire, A Mehta, F Meier, H Phuleria, E Schaffner, GA Thun, A Ineichen, M Ragettli, M Ritter, T Schikowski, G Stern, M Tarantino, M Tsai, M Wanner. This research used the ALICE and SPECTRE High Performance Computing Facilities at the University of Leicester. Page 17 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
Supplementary material Supplementary Information: File includes Supplementary Note, Supplementary Methods, Supplementary Figures and Supplementary Tables, as detailed below. Click here to access the data. Supplementary Note includes individual study descriptions. Supplementary Methods includes details of study level quality control procedures and eQTL analyses. Supplementary Figures: Supplementary Figure 1 - Quantile-quantile (QQ) and Manhattan plots for consortium-wide analyses, and the combined meta-analysis. Supplementary Figure 2 - Region Plots for novel loci. Supplementary Figure 3 - Forest Plots for novel loci. Supplementary Figure 4 - Trait Transformation Sensitivity Analysis Supplementary Tables: Supplementary Table 1 - Details of study specific genotyping platform, genotype calling procedure and software. Supplementary Table 2 - Association results for all SNPs identified in single variant association discovery analyses (P<10-4). Supplementary Table 3 - Association results for SNPs identified in single variant association discovery analyses (P<10-4), located in known lung function regions. Supplementary Table 4 - Single variant association result for the seven novel signals, in smoking and ancestry subgroups. Supplementary Table 5 - Single variant association result for rs1448044 and FVC in ever smokers and never smokers separately, and in all samples combined. Supplementary Table 6 - Association results for all genes identified in discovery SKAT analyses (meta-analysis P<10-4). Supplementary Table 7 - Association results for all genes identified in discovery Weighted sum test (WST) test analyses (P<10-4). Supplementary Table 8 - Evidence for the role of novel variants identified in single variant association analyses as eQTLs. Supplementary Table 9 - SIFT/Polyphen predictions for sentinel SNPs and proxies (r2>0.8). Supplementary Table 10 - Protein and RNA expression results all implicated genes from the single variant association analyses. Supplementary Table 11 - Look-up of association results for SNPs at 7 of the 12 loci which showed allele frequency differences between individuals from different regions in the UK. Supplementary Table 12 - All traits results for the seven novel lung function loci. Supplementary Table 13 - Genomic Inflation Factors: consortium and meta-analysis level. References 1. Rabe KF, Hurd S, Anzueto A, et al.: Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: GOLD executive summary. Am J Respir Crit Care Med. 2007; 176(6): 532–55. PubMed Abstract | Publisher Full Text 2. Palmer LJ, Knuiman MW, Divitini ML, et al.: Familial aggregation and heritability of adult lung function: results from the Busselton Health Study. Eur Respir J. 2001; 17(4): 696–702. PubMed Abstract | Publisher Full Text 3. Wilk JB, DeStefano AL, Joost O, et al.: Linkage and association with pulmonary function measures on chromosome 6q27 in the Framingham Heart Study. Hum Mol Genet. 2003; 12(21): 2745–51. PubMed Abstract | Publisher Full Text 4. Klimentidis YC, Vazquez AI, de Los Campos G, et al.: Heritability of pulmonary function estimated from pedigree and whole-genome markers. Front Genet. 2013; 4: 174. PubMed Abstract | Publisher Full Text | Free Full Text 5. Wilk JB, Djousse L, Arnett DK, et al.: Evidence for major genes influencing pulmonary function in the NHLBI Family Heart Study. Genet Epidemiol. 2000; 19(1): 81–94. PubMed Abstract | Publisher Full Text 6. Wilk JB, Chen TH, Gottlieb DJ, et al.: A Genome-Wide Association Study of Pulmonary Function Measures in the Framingham Heart Study. PLoS Genet. 2009; 5(3): e1000429. PubMed Abstract | Publisher Full Text | Free Full Text 7. Repapi E, Sayers I, Wain LV, et al.: Genome-wide association study identifies five loci associated with lung function. Nat Genet. 2010; 42(1): 36–44. PubMed Abstract | Publisher Full Text | Free Full Text 8. Soler Artigas M, Loth DW, Wain LV, et al.: Genome-wide association and largescale follow up identifies 16 new loci influencing lung function. Nat Genet. 2011; 43(11): 1082–90. PubMed Abstract | Publisher Full Text | Free Full Text 9. Hancock DB, Eijgelsheim M, Wilk JB, et al.: Meta-analyses of genome-wide Page 18 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
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Open Peer Review Current Referee Status: Version 2 03 July 2018Referee Report doi:10.21956/wellcomeopenres.15514.r33384 , Robin Beaumont Rachel M. Freathy InstituteofBiomedicalandClinicalScience,UniversityofExeter,Exeter,UK Thankyoutotheauthorsforrespondingtoandaddressingourcomments.Ihaveonefurthercommenton thereplicationanalysisusingUKBiobankdata.Thesensitivityanalyseswhichtheauthorscarriedout showedthatadjustingforcovariatespriortoinverse-normalizationdoesaffecttheresults.Whilethisdoes notaffectthemainconclusionsdrawn,itmayaffecttheresultsofthegene-basedtests,andinaddition, otherinvestigatorsusingthemethodsasaguidemaydrawinappropriateconclusionsifadjustingfor principalcomponentspriortoinverse-normalizingtheirphenotype.Ideally,theUKBiobankanalysis shouldberedonewiththeappropriatephenotypetransformation,andthemethodsandresultssections updatedaccordingly.However,iftheauthorsconsiderthatsucharevisionwouldbetooextensive,given thattheconclusionsdonotchange,itwouldatleastbehelpfultonotetheissueasalimitationinthe discussionandmakeitclearinthemethodsthatadjustingforcovariates(suchasprincipalcomponents) shouldbedoneafterinverse-normalisingthephenotype–soitcanbeusedappropriatelybyothers. Nocompetinginterestsweredisclosed.Competing Interests: We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. AuthorResponse17Jul2018 ,UniversityofLeicester,UKVictoria Jackson Thankyouforyouapprovalofourarticle,andyouradditionalcomment.Assuggested,wehave addedafurtherlimitationtothediscussionofthepaperoutliningtheissueregardingthetrait transformation.Thishasalsobeennotedinthemethods.Wehavealsoincludedtheresultsofthe sensitivityanalysesinthesupplement(SupplementaryFigure4). Nocompetinginterestsweredisclosed.Competing Interests: Version 1 04 April 2018Referee Report doi:10.21956/wellcomeopenres.13627.r30984 Page 21 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
doi:10.21956/wellcomeopenres.13627.r30984 , Lisa Strug Naim Panjwani ResearchInstitute,HospitalforSickChildren,Toronto,ON,Canada TheHospitalforSickChildren,Toronto,ON,Canada Theauthorshaveperformedalargegenome-wideassociationstudyinsubjectsofEuropean(36,998in thediscoverysetand111,556inthereplicationset)andAfrican(7,721inthediscoveryset)ancestriesfor variouslungfunctionmeasures:FEV1,FVCandFEV1/FVCratio.Bothcommonandrarevariantanalyses areperformed,andtheeffectofsmokingontheassociationsisalsoassessed.Thediscoveryset consistedofCHARGEandSpiroMetaconsortiametaanalysisusingtheHumanExomearray,whilethe replicationsetconsistedofgenotypesontheHumanCoreExomearrayandtheUKBiobank’scustom arrays.Atotalof7novelregionswereidentifiedbytheauthorsthatmettheoverall(discovery+replication) Bonferroni-adjustedP-valueof2.8x10^-7afteradjustmentforvariouscovariatessuchasage,sex,height, andancestryusingprincipalcomponents.AllidentifiednovelSNPsareofcommonfrequency,andtwoof theSNPsareinhighLDwithmissensevariantspredictedtobedamaging. Someareasforimprovement: Tworarevarianttestswerechosenandappliedtothedataasopposedtochoosingacombined test(e.g.Derkachetal2013GeneticEpidemiology).Acombinedtestwouldbemorepowerful. TheauthorsshouldexplainwhytherewasaninversenormalizationofthetraitsinSpiroMetabut notinCHARGE,andprovidesomesensitivityanalysis. ThereappeartobeverylargedifferencesinEffectAlleleFrequenciesbetweenthediscoveryand replicationsamples.Dotheauthorshaveanexplanationforthis?Thismightpointtolocalancestry differencesthatcouldberelevant,andshouldbefurtherinvestigated. TheeQTLanalysiscouldformallyinvestigatecolocalizationasopposedtocross-referencing individualassociatedSNPswithpublicrepositories,andthereareseveraldifferentmethodsthat achievethisgoal:e.g.COLOC,eCAVIAR,Sherlock,RTCorEnLoc. Inthereplicationanalysessection,itisstatedthat“Traitswereadjustedforage,age^2,height, sex,tenprincipalcomponentsandpack-years(eversmokersonly),andinverse normally ”Forclarity,theauthorsshouldbespecificaboutwhetherthetrait(FEV1,FVC,ortransformed. FEV1/FVC)wasinversenormalizedfirstandage,age^2,sex,10PCswerethenaddedas covariatesinthegeneticassociationmodel InthemethodssectionfortherarevarianttestingSkatappearstobeincorrectlyreferredtoasa Fisher’scombinedmethod. Theauthorsshouldprovidethejustificationfortheirvarioussignificancecriteriausedineachofthe analyses. TheauthorsshouldlisttheMAFalongsidethep-valuesreportedinthetextforclarityforthesingle variantanalysisresults Is the work clearly and accurately presented and does it cite the current literature? Yes 1 2 1 2 Page 22 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Nocompetinginterestsweredisclosed.Competing Interests: We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. ReaderComment12Jun2018 ,UniversityofLeicester,UKVictoria Jackson Thankyouthesecondsetofreviewersforyourhelpfulcomments.Again,wehaveaddressed specificpointsbelow,andandmadeappropriateamendmentstothemanuscript. 1. Two rare variant tests were chosen and applied to the data as opposed to choosing a combined test (e.g. Derkach et al 2013 Genetic Epidemiology). A combined test would be more powerful. Weagree,acombinedtestwouldhavebeenthepreferredchoiceforgene-basedassociation testing.However,inthisinstance,thegene-basedtestswerechosenduetopracticalreasons,as SKATandWST,thetwotestsutilised,werebothimplementedbythemeta-analysissoftwareused bythetwocontributingconsortia(RAREMETALandseqMeta).Sincethiswasameta-analysis, andonlysummarystatisticswereavailableforeachstudy,thegene-basedtestswewereableto utilisewererestrictedtothoseimplementedbythesetwosoftwarepackagesatthetimeofthe meta-analyses.Forexample,thesuggestedmethodbyDerkachetal.requirespermutationto calculateP-valueswithadequatelycontrolledtype1errors,whichwouldnothavebeenpossible withthesummarystatisticsavailable. 2. The authors should explain why there was an inverse normalization of the traits in SpiroMeta but not in CHARGE, and provide some sensitivity analysis. Asmentionedinresponsetotheotherreviewers’comments,weagreethatusingtherawtraitin CHARGEandthetransformedtraitinSpiroMetawasnotoptimal;bythetimewehadmadethe decisiontocombinetheresultsfromthetwoconsortia,allstudieshadalreadycompletedanalyses, andreanalysisacrossthemanycohortswouldnothavebeenfeasible. 3. There appear to be very large differences in Effect Allele Frequencies between the discovery Page 23 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018
3. There appear to be very large differences in Effect Allele Frequencies between the discovery and replication samples. Do the authors have an explanation for this? This might point to local ancestry differences that could be relevant, and should be further investigated. Thankyouforhighlightingthis.Therewasanerrorwiththeeffectallelefrequenciesforthe replicationsamplesinSupplementaryTable2;thesehavenowbeenamended,andtheallele frequenciesaremoreconsistentinthediscoveryandreplicationsamples.Wheretherearestill somedifferencesbetweenthediscoveryandreplicationallelefrequencies,thesearewherethe discoverymeta-analysisincludedindividualsofbothEuropeanandAfricanancestry,whereasthe replicationdatasetincludedindividualsofEuropeanancestryonly. 4. The eQTL analysis could formally investigate colocalization as opposed to cross-referencing individual associated SNPs with public repositories, and there are several different methods that achieve this goal: e.g. COLOC, eCAVIAR, Sherlock, RTC or EnLoc. Testsofcolocalisationaremoreusuallyundertakenindensegenome-widedata,whereasthe (oftenrare)putativecausalvariantsincludedontheexomearrayinourstudywererelatively sparselydistributed.Furthermore,wedidnothaveaccesstothelungeQTLdatarequiredto undertakeatestsofcolocalisation.WenowacknowledgethattheeQTLanalysisdidnotinclude formaltestsofcolocalisationinthediscussion,andintheexamplewehighlightthevariantsarein completeLD. 5. In the replication analyses section, it is stated that “Traits were adjusted for age, age , height, sex, ten principal components and pack-years (ever smokers only), and inverse normally transformed.” For clarity, the authors should be specific about whether the trait (FEV , FVC, or FEV /FVC) was inverse normalized first and age, age , sex, 10 PCs were then added as covariates in the genetic association model. Wehaveclarifiedinthemethodsforthereplicationanalysisthat“Traitswereadjustedforage,age ,height,sex,tenprincipalcomponentsandpack-years(eversmokersonly),andtheadjusted traitswereinversenormallytransformed.” 6. In the methods section for the rare variant testing Skat appears to be incorrectly referred to as a Fisher’s combined method. WithineachconsortiumwegeneratedresultsforSKAT.Subsequently,wecombinedtheSKAT resultsfromthetwoconsortiausingFisher’sMethodforcombingP-values.Wehaveclarifiedthis inthetextas“Forgeneswhichcontainedatleast2polymorphicSNPsinbothconsortia,we combinedtheresultsoftheconsortiumlevelgenebasedtestsusingeitherz-scoremeta-analysis (fortheWSTanalysis)orFisher’sMethodforcombiningP-values(inthecaseofSKAT).”. 7. The authors should provide the justification for their various significance criteria used in each of the analyses. JustificationfortheSNPsandgenestakenforwardtothereplicationstagehasnowbeenaddedto themethods: “WeidentifiedSNPsofinterestasthosewithanoverallP<10 andaconsistentdirectionofeffect andP<0·05observedinbothconsortia.RatherthanusingastrictBonferronicorrectionfordefining thesignificancethreshold,weadoptedthemorelenientP<10 thresholdinordertoincreasethe 2 1 12 2 -5 -5 Page 24 of 28 Wellcome Open Research 2018, 3:4 Last updated: 31 AUG 2018