1 Genetic discovery and translational decision support from ...1 Genetic discovery and translational decision support from ... ... 2

1

Geneticdiscoveryandtranslationaldecisionsupportfromexomesequencing1

of20,791type2diabetescasesand24,440controlsfromfiveancestries2

3

JasonFlannick1,2,JosepMMercader1,3,*,ChristianFuchsberger4,5,*,MiriamSUdler1,6,*,4

AnubhaMahajan7,8,*,JenniferWessel9,10,11,TanyaMTeslovich12,LizzCaulkins1,Ryan5

Koesterer1,ThomasWBlackwell4,EricBoerwinkle13,14,JenniferABrody15,Ling6

Chen6,SiyingChen4,CeciliaContreras-Cubas16,EmilioCórdova16,AdolfoCorrea17,7

MariaCortes18,RalphADeFronzo19,LawrenceDolan20,KimberlyLDrews21,8

AmandaElliott1,6,JamesSFloyd22,StaceyGabriel18,MariaEugeniaGaray-Sevilla23,9

HumbertoGarcía-Ortiz16,MyronGross24,SoheeHan25,SarahHanks4,NancyL10

Heard-Costa26,27,AnneUJackson4,MaritEJørgensen28,29,30,HyunMinKang4,Megan11

Kelsey21,Bong-JoKim25,HeikkiAKoistinen31,32,33,JohannaKuusisto34,35,JosephB12

Leader36,AllanLinneberg37,38,39,Ching-TiLiu40,JianjunLiu41,42,43,Valeriya13

Lyssenko44,45,AlisaKManning46,47,AnthonyMarcketta12,JuanManuelMalacara-14

Hernandez23,AngélicaMartínez-Hernández16,KarenMatsuo4,ElizabethMayer-15

Davis48,ElviaMendoza-Caamal16,KarenLMohlke49,AlannaCMorrison50,Anne16

Ndungu7,MaggieCYNg51,52,53,ColmO'Dushlaine12,AnthonyJPayne7,Catherine17

Pihoker54,BroadGenomicsPlatform18,WendySPost55,MichaelPreuss56,BruceM18

Psaty57,58,RamachandranSVasan27,59,NWilliamRayner7,8,60,AlexanderPReiner61,19

CristinaRevilla-Monsalve62,NeilRRobertson7,8,NicolaSantoro63,Claudia20

Schurmann56,WingYeeSo64,65,66,HeatherMStringham4,TimMStrom67,68,Claudia21

HTTam64,65,66,FarookThameem69,BrianTomlinson64,JasonMTorres7,RussellP22

Tracy70,71,RobMvanDam42,43,72,MarijanaVujkovic73,ShuaiWang40,RyanPWelch4,23

.CC-BY-NC-ND 4.0 International licenseacertified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under

The copyright holder for this preprint (which was notthis version posted July 31, 2018. ; https://doi.org/10.1101/371450doi: bioRxiv preprint

https://doi.org/10.1101/371450

http://creativecommons.org/licenses/by-nc-nd/4.0/

2

DanielRWitte74,75,Tien-YinWong76,77,78,GilAtzmon79,80,NirBarzilai79,John24

Blangero81,LoriLBonnycastle82,DonaldWBowden51,52,53,JohnCChambers83,84,85,25

EdmundChan42,Ching-YuCheng86,YoonShinCho87,FrancisSCollins82,PaulSde26

Vries50,RavindranathDuggirala81,BenjaminGlaser88,ClicerioGonzalez89,MaElena27

Gonzalez90,LeifGroop44,91,JaspalSinghKooner92,SooHeonKwak93,Markku28

Laakso34,35,DonnaMLehman19,PeterNilsson94,TimothyDSpector95,EShyong29

Tai42,43,77,TiinamaijaTuomi91,96,97,98,JaakkoTuomilehto99,100,101,102,JamesG30

Wilson103,CarlosAAguilar-Salinas104,ErwinBottinger56,BrianBurke21,DavidJ31

Carey36,JulianaChan64,65,66,JoséeDupuis27,40,PhilippeFrossard105,SusanR32

Heckbert106,MiYeongHwang25,YoungJinKim25,HLesterKirchner36,Jong-Young33

Lee107,JuyoungLee25,RuthLoos56,108,RonaldCWMa64,65,66,AndrewDMorris109,34

ChristopherJO'Donnell110,111,112,113,ColinNAPalmer114,JamesPankow115,Kyong35

SooPark92,116,117,AsifRasheed105,DanishSaleheen73,105,XuelingSim43,KerrinS36

Small95,YikYingTeo43,118,119,ChristopherHaiman120,CraigLHanis121,BrianE37

Henderson120,LorenaOrozco16,TeresaTusié-Luna104,122,FrederickEDewey12,Aris38

Baras12,ChristianGieger123,124,ThomasMeitinger67,68,125,KonstantinStrauch123,126,39

LeslieLange127,NielsGrarup128,TorbenHansen128,129,OlufPedersen128,Phil40

Zeitler21,DanaDabelea130,GoncaloAbecasis4,GraemeIBell23,NancyJCox131,Mark41

Seielstad132,133,RobSladek134,135,136,JamesBMeigs18,46,137,SteveRich138,JeromeI42

Rotter139,DiscovEHRCollaboration12,36,CHARGE,LuCamp,ProDiGY,GoT2D,ESP,43

SIGMA-T2D,T2D-GENES,AMP-T2D-GENES,DavidAltshuler1,6,46,140,141,142,NoëlP44

Burtt1,LauraJScott4,AndrewPMorris7,143,JoseCFlorez1,6,46,144,MarkI45

McCarthy7,8,145,MichaelBoehnke446



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1. ProgramsinMetabolismandMedical&PopulationGenetics,BroadInstitute,47

Cambridge,Massachusetts,USA.48

2. DivisionofGeneticsandGenomics,BostonChildren’sHospital,Boston,49

Massachusetts,USA.50

3. DiabetesUnitandCenterforGenomicMedicine,MassachusettsGeneralHospital,51

Boston,Massachusetts,USA.52

4. DepartmentofBiostatisticsandCenterforStatisticalGenetics,Universityof53

Michigan,AnnArbor,Michigan,USA.54

5. InstituteforBiomedicine,EuracResearch,Bolzano,Italy.55

6. DiabetesResearchCenter(DiabetesUnit),DepartmentofMedicine,56

MassachusettsGeneralHospital,Boston,Massachusetts,USA.57

7. WellcomeCentreforHumanGenetics,NuffieldDepartmentofMedicine,58

UniversityofOxford,Oxford,UK.59

8. OxfordCentreforDiabetes,EndocrinologyandMetabolism,RadcliffeDepartment60

ofMedicine,UniversityofOxford,Oxford,UK.61

9. DepartmentofEpidemiology,FairbanksSchoolofPublicHealth,Indiana62

University,Indianapolis,IN,46202,US.63

10. DepartmentofMedicine,SchoolofMedicine,IndianaUniversity,Indianapolis,IN,64

46202,US.65

11. DiabetesTranslationalResearchCenter,IndianaUniversity,Indianapolis,IN,66

46202,US.67

12. RegeneronGeneticsCenter,RegeneronPharmaceuticals,Tarrytown,NY,10591,68

USA.69



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13. HumanGeneticsCenter,DepartmentofEpidemiologyHumanGeneticsand70

EnvironmentalSciences,SchoolofPublicHealth,TheUniversityofTexasHealth71

ScienceCenteratHouston,Houston,Texas,USA.72

14. HumanGenomeSequencingCenter,BaylorCollegeofMedicine,Houston,Texas,73

USA.74

15. CardiovascularResearchUnit,DepartmentofMedicine,Universityof75

Washington,Seattle,WA,USA.76

16. InstitutoNacionaldeMedicinaGenómica,MexicoCity,Mexico.77

17. DepartmentofMedicine,UniversityofMississippiMedicalCenter,Jackson,78

Mississippi,USA.79

18. BroadInstituteofMITandHarvard,Cambridge,Massachusetts,USA.80

19. DepartmentofMedicine,UniversityofTexasHealthScienceCenter,SanAntonio,81

Texas,USA.82

20. CincinnatiChildren'sHospitalMedicalCenter,Ohio,Cincinnati,USA.83

21. BiostatisticsCenter,GeorgeWashingtonUniversity,Rockville,MD,USA.84

22. DepartmentofMedicineandEpidemiology,UniversityofWashington,Seattle,85

WA,USA.86

23. DepartmentsofMedicineandHumanGenetics,TheUniversityofChicago,87

Chicago,Illinois,USA.88

24. DepartmentofLaboratoryMedicineandPathology,UniversityofMinnesota,89

Minneapolis,Minnesota,USA.90

25. DivisionofGenomeResearch,CenterforGenomeScience,NationalInstituteof91

Health,Chungcheongbuk-do,RepublicofKorea.92



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26. DepartmentofNeurology,BostonUniversitySchoolofMedicine,Boston,93


27. NationalHeartLungandBloodInstitute'sFraminghamHeartStudy,Framingham,95


28. StenoDiabetesCenterCopenhagen,Gentofte,Denmark.97

29. NationalInstituteofPublicHealth,UniversityofSouthernDenmark,Copenhagen,98

Denmark.99

30. GreenlandCentreforHealthResearch,UniversityofGreenland,Nuuk,Greenland.100

31. DepartmentofPublicHealthSolutions,NationalInstituteforHealthandWelfare,101

Helsinki,Finland.102

32. UniversityofHelsinkiandDepartmentofMedicine,HelsinkiUniversityCentral103

Hospital,Helsinki,Finland.104

33. MinervaFoundationInstituteforMedicalResearch,Helsinki,Finland.105

34. InstituteofClinicalMedicine,InternalMedicine,UniversityofEasternFinland,106

Kuopio,Finland.107

35. DepartmentofMedicin,KuopioUniversityHospital,Kuopio,Finland.108

36. GeisingerHealthSystem,Danville,PA,17822,USA.109

37. DepartmentofClinicalMedicine,FacultyofHealthandMedicalSciences,110

UniversityofCopenhagen,Copenhagen,Denmark.111

38. CenterforClinicalResearchandPrevention,BispebjergandFrederiksberg112

Hospital,TheCapitalRegion,Copenhagen,Denmark.113

39. DepartmentofClinicalExperimentalResearch,Rigshospitalet,Copenhagen,114

Denmark.115



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40. DepartmentofBiostatistics,BostonUniversitySchoolofPublicHealth,Boston,116


41. GenomeInstituteofSingapore,AgencyforScienceTechnologyandResearch,118

Singapore.119

42. DepartmentofMedicine,YongLooLinSchoolofMedicine,NationalUniversityof120

Singapore,NationalUniversityHealthSystem,Singapore.121

43. SawSweeHockSchoolofPublicHealth,NationalUniversityofSingapore,122

Singapore.123

44. DepartmentofClinicalSciences,DiabetesandEndocrinology,LundUniversity124

DiabetesCentre,Malmö,Sweden.125

45. UniversityofBergen,Norway.126

46. DepartmentofMedicine,HarvardMedicalSchool,Boston,Massachusetts,USA.127

47. ClinicalandTranslationalEpidemiologyUnit,MassachusettsGeneralHospital,128

HarvardUniversity,Boston,MA,USA.129

48. UniversityofNorthCarolinaChapelHill,ChapelHill,NorthCarolina,USA.130

49. DepartmentofGenetics,UniversityofNorthCarolina,ChapelHill,NorthCarolina,131

USA.132

50. HumanGeneticsCenter,DepartmentofEpidemiologyHumanGeneticsand133

EnvironmentalSciences,SchoolofPublicHealth,TheUniversityofTexasHealth134


51. CenterforDiabetesResearch,WakeForestSchoolofMedicine,Winston-Salem,136

NorthCarolina,USA.137



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52. CenterforGenomicsandPersonalizedMedicineResearch,WakeForestSchoolof138

Medicine,Winston-Salem,NorthCarolina,USA.139

53. DepartmentofBiochemistry,WakeForestSchoolofMedicine,Winston-Salem,140

NorthCarolina,USA.141

54. SeattleChildren'sHospital,Washington,Seattle,USA.142

55. DivisionofCardiology,DepartmentofMedicine,JohnsHopkinsUniversity,143

Baltimore,Maryland,USA.144

56. CharlesR.BronfmanInstituteofPersonalizedMedicine,MountSinaiSchoolof145

Medicine,NewYork,NewYork,USA.146

57. CardiovascularHealthResearchUnit,DepartmentsofMedicine,Epidemiology,147

andHealthServices,UniversityofWashington,Seattle,Washington,USA.148

58. KaiserPermanenteWashingtonHealthResearchInstitute,Seattle,Washington,149

USA.150

59. PreventiveMedicine&Epidemiology,Medicine,BostonUniversitySchoolof151

Medicine,Boston,Massachusetts,USA.152

60. DepartmentofHumanGenetics,WellcomeTrustSangerInstitute,Hinxton,153

Cambridgeshire,UK.154

61. UniversityofWashington,Seattle,Washington,USA.155

62. InstitutoMexicanodelSeguroSocialSXXI,MexicoCity,Mexico.156

63. DepartmentofPediatrics,YaleUniversity,NewHaven,CT,USA.157

64. DepartmentofMedicineandTherapeutics,TheChineseUniversityofHongKong,158

HongKong,China.159



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65. LiKaShingInstituteofHealthSciences,TheChineseUniversityofHongKong,160

HongKong,China.161

66. HongKongInstituteofDiabetesandObesity,TheChineseUniversityofHong162

Kong,HongKong,China.163

67. InstituteofHumanGenetics,TechnischeUniversitätMünchen,Munich,Germany.164

68. InstituteofHumanGenetics,HelmholtzZentrumMünchen,GermanResearch165

CenterforEnvironmentalHealth,Neuherberg,Germany.166

69. DepartmentofBiochemistry,FacultyofMedicine,HealthScienceCenter,Kuwait167

University,Safat,Kuwait.168

70. DepartmentofPathologyandLaboratoryMedicine,RobertLarner,M.D.College169

ofMedicine,UniversityofVermont,Burlington,Vermont,USA.170

71. DepartmentofBiochemistry,RobertLarnerM.D.CollegeofMedicine,University171

ofVermont,Burlington,Vermont,USA.172

72. DepartmentofNutrition,HarvardSchoolofPublicHealth,Boston,Massachusetts,173

USA.174

73. DepartmentofBiostatisticsandEpidemiology,UniversityofPennsylvania,175

Philadelphia,Pennsylvania,USA.176

74. DepartmentofPublicHealth,AarhusUniversity,Aarhus,Denmark.177

75. DanishDiabetesAcademy,Odense,Denmark.178

76. SingaporeEyeResearchInstitute,SingaporeNationalEyeCentre,Singapore.179

77. Duke-NUSMedicalSchoolSingapore,Singapore.180

78. DepartmentofOphthalmology,YongLooLinSchoolofMedicine,National181

UniversityofSingapore,NationalUniversityHealthSystem,Singapore.182



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79. DepartmentsofMedicineandGenetics,AlbertEinsteinCollegeofMedicine,New183

York,USA.184

80. UniversityofHaifa,Facultyofnaturalscience,Haifa,Isarel.185

81. DepartmentofHumanGeneticsandSouthTexasDiabetesandObesityInstitute,186

UniversityofTexasRioGrandeValley,EdinburgandBrownsville,Texas,USA.187

82. MedicalGenomicsandMetabolicGeneticsBranch,NationalHumanGenome188

ResearchInstitute,NationalInstitutesofHealth,Bethesda,Maryland,USA.189

83. DepartmentofEpidemiologyandBiostatistics,ImperialCollegeLondon,London,190

UK.191

84. DepartmentofCardiology,EalingHospitalNHSTrust,Southall,Middlesex,UK.192

85. ImperialCollegeHealthcareNHSTrust,ImperialCollegeLondon,London,UK.193

86. Ophthalmology&VisualSciencesAcademicClinicalProgram(EyeACP),Duke-194

NUSMedicalSchool,Singapore195

87. DepartmentofBiomedicalScience,HallymUniversity,Chuncheon,Republicof196

Korea.197

88. EndocrinologyandMetabolismService,Hadassah-HebrewUniversityMedical198

Center,Jerusalem,Israel.199

89. UnidaddeDiabetesyRiesgoCardiovascular,InstitutoNacionaldeSaludPública,200

Cuernavaca,Morelos,Mexico.201

90. CentrodeEstudiosenDiabetes,MexicoCity,Mexico.202

91. InstituteforMolecularGeneticsFinland,UniversityofHelsinki,Helsinki,Finland.203

92. NationalHeartandLungInstitute,CardiovascularSciences,Hammersmith204

Campus,ImperialCollegeLondon,London,UK.205



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93. DepartmentofInternalMedicine,SeoulNationalUniversityHospital,Seoul,206

RepublicofKorea.207

94. DepartmentofClinicalSciences,Medicine,LundUniversity,Malmö,Sweden.208

95. DepartmentofTwinResearchandGeneticEpidemiology,King'sCollegeLondon,209

London,UK.210

96. FolkhälsanResearchCentre,Helsinki,Finland.211

97. DepartmentofEndocrinology,AbdominalCentre,HelsinkiUniversityHospital,212

Helsinki,Finland.213

98. ResearchProgramsUnit,DiabetesandObesity,UniversityofHelsinki,Helsinki,214

Finland.215

99. DiabetesPreventionUnit,NationalInstituteforHealthandWelfare,Helsinki,216

Finland.217

100. CenterforVascularPrevention,DanubeUniversityKrems,Krems,Austria.218

101. DiabetesResearchGroup,KingAbdulazizUniversity,Jeddah,SaudiArabia.219

102. InstitutodeInvestigacionSanitariadelHospitalUniversarioLaPaz(IdiPAZ),220

UniversityHospitalLaPaz,AutonomousUniversityofMadrid,Madrid,Spain.221

103. DepartmentofPhysiologyandBiophysics,UniversityofMississippiMedical222

Center,Jackson,Mississippi,USA.223

104. InstitutoNacionaldeCienciasMedicasyNutricion,MexicoCity,Mexico.224

105. CenterforNon-CommunicableDiseases,Karachi,Pakistan.225

106. CardiovascularHealthResearchUnitandDepartmentofEpidemiology,226

UniversityofWashington,Seattle,WA,USA.227

107. MinistryofHealthandWelfare,Seoul,RepublicofKorea.228



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108. TheMindichChildHealthandDevelopmentInsititute,IcahnSchoolofMedicineat229

MountSinai,NewYork,NewYork,USA.230

109. ClinicalResearchCentre,CentreforMolecularMedicine,NinewellsHospitaland231

MedicalSchool,Dundee,UK.232

110. SectionofCardiology,DepartmentofMedicine,VABostonHealthcare,Boston,233


111. HarvardMedicalSchool,Boston,Massachusetts,USA.235

112. BrighamandWomen’sHospital,Boston,Massachusetts,USA.236

113. IntramuralAdministrationManagementBranch,NationalHeartLungandBlood237

Institute,NIH,Framingham,Massachusetts,USA.238

114. PatMacphersonCentreforPharmacogeneticsandPharmacogenomics,Medical239

ResearchInstitute,NinewellsHospitalandMedicalSchool,Dundee,UK.240

115. DivisionofEpidemiologyandCommunityHealth,UniversityofMinnesota,241

Minnesota,MN,USA.242

116. DepartmentofMolecularMedicineandBiopharmaceuticalSciences,Graduate243

SchoolofConvergenceScienceandTechnology,SeoulNationalUniversity,Seoul,244

RepublicofKorea.245

117. DepartmentofInternalMedicine,SeoulNationalUniversityCollegeofMedicine,246

Seoul,RepublicofKorea.247

118. LifeSciencesInstitute,NationalUniversityofSingapore,Singapore.248

119. DepartmentofStatisticsandAppliedProbability,NationalUniversityof249

Singapore,Singapore.250



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120. DepartmentofPreventiveMedicine,KeckSchoolofMedicine,Universityof251

SouthernCalifornia,LosAngeles,California,USA.252

121. HumanGeneticsCenter,SchoolofPublicHealth,TheUniversityofTexasHealth253


122. InstitutodeInvestigacionesBiomédicas,DepartamentodeMedicinaGenómicay255

Toxicología,UniversidadNacionalAutónomadeMéxico,MexicoCity,Mexico.256

123. ResearchUnitofMolecularEpidemiology,InstituteofEpidemiology,Helmholtz257

ZentrumMünchen,GermanResearchCenterforEnvironmentalHealth,258

Neuherberg,Germany.259

124. GermanCenterforDiabetesResearch(DZDe.V.),Neuherberg,Germany.260

125. DeutschesForschungszentrumfürHerz-Kreislauferkrankungen(DZHK),Partner261

SiteMunichHeartAlliance,Munich,Germany.262

126. InstituteofMedicalInformatics,BiometryandEpidemiology,ChairofGenetic263

Epidemiology,Ludwig-Maximilians-Universität,Neuherberg,Germany.264

127. DepartmentofMedicine,UniversityofColoradoDenver,AnschutzMedical265

Campus,Aurora,Colorado,USA.266

128. NovoNordiskFoundationCenterforBasicMetabolicResearch,FacultyofHealth267

andMedicalSciences,UniversityofCopenhagen,Copenhagen,Denmark.268

129. FacultyofHealthSciences,UniversityofSouthernDenmark,Odense,Denmark.269

130. DepartmentofEpidemiology,ColoradoSchoolofPublicHealth,Aurora,CO,USA.270

131. VanderbiltGeneticsInstitute,VanderbiltUniversity,Tennessee,Nashville,USA.271

132. DepartmentofLaboratoryMedicine&InstituteforHumanGenetics,Universityof272

California,SanFrancisco,SanFrancisco,California,USA.273



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133. BloodSystemsResearchInstitute,SanFrancisco,California,USA.274

134. DepartmentofHumanGenetics,McGillUniversity,Montreal,Quebec,Canada.275

135. DivisionofEndocrinologyandMetabolism,DepartmentofMedicine,McGill276

University,Montreal,Quebec,Canada.277

136. McGillUniversityandGénomeQuébecInnovationCentre,Montreal,Quebec,278

Canada.279

137. DivisionofGeneralInternalMedicine,MassachusettsGeneralHospital,Boston,280


138. CenterforPublicHealthGenomics,UniversityofViriginiaSchoolofMedicine,282

Charlottesville,Virginia,USA.283

139. DepartmentsofPediatricsandMedicine,InstituteforTranslationalGenomicsand284

PopulationSciences,LosAngelesBioMedicalResearchInstituteatHarbor-UCLA285

MedicalCenter,Torrance,California,USA.286

140. DepartmentofGenetics,HarvardMedicalSchool,Boston,Massachusetts,USA.287

141. DepartmentofBiology,MassachusettsInstituteofTechnology,Cambridge,288


142. DepartmentofMolecularBiology,MassachusettsGeneralHospital,Boston,290


143. DepartmentofBiostatistics,UniversityofLiverpool,Liverpool,UK.292

144. CenterforGenomicMedicine,MassachusettsGeneralHospital,Boston,293


145. OxfordNIHRBiomedicalResearchCentre,OxfordUniversityHospitalsTrust,295

Oxford,UK.296



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Abstract297

Protein-codinggeneticvariantsthatstronglyaffectdiseaseriskcanprovide298

importantcluesintodiseasepathogenesis.Herewereportanexomesequence299

analysisof20,791type2diabetes(T2D)casesand24,440controlsfromfive300

ancestries.Weidentifyrare(minorallelefrequency<0.5%)variantgene-level301

associationsin(a)threegenesatexome-widesignificance,includingaT2D-302

protectiveseriesof>30SLC30A8alleles,and(b)within12genesets,includingthose303

correspondingtoT2Ddrugtargets(p=6.1×10-3)andcandidategenesfromknockout304

mice(p=5.2×10-3).Withinourstudy,thestrongestT2Drarevariantgene-level305

signalsexplainatmost25%oftheheritabilityofthestrongestcommonsingle-306

variantsignals,andtherarevariantgene-leveleffectsizesweobserveinestablished307

T2Ddrugtargetswillrequire110K-180Ksequencedcasestoexceedexome-wide308

significance.Tohelpprioritizegenesusingassociationsfromcurrentsmallersample309

sizes,wepresentaBayesianframeworktorecalibrateassociationp-valuesas310

posteriorprobabilitiesofassociation,estimatingthatreachingp<0.05(p<0.005)in311

ourstudyincreasestheoddsofcausalT2Dassociationforanonsynonymousvariant312

byafactorof1.8(5.3).Tohelpguidetargetorgeneprioritizationefforts,ourdata313

arefreelyavailableforanalysisatwww.type2diabetesgenetics.org.314

315



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Introduction316

Tobetterunderstandortreatdisease,humangeneticsoffersapowerfulapproachto317

identifymolecularalterationscausallyassociatedwithphysiologicaltraits1.318

Common-variantarray-basedgenome-wideassociationstudies(GWAS)have319

discoveredthousandsofgenomiclociassociatedwithhundredsofhumantraits2,320

andfurthercommonvariantanalysesindicatethatmostcomplextraitheritabilityis321

attributabletomodest-effectregulatoryvariants3-5.However,non-codingGWAS322

associationsarechallengingtolocalizetocausalvariantsorgenes6-10.323

324

Protein-codingvariantswithstrongeffectsonproteinfunctionordiseasecanoffer325

molecular“probes”intothepathologicalrelevanceofagene13-15andpotentially326

establishadirectcausal16,17linkbetweengenegainorlossoffunctionanddisease327

risk18,19–especiallywhenthereisevidenceofmultipleindependentvariant328

associations(an“allelicseries”)withinagene18-20.Severallinesofargument11,12329

predictthatstrong-effectvariants(allelicodds-ratios[OR]>2)willusuallyberare330

(minorallelefrequency[MAF]<0.5%)and,inmanycases,difficulttoaccurately331

studythroughcurrentGWASandimputationstrategies13,14.Wholegenomeor332

exomesequencing,bycontrast,allowsinterrogationofthefullspectrumofgenetic333

variation.334

335

Previousexomesequencingstudies,however,haveidentifiedfewexome-wide336

significantrarevariantassociations21-26forcomplexdiseasessuchastype2337

diabetes(T2D)24,27.Thispaucityoffindingsisdueinparttothelimitedsamplesizes338



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ofpreviousstudies,thelargestofwhichinclude<10,000diseasecasesandfallshort339

ofthesamplesizesthatanalytic12andsimulation-basedcalculations28-30predictare340

neededtoidentifyraredisease-associatedvariantsunderplausiblediseasemodels.341

Toexpandourabilitytouserarecodingvariantstomakegeneticdiscoveriesand342

accelerateclinicaltranslation,wecollectedandanalyzedexomesequencedatafrom343

20,791T2Dcasesand24,440controlsofmultipleancestries,representingthe344

largestexomesequenceanalysistodateforT2D.345

346

Geneticdiscoveryfromsingle-variantandgene-levelanalysis347

348

Studyparticipants(SupplementaryTable1)weredrawnfromfiveancestries349

(Hispanic/Latino[effectivesize(Neff)=14,442;33.8%],European[Neff=10,517;350

24.6%],African-American[Neff=5,959;13.9%],East-Asian[Neff=6,010;14.1%],351

South-Asian[Neff=5,833;13.6%])andyieldedequivalentstatisticalpowertodetect352

associationasabalancedstudyof~42,800individualsorapopulation-basedstudy353

(assuming8%T2Dprevalence)of~152,000individuals.Powertodetect354

associationwasimprovedcomparedtothepreviouslargestT2Dexomesequencing355

study24of6,504casesand6,436controls,increasing(forexample)from5%to90%356

foravariantwithMAF=0.2%andOR=2.5(SupplementaryFigure1).357

358

Exomesequencingto40xmeandepth,variantcallingusingbest-practice359

algorithms,andextensivedataqualitycontrol(Methods;SupplementaryFigures360

2-5,SupplementaryTable2)producedadatasetwith6.33Mvariants,ofwhich361



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2.3%arecommon(MAF>5%),4.2%low-frequency(0.5%<MAF<5%),and93.5%362

rare(MAF<0.5%)(SupplementaryTable3).Theseinclude2.26Mnonsynonymous363

variantsand871Kindels,morethantwicethenumbersanalyzedinthelargest364

previousT2Dexomesequencingstudy24.365

366

Wefirsttestedwhetheranyofthesevariants,regardlessofallelefrequency,367

exhibitedassociationwithT2D(“single-variant”test;Methods,Supplementary368

Figure6).Basedonapreviouslydemonstratedenrichmentofcodingvariantsfor369

diseaseassociations31,weusedanexome-widesignificancethresholdofp=4.3×10-7.370

Eighteenvariants(tennonsynonymous)insevenlocireachedthisthreshold;13of371

these(eightnonsynonymous)reachedthetraditionalgenome-widesignificance372

thresholdofp<5×10-8(Figure1a,SupplementaryTable4).These18associations373

representasubstantialincreaseovertheoneassociationreportedfromthe374

previouslargestT2Dexomesequencingstudy24.However,onlytwoofthese18have375

notbeenpreviouslyreportedby(muchlarger)GWAS:avariantinSFI1376

(rs145181683,p.Arg724Trp;SupplementaryFigure7)thatfailedtoreplicatein377

anindependentcohort(N=4,522,p=0.90,Methods),andavariantinMC4R378

(rs79783591,p.Ile269Asn).379

380

MC4Rp.Ile269AsnwasthesolevariantwithassociationOR>2(Hispanic/Latino381

MAF=0.89%;p=3.4×10-7,OR=2.17[95%CI:1.63-2.89]).MC4Rhaslongestablished382

effectsonbody-weightanddiabetes32-34,andp.Ile269Asnspecificallyhasbeen383



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showntodecreaseMC4Ractivity35,36withassociationstoobesityandT2Din384

smallerstudiesofaUnitedKingdomfamily37andaNativeAmericanpopulation36.385

386

Assingle-variantanalysishaslimitedpowertodetectassociationswithrarer387

variants12,wenextperformedtestsofassociationforsetsofvariantswithingenes.388

Weperformedtwogene-levelassociationtests:(a)aburdentest,whichassumesall389

analyzedvariantswithinageneareofthesameeffect,and(b)SKAT38,whichallows390

variabilityinvarianteffectsize(anddirection).391

392

Followingpreviousstudies22-24,weseparatelytestedsevendifferent“masks”of393

variantsgroupedbysimilarpredictedseverity.Asthisanalysisstrategyledto394

2×7=14p-valuesforeachgene,wedevelopedtwomethodstoconsolidatethese395

resultsforeachtest(Methods;SupplementaryFigures8-10).First,weretained396

onlythesmallestp-valuebutcorrectedfortheeffectivenumberofindependent397

maskstested39,onaverage3.6pergene(“minimump-valuetest”).Second,wetested398

allnonsynonymousvariants(i.e.missense,splicesite,andproteintruncating)but399

weightedeachvariantaccordingtoitsestimatedprobabilityofcausinggene400

inactivation12(“weightedtest”,inessenceassessingtheeffectofgene401

haploinsufficiencyfromcombinedanalysisofprotein-truncatingandmissense402

variants;Methods).Weverifiedthattheminimump-valueandweighted403

consolidationmethodswerebothwell-calibrated(SupplementaryFigure11)and404

betweenthemproducedbroadlyconsistentbutdistinctresults:acrossthetenmost405

significantly-associatedgenes,p-valueswerenominallysignificantunderboth406



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methodsforeightgenesbutvariedbyone-to-threeordersofmagnitude407

(SupplementaryTable5).WeemployedaconservativeBonferroni-corrected408

gene-levelexome-widesignificancethresholdofp=0.05/(2tests×2consolidation409

methods×19,020genes)=6.57×10-7.410

411

Usingthisstrategy,gene-levelassociationsreachedexome-widesignificancefor412

MC4R,SLC30A8,andPAM(Figure1b,SupplementaryTables5-6).Allthreegenes413

liewithinpreviouslyT2DGWASlociandcontainpreviouslyidentifiedcodingsingle-414

variantsignals:p.Arg325Trpandaseriesof12protectiveproteintruncating415

variants(PTVs)forSLC30A819,40,p.Asp563Glyandp.Ser539TrpforPAM24,41,and416

p.Ile269AsnforMC4R.417

418

Inadditionto11previouslyobservedPTVs,theSLC30A8gene-levelsignalincludes419

92variants(103intotalwithcombinedMAF=1.4%;p.Arg325Trpwasnotincluded420

ingene-levelanalysis)andisassociatedwithT2Dprotection(weightedp=1.3×10-8,421

OR=0.40[0.28-0.55]).Manyvariantscontributedtothissignal:whenwe422

progressivelyremovedvariantswiththesmallestsingle-variantp-values,removal423

of33wasrequiredtoextinguishnominal(p<0.05)gene-levelsignificance(Figure424

1cd,SupplementaryFigure12).AlthoughSLC30A8(anditsproteinproductZnT8)425

werefirstimplicatedinT2Doveradecadeago40,theirmoleculardisease426

mechanism(s)remainpoorlyunderstood42,43–inpartbecauseofseemingly427

conflictingobservationsofthecommonrisk-increasingallelep.Arg325Trp428

(suggestedtodecreaseproteinactivity44)andtherarerisk-decreasingPTVs(also429



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thoughttodecreaseproteinactivity19).Theprotectiveallelicseriesfromour430

analysisarguesthatdecreased,ratherthanincreased,riskisthemoretypicaleffect431

ofSLC30A8geneticvariation,anditfurtherprovidesmanyallelesthatcouldbe432

characterizedtooffermechanisticinsight.433

434

TheMC4R(combinedMAF=0.79%;minimump=2.7×10-10,OR=2.07[1.65-2.59])and435

PAM(combinedMAF=4.9%;weightedp=2.2×109,OR=1.44[1.28-1.62])gene-level436

signalsareduelargely–butnotentirely–toeffectsfromindividualvariants437

(p.Ile269AsnforMC4R,p.Asp563Glyandp.Ser539TrpforPAM).ForMC4R,gene-438

levelassociationdecreasedbutremainedsignificantafterremovingp.Ile269Asn439

(p=8.6×10-3;SupplementaryFigure13).Similarly,asshownpreviously34,45,440

associationwaslesssignificantafterconditioningonsampleBMI,bothforthe441

p.Ile269Asnsingle-variantsignal(p=1.0×10-5)andthegene-levelsignalnot442

attributabletop.Ile269Asn(p=0.035).443

444

Thegene-levelsignalinPAMalsoremainednominallysignificant(p<0.05)even445

afterremovingthe35strongestindividuallyassociatedPAMvariants,indicatinga446

contributionfromsubstantiallymorevariantsthanp.Asp563Glyandp.Ser539Trp447

(SupplementaryFigure14).Cellularcharacterizationofp.Asp563Glyand448

p.Ser539TrprecentlyidentifiedanovelmechanismforT2Driskthroughaltered449

insulinstorageandsecretion46.Ourresultsprovidemanymoregeneticvariants–450

identifiableonlythroughsequencing17–thatcouldbecharacterizedforfurther451

insightsintotheT2DriskmechanismmediatedbyPAM.452



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453

Wefinallyassessedthe50most-significantgene-levelassociations(asmeasuredby454

minimump-valueacrossourfouranalyses;Methods)intwoindependentexome455

sequencedatasets:14,118individuals(3,062T2Dcasesand9,405controlsof456

EuropeanorAfrican-Americanancestry)fromtheCHARGEdiscoverysequence457

project47(CHARGE,SupplementaryTable7;50genesavailable)and49,199458

individuals(12,973T2Dcasesand36,226controlsofEuropeanancestry)fromthe459

GeisingerHealthSystem(GHS,SupplementaryTable8;44genesavailable).In460

eachreplicationstudy,MC4R,SLC30A8,andPAMallshowedburdentest461

associationsdirectionallyconsistentwiththosefromouranalysis.MC4R(minimum462

p=0.0058)andSLC30A8(minimump=0.043)furtherdemonstratednominally463

significantassociationsintheGHSburdenanalysis,andMC4R(minimump=0.026)464

achievednominalsignificanceintheCHARGESKATanalysis.Theweaker465

associationsinthereplicationstudiescomparedtoourstudy(Supplementary466

Tables7and8)couldbeduetoawinner’scurseeffectcombinedwithdifferences467

inproceduresforvariantcalling,qualitycontrol,annotation,andassociationtesting.468

469

Morebroadly,acrossthegeneswithreplicationresultsavailableandwithburden470

p<0.05inouranalysis,weobservedanexcessofdirectionallyconsistentburdentest471

associations(31of46inCHARGE,one-sidedbinomialp=0.013;23of40inGHS,472

one-sidedbinomialp=0.21;overallone-sidedbinomialp=0.011;Supplementary473

Table9).Futurestudiesmaythereforeenableseveralmoreofthetopgene-level474

signalsfromouranalysistoreachexome-widesignificance.475



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476

Furtherinsightsfromgene-levelanalysis477

478

SLC30A8,MC4R,andPAMillustratehowexome-widesignificantgene-level479

associationsprovideallelicseriesthatcouldbecharacterizedforpathogenic480

insightsintopreviouslyT2D-associatedbutstillincompletelyunderstoodgenes.We481

nextinvestigatedtheutilityoflesssignificantgene-levelassociationstoeither(a)482

geneticallyprioritizegeneswithnopriorevidenceofT2Dassociation,(b)predict483

theeffectorgeneatestablishedT2DGWASloci,or(c)predictwhetherlossorgainof484

proteinfunctionincreasesdiseaserisk.Weconductedthisanalysisatthelevelof16485

setsofgenesconnectedtoT2Dfromdifferentevidencesources(e.g.genes486

harboringdiabetes-associatedMendelianorcommonvariants,T2Ddrugtargets48,487

orgenesimplicatedindiabetes-relatedphenotypesfrommousemodels49;488

SupplementaryTable10;Methods).489

490

First,foreachgeneset,weaskedwhetheritsgeneshadmoresignificantgene-level491

associationsthanexpectedbychance.Weusedaone-sidedWilcoxonRank-Sum492

Testtocomparegene-levelp-valueswithineachgenesettothoseforrandomsetsof493

geneswithsimilarnumbersofvariantsandaggregatefrequencies(Methods).494

Twelveofthe16genesetsachievedp<0.05set-levelassociations(Figure2a-e,495

SupplementaryFigure15),includingthoseforT2Ddrugtargets(p=6.1×10-3)and496

forgenesreportedfrommousemodelsofnon-autoimmunediabetes(p=5.2×10-3)or497

impairedglucosetolerance(p=7.2×10-6).Followingapreviousstudythat498



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retrospectivelyvalidateddrugtargetsfromthegeneticeffectsofPTVs27,these499

resultsdemonstratethevalueofgene-levelassociationstoprioritizecandidate500

genes–e.g.thosethatemergefromhigh-throughputexperimentalscreens50,51–for501

furtherinvestigation.Ourstudyemphasizestheaddedpowerofincludingmissense502

variantsinthisanalysis:set-levelp-valuesfromanalysisofPTVsalonewerep>0.05503

foralmostallgenesets(although,notably,thedrugtargetgenesetremained504

significantatp=0.0061;SupplementaryFigure16).505

506

Next,weinvestigatedwhethereffectorgenesthatmediateGWASassociations–507

whichmostlycorrespondtovariantsofuncertainregulatoryeffects–werealso508

enrichedforcodingvariantgene-levelassociations.Wetestedforassociations509

withintwosetsofpredictedeffectorgenes:acuratedlistof11genesharboring510

likelycausalcommoncodingvariants(reportedfromarecentstudy17with511

posteriorprobabilityofcausalassociation>0.25fromgeneticsalone;Methods),and512

20genessignificantinatranscriptassociationanalysiswithT2D52.Geneswith513

likelycausalcodingvariantsdemonstratedasignificantset-levelassociationrelative514

tocomparisongenesets(p=8.8×10-3)andtogeneswithinthesameloci(p=0.028;515

Figure2e),evenwhenweconditionedgene-levelassociationsonallsignificant516

commonvariantsignals.Mostofthissignalwasduetothegene-levelSLC30A8and517

PAMassociations(p=0.082fortheotherninegenes).Bycontrast,thetranscript-518

associationbasedgenesetdidnotexhibitasignificantassociation(p=0.72).519

520



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Extendingthisanalysis,wecuratedalistof94T2DGWASloci,and595genesthat521

laywithin250kbofanyT2DGWASindexvariant,froma2016T2Dgenetics522

review53.Amongthese595genes,40achievedap<0.05gene-levelsignal523

(SupplementaryTable11),greaterthanthe595×0.05=29.75expectedbychance524

(p=0.038).These40geneshadamongthemsignificantlymoreindirectprotein-525

proteininteractions(DAPPLE54p=0.03;observedmean=11.4,expectedmean=4.5)526

thandidthe184genesimplicatedbasedonproximitytoGWAStagSNPs(DAPPLE527

p=0.64),consistentwithagenesetofgreaterbiologicalcoherence.Rarecoding528

variantscouldtherefore,inprinciple,complementcommonvariantfinemapping6,55529

andexperimentaldata7,56tohelpinterpretT2DGWASassociations,althoughour530

resultsindicatethatmuchlargersamplesizeswillberequiredtoclearlyimplicate531

specificeffectorgenes.532

533

Finally,weassessedwhethergene-levelanalysiscouldhelppredictwhethergene534

inactivationincreasesordecreasesT2Drisk(i.e.theT2D“directional535

relationship”18,19).Foreachgeneset,wecomparedtheORsestimatedfromgene-536

levelweightedanalysisofpredicteddamagingcodingalleles(Methods)to537

directionalrelationshipspreviouslyreported.Gene-levelORswere100%538

concordantwiththeknownrelationshipsforthesetofeightT2Ddrugtargets(4/4539

inhibitortargetsOR<1,4/4agonisttargetsOR>1;one-sidedbinomialp=3.9×10-3;540

Figure2f).541

542



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Conversely,concordancesbetweengene-levelORestimatesandmouseknockout543

observationsweremoreequivocal(7/11diabetesgeneswithOR>1,binomial544

p=0.27;137/240increasedcirculatingglucosegeneswithOR>1,p=0.016;545

SupplementaryFigure17).Therelativelylowconcordancesforthesegenesets,546

despiteacleartrendtowardlower-than-expectedgene-levelp-valueswithinthem547

(SupplementaryFigure15),highlighthowcodingvariantsmightbeusedtoassess548

seeminglypromisingpreclinicalresults(particularlygiventheknownlimitationsof549

animalmodels57,58).Forexample,theprotectivegene-levelATMsignalweobserve550

(burdentestofPTVsOR=0.50,p=0.003)questionspreviousexpectations,basedon551

insulinresistanceandimpairedglucosetoleranceinAtmknockoutmice59,thatATM552

loss-of-functionshouldincreaseT2Drisk.EvidenceisevenlessfavorablethatATM553

haploinsufficiencystronglyincreasesT2Drisk,rejecting(forexample)OR>2at554

p=1.3×10-8.Thisobservationcouldberelevantintheongoingcharacterizationof555

ATMasapotentialmetformintarget60-62orifATMactivatorsareconsideredtotreat556

cardiovasculardisease63.557

558

ComparisonofrareandcommonvariantsinT2Dgeneticanalyses559

560

ThesubstantialnumberofrarecodingvariantT2Dassociationsweobserved561

promptedustore-evaluatearguments13,14,16,64abouttheirvalueingeneticstudies562

relativetocommonvariants,whichhavetheadvantageofbeingefficientlystudied563

(inmanymoresamplesthancurrentlycanbesequenced)througharray-based564

associationstudies55,65.Whilerecentstudieshaveemphasizedthemain565



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contributionofcommonvariantstoT2Dheritability17,21,24,66,theyhavelacked566

powertofullyevaluatetherelativemeritsofrareversuscommonvariants(or,by567

implication,sequencingversusarray-basedstudies)todiscoverdisease-associated568

loci,explaindiseaseheritability,orelucidateallelicseries.569

570

Forafaircomparisonofdiscoveriespossiblefromsequencingandarray-based571

studies,wecollectedgenome-widearraydatawithinthesameindividualswe572

sequenced(availablefor34,529[76.3%of]individuals;18,233casesand17,679573

controls).Wethenimputedvariantsusingbest-practicereferencepanels67,68and574

conductedsingle-variantanalysisfollowingthesameprotocolasforthesequence575

data(“imputedGWAS”;SupplementaryTable12,Methods).Eightoftheten576

exome-widesignificantnonsynonymoussingle-variantassociationsfromour577

sequenceanalysisweredetectableintheimputedGWASanalysis,togetherwith578

genome-widesignificantnoncodingvariantassociationsin14additionalloci579

(Figure3a,SupplementaryTable13).Alltensingle-variantsequenceassociations580

werealsopresentontheIlluminaExomeArray(Methods),implyingtheabilityof581

array-basedassociationstudiestodetectexome-widesignificantsingle-variant582

associationsatequivalentsignificanceandatfarlestcostthanexomesequence583

associationstudies.584

585

WenextcomparedthecontributionstoT2Dheritabilityfromthestrongest586

(common)single-variantassociationsfromtheimputedGWAStothosefromthe587

strongest(mostlyrarevariant)gene-levelassociationsfromthesequenceanalysis.588



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Usingageneticliabilitymodel69inwhichalldamagingvariantsinagenehavethe589

samedirectionofeffect(Methods),thethreeexome-widesignificantgene-level590

signalsexplainanestimated0.11%(MC4R),0.092%(PAM),and0.072%(SLC30A8)591

ofT2Dgeneticvariance.Theseestimatesareonly10-20%ofthevariances592

explainedbythethreestrongestindependentcommonvariantassociationsinthe593

imputedGWASofthesamesamples(TCF7L2,0.89%;KCNQ1,0.81%;andCDC123,594

0.35%)andifanythingoverstatetheheritabilityexplainedbyrarevariantsinthe595

gene-levelsignals,sincetheMC4RandPAMestimatesareattributablemostlytothe596

low-frequencyp.Ile269Asn(70.9%ofthegene-leveltotal)andp.Asp563Gly(83.3%)597

alleles.Weobtainedsimilarresultsinabroadercomparisonbetweenall(19)598

previouslyidentifiedindexSNPsachievingp<5×10-8intheimputedGWASandthe599

top19gene-levelsignalsfromoursequenceanalysis(Figure3b).600

601

TheseresultsargueagainstalargecontributiontoT2Dheritabilityfromrare602

variantsinthestrongestobservedgene-levelsignals,withonecaveat:asgene-level603

testsmayincludebenignallelesthatcandiluteevidenceforassociation,their604

aggregateeffectsmightunderestimatethetruecontributionofrarefunctional605

variantstoT2Dheritability12.However,whenweanalyzedallpossiblesubsetsof606

variationinthethreemostsignificantgene-levelsignals(Methods),noneexplained607

morethan20%oftheheritabilityofthesingle-variantTCF7L2association608

(maximumof0.18%forMC4R,0.15%forPAM,0.17%forSLC30A8).609

610



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Wefinallyassessedwhetheranarray-basedstudycouldhavedetectedtheallelic611

seriesweobservedfromexomesequenceanalysis.Amongthevariantscontributing612

totheexome-widesignificantgene-levelassociationsinSLC30A8,MC4R,andPAM,613

95.3%werenotimputable(r2>0.4;Methods)fromthe1000Genomesmulti-614

ancestryreferencepanel67,and74.6%ofthoseinEuropeanswerenotimputable615

fromthelargerEuropean-focusedHaplotypeReferenceConsortiumpanel68.616

Similarly,90.2%ofvariants(79.7%ofEuropeanvariants)areabsentfromthe617

IlluminaExomeArray.618

619

Additionally,genesetassociationsusinggene“scores”70(Methods)fromimputed620

GWASassociationsweresuggestive(fourgenesetsachievingp<0.05,nineachieving621

p<0.1;SupplementaryFigure18)butweakerthangenesetassociationsfromour622

sequenceanalysis.Someofthesegenesetassociationscanberecapturedinlarger623

array-basedstudies:scoresfromapublishedmulti-ancestryGWASof~110K624

samplesproducedp<0.05for12ofthe16genesetswestudied(Supplementary625

Figure19,Methods).However,evenherethegenes(andcorrespondingvariants)626

responsibleforthegenesetassociationswerebroadlydifferentbetweenthearray627

andsequence-basedstudies,asthetwomethodsoftenproduceduncorrelatedrank-628

orderingsofgeneswithingenesets(e.g.r=-0.11,p=0.57forthemousediabetesgene629

set;Figure3c).Collectively,theseresultsarguethatarray-basedGWASandexome630

sequencingarecomplementary,favoringlocusdiscoveryandenablingfull631

enumerationofpotentiallyinformativealleles,respectively.632

633



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Useofnominallysignificantassociationsintranslationaldecisionsupport634

635

TheT2Ddrugtargetsweanalyzedexemplifytheopportunitiesandchallengesof636

usingcurrentexomesequencedatasetsintranslationalresearch.Gene-level637

associationsaresignificantacrossthesetargetsasaset(Figure2b),andrare638

variantspredictthecorrectdiseasedirectionalrelationshipforeachgene(Figure639

2f).However,rarevariantgene-levelsignalsforthesegenesarenowherenear640

detectableatexome-widesignificanceinourcurrentsamplesize:80%powerwould641

require110,000-180,000sequencedcases(220,000-360,000exomesinabalanced642

study,equivalentineffectivesamplesizeto750,000-1,200,000exomesfroma643

populationwithT2Dprevalence8%;Figure4a).644

645

Consequently,manyofthemoremodestassociations(e.g.p=0.05)incurrentsample646

sizesmayinfactpointtotherapeuticallyrelevantvariantsorgenes647

(SupplementaryFigure20)71,72.Ifthefalsepositiveratefortheseassociations–648

whichisexpectedtobegreaterthanthatforassociationsexceedingexome-wide649

significance71-73–canbequantified74,75,thenamodestassociationsignalmay650

motivatefurtherexperimentationonagenewhilecompleteabsenceofan651

associationmayreduceenthusiasmforitsstudy.Forexample,theexpectedvalueof652

theexperimentcanbecalculatedbasedonthelikelihoodoftrueassociation,the653

costoftheexperiment,andthebenefitofitssuccess76,77(Figure4b).654

655



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Wesoughttoquantifythefalsepositiveassociationratefornonsynonymous656

variantsobservedinourdataset,dependingonthep-valueobservedinsingle-657

variantanalysis.Wedevelopedamethodtousetheconsistencyofsingle-variant658

associationstatisticsbetweenoursequenceanalysisandaprevious24exomearray659

study(re-analyzedtoincludeonlythe41,967individualsnotinourcurrentstudy;660

Methods),togetherwithpublishedestimatesofthefractionofnonsynonymous661

associationsthatarecausalfordisease17,78,79,toestimatetheposteriorprobability662

oftrueandcausalassociation(PPA)forvariantsreachingdifferentlevelsof663

statisticalsignificance.WeprovideanoverviewofthismethodinFigure4c-f,a664

detaileddescriptioninMethods,anditssensitivitytomodelingassumptionsin665

SupplementaryFigure21.666

667

Weappliedthismethodtothreeclassesofvariants:genome-wide,withinT2D668

GWASloci,andwithingenesimplicatedinT2Dthroughprior(non-genetic)669

evidence.Modelparametersinthemiddleoftherangeweexplored(Methods)670

predictthat1.5%(95%CI:0.74%-2.2%)ofnonsynonymousvariantsthatachieve671

p<0.05aretrulyandcausallyassociatedwithT2D,increasingto3.6%(1.4%-5.9%)672

forvariantswithp<0.005,and9.7%(3.9%-15.0%)forvariantswithp<5×10-4673

(SupplementaryFigure22).Underthismodel,541(270-810)ofthe36,604674

nonsynonymousvariantswithp<0.05inourdatasetrepresenttrueandcausal675

associations.676

677



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Withinthesetof94T2DGWASloci,weobservedevidenceofagreaterenrichment678

oftrueassociations:61.3%ofnonsynonymousvariantsachievingsequencep<0.05679

weredirectionallyconsistentintheindependentexomearrayanalysis(comparedto680

51.9%outsideofGWASloci).Were-calculatedamappingbetweensequencesingle-681

variantp-valueandPPAusingonlynonsynonymousvariantswithintheseloci.The682

resultingmodelpredictsthat2.0%(0.048%-4.0%)ofsuchvariantsoverall,8.1%683

(3.6%-12.4%)withsequencep<0.05,and17.2%(7.7%-24.1%)withsequence684

p<0.005representtrueandcausalT2Dassociations.Thissuggeststhatourdataset685

containsalargenumberofpotentiallystrong-effectvariantsinT2DGWASloci686

achievingnominalsignificance:of1059variantswithp<0.05,weestimateroughly687

60(26-93)of746withestimatedOR>2and41(18-63)of503withestimatedOR>3688

aretrueandcausalassociations(SupplementaryTables14-15).689

690

BeyondGWASloci,manyothergeneshaveevidence–forexamplefromanimal80or691

cellularstudies50,56–thatmayleadaresearcherto(oftensubjectively)believethey692

areinvolvedinT2Dpathogenesis.WeextendedourapproachforPPAestimationto693

incorporatepriorevidencethatageneisrelevanttoT2D81,calibratingitfroma694

modelofthepriorassociationlikelihoodwithinT2DGWASloci(Figure4e-f;695

Methods).Underourmodel(SupplementaryTable16),apriorbeliefthatagene696

has(forexample)probability25%ofbeinginvolvedwithT2Dyieldsestimatesthat697

variantswithinitachievingp<0.05andp<0.005have10.7%and26.2%698

probabilitiesofbeingtrueandcausalT2Dassociations.699

700



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Inthefuture,thesePPAcalculationscouldbeextendedtogene-levelassociations,701

whichwouldavoidconflictingresultsamongvariantswithinagenebutrequire702

larger-scalegene-levelreplicationdatathanwehadavailable.Additionalwork703

couldalsodevelopdataandmethodstoestimateobjective,ratherthansubjective,704

genepriorsandreducedependenceofourconclusionsonmodelingassumptions705

(SupplementaryFigure21).Still,thesePPAcalculationsprovideausefulinitial706

frameworktousegeneticsignalstosupportcost/benefitestimatesof“go/no-go”707

decisions82inthelanguageofdecisiontheory76,77(Figure4b).Tosupportuseofthis708

strategy,wehavemadeourexomesequenceassociationresultspublicallyavailable709

throughtheAMPT2DKnowledgePortal(www.type2diabetesgenetics.org),which710

supportsqueryingofallpre-computedsingle-variantassociationsandallowsusers711

todynamicallycomputesingle-variantandgene-levelassociationsaccordingto712

customcovariatesandcriteriaforsampleandvariantfiltering.713

714

Discussion715

716

OurresultspaintanuancedpictureofrarevariationandT2D,whichmayalsoapply717

toothercomplexdiseaseswithsimilargeneticarchitectures83.Ourgenesetanalyses718

showthatrarevariantgene-levelsignalsarelikelywidelydistributedacross719

numerousgenes,butthevastmajorityexplain,individually,vanishingamountsof720

T2Dheritability–evincedbythe>1Msampleslikelyrequiredtodetectexome-wide721

significantrarevariantsignalsinvalidatedtherapeutictargets.Gene-levelsignals722

thatdoreachexome-widesignificanceinouranalysis(suchasthoseinMC4Rand723



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PAM)arenoteworthynotbecausetheyincludeunusuallystrongrarevariant724

associationsbutbecausetheyincludetypicalrarevariantassociationsboostedfrom725

nominaltoexome-widesignificancebylowfrequencyvariant(s)–which,726

empirically,canalsobedetectedbyarray-basedstudies.Therefore,formany727

complextraits(particularlythosewithmodestselectivepressurelikeT2D),the728

primaryvalueofexomesequencingbeyondarray-basedGWASmaybetoaid729

experimentalgenecharacterization84byidentifyingabroadseriesofrarecoding730

alleles–ideallythroughmulti-ancestrysamplestocaptureasbroadasetofalleles731

aspossible–ratherthantodiscovernewdiseaseloci.Whole-genomesequencing732

willlikely,oneday,becomesufficientlycosteffectivetosubsumebotharray-based733

GWASandexomesequencing;evennow,itisatminimumanessentialmeansto734

expandimputationreferencepanelstopowergeneticdiscoveryfromGWAS.735

736

Ourresultsalsooutlineastrategyforusingexomesequencedatatoprioritizeor737

validategenesunderstudybybiologistsorpharmaceuticalindustryscientists.738

WehavepresentedaprincipledandempiricallycalibratedBayesianapproach739

(Figure4,SupplementaryTable16)toestimatetheassociationprobabilityfor740

anyvariantinourdataset.Whilecurrentlylimitedbyavailabledataandmodeling741

assumptions,itprovidesafirststeptoincreasetheinterpretabilityofexome742

sequenceassociationsevenabsentexome-widesignificance.Resultsandcustomized743

analysesfromourstudycanbeaccessedthroughapublicwebportal744

(www.type2diabetesgenetics.org),advancingthevisiontobroadlyuseexome745

sequencedataacrossmanyavenuesofbiomedicalresearch.746



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34

Figurelegends747

748

Figure1:Exome-wideassociationanalysis.(a)AManhattanplotofexome749

sequencesingle-variantassociations.Genesclosesttovariantsachievingp<4.3×10−7750

(redline;atmostonepereach250KBregion)arelabeled.(b)AManhattanplotof751

gene-levelassociations;p-valuesshownaretheminimumacrossthefourgene-level752

analysesaftercorrectionforfouranalyses(Methods),withthemostsignificant753

geneslabeled.Redline:p=6.5×10-7.(c)Gene-levelassociationp-valuesforSLC30A8,754

usingtheburdentestonallelesinthe1/51%mask(themask,asdefinedin755

Methods,achievinggreateststatisticalsignificanceforSLC30A8),afterprogressive756

removalofvariantsinorderofincreasingsingle-variantassociationp-value.Theleft757

y-axis(blackline)showstheprogressivegene-levelp-value,thedashedlinep=0.05.758

Therighty-axis(blueline)showstheestimatedeffectsize(log10(OR)),withshaded759

blueindicatingthe95%confidenceintervalanddottedlineindicatingeffectsize=0.760

(d)VariantsobservedinSLC30A8within1/51%mask.Variantsarecoloredblue(if761

OR<1)orred(OR>1).Case(red)andcontrol(blue)frequenciesareshownfor762

eachvariant,withblackboxesshadedaccordingtothecontributionofeachvariant763

tothegene-levelsignal(computedbythedifferenceinlog10(p-value)afterremoval764

ofthevariantfromthetest).OR:oddsratio.765

766

Figure2:Genesetanalysis.(a-e)Boxplotsoftherankpercentiles(1beingthe767

highest)forgene-levelassociationswithin(a)11genesimplicatedinMaturity768

OnsetDiabetesoftheYoung(MODY);(b)8genesannotatedintheDrugBank769



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35

databaseastheprimarytargetsofT2Dmedications;(c)31genesannotatedinthe770

MouseGenomeInformatics(MGI)databaseasharboringknockoutmutations771

causingnon-insulindependentdiabetes;(d)323genesannotatedintheMGI772

databaseasharboringknockoutmutationscausingimpairedglucosetolerancein773

mice;and(e)11geneswithstronggeneticevidenceforharboringcommoncausal774

codingvariants.P-valuescorrespondtoaone-sidedWilcoxonRank-Sumtest775

comparingtheassociationstothoseofmatchedcomparisongenes.(f)Estimated776

oddsratios(OR)ofdeleteriousnonsynonymousvariantsintheeightT2Ddrug777

targets.Targetsofagonistsarecoloredredandtargetsofinhibitorsarecolored778

blue.Errorbarsindicateonestandarderror.779

780

Figure3:Comparisonofexomesequencingtoarray-basedGWAS.(a)A781

Manhattanplotofsingle-variantassociationsinanarray-basedimputedGWASof782

thesubset(76%)ofthesamplesintheexomesequenceanalysisforwhicharray783

datawereavailable.Labelsandy-axisareequivalenttoFigure1a.(b)Theobserved784

liabilityvarianceexplained(LVE)bythetop19gene-levelassociationsfromthe785

exomesequenceanalysis(red;Exomes)andthetop19single-variantassociations786

(consideringonlyoneper250kb)fromtheimputedGWAS(blue;ImputedGWAS),787

aswellastheirratio(black;Ratio).SignalsarerankedbyLVEratherthanp-value.788

(c)Acomparisonofgenerankpercentilesaccordingtoexomesequencegene-level789

analysis(x-axis)andgenerankpercentilesaccordingtoproximitytoGWASsignals790

fromapublishedtransethnicT2DGWAS(y-axis;Methods).Genesshownarefrom791



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36

thesetof31genesimplicatedinnon-insulindependentdiabetesfromknockout792

mice(thesetinFigure2c).793

794

Figure4:Translationaldecisionsupportfromexomesequencedata.(a)795

Estimatedpower,asafunctionoffuturesamplesize,todetectT2Dgene-level796

associations(atsignificancep=6.25×10-7)withaggregatefrequencyandoddsratios797

equaltothoseestimatedfromouranalysisineightestablishedT2Ddrugtargets(in798

Figure2f).(b)Aproposedworkflowforusingexomesequencedataingene799

characterization.Dependingonthepriorbeliefinthedisease-relevanceofthegene,800

thecostofexperimentalcharacterization,andthebenefitofvalidatingthegene,a801

decisiontoconductafurtherexperimentcouldbeinformedbytheprobabilitythat802

thegeneisrelevanttodisease,asestimatedfromexomesequenceassociation803

statistics(availablethroughwww.type2diabetesgenetics.org).(c-f)Tosupportthis804

workflow,weestimatedtheposteriorprobabilityoftrueandcausalassociation805

(PPA)fornonsynonymousvariantsinoursequenceanalysisbasedon(c)806

concordancewithindependentexomechipdataandpublishedestimatesofthe807

fractionofcausalcodingassociations(Methods).(d)PPAestimatesfor808

nonsynonymousvariantswithinT2DGWASlociareshownasafunctionofp-value809

(righty-axis,black;95%confidenceinterval,gray)togetherwiththetotalnumberof810

suchvariants(lefty-axis,red).ForvariantsoutsideofT2DGWASloci,wedeveloped811

amethodtofurthercompute(e)Bayesfactors,whichmeasuretheoddsoftrueand812

causalassociation,asafunctionofp-value,usingamodeloftheprioroddsoftrue813

andcausalassociationforvariantsinGWASloci(Methods).TheseBayesfactorscan814



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37

be(f)combinedwithasubjectivepriorbeliefintheT2D-relevanceofagene(y-axis)815

toproducetheestimatedposteriorprobabilityoftrueandcausalassociationforany816

nonsynonymousvariantintheexomesequencedatasetbasedonitsobserved817

log10(p-value)(x-axis).Posteriorestimatesareshadedproportionaltovalue(red:818

low;white:high).Valuesshownareforthedefaultmodelingassumptionsof33%of819

missensevariantscausinggeneinactivationand30%oftruemissenseassociations820

representingthecausalvariant. 821



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38

Funding822

BroadInstitute,USA:SequencingforT2D-GENEScohortswasfundedbythe823

NationalInstituteofDiabetesandDigestiveandKidneyDiseases(NIDDK)grant824

U01DK085526:MultiethnicStudyofTypeDiabetesGenesandNationalHuman825

GenomeResearchInstitute(NHGRI)grantU54HG003067:LargeScaleSequencing826

andAnalysisofGenomes.827

SequencingforGoT2DcohortswasfundedbyNationalInstituteofHealth(NIH)828

1RC2DK088389:Low-PassSequencingandHighDensitySNPGenotypinginType2829

Diabetes.830

SequencingforProDiGYcohortswasfundedbyNationalInstituteofDiabetesand831

DigestiveandKidneyDiseases(NIDDK)U01DK085526.832

SequencingforSIGMAcohortswasfundedbytheCarlosSlimFoundation:Slim833

InitiativeinGenomicMedicinefortheAmericas(SIGMA).834

AnalysiswassupportedbytheNationalInstituteofDiabetesandDigestiveand835

KidneyDiseases(NIDDK)grantU01DK105554:AMPT2D-GENESData836

CoordinationCenterandWebPortal.837

TheMountSinaiIPMBiobankProgramissupportedbyTheAndreaandCharles838

BronfmanPhilanthropies.839

TheWakeForeststudywassupportedbyNIHR01DK066358.840



https://doi.org/10.1101/371450


39

Oxfordcohortsandanalysisisfundedby:TheEuropeanCommission(ENGAGE:841

HEALTH-F4-2007-201413);MRC(G0601261,G0900747-91070);National842

InstitutesofHealth(RC2-DK088389,DK085545,R01-DK098032,U01-DK105535);843

WellcomeTrust(064890,083948,085475,086596,090367,090532,092447,844

095101,095552,098017,098381,100956,101630,203141)845

TheFUSIONstudyissupportedbyNIHgrantsDK062370andDK072193.846

TheresearchfromtheKoreancohortwassupportedbyagrantoftheKoreaHealth847

TechnologyR&DProjectthroughtheKoreaHealthIndustryDevelopmentInstitute848

(KHIDI),fundedbytheMinistryofHealth&Welfare,RepublicofKorea(grant849

number:HI14C0060,HI15C1595).850

TheMalmöPreventiveProjectandtheScaniaDiabetesRegistrywere851

supportedbyaSwedishResearchCouncilgrant(Linné)totheLundUniversity852

DiabetesCentre.853

TheBotniaandThePPP-Botniastudies(L.G.,T.T.)havebeenfinancially854

supportedbygrantsfromFolkhälsanResearchFoundation,theSigridJuselius855

Foundation,TheAcademyofFinland(grantsno.263401,267882,312063toLG,856

312072toTT),NordicCenterofExcellenceinDiseaseGenetics,EU(EXGENESIS,857

EUFP7-MOSAICFP7-600914),OllqvistFoundation,SwedishCulturalFoundationin858

Finland,FinnishDiabetesResearchFoundation,FoundationforLifeandHealthin859

Finland,SigneandAneGyllenbergFoundation,FinnishMedicalSociety,Paavo860

NurmiFoundation,HelsinkiUniversityCentralHospitalResearchFoundation,861



https://doi.org/10.1101/371450


40

PerklénFoundation,NärpesHealthCareFoundationandAhokasFoundation.The862

studyhasalsobeensupportedbytheMinistryofEducationinFinland,Municipal863

HeathCareCenterandHospitalinJakobstadandHealthCareCentersinVasa,864

NärpesandKorsholm.TheskilfulassistanceoftheBotniaStudyGroupisgratefully865

acknowledged.866

TheJacksonHeartStudy(JHS)issupportedbycontractsHHSN268201300046C,867

HHSN268201300047C,HHSN268201300048C,HHSN268201300049C,868

HHSN268201300050CfromtheNationalHeart,Lung,andBloodInstituteandthe869

NationalInstituteonMinorityHealthandHealthDisparities.Dr.Wilsonissupported870

byU54GM115428fromtheNationalInstituteofGeneralMedicalSciences.871

TheDiabeticCohort(DC)andMulti-EthnicCohort(MEC)weresupportedby872

individualresearchgrantsandclinicianscientistawardschemesfromtheNational873

MedicalResearchCouncil(NMRC)andtheBiomedicalResearchCouncil(BMRC)of874

Singapore.875

TheDiabeticCohort(DC),Multi-EthnicCohort(MEC),SingaporeIndianEye876

Study(SINDI)andSingaporeProspectiveStudyProgram(SP2)weresupported877

byindividualresearchgrantsandclinicianscientistawardschemesfromthe878

NationalMedicalResearchCouncil(NMRC)andtheBiomedicalResearchCouncil879

(BMRC)ofSingapore.880

TheLongevitystudyatAlbertEinsteinCollegeofMedicine,USAwasfundedby881

TheAmericanFederationforAgingResearch,theEinsteinGlennCenter,and882



https://doi.org/10.1101/371450


41

NationalInstituteonAging(PO1AG027734,R01AG046949,1R01AG042188,883

P30AG038072).884

TheTwinsUKstudywasfundedbytheWellcomeTrustandEuropean885

Community’sSeventhFrameworkProgramme(FP7/2007-2013).TheTwinsUK886

studyalsoreceivessupportfromtheNationalInstituteforHealthResearch(NIHR)-887

fundedBioResource,ClinicalResearchFacilityandBiomedicalResearchCentre888

basedatGuy'sandStThomas'NHSFoundationTrustinpartnershipwithKing's889

CollegeLondon.890

FraminghamHeartStudyissupportedbyNIHcontractNHLBIN01-HC-25195and891

HHSN268201500001I.ThisresearchwasalsosupportedbyNIAAG08122and892

AG033193,NIDDKU01DK085526,U01DK078616andK24DK080140,NHLBIR01893

HL105756,andgrantsupplementR01HL092577-06S1forthisresearch.Wealso894

acknowledgethededicationoftheFHSstudyparticipantswithoutwhomthis895

researchwouldnotbepossible.896

TheMexicoCityDiabetesStudyhasbeensupportedbythefollowinggrants:897

RO1HL24799fromtheNationalHeart,Lung,andBloodInstitute;ConsejoNacional898

deCienciayTecnologıá2092,M9303,F677-M9407,251M,2005-C01-14502,and899

SALUD2010-2151165;andConsejoNacionaldeCienciayTecnologıá(CONACyT)900

[FondodeCooperaciońInternacionalenCienciayTecnologıá(FONCICYT)C0012-901

2014-01-247974.902



https://doi.org/10.1101/371450


42

TheKAREcohortwassupportedbygrantsfromKoreaCentersforDiseaseControl903

andPrevention(4845–301,4851–302,4851–307),andanintramuralgrantfrom904

theKoreaNationalInstituteofHealth(2016-NI73001-00).905

TheDiabetesinMexicoStudywassupportedbyConsejoNacionaldeCienciay906

TecnologíagrantnumberS008-2014-1-233970andbyInstitutoCarlosSlimdela907

Salud,AC.908

TheAtherosclerosisRiskinCommunitiesstudyhasbeenfundedinwholeorin909

partwithFederalfundsfromtheNationalHeart,Lung,andBloodInstitute,National910

InstitutesofHealth,DepartmentofHealthandHumanServices(contractnumbers911

HHSN268201700001I,HHSN268201700002I,HHSN268201700003I,912

HHSN268201700004IandHHSN268201700005I).Theauthorsthankthestaffand913

participantsoftheARICstudyfortheirimportantcontributions.Fundingsupport914

for“BuildingonGWASforNHLBI-diseases:theU.S.CHARGEconsortium”was915

providedbytheNIHthroughtheAmericanRecoveryandReinvestmentActof2009916

(ARRA)(5RC2HL102419).CHARGEsequencingwascarriedoutattheBaylor917

CollegeofMedicineHumanGenomeSequencingCenter(U54HG003273and918

R01HL086694).FundingforGOESPwasprovidedbyNHLBIgrantsRC2HL-103010919

(HeartGO)andexomesequencingwasperformedthroughNHLBIgrantsRC2HL-920

102925(BroadGO)andRC2HL-102926(SeattleGO).921

TheinfrastructurefortheAnalysisCommonsissupportedbyR01HL105756922

(NHLBI,B.M.P.),U01HL130114(NHLBI,B.M.P.)and5RC2HL102419(NHLBI,E.B.).923



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43

TheNHLBIExomeSequencingProject(ESP)wassupportedthroughtheNHLBI924

GrandOpportunity(GO)programandfundedthroughbygrantsRC2HL103010925

(HeartGO),RC2HL102923(LungGO),andRC2HL102924(WHISP)forproviding926

dataandDNAsamplesforanalysis.TheexomesequencingfortheNHLBIESPwas927

supportedbyNHLBIgrantsRC2HL102925(BroadGO)andRC2HL102926928

(SeattleGO).929

ThisresearchwassupportedbytheMulti-EthnicStudyofAtherosclerosis(MESA)930

contractsHHSN268201500003I,N01-HC-95159,N01-HC-95160,N01-HC-95161,931

N01-HC-95162,N01-HC-95163,N01-HC-95164,N01-HC-95165,N01-HC-95166,932

N01-HC-95167,N01-HC-95168,N01-HC-95169,UL1-TR-000040,UL1-TR-001079,933

andUL1-TR-001420.Theprovisionofgenotypingdatawassupportedinpartbythe934

NationalCenterforAdvancingTranslationalSciences,TSCIgrantUL1TR001881,935

andtheNationalInstituteofDiabetesandDigestiveandKidneyDiseaseDiabetes936

Research(DRC)grantDK063491.937

TheSanAntonioMexicanAmericanFamilyStudies(SAMAFS)aresupportedby938

thefollowinggrants/institutes.TheSanAntonioFamilyHeartStudy(SAFHS)and939

SanAntonioFamilyDiabetes/GallbladderStudy(SAFDGS)weresupportedbyU01940

DK085524,R01HL0113323,P01HL045222,R01DK047482,andR01DK053889.941

TheVeteransAdministrationGeneticEpidemiologyStudy(VAGES)studywas942

supportedbyaVeteransAdministrationEpidemiologicgrant.TheFamily943

InvestigationofNephropathyandDiabetes-SanAntonio(FIND-SA)studywas944



https://doi.org/10.1101/371450


44

supportedbyNIHgrantU01DK57295.TheSAMAFSresearchteamacknowledges945

lateDr.HannaE.Abboud’scontributionstotheresearchactivitiesoftheSAMAFS.946

Samplescollection,researchandanalysisfromtheHongKongDiabetesRegister947

(HKDR)attheChineseUniversityofHongKong(CUHK)weresupportedbythe948

HongKongFoundationforResearchandDevelopmentinDiabetesestablished949

undertheauspicesoftheChineseUniversityofHongKong,theHongKong950

GovernmentResearchGrantsCommitteeCentralAllocationScheme(CUHK1/04C),951

aResearchGrantsCouncilEarmarkedResearchGrant(CUHK4724/07M),the952

InnovationandTechnologyFund(ITS/088/08andITS/487/09FP),andthe953

ResearchGrantsCommitteeTheme-basedResearchScheme(T12-402/13N).954

TheTODAYcontributiontothisstudywascompletedwithfundingfromNIDDKand955

theNIHOfficeoftheDirector(OD)throughgrantsU01-DK61212,U01-DK61230,956

U01-DK61239,U01-DK61242,andU01-DK61254;fromtheNationalCenterfor957

ResearchResourcesGeneralClinicalResearchCentersProgramgrantnumbers958

M01-RR00036(WashingtonUniversitySchoolofMedicine),M01-RR00043-45959

(Children’sHospitalLosAngeles),M01-RR00069(UniversityofColoradoDenver),960

M01-RR00084(Children’sHospitalofPittsburgh),M01-RR01066(Massachusetts961

GeneralHospital),M01-RR00125(YaleUniversity),andM01-RR14467(University962

ofOklahomaHealthSciencesCenter);andfromtheNCRRClinicalandTranslational963

ScienceAwardsgrantnumbersUL1-RR024134(Children’sHospitalof964

Philadelphia),UL1-RR024139(YaleUniversity),UL1-RR024153(Children’s965

HospitalofPittsburgh),UL1-RR024989(CaseWesternReserveUniversity),UL1-966



https://doi.org/10.1101/371450


45

RR024992(WashingtonUniversityinStLouis),UL1-RR025758(Massachusetts967

GeneralHospital),andUL1-RR025780(UniversityofColoradoDenver).Thecontent968

issolelytheresponsibilityoftheauthorsanddoesnotnecessarilyrepresentthe969

officialviewsoftheNationalInstitutesofHealth.970

Acknowledgements971

RuthLoosissupportedbytheNIH(R01DK110113,U01HG007417,R01DK101855,972

R01DK107786).973

AndrewPMorrisissupportedbytheNIH-NIDDK(U01DK105535);andaWellcome974

TrustSeniorFellowinBasicBiomedicalScience(awardWT098017).975

JoseCFlorezisanMGHResearchScholarandissupportedbyNIDDKK24976

DK110550.977

GraemeIBellissupportedbyP30DK020595.978

MichiganStateUniversityissupportedbyNIHGrant1K23DK114551-01.979

MarkIMcCarthyisaWellcomeTrustSeniorInvestigator(WT098381);anda980

NationalInstituteofHealthResearch(NIHR)SeniorInvestigator.Theviews981

expressedinthisarticlearethoseoftheauthor(s)andnotnecessarilythoseofthe982

NHS,theNIHR,ortheDepartmentofHealth.983

YoonShinChoacknowledgedsupportfromtheNationalResearchFoundationof984

Korea(NRF)grant(NRF-2017R1A2B4006508).985



https://doi.org/10.1101/371450


46

Ching-YuChengissupportedbyClinicianScientistAward(NMRC/CSA-986

SI/0012/2017)oftheSingaporeMinistryofHealth’sNationalMedicalResearch987

Council.988

LuCAMP:WewishtothankA.Forman,T.H.LorentzenandG.J.Klavsenfor989

laboratoryassistance,P.Sandbeckfordatamanagement,G.Lademannfor990

secretarialsupport,andT.F.Toldstedforgrantmanagement.Thisprojectwas991

fundedbytheLundbeckFoundationandproducedbyTheLundbeckFoundation992

CentreforAppliedMedicalGenomicsinPersonalisedDiseasePrediction,993

Prevention,andCare(www.lucamp.org).TheNovoNordiskFoundationCenterfor994

BasicMetabolicResearchisanindependentResearchCenterattheUniversityof995

CopenhagenpartiallyfundedbyanunrestricteddonationfromtheNovoNordisk996

Foundation(www.metabol.ku.dk).FurtherfundingcamefromtheDanishCouncil997

forIndependentResearchMedicalSciences.TheInter99wasinitiatedbyTorben998

Jørgensen(principalinvesitigator[PI]),KnutBorch-Johnsen(co-PI),HansIbsen,and999

TroelsF.Thomsen.ThesteeringcommitteecomprisestheformertwoandCharlotta1000

Pisinger.ThestudywasfinanciallysupportedbyresearchgrantsfromtheDanish1001

ResearchCouncil,theDanishCentreforHealthTechnologyAssessment,Novo1002

Nordisk,theResearchFoundationofCopenhagenCounty,theMinistryofInternal1003

AffairsandHealth,theDanishHeartFoundation,theDanishPharmaceutical1004

Association,theAugustinusFoundation,theIbHenriksenFoundation,theBecket1005

Foundation,andtheDanishDiabetesAssociation.DanielWitteissupportedbythe1006

DanishDiabetesAcademy,whichisfundedbytheNovoNordiskFoundation.1007



https://doi.org/10.1101/371450


47

WethankallstudyparticipantsoftheDiabeticCohort(DC),Multi-EthnicCohort1008

(MEC),SingaporeIndianEyeStudy(SINDI)andSingaporeProspectiveStudy1009

Program(SP2)fortheircontributionsandtheNationalUniversityHospitalTissue1010

Repository(NUHTR)forbiospecimensamplestorage.1011

WethanktheJacksonHeartStudy(JHS)participantsandstafffortheir1012

contributionstothiswork.1013

ThisstudywasprovidedwithbiospecimensanddatafromtheKoreanGenome1014

AnalysisProject(4845-301),theKoreanGenomeandEpidemiologyStudy(4851-1015

302),andtheKoreaBiobankProject(4851-307,KBP-2013-11andKBP-2014-68)1016

thatweresupportedbytheKoreaCentersforDiseaseControlandPrevention,1017

RepublicofKorea.1018

ThePakistanGenomicResource(PGR)wouldliketothankallthestudy1019

participantsfortheirparticipation.PGRisfundedthroughendowmentsawardedto1020

CNCD,Pakistan.1021

TheKORAstudywasinitiatedandfinancedbytheHelmholtzZentrumMünchen—1022

GermanResearchCenterforEnvironmentalHealth,whichisfundedbytheGerman1023

FederalMinistryofEducationandResearch(BMBF)andbytheStateofBavaria.1024

Furthermore,KORAresearchwassupportedwithintheMunichCenterofHealth1025

Sciences(MC-Health),Ludwig-Maximilians-Universität,aspartofLMUinnovativ.For1026

thispublication,biosamplesfromtheKORABiobankaspartoftheJointBiobank1027

Munich(JBM)havebeenused.1028



https://doi.org/10.1101/371450


48

RonaldCMaandJulianaCChanacknowledgedsupportfromtheHongKong1029

ResearchGrantsCouncilTheme-basedResearchScheme(T12-402/13N),Research1030

GrantsCouncilGeneralResearchFund(Ref.14110415),theFocusedInnovation1031

Scheme,theVice-ChancellorOne-offDiscretionaryFund,thePostdoctoral1032

FellowshipSchemeoftheChineseUniversityofHongKong,aswellastheChinese1033

UniversityofHongKong-ShanghaiJiaoTongUniversityJointResearchCollaboration1034

Fund.WewouldalsoliketothankallmedicalandnursingstaffofthePrinceof1035

WalesHospitalDiabetesMellitusEducationCentre,HongKong.1036

AuthorContributions1037

Leadership.J.F.,N.P.B.,J.C.F.,M.I.M.,M.B.Analysisteam.J.M.M.,C.F.,M.S.U.,1038

A.Mahajan,T.W.B.,L.Chen,S.C.,A.E.,S.Hanks,A.U.J.,K.M.,A.N.,A.J.P.,N.W.R.,N.R.R.,1039

H.M.S.,J.M.T.,R.P.W.,L.J.S.,A.P.M.Projectmanagement/Supportroles.L.Caulkins,1040

R.K.,M.C.Datageneration.BroadGenomicsPlatform.T2D-GENES.A.C.,R.A.D.,S.G.,1041

S.Han,H.M.K.,B.-J.K.,H.A.K.,J.K.,J.Liu,K.L.M.,M.C.N.,M.P.,R.S.V.,C.S.,W.Y.S.,C.H.T.,1042

F.T.,B.T.,R.M.v.D.,M.V.,T.-Y.W.,G.Atzmon,N.B.,J.B.,D.W.B.,J.C.C.,E.Chan,C.-Y.C.,1043

Y.S.C.,F.S.C.,R.D.,B.G.,J.S.K.,S.H.K.,M.L.,D.M.L.,E.S.T.,J.T.,J.G.W.,E.Bottinger,J.C.,J.D.,1044

P.F.,M.Y.H.,Y.J.K.,J.-Y.L.,J.Lee,R.L.,R.C.M.,A.D.M.,C.N.P.,K.S.P.,A.R.,D.S.,X.S.,Y.Y.T.,1045

C.L.H.,G.Abecasis,G.I.B.,N.J.C.,M.S.,R.S.,J.B.M.,D.A.GoT2D.V.L.,L.L.B.,L.G.,P.N.,1046

T.D.S.,T.T.,K.S.S.LuCAMP.M.E.J.,A.L.,D.R.W.,N.G.,T.H.,O.P.ProDiGY.L.D.,K.L.D.,1047

M.K.,E.M.-D.,C.P.,N.S.,B.B.,P.Z.,D.D.SIGMA.C.C.-C.,E.Córdova,M.E.G.-S.,H.G.-O.,1048

J.M.M.-H.,A.M.-H.,E.M.-C.,C.R.-M.,C.Gonzalez,M.E.G.,C.A.A.-S.,C.H.,B.E.H.,L.O.,T.T.-1049

L.CHARGE.J.W.,E.Boerwinkle,J.A.B.,J.S.F.,N.L.H.-C.,C.-T.L.,A.K.M.,A.C.M.,B.M.P.,1050



https://doi.org/10.1101/371450


49

S.W.,P.S.d.V.,J.D.,S.R.H.,C.J.O'D.,J.P.,J.B.M.Regeneron.T.M.T.,J.B.L.,A.Marcketta,1051

C.O'D.,D.J.C.,H.L.K.,F.E.D.,A.B.,D.C.KORA.T.M.S.,C.Gieger,T.M.,K.S.1052

ESP.E.Boerwinkle,M.G.,N.L.H.-C.,A.C.M.,W.S.P.,B.M.P.,A.P.R.,R.P.T.,C.J.O'D.,L.L.,1053

S.R.,J.I.R.1054

1055

Disclosures1056

PhilipZeitlerisaconsultantforMerck,Daichii-Sankyo,Boerhinger-Ingelheim,and1057

Janssen.1058

1059

BruceMPsatyservesontheDSMBofaclinicaltrialfundedbyZollLifeCorandon1060

theSteeringCommitteeoftheYaleOpenDataAccessProjectfundedbyJohnson&1061

Johnson. 1062



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Methods1063

Sampleselection1064

Wedrewsamplesforexomesequencingfromsixconsortia(SupplementaryTable1065

1):1066

1. TheT2D-GENES(Type2DiabetesGeneticExplorationbyNext-generation1067

sequencinginmulti-EthnicSamples)consortium,anNIDDK-funded1068

internationalresearchconsortiumseekingtoidentifygeneticvariantsforT2D1069

throughmultiethnicsequencingstudies24.1070

2. TheSlimInitiativeinGenomicMedicinefortheAmericas:Type2Diabetes1071

(SIGMAT2D),aninternationalresearchconsortiumfundedbytheCarlosSlim1072

FoundationtoinvestigategeneticriskfactorsofT2DwithinMexicanandLatin1073

Americanpopulationsandtranslatethosefindingtoimprovedmethodsof1074

treatmentandprevention85.1075

3. TheGeneticsofType2Diabetes(GoT2D)consortium,anNIDDK-funded1076

internationalresearchconsortiumseekingtounderstandtheallelicarchitecture1077

ofT2Dthroughlow-passwhole-genomesequencing,deepexomesequencing,1078

andhigh-densitySNPgenotypingandimputation24.1079

4. TheExomeSequencingProject(ESP),anNHLBI-fundedresearchconsortiumto1080

investigatenovelgenesandmechanismscontributingtoheart,lung,andblood1081

disordersthroughwholeexomesequencing86.1082

5. TheLundbeckFoundationCentreforAppliedMedicalGenomicsinPersonalised1083

DiseasePrediction,Prevention,andCare(LuCamp)study,whichresearches1084

wholeexomevariationinDanishmetabolicdiseasesincludingdiabetes21.1085



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6. TheProDiGY(ProgressinDiabetesGeneticsinYouth)consortium,anNIDDK-1086

fundedresearchconsortiumtoinvestigategeneticvariantsforchildhoodT2D.1087

Eachconsortiumprovidedindividual-levelinformationonT2Dcase-controlstatus1088

accordingtostudy-specificcriteriaaswellaskeycovariatesincludingage,sex,and1089

BMI(SupplementaryTable1).Inaddition,severalconsortiaprovideddataon1090

fastingglucose,2-hourglucosefollowingglucosechallenge,anduseofanti-1091

hyperglycemicmedications.Weexcludedascontrolsindividualswitha2-hour1092

glucosevalue≥11.1mmol/L(whichmeetsdiagnosticcriteriaforT2D)orwithany1093

twoofthefollowingfeaturessuggestiveofT2D:fastingglucose≥7mmol/L,1094

hemoglobinA1c≥6.5%,orrecordedastakingananti-hyperglycemicmedication.1095

Weoptedtorequiretwoofthepreviousfeaturessincethereisroomforerrorin1096

each:fastingvaluesusedinT2Ddiagnosticcriteriaarerequiredtorepresentatleast1097

aneight-hourfast,accuracyvariesacrosshemoglobinA1cassays,andanti-glycemic1098

medicationsareoccasionallytakenbynon-diabeticindividuals.1099

1100

Allsampleswereapprovedforusebytheirhomeinstitution’sinstitutionalreview1101

boardorethicscommittee,aspreviouslyreported21,24,85,86.Samplesnewly1102

sequencedatTheBroadInstituteaspartofT2D-GENES,SIGMA,andProDiGYare1103

coveredunderPartnersHumanResearchCommitteeprotocol#2017P000445/PHS1104

“DiabetesGeneticsandRelatedTraits”.1105

1106



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Availabilityofsequencedataandphenotypesforthisstudyisavailableviathe1107

databaseofGenotypesandPhenotypes(dbGAP)and/ortheEuropeanGenome-1108

phenomeArchive,asindicatedinSupplementaryTable1.1109

1110

SampleSequencing1111

Forroughlyhalfthestudyparticipants(someofT2D-GENES24,GoT2D24,SIGMA-1112

T2D85,LuCAMP21,ESP86),exomesequencedatawereavailablefromprevious1113

studies.Fortheseindividuals(SupplementaryTable1),weobtainedaccesstoand1114

aggregatedBAMfilescontainingunalignedsequencereads,whichweregenerated1115

andanalyzedaspreviouslydescribed23,62,79,80.1116

1117

Fortheremainingparticipants,de-identifiedDNAsamplesweresenttotheBroad1118

InstituteinCambridge,MA,USAwheresampleswith(a)sufficienttotalDNA1119

quantityandminimumDNAconcentrations(asestimatedbyPicogreen)and(b)1120

highqualitygenotypes(asmeasuredbya24SNPSequenomiPLEXassay)were1121

advancedforsubsequentsequencing.Libraryconstructionwasperformedas1122

previouslydescribed87withsomeslightmodifications.InitialgenomicDNAinput1123

intoshearingwasreducedfrom3µgto50ngin10µLofsolutionandenzymatically1124

sheared.Foradapterligation,dual-indexedIlluminapairedendadapterswere1125

replacedwithpalindromicforkedadapterswithunique8baseindexsequences1126

embeddedwithintheadapterandaddedtoeachend.1127

1128

In-solutionhybridselectionwasperformedusingtheIlluminaRapidCapture1129



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Exomeenrichmentkitwith38Mbtargetterritory(29Mbbaited),including98.3%of1130

theintervalsintheRefseqexomedatabase.Dual-indexedlibrarieswerepooledinto1131

groupsofupto96samplespriortohybridization,withliquidhandlingautomated1132

onaHamiltonStarletLiquidHandlingsystem.Theenrichedlibrarypoolswere1133

quantifiedviaPicoGreenafterelutionfromstreptavidinbeadsandthennormalized1134

toarangecompatiblewithsequencingtemplatedenatureprotocols.1135

1136

Followingsamplepreparation,thelibrariespreparedusingforked,indexed1137

adapterswerequantifiedusingquantitativePCR(KAPABiosystems),normalizedto1138

2nM,andpooledbyequalvolumeusingtheHamiltonStarlet.Poolswerethen1139

denaturedusing0.1NNaOH.Denaturedsamplesweredilutedintostriptubesusing1140

theHamiltonStarlet.1141

1142

Clusteramplificationofthetemplateswasperformedaccordingtothe1143

manufacturer’sprotocol(Illumina)usingtheIlluminacBot.Flowcellswere1144

sequencedonHiSeq4000Sequencing-by-SynthesisKits,thenanalyzedusing1145

RTA2.7.3.1146

1147

Variantcallingandqualitycontrol1148

Sequencingreadsforallsamples(bothnewlysequencedandpreviouslysequenced)1149

wereprocessedandalignedtothehumangenome(buildhg19)usingthePicard1150

(broadinstitute.github.io/picard/),BWA88,andGATK89softwarepackages,following1151

best-practicepipelines;datafrompreviouslypublishedstudiesweretreatedthe1152



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sameasdatafromthenewstudy(i.e.beginningfromunalignedreads)toensure1153

uniformityofprocessing.Singlenucleotideandshortindelvariantswerethencalled1154

usingaseriesofGATKcommands(versionnightly-2015-07-31-g3c929b0):1155

ApplyRecalibration,CombineGVCFs,CombineVariants,GenotypeGVCFs,1156

HaplotypeCaller,SelectVariants,andVariantFiltration.Variantswerecalledwithin1157

50bpofanyregiontargetedforcaptureinanysequencedcohort.1158

1159

Wecomputedhardcalls(theGATK-calledgenotypesbutsetasmissingata1160

genotypequality[GQ]<20threshold)anddosages(theexpectedalternateallele1161

count,definedasPr(RX|data)+2Pr(XX|data),whereRisthereferencealleleandX1162

thealternativeallele)foreachindividualateachvariantsite.Weusedhardcallsfor1163

qualitycontrolanddosagesindownstreamassociationanalyses.Wecomputed1164

dosagesontheXchromosome(outsideofthepseudo-autosomalregion)accounting1165

forsex,treatingmalesashaploid.1166

1167

Toperformdataqualitycontrol,wefirstcalculatedarangeofmetricsmeasuring1168

samplesequencingquality(SupplementaryFigure2).Wethenstratifiedsamples1169

byancestryandsequencecapturetechnologyandexcludedfromfurtheranalysis1170

samplesthatwereoutliersaccordingtoanymetric,basedonvisualinspectionby1171

comparisontoothersampleswithinthesamestratum.Afulllistofmetricsusedfor1172

exclusionandthenumberofsamplesexcludedbasedoneachmetricisshownin1173

SupplementaryTable2.1174

1175



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Afterexclusionofsamples,wecalculatedanadditionalsetofvariantmetricsand1176

excludedanyvariantwithoverallcallrate<0.3,heterozygosityof1,orheterozygote1177

allelebalanceof0or1(i.e.100%or0%ofreadscallednon-referencefor1178

heterozygousgenotypes).Weintentionallychosethesenon-stringentinitialvariant1179

quality-controlthresholdsduetotheheterogeneityofcaptureandsequencing1180

technologiesusedinourstudy;weperformedmuchmorestringentvariantquality1181

controlduringsingle-variantorgene-levelassociationanalysis.Werefertothe1182

49,484samplesand7.02Mvariantspassingthisfirstroundofnon-stringentquality1183

controlasthe“clean”dataset.1184

1185

Additionalqualitycontrolforassociationanalysisinsequencedata1186

Followinginitialsampleandvariantqualitycontrol,weperformedadditional1187

exclusionsofsamplesfromassociationanalysis.First,wecomputedatransethnic1188

setof“ancestry”SNPsforuseinidentity-by-descent(IBD)andprincipalcomponent1189

(PC)analysis.Webeganthisanalysiswithvariantsinthecleandataset(a)with1190

genotypecallrate>95%,(b)withminorallelefrequency(MAF)>1%ineach1191

ancestry,and(c)furtherthan250KbfromtheHLAregionoranestablishedT2D1192

associationsignal.WeLD-prunedvariantsusingPLINK90basedonmaximumr2=0.21193

(parameters–indep-pairwise5050.2).Weusedtheremaining171Kvariantsto1194

estimatepairwiseindividualIBDusingPLINK,andthetop10PCsofgenetic1195

ancestryusingEIGENSTRAT91.ForeachpairofindividualswithIBD>0.9,we1196

excludedtheindividualwiththelowercallrate(337duplicateexclusionsin1197

SupplementaryFigure2).Wethenexcluded,foreachofthefiveancestries,any1198



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individualwhoappeared,basedonvisualinspectionofthefirsttwotransethnicPCs,1199

tolieoutsideofthemainPCclustercorrespondingtothatancestry(133ethnic1200

outliersinSupplementaryFigure2).Finally,weusedthesubsetoftransethnic1201

ancestrySNPsontheXchromosometocomparegeneticsextoreportedsex,using1202

PLINK,andexcludedalldiscordantindividuals(273sexdiscordancesin1203

SupplementaryFigure2).1204

1205

Atthisstagewealsoexcludedthe3,510childhooddiabetescasesfromtheSEARCH1206

andTODAYstudies.Weinitiallyhopedtoincludethesesamplesascasesinboth1207

single-variantandgene-levelanalysis,usingeitherPCsorlinearmixedmodelsto1208

adjustforanyancestrydifferencesbetweenthemandtheothersamples.However,1209

whilesingle-variantassociationstatistics(computedviaameta-analysisof1210

ancestry-levelassociations)remainedwell-calibratedwiththesestudiesincluded1211

(SupplementaryFigure23ab),gene-levelanalysisyieldedadramaticallyinflated1212

QQplot(SupplementaryFigure23cd).ExclusionoftheSEARCHandTODAYstudy1213

samples,samplesfailingqualitycontrol,andvariantsthatbecamemonomorphicas1214

aresultofthesesampleexclusions,yieldedan“analysis”datasetof45,2311215

individualsand6.33Mvariants.1216

1217

Afterthesethreeroundsofsampleexclusions,weidentifiedfivesetsofancestry-1218

specific“ancestry”SNPs.Weusedthesameprocedureasforthetransethnic1219

ancestrySNPs(describedabove),exceptthatweappliedtheMAFthresholdonly1220

withintheappropriateancestry.WeusedtheseancestrySNPstoestimate,foreach1221



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ancestry,pairwiseIBDvalues,geneticrelatednessmatrices(GRMs),andPCsforuse1222

indownstreamassociationanalysis.1223

1224

Additionally,fromtheIBDvalues,wegeneratedalistofunrelatedindividualswithin1225

eachancestrybyexcludingtheindividualwiththelowercallrateinanypairof1226

individualswithIBD>0.3(leadingto2,157excludedindividuals).Theresulting1227

“unrelatedsanalysis”setconsistedof43,090individuals(19,828casesand23,2621228

controls)andyielded6.29Mnon-monomorphicvariants.Weusedthissetof1229

individualsandvariantsforsingle-variantandgene-leveltests(describedbelow)1230

thatrequiredanunrelatedsetofindividualsforanalysis.1231

1232

Wecarriedoutpowercalculations92forsingle-variantorgene-leveltestsassuminga1233

diseaseprevalenceof0.08toconvertpopulationfrequenciesandORstocaseand1234

controlfrequencies,andasamplesize(19,828casesand23,262controls)froman1235

analysisofonlyunrelatedindividuals.Ourpowercalculationsassumedthatallelic1236

effectswerehomogeneousacrossancestries.1237

1238

Variantannotation1239

WeannotatedvariantswiththeENSEMBLVariantEffectPredictor93(VEP,version1240

87).AnnotationswereproducedforallENSEMBLtranscriptswiththe–flag-pick-1241

alleleoptionusedtoassigna“bestguess”annotationtoeachvariantaccordingto1242

thefollowingorderedcriteriafortranscripts94:transcriptsupportlevel(TSL,i.e.1243

supportedbymRNA),biotype(i.e.protein_coding),APPRISisoformannotation(i.e.1244



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principal),deleteriousnessofannotation(i.e.prefertranscriptswithhigherimpact1245

annotations),CCDS95statusoftranscript(i.e.ahigh-qualitytranscriptset),canonical1246

statusoftranscript,andtranscriptlength(i.e.longerpreferred).WeusedtheVEP1247

LofTee(https://github.com/konradjk/loftee)anddbNSFP(version3.2)96pluginsto1248

generateadditionalbioinformaticpredictionsofvariantdeleteriousness;fromthe1249

dbNSFPplugin,wetookannotationsfrom15differentbioinformaticalgorithms1250

(listedinSupplementaryFigure8)aswellastherecentmCAP97algorithm.As1251

theseannotationswerenottranscript-specific,weassignedthemtoalltranscripts1252

forthepurposeofdownstreamanalysis.1253

1254

Allsingle-variantanalysesreportedinthemanuscriptorfiguresareshownusing1255

the“bestguess”annotationforeachvariant(asdescribedabove).1256

1257

Single-variantassociationanalysisinsequencedata1258

Toperformsingle-variantassociationanalysis,westratifiedsamplesbycohortof1259

originandsequencingtechnology(i.e.samplesfromthesamecohortbutsequenced1260

atdifferenttimeswereanalyzedseparately).SamplesfromtheESPstudywere1261

treateddifferently,duetothelargenumberofcohortsandsequencingtechnologies1262

withinthestudy;westratifiedESPsamplesbyancestry(ratherthancohort)anddid1263

notfurtherstratifythembysequencingtechnology.Thisprocedureyielded251264

distinctsamplesubgroups(SupplementaryFigure6).1265

1266



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Wethenexcludedvariantsseparatelyforeachsubgroup,basedonsubgroup-1267

specificmeasuresofcallrate,Hardy-Weinbergequilibrium(HWE),differentialcase-1268

controlmissingness,andalternateallelegenotypequality.Specificfiltersusedto1269

excludevariantsfromallsubgroupsareshowninSupplementaryFigure6;in1270

general,filterswerestrict–particularlyformultiallelicvariantsandX-chromosome1271

variants.1272

1273

Forsomesubgroups,weusedstricterfiltersontopofthebasicfiltersifsubgroup-1274

specificquantile-quantile(QQ)plotsshowedanexcessofsignificantassociations.In1275

particular,theAshkenazisubgroupfromtheT2D-GENESstudyshowedminimum1276

heterogeneityinsequencingqualitybetweencasesandcontrols(owingto1277

resequencingperformedsubsequenttotheoriginalstudypublication)andrequired1278

significantfilterstoremoveartifactualassociations.Inaddition,duetoasignificant1279

imbalancebetweenthenumberofcasesandcontrolsintheESPstudies,we1280

excludedanyvariantsfromthatsubgroupwhichhadanassociationp-valueless1281

than0.3timesthep-valuefromFisher’sexacttest(undertheassumptionthat1282

covariatesintheanalysiswereinducingstatisticalartifacts).Thenumbersof1283

variantspassingthesefiltersineachsubgroupareshowninSupplementaryFigure1284

6.1285

1286

Foreachofthe25samplesubgroups,weconductedtwosingle-variantassociation1287

analyses.Inbothsingle-variantanalysis,wecollapsedallnon-referenceallelesat1288

multiallelicsitesintoasingle“non-reference”allele.1289



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1290

First,weanalyzedall(includingrelated)samplesviatheEMMAXtest98,as1291

implementedintheEPACTS(genome.sph.umich.edu/wiki/EPACTS)software1292

package,usingtheGRMcomputedfromtheancestry-specificancestryvariants.We1293

includedinthemodelcovariatesforsequencingtechnology(whereappropriate)1294

butnotforPCsofgeneticancestry.Wedidnotincludecovariatesforage,sex,or1295

BMI.1296

1297

Second,weanalyzedunrelatedsamplesviatheFirthlogisticregressiontest99,also1298

asimplementedinEPACTS;weincludedinthemodelcovariatesforsequencing1299

technologyandforPCsofgeneticancestry(computedfromtheancestry-specific1300

ancestryvariants).ThenumberofPCsweincludedvariedbysubgroup;toselectthe1301

PCstobeincluded,weregressedT2Dstatusonsequencingtechnologyandthefirst1302

tenPCsandincludedinthemodelanyPCthatdemonstratednominal(p<0.05)1303

associationwithT2D,aswellasallhigher-orderPCs.1304

1305

Foreachofthe25×2=50single-variantanalyses,weinspectedQQplotsofvariant1306

associationstatisticsandincreasedthestringencyofthevariantfiltersifthe1307

distributionofassociationstatisticsappearedpoorlycalibrated.Thefiltersshownin1308

SupplementaryFigure6representthefinalvaluesatwhichwearrived.1309

1310

Wethenconducteda25-groupfixed-effectinverse-varianceweightedmeta-analysis1311

foreachoftheFirthandEMMAXtests,usingMETAL100.WeusedEMMAXresultsfor1312



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associationp-valuesandFirthresultsforeffectsizeestimates.Forcomparison,we1313

conductedtwoadditionalmeta-analyseswithassociationZ-scoresweightedby(a)1314

sample-sizeand(b)thenumberofvariantcarriers.Wefoundthatthesample-size1315

weightedmeta-analysishadsignificantlyreducedpowertodetectassociationfor1316

variantswithfrequenciesthatvariedwidelybysamplesubgroup;forexample,1317

1,425East-Asianindividualscarriedp.Arg192HisinPAX4(N=6,032;p=1.2×10-21)1318

comparedtoonly28carriersacrossallotherancestries(N=39,199;p>0.2),yielding1319

aninverse-varianceweightedmeta-analysisp=7.6×10-22andasample-sizeweighted1320

meta-analysisp=1.0×10-6.Bycontrast,thenumber-of-carrierweightedmeta-1321

analysisyieldedsimilarresultsastheinverse-varianceweightedmeta-analysis.We1322

electedtousetheinverse-varianceweightedmethodduetoitswidespreaduse100.1323

Wedidnotconductrandom-effectsmeta-analyses.1324

1325

Replicationofrs1451816831326

Toassesswhetherthers145181683variantinSFI1(p=3.2×10-8intheexome1327

sequenceanalysis)representedatruenovelassociation,weobtainedassociation1328

statisticsfromthe4,522Latinospreviouslyanalyzedaspartofan8,214sample1329

LatinoGWASpublishedbytheSIGMA-T2Dconsortium101whodidnotoverlapwith1330

thecurrentstudy.Basedontheoddsratio(1.19)estimatedinouranalysisandthe1331

MAF(12.7%)inthereplicationsample,powerwas91%toachievep<0.05undera1332

one-sidedassociationtest.Theobservedevidence(p=0.90,OR=1.00)didnot1333

supportrs145181683asatrueT2Dassociation.1334

1335



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Gene-levelanalysis1336

Wefirstfilteredvariants(or,moreaccurately,alleles,sinceincontrasttosingle-1337

variantanalysis,wetreatedmultiallelicvariantsascollectionsofindependent1338

biallelicvariants)accordingtosevendifferentannotation“masks”,rankedinorder1339

ofincreasingdeleteriousness.Thestrongestmaskconsistedofallelespredictedto1340

causelossoffunctionbytheLofTeealgorithm1341

(https://github.com/konradjk/loftee),whileweakermasksalsoincludedalleles1342

predicteddeleteriousbyprogressivelyfewerbioinformaticalgorithms.Eachmask1343

includedallallelesinhigherrankedmasksaswellasadditionalallelesspecificto1344

themask.Inthetwolowestrankedmasks(the1/51%and0/51%masks,which1345

includedallelespredicteddeleteriousbyoneorzerotools,respectively),wefiltered1346

allelesspecifictoeachmaskaccordingtoallelefrequencyusingacutoffofMAF=1%,1347

withMAFcomputedasthemaximumMAFacrossthefiveancestries.Afulllistand1348

definitionsofmasksareshowninSupplementaryFigure8;thecriterialistedin1349

thefigureareforallelesspecifictoeachmask.1350

1351

Tovalidatethattheseverityorderingofmaskscorrespondedtoanincreasing1352

likelihoodthatanalleleinthemaskwasdeleterious,weusedpreviouslypublished1353

dataassessingtheextenttowhichallmissensevariantsinthegenePPARGimpeded1354

adipocytedifferentiation(i.e.wereannotatedascausingPPARGlossoffunction).1355

Thesedatashowedatrendwherebyallelesinmoreseveremaskshadlower1356

predictedfunctionality(SupplementaryFigure9).1357

1358



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Foreachmask,wegroupedallelesbygeneaccordingtoVEPannotationsof1359

impactedtranscript;weassignedvariantsintranscriptsofmultiplegenestoallsuch1360

genes.Foreachgene,wecreateduptothreegroupingsofalleles,correspondingto1361

differenttranscriptsetsofthegene.First,the“best”groupingconsistedofallelesin1362

themaskaccordingtothe“bestguess”allele-levelannotations.Second,the“all”1363

groupingconsistedofallelesinthemaskaccordingtoanytranscriptofthegene.1364

Third,the“filter”groupingconsistedofallelesinthemaskaccordingtoprotein-1365

codingtranscriptsofthegenewithTSL<3.Formanygenes,twoormoreofthese1366

allelegroupingswereidentical.1367

1368

Additionally,weassignedmask-specificalleleweightsaccordingtotheiraggregate1369

predicteddeleteriousness.Tocalculateweights,weusedapreviouslypublished1370

model12inwhichmissensevariantsareamixtureoffullybenignvariantsandfully1371

loss-of-functionvariants,withaparameter0≤x≤1determiningthefractionofloss-1372

of-functionvariants.WeassumedallallelesintheLofTeemaskwerefullloss-of-1373

functionvariants(x=1)andthatallsynonymousalleleswerefullybenign(x=0).We1374

thencalculatedthe(binned)frequencydistribution,truncatedatMAF<1%,of1375

biallelicLofTeeandbiallelicsynonymousalleles,usingtheseasreference1376

distributionsofthefrequencyofloss-of-functionandbenignalleles,respectively.1377

Foreachmask,wethencalculatedthebinnedandtruncatedfrequencydistribution1378

forallelesspecifictothemask(SupplementaryFigure10)andestimatedavalue1379

forx(byenumeratingandtestingarangeofpossiblevaluesbetween0and1)that1380

maximizedthelikelihoodoftheobservedfrequencydistribution.Wethenusedthe1381



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64

estimatedvaluesofxforalleleweights,asshowninSupplementaryFigure8.1382

Becauseeachmaskconsistednotonlyofallelesspecifictothemaskbutalsoof1383

allelespresentinhigherrankedmasks,alleleswithinanygivenmaskhadarangeof1384

weights.1385

1386

Priortorunninggene-leveltests,weperformedadditionalqualitycontrolonsample1387

genotypes.Foreachofthe25samplesubgroups(thesamesubgroupsusedfor1388

single-variantanalysis),weidentifiedallvariantswithlowsubgroup-specificcall1389

rates,highsubgroup-specificdeviationsfromHWE,orhighsubgroup-specific1390

differencesbetweencaseandcontrolcallrates(specificcriteriaareshownin1391

SupplementaryFigure8).Foreachvariantfailinganyofthesecriteria,all1392

genotypesforindividualsinthesubgroup(regardlessofallele)weresetas1393

“missing”;formultiallelicvariants,allsubgroupgenotypesweresetasmissingifany1394

allelefailedanyqualitycontrolcriterion.1395

1396

Wethenconductedaseriesoftestsacrossthemasks.Weusedaburdentestand1397

SKAT38,bothasimplementedintheEPACTSsoftwarepackage.Theburdentest1398

assumesthattheeffectsizesofallanalyzedvariantsarethesame,whiletheSKAT1399

testallowseffectsizestovary102.Weconductedeachtestacrossallunrelated1400

individualspooledtogether(i.e.incontrasttosingle-variantanalysis,weperformed1401

a“mega-analysis”ratherthanameta-analysis)andincludedtenPCcovariates1402

(computedfromthetransethnicancestrySNPs)aswellasindicatorcovariatesfor1403

the25samplesubgroups(thesameasdefinedinsingle-variantanalysis).Wedid1404



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65

notincludecovariatesforage,sex,orBMIinouranalysis,astheyhadlittleeffecton1405

ourresults.1406

1407

Weimplementedsubgroup-specificgenotypefilters(asdefinedintheprevious1408

qualitycontrolstep)bymodifyingtheEPACTSsoftwaretosetspecifiedgenotypes1409

tomissingduringassociationtesting;weachievedallele-specifictestsfor1410

multiallelicvariants(i.e.inwhichonlyoneallelewaspresentinthemask)ina1411

similarmannerbysettingnon-referencegenotypestomissingforsamplesthat1412

carriedanalleleoutsideofthemask.WealsomodifiedtheEPACTSsoftwareto1413

acceptallele-specificweightsbymultiplyinggenotypes(ormoreaccurately,1414

genotypedosages)bytherelevantweightpriortoconductingtheformalburdenor1415

SKATanalysis.1416

1417

Consolidationoftestsacrossmasks1418

Historically,exomesequencingstudieshaveproducedseparategene-level1419

associationresultsforeachallelicmask.Whilestraightforwardtoreport,1420

interpretingmultiplep-valuesforeachgenecanbechallenging–particularlyifthe1421

goalistodeterminewhetheraspecificgenedemonstratesassociationwitha1422

phenotype.Toaddressthischallenge,wedevelopedtwomethodstocollapse1423

associationresultsacrossdifferentallelicmasks.1424

1425

Thefirstmethod(“weightedtest”)collapsesassociationsunderamodelwhereby1426

thephenotypiceffectsofallelesaredirectlyproportionaltotheirbioinformatically1427



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estimateddeleteriousness.Inthe“weightedburden”test,weusedthesumofthe1428

weightsofallelescarriedbyanindividualasapredictorvariableinplaceofthetotal1429

numberofallelescarried.Inthe“weightedSKAT”test,wemultipliedthedefault1430

weightsusedintheSKATEPACTSimplementationbytheallelicweightswe1431

calculated.Fortheseweightedtestsweincludedallallelesinthe0/51%maskin1432

theanalysis.1433

1434

Becausebioinformaticallypredictedseverityisanimperfectproxytoactual1435

phenotypicseverity,wedevelopedasecondmethod,the“minimump-valuetest”,to1436

collapseassociationsacrossmasks.Wechosetheminimump-valuetesttoprovidea1437

principledextensionofanadhocbutintuitivewaytointerpretmultiplep-valuesfor1438

agivengene:takethesmallestp-valueobservedacrosseachmaskandthencorrect1439

fortheeffectivenumberoftestsperformedforthegene.1440

1441

Toconducttheseminimump-valuetests,wefirstrantheburdenandSKATanalyses1442

foreachofthesevenmasksseparately,followingusualexomesequenceanalysis1443

protocolsbyusingnoweightsandincludingallallelesineachmask.Foreachgene,1444

wethenconvertedthesevenp-valuesintoasinglep-valueviatheformula1445

1− 1− 𝑝!"# !

whereeistheeffectivenumberofindependenttestsperformedacrossthemasks.1446

Toestimatee,weappliedapreviousapproach39originallydevelopedtocompute1447

theeffectivenumberofindependentp-valuesacrossasetofSNPs:1448



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𝑀 − 𝐼 𝜆! > 1 𝜆! − 1!

!!!

whereinourcaseMequalsthenumberofmasks(usuallyseven,exceptforgenes1449

thatlackvariantsinoneormoremasksorforwhichtwomasksareidentical)andλi1450

aretheeigenvaluesoftheM×Mmatrixofcorrelationsamongthep-valuesofthe1451

mask-leveltests.Tocomputethemaskp-valuecorrelationmatrix,wefollowedthe1452

previousapproachbyfirstcalculatingthemaskgenotypecorrelationmatrix(i.e.,for1453

eachmask,producingavectorwiththenumberofvariantsinthemaskcarriedby1454

eachindividual,andthencalculatingcorrelationsofthevectors)andthen1455

transformingthegenotypecorrelationmatrixaccordingtothepreviously1456

empiricallyderived39polynomialequation:1457

𝑦 = 0.2982𝑥! − 0.0127𝑥! + 0.0588𝑥! + 0.0099𝑥! + 0.6281𝑥! − 0.0009𝑥

wherexisthemeasuredcorrelationbetweenthenumberofallelescarriedandyis1458

theestimatedcorrelationbetweenp-values.1459

1460

Wenotethatthispolynomialequationwasinitiallydevelopedtotranslate1461

correlationsbetweenindividualvariantsandp-values,ratherthancorrelations1462

betweenaggregatesetsofvariantsandp-values,andthusmaynotbeasaccuratein1463

oursetting.However,genomiccontrolestimates(λ=0.67)andQQplots1464

(SupplementaryFigure11)suggestedthatifanythingourmultipletestcorrection1465

wasconservativeformostgenes.Furthermore,evenifourgene-levelp-valueswere1466

Bonferronicorrectedforallsevenmasks,theresultsofourstudywouldremain1467

largelyunchanged:eachofSLC30A8,MC4R,andPAMwouldstillexceedexome-wide1468



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significance(forboththeweightedandminimump-valuetests),andthegeneset1469

testswouldremainnearlyidentical(astheyarebasedongene-levelp-valueranks1470

ratherthanabsolutevalues).Futureworkcouldinvestigatetheapplicationofother1471

methodspreviouslydevelopedtocorrectforcorrelatedp-values103,104.1472

1473

Theapplicationoftwodifferentmethodsforcollapsingp-valuesacrossmasksfor1474

eachoftwotestsyieldedfouranalysesforeachgene,correspondingtoaweighted1475

burdenanalysis,aweightedSKATanalysis,anminimump-valueburdenanalysis,1476

andanminimump-valueSKATanalysis.Infact,foreachofthefouranalyses,1477

multiplep-valueswerepossibleforeachgene(correspondingtothedifferent1478

transcriptsetsusedforannotation).Toproduceasinglegene-levelp-valueforeach1479

ofthefouranalyses,wethuscollapsed(foreachgene)thesetofp-valuesacross1480

transcriptsetsintoasinglegene-levelp-valueusingthesameprocedureasforthe1481

minimump-valuetest(i.e.takingtheminimump-valuecorrectedfortheeffective1482

numberoftestsperformed).1483

1484

Forsomegenes(SupplementaryFigures12-14)weconductedadditionalgene-1485

levelanalysestodissecttheaggregatesignalsobserved.First,weperformedtests1486

foreachmaskseparately,includingonlyvariantsspecifictothemask(ratherthan1487

allvariants),tounderstandwhethertheaggregatesignalwasobservedinonlyone1488

asopposedtomultiplemasks.Second,weperformedtestsbyprogressively1489

removingvariantsinorderoflowestsingle-variantanalysisp-value,tounderstand1490

the(minimum)numberofvariantsthatcontributedstatisticallytotheaggregate1491



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signal.Third,weperformedtestsconditionaloneachvariantseparately(i.e.1492

calculatingseparatemodelswitheachindividualvariantasacovariate),withthe1493

resultingp-valuescomparedtothefullgene-levelp-value,toassessthecontribution1494

ofeachvariantindividuallytothesignal.1495

1496

AnalysisofexomesfromtheGeisingerHealthSystem(GHS)1497

Weobtainedgene-levelassociationresultspreviouslycomputedfromananalysisof1498

49,199individuals(12,973T2Dcasesand36,226controls)fromtheGeisinger1499

HealthSystem.Werequestedassociationsummarystatisticsforthe50geneswith1500

thestrongestgene-levelassociationsfromouranalysis;44geneshadprecomputed1501

summarystatisticsavailable;pseudogeneUBE2NLandXchromosomegenes1502

MAP3K15,SLC16A2,MAGEB5,DGKK,andMAGEE2werenotavailable.1503

1504

GHSsequencedatawereprocessedandanalyzedaspreviouslydescribed27and1505

associationresultswereproducedforfour(nested)variantmasks:1506

1. M1:predictedloss-of-functionvariants,accordingtotheVEP,withMAF<1%–1507

similartotheLofTeemaskbutwithanadditionalMAF<1%filterandwithoutthe1508

LofTeefilteronprotein-truncatingvariantsannotatedbytheVEP.1509

2. M2:nonsynonymousvariantspredicteddeleteriousby5/5prediction1510

algorithmswithMAF<1%–similartothe5/5maskbutwithanadditionalfilter1511

onMAF<1%.1512

3. M3:allnonsynonymousvariantspredicteddeleteriousby≥1/5bioinformatic1513

algorithmswithMAF<1%–similartothe1/51%mask.1514



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4. M4:allnonsynonymousvariantswithMAF<1%–similartothe0/51%mask,1515

althoughnotidenticalasthe1%filterwasusedforallvariantsincludingthosein1516

theLofTeeand5/5masks.1517

1518

Foreachmask,associationresultswerecomputedvialogisticregressionunderan1519

additiveburdenmodel(withphenotyperegressedonthenumberofvariants1520

carriedbyeachindividual)withage,age2,andsexascovariates.Althoughthis1521

analysisprocedurewasbroadlyconsistentwiththeoneweusedforourexome1522

sequenceanalysis,wewerenotabletosynchronizeourproceduresforquality1523

control,annotation,andcollapsingassociationstatisticsacrossmasks.1524

1525

ToproduceasingleGHSp-valueforeachgene,weappliedtheminimump-value1526

procedureacrossthefourmask-levelresults.Weestimatedthecorrelationmatrix1527

usingthesameprocedureasforourexomesequenceanalysis,usingthecombined1528

GHSallelefrequenciesreportedacrossthefour(nested)masks.1529

1530

AnalysisofexomesfromtheCHARGEconsortium1531

WecollaboratedwiththeCHARGEconsortiumtoanalyzethe50geneswiththe1532

strongestgene-levelassociationsfromouranalysisin12,467individuals(3,0621533

T2Dcasesand9,405controls)fromtheirpreviouslydescribedstudy105.CHARGE1534

DNAsampleswereprocessedatBaylorCollegeofMedicineHumanGenome1535

SequencingCenterusingtheVCRome2.1designandsequencedinpaired-endmode1536

inasinglelaneontheIlluminaHiSeq2000ortheHiSeq2500platformwithamean1537



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78-foldcoverage.Allsampleswerecalledtogetheranddetailsonsequencing,1538

variantcalling,andvariantqualitycontrolweredescribedindetailbyYuetal.1061539

1540

VariantsintheCHARGEexomeswereannotatedandgroupedintosevenmasks1541

usingthesameprocedureasfortheoriginalexomesequenceanalysis.Foreach1542

mask,CHARGEburdenandSKATassociationtestswereperformedintheAnalysis1543

Commons107usingalogisticmixedmodel108assuminganadditivegeneticmodel1544

andadjustedforage,sex,study,race,andkinship.1545

1546

ToproduceasingleCHARGEp-valueforeachgene,weappliedtheminimump-value1547

procedureacrossthefourmask-levelresults,asfortheGHSanalysis.1548

1549

Evaluationofdirectionalconsistencybetweenexomesequence,CHARGE,andGHS1550

analyses1551

Weexaminedtheconcordanceofdirectionofeffectsizeestimates(i.e.OR>1or1552

OR<1)betweenouroriginalexomesequenceanalysisandthosefromCHARGEand1553

GHS.Weusedburdenteststatisticsforthisanalysis,asSKATtestsdonotproduce1554

directionofeffects.Ofthe50genesadvancedforreplication,weconsideredthe461555

thatreachedburdenp<0.05foratleastonemask(i.e.ignoringthosewithevidence1556

forassociationonlyundertheSKATmodel).Wecomparedthedirectionofeffectto1557

thatestimatedbyburdenanalysisofthesame(oranalogous)maskintheGHSor1558

CHARGEanalysis.ForCHARGE,wecompareddirectionofeffectforthesamemask.1559

ForGHS,wecomparedusethefollowingapproximatemappingbetweenmasks:1560



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LofTeetoM1;15/15,10/10,5/5,and5/5+LofTeeLCtoM2;1/51%toM3;and0/51561

1%toM4.Wethenconductedaone-sidedexactbinomialtesttoassesswhetherthe1562

fractionofresultswithconsistentdirectionofeffectswassignificantlygreaterthan1563

expectedbychance.1564

1565

GenerationofcandidateT2D-relevantgenessets1566

Toassesswhethergene-levelassociationstrengthcouldbeaninformativemetricto1567

usewhenprioritizingcandidategenesforfurtherstudyorexperimentation,we1568

comparedgene-levelassociationsforgenesinavarietyofgenesets1569

(SupplementaryTable10)togene-levelassociationstatisticsforrandomsetsof1570

genesmatchedwiththetargetsetbasedonthenumberandfrequenciesofvariants1571

(asdescribedbelow).Wedidsofor16setsofgenes:1572

1. ElevengenesharboringmutationsthatcauseMaturityOnsetDiabetesoftheYoung1573

(MODY).Weselectedgenesfromasetpreviouslydescribed24afterexcludingtwo1574

genes(ABCC8andKCNJ11)thatcancausemonogenicdiabetesorcongenital1575

hyperinsulinismdependingonwhetherthemutationstheyharborareactivating1576

orinactivating.1577

2. Eightgenesannotatedastargetsforantidiabeticmedications.Wedownloaded1578

medicationsannotatedas“DrugsUsedinDiabetes”or“BloodGlucoseLowering”1579

fromtheDrugBankdatabaseversion5.048.Afterexclusionofmedicationswith1580

morethantwoannotatedtargets,weadvancedforanalysisonlygenes(a)1581

annotatedasatargetofatleasttwocompoundsand(b)forwhichthe1582

therapeutictargetmodulationstrategywasconsistentlyannotatedacrossall1583



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medications,whereannotationsof“inhibitor”,“antagonist”,and“inverse1584

agonist”wereinterpretedasreducingactivity,whileannotationsof“agonist”,1585

“activator”,or“inducer”wereinterpretedasincreasingactivity.These1586

restrictionsexcludedABCC8fromanalysis,asitwasannotatedasthetargetof1587

bothaninhibitorandanagonist;weelectedtomaintainthisexclusion,despite1588

multiplelinesofevidence109indicatinginhibitionofABCC8tobetheappropriate1589

anti-diabeticstrategy,tomaintainconsistentcriteriaacrossallgenesselectedfor1590

analysis.Additionally,weexcludedKCNJ11(whichwithABCC8encodestheATP-1591

sensitiveK(ATP)channeltargetedbysulfonylureas)fromanalysisbecauseboth1592

medicationslistedinDrugBankastargetingithadmorethantwotargets1593

(Glyburide,8,andGlimepiride,3).TheresultinggenesetwasthusGLP1R,IGF1R,1594

PPARG,INSR,SLC5A2,DPP4,KCNJ1,andKCNJ8.1595

3-14.TwelvesetsofgenesreportedasrelevanttoT2Dinmousemodels.Withinthe1596

MouseGenomeInformaticsDatabase,wesearchedforgenesmatchingvarious1597

diabetes-relevant“phenotypes,alleles,anddiseasemodels”underthebroader1598

categoryof“mousephenotypesandmousemodelsofhumandisease”.We1599

constructedagenesetforeachphenotypedefinedinthedatabase,manyof1600

whichoverlapped.Forphenotypesassociatedwithincreaseddiabetesrisk,we1601

used:(3)“type2diabetesortypeiidiabetes”(i.e.non-insulindependent1602

diabetes;31genes),(4)“diabetesmellitus”(72genes),(5)“impairedglucose1603

tolerance”(327genes),(6)“increasedcirculatingglucose”(365genes),(7)1604

“insulinresistance”(181genes),and(8)“decreasedinsulinsecretion”(1331605

genes).Forphenotypesassociatedwithdecreaseddiabetesrisk,weused:(9)1606



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“improvedglucosetolerance”(239genes),(10)“decreasedcirculatingglucose”1607

(481genes),(11)“increasedinsulinsensitivity”(178genes),and(12)“increased1608

insulinsecretion”(51genes).Forphenotypesassociatedwithdiabetesriskbut1609

withuncleardirectionofeffect,weused(13)“decreasedcirculatinginsulin”1610

(321genes)and(14)“increasedcirculatinginsulin”(215genes).1611

15. ElevengenessuspectedofharboringcommoncodingcausalvariantswithinT2D1612

GWASloci.Weanalyzedthesetofgenesfromarecentexomearrayanalysis171613

whichcontainedacodingvariantGWASsignalforwhichtheunweighted1614

posteriorprobabilityofcausalityexceeded25%.Althoughthefinalvalues1615

reportedbythestudyincludeanelevatedpriorforcodingvariants,weelectedto1616

usea25%unweightedposteriorthresholdtoenrichforthegeneswiththe1617

highestlikelihoodofmediatingtheobservedGWASsignal.Foranalysisofthis1618

geneset,werecomputedgene-levelassociationstatisticswithinthesetby1619

conditioningonallGWAStagSNPs(withinthelocus)reportedintheexome1620

arrayanalysis17;weusedp-valuesfromtheseconditionalgene-levelassociations1621

inthegenesetanalysis.1622

16. TwentygeneswithT2D-associatedtranscriptlevels.Weselectedgeneswith1623

significantassociationsinapre-publication52tissue-wideT2Dassociation1624

analysis(i.e.testingforassociationbetweenthegeneticcomponentoftissue-1625

levelgeneexpressionandT2D),withassociationsconsideredsignificantifthey1626

survivedBonferronicorrectionforalltestedgenesandalltestedtissues.Results1627

werecomputedwiththeMetaXcansoftwarepackage110usingSNPregression1628

coefficientstakenfromalargetrans-ethnicT2DGWASmeta-analysis111and1629



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geneexpressionpredictionmodelsfromthePredictDBwebsite1630

(http://predictdb.org).1631

1632

Genesetanalysis1633

Foreachgeneset,ourgoalwastocomparethegenelevelp-valueswithinthesetto1634

thoseofgeneschosenatrandomfromthegenome.Tocontrolforgenevariabilityin1635

thenumberandfrequencyofvariantswithinthem,whichcouldconfound1636

comparisons,weconstructedcomparisongenesbymatchingonfourproperties:the1637

(1)numberofvariantsinanyofthesevenvariantmasks;(2)totalallelecountsover1638

allvariantsinanyofthesevenmasks;(3)numberoftestsacrossallvariantmasks1639

andtranscriptsets;and(4)effectivenumberoftestsacrossallvariantmasksand1640

transcriptsets(ascomputedfortheminimump-valuetest).Wescaledeach1641

propertytozeromeanandunitvariance.Foreachgene,wethenusedthe501642

nearestneighbors(definedusingEuclideandistanceinthescaledpropertyspace)1643

asmatchedcomparisongenes.1644

1645

Toconductagenesetanalysis,wethencombinedthegenesinthegenesetwithall1646

ofthecomparisongenesmatchedtoeachgeneintheset.Withinthecombinedlistof1647

genes,werankedgenesusingthep-valuesobservedfortheminimump-value1648

burdentest.Wethenusedaone-sideWilcoxonrank-sumtesttoassesswhether1649

genesinthegenesethadsignificantlyhigherranksthanthecomparisongenes.1650

1651



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Forgenesetanalysis,weusedtheminimump-valuetest,ratherthantheweighted1652

test,undertherationalethat(a)weaimedtodetectassociationswithasmanygenes1653

aspossibleusinginformationfromasmanyvariantsaspossibleand(b)the1654

weightedtestmightnotdetectgenesthatdidnotfollowitsmodelofastrong1655

correlationbetweenvarianteffectsizesandmolecularannotation.Weusedthe1656

burdentestratherthanSKATbasedonadesiretohavemoreinterpretable1657

associationstatistics(e.g.effectsizeestimates).However,wedidnotquantitatively1658

andsystematicallycomparethepowerofeachofouranalysesinthissetting.1659

1660

Useofgene-levelassociationstopredicteffectorgenes1661

Inmostsituations,GWASassociationsimplicatecommonregulatoryvariants,which1662

seldomlocalizetospecificgenes.Toassesswhethergene-levelassociationsfrom1663

exomesequencing–whicharecomposedmostlyofrarevariantsindependentfrom1664

anyGWASassociations–couldprioritizepotentialeffectorgeneswithinknownT2D1665

GWASloci,wecataloguedallgeneswithineachlocusreachingp<0.05forthe1666

minimump-valueburdentest.Wetookalistof94GWASlocifromarecentreview1667

article53andadvancedforanalysisthe595geneswithin250kbofanindexSNP.1668

1669

Wethensoughttocomparetwomethodstopredicteffectorgeneswithintheseloci.1670

First,weusedp<0.05accordingtotheminimump-valuegene-leveltestfromour1671

exomesequenceanalysistopredictcandidateeffectorgenes,producingalistof401672

genes(across32loci).Second,weusedproximitytotheindexSNP(aspredictedby1673



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DAPPLE54)topredictcandidateeffectorgenes,producingalistof184genes(at1674

somelociDAPPLEannotatedmorethanonecandidateeffectorgene).1675

1676

Asaccuratelyassessingwhichofthesetwogenesetsismoreenrichedfortrue1677

effectorgeneswouldrequire(atminimum)significantexperimentalwork,weused1678

therelativenumberofproteininteractionswithineachgenesetasone(imperfect)1679

measureoftheirrespectivebiological“coherence”.Toassesswhethereachset1680

encodesproteinswithmoreinteractionsthanwouldbeexpectedbychance,weran1681

DAPPLEthroughthepublicGenePatternportal1682

(https://software.broadinstitute.org/cancer/software/genepattern)withdefault1683

valuesforallparameters.The40geneswithminimump<0.05weresignificantly1684

moreenrichedforproteininteractions(p=0.03;observedmean=11.4,expected1685

mean=4.5)thanwerethe184genesimplicatedbasedonproximitytotheindexSNP1686

(p=0.64;observedmean=21.1,expectedmean=21.9).1687

1688

Whiletheseresultssuggestthatgene-levelassociationsmaybeusefulfor1689

prioritizingeffectorgenes,wenotethattheydonotimplicateanyspecificgenesand1690

thatDAPPLEisonlyonemeanstoassessbiologicalcoherenceofageneset(through1691

directandindirectproteininteractions).Evaluationofthebiologicalcandidacyof1692

thesegenesmayultimatelyrequirein-depthfunctionalstudies56.1693

1694

Useofgene-levelassociationstopredictdirectionofeffect1695



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Intherapeuticdevelopment,itisoftenvaluabletoknowthedirectionofeffect1696

linkinggenemodulationtodiseaserisk–thatis,whetherinactivationoractivation1697

ofaproteinincreasesdiseaserisk.Wethusassessedwhethergene-levelassociation1698

analysisofpredicteddeleteriousvariantscouldbeusedtopredictthisdirectionof1699

effect.Forthisanalysis,weusedoddsratiosestimatedfromamodifiedweighted1700

burdentestprocedure,whichonlyincludedallelesfromthefourmaskswiththe1701

predictedmostdeleteriousvariants:LofTee,16/16,11/11,and5/51702

(SupplementaryFigure8).Weightsforvariantswereidenticaltothoseusedinthe1703

exome-wideweightedburdentest.Wechosethesefourmasksforanalysisto1704

balanceadesireforgreateraggregateallelecountpergene(i.e.missensevariantsin1705

additiontoprotein-truncatingvariants)withaneedtostronglyenrichfor1706

deleteriousvariants(>73%estimatedtobedeleteriousinmasksanalyzedvs.<50%1707

intheothermasks(SupplementaryFigure8).Inaddition,weusedtheweighted1708

testbecauseitwasexplicitlydesignedtoestimateaneffectofgene1709

haploinsufficiencybasedonbothprotein-truncatingandmissensevariants.1710

1711

TocomparethesedirectionofeffectestimatestothoseexpectedforT2Ddrug1712

targets,weassumedagonisttargetstohavetrueOR>1andinhibitorstohavetrue1713

OR<1.Foracomparisontoexpectationsformousegeneknockouts,wefirst1714

excluded473genesannotated,basedonmembershipinmultiplegenesets,tohave1715

bothexpectedOR>1andexpectedOR<1(thesegeneswereexcludedonlyfromthe1716

directionofeffectcomparisons;theyweremaintainedinallothergeneset1717

analyses).Thisleft389geneswithanexpectedOR>1,associatedexclusivelywith1718



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mousetraitsindicativeofincreasedrisk(overlappingsetsof11“type2diabetesor1719

typeiidiabetes”,46“diabetesmellitus”,204“impairedglucosetolerance”,2451720

“increasedcirculatingglucose”,104“insulinresistance”,and63“decreasedinsulin1721

secretion”),and467geneswithanexpectedOR<1,associatedexclusivelywithtraits1722

indicativeofdecreasedrisk(overlappingsetsof164“improvedglucosetolerance”1723

genes,358“decreasedcirculatingglucose”genes,95“increasedinsulinsensitivity”1724

genes,and18“increasedinsulinsecretion”genes).Genesetsfor“decreased1725

circulatinginsulin”and“increasedcirculatinginsulin”wereexcludedfromthis1726

directionofeffectcomparisonduetotheunclearrelationshipbetweenthese1727

phenotypesandT2Drisk.1728

1729

AggregationandgenerationofSNParraydata1730

Becausethemostsignificantsingle-variantassociationsthatemergedfromour1731

exomesequenceanalysiswerewithcommonvariants,weaskedwhetheranarray-1732

basedgenome-wideassociationstudyinthesamesamplescouldhaveprovideda1733

lessexpensivemethodtodetectthesesameassociations.Toaddressthisquestion,1734

weaggregatedallavailableSNParraydatafortheexome-sequencedsamples1735

(SupplementaryTable12).DatafortheGoT2D24,SIGMA85,andT2D-GENES1736

consortiahavebeenpreviouslyanalyzed(unpublishedT2D-GENESdatawere1737

collectedfromarangeofSNParraysincludingAffymetrix5.0and6.0,Illumina1738

HumanHap610Kand1M,andtheIlluminaCardioMetabochip).Thenewly1739

sequencedsamplesfromtheT2D-GENESandSIGMAconsortiaweregenotypedona1740

custom“GenomesForLife”(G4L)IlluminaInfiniumarray,including243,6621741



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variantschosentouniquelyidentifyeachindividualinastudyandtoprovidea1742

backboneforimputationofcommonvariation.TheG4Larraywasprocessedbythe1743

ArrayslabofBroadGenomicsandcalledusingtheIlluminaGenCall(Autocall)1744

algorithm.1745

1746

AnalysisofSNParraydata1747

Aftergenotyping,the34,529samples(18,233casesand17,679controls;1748

SupplementaryTable12)bothintheexomesequenceanalysisandwithaSNP1749

arraycall-rate>95%wereadvancedforimputation.Toomitvariantsthatmight1750

degradeimputationquality,priortoimputationweexcludedvariantswithlow1751

genotypecallrate(<95%),strongdeviationfromHardy-Weinbergequilibrium1752

(p<10-6),differentialgenotypecallratebetweencasesandcontrols(p<10-5),orlow1753

frequency(MAF<1%).Wethenimputedautosomalvariants(SNVs,shortindels,and1754

largedeletions)viatheMichiganImputationServer112foreachoftworeference1755

panels:theallancestries1000GenomesPhase3(1000G)referencepanelof2,5041756

individuals67andtheHaplotypeReferenceConsortium(HRC)Panelof32,4701757

individuals68.Weusedthe1000G-basedimputationforallassociationanalysesand1758

theHRC-basedimputationtoassessthenumberofexomesequencevariants1759

imputablefromthelargestavailableEuropeanreferencepanel.Wenotethatthe1760

HRCpanelincludesonlySNPs(i.e.noindels)andonlyvariantsobservedatleastfive1761

timesinthesequencedatacontributedtotheHRC.1762

1763



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Afterimputation,weperformedsampleandvariantqualitycontrol,aswellas1764

associationtests,analogoustotheexomesequencesingle-variantanalysis.By1765

contrastwiththeexomesequenceanalysis,wefoundthattheEMMAXtestproduced1766

moresuspiciouslookingassociationsthandidtheFirthtestandthususedonlythe1767

Firthtest(i.e.forbothp-valuesandORs)intheimputedGWASanalysis.1768

1769

Todeterminewhichvariantsintheexomesdatasetwereimputablefromthe1000G1770

orHRCpanel,wecalculatedwhichoftheexomevariantspassedimputedGWAS1771

qualitycontrolinanysamplesubgroup,withafurtherrestrictionofachievingr2>0.41772

inthatsubgroup.Onlyvariantsintheexomesdatasetthatwerepolymorphicinthe1773

imputedGWASsampleswereincludedinthisanalysis.Forcalculationsinvolving1774

theHRC-imputedGWAS(giventhattheHRCpanelisEuropean-specific),weonly1775

consideredvariantsvariableinfourEuropeancohorts(METSIM,Ashkenazi,1776

GoDARTS,andFHS)intheanalysis.1777

1778

GenesetanalysisusingSNParraydata1779

Inadditiontosingle-variantanalysis,weconductedgenesetanalysiswiththe1780

imputedGWASdata.WefirstusedthemethodimplementedinMAGENTA70to1781

assigngenescoresfromtheimputedGWASsingle-variantassociationresults;1782

MAGENTAgenescoresarebasedonproximitytoaGWASleadSNPaftercorrection1783

forpotentialconfoundingfactors.Inthesamewayasforgenesetanalysisfromthe1784

exomesequencegene-levelresults,wethenconductedaone-sidedWilcoxonrank-1785

sumtesttocomparethegenescorestothoseofmatchedcomparisongenes.1786



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1787

AstheimputedGWASgenesetanalysisproducedfewersignificantgeneset1788

associationsthandidtheexomesequencegenesetanalysis,weinvestigated1789

whetheralargerarray-basedassociationstudywouldproducemoresignificant1790

genesetassociations(i.e.whetherthelackofgenesetassociationsintheimputed1791

GWASwasduetoafundamentallackofassociatedcommonvariantsnearthegenes1792

inthegenesetorsimplyduetoaninsufficientsamplesize).Forthisanalysis,we1793

downloadedsingle-variantassociationstatisticsfromthelargestavailablemulti-1794

ethnicarray-basedGWASforT2D111,convertedthemtoMAGENTAgenescores,and1795

thenforeachgenesetconductedaWilcoxonrank-sumtestasdescribedabove.1796

1797

LVEcalculations1798

Tocalculateliabilityvarianceexplained(LVE),weusedapreviouslypresented1799

formula69tocalculatetheLVEofavariantwiththreegenotypes(AA,Aa,andaa)and1800

correspondingrelativerisks(1,RR1,andRR2).Forthesecalculationsweassumed1801

HWE,implyingthefrequenciesofthethreegenotypestobePaa=Pa2,PAa=2Pa(1-Pa),1802

andPAA=(1-Pa)2,wherePaistheminorallelefrequency.Underthisassumption,LVE1803

canbeexpressedas1804

𝐿𝑉𝐸 = 𝑃!! 𝜇!! − 𝜇 ! + 2𝑃!(1− 𝑃!) 𝜇!" − 𝜇 ! + 1− 𝑃! ! 𝜇!! − 𝜇 !

where𝜇 = 2𝑃!(1− 𝑃!)𝜇!" + 1− 𝑃! !𝜇!!,and1805

𝜇!! = 0; 𝜇!" = 𝑇 −Φ!! 1− 𝑓!" ; 𝜇!! = 𝑇 −Φ!! 1− 𝑓!!

HereΦ!!isthenormalquantiledistribution,𝑇 = Φ!!(1− 𝑓!!),andfaa,fAa,andfAA1806

aredefinedas1807



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𝑓!! =𝐾

𝑃!! + 2𝑃!(1− 𝑃!)𝑅𝑅! + 1− 𝑃! !𝑅𝑅!; 𝑓!" = 𝑅𝑅!𝑓!!; 𝑓!! = 𝑅𝑅!𝑓!!

whereKisthediseaseprevalence.1808

1809

Theinputstotheseformulaeareestimatesofallelefrequency(foreitherindividual1810

variantsorsetsofvariants,dependingonwhethervariant-levelorgene-level1811

varianceistobecalculated),relativerisk,anddiseaseprevalence.Forindividual1812

variants,weusedthepointestimateoftheMAFfromouranalysistoestimateallele1813

frequency,whileforgenesweusedthepointestimateofcombinedallelefrequency1814

(acrossallalleles)inplaceofMAF.WeestimatedrelativerisksfromanalysisORs1815

andMAFs(𝑃!)underanassumedprevalenceofK=0.08andanadditivegenetic1816

model,byiterativelysolvingtwoequations69:1817

𝑓!! =𝐾

𝑃!! + 2𝑃! 1− 𝑃! 𝑅𝑅! + 1− 𝑃!

!𝑅𝑅!

1818

𝑅𝑅! =𝑂𝑅!

1+ 𝑓!!(𝑂𝑅! − 1)

wherei=1,2correspondtotheheterozygousandmajor-allelehomozygous1819

genotypes.Weusedamultiplicativemodelforodds-ratios;i.e.OR2=OR12.1820

1821

WeperformedLVEcalculationsasanintegraloverthedistributionofpotential1822

relativerisks,assumingthatthelogarithmofoddsratiosORifollowednormal1823

distributionswithmeansandvarianceequaltothoseestimatedfromouranalysis.1824

WhenpresentingthestrongestLVEvaluesfortheimputedGWASanalysis,weonly1825



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consideredvariantsgenotypedinatleast10,000individualstoavoidpotential1826

artifactsresultingfromaspuriousassociationinasmallsamplesubgroup.1827

1828

Forgene-levelLVEcalculations,weusedthevariantmaskwithlowestp-valueto1829

calculateLVE.Aseachmaskmayhaveincludedamixtureofdisease-associatedand1830

benignalleles,thecalculatedLVEmayunderestimatethetrueLVEfordisease-1831

associatedalleleswithinthegene.TocalculateanupperboundontheLVEbyonly1832

disease-associatedalleles,weperformedaseriesofLVEcalculationsfor1833

progressivelylargersetsofalleles,ateachstepincludingallelesbyorderof1834

decreasingsingle-variantsignificance.Weperformedtwocalculationsforeachgene,1835

oneforriskallelesandoneforprotectivealleles,takingthemaximumofthetwoas1836

thefinalupperboundestimatedforLVEbythegene.WedidnotcalculateanLVE1837

boundunderamodelwherebyalleleswithinthegenecanbothincreaseand1838

decreaseriskofdisease.1839

1840

Estimatedpowertodetectgene-levelassociationswithT2Ddrugtargets1841

Toestimatethepoweroffuturestudiestodetectgene-levelassociationsingenes1842

witheffectsizessimilartothoseforestablishedT2Ddrugtargets,weused1843

aggregateallelefrequenciesandoddsratiosestimatedfromourgene-levelanalysis1844

andanassumedprevalenceofK=0.08tocalculateaproxyfortruepopulation1845

frequenciesandrelativerisks.Ineachcase,weusedoddsratiosandfrequencies1846

fromthevariantmaskyieldingthestrongestgene-levelassociation.Becauseon1847

averagethesedrugtargetshad5effectivetestspermask,weusedanexome-wide1848



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significancethresholdofα=1.25×10-7forpowercalculations.Wecalculatedpower1849

aspreviouslydescribed92.1850

1851

Estimatedfractionoftrueassociations1852

Wesoughttoquantifytheproportionoftrueassociations(PPA)fornonsynonymous1853

variantsobservedinourdatasetasafunctionofassociationstrengthasmeasured1854

bysingle-variantp-value.Wedefineatrueassociationasavariantwhich,when1855

studiedinlargersamplesizes,willeventuallyachievestatisticalsignificanceowing1856

toatrueOR≠1.Wedistinguishtrueassociationfromcausalassociation:causally1857

associatedvariantsarethesubsetoftrulyassociatedvariantsinwhichthevariant1858

itselfiscausalfortheincreaseindiseaserisk,asopposedtobeingtrulyassociated1859

duetoLDwithadifferentcausallyassociatedvariant.1860

1861

ToestimatePPA,weusedastrainingdataapreviousexomearraystudyfromthe1862

GoT2Dconsortiumspanning13Europeancohorts24.Astwoofthe13cohorts1863

includedinthepreviousstudycontributedsamplestothecurrentexomesequence1864

analysis,were-calculatedafixed-effectsinverse-varianceweightedmeta-analysis1865

foreveryvariantintheexomearraystudyafterexcludingallsamplesfromthese1866

twooverlappingcohorts.Thisyieldedacollectionofexomearrayassociation1867

statisticsfor206,373variants,withamaximumsamplesizeof50,567(maximum1868

effectivesamplesize41,967).1869

1870



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Wethencomparedvariantdirectionofeffectestimatedfromourexomesequence1871

analysisof45,231individualstothoseestimatedfromtheindependentexomearray1872

analysisof41,967individuals.Toproduceanuncorrelatedsetofassociationstests1873

forthisanalysis,weprunedallcollectionsofvariantsusingtheLD-clumpprocedure1874

(parameters–clump-p10.1–clump-p20.1–clump-r20.01)ofthePLINKsoftware1875

package90,whichrequiredvariantstohavepairwiser2<0.01.Weperformedthis1876

procedurefor(a)nonsynonymousvariantswithin94previouslyestablishedT2D1877

GWASlociand(b)nonsynonymousvariantsexome-wide.Forthe1,0591878

nonsynonymousvariantswithinestablishedT2DGWASlociachievingp<0.05inthe1879

exomesequenceanalysis,thedirectionsofeffectwereconcordant(bothOR>1or1880

bothOR<1)withtheexomearrayanalysisfor61.3%ofvariants.Thisfraction1881

decreased(asexpected)forhigherp-valuethresholds(e.g.49.4%atp>0.5)and1882

whenonlyvariantsoutsideofT2DGWASlociwereanalyzed(51.9%atp<0.05).1883

1884

Toestimatethefractionoftrueassociationsamongthesetofvariantsachieving1885

significancebelowathresholdp(e.g.p<0.05),wemodeledthesetofvariantsasa1886

mixtureofproportionsxpoftrulyassociatedvariants(OR≠1)and(1-xp)oftrulynon-1887

associatedvariants(OR=1).Weassumednon-associatedvariantshavea50%1888

chanceofaconcordantdirectionofeffectbetweenthetwoanalyses,andtruly1889

associatedvariantshaveagreaterchanceaccordingtotheirestimatedeffectsize.1890

Specifically,assumingthattheobservedeffectsizeforavariantfollowsanormal1891

distributionwithmeanequaltothetrueeffectandvariancethatscalesinversely1892



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withsamplesize,weestimatedtheprobabilitypiofproducingaconcordanteffect1893

forvariantvias1894

p! = Pr 𝑁 |β|,𝜎𝑁!"𝑁!"

> 0

where|β|istheabsolutevalueoftheestimated(fromtheexomesequenceanalysis)1895

logarithmoftheoddsratio,𝜎istheestimatedstandarderrorofthelogarithmofthe1896

oddsratio,Nexistheeffectivesamplesizeoftheexomesequenceanalysis,andNeais1897

theeffectivesamplesizeoftheexomearrayanalysis.1898

1899

Theexpectedfractionofvariantsexhibitingconcordantdirectionofeffectisthen1900

𝑓! =𝑝! 𝑥!

!!!!!𝑉!

+ 0.5 1− 𝑥!

whereVpisthenumberofvariantsintheset.Basedontheobservedfraction𝑓!of1901

variantswithconcordantdirectionsofeffect,wethusestimatedxpby1902

𝑥! =

𝑓! 𝑉! − 0.5 𝑉!𝑝! − 0.5 𝑉!!

!!! (1)

Tocalculatea95%confidenceinterval(CI)forxp,wefirstestimateda95%CIforfp1903

usingtheJeffreysintervalmethod113,asimplementedintheRsoftwarepackage1904

(https://www.r-project.org),andwethenusedequation(1)toconvertitslower1905

andupperboundstolowerandupperboundsonthecorrespondingconfidence1906

intervalforxp.1907

1908

Probabilityofcausalassociation1909



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Theestimatedvaluesforxpcanbeinterpretedasestimatesoftheposterior1910

probabilitythatavariantwithp<0.05inouranalysisistrulyassociatedwithT2D1911

ratherthanduetochance.Asourultimategoalwastoquantifytheprobabilityof1912

causalassociation,ratherthanjusttrueassociation,wemodeledtheprobabilityof1913

variantassociationasafunctionof(a)theprobabilityofcausalassociation(PPAc),1914

influencedinturnbythelikelihoodthatthevariantresultsingeneloss-of-function1915

aswellasthelikelihoodthatthegeneisrelevanttoT2D;and(b)theprior1916

probabilityofindirectassociation(PPAi),influencedinturnbythelikelihoodthat1917

thevariantisinLDwithanearbybutdifferentvariantthatiscausallyassociated1918

withT2D.Undertheassumptionthatcausalandindirectassociationsaredisjoint1919

events,thismodelexpressesPPAas1920

𝑃𝑃𝐴 = 𝑃𝑃𝐴! + 𝑃𝑃𝐴!

1921

Preciselydeterminingwhichcodingvariantassociationsareinfactcausalrequires1922

finemappingofallnearbyvariantsinlargesamplesizes6,whichiscurrently1923

infeasibleforthemostlyrarevariantsobservedinourstudy.Sincewecouldnot1924

accuratelycalculatespecificvaluesofPPAcandPPAiforeachvariant,weinstead1925

usedestimatesoftheaveragetheproportionofassociationsthatarecausal(α),1926

where𝛼istheprobabilityofcausalassociationconditionalonatrueassociation,1927

ratherthantheabsoluteprobabilityofcausalassociation.Weconsideredtwomeans1928

toestimateα.1929

1930



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First,recentanalyseshaveattemptedtoassessthecontributionofnonsynonymous1931

variantstoT2Dorsimilartraits,eitherbydirectlyestimatingtheproportionof1932

associationsthatareduetononsynonymousvariants79orbymeasuringthe1933

proportionofheritabilityexplainedbynonsynonymousvariants78.Theseanalyses1934

suggestthat~10%ofT2Dassociationsarelikelytobeduetononsynonymous1935

variants.Asthesecalculationsapplytoallassociationsinthegenome,ratherthan1936

thoseinwhichatleastonenonsynonymousvariantachievessignificance,theylikely1937

underestimatetheproportionofnonsynonymousassociationsthatarecausal.1938

1939

Second,arecentexomearraystudyidentified40exome-widesignificant1940

nonsynonymousvariantassociationsandthencalculatedtheprobabilityofcausal1941

associationforeach(viacrediblesetanalysis)17.Thereportedaverageprobabilityof1942

causalassociationacrossthesevariantsof49.2%providesadirectestimateofα.1943

Thisestimateislikelylessbiasedthanthatbasedongenome-wideanalysesofall1944

T2Dassociations,butitisbasedonasmallnumberofassociationsandthushasa1945

highvariance.1946

1947

Basedontheseconsiderations,weconsideredvaluesof10%,30%,and50%forα.1948

andused30%asourdefaultvalueforanalysesreportedinthemainmanuscript.1949

Foranyvalueofxp,representingthefractionoftrueassociationsatagivenp-value1950

threshold,wecalculatedavaluefor𝑥!! ,representingthefractionofcausal1951

associationsatagivenp-valuethreshold,as𝑥!! = 𝛼𝑥!.Underthismodel,usinga1952



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differentvalueforα(e.g.50%or10%)wouldscalePPAcestimateslinearly(e.g.5/31953

or1/3ashigh).1954

1955

Incorporationofpriorlikelihoodintoposteriorprobabilityestimations1956

Followingpreviouswork81,theposteriorprobabilityofcausalassociation𝑥!! canbe1957

expressedasacombinationoftheprioroddsofcausalassociationforthevariant,π1958

(i.e.thebelief,priortoobservinganygeneticassociationdata,thatthevariantis1959

causallyassociatedwithT2D),andtheBayesfactorforcausalassociationofthe1960

variantcalculatedfromgeneticassociationdata,BFc:1961

𝑃𝑂! = 𝐵𝐹!𝜋

1− 𝜋 (2)

wherePOcistheposterioroddsofcausalassociationexpressedas1962

𝑃𝑂! = 𝑃𝑃𝐴!/(1− 𝑃𝑃𝐴!) (3)

Weusea“c”subscriptinPOcandBFctoemphasizethattheyareposteriorodds(and1963

Bayesfactors)forcausalassociation,ratherthanjusttrueassociation.1964

1965

Givenanestimate𝑥!! oftheposteriorprobabilityofcausalassociation(i.e.PPAc)for1966

aclassofvariants(e.g.thosesatisfyingp<0.05),aswellasapriorprobabilityof1967

causalassociationπforthesameclassofvariants,wecancalculateanestimateof1968

theaverageBayesfactorforvariantsintheclassas: 1969

𝐵𝐹!! =

𝑥!!

1− 𝑥!!1− 𝜋𝜋 (4)

Here,𝐵𝐹!! denotestheaverageBayesfactorforcausalassociation(i.e.theratioof1970

thelikelihoodoftheobserveddataunderthemodelofcausalassociationtothe1971



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91

likelihoodoftheobserveddataunderthemodelofnoassociation)forvariantswith1972

p-valuebelowagivenp.Wenotethatthisequationindirectlyinfersanaverage1973

Bayesfactorfromadirectestimateofanaverageposterior(xpc)andaspecified1974

priorπ,whichisdifferentfromhowBayesfactorsareusuallycalculated.1975

1976

Undertheassumptionthattherelationshipbetweenavariant’sπandPOcis,given1977

itsobservedp-value,conditionallyindependentofallothervariantproperties(i.e.1978

dependenceonpropertiessuchassamplesizeisentirelycapturedbytheobserved1979

p-value),wecalibratedtherelationshipbetweenp-valueandBFpcusing1980

nonsynonymousvariantswithinGWASloci.Wemodeledπforsuchvariants1981

assuming(a)onaverage1.1geneswithin250kbofeachGWASsignalharbors1982

codingvariantsassociatedwithT2D;(b)missensevariantsareamixtureoffully1983

benignandfullyprotein-inactivatingvariants12;(c)onlyinactivatingmissense1984

variants;and(d)one-thirdofmissensevariantsareinactivating(asestimatedby1985

theaverageweightofmissensevariantsinourmasks).Basedonthe595genes1986

withinthe94T2DGWASlociinouranalysis,thisyieldedapriorestimateof1987

0.057 = 1.1× 94 595 × 0.33.1988

1989

ThegenepriorwasinspiredbytheoftenimplicitexpectationthataGWASsignal1990

usuallyrepresentsasinglecausalvariant114affectingasinglegene(although1991

multipleeffectorgenesmaybemorecommonthanpreviouslythought3).Toassess1992

thesensitivityofourresultstotheassumptionof1.1disease-relevantgenesper1993



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T2DGWASlocus,werepeatedallcalculationswiththeadditionalchoicesof0.5and1994

2genesperGWASlocus(SupplementaryFigure21ab).1995

1996

Wecalculatedthevariantpriorbasedonthemeanweightofvariantsinourdataset1997

ascomputedforthe“weighted”gene-leveltest,astheseweightsweredesignedto1998

directlyestimatetheprobabilitythatvariantsinamaskcausefulllossoffunction.1999

Thiscalculationproducedapriorestimateof34.2%fornonsynonymousvariantsin2000

ourdataset,notfarfromapreviouslyreportedvalueof25%12.Wethususedavalue2001

of33%forthevariantpriorinourmainanalysis,withvaluesof40%and25%used2002

forcomparison(SupplementaryFigure21cd).2003

2004

Throughthepriorprobabilityofcausalassociationfornonsynonymousvariantsin2005

T2DGWASlociof0.057,andequations(1)-(4)above,weproducedalookuptable2006

mappingvariantp-valuestoBayesfactorsofcausalassociation(BFc).Forany2007

subsequentvariantvwithobservedp-valuep(v)andauser-specifiedprioronthe2008

relevanceofitsgenetoT2D,wethencalculateditsposteriorlikelihoodof2009

associationbymappingp(v)toBFcandthenemployingequations(2)and(3)to2010

calculateanestimatedposteriorprobabilityofcausalassociation(PPAc).Although2011

notpresentedhere,lowerandupperconfidenceintervalsonPPAccanalsobe2012

estimatedbyrepeatingthisprocedureusingthelowerandupperconfidence2013

intervalsforxpcinequation(4).2014

2015

SensitivityofPPActomodelingparameters2016



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Theabovecalculationsrelyontwoparameters,thespecificvaluesofwhichwill2017

affectfinalPPAcestimates.First,theyrequireaparameterfortheproportionoftrue2018

nonsynonymousassociationsthatarecausal.Asdescribedaboveandinthetext,we2019

usedavalue–of30%–inbetweenapublishedestimateoftheproportionof2020

nonsynonymousassociationswithinGWASlocithatarecausal(49.2%)anda2021

publishedestimateoftheproportionofcausalassociationsthatarenonsynonymous2022

(~10%).Usingadifferentvalue(e.g.50%or10%)wouldscalethePPAcestimates2023

linearly(e.g.5/3or1/3ashigh).2024

2025

Inaddition,calculationsinvolvingauser-specifiedpriorrequireaparameterforthe2026

proportionofnonsynonymousvariantsinGWASlocithatcausallyinfluenceT2D2027

risk(priortoanyobservedassociations).ThisparameterdoesnotaffectPPAc2028

estimatesgenome-wideorwithinGWASloci,aswedirectlyestimatePPAcestimates2029

forthesegenesfromourdataandthereforedonotrequireauser-specifiedprior.2030

Althoughwedecomposethisparameterintotwo–aparameterfortheproportionof2031

geneswithinT2DGWASlocithatarerelevanttodiseaseandaparameterforthe2032

proportionofmissensevariantswithinagenethatresultinlossoffunction–only2033

theproductofthetwoparametersisusedinthemodel.SupplementaryFigure212034

showstheimpactofdifferentvaluesforthesetwoparameters.2035



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PAX4

SLC30A8WFS1 KCNJ11SLC16A11

SFI1MC4R

b

0

2

4

6

8

10

Gene−level associations

Chromosome

−lo

g 10(p

)

1 2 3 4 5 6 7 8 10 12 14 17 20 23

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MC4R

PAM

SLC30A8

IGFBPL1BICD1

ING3 HNF1AMAP3K15

PDX1

c

0 20 40 60 80

1e−

071e

−05

1e−

031e

−01

SLC30A8 progressive gene−level analysis

# variants removed

P−

valu

e

02

46

log(

Odd

s R

atio

)

d SLC30A8 variants

��

p.Glu2

Gly

@@@@@

p.Thr7Met

��

p.Asn

11His

@@@@@

p.Met17CysfsTer6

��

p.Tyr1

8His

@@@@@

p.Tyr18Cys

��

p.Ala1

9Val

@@@@@

c.71+2T>A

��

p.Lys

34Glu

@@@@@

p.Glu44Asp

��

p.Leu

45Met

@@@@@

p.Gly49Ser

��

p.Cys

53Ty

r

@@@@@

p.His54Leu

��

p.Glu6

1Glnf

sTer

26

@@@@@

p.Glu66Lys

��

p.Leu

74Arg

@@@@@

p.Ile80Met

��

p.Phe

82Le

u

@@@@@

p.Ala87Thr

��

c.271

+1G>A

@@@@@

p.Ser113Asn

��

p.Leu

118P

ro

@@@@@

p.Ser124Leu

��

p.Ser

125L

eu

@@@@@

p.Arg131Trp

��

p.Gly1

35Val

@@@@@

p.His137Pro

��

p.Arg

138T

er

@@@@@

c.419-1G>C

��

p.Glu1

40Ala

@@@@@

p.Ile141Leu

��

p.Cys

150T

yr

@@@@@

p.Trp152Arg

��

p.Val1

57Met

@@@@@

p.Arg165His

��

p.Arg

165C

ys

@@@@@

p.Gln174Ter

��

p.Ala1

75Val

@@@@@

p.Ile179Thr

��

p.Ser

182P

ro

@@@@@

p.Ala188Ser

��

p.Asn

189A

sp

@@@@@

c.572+1G>A

��

p.Cys

200T

er

@@@@@

p.His203Arg

��

p.Asn

211S

er

@@@@@

p.Val214Ile

��

p.Ala2

16Pro

@@@@@

p.Val219Glu

��

p.Gln2

27Te

r

@@@@@

p.Ser230Arg

��

p.Ser

230A

sn

@@@@@

p.Ile237Thr

��

p.Lys

241A

sn

@@@@@

p.Lys241Gln

��

p.Tyr2

44Cys

@@@@@

p.Tyr244His

��

p.Asp

248A

sn

@@@@@

p.Pro249Ser

��

p.Ile2

66Val

@@@@@

p.Ile272Val

��

p.Ile2

72Met

@@@@@

p.Tyr284Ter

��

p.Asp

295G

lu

@@@@@

p.Asp295Asn

��

p.Gly2

96Arg

@@@@@

p.Leu308Gln

��

p.Met3

10Val

@@@@@

p.Gln312Arg

��

p.Val3

19Ile

@@@@@

p.Thr321Ile

��

p.Ser

327T

hrfsT

er55

@@@@@

p.Ser327Thr

��

p.Gln3

28Te

r

@@@@@

p.Val330Phe

��

p.Arg

331G

ln

@@@@@

p.Ala335Asp

��

p.Leu

347P

ro

@@@@@

p.Ile349Phe

��

p.Ile3

49Asn

@@@@@

p.Met351Leu

��

p.Asp

356H

is

@@@@@

p.Pro359Leu

��

p.Pro

367S

er

Case

Control

Contribution



https://doi.org/10.1101/371450


a

Bac

kgro

und

Gen

e se

t

0.0

0.2

0.4

0.6

0.8

1.0

●●●●●●

●

●

●

●

●

p=0.0012

Per

cent

iles

Monogenic b

●

Bac

kgro

und

Gen

e se

t

0.0

0.2

0.4

0.6

0.8

1.0

●●●●●●

●

●

p=0.0061

Drug targets c

Bac

kgro

und

Gen

e se

t

0.0

0.2

0.4

0.6

0.8

1.0

●●●●●●●●●

●●●●

●●●●

●●●

●

●●●

●●

●●

●●

●

p=0.0052

Mouse NIDD d

Bac

kgro

und

Gen

e se

t

0.0

0.2

0.4

0.6

0.8

1.0

p=7.2e−06

Mouse impairedglucose tolerance e

Bac

kgro

und

Gen

e se

t

0.0

0.2

0.4

0.6

0.8

1.0

●●

●

●

●●●●

●●

●

p=0.0088

GWAS genes f

1/ 2

.71/

1.6

11.

62.

7

Odd

s R

atio

T2D drug target effect sizes

GL

P1R

IGF

1R

PP

AR

G

INS

R

Agonists

1/ 2

.71/

1.6

11.

62.

7

Odd

s R

atio

T2D drug target effect sizes

SL

C5A

2

DP

P4

KC

NJ

1

KC

NJ

8

Inhibitors



https://doi.org/10.1101/371450


a

0

5

10

15

20

25

30

Imputed GWAS associations

Chromosome

−lo

g 10(p

)

1 2 3 4 5 6 7 8 9 11 13 15 18 22

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●

●

●●●●●●●●●●

●●

●●

●

●

●

●

TCF7L2

KCNQ1

CDC123

CDKAL1SLC30A8IGF2BP2

CTBP1ASCL2KCNJ11 HNF4A

KIF11ZMIZ1IRS1 JAZF1 SFI1

GPSM1SPRY2EML4PPARGWFS1

b

●● ●

●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

5 10 15

0.00

20.

004

0.00

60.

008

LVE of top 50 Imputed GWAS and sequence associations

Rank

LVE

●

●

●

● ●● ●

●● ● ● ● ● ● ● ● ● ● ●

●●

●

●

●

●

●

●

●

● ● ●●

●● ● ● ●

●

0.15

0.20

0.25

0.30

Rat

ioExomesImputed GWASRatio

c

●

●

●

●

●

●

●

●

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●

●

●

●

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●

●

●

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●●

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●

●

●

●

0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Rank comparison for mouse NIDD genes

Exomes Percentile

GW

AS

Per

cent

ile

GADD45GIP1

PPP1R3CPPP1R3A

SNAP25

SLC2A4

SLC2A2

CYB5R4

CDKAL1

PRKCI

PPARD

HNF1A

HMGA1

FOXM1

FEM1B

PDX1

PBX1NOS3

MAFA

MADD

LEPRIRS2

IRS1

CTF1

ASIP

AKT2

LEP

INS

GCK

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https://doi.org/10.1101/371450


Exomes

p-value cutoff

Exome chip

Fraction concordant

Frac

tion

true

asso

ciat

ions

Calibrate from prior model at GWAS loci

1.1 effector genes per locus

1/3 of missense mutations loss-of-function

0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Researcher gene or variant prior

Compare direction of effect

Fraction of causal coding associations

Mahajan et al, 2018 (0.49) Finucane et al, 2015 (~0.10)

Pickrell, 2014 (~0.10)

0.001 0.005 0.020 0.050

020

040

060

080

0

Causal associations at T2D GWAS loci

P−value

# as

soci

atio

ns

0.0

0.2

0.4

0.6

0.8

1.0

Est

imat

ed fr

actio

n ca

usal

ass

ocia

tions

TotalFrac. causal

0.001 0.005 0.020 0.050

23

45

Map to Bayes Factor

P−value

Bay

es F

acto

r

1.0 1.5 2.0 2.5 3.0 3.5 4.0

0.0

0.2

0.4

0.6

0.8

Probability of nonsynonymous variant association

Observed −log10(P)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Prio

r pr

obab

ility

of g

ene

rele

vanc

e

Gene

PriorCostBenefitVariantassoc.

Gene-levelassoc. Customize

PosteriorDecision

Portal

0 50000 150000 250000

0.0

0.2

0.4

0.6

0.8

1.0

Predicted power to detect known T2D drug targets

Sample Size

Pow

er

INSRGLP1RPPARGDPP4SLC5A2IGF1RKCNJ1KCNJ8

a b Decision support from exome sequence data

c d e f



https://doi.org/10.1101/371450


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