1 genetic discovery and translational decision support from ...1 genetic discovery and translational...
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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
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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
.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
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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
.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
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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
.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
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26. DepartmentofNeurology,BostonUniversitySchoolofMedicine,Boston,93
Massachusetts,USA.94
27. NationalHeartLungandBloodInstitute'sFraminghamHeartStudy,Framingham,95
Massachusetts,USA.96
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
Massachusetts,USA.117
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
ScienceCenteratHouston,Houston,Texas,USA.135
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
.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
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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
.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
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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
.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
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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
Massachusetts,USA.234
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
.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
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120. DepartmentofPreventiveMedicine,KeckSchoolofMedicine,Universityof251
SouthernCalifornia,LosAngeles,California,USA.252
121. HumanGeneticsCenter,SchoolofPublicHealth,TheUniversityofTexasHealth253
ScienceCenteratHouston,Houston,Texas,USA.254
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
.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
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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
Massachusetts,USA.281
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
Massachusetts,USA.289
142. DepartmentofMolecularBiology,MassachusettsGeneralHospital,Boston,290
Massachusetts,USA.291
143. DepartmentofBiostatistics,UniversityofLiverpool,Liverpool,UK.292
144. CenterforGenomicMedicine,MassachusettsGeneralHospital,Boston,293
Massachusetts,USA.294
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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16
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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17
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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18
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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19
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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20
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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21
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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24
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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26
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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27
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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28
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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29
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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30
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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31
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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32
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
.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
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33
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
.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
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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
.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
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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
.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
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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
deCienciayTecnologı´a2092,M9303,F677-M9407,251M,2005-C01-14502,and899
SALUD2010-2151165;andConsejoNacionaldeCienciayTecnologı´a(CONACyT)900
[FondodeCooperacio´nInternacionalenCienciayTecnologı´a(FONCICYT)C0012-901
2014-01-247974.902
.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
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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
.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
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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
.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
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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
.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
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
.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
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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
.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
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
.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
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
.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
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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
.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
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50
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
.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
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51
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
.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
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52
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
.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
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53
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
.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
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54
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
.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
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55
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
.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
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56
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
.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
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57
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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58
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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59
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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60
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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61
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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62
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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63
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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66
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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67
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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68
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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69
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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70
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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71
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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72
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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76
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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77
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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79
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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80
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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82
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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83
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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84
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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85
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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86
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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87
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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88
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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90
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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92
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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93
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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94
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PAX4
SLC30A8WFS1 KCNJ11SLC16A11
SFI1MC4R
b
0
2
4
6
8
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Gene−level associations
Chromosome
−lo
g 10(p
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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
.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
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
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.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
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Imputed GWAS associations
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TCF7L2
KCNQ1
CDC123
CDKAL1SLC30A8IGF2BP2
CTBP1ASCL2KCNJ11 HNF4A
KIF11ZMIZ1IRS1 JAZF1 SFI1
GPSM1SPRY2EML4PPARGWFS1
b
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5 10 15
0.00
20.
004
0.00
60.
008
LVE of top 50 Imputed GWAS and sequence associations
Rank
LVE
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0.15
0.20
0.25
0.30
Rat
ioExomesImputed GWASRatio
c
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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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.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
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
.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