biostudies-literatureJia GU.S. Department of Health & Human Services | NIH | National Center for Advancing Translational SciencesNCATS NIH HHSNICHD NIH HHSNCRR NIH HHSUnited States Department of Defense | Defense Advanced Research Projects AgencyGift from Liz and Kent DautenNIAID NIH HHSU.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious DiseasesNHLBI NIH HHSNIMH NIH HHSU.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood InstituteU.S. Department of Health & Human Services | NIH | NIH Office of the DirectorU.S. Department of Health & Human Services | NIH | National Institute of Mental HealthNIDDK NIH HHSMedical Research CouncilODCDC CDC HHSNHGRI NIH HHSNINDS NIH HHSRafael Rivera III Memorial Foundation for Asthma ResearchNIH HHSNIGMS NIH HHSU.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases6712https://www.ebi.ac.uk/biostudies/studies/S-EPMC9640644biostudies-literatureUnknown13(1)Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across differentNature communicationsDiscerning asthma endotypes through comorbidity mapping.PMC9640644K08 HL153955RC2 GM092618U01 HL108634U19 AI095230R01 MH113362U01 HG009086P30 DK020595UL1 RR024975UG3/UH1 OD023282S10 RR025141U19 AI162310MC_QA137853R01 HL129735ARO contract W911NF1410333MC_PC_17228UL1 TR002243UL1 TR002389U01 HG004798U01 HG006378S10 OD017985UL1 TR000445R01 NS032830R01 HL122712UG3 OD023282UH3 OD023282P50 GM115305R01 HL104608R01 HL12271R01 HD074711U19 AI62310R01 MH107666U19 HL065962Kamatani YOber CHogarth DKIm HKJia GZhong XTerao CLyttle CSPividori MSchoettler NSperling AISolway JNaureckas ETCox NJAkiyama MRzhetsky AWhite SRMatsuda KKubo MfalseDiscerning asthma endotypes through comorbidity mapping.Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across different2022-01-01T00:00:00Z2022 Nov2026-05-29T18:58:02.571Z2025-04-19T22:49:20.528ZS-EPMC96406443634452210.1038/s41467-022-33628-8