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Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records
Autoimmune diseases represent a significant medical burden affecting up to 5–8% of the U.S. population. While genetics is known to play a role, studies of common autoimmune diseases are complicated by phenotype heterogeneity, limited sample sizes, and a single disease approach. Here we performed a t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5071319/ https://www.ncbi.nlm.nih.gov/pubmed/27812365 http://dx.doi.org/10.3389/fgene.2016.00185 |
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author | Restrepo, Nicole A. Butkiewicz, Mariusz McGrath, Josephine A. Crawford, Dana C. |
author_facet | Restrepo, Nicole A. Butkiewicz, Mariusz McGrath, Josephine A. Crawford, Dana C. |
author_sort | Restrepo, Nicole A. |
collection | PubMed |
description | Autoimmune diseases represent a significant medical burden affecting up to 5–8% of the U.S. population. While genetics is known to play a role, studies of common autoimmune diseases are complicated by phenotype heterogeneity, limited sample sizes, and a single disease approach. Here we performed a targeted genetic association study for cases of multiple sclerosis (MS), rheumatoid arthritis (RA), and Crohn's disease (CD) to assess which common genetic variants contribute individually and pleiotropically to disease risk. Joint modeling and pathway analysis combining the three phenotypes were performed to identify common underlying mechanisms of risk of autoimmune conditions. European American cases of MS, RA, and CD, (n = 119, 53, and 129, respectively) and 1924 controls were identified using de-identified electronic health records (EHRs) through a combination of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) billing codes, Current Procedural Terminology (CPT) codes, medication lists, and text matching. As expected, hallmark SNPs in MS, such as DQA1 rs9271366 (OR = 1.91; p = 0.008), replicated in the present study. Both MS and CD were associated with TIMMDC1 rs2293370 (OR = 0.27, p = 0.01; OR = 0.25, p = 0.02; respectively). Additionally, PDE2A rs3781913 was significantly associated with both CD and RA (OR = 0.46, p = 0.02; OR = 0.32, p = 0.02; respectively). Joint modeling and pathway analysis identified variants within the KEGG NOD-like receptor signaling pathway and Shigellosis pathway as being correlated with the combined autoimmune phenotype. Our study replicated previously-reported genetic associations for MS and CD in a population derived from de-identified EHRs. We found evidence to support a shared genetic etiology between CD/MS and CD/RA outside of the major histocompatibility complex region and identified KEGG pathways indicative of a bacterial pathogenesis risk for autoimmunity in a joint model. Future work to elucidate this shared etiology will be key in the development of risk models as envisioned in the era of precision medicine. |
format | Online Article Text |
id | pubmed-5071319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50713192016-11-03 Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records Restrepo, Nicole A. Butkiewicz, Mariusz McGrath, Josephine A. Crawford, Dana C. Front Genet Genetics Autoimmune diseases represent a significant medical burden affecting up to 5–8% of the U.S. population. While genetics is known to play a role, studies of common autoimmune diseases are complicated by phenotype heterogeneity, limited sample sizes, and a single disease approach. Here we performed a targeted genetic association study for cases of multiple sclerosis (MS), rheumatoid arthritis (RA), and Crohn's disease (CD) to assess which common genetic variants contribute individually and pleiotropically to disease risk. Joint modeling and pathway analysis combining the three phenotypes were performed to identify common underlying mechanisms of risk of autoimmune conditions. European American cases of MS, RA, and CD, (n = 119, 53, and 129, respectively) and 1924 controls were identified using de-identified electronic health records (EHRs) through a combination of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) billing codes, Current Procedural Terminology (CPT) codes, medication lists, and text matching. As expected, hallmark SNPs in MS, such as DQA1 rs9271366 (OR = 1.91; p = 0.008), replicated in the present study. Both MS and CD were associated with TIMMDC1 rs2293370 (OR = 0.27, p = 0.01; OR = 0.25, p = 0.02; respectively). Additionally, PDE2A rs3781913 was significantly associated with both CD and RA (OR = 0.46, p = 0.02; OR = 0.32, p = 0.02; respectively). Joint modeling and pathway analysis identified variants within the KEGG NOD-like receptor signaling pathway and Shigellosis pathway as being correlated with the combined autoimmune phenotype. Our study replicated previously-reported genetic associations for MS and CD in a population derived from de-identified EHRs. We found evidence to support a shared genetic etiology between CD/MS and CD/RA outside of the major histocompatibility complex region and identified KEGG pathways indicative of a bacterial pathogenesis risk for autoimmunity in a joint model. Future work to elucidate this shared etiology will be key in the development of risk models as envisioned in the era of precision medicine. Frontiers Media S.A. 2016-10-20 /pmc/articles/PMC5071319/ /pubmed/27812365 http://dx.doi.org/10.3389/fgene.2016.00185 Text en Copyright © 2016 Restrepo, Butkiewicz, McGrath and Crawford. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Restrepo, Nicole A. Butkiewicz, Mariusz McGrath, Josephine A. Crawford, Dana C. Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records |
title | Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records |
title_full | Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records |
title_fullStr | Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records |
title_full_unstemmed | Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records |
title_short | Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records |
title_sort | shared genetic etiology of autoimmune diseases in patients from a biorepository linked to de-identified electronic health records |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5071319/ https://www.ncbi.nlm.nih.gov/pubmed/27812365 http://dx.doi.org/10.3389/fgene.2016.00185 |
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