Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets
Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional stu...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Elsevier B.V.
2011
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| Online Access: | http://hdl.handle.net/1721.1/66693 https://orcid.org/0000-0002-7887-4301 |
| _version_ | 1826193070789492736 |
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| author | Hu, Xinli Kim, Hyun Stahl, Eli Plenge, Robert Daly, Mark J. Raychaudhuri, Soumya |
| author2 | Whitaker College of Health Sciences and Technology |
| author_facet | Whitaker College of Health Sciences and Technology Hu, Xinli Kim, Hyun Stahl, Eli Plenge, Robert Daly, Mark J. Raychaudhuri, Soumya |
| author_sort | Hu, Xinli |
| collection | MIT |
| description | Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10−6) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10−5). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10−6). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (pGWAS < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10−4). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available. |
| first_indexed | 2024-09-23T09:33:18Z |
| format | Article |
| id | mit-1721.1/66693 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:33:18Z |
| publishDate | 2011 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | mit-1721.1/666932022-09-26T12:13:56Z Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets Hu, Xinli Kim, Hyun Stahl, Eli Plenge, Robert Daly, Mark J. Raychaudhuri, Soumya Whitaker College of Health Sciences and Technology Harvard University--MIT Division of Health Sciences and Technology Hu, Xinli Hu, Xinli Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10−6) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10−5). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10−6). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (pGWAS < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10−4). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available. National Institutes of Health (U.S.) (NIH/NIAMS Development Award (1K08AR055688)) National Institute of Arthritis and Musculoskeletal and Skin Diseases (U.S.) Harvard University--MIT Division of Health Sciences and Technology 2011-10-31T17:36:19Z 2011-10-31T17:36:19Z 2011-10 2011-08 Article http://purl.org/eprint/type/JournalArticle 0002-9297 1537-6605 http://hdl.handle.net/1721.1/66693 Hu, Xinli et al. “Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets.” The American Journal of Human Genetics 89 (2011): 496-506. https://orcid.org/0000-0002-7887-4301 en_US http://dx.doi.org/10.1016/j.ajhg.2011.09.002 American Journal of Human Genetics Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Elsevier B.V. Cell Press |
| spellingShingle | Hu, Xinli Kim, Hyun Stahl, Eli Plenge, Robert Daly, Mark J. Raychaudhuri, Soumya Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets |
| title | Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets |
| title_full | Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets |
| title_fullStr | Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets |
| title_full_unstemmed | Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets |
| title_short | Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets |
| title_sort | integrating autoimmune risk loci with gene expression data identifies specific pathogenic immune cell subsets |
| url | http://hdl.handle.net/1721.1/66693 https://orcid.org/0000-0002-7887-4301 |
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