Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases
Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations...
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| Format: | Article |
| Language: | en_US |
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Oxford University Press
2016
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| Online Access: | http://hdl.handle.net/1721.1/105218 |
| _version_ | 1811091107339042816 |
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| author | Li, Yue Kellis, Manolis |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Li, Yue Kellis, Manolis |
| author_sort | Li, Yue |
| collection | MIT |
| description | Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants from summary statistics across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the nine autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models. |
| first_indexed | 2024-09-23T14:57:08Z |
| format | Article |
| id | mit-1721.1/105218 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:57:08Z |
| publishDate | 2016 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/1052182022-10-01T23:33:37Z Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases Li, Yue Kellis, Manolis Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Li, Yue Kellis, Manolis Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants from summary statistics across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the nine autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models. National Institutes of Health (U.S.) (Grants R01-HG004037, RC1- HG005334, R01-HG008155 and R01 HG004037) 2016-11-04T20:46:59Z 2016-11-04T20:46:59Z 2016-06 2016-07 Article http://purl.org/eprint/type/JournalArticle 0305-1048 1362-4962 http://hdl.handle.net/1721.1/105218 Li, Yue, and Manolis Kellis. “Joint Bayesian Inference of Risk Variants and Tissue-Specific Epigenomic Enrichments across Multiple Complex Human Diseases.” Nucleic Acids Research 44.18 (2016): e144–e144. en_US http://dx.doi.org/10.1093/nar/gkw627 Nucleic Acids Research Creative Commons Attribution-NonCommercial 4.0 International http://creativecommons.org/licenses/by-nc/4.0/ application/pdf Oxford University Press Oxford University Press |
| spellingShingle | Li, Yue Kellis, Manolis Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases |
| title | Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases |
| title_full | Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases |
| title_fullStr | Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases |
| title_full_unstemmed | Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases |
| title_short | Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases |
| title_sort | joint bayesian inference of risk variants and tissue specific epigenomic enrichments across multiple complex human diseases |
| url | http://hdl.handle.net/1721.1/105218 |
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