{"doi":"10.1101/059329","title":"RiVIERA-beta: Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases","abstract":"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-beta ( Ri sk V ariant I nference using E pigenomic R eference A nnotations) for inference of driver variants by modelling summary statistics p-values in Beta density function across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA-beta promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA-beta 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-beta identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the 9 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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2016,"id":3070,"datarank":0.5421890769972864,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.30077339013217125,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.30077339013217125,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":4,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0463,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2016-06-16","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":14693,"name":"Sharon L. R. Kardia","orcid":"0000-0002-9853-3379","position":1,"is_corresponding":false},{"id":5418,"name":"Yue Li","orcid":"0000-0002-1529-2953","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}