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.