This study addresses the challenge of interpreting noncoding variants identified by genome-wide association studies (GWAS) in autoimmune diseases. The authors developed a fine-mapping algorithm, Probabilistic Identification of Causal SNPs (PICS), to identify candidate causal variants from genotyping data. They integrated these predictions with transcription and cis-regulatory element annotations derived from chromatin and RNA mapping in primary immune cells. The results show that ~90% of causal variants are noncoding, with ~60% mapping to immune-cell enhancers. These enhancers often gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10–20% directly alter recognizable transcription factor binding motifs. Most noncoding risk variants affect non-canonical sequence determinants not well-explained by current gene regulatory models. The study provides a resource for understanding autoimmunity, linking causal variants to specific immune enhancers and suggesting that many noncoding variants act by altering non-canonical regulatory sequences.This study addresses the challenge of interpreting noncoding variants identified by genome-wide association studies (GWAS) in autoimmune diseases. The authors developed a fine-mapping algorithm, Probabilistic Identification of Causal SNPs (PICS), to identify candidate causal variants from genotyping data. They integrated these predictions with transcription and cis-regulatory element annotations derived from chromatin and RNA mapping in primary immune cells. The results show that ~90% of causal variants are noncoding, with ~60% mapping to immune-cell enhancers. These enhancers often gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10–20% directly alter recognizable transcription factor binding motifs. Most noncoding risk variants affect non-canonical sequence determinants not well-explained by current gene regulatory models. The study provides a resource for understanding autoimmunity, linking causal variants to specific immune enhancers and suggesting that many noncoding variants act by altering non-canonical regulatory sequences.