Integrative Data Analysis to Uncover Transcription Factors Involved in Gene Dysregulation of Nine Autoimmune and Inflammatory Diseases

Abstract

Autoimmune and inflammatory diseases are a group of > 80 complex diseases caused by loss of immune tolerance for self-antigens. The biological mechanisms of autoimmune diseases are largely unknown, preventing the development of effective treatment options. Integrative analysis of genome-wide association studies and chromatin accessibility data has shown that the risk variants of autoimmune diseases are enriched in open chromatin regions of immune cells, supporting their role in gene regulation. However, we still lack a systematic and unbiased identification of transcription factors involved in disease gene regulation. We hypothesized that for some of the disease-relevant transcription factors, their binding to DNA is affected at multiple genomic sites rather than a single site, and these effects are cell-type specific. We developed a statistical approach to assess enrichment of transcription factors in being affected by disease risk variants at multiple genomic sites. We used genetic association data of nine autoimmune diseases and identified 99% credible set SNPs for each trait. We then integrated the credible SNPs and chromatin accessibility data of 376 samples comprising 35 unique cell types, and employed a probabilistic model to identify the SNPs that are likely to change binding probability of certain transcription factors at specific cell types. Finally, for each transcription factor, we used a statistical test to assess whether the credible SNPs show enrichments in terms of changing the binding probability of that transcription factor at multiple sites. Our analysis resulted in identification of significantly enriched transcription factors and their relevant cell types for each trait. The prioritized immune cell types varied across the diseases. Our analysis proved that our predicted regulatory sites are active enhancers or promoters in the relevant cell types. Additionally, our pathway analysis showed that the majority of the significant biological pathways are immune-related. In summary, our study has identified disease-relevant transcription factors and their relevant cell types, and will facilitate discovering specific gene regulatory mechanisms of autoimmune diseases.