Abstract
Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Here we show an eQTL (rs6679677) associated with changes to AP4B1-AS1 transcript levels in lung tissue makes the largest gene regulatory contribution to the risk of T1D development. Luciferase reporter assays confirmed allele-specific enhancer activity for the rs6679677 tagged locus in lung epithelial cells (i.e. A549 cells; C > A reduces expression, p = 0.005). Our results identify tissue-specific eQTLs for SNPs associated with T1D. The strongest tissue-specific eQTL effects were in the lung and may help explain associations between respiratory infections and risk of islet autoantibody seroconversion in young children.
Highlights
Type 1 diabetes (T1D) etiology is complex
We reasoned that we could use genotypes for T1D cases and controls to machine learn the tissue-specific-expression scores for T1D-associated variants. This approach would enable the ranking of the tissue-specific regulatory changes that contribute to the conversion of genetic risk to T1D pathology
We assigned SNPs associated with T1D to the genes they modulate through Hi-C chromatin interactions captured from primary tissues and immortalised cells
Summary
Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Consistent with our understanding of T1D pathology, we reported that the differentially expressed genes were enriched for immune activation and response pathways[6] This still did not provide any important information into the relative contributions of the tissue-specific gene regulatory effects we identified. We reasoned that we could use genotypes for T1D cases and controls to machine learn the tissue-specific-expression scores for T1D-associated variants This approach would enable the ranking of the tissue-specific regulatory changes that contribute to the conversion of genetic risk to T1D pathology. We integrated a regularised logistic regression model on European ancestry genotypes of T1D case and control to identify transcriptional changes in the lung involving AP4B1AS1 and CTLA4 (associated with rs6679677) as the largest individual contributors, through a gene regulatory mechanism, to the conversion of the genetic risk for the development of T1D. A plasmid-based luciferase reporter expression assay was performed to validate the allele-specific enhancer activity of the locus marked by rs6679677 in lung cells
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
