Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk.
Daniel HoDenis M NyagaWilliam SchierdingRichard SafferyJo K PerryJohn A TaylorMark Hedley VickersAndreas W Kempa-LiehrJustin Martin O'SullivanPublished in: Communications biology (2021)
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.
Keyphrases
- type diabetes
- transcription factor
- genome wide
- poor prognosis
- machine learning
- case control
- binding protein
- induced apoptosis
- cardiovascular disease
- healthcare
- insulin resistance
- big data
- high throughput
- long non coding rna
- electronic health record
- high resolution
- metabolic syndrome
- signaling pathway
- genome wide association study
- mass spectrometry
- health information
- social media
- cell death
- weight loss