Data from: A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes
Cite this dataset
Chu, Xiaojing et al. (2022). Data from: A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes [Dataset]. Dryad. https://doi.org/10.5061/dryad.4f4qrfjd0
Background: The large inter-individual variability in immune-cell cell composition and function determines immune responses in general and susceptibility to immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.
Methods: Blood samples were collected from 243 patients with T1D of Dutch descent. We applied genetic association analysis on > 200 immune cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.
Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTL) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.
Conclusion: This study provides new insights into the genetic factors that affect immunological responses in T1D.
The QTL dataset was generated by mapping immune phenotypes (cell proportion and cytokine production in response to stimulation) to either heathy donors (500FG) or Type 1 diabetes patients (300DM).
All donor information was Anonymized.
European Research Council, Award: 948207
European Research Council, Award: 833247
Dutch Research Council
European Foundation for the Study of Diabetes
China Scholarship Council, Award: 201706040081