(#24) Whole exome sequencing and analysis of 207 nPOD cases

PRESENTED BY: Chester Chamberlain

Authors
First NameLast NameAffiliation/Institution
ChesterChamberlainUniversity of California San Francisco
MichaelGermanUniversity of California San Francisco
MarkAndersonUniversity of California San Francisco
 

Purpose

Type 1 diabetes (T1D) is an autoimmune disease caused by the immune-mediated destruction of pancreatic beta cells. Some people initially diagnosed with T1D are later found to have a protein coding variant in a beta cell gene that reduces pancreatic insulin production, or an immune cell gene that causes the autoimmune attack on the insulin-producing beta cell. We generated a genetic dataset using cases selected from the Network of Pancreatic Organ Donors (nPOD) that can be used to screen for coding variants in known or suspected diabetes causing genes, and that can serve as a sequence dataset for a control population of T1D individuals.
 

Methods

Whole exome sequencing (WES) was performed on genomic DNA from 207 nPOD cases, including 147 cases clinically diagnosed with T1D. WES data was processed to generate a list of coding variants for each nPOD case. Coding variants were annotated with a variety of useful metrics, such as the Combined Annotation Dependent Depletion (CADD) score for predicting protein deleteriousness, the American College of Medical Genetics (ACMG) classification for predicting disease pathogenicity, and the Genome Aggregation Database (gnomAD) allele frequency for predicting how common a variant may be in the human population.
 

Summary of Results

Our current analysis suggests that several T1D cases in the nPOD collection may harbor coding variants in a beta cell or immune cell genes that underlies their diabetes. These candidates will require additional studies to determine their pathogenicity.
 

Conclusions

These data will provide a valuable resource to nPOD investigators and the broader T1D research community. The study of protein-coding variants in the T1D population may provide insight into disease mechanisms and enable screening methods for monogenic diabetes.