Deep Learning Based Genetic Risk Prediction for Type 1 Diabetes

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Project Summary Type I diabetes (T1D) is an autoimmune disease of childhood caused by a combination of genetic and environmental factors. In a subset of individuals with a genetic predisposition to T1D, environmental triggers instigate an autoimmune response which targets and damages pancreatic islets, leading to pre-diabetes and ultimately diabetes. A critical barrier in T1D prevention research is to identify and directly enroll children with a strong genetic predisposition for developing T1D into prevention trials. A robust genetic risk score (GRS) would allow for the identification of children at high-risk of T1D, their recruitment into T1D prevention trials, and subsequent testing of novel interventions. I aim to 1) optimize a multi-layer feedforward neural network genetic risk predictor that can be used to enroll newborns directly into T1D prevention trials; and 2) identify putative, novel T1D-causing SNPs, and their interactions. Completion of aim 1 would provide a better GRS to the T1D research community, which can be used to identify children with higher genetic risk of T1D development, increasing the statistical power of future T1D prevention clinical trials. Completion of aim 2 will provide a deeper biological understanding of the molecular drivers of T1D development, and potential new therapeutic targets for T1D prevention trials. Successful completion of this project will both help understand the genetic causes of type 1 diabetes and help prevent the disease.