Deep Learning Based Genetic Risk Prediction for Type 1 Diabetes

项目摘要 I型糖尿病（T1 D）是一种儿童自身免疫性疾病， 环境因素在具有T1 D遗传易感性的个体亚组中，环境触发因素 引发靶向和破坏胰岛的自身免疫反应，导致糖尿病前期 最终导致糖尿病。T1 D预防研究的一个关键障碍是识别和直接招募儿童 具有很强的遗传倾向，可将T1 D发展为预防试验。一个强大的遗传风险评分 (GRS)将允许识别T1 D高风险儿童，将他们招募到T1 D预防工作中 试验，以及随后对新干预措施的测试。我的目标是1）优化多层前馈神经网络 网络遗传风险预测因子，可用于将新生儿直接纳入T1 D预防试验;以及2） 鉴定推定的、新的T1 D引起的SNP及其相互作用。目标1的完成将提供一个 为T1 D研究社区提供更好的GRS，可用于识别具有较高遗传风险的儿童 T1 D的发展，增加未来T1 D预防临床试验的统计能力。完成 目标2将提供对T1 D发展的分子驱动因素的更深入的生物学理解， T1 D预防试验的潜在新治疗靶点。该项目的成功完成将使双方 有助于了解1型糖尿病的遗传原因，并有助于预防这种疾病。

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.