Deep Learning Based Genetic Risk Prediction for Type 1 Diabetes

基于深度学习的 1 型糖尿病遗传风险预测

基本信息

批准号：
9976989
负责人：
Paul Tran
金额：
$ 4.11万
依托单位：
AUGUSTA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-10 至 2022-07-09
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9976989
关键词：
Adopted Alleles Alternative Splicing Area Autoimmune Diseases Autoimmune Responses Bioinformatics Biological Child Childhood Communities Complex Data Data Analyses Data Set Decision Making Development Diabetes Mellitus Disease Enhancers Enrollment Environmental Risk Factor Future Genes Genetic Genetic Population Study Genetic Predisposition to Disease Genetic Risk Genotype HLA Antigens Individual Insulin-Dependent Diabetes Mellitus Intervention Islets of Langerhans Logistic Regressions Longitudinal Studies Measures Medical Modeling Molecular Neural Network Simulation Newborn Infant Pathogenesis Pathway Analysis Population Prediabetes syndrome Premature Birth Prevention Research Prevention trial Publishing Quantitative Trait Loci Receiver Operating Characteristics Research Risk Sensitivity and Specificity Statistical Models Testing Time Training Variant base case control deep learning feedforward neural network genetic profiling genome wide association study high risk improved molecular subtypes neural network new therapeutic target novel predictive modeling prevent prevention clinical trial promoter recruit statistical and machine learning

项目摘要

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.

项目概要 I 型糖尿病 (T1D) 是一种由遗传和遗传因素共同引起的儿童期自身免疫性疾病。环境因素。在具有 T1D 遗传易感性的个体子集中，环境触发因素引发针对胰岛并损害胰岛的自身免疫反应，导致糖尿病前期最终患上糖尿病。 T1D 预防研究的一个关键障碍是识别并直接招募儿童具有将 T1D 纳入预防试验的强烈遗传倾向。强大的遗传风险评分 (GRS) 将允许识别 T1D 高危儿童，并招募他们参与 T1D 预防工作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器，可用于将新生儿直接纳入 T1D 预防试验；和 2) 识别推定的、新颖的 T1D 致病 SNP 及其相互作用。完成目标 1 将提供为 T1D 研究界提供更好的 GRS，可用于识别具有较高遗传风险的儿童 T1D 发展的统计能力，增加未来 T1D 预防临床试验的统计能力。完成目标 2 将提供对 T1D 发展的分子驱动因素更深入的生物学理解，以及 T1D 预防试验的潜在新治疗靶点。该项目的顺利完成将帮助了解 1 型糖尿病的遗传原因并帮助预防该疾病。