权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Validating the performance and inclusivity of a novel functionally-informed predictive genetic test method for polygenic disease

验证多基因疾病的新型功能信息预测基因测试方法的性能和包容性

基本信息

批准号：
10759476
负责人：
Tejal Patwardhan
金额：
$ 38.75万
依托单位：
MARTINGALE LABS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-03 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10759476
关键词：
African Animal Model Area Benchmarking Businesses Cardiovascular Diseases ClinVar Clinical Code Colorectal Cancer Data Set Development Disease Disease model Disparity East Asian Elements Enhancers Ethnic Origin Ethnic Population European Foundations Genes Genetic Screening Genome Goals Health Health Insurance Health Insurance Portability and Accountability Act Individual Insurance Carriers Latino Life Style Literature Machine Learning Malignant Neoplasms Malignant neoplasm of prostate Maps Measures Medical Mendelian disorder Methods Modeling Output Performance Pharmaceutical Preparations Phase Play Population Prevention strategy Preventive healthcare ROC Curve Research Research Personnel Risk Role Sampling South Asian Specificity Technology Testing Time Training Untranslated RNA Variant Veterinary Medicine Work biobank cardiovascular disorder risk causal variant disorder risk diverse data experience genetic element genetic predictors genetic testing genetic variant genome annotation genome-wide human morbidity human mortality improved in silico learning strategy machine learning model machine learning prediction malignant breast neoplasm multidisciplinary novel polygenic risk score predictive modeling risk mitigation risk prediction risk prediction model screening supervised learning trait usability web services whole genome

项目摘要

PROJECT SUMMARY We now know that over 90% of causal variants for common diseases, including cardiovascular disease and many cancers, lie in non-coding regions of the genome. Therefore, a complete picture of common disease risk requires analyzing variants across the whole genome, not just the coding genome. Currently available methods to screen for common disease risk focus on monogenic coding variants and are limited in accuracy, inclusivity, and interpretability by not incorporating functional whole-genome variants into their training methods and output interpretation. Thus, there is still an unmet need for the use of systematic whole-genome functional mapping in building predictive risk models. Drawing on prior research and experience from their time at Harvard, Stanford, MIT, the Broad Institute, and Mount Sinai, the Martingale Labs, Inc. team has developed the first comprehensive functionally informed polygenic model for disease risk. This new polygenic prediction model combines the power of novel genome-wide functional variant annotations with a Bayesian supervised machine learning (ML) prediction method to improve the accuracy, ethnic inclusivity, and interpretability of predictive genetic tests. The goal of this Phase I project is to ready Martingale Labs’ whole-genome predictive machine learning platform for scale by validating and quantifying the model’s predictive accuracy and ethnic inclusivity as compared to current clinically available methods. These models can be implemented in clinical settings as predictive genetic tests to help stratify individuals by risk and tailor preventative strategies such as screenings and preventative medications to minimize disease risk. In this project, we focus on the example of cardiovascular disease. We will expand the quantification of our model performance to other common diseases in Phase II, starting with breast, prostate, and colorectal cancer. At the current rate of testing, even with conservative reimbursement by current health insurers, our proposed genetic testing product could capture an $8 Billion annual revenue opportunity. Our project involves the development of a new type of deep technology by creating the first supervised learning models that incorporate functional annotations of the whole genome. This technology’s use extends beyond medical applications to other novel and useful applications, such as animal models or veterinary medicine.

项目总结