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

• 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
• 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

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.

项目摘要 我们现在知道，超过90％的普通疾病因果变异，包括心血管疾病和 许多癌症位于基因组的非编码区域。因此，一张普通疾病风险的完整图片 需要在整个基因组中进行分析的变体，而不仅仅是编码基因组。当前可用的方法 筛查普通疾病风险的重点是单基因编码变体，并且准确性，包容性，包括 通过不编码功能性全基因组变体来解释性，并输出 解释。那仍然需要使用系统的全基因组功能映射 建立预测风险模型。从他们在斯坦福大学的哈佛大学开始的先前研究和经验。 MIT，Broad Institute和Mount Sinai，Martingale Labs，Inc。团队开发了第一个综合 功能知情的疾病风险多基因模型。这个新的多基因预测模型结合了力量 贝叶斯监督机学习（ML）的新型全基因组功能变体注释 提高预测基因检验的准确性，种族包容性和可解释性的预测方法。 这个阶段我项目的目标是准备马丁加尔实验室的全基因组预测机学习 通过验证和量化模型的预测准确性和种族包容性来扩展平台 与当前的临床可用方法相比。这些模型可以在临床环境中实现 作为预测性遗传测试，可以通过风险和量身定制的预防策略（例如筛查）来分层个人 和预防药物以最大程度地减少疾病风险。在这个项目中，我们关注心血管的例子 疾病。我们将将模型性能的数量扩展到第二阶段的其他常见疾病， 从乳房，前列腺和结直肠癌开始。以当前的测试速度，即使有保守的 通过当前健康保证的报销，我们提出的基因测试产品可能会捕获80亿美元 年收入机会。我们的项目涉及通过创建新型深技术的开发 结合了整个基因组功能注释的第一个监督学习模型。这 技术的使用范围超出了医疗应用，到其他新颖和有用的应用，例如动物 模型或兽医医学。