Next-Generation Algorithms in Statistical Genetics Based on Modern Machine Learning

基于现代机器学习的下一代统计遗传学算法

• 批准号: 10714930
• 负责人: Volodymyr Kuleshov
• 金额: $ 40.48万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714930
• 关键词: Algorithms; Area; Biological Assay; Clinical; Collection; Computer software; Computing Methodologies; Data; Data Set; Development Plans; Disease; Environment; Environmental Risk Factor; Foundations; Genetic; Genetic Risk; Genetic Structures; Genetic Variation; Genome; Genomics; Genotype; Haplotypes; Health; Healthcare; Human; Human Genome; Image; Link; Linkage Disequilibrium; Machine Learning; Medical; Methods; Modeling; Modernization; Outcome; Pharmacogenomics; Phenotype; Preventive Medicine; Research; Risk; Scientist; Statistical Algorithm; System; Technology; Vision; causal variant; cost; genome wide association study; genomic predictors; improved; machine learning algorithm; next generation; novel; open source; risk prediction; software systems; trait; unstructured data

PROJECT SUMMARY/ABSTRACT Advances in technology are enabling the collection of massive datasets of millions of human genomes. The long-term vision underlying this proposal is to leverage modern datasets and machine learning (ML) to under- stand how genetics and the environment determine traits and outcomes important to improving human health. Modern ML thrives on vast datasets of millions of unstructured datapoints (genomes, clinical notes, images), and stands to greatly impact statistical genetics, the field which studies the genotype-phenotype link. Improve- ments in statistical genetics have in turn the potential to elucidate the genetic basis of disease and support per- sonalized medical therapies. This proposal advances the above vision via two thrusts: (1) developing novel machine learning (ML) algo- rithms motivated by problems in statistical genetics; (2) creating open-source software systems for scientists and clinicians based on the above algorithms. Specifically, we describe a plan for the development of computa- tional methods in three broad areas in statistical genetics: modeling linkage disequilibrium and the structure of genetic variation, analyzing genome-wide association study data, and predicting risk from genetic and environ- mental factors. Within in each area, we aim to develop open-source software for key applied problems includ- ing genetic imputation, haplotyping, low-pass sequencing, causal variant identification, and risk scoring. Our research seeks to establish a foundation for statistical genetics based on modern ML and also advance ML in directions that may not be pursued in other application domains. Our methods will support technologies that have immediate applications in healthcare and that help reveal novel genetic factors that influence dis- ease; improve the accuracy of genomic prediction in domains from preventive medicine to pharmacogenomics; significantly reduce the cost of genomic sequencing assays, and ultimately improve human health.

项目摘要/摘要 技术的进步使得能够收集数百万人类基因组的大量数据集。这 该提案的长期视觉是利用现代数据集和机器学习（ML） 立即遗传学和环境如何确定对改善人类健康至关重要的特征和结果。 现代ML在数百万个非结构化数据点（基因组，临床注释，图像）的大量数据集上壮成长 并严重影响统计遗传学，这是研究基因型 - 表型链接的领域。提升- 统计遗传学中的生物反过来阐明了疾病的遗传基础和支持的潜力 Sonalized医疗疗法。 该提议通过两个推力提高了上述愿景：（1）开发新颖的机器学习（ML）算法 - 由统计遗传学问题激励的riths； （2）为科学家创建开源软件系统 和基于上述算法的临床医生。具体而言，我们描述了计算机制定的计划 统计遗传学三个广泛领域的典型方法：建模连锁不平衡和结构 遗传变异，分析全基因组关联研究数据，并预测遗传和环境的风险 心理因素。在每个区域内，我们旨在为关键应用问题开发开源软件，包括 遗传插补，单倍型，低通测序，因果变异鉴定和风险评分。 我们的研究旨在为基于现代ML的统计遗传学建立基础，并进步 ML在其他应用程序域中可能无法追求的方向。我们的方法将支持技术 在医疗保健中有直接应用的，这有助于揭示影响疾病的新遗传因素 舒适;提高从预防医学到药物基因组学的领域基因组预测的准确性； 显着降低基因组测序测定的成本，并最终改善人类健康。