Machine Learning to Identify Complex Interactions in Genome-Wide Association Data

机器学习识别全基因组关联数据中的复杂相互作用

• 批准号: 7348470
• 负责人: DAVID McLeod HERRINGTON
• 金额: $ 39.95万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-21 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7348470
• 关键词: Accounting; Adverse effects; Algorithms; Atherosclerosis; Bibliography; Binding Sites; Cardiovascular system; Cholesterol; Classification; Collaborations; Complex; Condition; Consultations; Data; Data Set; Databases; Development; Disease; Entropy; Environment; Environmental Exposure; Environmental Risk Factor; Exons; Functional disorder; Funding; Genes; Genetic; Genome; Genome Scan; Genotype; Goals; Individual; Internet; LDL Cholesterol Lipoproteins; Linkage Disequilibrium; Logistic Regressions; Machine Learning; Medicine; Methods; Metric; Modeling; Nucleic Acid Regulatory Sequences; Numbers; Pathway Analysis; Pathway interactions; Penetrance; Performance; Persons; Pharmacogenetics; Phenotype; Predisposition; Preventive; Preventive Intervention; Principal Investigator; Probability; Public Health; Publications; RNA Splicing; Range; Research; Research Design; Research Personnel; Risk; SNP genotyping; Sampling; Sensitivity and Specificity; Signal Transduction; Simulate; Site; Source Code; Staging; Stratification; Techniques; Therapeutic Intervention; Triplet Multiple Birth; Validation; Variant; abstracting; base; burden of illness; density; design; disease phenotype; disorder risk; gene environment interaction; genome wide association study; improved; insight; novel; novel strategies; novel therapeutics; open source; predictive modeling; programs; simulation; trait; transcription factor; web-accessible

DESCRIPTION (provided by applicant): The focus of this application is the development and validation of new computational approaches to identify complex interactions among genetic and environmental factors (features) which could be used to help identify individuals at high risk for a specific disease or dysfunction, and provide novel insights into the pathophysiology of the conditions in question. Specific Aims of the application include: 1 )To adapt a variety of statistical machine learning methods to the analysis of simulated high density genome scan and environmental exposure data and to evaluate their ability to identify SNPs and environmental factors that are jointly predictive of a binary trait; 2)To apply the described feature selection and model building techniques to the genome-wide SNP genotype data collected from two NHLBI-funded genome-wide association studies: a) the SNPs and Atherosclerosis (SEA) study predicting premature atherosclerosis, and b) the Cholesterol and Pharmacogenetics of Statins (CAPS) Study predicting LDL cholesterol; 3) to develop a study-specific publicly accessible web-site designed to help disseminate the methods and results of the project and 4) to support the NIH-wide Genes and Environment Initiative (GEI). This proposal represents a unique collaboration focusing on the development of new methods to more effectively identify interacting genetic and environmental factors that account for variation in risk for common cardiovascular and other disease phenotypes. If the risk is determined, in part by a gene-environment interaction, the preventive intervention could include altering the environmental exposure. Furthermore, determining specific genetic and/or environmental factors that jointly influence risk may reveal new biologic pathways that would be appropriate targets for novel therapeutic interventions. Together, improved risk stratification and new pathophysiologic insights would be expected to reduce the burden of disease and accelerate the realization of true personalized medicine. Relevance of this research to public health: This project aims to develop new approaches to identify the relationship between genetic and environmental factors which could then be used to identify people at high risk for a disease. Determining specific genetic and/or environmental factors that influence a person's risk of disease may help doctors reduce risk for disease and reveal new treatments for disease. (End of Abstract)

描述（由申请人提供）： 该应用的重点是开发和验证新的计算方法，以识别遗传和环境因素之间的复杂相互作用（特征），这些方法可用于帮助识别患有特定疾病或功能障碍的高风险的人，并提供有关所涉及疾病病理生理学的新见解。该应用程序的具体目的包括：1）使各种统计机器学习方法适应模拟的高密度基因组扫描和环境暴露数据的分析，并评估其识别共同预测二元性状的SNP和环境因素的能力； 2）将所描述的特征选择和模型构建技术应用于全基因组SNP基因型数据，这些数据从两项NHLBI资助的全基因组资助的关联研究中收集到：a）a​​）SNP和动脉粥样硬化（SEA）研究预测过早动脉粥样硬化的研究，以及B）胆固醇和药物遗传学（CAPS）研究（CAPS）研究LDLDL CHOLSTEROL; 3）开发特定于研究的公共访问网站，旨在帮助传播项目的方法和结果，以及4）支持NIH范围内的基因和环境计划（GEI）。该提案代表着一个独特的合作，重点是开发新方法，以更有效地识别相互作用的遗传和环境因素，以解释常见心血管和其他疾病表型的风险差异。如果确定风险，部分通过基因环境相互作用确定，预防性干预可能包括改变环境暴露。此外，确定共同影响风险的特定遗传和/或环境因素可能会揭示新的生物学途径，这将是新型治疗干预措施的适当靶标。预计将共同改善风险分层和新的病理生理见解，以减轻疾病的负担并加速真正的个性化医学。这项研究与公共卫生的相关性：该项目旨在开发新的方法来识别遗传和环境因素之间的关系，然后可以用来识别患有疾病高风险的人。确定影响一个人疾病风险的特定遗传和/或环境因素可能有助于医生降低疾病的风险并揭示新的疾病治疗方法。 （抽象的结尾）