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: 7667260
• 负责人: DAVID McLeod HERRINGTON
• 金额: $ 39.81万
• 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 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7667260
• 关键词: Accounting; Adverse effects; Algorithms; Atherosclerosis; Bibliography; Binding Sites; Cardiovascular system; Cholesterol; Classification; Collaborations; Complex; Consultations; Data; Data Set; Databases; Development; Disease; Entropy; Environment; Environmental Exposure; Environmental Risk Factor; Exons; Functional disorder; Funding; Genes; Genetic; Genome Scan; Genotype; Goals; Individual; LDL Cholesterol Lipoproteins; Linkage Disequilibrium; Logistic Regressions; Machine Learning; Medicine; Methods; Metric; Modeling; Nucleic Acid Regulatory Sequences; Pathway Analysis; Pathway interactions; Penetrance; Performance; Persons; Pharmacogenetics; Phenotype; Predisposition; Preventive; Preventive Intervention; Principal Investigator; Probability; Public Health; Publications; RNA Splicing; 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; data sharing; density; design; disease phenotype; disorder risk; gene environment interaction; genome wide association study; genome-wide; high risk; improved; insight; meetings; novel; novel strategies; novel therapeutic intervention; open source; predictive modeling; premature atherosclerosis; programs; simulation; trait; transcription factor; web site

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）： 该应用程序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（特征）之间的复杂相互作用，这些因素可用于帮助识别特定疾病或功能障碍的高风险个体，并提供有关条件的病理生理学的新见解。1）使各种统计机器学习方法适应模拟高密度基因组扫描和环境暴露数据的分析，并评估它们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从两个NHLBI资助的基因组收集的全基因组SNP基因型数据，广泛关联研究：a）预测过早动脉粥样硬化的SNP和动脉粥样硬化（SEA）研究，和B）预测LDL胆固醇的胆固醇和他汀类药物的药物遗传学（CAPS）研究; 3）开发一个特定的研究公开访问的网站，旨在帮助传播该项目的方法和结果和4）支持NIH范围内的基因和环境倡议（GEI）。该提案代表了一种独特的合作，专注于开发新方法，以更有效地识别相互作用的遗传和环境因素，这些因素可解释常见心血管和其他疾病表型风险的变化。如果风险部分由基因-环境相互作用决定，则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，这些途径可能是新的治疗干预措施的适当靶点。总之，改进的风险分层和新的病理生理学见解有望减少疾病负担，加速实现真正的个性化医疗。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确定遗传和环境因素之间的关系，然后可以用来确定疾病的高风险人群。确定影响一个人患病风险的特定遗传和/或环境因素可能有助于医生降低患病风险，并揭示新的疾病治疗方法。(End摘要）

项目成果

Comparative analysis of methods for detecting interacting loci.

• DOI: 10.1186/1471-2164-12-344
• 发表时间: 2011-07-05
• 期刊: BMC genomics
• 影响因子: 4.4
• 作者: Chen L;Yu G;Langefeld CD;Miller DJ;Guy RT;Raghuram J;Yuan X;Herrington DM;Wang Y
• 通讯作者: Wang Y

A Ground Truth Based Comparative Study on Detecting Epistatic SNPs.

• DOI: 10.1109/bibmw.2009.5332132
• 发表时间: 2009-11-01
• 期刊: Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
• 作者: Chen L;Yu G;Miller DJ;Song L;Langefeld C;Herrington D;Liu Y;Wang Y
• 通讯作者: Wang Y