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

描述(由申请人提供)： 这一应用的重点是开发和验证新的计算方法，以确定遗传和环境因素(特征)之间的复杂相互作用，这些方法可用于帮助识别特定疾病或功能障碍的高危个体，并为所述条件的病理生理学提供新的见解。应用的具体目标包括：1)将各种统计机器学习方法应用于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SNPs和环境因素联合预测二元性状的能力；2)将所描述的特征选择和建模技术应用于从NHLBI资助的两项全基因组关联研究中收集的全基因组SNP基因数据：a)SNPs与动脉粥样硬化(SEA)研究预测过早动脉粥样硬化，以及b)胆固醇和他汀类药物的药物遗传学(CAPs)研究预测低密度脂蛋白胆固醇；3)开发一个专门针对研究的可公开访问的网站，旨在帮助传播该项目的方法和结果；4)支持全美卫生研究院的基因与环境倡议(GEI)。这项建议代表了一项独特的合作，重点是开发新的方法，以更有效地确定导致常见心血管疾病和其他疾病表型风险变化的相互作用的遗传和环境因素。如果风险在一定程度上是由基因-环境相互作用确定的，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/或环境因素可能揭示新的生物途径，将成为新的治疗干预的合适靶点。总之，改进的风险分层和新的病理生理学见解有望减轻疾病负担，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因素和环境因素之间的关系，然后用这些方法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，并揭示疾病的新疗法。(摘要结束)

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)