Predictive Modeling with Clinical and Genomic Data in COPD

利用 COPD 的临床和基因组数据进行预测建模

• 批准号: 8500430
• 负责人: Peter Castaldi
• 金额: $ 12.83万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8500430
• 关键词: Affect; American; Ancillary Study; Architecture; Area; Bioinformatics; Biological; Biology; Biomass; Caring; Case-Control Studies; Cause of Death; Chronic Obstructive Airway Disease; Clinical; Clinical Research; Collaborations; Complex; Computational algorithm; Computing Methodologies; Data; Data Set; Development; Diabetes Mellitus; Diagnostic; Disease; Disease Association; Disease susceptibility; Environment; Environmental Exposure; Epistatic Gene; Foundations; Frequencies; Funding; Future; General Population; Genes; Genetic; Genetic Predisposition to Disease; Genomics; Genotype; Goals; Head; Health; Health Policy; Heart Diseases; Hematological Disease; Hereditary Disease; Individual; Institutes; Investigation; Laboratories; Lead; Life; Link; Lung; Lung diseases; Machine Learning; Medical center; Mentors; Meta-Analysis; Methods; Mission; Modeling; Morbidity - disease rate; Non-Insulin-Dependent Diabetes Mellitus; Norway; Outcome; Pathway interactions; Patients; Pattern; Performance; Persons; Phenotype; Physicians; Population; Positioning Attribute; Predisposition; Prevention; Public Health; Pulmonary Emphysema; Pulmonary Function Test/Forced Expiratory Volume 1; Relative (related person); Research; Research Personnel; Resources; Risk Factors; Sample Size; Sampling; Single Nucleotide Polymorphism; Smoke; Smoking; Techniques; Technology; Testing; Time; Tobacco; Training; Training Programs; Translating; Translations; Validation; Vision; Work; base; case control; cigarette smoking; clinical care; clinical practice; cohort; computer science; design; gene environment interaction; gene interaction; genetic association; genetic epidemiology; genetic variant; genome wide association study; improved; insight; interest; meetings; mortality; multidisciplinary; next generation sequencing; novel; patient population; population health; predictive modeling; prognostic; respiratory; success; tool; treatment trial

Candidate: Dr. Peter Castaldi is a physician completing a period of F32-funded support. On July 1st, 2009 he will begin a full-time position at Tufts Medical Center and the Institute for Clinical Research and Health Policy Studies (ICRHPS). This position involves a 25% clinical commitment. His principal research interests are the genetic epidemiology of COPD and the translation of genomic discoveries into clinical practice and public health. His particular interests are genetic meta-analysis, gene-environment interaction, and predictive modeling with regression-based and machine-learning methods. His immediate goals are 1.) to identify novel genetic associations with COPD susceptibility and COPD- related phenotypes through the combined analysis of multiple genome-wide association (GWA) studies, 2.) to identify epistatic and gene-by-smoking interactions, and 3.) to develop accurate predictive models in chronic obstructive pulmonary disease (COPD) using clinical and genomic information. His long-term goal is to be an independent investigator with expertise in bioinformatics. His vision for achieving this goal involves developing expertise in bioinformatics so as to be able to participate in and eventually lead multidisciplinary teams in the application of computational methods to genomic datasets in order to answer important clinical questions that will improve the care of patients and population health. Environment: Dr. Castaldi will receive training in a rich, interdisciplinary environment. His principal mentor, Dr. Joseph Lau, is the head of the Center for Clinical Evidence Synthesis in the ICRHPS, and he is a worldwide leader in the field of meta-analysis and evidence synthesis. At Tufts, in addition to regular meetings with Dr. Lau, Peter will receive training in genetic evidence synthesis from leaders in the field, including Drs. John Ioannidis and Tom Trikalinos. The co-mentor of this application, Dr. Edwin Silverman, is a leading researcher in COPD genetics at the Channing Laboratory. At the Channing Laboratory, Peter will receive excellent training in respiratory genetics and genetic epidemiology, and he will have resources to state of the art high-throughput genotyping, next-generation sequencing technologies, and bioinformatics support. Dr. Castaldi will also continue his collaboration with Dr. Donna Slonim in the Tufts Computer Science Department, who will provide assistance with application of computational algorithms to genomic data and guidance as Dr. Castaldi continues to build a practical and theoretical foundation in Bioinformatics. Research: COPD is a major cause of morbidity and mortality that is of increasing public health importance. While the principal risk factor for COPD, smoking, is well-established, there is variable susceptibility in the general population to the lung damage caused by cigarette smoke. There is strong evidence supporting a genetic component to COPD susceptibility. Understanding how genes and environment interact to produce clinical COPD will allow for more accurate diagnostic tools and open new avenues of investigation for the development of COPD therapies. We propose to 1.) identify novel genetic associations with COPD susceptibility and 4 COPD-related phenotypes by performing meta-analysis on patient-level data from 4 large COPD GWA studies, 2.) identify gene-by-smoking and gene-gene interactions, and 3.) develop predictive models for COPD susceptibility and COPD-related phenotypes. In order to maximize the information obtained from genomic data, we will combine data from multiple studies (the National Emphysema Treatment Trial Genetics Ancillary Study, the Norway Case-Control Study, COPDGene, and ECLIPSE - total sample size=7,962) to increase power and employ regression-based and machine-learning methods to identify complex patterns of interaction in genotype data. Our study is designed to both explore and subsequently rigorously validate discovered main effect and interaction associations. Using predictive models, we will quantify the incremental predictive benefit of including genetic main effects and genetic interaction data to traditional clinical variables. Relevance: The proposed work will identify new genes associated with COPD and place them in a multivariate context so as to develop a better understanding of how genetic differences and environmental exposures contribute to the development of COPD. The models generated by this work will facilitate the translation of genomic discoveries to clinical practice and public health, in keeping with the NHLBI's mission to promote the prevention and treatment of heart, lung, and blood diseases and enhance the health of all individuals so that they can live longer and more fulfilling lives.

候选人：Peter Castaldi博士是一名医生，完成了F32资助的后期。 2009年7月1日，他将在塔夫茨医学中心和临床研究与健康政策研究所（ICRHPS）开始全职职位。该职位涉及25％的临床承诺。他的主要研究兴趣是COPD的遗传流行病学以及将基因组发现转化为临床实践和公共卫生。他的特殊利益是遗传荟萃分析，基因环境相互作用以及基于回归和机器学习方法的预测建模。 他的近期目标是1.）通过对多个基因组关联（GWA）研究的组合分析（GWA）研究确定与COPD易感性和COPD相关的表型的新型遗传关联，2。）以使用临床和基因的临床疾病和基因上的临床疾病（COPD）中的准确预测模型来识别上症和基因的相互作用，以及3.）。他的长期目标是成为具有生物信息学专业知识的独立研究者。他实现这一目标的愿景涉及发展生物信息学方面的专业知识，以便能够参与并最终领导多学科团队将计算方法应用于基因组数据集中，以回答重要的临床问题，以改善患者和人口健康的护理。 环境：Castaldi博士将在丰富的跨学科环境中接受培训。他的主要导师约瑟夫·劳（Joseph Lau）博士是ICRHPS临床证据中心负责人，他是荟萃分析和证据综合领域的全球领导者。在塔夫特（Tufts），除了与劳博士（Lau）的定期会议外，彼得还将接受包括Drs在内的领导者的遗传证据综合培训。 John Ioannidis和Tom Trikalinos。该应用程序的联合学者埃德温·西尔弗曼（Edwin Silverman）博士是Channing实验室COPD遗传学的主要研究人员。在Channing实验室，彼得将获得呼吸遗传学和遗传流行病学的出色培训，他将拥有最先进的高通量基因分型，下一代测序技术和生物信息学支持的资源。卡斯塔尔（Castaldi）博士还将继续与塔夫茨计算机科学系的唐娜·斯洛尼姆（Donna Slonim）博士合作，随着卡斯塔尔迪（Castaldi）博士继续在生物信息学中建立实用和理论基础，他将在计算算法应用于基因组数据和指导中提供帮助。 研究：COPD是发病率和死亡率的主要原因，这是公共健康重要性提高的。尽管吸烟的主要风险因素是良好的，但普通人群对香烟烟雾造成的肺部损害的敏感性可变。有强有力的证据支持COPD易感性的遗传成分。了解基因和环境如何相互作用以产生临床COPD将允许更准确的诊断工具，并开放开发COPD疗法的新途径。我们建议对1.）确定与COPD易感性的新型遗传关联和4种与COPD相关的表型，通过对来自4个大型COPD GWA研究的患者级数据进行荟萃分析，2。）确定基因逐种基因和基因 - 基因的相互作用，以及3.）开发COPD易敏感性和COPD易感性和COPD - 与COPD易感性的预测模型。为了最大程度地利用从基因组数据获得的信息，我们将结合多个研究（国家肺气肿治疗遗传学辅助研究，挪威病例对照研究，科普德烯和eclipse-总样本量= 7,962）的数据，以增加功率和雇用基于回归的和雇用的基于基于基于基因型的互动模式的基于基于基于基因类别的数据的方法。我们的研究旨在探索和随后严格验证发现的主要效果和相互作用关联。使用预测模型，我们将量化将遗传主要影响和遗传相互作用数据包括在传统临床变量中的增量预测益处。 相关性：拟议的工作将确定与COPD相关的新基因，并将其置于多元环境中，以便更好地了解遗传差异和环境暴露如何有助于COPD的发展。这项工作产生的模型将有助于将基因组发现转化为临床实践和公共卫生，以符合NHLBI促进预防和治疗心脏，肺和血液疾病的使命，并增强所有人的健康，以便他们可以生活更长，更充实的生活。