Predictive Modeling with Clinical and Genomic Data in COPD

候选人：彼得·卡斯塔尔迪博士是一名医生，完成了F32资助的一段时间的支持。2009年7月1日，他将开始在塔夫茨医学中心和临床研究与卫生政策研究所(ICRHPS)担任全职职位。这个职位需要25%的临床投入。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。他特别感兴趣的是遗传荟萃分析、基因-环境交互作用，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。)通过多个全基因组关联(GWA)研究的联合分析，确定与COPD易感性和COPD相关表型的新的遗传关联。识别上位性和吸烟对基因的交互作用，以及3.)利用临床和基因组信息开发准确的慢性阻塞性肺疾病(COPD)预测模型。他的长期目标是成为一名拥有生物信息学专业知识的独立调查员。他实现这一目标的愿景包括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，以便回答将改善患者护理和人口健康的重要临床问题。 环境：卡斯塔尔迪博士将在丰富的跨学科环境中接受培训。他的主要导师刘若瑟博士是ICRHPS临床证据合成中心的负责人，他是荟萃分析和证据合成领域的全球领导者。在塔夫茨，除了与刘博士的定期会面外，彼得还将接受该领域领导人的基因证据合成培训，其中包括约翰·约安尼迪斯博士和汤姆·特里卡利诺斯博士。该应用程序的联合导师Edwin Silverman博士是钱宁实验室COPD遗传学的主要研究员。在钱宁实验室，Peter将接受呼吸遗传学和遗传流行病学方面的出色培训，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资源。卡斯塔尔迪博士还将继续与塔夫茨计算机科学部的唐娜·斯洛尼姆博士合作，后者将在卡斯塔尔迪博士继续在生物信息学方面建立实践和理论基础的同时，协助将计算算法应用于基因组数据和指导。 研究：慢性阻塞性肺病是发病率和死亡率的主要原因，对公共卫生的重要性日益增加。虽然吸烟是慢性阻塞性肺病的主要危险因素，但在普通人群中，吸烟引起的肺损伤的易感性是不同的。有强有力的证据支持COPD易感性的遗传成分。了解基因和环境是如何相互作用产生临床COPD的，将允许更准确的诊断工具，并为COPD疗法的发展开辟新的研究途径。我们建议1。)通过对4项大型COPD GWA研究的患者水平数据进行荟萃分析，确定新的基因与COPD易感性和4种COPD相关表型的相关性。确定吸烟与基因之间的相互作用以及基因与基因之间的相互作用。开发COPD易感性和COPD相关表型的预测模型。为了最大化从基因组数据中获得的信息，我们将结合来自多项研究(国家肺气肿治疗试验遗传学辅助研究、挪威病例对照研究、COPDgene和日食-总样本量=7,962)的数据，以增加能力，并使用基于回归和机器学习的方法来识别基因数据中复杂的相互作用模式。我们的研究旨在探索并随后严格验证已发现的主效应和相互作用关联。使用预测模型，我们将量化将遗传主效应和遗传交互作用数据纳入传统临床变量的增量预测收益。 相关性：拟议的工作将确定与慢性阻塞性肺病相关的新基因，并将它们置于多元背景下，以便更好地了解遗传差异和环境暴露如何促进慢性阻塞性肺疾病的发展。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，符合NHLBI的使命，即促进心、肺和血液疾病的预防和治疗，并提高所有人的健康，使他们能够活得更长、更有成就感。

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.