Statistical and Computational Methods for Large-Scale Sequencing Studies

 描述(申请人提供)：本建议的主要目标是支持韩臣博士的职业发展，从一名见习生过渡到一名统计遗传学和基因组学的独立研究员，在复杂疾病和性状的大规模测序关联研究方面拥有专业知识，如心血管疾病、呼吸系统疾病、代谢性疾病，包括冠心病、高血压、哮喘、急性肺损伤/急性呼吸窘迫综合征、阻塞性睡眠呼吸暂停综合征和2型糖尿病。陈博士目前是哈佛大学公共卫生学院生物统计学系的博士后研究员，他开发了全基因组关联研究(GWAS)、测序关联研究和荟萃分析的统计方法。此前，美国的少数族裔群体，如非洲裔美国人和西班牙裔美国人，在基因关联研究中的代表性不足。为更好地了解、预防和治疗这些族裔群体中的复杂疾病，越来越迫切地需要设计和进行GWA和测序研究。为了实现这一目标，重要的是开发先进的统计和计算方法，以应对分析这些数据的挑战。具体地说，申请人建议开发统计和计算方法，以1)在测序研究中考虑种群结构和相关性；以及2)在跨种族测序研究中对遗传异质性进行检验，并对基因-环境相互作用进行考虑。这将为生物功能研究提供新的见解，更准确地预测疾病风险，并促进个性化药物的发展。建议的方法将应用于正在进行的OSA测序研究，这种疾病影响到美国10%以上的人口，特别是非洲裔美国人和西班牙裔美国人，并与严重的心脏代谢疾病有关。在指导期间，申请人将学习更多有关相关数据分析的现代统计模型，如高级参数、半参数和加性混合模型，并在主要导师林锡鸿博士的指导下为拟议的研究开发新的统计框架。申请者还将在苏珊·雷德林博士(共同导师)的指导下扩大关于复杂人类疾病的知识，并通过课程作业、讲习班和研讨会拓宽他在人口遗传学和计算机科学方面的背景。凭借在辅导期获得的技能，申请者将使统计模型适应不同的数据和研究问题，并将它们应用于测序关联研究，以更好地了解复杂人类疾病的遗传结构。完成这一奖项后，申请者将成为统计遗传学和基因组学领域富有成效的独立研究人员，拥有大规模测序研究的专业知识，并应用于复杂的人类疾病研究。

 DESCRIPTION (provided by applicant): The primary goal of this proposal is to support Dr. Han Chen's career development in transition from a trainee into an independent researcher in statistical genetics and genomics with expertise in large-scale sequencing association studies for complex diseases and traits, such as cardiovascular, respiratory, metabolic diseases, including Coronary Heart Disease (CHD), hypertension, asthma, Acute Lung Injury / Acute Respiratory Distress Syndrome (ALI/ARDS), Obstructive Sleep Apnea (OSA) and Type 2 Diabetes (T2D). Dr. Chen is currently a postdoctoral research fellow in the Department of Biostatistics at Harvard T. H. Chan School of Public Health, and he has developed statistical methods for genome-wide association studies (GWAS), sequencing association studies and meta-analysis. Minority ethnic groups in the United States such as African- Americans and Hispanic-Americans have previously been underrepresented in genetic association studies. There is an increasingly pressing need to design and conduct GWAS and sequencing studies to better understand, prevent and treat complex diseases in these ethnic groups. To achieve this goal, it is important to develop advanced statistical and computational methods to address the challenges in analyzing these data. Specifically, the applicant proposes to develop statistical and computational methods to 1) account for population structure and relatedness in sequencing studies; and 2) test for genetic heterogeneity and test for gene-environment interaction accounting for heterogeneous environmental effects in trans-ethnic sequencing studies. This will provide new insights into biological functional studies, more accurate disease risk prediction, and advance personalized medicine. The proposed methods will be applied to ongoing sequencing studies for OSA, a condition that affects more than 10% of the population in the United States, especially African- Americans and Hispanic-Americans, and is associated with profound cardio-metabolic morbidity. During the mentored period, the applicant will learn more about modern statistical models for correlated data analysis such as advanced parametric, semiparametric, and additive mixed models, and develop the new statistical frameworks for the proposed research under the guidance of Dr. Xihong Lin (primary mentor). The applicant will also expand knowledge on complex human diseases under the guidance of Dr. Susan Redline (co-mentor), and broaden his background in population genetics and computer science through coursework, workshops and seminars. With skills acquired in the mentored period, the applicant will adapt the statistical models to different data and research questions, and apply them in sequencing association studies to better understand the genetic architecture of complex human diseases. Upon the completion of this award, the applicant will have become a productive and independent researcher in statistical genetics and genomics with expertise in large- scale sequencing studies with applications to complex human disease research.