Statistical and Computational Methods for Large-Scale Sequencing Studies

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 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.