? This career award will help the PI achieve the following career goals during the next five years by building on his strength in the statistical methodology, particularly the recursive partitioning based techniques, and on his experience of analyzing data related to substance use, and: ? ? (1) To further broaden his knowledge and enhance his understanding of human genetics, particularly related to genetic etiology of substance use disorders and interactions with environment. ? (2) To develop statistical methods that are of broad applications in analyzing biomedical data; to develop tree-based statistical methods and software for genetic analyses; to establish latent variable models and software for genetic analyses of ordinal traits; and to apply the new methods in on-going NIDA funded studies such as to better understand the genetic epidemiology of substance use disorders by considering an ordinal phenotypic definition that is a better reflection of how the substance use disorders were defined and recorded; and to identify candidate genes for tobacco use, and its comorbidity with alcohol use and psychiatric disorders including anxiety. ? (3) To establish and sustain a leadership role in statistical methodology and analysis for studying genetic epidemiology of substance use and related disorders by disseminating our methods and results through lectures, seminars, and conferences. ? (4) To strengthen his intellectual network inside and outside Yale particularly in studies of substance use. Understanding genetic epidemiology and environmental effects for complex diseases including substance use disorders remains a tremendous challenge. This award enables the PI to invest a major effort in a relatively long term to develop some of the critically needed, timely statistical methods and software, which help us advance our understanding of substance use disorders. ? ? ? ?
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