Methods for Genetic Association Analysis of Longitudinal and Multiple Phenotypes

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DESCRIPTION (provided by applicant): Dr. Salem's ultimate career goal is to be a successful and independent human genetics researcher, applying statistical and epidemiological methods to understand the genetic architecture of complex traits and disease. During the time period of his K99 grant, Dr. Salem will acquire the requisite skills in advanced statistical methodology and phenotype harmonization to achieve this goal through a combination of formal course work, attendance of seminars, mentoring and hands on research experience. Biomedical researchers face many challenges when dissecting the genetic basis of complex traits and diseases of medical and public health importance such as diabetes, hypertension, and cardiovascular disease. These traits and diseases are notoriously difficult to study as they are influenced by the interplay of multiple genetic, environmental, and behavioral factors. GWAS have identified thousands of common polymorphisms contributing to many complex traits and disease. These studies have tended to focus on a single phenotype at a single time point. The complexity of polygenic traits may not admit to such simple characterizations. Use of more informative phenotypes, study designs and analyses, has the potential to shed light on new biology. One way to improve the yield of association studies both in terms of novel loci and biological insights is for researchers to consider more elaborate analyses. Hypotheses that leverage more sophisticated approaches may yield new discoveries. For example, the traditional use of a phenotypic measurement at a single point at a specific time in current case-control candidate gene and GWA studies does not do justice to the likely age or time dependence and/or general developmental pathogenesis of most biomedical traits and diseases, nor does it illuminate the potential shared genetics between phenotypes. This project aims to develop a resource of ~145,000 subjects from dbGaP and evaluate the contribution of genes on temporal changes and the interplay between traits via statistical methods development and application. This project will be guided by an important public health and clinical problem, metabolic disease. Through his prior research experience and training, Dr. Salem has acquired a strong foundation in statistics and genetics. He now seeks further training in the advanced statistical methodologies and phenotype characterization required to fully understand complex traits. He will be mentored by Dr. Joel Hirschhorn, a leading investigator in the genetics of obesity and height, and will be co-mentored by leaders in the area of statistics and phenotype harmonization. The research proposed in this K99 application has broad implications for understanding complex traits and disease, particularly metabolic syndrome. It also has the potential to significantly understanding of and impact the diagnosis and treat of metabolic syndrome. Dr. Salem is confident that completion of the work and training plan will prepare him for a successful career as an independent investigator in human genetics.