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Linear and Generalized Linear Mixed Models for Genetic Case Control Studies
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Omer Weissbrod, Ph.D. Thesis Seminar
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Wednesday, 14.12.2016, 13:00
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Taub 601
In recent years, genetic studies have revolutionized our understanding of common diseases like diabetes and cancer. However, the analysis of genetic studies of disease poses substantial statistical and computational challenges, owing to the data collection scheme and to the extremely large data dimensionality. We propose a unified modeling framework that addresses these challenges in order to solve the three main problems associated with genetic disease studies: Searching for disease causing mutations, predicting risk of being affected with a disease, and inferring the overall genetic architecture of diseases. Our proposed solutions are based on generalized linear mixed models, and employ techniques from diverse fields, such as multiple kernel learning from machine learning and the method of moments from statistics. The proposed methodologies lead to state of the art results in the three main problems of genetic studies of disease, and have been published in top scientific journals. The talk is aimed towards a general audience, and does not require statistical or biological background.
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