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Abstract:
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Genome wide association studies (GWAS) have identified thousands of genetic variants that are associated with hundreds of diseases/traits in the past decade, including many for neurodevelopment, neurodegenerative, and psychiatric disorders. However, it is challenging to identify the functional variants from a large pool of associated variants, and even more challenging to understand the biological pathways through which they affect the phenotypes. Data collected from studies where both genotype data and molecular, imaging, and other intermediate phenotypes are available can be informative in elucidating the functional mechanisms of genetic variants inferred from GWAS. In this presentation, we discuss statistical methods that can integrate genetics data with other data types, including imaging data, to better identify disease associated variants as well as to depict the functional pathways from genetic variants to imaging traits and then to phenotypes. This is joint work with Dingjue Ji, Yiliang Zhang, Chintan Mehta, Jeff Gruen, and other collaborators.
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