Large data analysis of genetic and imaging data
published: Nov. 28, 2016, recorded: November 2016, views: 54
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In recent years neuroeconomic analysis of economic and strategic behavior has provided insights into the biological pathways affecting this behavior, and insights into the individual differences (for classical features in economic analysis, such as attitude to risk and time discounting, but also of Personality Traits and Intelligence). Genetic analysis mostly based on Genome Wide Association Studies (GWAS) has provided useful quantitative estimates of the association; the identification of significant Single Nucleotide Polymorphisms (SNP’s) however is limited to purely correlational results. The task of identifying pathways is made extremely difficult by the highly polygenic nature of the phenotypes of interest for economic analysis, and by the high dimensions of both imaging and genetic data. On the other hand, a correct understanding of the biological pathways to behavior of SNP’s identified with GWAS to behavior is essential for economists when they have to suggest policies. An integration of the neural data (in first place, structural and functional imaging data) and genetic data is essential for future progress. The fundamental difficulty is that the polygenic nature of the phenotypes makes the candidate gene approach particularly inadequate and misleading. The method of Gene Set Enrichment Analysis (GSEA) provides a promising alternative, and has already been successfully used. We will outline a strategy based on the integration of hierarchical Bayesian models with ideas form GSEA adapted to Bayesian methodology.
Download slides: BIDSAconference2016_rustichini_large_data_01.pdf (2.8 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !