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GxE interactions in genome-wide association studies

Published on November 30, 2016 Archived on February 6, 2025   51 min

Other Talks in the Series: Statistical Genetics

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0:00
Hello, my name is David Conti, and I'm from the University of Southern California. And today, I am going to talk about "GxE Environment Interactions in Genome-Wide Association Studies".
0:10
Typically, in genetic association studies, we start with a phenotype. And in this example, we're starting with cigarette smoking, and the behavior of who smokes and who doesn't. From that step of the phenotype, we often try to key in on the biology and look at, who's absorbing that nicotine and how they absorb it differently, in terms of nicotine metabolism. That also then leads into effects of the brain such arousal, mood modulation, and pleasure. Over time, as individuals start ... continue to smoke, there's a build-up of tolerance and physical dependence. This leads to drug abstinence and withdrawal symptoms, when one tries to quit smoking. And then, of course, the metabolism kicks in. And there's a craving for nicotine to self-medicate that withdrawal symptoms and to smoke again. Based on this understanding of the phenotype, we often then key in more on the resolution.
1:02
We focus on the biology that's behind this. And in this slide, we can look at in the upper right figure, in the liver cell, we have nicotine going to cotinine. That's going to be a process that is mediated by certain genes, CYP2A6, CYP2B6. And then also the process of how nicotine crosses the blood brain barrier, and how that gets into the dopamine system and the neuronic system. And what genes are involved in there. Based again on that biology in a genetic context, we can move forward.
1:34
In which we sort of key in on the key genes that are involved in that pathways, and the SNPs that might be involved. And the SNPs being the single-nucleotide polymorphisms within each gene. Within each gene, we can select these SNPs that include putative functional SNPs as well as SNPs to capture the overall genetic architecture, or the linkage disequilibrium, or LD, between the SNPs. We also can include SNPs that are informative for ancestry, so we can control for potential confounding due to population stratification. So within this setting, as we choose the set of SNPs, we can then analyze this data and see how any of these individual SNPs are associated to the outcome of interest.

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