Please wait while the transcript is being prepared...
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.
