Hello, and welcome back to introduction to genetics and evolution.
We've been talking so far about mapping complex traits.
We first talked about some of the principles associated with such mapping of
complex traits.
And we've also talked about how you do it using a cross, and
we used this trajectory of associations string.
Now lets look at an alternative approach.
You know with simple genetic traits we've talked about mapping
using data from overall populations.
Well we can do the same thing here, in the case of complex traits.
Now you may be thinking first, why would we wanna do this?
Well, when your QTL mapping across a pedigree,
what happens is you're localizing the genes affecting this difference
between the original strains or people.
So let's say you're using a pedigree, you're looking at where are the genes
contributing to breast cancer predisposition in this particular family.
So let's say you're mapping a gene and
let's say in this case it's diabetes rather than breast cancer in one family.
And you map it to chromosome 18 arm p location 22.
Let's say there's a very strong effect next to a marker.
Then you look at another family that has diabetes and
you see absolutely no affect to this region.
Even though they have a lot of diabetes and
even though it is clearly running in the family, so it has a genetic component.
Why would you see this dichotomy?
Why would it have a strong effect in this family and no effect in this family?
Well the answer is, again,
there are many different genes that can contribute to disease predispositions.
And you may see in one family there's a particular mutation that's contributing
to diabetes predisposition,
and there's a completely different one in another family.
So when you study a trait in one single family,
you're only identifying what's causing that variation within that family.
It's important, now, to look at the broader population,
if you want to understand what causes diabetes more broadly.
So the other approach that people use is this mapping
in populations by association study.
And a common variant of this is referred to as the genome wide association study.
This has become extremely popular lately with the ability
to examine markers across the whole genome simultaneously.
And of course with having a human genome sequence.
We discussed this in general in the context of simple traits.
And the same principle applies as before.
If a marker is close to the disease causing gene,
then individuals having one allele will be more likely to have the disease than
individuals having the other allele.
So there is an association between genotype at the markers, and
likelihood of disease, or whatever the phenotype is.
The same principle is true not only in simple genes and things, but
also in multiple gene traits, such as disease.