Explore
For Enterprise
Join for Free
Log In

Arts and Humanities
Business
Computer Science
Data Science
Information Technology
Health
Math and Logic
Personal Development
Physical Science and Engineering
Social Sciences
Language Learning
Degrees and Certificates

Explore all of Coursera

Browse
Search
For Enterprise
Log In
Join for Free

Mapping Traits Relative to Markers in a Population (S)

Loading...

Introduction to Genetics and Evolution

Duke University

4.8 (643 ratings) | 56K Students Enrolled

Introduction to Genetics and Evolution is a college-level class being offered simultaneously to new students at Duke University. The course gives interested people a very basic overview of some principles behind these very fundamental areas of biology. We often hear about new "genome sequences," commercial kits that can tell you about your ancestry (including pre-human) from your DNA or disease predispositions, debates about the truth of evolution, why animals behave the way they do, and how people found "genetic evidence for natural selection." This course provides the basic biology you need to understand all of these issues better, tries to clarify some misconceptions, and tries to prepare students for future, more advanced coursework in Biology (and especially evolutionary genetics). No prior coursework is assumed.

Skills You'll Learn

Biology, Genetics, Evolution, Genomics

Reviews

4.8 (643 ratings)

5 stars
558 ratings
4 stars
67 ratings
3 stars
11 ratings
2 stars
6 ratings
1 star
1 ratings

IW

Jan 08, 2016

Excellent course. Would recommend it to anyone who wants to delve in the subject matter. Wonderful instructor and interesting lecture videos. One of the best courses I have taken in Coursera.

VL

Jan 17, 2017

I really enjoyed the classes and the supplemental materials available. I loved how the lessons were taught and explained by Dr. Mohamed Noor and I really enjoyed the content of the classes.

From the lesson

Genetics II

This module delves somewhat more deeply into genetics and specifically the concept of "recombination." It begins to discuss how recombination is leveraged in classic genetic works as well as mapping simple genetic traits using crosses or data from natural populations.

Mapping Traits Relative to Markers in a Cross (S)11:03

Mapping Traits Relative to Markers in a Population (S)21:54

Taught By

Dr. Mohamed Noor
Earl D. McLean Professor and Chair,

Hello and welcome back to Introduction to Genetics and Evolution.

In the most recent videos,

we've been talking about how you map a simple genetic trait.

So a trait whose variation is caused by variation at a single gene.

In the previous video, we talked about mapping a simple genetic trait

using a cross, or basically a pedigree.

In this video, we're going to take it a little bit further.

We're going to look at different sort of genetic mapping.

But let me stress the underlying principle of all genetic mapping,

including what we'll be talking about in this video,

is an association between genotypes at markers.

So, having AA or aa at a particular marker, and

the appearance of a trait or disease.

Yeah, so let's look at this.

Can we map diseases without controlled crosses or pedigrees?

This is a big question.

Now obviously it's not socially acceptable to force people

to mate with others especially ones who may have diseases.

So if you wanted to map the genetic basis of say,

predisposition to some sort of cancer.

You can't find, well here's a cancer person, here's somebody who's a carrier,

let's make them have kids.

Things like that, that obviously would never fly.

And you also can't always find enough subjects who happen

to have bred in an informative way.

All right, you can't find these individuals who,

you can't find tons of people who have a particular kind of cancer and

happen to have kids with somebody who was a carrier for the same sort of thing.

So is there another route?

The answer is yes.

What we can do is we can leverage population data over time.

Now, on average, this is a just broad, off the cuff average, human generation times,

historical average, is on the order of about 20 years.

So when we looked at recombination previously,

we were looking at what happened in one individual.

So basically we were looking at a time frame in total of about 20 years,

or one generation.

One bout of myosis.

One iteration of some crossing over.

Now, when you're looking at genes that are very close together.

So genes that are maybe, you know thousands, or even tens of thousands, or

even potentially hundreds of thousands, there's a very, bases apart.

There's a very low probability of having a cross over between them.

There's a very low probability of exchange.

Now, if you're looking at bases that are say, hundreds of thousands of bases apart,

although there's a very low probability of exchange in one generation.

If you look over the thousands of years of human history,

there actually has been a lot of recombination.

So just putting in a little bit of math to it.

If the probability of exchange is only 0.1%, so

it's very low, in one generation, between two say snip markers.

There would actually be a 99.3% probability of exchange when you iterate

that over 5,000 generations.

You can think of that like flipping a coin, that,

yeah your odds are 50% of getting heads with one coin flip.

But what are your odds of getting heads if you flip the coin 50 times

that you get at least one heads?

It's very, very high.

Same sort of thing happens, even when you have very low probabilities of exchange.

Now, over human history,

most neighboring genes will have had their alleles shuffled.

And this will be true even for very close genes, but even sometimes within genes.

You'll actually find some areas Inside a gene that will actually have

a crossover event.

So you'll have, for example, the father's copy, mother's copy,

we'll have a crossover event that makes it so

this end of mother's copy links up with this end of the father's copy.

Now let me show you this with a picture.

So lets imagine we're starting a population with four chromosomes.

Lets say that going to this island, we have two individuals.

One of them is heterozygous blue over red and

one of them is heterozygous brown over purple.

And this, Adam and Eve of this island, basically start a population and

their kids have kids, their kids have kids, etc, etc.

And they live on this island for many, many years.

Okay, so we have these four chromosomes that started everything.

It happens to be that the blue chromosome has a disease causing mutation,

okay, so that's a little star that's under the D in the blue.

Let's say we have some genetic markers, so we can look at genotype at marker one.

See if it's blue, red, brown or purple.

We can look at genotype at marker two, and say it's blue, red, brown or purple.

And what we see is that after all these generations, after many generations,

instead of having one crossover event that's happened,

we have this sort of shuffled up chromosomes as you can see here.

Depending on how many generations you may actually see a lot more shuffling than is

depicted here.

But this is sort of a moderate amount of shuffling there.

So this might be after, say,

hundreds of generations rather than thousands of generations.

We see this sort of shuffling in here.

Now, what you see, nonetheless, even though there's been this shuffling.

We see the same principle that we saw before with genetic mapping and crossing.

That things that are very close to each other tend to remain associated.

Things that are farther apart don't have that same tendency.

So when we look at D and M1, you tend to have the same type there, blue with blue,

brown with brown, red with red, purple with purple, blue with blue, etc.

For D and M2 we don't see the association.

Here it's red with purple, blue with brown, brown with blue, etc.

They've become dissociated from each other, D and M2 become dissociated.

Whereas D and M1 remain associate.

So, we can actually, if we could genotype what's going on at markers one and markers

two, we can still see an association between genotype and phenotype.

In this particular case, ones that have the blue allele at marker one

are likely to have disease causing mutation as well.

Now let me build on this with another facet.

Recombination, or actually crossing over, is not homogenous.

When you look at a very, very fine scale.

So far, we've been looking at whole chromosomes.

And we see one crossover event in one generation, sometimes two,

things like that.

But if you look at a very, very fine scale and over very long periods of time,

we see these crossovers don't just happen completely randomly.

There are a couple of spots across the chromosome that they tend to happen.

These are referred to as hotspots of recombination.

You find them every few thousand bases in humans, and

the rest of the genome basically has a recombination fraction or rf of zero.

That you essentially don't see recombination in these different areas.

So looking at this, this is a stretch of genes across one human chromosome.

And this shows you the recombination fraction.

These peaks in recombination are crossover hot spots.

So you see, for example,

there's a crossover hot spot here between DMB and PSMB9.

So, you might expect, over a very long time, this is a place where

a crossover may happen, and DMB can become dissociated from PSMB9.

In contrast, PSMB9 and TAP1, they're both very close to each other and

there's no recombination hotspot.

So even over a very long time,

it seems like these two are likely to be stuck together.

They're in the same window between these two peaks.

You can think of these peaks as defining a boundary.

And in here, in this window part, you essentially have, recombination of 0.

But here in these peaks, you do have recombination.

So this can become dissociated from this, but

this cannot become dissociated from that.

So what are the implications of this?

And I'll show you an application of this, so

it'll make a little bit more sense in just a moment.

We have shuffling occurring between these windows every few thousand base pairs.

Let me emphasize, when I'm saying this shuffling is occurring,

I don't mean every generation it happens.

I just mean there is some chance that,

over thousands of generations you can have this shuffling.

You can have a crossover event happen at one of these hotspots.

Potentially once or a few times every couple of thousand generations.

But in contrast to that, we have some areas where no shuffling is occurring and

those are the areas within the windows.

The areas between the recombination hotspots.

There's basically no recombination, or

you can say that they are in linkage disequilibrium, is often refered to as LD.

They're in linkage disequilibrium.

Essentially the alleles at one are stuck with

the alleles they started with at the other one.

Now, some of these windows are going to contain

genes that have alleles causing disease.

What we can do is we can leverage these recombination hotspots and

windows between them to find the disease causing gene.

Let me illustrate this with an example.

Now, again as we said,

we have these windows within which recombination doesn't happen.

And we have recombination hotspots where recombination can happen.

So I've depicted here this little lightning bolt indicates

a recombination hotspot.

Now I've made it so the recombination hotspot is only one base long.

That's not actually true, but let's just pretend this for the second.

So I'll draw a line down here and saying that is the site that

recombination can happen, sometime over the course of thousands of generations.

Now within these windows here, so between recombination hotspots.

Maybe there's another one over here.

Within these windows you won't have recombination.

So what you'll see is that will tend to be an association between

the genotypes at these SNPs.

So let's look at, for example SNP1, the C is associated with G,

the T is associated with C, C with G, C with G, T with C, etc.

So, you see that there's a strong association there

within one of these windows.

Now, let's look between windows.

So, let's take SNP3 and SNP4.

So, this is in one window.

This is in a different window.

A is with G.

This case, G is with G.

This case, A is with G.

A is with C.

G is with C.

So, we see that, over the course of time, the alleles in this window,

have become dissociated from the alleles in this window.

However, within the windows,

these different SNPs stay very strongly associated.

Then in this particular case, we have one causing a disease.

Now, let's focus on that and let's pretend we didn't look at all these SNPs.

Let's just look at a couple of them.

Make it a little bit simpler here.

What we see looking at this is that,

if you're looking at it for an association between genotype and

phenotype, which is what we're always trying to do with genetic mapping.

We see here that this individual would have the disease because it has this

A mutation.

We don't actually know this A mutation cuz we're not looking at it we're only

looking at the snips.

But G's tend to have the disease mutation.

C's do not.

This G actually is okay.

This G has the disease mutation.

C is not. C is not.

So we see that, on average, if you have a G, you're more likely to have the disease.

If you have a C, you're very unlikely to have the disease.

So there is an association here between the genotype at SNP2 and

whether you have the disease.

And that's because SNP2 is linked to the mutation causing it.

Now let's try the same thing with SNP5.

In this case an individual with T has the disease.

This individual with T does not.

T does not, A has the disease, A does not, A does not.

There's no particular association between the genotype at SNP5 and

whether you have the disease.

And the reason for that is because when you're looking,

even over the course of many generations,

SNP5 has become dissociated from the disease-causing mutation, okay?

So, our prediction again is that, like with all gene mapping,

the disease gene mapping is associating the genotype.

And this is looking at the marker, in this case the SNPs at one location.

With the phenotype, which is the disease.

And we again, we don't know the SNP or the mutation causing the disease or

trying to figure out where it is.

If a marker is very close to the disease-causing gene.

Then individuals having one allele at that marker will be more likely to have

the disease than individuals having a different allele.

Essentially, the marker will be in LD or

in linkage disequilibrium with the disease gene.

And this does not mean, I want to emphasize that,

this does not mean that the marker gene or SNP causes the disease.

So that the answer to this would be a very resounding no.

This is a common misconception.

People say that, oh look, that's associated with the disease and

therefore it causes it.

No, it just means it's in linkage disequilibrium with the alleles causing.

So let's give you an example to try this out.

Let's say that you were trying to map Irritable Bowel Syndrome.

And you're sampling a population for alleles of two markers, and

you're looking at incidence of this disease.

Now, just to make things a little bit simpler,

we're just gonna forget the heterozygotes.

We'll just go with AA and aa, okay?

So, AA individuals, you look at 100 individuals, and 20 of them have IBS.

aa, you have 200 individuals and 40 have IBS.

Is there an association between genotype and phenotype?

Well you might say, yes.

Because look, a lot more of these have IBS.

On the other hand, you sampled more individuals.

Let's look at what actually is going on here.

In this case, out of 100 individuals, 20 have IBS, so

your probability of having IBS in this case would be 20/100 = 0.2.

For aa, you have 40/200 so again your

probability of having the disease is 0.2, 20%.

So, there really is no association between the genotype at SNP 1 and

the disease, so we just cross this out.

What about SNP 2?

Well here we are, 50 individuals, 45 have IBS or 250 individuals, 15 have IBS.

Well, let's look at this then.

In this case, we have 45/50 for big b, for BB.

For bb, you have 15/250.

That's a huge difference, [LAUGH], you can do the math there,

that's a huge difference between those two types.

So in this case,

we can say that your genotype at SNP 2 is associated with Irritable Bowel Syndrome.

This does not mean that SNP 2 causes it,

it just means that SNP 2 is probably closely linked to a gene that causes it.

It could be, it could be that SNP 2 actually does cause it, but

it's improbable you would have happened to have just found that.

So I won't rule it out, but in this case, SNP 2 is associated with it.

SNP 2 must be near the disease causing mutation or, possibly but

unlikely, it could be it.

Now, you may be thinking, the genome's a big place.

There's over 3 billion bases in the human genome.

And if there's a hotspot, as I mentioned before, every about 3,000 base pairs,

how many markers would you need to study to find disease genes?

Well, basically, how many windows are there?

So there's 3 billion bases total, there's a hotspot every 3,000 base pairs,

how many markers do you need to find?

Well, the answer's pretty simple.

Basically you just divide this by that.

So essentially, you need about a million markers.

You need about a million markers to sample the whole genome to try to find disease

causing genes, given these, given these windows.

Now that may sound very difficult, but technology helps.

In fact, this is really not a problem at all now, thanks to recent innovations.

We have microarray chips that can actually tell us the genotype of

over a million markers at once just from your spit.

This is what some of the companies I mentioned a while back,

like 23andMe do when you send them that little tube of your spit.

They basically are genotyping you for over a million markers, and they'll do this for

you at a fairly low cost.

And they can tell you from that, your susceptibility to many mapped diseases.

They are basically using published data on those diseases and

they're mapping them Now, I'll give you an example to try.

I give you here 1000 people, 950 of them are healthy and

50 of them have cystic fibrosis.

You're trying to map if there's a gene associated with any of these markers

that may be causing cystic fibrosis.

So I want you to go ahead and

try it out and do the same sort of thing that we just did.

And see are any of these markers, possibly more than one of them,

associated with the gene contributing to cystic fibrosis?

Well, hopefully that wasn't too challenging.

Let's give it a shot.

See where is the strongest association between gene and template.

And normally you want to do some statistics with this.

We're gonna skip the statistical facets,

we just wanna see where you see the strongest association.

Now, in looking at this, let me just do two of them.

I'll do one negative, and then I'll do the right answer.

So this first one.

You're looking at, let's say for example,

600 of these 1000 people have the big AA allele, 400 of them have aa.

Among the ones with AA, 28 have cystic fibrosis.

So you just pull out your calculator and

what fraction of AA individuals have cystic fibrosis?

28 divided by 600.

And that would be 4.6%.

So what about for little a little a?

Well, we've got 22 divided by

400 and that is 5.5%.

So maybe there's a little hint of an association, maybe not.

That could just be random.

Again, we're not gonna worry about the statistics.

I just want you to look for where is the strongest association.

You also get Marker 3.

There, we have 45 out of 100.

So, 100 have CC, 900 have cc.

Again, we've just pretended there are no heterozygotes.

So, for the first one it's 45 divided by 100.

Well, I can do that one in my head.

That would be 45%.

Okay? So, if your big C big C,

your odds of having Cystic Fibrosis is 45%.

What about little c, little c?

Well, it'd be five divided by nine, oops.

Five divided by 900, that would be 0.5.

Well that's a huge difference!

0.5%, 45%.

There's basically a 90 times higher chance in Cystic Fibrosis

if you're a CC than if you're a cc.

So clearly in this case there's a very strong association of Marker C

with cystic fibrosis.

So what does this mean?

The CC genotype at Marker 3 causes cystic fibrosis right?

Wrong.

It is not the case.

It is linked to variation causing.

Now one question we wonder is why do some people with cc still have cystic fibrosis?

And in fact, why doesn't everybody who is CC have it?

Well there's many answers to this, and

this comes back to the italicized word in our video title, right?

I said we were mapping very simple genetic trees.

But simple is sometimes somewhat unrealistic.

So you can have environmental effects.

A lot of diseases have multiple genes contributing to them.

And we'll talk about, as we go into some of the subsequent videos,

some of these confounding factors that make it so

a lot of the mapping is not as simple as I've made it to seem here.

But sometimes you are lucky.

Sometimes there are traits or, with allelic variation that is very strong.

Now, let me conclude this video with just a quick re-cap.

Now, this is showing you the distinctions between association mapping

in a population, like we just did.

And a cross pedigree mapping as we have done in previous videos.

And with a cross, you're mapping in known families.

So you're taking individuals that, for example, are carriers.

And seeing what happens when they have kids with individuals that have

the disease, or maybe carrier with carrier.

Your resolution, just to give you some idea how

precisely you can localize these genes causing disease.

This is just a ballpark.

This is not a, you're looking at a resolution of about 2 million base pairs.

You've narrowed it down from the whole genome to about 2 million base pairs.

It's better than nothing.

In this you have, for crosses you have one generation of recombination.

You're looking at just one bout of crossing over.

And importantly, this actually works fairly well,

even if the mutation is rare in the overall population.

So that is actually a plus.

With populations, you're actually, rather than mapping in known families,

you're mapping across a population, right?

You're just taking a bunch of of unrelated individuals,

they just all happen to be in the same species and the same population.

The resolution here is much better.

Rather than 2 million base pairs your resolution here is about 3,000 base pairs.

Because it's about those windows between recombination class.

Importantly the reason that you have so much high precision here is because

here you're leveraging many generations of recombination.

Whereas in the cross your leveraging one generation of recombination.

So your precision is higher because you have a lot of many more recurring of

events to happen, therefore you can more precisely localize what's going on.

You know the down side to this one is that this doesn't tend to work

if the mutation causes a disease.

It's very rare in the population you're seeing.

Now one question people often ask is,

what is the recombinationary between the two sides of the hotspot?

They think it's something like ten cM, or 10% of recombination.

No, let me emphasize.

For reference, the recombination fraction

across a hotspot in one generation is probably less than 0.01 cM.

This is why you have to look across many, many,

many generations of recombination to see any effect from them.

Well I hope this was helpful.

Thank you for joining us.

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.

Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.

© 2019 Coursera Inc. All rights reserved.

Download on the App Store

Get it on Google Play

Coursera
About
Leadership
Careers
Catalog
Certificates
MasterTrack™ Certificates
Degrees
For Enterprise
For Government

Community
Learners
Partners
Developers
Beta Testers
Translators

Connect
Blog
Facebook
LinkedIn
Twitter
Instagram
YouTube
Tech Blog

More
Terms
Privacy
Help
Accessibility
Press
Contact
Directory
Affiliates