[Dr. Wildin] Hi, this is Dr. Bob Wildin.
This is a lecture from Genomics,
Genetics for Clinicians,
and this is in module nine,
which is about genetics through the lifespan,
essentially adult onset disorders.
There are a number of monogenic disorders
that are discussed primarily in the reading material.
This lecture is about complex disease, GWAS,
or genome-wide association studies and genetic risk scores.
So complex disease in genetics means,
simply, that it is not simple, it is not monogenic,
it's not, you get the gene, you get the disorder.
It is not entirely genetic,
but may be influenced by genetics,
so there are other factors that are involved.
The pathophysiology of the disorder
may involve multiple steps
and multiple body systems or organs.
An example might be autoimmune disease
that attacks a particular organ,
and the origin of the pathogenesis is in the immune system,
but the organ is the part of the body that fails.
Complex diseases may be susceptible
to trigger conditions and circumstances,
and so may never appear
if those circumstances are not encountered in one's life.
These features make it much harder
to do traditional genetic research
where you identify a disease,
and you follow it through the family,
and you do linkage studies to identify
where on the chromosomes the disease gene may lie,
and then clone the disease gene and study the biology,
that's really impossible to do in complex diseases.
It also makes it potentially harder
to identify common therapeutic targets,
so a drug or some other intervention
that will have the same beneficial effect
across a diverse patient population
that has the same "disease", in quotes.
Now, that's one way of looking at it,
another way of looking at it is that
the additive nature of complex disease burdens means
that there may be one small additive element
which pushes the disease into being,
and any one of the elements that led up to that point
may potentially be targetable to bring down the intensity,
if not the expression of the disease.
Polygenic inheritance.
So "poly" means many, "genetic" means genes,
so polygenic inheritance means that a disease is inherited
when multiple genes or genetic variants
are inherited together.
So this initiates the concept of a cumulative genetic burden
that leads to disease.
One gene is not enough,
you have to have variants predisposed to disease
in multiple genes.
There can be multiple combinations of variations
in different genes that can add up to disease,
so not everybody inherits the same five variants,
or variants from the same five genes,
in order to get the disease, there may be a variety of ways,
a variety of collections in your basket,
in order to get you to the point of expressing a disease
like type two diabetes.
So there's a concept of a threshold
of getting enough of these genes with negative effects
in a particular area
in order to express the disease externally,
and obviously, it's really a gradation,
and so you may have a subclinical case of something
when you have a little bit less of the disease gene burden,
and a more severe case
when you have a full blown basketful of goodies.
Different families may therefore have
different combinations of contributing genes.
Familial clustering with rapid dissipation
beyond first-degree relatives
is the pattern that you see in pedigree analysis.
So if you're doing pedigree analysis,
and you see that there's a tight cluster
of first-degree relatives who have a disease,
but the second-degree relatives, up and down,
or cousins and so forth,
have a much lower frequency of the disease,
one explanation for that
is that they have a polygenic disorder.
And the reason that it falls off so quickly
as you move away from the core group,
is because in order reach
that threshold of genetic burden for the disease,
you have to co-inherit a bunch of different genes
on different chromosomes,
and the likelihood of that ends up being
the product of half for each one of those,
or very quickly, quite small.
Multifactorial and polygenic are often confused,
multifactorial is essentially polygenic plus other stuff.
This diagram is trying to describe what that means.
So polygenic, not monogenic,
essentially sets up a susceptibility to a disease,
and then you add in an environmental exposure,
whether it be a medicine, a substance,
radiation, in the case of people,
for example, with very light skin,
low pigment level in their skin,
may have problems with increased skin cancer,
because they have more sensitive skin,
if they stay out of the sun,
their skin's cancer risk goes down.
Other environmental exposures can be trauma,
or physical things that can happen to you, surgery.
Think about malignant hyperthermia,
a genetic predisposition
to a severe life-threatening adverse reaction
when given certain volatile anesthetic agents,
inhaled anesthetic agents,
and stress and physical activity,
for some disorders are triggers,
McArdle's disease is an example of that.
A metabolic state like obesity can predispose you
to type 2 diabetes,
but you probably have to have a set of genes,
a polygenic risk component underlying it,
before you actually express the disease.
On the other hand,
even if you have that polygenic risk component
and you can reduce the trigger exposure,
that is obesity, you may be able to reverse the disease.
Immunogenic stimulus is also
another potential environmental exposure.
If you are not exposed to measles,
then you may not get an autoimmune disease later in life.
And then there's the third component,
here on the right, in the question marks,
which are basically saying
that there may be other factors that we can't identify,
things that are not necessarily exposures, medications,
and they're not genes, but they may be other factors
that are not under our control, for example,
a particular timing of an environmental exposure
in a particular age group.
So for example, in adolescence when one is growing rapidly,
a particular exposure may have more effect
on a bone physiology than in an adult.
Similarly, in a post-menopausal woman,
the polygenic risk for osteoporosis
may be closer to the surface and more expressed
than it is at a younger age
when estrogen is present and protective.
So in order to get at some of these,
sort of subtle effects of polygenic risk,
as I stated earlier, it's really not possible
to do the traditional kinds of genetic research
and identify those genes in the usual family-based way.
So a technique has evolved,
which is called genome wide-association studies, or GWAS.
Now, the purpose of GWAS is to identify
regions, small regions of the genome
that contain variation,
which when compared across a population,
tends to segregate more or less with disease expression.
So GWAS studies use normal sequence variation in the genomes
or what we would call polymorphisms,
often they're using single nucleotide polymorphisms,
or SNPs,
and they ask which variations are observed more often
when the individual has or lacks the condition.
So, do you have allele A, or allele B?
And of course, in many cases
there will be people in the disease condition group
that have either allele A or allele B,
or two copies of B and one copy,
or two copies of A, or one copy of A and B,
and the same is true of the control population.
So what you're looking for is an enrichment
of certain alleles in the disease population,
that might give you a clue
that that locus on the chromosomes may have a gene nearby
that is important in the pathogenesis of this disease,
in at least some people in the population.
It's important to mention
that these variations can go in both directions.
So some variations may be disease enhancing,
disease risk enhancing,
and some may be protective.
And you'll see this if you read the GWAS literature,
that those variants go in both direction.
So the next step is
to analyze these results across a large population.
Now, the earlier GWAS studies were done
with relatively small populations,
because we had to go out and collect DNA
for a particular disease group,
and we had to collect a whole population
of people with that disease and without that disease.
With the advent of large groups of people
who were having their whole exome sequenced,
or whole genome sequenced
during population genomic research,
we are now having access to a large number of people
who have had their genome entirely sequenced,
or have had a bunch of genotypes done
all the way across their genome, and that data can be used
along with their health information
to perform these studies.
So now we can actually perform studies
on a hundred thousand individuals,
or 200,000, or even 500,000 individuals.
as is the case
with some of the SNP/ChIP-based genotyping studies.
This gives us a whole lot more power to understand the role
that each of the small increment genetic risk loci play
in the disease.
So the GWAS studies ask if a variation,
once you find a GWAS with a signal, then you have to ask,
is that polymorphism that I looked at,
that genetic A-to-T, or G-to-C,
is that itself a functional variant?
In other words, does that specific variant change
the amino acid sequence of a protein,
or does it change a regulatory element
that changes the expression of a protein,
or perhaps changes the methylation of CPG islands,
and thus changes gene expression epigenetically?
Or, is it simply a polymorphic marker
that is linked to a nearby gene,
and therefore travels together with it through families,
and it's the nearby gene
that's actually more important in the disease,
and variation in that, than the polymorphism
that was being typed in this particular individual.
So that's a difficult process to validate,
but in the end, that's one of the goals,
and we'll talk about that later.
Single variants themselves have really no power
to identify risk individuals, and so the power
of that differential enrichment of a single variant
in a large population,
compared to a control population,
has really no power on its own.
But it may give us a clue to pathophysiology,
or other related information
that will help us understand the disease better.
A large number of associated variants,
so variants for a particular condition all across the genome
can be sort of added up in a polynomial file,
a polynomial fashion, to create a disease risk score.
And this is something that's really come up in the past,
less than five years, these disease risk scores,
as we have begun doing these kind of studies
on very large populations,
and being able to say, well, in some populations,
some subpopulations, some ancestral populations,
certain variants are important in a disease,
and in another ancestral population, different variants are.
So we try to combine them together
to get a better sense of that,
as well as which variants are additive on top of each other,
to increase the risk even further.
Now, this was a article in preparing for this lecture
that I came across,
that really kind of described the different uses
in a very short abstract.
So I'm gonna read this,
"Hypertension continues to be a major risk factor
for global mortality,
and recent genome-wide association studies
have expanded in size,
leading to the identification of further genetic loci,
identifying, influencing blood pressure.
In light of the new knowledge
from the largest cardiovascular GWAS to date,
we review the potential impact of genomics
on discovering potential drug targets,
risk stratification with genetic risk scores,
drug selection with pharmacogenetics, and exploring insights
provided by gene-environment interactions.
So that's kind of what I'm gonna talk about
in the next few slides.
So one goal, and really an early goal of GWAS, is to say,
what are the different genes that actually play a role
in the pathogenesis of this disease?
And we can't find those
through rare disease single-gene studies,
so let's try these GWAS things
and see if we can pick out things that really stick out,
pick out genes that really stick out
as being enriched in the disease population,
and study those more carefully.
And this is just an example of that.
So on the bottom we have,
let's see if I can get a pen going here,
we have the chromosomes,
no,
gonna try that again,
we have the chromosomes,
and they're just lined up from chromosome one, over here,
to chromosome 22, over here.
And each one of these dots, black or blue,
represents a single nucleotide polymorphism
that has been typed.
And then, its likelihood of appearing preferentially
in the disease population
is plotted on this p-value log-based 10 scale,
also called a log score.
And what you see,
is that there are many
that really don't have any significance at all,
many, many, many, most of them don't,
and there are a few that have a significance
that approaches the threshold
that this particular study has set up,
and there are a handful
that are present at above the statistical cutoff
for this particular study.
So this little peak here would indicate
that there may be a gene at this locus on chromosome nine
that would be of interest
as a important drug target for the disease being studied.
And in this particular Manhattan plot,
it's called a Manhattan plot
because it looks like the skyline of Manhattan,
like in the lead-in slide for this slide deck.
So this is an interesting finding,
and is interesting potential to drug developers,
because there isn't a lot of other competition for this,
this seems to be sort of a predominant peak.
So goal two for GWAS
is to stratify complex risk individuals
for the purposes of preventative intervention.
So in this study,
they looked at coronary artery disease,
and they created a genomic risk score for that
using a whole bunch of these GWAS markers
that had signals in that Manhattan plot.
And over here on the right,
they have the genetic risk score quintiles in males,
and you can see that as your quintiles,
so the higher your genetic risk score,
if you're in the highest 25% of the genetic risk score,
then your cumulative risk of coronary artery disease,
as you age, is substantially higher, up here,
than the group in the lowest quintile, down here.
I guess these are fifths, not quarters.
So that would suggest that you may have increased risk
of cardiovascular coronary artery disease,
you know, starting between age 50 and 60,
whereas in the lowest quintile, your risk is quite low.
So this kind of information may help us in the future
further categorize people who need preventative intervention
or further evaluation of their risks,
and those who either can wait longer,
or those who don't need intervention at all.
Interestingly, these genetic risk scores
for coronary artery disease are,
you might say, well, we already have risk scores
for coronary artery disease,
they have to do with your cholesterol level,
your age, your sex, whether you're obese or not,
what your exercise profile is, and so forth,
is sort of Framingham-type risk profiles.
And it's very interesting that
this genomic risk score for the coronary artery disease
actually has an almost zero correlation
with the Framingham and American Heart Association
traditional risk scoring systems.
So this is measuring something else,
this is measuring something different
than those traditional sort of lifestyle
and regular laboratory measurements are measuring,
and so it may actually help us refine, dramatically,
who will really benefit from statins,
and who doesn't need to take them
and who may benefit from the most intensive interventions,
even in the absence of monogenic risk diseases
like familial hypercholesterolemia.
So interestingly, you know,
in familial hypercholesterolemia,
your risk of inheriting the disease from your parent is 50%
because it's autosomal dominant,
and your risk of having disease is pretty close to 50%,
of coronary artery disease, is pretty close to 50%
as you get into your fifties and sixties.
So interestingly, some of these genomic risk scores,
or polygenic risk scores,
can approach the same sort of level of penetrance
on a polygenic basis
as some of the dominant disorders
that also present in the same way,
or fit into the same general clinical group.
So,
I'll leave you to read the rest of this slide on your own,
but basically the genomic risk score allows you
a greater association
with future coronary artery disease risk
than any single conventional risk factor,
it is independent,
and yet compliments conventional risk factors,
it provides meaningful lifetime risk estimates
of coronary artery disease,
and it is quantifiable quite early, at or even before birth,
and shows potential for risk screening in early life.
So what we know about coronary artery disease is
that your risk for that begins to develop
actually early in childhood,
so the interventions to prevent that long term
may best be applied beginning in childhood.
We've never had a way to really quantify that
in a meaningful way before.
So at this point,
these GWAS studies for risk stratification and prevention
are still experimental,
but I would say, within the next five years,
these are going to start creeping into clinical practice
for their clinical utility in prevention
once those studies are completed.
A third goal of GWAS is
to explain variation in monogenic disease.
So we've talked about Huntington's disease in the past,
and we understand that the severity of Huntington's disease
is proportional to the number of trinucleotide repeats
in that expanded CGG repeat region.
Same thing for Fragile X.
However, if you look at Huntington's disease patients
with the same number of repeats,
there is a range of age of onset of the disease,
and age of death,
even though they have the same number of repeats.
So there are other factors that can influence
the expression of that disease, and that's sometimes called
the genetic landscape of the disease,
what are the other genetic factors
that can participate in making the disease worse,
or maybe slightly less worse than average?
So this is an effort to explain the phenomena
that we've talked about a lot,
decreased penetrance and variable expressivity.
Here's an example of a monogenic disease,
which is familial hypercholesterolemia or FH.
And over here on the left you have the LDL levels,
LDL cholesterol levels,
and most people wanna keep their LDL cholesterol
below, say 140,
but a lot of people have LDL cholesterol
that's higher than that.
That group can be common hypercholesterolemia,
or polygenic, multifactorial,
diet-related hypercholesterolemia,
or they may be a heterozygote, that's what the HE is for,
heterozygote familial hypercholesterolemia.
And if their LDL cholesterol is quite high,
they may be a homozygote for familial hypercholesterolemia.
So here's what we consider a dominant disease
that gets worse
when you have both copies of your gene affected.
However, there's a lot of variability
even within these groups, here,
and we would like to understand more
what the etiology of that variability is,
because those are other potentially modifiable,
or drug-targetable risk factors.
So examples are multiple LDL-raising SNPs,
or a high Lp(a),
and for increasing common hypocholesterolemia,
heterozygous pathogenic variants in LDL receptors,
PCSK9, which is a cholesterol receptor in the gut,
and APOB, which is also described in the book,
and then homozygous LDL receptor defects
and compound heterozygote,
which means you have two different heterozygous variants,
one on the your mom's copy and one on your dad's copy.
All right, so there's also these genetic background,
or, we'll call 'em background variations,
and there are both protective variations
and additional pathogenic variations,
and all of those essentially contribute
to your overall cardiovascular disease risk.
So to really accurately assess your risk,
you need to take into account all of these different things,
and these factors may,
if we explore those through GWAS,
we may begin to get our handle
on some things that can modify the underlying disease.
All right, fourth goal for GWAS
is assessing comorbidity risks.
This is a conclusion statement
from the abstract of an article that's quoted below,
that was looking at risk of,
in a depression cohort,
there was a polygenic risk score developed for depression,
major depressive disorder,
and they happened to notice
that the risk score also gave them
an increased a correlation with the risk of ischemic stroke.
So these two diseases,
we might not have thought were related to each other,
but this kind of information is beginning to give us links
between the pathogenesis of one disease
that we've characterized individually,
and the pathogenesis
of what we thought was a completely different disease,
and they may have similar risk factors.
Okay, so a fifth goal of GWAS
is to learn about pathophysiology, as I've been intimating.
This figure here is a whole exome sequencing study
that identified rare,
novel, rare and common Alzheimer's-associated variants
that were involved in immune response
and transcription regulation.
So you've read about those,
we've heard about those in previous modules.
And here's a study where they're basically looking
for other potential partners
in the pathophysiology
for common late-onset Alzheimer's disease.
And this is again, a Manhattan plot,
and they're finding some variants,
there,
and of course they find APOE, which we knew they would find,
because that's a well-known variant,
but they found others which have some signal, here,
on different chromosomes,
that may be worth looking at further.
All right, so what is the future of genetic risk scores?
I've kind of hinted at this throughout the lecture.
One is that,
and this is actually a review article that I'm quoting here,
first is, "The use of risk factors
for decision-making in cardiovascular disease
has a long history in medicine.
Early attempts to augment traditional risk factors
with genetic scores
were hampered by too little understanding
of the genetic basis of complex cardiovascular disease,"
and I would argue as well,
too small a group
in the populations that were being studied.
"Newer studies based on hundreds of thousands of people
and now millions of genetic variants all at once
indicate that genetic risk scores can now outperform
traditional risk factors in risk prediction,"
or be combined with them for a more strenuous prediction.
And they, "Propose that the time has come
to incorporate these genetic risk scores
into clinical practice."
And they suggest that, "Studies should focus
on the most appropriate way to do this
and maximize the benefit for our patients."
So this is really sort of groundbreaking.
Before 2018, somebody told me, you know,
GWAS was gonna be really important in clinical care,
I would've said, nah, nah, never.
But the more recent data that's coming out
is really suggesting
that this will be a real tool in clinical medicine
once the clinical utility studies have been completed.
Now,
are there gaps in this approach?
Yes.
So one of the gaps is
that the variants that are used in the GWAS study,
as much as they try to get variants that are universal,
may be present at different frequencies
in different ancestral populations.
So in an African population, versus an Asian population,
versus a northern European population, you're going to find
vastly different profiles of genetic variation,
so that a polygenic risk score
that was developed
on the basis of information from European populations
may have much less power in other ancestral populations,
because the frequency of the variants that you looked at
just aren't very high there.
So one of the challenges to making polygenic risk scores
truly valuable in clinical work,
is to overcome that by really charging forward
with sequencing lots and lots of individuals
of many different ethnic backgrounds,
and many different ancestral strands,
all over the world,
to capture as much of the variation as possible,
and then to repeat these kinds of studies
in those populations
so that we know that the same, or different variants apply
in that particular circumstance.
So that's a pretty big barrier,
I think that genomic risk scores, or polygenic risk scores
will continue to be moved forward even with that gap,
but it's something that we have to keep in mind
when considering the applicability across the board.
So I think that's the last slide
that I'm going to talk about.
And I will point out
that the slide deck includes slides
that came from the book publisher,
and I basically kept the slides on there
about type 1 diabetes.
I'm not gonna go through that in this lecture, I wanna stop,
but it's probably a reasonable exercise,
after hearing this story,
to walk through those slides on your own
to think about the way
that the information that I've just presented
colors the way you think about type 1 diabetes
or in any of the other disorders of complex inheritance
that we've been talking about in this module.
So that's the end of the lecture for module nine,
thanks for your attention and thank you for learning.