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[Dr. Wildin] Hi, this is Dr. Bob Wildin.

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This is a lecture from Genomics,

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Genetics for Clinicians,

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and this is in module nine,

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which is about genetics
through the lifespan,

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essentially adult onset disorders.

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There are a number of monogenic disorders

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that are discussed primarily
in the reading material.

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This lecture is about
complex disease, GWAS,

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or genome-wide association
studies and genetic risk scores.

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So complex disease in genetics means,

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simply, that it is not
simple, it is not monogenic,

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it's not, you get the
gene, you get the disorder.

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It is not entirely genetic,

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but may be influenced by genetics,

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so there are other
factors that are involved.

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The pathophysiology of the disorder

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may involve multiple steps

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and multiple body systems or organs.

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An example might be autoimmune disease

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that attacks a particular organ,

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and the origin of the pathogenesis
is in the immune system,

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but the organ is the part
of the body that fails.

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Complex diseases may be susceptible

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to trigger conditions and circumstances,

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and so may never appear

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if those circumstances are
not encountered in one's life.

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These features make it much harder

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to do traditional genetic research

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where you identify a disease,

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and you follow it through the family,

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and you do linkage studies to identify

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where on the chromosomes
the disease gene may lie,

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and then clone the disease
gene and study the biology,

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that's really impossible
to do in complex diseases.

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It also makes it potentially harder

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to identify common therapeutic targets,

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so a drug or some other intervention

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that will have the same beneficial effect

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across a diverse patient population

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that has the same "disease", in quotes.

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Now, that's one way of looking at it,

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another way of looking at it is that

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the additive nature of
complex disease burdens means

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that there may be one
small additive element

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which pushes the disease into being,

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and any one of the elements
that led up to that point

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may potentially be targetable
to bring down the intensity,

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if not the expression of the disease.

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Polygenic inheritance.

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So "poly" means many,
"genetic" means genes,

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so polygenic inheritance means
that a disease is inherited

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when multiple genes or genetic variants

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are inherited together.

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So this initiates the concept
of a cumulative genetic burden

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that leads to disease.

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One gene is not enough,

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you have to have variants
predisposed to disease

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in multiple genes.

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There can be multiple
combinations of variations

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in different genes that
can add up to disease,

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so not everybody inherits
the same five variants,

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or variants from the same five genes,

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in order to get the disease,
there may be a variety of ways,

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a variety of collections in your basket,

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in order to get you to the
point of expressing a disease

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like type two diabetes.

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So there's a concept of a threshold

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of getting enough of these
genes with negative effects

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in a particular area

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in order to express
the disease externally,

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and obviously, it's really a gradation,

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and so you may have a
subclinical case of something

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when you have a little bit less
of the disease gene burden,

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and a more severe case

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when you have a full blown
basketful of goodies.

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Different families may therefore have

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different combinations
of contributing genes.

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Familial clustering with rapid dissipation

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beyond first-degree relatives

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is the pattern that you
see in pedigree analysis.

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So if you're doing pedigree analysis,

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and you see that there's a tight cluster

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of first-degree relatives
who have a disease,

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but the second-degree
relatives, up and down,

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or cousins and so forth,

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have a much lower
frequency of the disease,

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one explanation for that

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is that they have a polygenic disorder.

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And the reason that it
falls off so quickly

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as you move away from the core group,

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is because in order reach

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that threshold of genetic
burden for the disease,

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you have to co-inherit a
bunch of different genes

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on different chromosomes,

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and the likelihood of that ends up being

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the product of half for each one of those,

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or very quickly, quite small.

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Multifactorial and polygenic
are often confused,

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multifactorial is essentially
polygenic plus other stuff.

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This diagram is trying to
describe what that means.

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So polygenic, not monogenic,

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essentially sets up a
susceptibility to a disease,

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and then you add in an
environmental exposure,

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whether it be a medicine, a substance,

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radiation, in the case of people,

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for example, with very light skin,

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low pigment level in their skin,

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may have problems with
increased skin cancer,

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because they have more sensitive skin,

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if they stay out of the sun,

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their skin's cancer risk goes down.

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Other environmental
exposures can be trauma,

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or physical things that
can happen to you, surgery.

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Think about malignant hyperthermia,

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a genetic predisposition

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to a severe life-threatening
adverse reaction

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when given certain
volatile anesthetic agents,

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inhaled anesthetic agents,

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and stress and physical activity,

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for some disorders are triggers,

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McArdle's disease is an example of that.

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A metabolic state like
obesity can predispose you

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to type 2 diabetes,

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but you probably have
to have a set of genes,

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a polygenic risk component underlying it,

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before you actually express the disease.

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On the other hand,

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even if you have that
polygenic risk component

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and you can reduce the trigger exposure,

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that is obesity, you may be
able to reverse the disease.

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Immunogenic stimulus is also

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another potential environmental exposure.

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If you are not exposed to measles,

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then you may not get an
autoimmune disease later in life.

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And then there's the third component,

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here on the right, in the question marks,

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which are basically saying

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that there may be other
factors that we can't identify,

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things that are not necessarily
exposures, medications,

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and they're not genes, but
they may be other factors

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that are not under our
control, for example,

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a particular timing of
an environmental exposure

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in a particular age group.

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So for example, in adolescence
when one is growing rapidly,

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a particular exposure may have more effect

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on a bone physiology than in an adult.

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Similarly, in a post-menopausal woman,

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the polygenic risk for osteoporosis

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may be closer to the
surface and more expressed

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than it is at a younger age

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when estrogen is present and protective.

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So in order to get at some of these,

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sort of subtle effects of polygenic risk,

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as I stated earlier,
it's really not possible

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to do the traditional
kinds of genetic research

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and identify those genes in
the usual family-based way.

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So a technique has evolved,

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which is called genome
wide-association studies, or GWAS.

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Now, the purpose of GWAS is to identify

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regions, small regions of the genome

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that contain variation,

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which when compared across a population,

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tends to segregate more or
less with disease expression.

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So GWAS studies use normal
sequence variation in the genomes

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or what we would call polymorphisms,

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often they're using single
nucleotide polymorphisms,

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or SNPs,

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and they ask which variations
are observed more often

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when the individual has
or lacks the condition.

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So, do you have allele A, or allele B?

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And of course, in many cases

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there will be people in
the disease condition group

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that have either allele A or allele B,

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or two copies of B and one copy,

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or two copies of A, or
one copy of A and B,

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and the same is true of
the control population.

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So what you're looking
for is an enrichment

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of certain alleles in
the disease population,

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that might give you a clue

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that that locus on the
chromosomes may have a gene nearby

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that is important in the
pathogenesis of this disease,

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in at least some people in the population.

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It's important to mention

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that these variations can
go in both directions.

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So some variations may
be disease enhancing,

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disease risk enhancing,

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and some may be protective.

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And you'll see this if you
read the GWAS literature,

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that those variants go in both direction.

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So the next step is

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to analyze these results
across a large population.

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Now, the earlier GWAS studies were done

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with relatively small populations,

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because we had to go out and collect DNA

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for a particular disease group,

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and we had to collect a whole population

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of people with that disease
and without that disease.

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With the advent of large groups of people

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who were having their
whole exome sequenced,

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or whole genome sequenced

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during population genomic research,

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we are now having access
to a large number of people

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who have had their genome
entirely sequenced,

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or have had a bunch of genotypes done

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all the way across their genome,
and that data can be used

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along with their health information

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to perform these studies.

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So now we can actually perform studies

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on a hundred thousand individuals,

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or 200,000, or even 500,000 individuals.

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as is the case

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with some of the SNP/ChIP-based
genotyping studies.

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This gives us a whole lot more
power to understand the role

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that each of the small
increment genetic risk loci play

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in the disease.

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So the GWAS studies ask if a variation,

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once you find a GWAS with a
signal, then you have to ask,

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is that polymorphism that I looked at,

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that genetic A-to-T, or G-to-C,

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is that itself a functional variant?

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In other words, does that
specific variant change

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the amino acid sequence of a protein,

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or does it change a regulatory element

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that changes the expression of a protein,

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or perhaps changes the
methylation of CPG islands,

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and thus changes gene
expression epigenetically?

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Or, is it simply a polymorphic marker

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that is linked to a nearby gene,

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and therefore travels together
with it through families,

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and it's the nearby gene

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that's actually more
important in the disease,

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and variation in that,
than the polymorphism

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that was being typed in
this particular individual.

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So that's a difficult process to validate,

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but in the end, that's one of the goals,

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and we'll talk about that later.

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Single variants themselves
have really no power

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to identify risk
individuals, and so the power

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of that differential
enrichment of a single variant

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in a large population,

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compared to a control population,

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has really no power on its own.

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But it may give us a
clue to pathophysiology,

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or other related information

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that will help us understand
the disease better.

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A large number of associated variants,

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so variants for a particular
condition all across the genome

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can be sort of added up
in a polynomial file,

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a polynomial fashion, to
create a disease risk score.

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And this is something that's
really come up in the past,

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less than five years,
these disease risk scores,

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as we have begun doing
these kind of studies

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on very large populations,

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and being able to say,
well, in some populations,

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some subpopulations, some
ancestral populations,

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certain variants are
important in a disease,

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and in another ancestral
population, different variants are.

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So we try to combine them together

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to get a better sense of that,

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as well as which variants are
additive on top of each other,

262
00:16:05,640 --> 00:16:07,353
to increase the risk even further.

263
00:16:10,170 --> 00:16:14,580
Now, this was a article in
preparing for this lecture

264
00:16:14,580 --> 00:16:15,413
that I came across,

265
00:16:15,413 --> 00:16:17,790
that really kind of
described the different uses

266
00:16:17,790 --> 00:16:19,230
in a very short abstract.

267
00:16:19,230 --> 00:16:20,617
So I'm gonna read this,

268
00:16:20,617 --> 00:16:23,460
"Hypertension continues
to be a major risk factor

269
00:16:23,460 --> 00:16:24,840
for global mortality,

270
00:16:24,840 --> 00:16:27,420
and recent genome-wide association studies

271
00:16:27,420 --> 00:16:29,010
have expanded in size,

272
00:16:29,010 --> 00:16:32,070
leading to the identification
of further genetic loci,

273
00:16:32,070 --> 00:16:34,473
identifying, influencing blood pressure.

274
00:16:35,340 --> 00:16:36,330
In light of the new knowledge

275
00:16:36,330 --> 00:16:39,540
from the largest
cardiovascular GWAS to date,

276
00:16:39,540 --> 00:16:42,060
we review the potential impact of genomics

277
00:16:42,060 --> 00:16:44,940
on discovering potential drug targets,

278
00:16:44,940 --> 00:16:48,630
risk stratification with
genetic risk scores,

279
00:16:48,630 --> 00:16:52,020
drug selection with pharmacogenetics,
and exploring insights

280
00:16:52,020 --> 00:16:55,290
provided by gene-environment interactions.

281
00:16:55,290 --> 00:16:56,720
So that's kind of what
I'm gonna talk about

282
00:16:56,720 --> 00:16:58,830
in the next few slides.

283
00:16:58,830 --> 00:17:03,210
So one goal, and really an
early goal of GWAS, is to say,

284
00:17:03,210 --> 00:17:06,000
what are the different genes
that actually play a role

285
00:17:06,000 --> 00:17:09,211
in the pathogenesis of this disease?

286
00:17:09,211 --> 00:17:11,760
And we can't find those

287
00:17:11,760 --> 00:17:14,280
through rare disease single-gene studies,

288
00:17:14,280 --> 00:17:15,960
so let's try these GWAS things

289
00:17:15,960 --> 00:17:19,260
and see if we can pick out
things that really stick out,

290
00:17:19,260 --> 00:17:20,910
pick out genes that really stick out

291
00:17:20,910 --> 00:17:24,510
as being enriched in
the disease population,

292
00:17:24,510 --> 00:17:26,250
and study those more carefully.

293
00:17:26,250 --> 00:17:29,160
And this is just an example of that.

294
00:17:29,160 --> 00:17:32,073
So on the bottom we have,

295
00:17:33,810 --> 00:17:36,450
let's see if I can get a pen going here,

296
00:17:36,450 --> 00:17:38,580
we have the chromosomes,

297
00:17:38,580 --> 00:17:39,413
no,

298
00:17:40,800 --> 00:17:41,883
gonna try that again,

299
00:17:45,690 --> 00:17:47,490
we have the chromosomes,

300
00:17:47,490 --> 00:17:52,490
and they're just lined up from
chromosome one, over here,

301
00:17:52,560 --> 00:17:56,220
to chromosome 22, over here.

302
00:17:56,220 --> 00:17:59,970
And each one of these dots, black or blue,

303
00:17:59,970 --> 00:18:04,830
represents a single
nucleotide polymorphism

304
00:18:04,830 --> 00:18:06,093
that has been typed.

305
00:18:06,960 --> 00:18:11,960
And then, its likelihood
of appearing preferentially

306
00:18:13,230 --> 00:18:15,000
in the disease population

307
00:18:15,000 --> 00:18:19,500
is plotted on this p-value
log-based 10 scale,

308
00:18:19,500 --> 00:18:21,093
also called a log score.

309
00:18:22,350 --> 00:18:24,060
And what you see,

310
00:18:24,060 --> 00:18:27,660
is that there are many

311
00:18:27,660 --> 00:18:30,630
that really don't have
any significance at all,

312
00:18:30,630 --> 00:18:32,790
many, many, many, most of them don't,

313
00:18:32,790 --> 00:18:37,790
and there are a few
that have a significance

314
00:18:37,800 --> 00:18:39,510
that approaches the threshold

315
00:18:39,510 --> 00:18:42,273
that this particular study has set up,

316
00:18:43,350 --> 00:18:45,520
and there are a handful

317
00:18:46,440 --> 00:18:51,440
that are present at above
the statistical cutoff

318
00:18:52,500 --> 00:18:54,780
for this particular study.

319
00:18:54,780 --> 00:18:58,680
So this little peak here would indicate

320
00:18:58,680 --> 00:19:02,970
that there may be a gene at
this locus on chromosome nine

321
00:19:02,970 --> 00:19:06,480
that would be of interest

322
00:19:06,480 --> 00:19:11,480
as a important drug target
for the disease being studied.

323
00:19:13,560 --> 00:19:17,400
And in this particular Manhattan plot,

324
00:19:17,400 --> 00:19:18,300
it's called a Manhattan plot

325
00:19:18,300 --> 00:19:21,180
because it looks like
the skyline of Manhattan,

326
00:19:21,180 --> 00:19:23,733
like in the lead-in slide
for this slide deck.

327
00:19:25,410 --> 00:19:28,440
So this is an interesting finding,

328
00:19:28,440 --> 00:19:31,650
and is interesting potential
to drug developers,

329
00:19:31,650 --> 00:19:35,520
because there isn't a lot of
other competition for this,

330
00:19:35,520 --> 00:19:39,003
this seems to be sort
of a predominant peak.

331
00:19:43,200 --> 00:19:46,080
So goal two for GWAS

332
00:19:46,080 --> 00:19:49,650
is to stratify complex risk individuals

333
00:19:49,650 --> 00:19:52,653
for the purposes of
preventative intervention.

334
00:19:54,450 --> 00:19:56,460
So in this study,

335
00:19:56,460 --> 00:20:00,390
they looked at coronary artery disease,

336
00:20:00,390 --> 00:20:03,870
and they created a genomic
risk score for that

337
00:20:03,870 --> 00:20:07,470
using a whole bunch of these GWAS markers

338
00:20:07,470 --> 00:20:11,043
that had signals in that Manhattan plot.

339
00:20:12,360 --> 00:20:15,210
And over here on the right,

340
00:20:15,210 --> 00:20:18,717
they have the genetic risk
score quintiles in males,

341
00:20:21,900 --> 00:20:25,260
and you can see that as your quintiles,

342
00:20:25,260 --> 00:20:26,850
so the higher your genetic risk score,

343
00:20:26,850 --> 00:20:30,450
if you're in the highest 25%
of the genetic risk score,

344
00:20:30,450 --> 00:20:33,630
then your cumulative risk
of coronary artery disease,

345
00:20:33,630 --> 00:20:37,833
as you age, is substantially
higher, up here,

346
00:20:38,820 --> 00:20:43,820
than the group in the
lowest quintile, down here.

347
00:20:44,880 --> 00:20:47,940
I guess these are fifths, not quarters.

348
00:20:47,940 --> 00:20:52,940
So that would suggest that
you may have increased risk

349
00:20:53,310 --> 00:20:57,510
of cardiovascular coronary artery disease,

350
00:20:57,510 --> 00:21:00,810
you know, starting between age 50 and 60,

351
00:21:00,810 --> 00:21:04,890
whereas in the lowest quintile,
your risk is quite low.

352
00:21:04,890 --> 00:21:07,690
So this kind of information
may help us in the future

353
00:21:08,940 --> 00:21:13,530
further categorize people who
need preventative intervention

354
00:21:13,530 --> 00:21:16,053
or further evaluation of their risks,

355
00:21:17,310 --> 00:21:19,530
and those who either can wait longer,

356
00:21:19,530 --> 00:21:21,903
or those who don't need
intervention at all.

357
00:21:24,270 --> 00:21:27,780
Interestingly, these genetic risk scores

358
00:21:27,780 --> 00:21:31,320
for coronary artery disease are,

359
00:21:31,320 --> 00:21:34,860
you might say, well, we
already have risk scores

360
00:21:34,860 --> 00:21:36,120
for coronary artery disease,

361
00:21:36,120 --> 00:21:38,220
they have to do with
your cholesterol level,

362
00:21:38,220 --> 00:21:42,570
your age, your sex, whether
you're obese or not,

363
00:21:42,570 --> 00:21:45,060
what your exercise
profile is, and so forth,

364
00:21:45,060 --> 00:21:49,353
is sort of Framingham-type risk profiles.

365
00:21:50,370 --> 00:21:53,010
And it's very interesting that

366
00:21:53,010 --> 00:21:56,650
this genomic risk score for
the coronary artery disease

367
00:21:57,660 --> 00:22:00,060
actually has an almost zero correlation

368
00:22:00,060 --> 00:22:03,390
with the Framingham and
American Heart Association

369
00:22:03,390 --> 00:22:05,970
traditional risk scoring systems.

370
00:22:05,970 --> 00:22:09,600
So this is measuring something else,

371
00:22:09,600 --> 00:22:11,820
this is measuring something different

372
00:22:11,820 --> 00:22:14,640
than those traditional sort of lifestyle

373
00:22:14,640 --> 00:22:18,300
and regular laboratory
measurements are measuring,

374
00:22:18,300 --> 00:22:22,680
and so it may actually help
us refine, dramatically,

375
00:22:22,680 --> 00:22:24,930
who will really benefit from statins,

376
00:22:24,930 --> 00:22:27,040
and who doesn't need to take them

377
00:22:28,020 --> 00:22:33,020
and who may benefit from the
most intensive interventions,

378
00:22:37,800 --> 00:22:41,670
even in the absence of
monogenic risk diseases

379
00:22:41,670 --> 00:22:43,773
like familial hypercholesterolemia.

380
00:22:44,760 --> 00:22:47,370
So interestingly, you know,

381
00:22:47,370 --> 00:22:49,170
in familial hypercholesterolemia,

382
00:22:49,170 --> 00:22:53,970
your risk of inheriting the
disease from your parent is 50%

383
00:22:53,970 --> 00:22:56,700
because it's autosomal dominant,

384
00:22:56,700 --> 00:23:00,480
and your risk of having
disease is pretty close to 50%,

385
00:23:00,480 --> 00:23:03,540
of coronary artery disease,
is pretty close to 50%

386
00:23:03,540 --> 00:23:06,060
as you get into your fifties and sixties.

387
00:23:06,060 --> 00:23:11,060
So interestingly, some of
these genomic risk scores,

388
00:23:11,520 --> 00:23:13,890
or polygenic risk scores,

389
00:23:13,890 --> 00:23:18,890
can approach the same sort
of level of penetrance

390
00:23:22,980 --> 00:23:24,270
on a polygenic basis

391
00:23:24,270 --> 00:23:27,690
as some of the dominant disorders

392
00:23:27,690 --> 00:23:30,570
that also present in the same way,

393
00:23:30,570 --> 00:23:32,943
or fit into the same
general clinical group.

394
00:23:35,250 --> 00:23:36,083
So,

395
00:23:38,220 --> 00:23:42,093
I'll leave you to read the
rest of this slide on your own,

396
00:23:43,380 --> 00:23:47,070
but basically the genomic
risk score allows you

397
00:23:47,070 --> 00:23:49,050
a greater association

398
00:23:49,050 --> 00:23:51,960
with future coronary artery disease risk

399
00:23:51,960 --> 00:23:54,483
than any single conventional risk factor,

400
00:23:55,590 --> 00:23:57,453
it is independent,

401
00:23:59,910 --> 00:24:03,990
and yet compliments
conventional risk factors,

402
00:24:03,990 --> 00:24:07,500
it provides meaningful
lifetime risk estimates

403
00:24:07,500 --> 00:24:09,900
of coronary artery disease,

404
00:24:09,900 --> 00:24:14,900
and it is quantifiable quite
early, at or even before birth,

405
00:24:14,940 --> 00:24:18,663
and shows potential for risk
screening in early life.

406
00:24:19,620 --> 00:24:22,650
So what we know about
coronary artery disease is

407
00:24:22,650 --> 00:24:25,860
that your risk for that begins to develop

408
00:24:25,860 --> 00:24:28,053
actually early in childhood,

409
00:24:28,980 --> 00:24:33,120
so the interventions to
prevent that long term

410
00:24:33,120 --> 00:24:36,273
may best be applied
beginning in childhood.

411
00:24:38,100 --> 00:24:40,710
We've never had a way
to really quantify that

412
00:24:40,710 --> 00:24:42,390
in a meaningful way before.

413
00:24:42,390 --> 00:24:44,430
So at this point,

414
00:24:44,430 --> 00:24:48,960
these GWAS studies for risk
stratification and prevention

415
00:24:48,960 --> 00:24:51,330
are still experimental,

416
00:24:51,330 --> 00:24:53,940
but I would say, within
the next five years,

417
00:24:53,940 --> 00:24:58,560
these are going to start
creeping into clinical practice

418
00:24:58,560 --> 00:25:03,560
for their clinical utility in prevention

419
00:25:03,750 --> 00:25:05,673
once those studies are completed.

420
00:25:08,610 --> 00:25:10,050
A third goal of GWAS is

421
00:25:10,050 --> 00:25:14,130
to explain variation in monogenic disease.

422
00:25:14,130 --> 00:25:17,850
So we've talked about
Huntington's disease in the past,

423
00:25:17,850 --> 00:25:22,350
and we understand that the
severity of Huntington's disease

424
00:25:22,350 --> 00:25:27,350
is proportional to the number
of trinucleotide repeats

425
00:25:28,140 --> 00:25:30,783
in that expanded CGG repeat region.

426
00:25:31,650 --> 00:25:33,600
Same thing for Fragile X.

427
00:25:33,600 --> 00:25:37,350
However, if you look at
Huntington's disease patients

428
00:25:37,350 --> 00:25:40,650
with the same number of repeats,

429
00:25:40,650 --> 00:25:45,650
there is a range of age
of onset of the disease,

430
00:25:46,380 --> 00:25:47,673
and age of death,

431
00:25:48,720 --> 00:25:51,150
even though they have the
same number of repeats.

432
00:25:51,150 --> 00:25:54,540
So there are other
factors that can influence

433
00:25:54,540 --> 00:25:59,520
the expression of that disease,
and that's sometimes called

434
00:25:59,520 --> 00:26:01,230
the genetic landscape of the disease,

435
00:26:01,230 --> 00:26:03,480
what are the other genetic factors

436
00:26:03,480 --> 00:26:08,480
that can participate in
making the disease worse,

437
00:26:09,750 --> 00:26:12,483
or maybe slightly less worse than average?

438
00:26:13,800 --> 00:26:17,400
So this is an effort to
explain the phenomena

439
00:26:17,400 --> 00:26:18,900
that we've talked about a lot,

440
00:26:18,900 --> 00:26:21,963
decreased penetrance and
variable expressivity.

441
00:26:22,860 --> 00:26:25,650
Here's an example of a monogenic disease,

442
00:26:25,650 --> 00:26:30,060
which is familial
hypercholesterolemia or FH.

443
00:26:30,060 --> 00:26:35,060
And over here on the left
you have the LDL levels,

444
00:26:35,370 --> 00:26:38,070
LDL cholesterol levels,

445
00:26:38,070 --> 00:26:43,070
and most people wanna
keep their LDL cholesterol

446
00:26:43,170 --> 00:26:44,883
below, say 140,

447
00:26:45,780 --> 00:26:49,980
but a lot of people have LDL cholesterol

448
00:26:49,980 --> 00:26:51,180
that's higher than that.

449
00:26:53,040 --> 00:26:57,540
That group can be common
hypercholesterolemia,

450
00:26:57,540 --> 00:26:59,970
or polygenic, multifactorial,

451
00:26:59,970 --> 00:27:03,480
diet-related hypercholesterolemia,

452
00:27:03,480 --> 00:27:07,320
or they may be a heterozygote,
that's what the HE is for,

453
00:27:07,320 --> 00:27:10,383
heterozygote familial
hypercholesterolemia.

454
00:27:11,850 --> 00:27:16,173
And if their LDL
cholesterol is quite high,

455
00:27:17,100 --> 00:27:21,810
they may be a homozygote for
familial hypercholesterolemia.

456
00:27:21,810 --> 00:27:25,230
So here's what we consider
a dominant disease

457
00:27:25,230 --> 00:27:26,063
that gets worse

458
00:27:26,063 --> 00:27:29,163
when you have both copies
of your gene affected.

459
00:27:31,320 --> 00:27:33,170
However, there's a lot of variability

460
00:27:35,910 --> 00:27:39,030
even within these groups, here,

461
00:27:39,030 --> 00:27:42,510
and we would like to understand more

462
00:27:42,510 --> 00:27:45,780
what the etiology of that variability is,

463
00:27:45,780 --> 00:27:49,140
because those are other
potentially modifiable,

464
00:27:49,140 --> 00:27:54,063
or drug-targetable risk factors.

465
00:27:58,650 --> 00:28:03,630
So examples are multiple LDL-raising SNPs,

466
00:28:03,630 --> 00:28:05,247
or a high Lp(a),

467
00:28:07,290 --> 00:28:12,290
and for increasing common
hypocholesterolemia,

468
00:28:13,320 --> 00:28:17,130
heterozygous pathogenic
variants in LDL receptors,

469
00:28:17,130 --> 00:28:21,033
PCSK9, which is a cholesterol
receptor in the gut,

470
00:28:22,140 --> 00:28:27,030
and APOB, which is also
described in the book,

471
00:28:27,030 --> 00:28:32,030
and then homozygous LDL receptor defects

472
00:28:32,220 --> 00:28:35,970
and compound heterozygote,

473
00:28:35,970 --> 00:28:39,450
which means you have two
different heterozygous variants,

474
00:28:39,450 --> 00:28:42,843
one on the your mom's copy
and one on your dad's copy.

475
00:28:44,670 --> 00:28:49,290
All right, so there's also
these genetic background,

476
00:28:49,290 --> 00:28:51,570
or, we'll call 'em background variations,

477
00:28:51,570 --> 00:28:54,840
and there are both protective variations

478
00:28:54,840 --> 00:28:57,750
and additional pathogenic variations,

479
00:28:57,750 --> 00:28:59,580
and all of those essentially contribute

480
00:28:59,580 --> 00:29:02,790
to your overall
cardiovascular disease risk.

481
00:29:02,790 --> 00:29:06,210
So to really accurately assess your risk,

482
00:29:06,210 --> 00:29:10,770
you need to take into account
all of these different things,

483
00:29:10,770 --> 00:29:15,630
and these factors may,

484
00:29:15,630 --> 00:29:17,730
if we explore those through GWAS,

485
00:29:17,730 --> 00:29:19,500
we may begin to get our handle

486
00:29:19,500 --> 00:29:24,363
on some things that can
modify the underlying disease.

487
00:29:27,540 --> 00:29:29,310
All right, fourth goal for GWAS

488
00:29:29,310 --> 00:29:31,803
is assessing comorbidity risks.

489
00:29:32,820 --> 00:29:36,000
This is a conclusion statement

490
00:29:36,000 --> 00:29:39,903
from the abstract of an
article that's quoted below,

491
00:29:40,830 --> 00:29:43,980
that was looking at risk of,

492
00:29:43,980 --> 00:29:46,020
in a depression cohort,

493
00:29:46,020 --> 00:29:50,433
there was a polygenic risk
score developed for depression,

494
00:29:51,330 --> 00:29:53,820
major depressive disorder,

495
00:29:53,820 --> 00:29:56,040
and they happened to notice

496
00:29:56,040 --> 00:29:59,550
that the risk score also gave them

497
00:29:59,550 --> 00:30:04,550
an increased a correlation with
the risk of ischemic stroke.

498
00:30:05,910 --> 00:30:09,450
So these two diseases,

499
00:30:09,450 --> 00:30:12,780
we might not have thought
were related to each other,

500
00:30:12,780 --> 00:30:17,040
but this kind of information
is beginning to give us links

501
00:30:17,040 --> 00:30:19,470
between the pathogenesis of one disease

502
00:30:19,470 --> 00:30:21,420
that we've characterized individually,

503
00:30:21,420 --> 00:30:22,530
and the pathogenesis

504
00:30:22,530 --> 00:30:25,590
of what we thought was a
completely different disease,

505
00:30:25,590 --> 00:30:28,953
and they may have similar risk factors.

506
00:30:31,740 --> 00:30:34,950
Okay, so a fifth goal of GWAS

507
00:30:34,950 --> 00:30:38,163
is to learn about pathophysiology,
as I've been intimating.

508
00:30:39,510 --> 00:30:43,530
This figure here is a whole
exome sequencing study

509
00:30:43,530 --> 00:30:45,480
that identified rare,

510
00:30:45,480 --> 00:30:49,800
novel, rare and common
Alzheimer's-associated variants

511
00:30:49,800 --> 00:30:52,620
that were involved in immune response

512
00:30:52,620 --> 00:30:54,570
and transcription regulation.

513
00:30:54,570 --> 00:30:55,890
So you've read about those,

514
00:30:55,890 --> 00:30:59,190
we've heard about those
in previous modules.

515
00:30:59,190 --> 00:31:02,730
And here's a study where
they're basically looking

516
00:31:02,730 --> 00:31:07,730
for other potential partners

517
00:31:08,280 --> 00:31:09,960
in the pathophysiology

518
00:31:09,960 --> 00:31:14,817
for common late-onset Alzheimer's disease.

519
00:31:14,817 --> 00:31:17,550
And this is again, a Manhattan plot,

520
00:31:17,550 --> 00:31:20,043
and they're finding some variants,

521
00:31:23,790 --> 00:31:25,020
there,

522
00:31:25,020 --> 00:31:27,420
and of course they find APOE,
which we knew they would find,

523
00:31:27,420 --> 00:31:29,520
because that's a well-known variant,

524
00:31:29,520 --> 00:31:33,363
but they found others which
have some signal, here,

525
00:31:35,193 --> 00:31:36,240
on different chromosomes,

526
00:31:36,240 --> 00:31:38,253
that may be worth looking at further.

527
00:31:41,970 --> 00:31:44,430
All right, so what is the
future of genetic risk scores?

528
00:31:44,430 --> 00:31:49,380
I've kind of hinted at this
throughout the lecture.

529
00:31:49,380 --> 00:31:50,697
One is that,

530
00:31:50,697 --> 00:31:55,083
and this is actually a review
article that I'm quoting here,

531
00:31:56,460 --> 00:31:58,470
first is, "The use of risk factors

532
00:31:58,470 --> 00:32:01,290
for decision-making in
cardiovascular disease

533
00:32:01,290 --> 00:32:02,913
has a long history in medicine.

534
00:32:04,260 --> 00:32:06,750
Early attempts to augment
traditional risk factors

535
00:32:06,750 --> 00:32:08,490
with genetic scores

536
00:32:08,490 --> 00:32:10,080
were hampered by too little understanding

537
00:32:10,080 --> 00:32:13,140
of the genetic basis of complex
cardiovascular disease,"

538
00:32:13,140 --> 00:32:14,763
and I would argue as well,

539
00:32:15,930 --> 00:32:19,950
too small a group

540
00:32:19,950 --> 00:32:22,927
in the populations that
were being studied.

541
00:32:22,927 --> 00:32:25,860
"Newer studies based on
hundreds of thousands of people

542
00:32:25,860 --> 00:32:29,070
and now millions of genetic
variants all at once

543
00:32:29,070 --> 00:32:32,160
indicate that genetic risk
scores can now outperform

544
00:32:32,160 --> 00:32:35,130
traditional risk factors
in risk prediction,"

545
00:32:35,130 --> 00:32:38,913
or be combined with them for
a more strenuous prediction.

546
00:32:40,200 --> 00:32:42,450
And they, "Propose that the time has come

547
00:32:42,450 --> 00:32:45,030
to incorporate these genetic risk scores

548
00:32:45,030 --> 00:32:46,617
into clinical practice."

549
00:32:47,580 --> 00:32:49,860
And they suggest that,
"Studies should focus

550
00:32:49,860 --> 00:32:52,230
on the most appropriate way to do this

551
00:32:52,230 --> 00:32:55,770
and maximize the benefit
for our patients."

552
00:32:55,770 --> 00:32:58,920
So this is really sort of groundbreaking.

553
00:32:58,920 --> 00:33:02,940
Before 2018, somebody told me, you know,

554
00:33:02,940 --> 00:33:05,280
GWAS was gonna be really
important in clinical care,

555
00:33:05,280 --> 00:33:08,190
I would've said, nah, nah, never.

556
00:33:08,190 --> 00:33:11,820
But the more recent data that's coming out

557
00:33:11,820 --> 00:33:12,990
is really suggesting

558
00:33:12,990 --> 00:33:17,710
that this will be a real
tool in clinical medicine

559
00:33:18,720 --> 00:33:22,173
once the clinical utility
studies have been completed.

560
00:33:23,340 --> 00:33:24,173
Now,

561
00:33:27,255 --> 00:33:30,840
are there gaps in this approach?

562
00:33:30,840 --> 00:33:31,673
Yes.

563
00:33:31,673 --> 00:33:32,670
So one of the gaps is

564
00:33:32,670 --> 00:33:37,670
that the variants that are
used in the GWAS study,

565
00:33:39,660 --> 00:33:44,163
as much as they try to get
variants that are universal,

566
00:33:45,090 --> 00:33:49,500
may be present at different frequencies

567
00:33:49,500 --> 00:33:51,690
in different ancestral populations.

568
00:33:51,690 --> 00:33:56,010
So in an African population,
versus an Asian population,

569
00:33:56,010 --> 00:33:59,910
versus a northern European
population, you're going to find

570
00:33:59,910 --> 00:34:04,740
vastly different profiles
of genetic variation,

571
00:34:04,740 --> 00:34:09,740
so that a polygenic risk score

572
00:34:10,590 --> 00:34:12,300
that was developed

573
00:34:12,300 --> 00:34:17,300
on the basis of information
from European populations

574
00:34:18,090 --> 00:34:22,620
may have much less power in
other ancestral populations,

575
00:34:22,620 --> 00:34:24,780
because the frequency of the
variants that you looked at

576
00:34:24,780 --> 00:34:26,313
just aren't very high there.

577
00:34:27,240 --> 00:34:32,190
So one of the challenges to
making polygenic risk scores

578
00:34:32,190 --> 00:34:35,760
truly valuable in clinical work,

579
00:34:35,760 --> 00:34:40,760
is to overcome that by
really charging forward

580
00:34:40,800 --> 00:34:45,000
with sequencing lots
and lots of individuals

581
00:34:45,000 --> 00:34:47,490
of many different ethnic backgrounds,

582
00:34:47,490 --> 00:34:51,660
and many different ancestral strands,

583
00:34:51,660 --> 00:34:53,520
all over the world,

584
00:34:53,520 --> 00:34:56,550
to capture as much of the
variation as possible,

585
00:34:56,550 --> 00:34:59,040
and then to repeat these kinds of studies

586
00:34:59,040 --> 00:35:00,750
in those populations

587
00:35:00,750 --> 00:35:05,750
so that we know that the same,
or different variants apply

588
00:35:08,670 --> 00:35:11,700
in that particular circumstance.

589
00:35:11,700 --> 00:35:16,680
So that's a pretty big barrier,

590
00:35:16,680 --> 00:35:19,560
I think that genomic risk
scores, or polygenic risk scores

591
00:35:19,560 --> 00:35:24,560
will continue to be moved
forward even with that gap,

592
00:35:25,140 --> 00:35:28,320
but it's something that
we have to keep in mind

593
00:35:28,320 --> 00:35:32,133
when considering the
applicability across the board.

594
00:35:33,000 --> 00:35:35,310
So I think that's the last slide

595
00:35:35,310 --> 00:35:36,963
that I'm going to talk about.

596
00:35:38,820 --> 00:35:42,810
And I will point out

597
00:35:42,810 --> 00:35:45,600
that the slide deck includes slides

598
00:35:45,600 --> 00:35:47,613
that came from the book publisher,

599
00:35:48,480 --> 00:35:50,640
and I basically kept the slides on there

600
00:35:50,640 --> 00:35:53,073
about type 1 diabetes.

601
00:35:54,150 --> 00:35:57,780
I'm not gonna go through that
in this lecture, I wanna stop,

602
00:35:57,780 --> 00:36:01,170
but it's probably a reasonable exercise,

603
00:36:01,170 --> 00:36:03,150
after hearing this story,

604
00:36:03,150 --> 00:36:06,310
to walk through those slides on your own

605
00:36:07,170 --> 00:36:10,680
to think about the way

606
00:36:10,680 --> 00:36:15,680
that the information
that I've just presented

607
00:36:15,750 --> 00:36:20,340
colors the way you think
about type 1 diabetes

608
00:36:20,340 --> 00:36:23,880
or in any of the other
disorders of complex inheritance

609
00:36:23,880 --> 00:36:27,900
that we've been talking
about in this module.

610
00:36:27,900 --> 00:36:32,310
So that's the end of the
lecture for module nine,

611
00:36:32,310 --> 00:36:34,673
thanks for your attention
and thank you for learning.