- Welcome, everyone, to Wednesday Nite @ the Lab.
I'm Tom Zinnen.
I work here at the UW Madison Biotechnology Center.
I also work for UW Extension Cooperative Extension,
and on behalf of those folks and our other co-organizers,
Wisconsin Public Television, the Wisconsin Alumni Association,
and the UW Madison Science Alliance,
thanks again for coming to Wednesday Nite @ the Lab.
We do this every Wednesday night, 50 times a year.
Tonight, it's my pleasure to introduce to you Peter Muir.
He's with the Department of Surgical Sciences
in the School of Veterinary Medicine.
He's going to talk about man's best friend,
the genome-wide association analysis
of complex traits of dogs.
Peter was born in Plymouth in the UK
and went to high school there.
He went to Bristol University for his original PhD and DVM.
Then he went to the University of Sydney in Australia
for a couple of years, came here to Madison,
then went to the University of California Davis,
and then was at the Royal Veterinary College
at the University of London.
And then, in 1999, he came back here to Madison
to be on the faculty, where he's been every since.
I'm looking forward to tonight's talk, "We All Have Knees."
Bees have knees; more importantly, dogs have knees,
and they have ligaments that get stressed kind of like ours do.
It's one of the great things veterinarians get to do.
They get to study more than us, and therefore,
they can learn from lots and lots of different animals.
Please join me in welcoming Professor Peter Muir
to Wednesday Nite @ the Lab.
[applause]
- All right.
Thank you very much for that kind introduction.
Can everybody hear me okay
at the back of the lab or back of the room?
Okay.
So, I'm just going to start
this talk by giving you
a brief outline of what I want to speak about.
I've divided this topic into some sort of distinct sections.
So, initially, I was going to just make a few comments
about this topic of complex trait GWAS,
GWAS obviously standing for genome-wide association study.
And to sort of extend that,
I'm going to say something
about dog breeds and how they were created over time.
There's this idea that dog breeding is the world's greatest
genetic experiment ever undertaken by humankind.
And so we'll talk about that.
I'm also going to talk about this condition of ACL rupture,
which is common in people and common in dogs as well.
And then talk a little bit about our work
and what we've done to study this topic
and why it might be advantageous to study this in dogs.
And one important point is that today there are no published
studies looking at ACL rupture with GWAS in human patients.
And so, just to conclude then, I'm going to say something about
future directions and how we see our work unfolding over time.
So, I apologize if I'm explaining things
that you already know, but I just want to start off
with a couple of definitions.
So a genome is the entire set of DNA for a person or an animal.
And almost all of this
is identical between individuals in the same species.
But, as you can see in this slide here,
every once in a while there is a base pair difference,
which is referred to as a single nucleotide polymorphism.
So a difference or variation at a single locus.
The other thing that I want to mention is
what's the definition of an allele.
And so this is one or more forms of a gene or a genetic locus,
and there's one copy on each chromosome.
One of the interesting things is that GWAS
is really a 21st century field.
So, although the ideas were out there a little bit in the 1990s,
it's really only been a very nascent field
that's really grown a lot in the 2000s.
And so the first draft of the dog genome
was published in 2005.
So, certainly for dogs, the field is barely 10 years old.
And what's occurred over time is the magnitude or the scope
of these studies has increased, and now, these days,
certainly in human patients, consortia studies with very,
very large numbers of people are now more commonplace.
So, when these studies are designed,
there's different ways the investigators
can set up the design.
More often than not, a case control design is used,
and that's certainly something that we've used for our work
where subjects are either
cases affected with a disease or condition
or they're controls.
But certain diseases can have a marker that is
a quantitative trait that can be a continuous variable.
And so some associations are quantitative trait associations.
It's also possible if there are many markers for a disease
to undertake a multivariate association,
and this can sometimes have some advantages statistically.
When we studied marker associations in GWAS,
more often than not the standard way of approaching this
has been to do a single marker association.
So, statistically look at one marker at a time.
But, more recently, statistical models in this field
have become very complex,
and now there are regression models
that will analyze
all of the markers together.
And I'm not a statistician, but this is a big challenge
statistically, both from, I think,
designing the statistical algorithm,
but also from a computational point of view.
And one of the challenges for any of you who might have a
statistical background is that the number of markers that are
studied in these projects are typically much in excess
of the sample number of subjects or patients.
So, I want to say a little bit about complex traits.
And so, what I'm trying to illustrate here
is that complex traits are on a continuum where, obviously,
certain diseases may be considered to be principle
or exclusively an environmental condition.
Just one example might be some types of infectious disease.
And at the other extreme, there are Mendelian diseases
that are thought to have a pure genetic basis.
And so complex traits reside in the middle of this somewhere,
and what we typically do is estimate heritability to try and
understand what proportion of the trait
might be explained by genetic background.
And so we've done that and other investigators have done that for
dog ACL rupture, and it's been estimated that heritability is
in the range of 0.27 to 0.51.
And so this is a moderate value, which is good from trying to
dissect the genetic background of the disease.
One important concept to think about is when we're undertaking
studies of complex traits, is that the alleles that explain
the genetic component of the disease are common
in the population.
So when we look at a population of cases,
as in this diagram here in controls on the right,
we can see that the risk allele, in the light blue
in this diagram, is common in both populations.
And so when we calculate an odds ratio,
typically the odds ratio for the risk alleles are not very big.
So they're sort of moderate in size.
And when we undertake this type of analysis,
more often than not, we're using SNPs
as the allelic markers for the association.
Now, as I mentioned earlier,
when we're wanting to study complex traits,
these studies typically need
many markers and subjects.
And so there's a very famous study published in 2007 that
looked at 500,000 SNP markers in their study
and had thousands of people.
So, in this case for rheumatoid arthritis,
approximately 2,000 cases and 3,000 controls.
And so one way of looking at these data is to make this plot
on the right, which is referred to as a Q-Q plot,
where the negative log of the observed P value of
the association for each marker is plotted against
the expected value from the chi-squared distribution.
And so each of these little circles represents one of the
500,000 markers, and what we're interested in obviously focusing
on is the markers that deviate from the identity line
in this plot and are associated with the disease.
When these analyses are done, another important concept is
that these large populations of humans and/or dogs
can tend to cluster together.
And so we find that certain groups of individuals may be
more similar genetically than other sort of subgroups
within the sample population.
And so, this is an important point because this clustering
can lead, potentially, to spurious associations,
or false positive associations.
And so the clustering needs to be studied and potentially
accounted for in the statistical models
that are used to analyze these data.
And, as I mentioned at the beginning,
these days it's more common to undertake,
particularly in humans, consortia studies.
And so in this plot, which is referred to as a Manhattan plot,
now we have the chromosomes across the genome
and, again, each marker is a little circle.
And on the X-axis here,
we have the negative log of the associated P value.
And so one thing to notice as the number of patients
increases, and obviously you can see that there are large numbers
of patients here, you end up potentially with generating
very, very significant P values.
So these ones are P is less than 10 to the minus 100.
But, obviously, with many markers and so forth,
then you need to correct the P value
for genome-wide significance.
And so you can see in this study that the gray dotted line here
represents genome-wide significance.
And so for this trait of rheumatoid arthritis,
there are many markers across the genome that do associate
with the trait.
And so, therefore, rheumatoid arthritis is a complex trait
with a genetic contribution to it.
But the point is that you need very large numbers of subjects
in order to gain enough power in your association to really
understand what's going on.
So another important point is that
with many of these complex traits diseases
are quite common in human or dog populations.
And so,
as I mentioned earlier, we're trying to estimate
heritability to sort of understand
how big the genetic contribution to the condition might be.
And so, typically when we do that,
we're estimating narrow-sense heritability,
and this is either done from SNPs
or from pedigree information.
And one of the nice things about dogs is it's actually fairly
easy for us to obtain their pedigree information in order
to construct a data matrix that can estimate heritability.
And what's been learned from over time in this field is that
when the early GWAS studies were done and they were studying
a disease where heritability had already been estimated,
it was found that the GWAS SNPs often only explained
a small proportion of the phenotype.
And so that sort of lead to this question of
what about this missing heritability?
And so, really, there's sort of two ideas explaining that.
One idea is that there are other facets to genetic variation,
such as structural variation in the genome,
that could explain the missing heritability that therefore
wouldn't necessarily be captured by the SNP association.
And the other idea is that there's a tendency in the past
for these studies to be underpowered.
And so if there are not enough subjects and not enough markers
and you don't have enough statistical power,
then some of the genetic variation
that may explain the disease
could be missed or not captured in your association study.
So just to summarize this first section,
GWAS has become a powerful discovery tool for studying
complex traits.
And we know, for human disease in particular,
that large numbers of individuals are needed for a
robust study, and obviously that is a huge amount of work an a
lot of expense and so it potentially makes a research
project really a substantial and very expensive undertaking but
potentially very valuable.
And the phenotype in these diseases is often explained by
many genetic variants with small to moderate effects that in
general are thought to be additive.
So we have this common diseases have common variants in the
population that, in certain individuals, can come together,
and if enough of them are there, disease will happen.
Here's kind of a fun section now
to say something about dog breeds.
If you're interested, there's this really fabulous paper,
which has one of my favorite titles I've ever come across in
my career: "Man's Best Friend is Biology's Best in Show."
And this is a review article published in 2010.
And it's well worth looking into,
if you want to learn more about the background.
So, obviously, dogs, as everybody knows,
come in many different shapes and sizes.
And so, currently, there are more than 400 breeds of dogs
that have been created by intense selection.
And obviously these breeds have been selected to perform
different functions.
That was the purpose for breed creation in general in the past.
And this selection process
has occurred over about 15,000 years.
But most modern dog breeds are not very old.
They are only a few hundred generations old,
and maybe as little as a hundred or more years old in some cases.
And so this has led to dogs having a unique genomic
architecture as a species.
So, dog breeds are phenotypically very diverse.
And as you can see in these images,
there are dog breeds used for herding,
for hunting and coursing, for retrieving in water.
Dogs obviously can be sled dogs.
They can be for transportation.
Down here we have a pointer, that's also hunting,
and some foxhounds.
And so one of the motivations for breed creation was to,
when dogs had more of a working function in life,
was to have breeds that were optimized for different tasks.
And so in current times as a veterinarian,
we may have this tiny little dog, like this Chihuahua,
and then a really big dog, like this Great Dane.
And so, phenotypically, they're very, very different,
but they are still members of the same species.
And so, from an evolutionary point of view down here,
dogs are domesticated from gray wolves.
And so we have this ancient bottleneck associated
with domestication of the wolf.
And then, up here, we have the more modern bottleneck
associated with modern breed creation.
So there are two population bottlenecks to that evolutionary
history of dogs.
And the genetic architecture of different breeds of dog has been
studied, particularly with regards to this idea of ancient
breeds of dog versus modern and how they may be related to the
species of origin, the gray wolf.
And so, in medieval Europe, this breed of dog here
would be referred to as a Dyrehund.
And today we know this breed as a Norwegian Elkhound.
And so this would be a good example of a breed of dog
that's existed for a long time,
going back probably at least a thousand years.
This breed of dog down here, the Australian Shepherd,
which was largely created in the US,
is a modern breed that's barely more than a hundred years old.
So before I move further into this section,
I just want to say something about linkage disequilibrium
because this is an important concept.
And linkage disequilibrium is
the nonrandom association of alleles at two or more loci.
And so essentially what this means is that stretches of DNA
can be inherited together, and so in newer populations,
such as these modern breeds,
long stretches of the DNA are inherited together.
And this is broken down by random mating.
But, of course, dogs are not bred randomly.
Gray wolves are or do, but not dogs.
And so just to give you a sense of that,
popular stud dogs are breeders that are highly valued or prized
may sire more than 400 litters of puppies in their lifetime.
So dogs are pretty selectively line bred.
Now, when we're interested in studying LD,
we can typically do that with what's called a triangle plot.
And so in this plot, you can see at the top here
are some SNP markers,
and then there's a box that
matches one marker with adjacent markers.
And in each of these boxes, I think you can see that there's a
number, which is the correlation coefficient between the two.
And so this triangle plot is essentially a heat map where
white is very little to no correlation and the bright red
is a very strong correlation.
And so from the heat map of the triangle plot,
we can determine that there are segments of DNA,
such as this block here, that are inherited together.
And so this is referred to as a haplotype block.
And so if we look in a human population,
Caucasian population is a relatively modern population,
and we can see quite a lot of red in this triangle plot.
And then if we go down to the bottom here,
the Yoruba population is an ancient African population,
and we can see that there's a lot less red.
And so from a GWAS point of view,
when these haplotype blocks exist in the genome,
we just really need one marker in each block obviously to test
that block for association with a disease.
Now, what about the dog?
Well, here's the Caucasian plot at the top here,
and here's a chromosome for a dog.
And you can see, of course, that the dogs have a lot more red
and the blocks are much bigger.
And so within a breed of dogs,
the average LD block is about a megabase.
Whereas, in human populations, it's more in the, like,
tens of thousands to 20,000 bases.
So, big difference.
And this plot here just shows these data or this concept in
a different way, where we have
distance across the genome here on the X-axis
and the correlation R-squared on the Y-axis.
And so we can see that the gray wolves have fairly short LD
and their correlation or their R-squared decays very quickly
across the genome.
Whereas, within breeds, and particularly some of these
modern breeds, we can see that we can move a long way across
the genome and the SNP markers are still very associated.
So, as I say, the contrast between dogs and people means
that GWAS in dogs needs fewer markers
to undertake the association
because the LD blocks in the genome are much bigger.
And here we are again talking a little bit about the genomic
architecture of the dog.
So, on this diagram, we can see the ancient bottleneck
that I mentioned with domestication of the wolf.
And then the modern bottleneck that I mentioned
associated with breed creation.
And so modern breeds here have this add mixture of short
linkage disequilibrium from ancient times
and long linkage disequilibrium from modern breed creation.
And this add mixture has not yet really been broken down
by random mating.
And so that is what I mean by the unique genomic architecture
that dog breeds have.
So when we're thinking about a disease and the genetic bases of
a complex trait disease, the disease associated alleles
likely proceed modern breed creation.
And so it's pretty common as a veterinarian to recognize that
there are common phenotypes between breeds.
So, for example, ACL rupture
is common in different breeds of dog.
And so that suggests that there are probably common causal
variants in the different breeds
that contribute to the genetic risk.
And so when we're thinking about GWAS,
we need a marker density that hopefully can capture that
ancestral haplotype blocks, and therefore be of an appropriate
density to sort of map the disease to the genome,
and so it's been proposed as the field has evolved to do this
in a two-phase way.
So, in the first discovery phase,
we take a single breed of dog.
So, obviously, here we have black and white dogs,
but they're the same breed so they're the same shape.
And we do our association and then find a locus that
associates with, in this case, the color and expect that it'll
be a region of about a megabase.
And then, if we have a second breed,
or potentially several breeds, that have the same disease,
we can take our area of interest,
our candidate locus from the first phase,
and then we'll finally map that or study that in more detail and
take advantage of the short range, ancient,
short range LD and, by studying it between breeds,
potentially then narrowed down the locus to a much smaller
interval.
And so this theory has been used to some extent in the field,
but some of the evolution of technology,
which I'll say something about at the end of my talk,
has sort of overtaken some of these ideas.
And so it's still a bit up for discussion about how useful this
type of approach is.
To give you a couple of examples,
I don't know if any of you are familiar with this breed,
the Rhodesian Ridgeback, but maybe what you didn't know is
that some Rhodesian Ridgebacks don't have a ridge.
And so that's one example of a genetic phenotype that's been
studied and shown the genetic basis of the ridgeless dog
was discovered by GWAS.
And another example would be some breeds of dogs are affected
with obsessive compulsive disorder.
And there's this nice paper that was published in 2014 that had
sort of moderate numbers of dogs down here,
and found quite large numbers of variants
associated with the disease.
And what's interesting in this area is this is one example
where initial discoveries in dogs were then led on to
discovery of novel disease association in humans,
human patients.
So, just to summarize this section,
there are different genomic animal models that geneticists
have studied, but the dog, if you look down this column here,
is unique and very advantageous, particularly for complex traits,
because there are many dog diseases that are similar
to humans.
Dogs have a shared environment with people,
which is potentially important.
It's relatively easy to get pedigree information about dogs.
There's this very strong breed portfolio of different breeds
that exist and this unique genetic architecture that exists
in dogs too.
So, the key points here is dogs have a unique genomic
architecture where the linkage disequilibrium within a breed is
much greater than in humans.
And in modern breeds, short range LD has not been broken
down by recombination.
So this is thought to be very advantageous for studies of
complex trait disease.
Okay, so the next section I want to do is say something about the
disease of interest that we've been particularly focused on for
quite some years now, which is this condition of ACL rupture.
So the cruciate ligaments, there are two cruciate ligaments,
the anterior and posterior cruciate ligament that exist
inside the knee joint.
And we can see here, this one here is the posterior,
and this one here is the anterior.
And you can see that they cross over each other
and sort of twist around each other, and
this crossing over is obviously what gave them the name.
And together with the collateral ligaments,
the lateral and medial collateral ligaments,
these are important stabilizers of the knee joint.
The anatomy of these ligaments
is very similar in dogs and people.
So in human patients, this is an important condition.
It has a gender predisposition and obviously is very common
in athletic and sporting people,
but it's also common in sedentary people too.
And if you follow the Green Bay Packers,
then this is an important player here who unfortunately
experienced this injury.
And it is a life changing health problem.
If you are a human patient with ACL rupture,
you're very likely to get knee arthritis
as a result of the injury.
And you're also at some risk, actually,
of developing a second ACL rupture.
And most ruptures occur not from contact injury but from landing
or twisting and that type of movement.
And it is officially common in surgical treatments done a lot
to essentially mean that it's an economically important disease
to the human population, and certainly large amounts of money
are spent on the healthcare for both human patients
and for canine patients as well.
There have been some genetic studies in humans,
and so some candidate gene studies have shown
significant associations.
And the focus of interest for the candidate gene studies has
been to look at genes that regulate ligament matrix
homeostasis and composition.
And this has typically been the collagen genes have obviously
been the initial focus in this way because the collagen protein
has extremely important role in the hierarchical architecture
of tendons and ligaments
and is very important for mechanical properties.
But today, as far as I'm aware, there's not heritability
estimate for ACL rupture in people,
and there are no published GWAS discovery studies.
So, what about dogs?
Well, this condition is very common in dogs.
It's the most common reason that dogs have orthopedic surgery at
the veterinarian.
And in high risk breeds, it actually affects about 10% of
the breed, which is huge if you actually think about how many
Labradors or Rottweilers or Newfoundlands might there be in
the whole of the US.
So this incidence comparing the species means that
in the high-risk breeds it's about 100 times higher incidence
than human patients.
And so the biggest known factor for initiation of the disease
is breed.
And I'll comment on that in a minute.
For disease progression, other factors have been implicated,
and without going into a lot of detail,
inflammation within the knee joint and the morphology
or the anatomy of the knee are two things
that have been studied quite a lot in that regard.
There was a nice paper published in 2008 that looked at the
epidemiology of the disease and essentially created a nice list
of many common breeds of dog with their breed prevalences.
You can see here, up at this list,
the top is a Newfoundland with 8.9% prevalence.
And the next breed is the Rottweiler, 8.29%.
So, very common in certain breeds of dog.
And, obviously, third on the list here is the Labrador
Retriever.
We chose the Labrador Retriever for our studies,
and you may say, "Well, why did you do that
if it's not most commonly affected?"
But it is the most common breed of dog in the US.
And so if you put the prevalence and the numbers of dogs
together, it means that there are lots and lots and lots of
Labradors out there that experience this disease.
So-- And as I mentioned earlier,
there are heritability estimates in the dog that certainly
suggest that this is a disease with moderate heritability.
So I just want to say a little bit more about the sort of
epidemiology of the background of the disease in dogs.
And so we know a fair bit about its age at presentation.
And this is important when we're thinking about the controls
for our GWAS.
And so we know that once the Labrador Retriever gets
to about eight years of age, it's pretty unlikely
to experience cruciate rupture.
And so it's very interesting.
I've talked to owners of quite young Labradors that might only
be a few years old, and they have disease,
and the dog's sort of got reduced mobility.
And I've talked to other owners that might have a 15-year-old
Labrador that goes hunting with them for pheasant hunting,
and the dog's fine and it doesn't have the disease So this
disease does have some impacts on dog mobility.
This table just summarizes a little bit of information about
sex steroids and the risk of the disease.
Now, obviously in dogs, it's pretty routine in the US to
either spay female dogs or to neuter the male dogs.
And so whether it's a male or a female dog,
neutering does an influence on the disease,
and the risk of the disease goes up.
And so this little table just summarizes that.
We can see here for the spayed females and males that the odds
ratio for ACL rupture is higher.
We published a paper from our lab some time ago.
We were very interested in studying second cruciate
ruptures in dogs.
And this paper was published in 2014.
And it's in Plus One, so it's freely accessible,
and I'd say it's an interesting one to read
if you're interested.
So what we did is look at dogs that had diagnosed with ACL
rupture in one knee, and then what we did is actually studied
the second knee.
And so we made radiographs of the knees and graded them for
radiographic change, for either the volume of joint fluid,
or essentially a marker of inflammation in the joint,
and also for the amount of arthritis that
we could see on the radiographs.
And these curves are what are referred to
as Kaplan-Meier curves.
So they're a survival curve where the terminal event,
either ACL rupture, causes the curve to drop down.
And so, obviously, if you can see at the top here,
the red curve obviously looks different from the other two,
and it is.
And so the dogs with more severe effusion or inflammation
radiographically in their joint at diagnosis quite quickly
went on to get a second rupture.
Whereas, the dogs with less severe change were less likely
to get a second rupture over time.
And you can kind of see the same pattern to the data when we
grade the radiographs for osteophyte inflammation.
So in the vet school these days, when we have a lame dog and we
make the diagnosis, we often take radiographs and we actually
take radiographs of both knees to sort of stage the dog and
warn the owner and say something from a predictive point of view
of how likely it is for the dog to be developing a second ACL
rupture.
And so, from a mechanism point of view,
what does this data say?
What it certainly suggests is one piece of data that suggests
that synovitis, or inflammation inside the knee joint,
does influence the progression of the disease over time as more
and more fibers rupture in the ligament.
So, here are some arthroscopic images.
Just like in human surgery, in the vet school it's pretty
routine these days to do arthroscopic surgery.
And so in the normal joint, the synovial lining of the joint
should be almost white with very small number of blood vessels.
But as the ligament starts to rupture,
the synovium becomes fronded and proliferates
and much more inflamed.
And down here on the right you can see that the synovium's
intimately associated with the cruciate ligaments.
And so that inflammatory change sort of makes sense as to how
that inflammatory change could then influence the homeostasis
of the ligaments that reside inside the knee joint.
And I have a little video to play.
So, here, we have this little arthroscopic probe,
and what it's doing now is showing you
that the splits and damage to the fibers
are actually the posterior cruciate ligament.
So it's very common that both ligaments are damaged,
but the rupture event is typically in the ACL,
and that's really from a biomechanical reason.
And so, now we can start to see some of the inflammation
in the joint.
And down here is the torn ACL.
And if we look carefully at the end, you'll see it in a moment,
the end of it has a little blood areas.
So the ligament is trying to heal.
The end of the torn ligament is trying to heal,
but because it resides in this intra-articular environment,
once the rupture has occurred
normal wound healing just can't occur.
And so once the rupture has occurred,
the ligament won't heal.
Okay, so other factors that have been studied
include body condition.
So, higher body weight increases rupture risk.
And new morphology can influence this too.
So, these ligaments, as I mentioned,
are intra-articular but extra synovial,
and they wrap around each other inside this notch,
the intercondylar notch in the femur or the thigh bone.
And an important point is that little blood supply to the
ligaments is derived from the bony attachments.
It all comes from the overlying soft tissue.
A couple of things that have been studied from a morphology
point of view is, as the arthritis develops,
you can see that here in this image,
that the notch of the femur can narrow,
and that notch is thought to impinge the cruciate ligament
and promote rupture.
And, also, dogs and people have a posterior or a slope
to the tibial plateau, as you can see here,
relative to the long axis of the bone.
And that slope has maybe a small effect on risk of rupture.
And so in this data here, we've plotted that time to
contralateral rupture we talked about a couple slides ago
against the TPA or the posterior slope angle.
And we can see that is a slight drop-off to the regression line.
And over here on the right, interestingly, in this model,
biomechanical model from these images here,
we can see that when the cruciate ligament is cut
in a cadaver knee that the tibia sublaxates
in an anterior direction.
And when we level the plateau surgical, as per this cut here,
we can actually mechanically stabilize the joint without
actually repairing the ligament.
And so, there's been great interest in this morphology,
particularly the posterior slope, or the TPA,
tibia plateau angle, because it's become incorporated
into a very common surgical treatment
that's used clinically to treat dogs.
In the vet school, we operate on dogs almost every week
with this surgery.
So clients come in and we do this surgery called the
tibia plateau leveling osteotomy
for the client and dogs.
It usually works really well.
Helps the dogs with mobility.
But it's a very common surgery and done commonly in specialty
practice and other vet schools throughout the US.
So just to summarize, dog ACL rupture is an excellent model
for human ACL rupture.
This topic is like an important example of One Health Research.
Bilateral ACL rupture is common in both species.
The disease risk is influenced by sex steroids.
And, importantly as well, for some diseases,
dogs and humans have a shared environment.
So now I'm just going to talk a little bit,
having given you this background about some of the work that
we've been interested in doing.
So, as I mentioned, I think, briefly,
we conducted a within breed association study,
and hopefully it makes sense as to why we did that.
And it was a case control study.
And when we started recruiting, our aim was to recruit more than
a hundred dogs, a hundred Labrador Retrievers
in each group.
And because of this age effect, we only used dogs
more than eight years of age for our controls.
And we also wanted to confirm that they had normal knee
radiographs.
And obviously when diseases have a prevalence of 5% to 10%,
if you just get some healthy dogs, then obviously,
statistically, it's quite likely you could accidentally have some
disease dogs in your control group.
And so this phenotyping piece is actually very important.
There is a standard SNP genotyping array that's been
available for some time for dogs.
And so one of the very commonly used ones is from this company
called Alumina, and that's what we used for our work.
And so the Alumina array has over 170,000 markers on it.
This is what the data looks like.
I mean, I think from computational things,
people are always like, well, what does the data look like?
Well this is what it looks like. [laughter]
So, essentially, those 170-odd-thousand markers
are in a row.
So each dog is a row of data.
And we have the first few columns are the phenotyping and
clinical information, and then the rest of the data are the
genotypes.
And so the data is in two files.
There's this file and then that file,
which is a map of how the order of the SNPs relates to the
genetic information in the genotype file.
So these are large files, obviously,
and there's quite a lot of learning needed to understand
how to run the programs to handle these large data sets,
which is definitely something my lab has grown up a lot with
over the last few years.
When we attain the genotyping data,
we need to do some quality control to make sure that
we have good quality data.
And without going into a lot of information or a lot of detail,
if we have samples with a low genotyping rate, that infers,
suggests bad data, and so they would be filtered out.
If we have SNPs, individuals SNPs,
with a low genotyping rate, then we'll filter those out.
And if we have SNPs that don't vary very much in terms of the
genotypes in the population-- So they have a minor allele
frequency of less than 5%, then they're typically filtered out
because they're not really going to inform the association.
And then we also look for SNPs, which is usually not very many,
that might deviate from Hardy-Weinberg equilibrium.
So when we're done with this filtering,
the 170-odd-thousand usually goes down to something
like 100,000 to 120,000.
Something like that.
Which we have then left
to perform our association analysis.
So, as I mentioned at the beginning,
there's a number of different statistical models that have
been described as to how you can do this.
But, basically, many of them are a variant of a linear mix model.
And these algorithms essentially look at the markers
one at a time.
So a single marker analysis.
And these programs here are acronyms that we've used for
some widely adopted programs in the field.
We've also begun to study more advanced or sophisticated
regression models that analyze all the SNPs together,
which is computationally more intensive,
but as desktop workstations have become more powerful,
this type of analysis has become more routine.
One interesting topic here is to think about genome-wide
significance, where, obviously,
we set an alpha level of 0.05%, 5%, 0.05%,
usually in scientific experiments.
But we need to correct that in these association studies.
And so it's a big topic as to how do you do that.
Bonferroni correction is too conservative
because, as we've discussed,
the markers are not independent because they're linked together
in haplotype blocks.
So other approaches that have been considered is to do some
sort of permutation testing with random phenotypes to estimate
genome-wide significance, or sometimes just empirically you
can set a lower, less stringent threshold for association which
is often used for pathway analysis to look for enrichment
of a biologic signally pathway.
But there's definitely room for improvement statistically
on this topic.
And this is something that we've been discussing a lot amongst
the lab, my lab, and people, statisticians that we
collaborate with here on campus.
And a recent paper did propose a more sophisticated way of trying
to create a breed specific genome-wide significance because
the LD can vary between breeds, where permutation testing data
was used to draw a Q-Q plot with empirical confidence intervals
from the data.
Then, obviously, where the SNPs leave the upper 95% confidence,
if you draw that across,
in theory, that should set
what a robust genome-wide significance is.
And so that's one new idea that's been out there
as to how to do this.
In our initial discovery GWAS, this was our Q-Q plot.
And so we did find a locus that met genome-wide significance.
And when we set a less stringent cutoff for pathway analysis,
we found, as you can see here, many loci across the genome that
looked like they contributed to the disease.
In the most significant locus that we found,
there are about 10 genes that reside in this locus that
regulate a number of different biological phenomena.
And so, obviously as we move forward, that's one area,
canine genome that we're interested in studying a lot
more detail.
And overall, we identified 129 associated SNPs.
And when we did our LD clumping, some of them clump together,
which left us with 99 candidate loci.
This graph here is actually a fairly recent product of some of
the work that we've done.
And it may not look like much but the people in our lab put a
huge amount of effort to compute this graph,
which actually took quite a lot of effort to compute it.
But this output is from this Bayesian mixture model called a
program called BayesR, which analyzes all the SNPs together.
And the idea with that type of analysis
is better able to capture weaker associations
between certain SNPs and the trait.
And so when we fitted our data
using the BayesR model,
BayesR said that there are just about 3,600 SNPs that associate
with the disease of ACL rupture in the dog.
And the nature of the mixture model then is that BayesR
partitions the SNPs with non-zero effect.
So the SNPs that have some effects on the trait into
essentially SNPs with large and gray medium, in orange,
or small, in blue, effects on the disease.
So, when you look at the gray bars here across the genome,
and the X-axis obviously plots the percent variants explained,
you can see that the gray sort of maybe explains about sort of
two-fifths of the genetic background of the disease.
And so, obviously, those SNPs are relatively small number
at 41.
But, obviously, the orange and blue also make a big
contribution to the genetic background to the disease.
And, obviously, the medium and small effect SNPs are much
larger in number.
So what this analysis does is tell you quite a lot of
information about the genetic architecture of
ACL rupture in much more detail.
And it also tells you that it's highly polygenic.
So, again, just to summarize.
Dog ACL rupture is highly polygenic.
Quite a large proportion of the phenotypic variants explained
by only a relatively small number of SNPs.
And so, obviously, going forward,
the genetic loci that those SNPs reside in are definitely going
to be of focus for more investigation as the discovery
work moves forward.
But we also have this sort of problem or phenomenon that
a large proportion of variation is also associated with SNPs or
genetic variants that have just very small or moderate size
effects on the disease.
So, just to finish up, I'm going to say a few words
about future work.
What about things that we would like to do going forward?
Well, we would like to do a replication study.
So it's very-- With discovery GWAS,
we talked about the idea that false associations can occur.
It's statistically a difficult sort of field.
And so one thing that it's a good idea to do is to see if you
can replicate your results in a different second population.
Obviously replication in a second population of Labrador
Retrievers.
But it's also a good idea to try and validate your discovery
results in a different population with a different
genetic background.
So our goal right now, and we're right in the middle of doing
more recruiting, actually, for both breeds,
is to look at the Rottweiler.
And I mentioned Rottweilers actually have a much higher
breed prevalence than Labrador Retrievers.
Although, Rottweilers are not as common or popular breed of dog.
We've also been thinking about the idea,
and still thinking about it right now, is
should we really try and study
some sort of a control breed?
And there an iconic control breed with a very,
very low prevalence of the disease would be the Greyhound.
And Greyhounds have largely been selected to race.
And obviously if they're racing, they need to be orthopedically
fit, and so that sort of makes sense that the alleles that
might associate with ACL rupture or other orthopedic diseases are
not very prevalent in that breed.
And we're also trying to explore this idea of doing some sort of
consortium project with other vet school investigators or
other research groups to try an address this piece about
statistical power and get really large numbers of dogs into an
association analysis.
Why is this work important?
Well, obviously, this is discovery research,
so we expect to make discoveries about novel genetic variants
that contribute to ACL rupture.
We need to sort of then obviously confirm those
relationships experimentally.
But as that work unfolds, there's the potential that we
could identify a drug target that could come up with a better
treatment for the early phase of the disease perhaps.
And in veterinary medicine, obviously dogs are bred
selectively so, clearly, genetic testing could potentially have a
role in breed improvement and also in risk management.
This is an acquired disease, so if we knew a priority that a
particular individual patient had a very high genetic risk for
the disease, then we veterinarians could provide
advice to those owners about how to try and mitigate that risk.
And, finally, I think that what I'd like to highlight
is that in veterinary medicine
One Health Research is a very important topic.
Certainly, it's a topic that's capturing more attention
in terms of sort of human medicine and human health
sciences research.
And so I think what I've hopefully shown you today
is this is a good example of One Health Research
where work in dogs can benefit people and vice versa.
And so GWAS, as susceptibility loci are discovered and we can
learn more about biological pathways,
then we can potentially identify better markers for disease,
and that could therefore improve clinical care.
And, obviously, clinical care can be at the level of
diagnosis, prediction of disease,
and so probably for these complex traits,
very common both in human populations and dogs,
genomic prediction may well be actually a more important test,
clinical test in medicine.
Because these diseases are highly polygenic,
it may well take genomic prediction but with a large
panel of markers to actually predict the risk well.
And, obviously, those types of-- That type of knowledge could
inform prognosis about disease, it could lead to development of
new treatments, and also provide perhaps better ways of
monitoring response to treatment.
So I'd definitely like to leave you with this sort of thought as
you walk out of the room this evening to think about,
you know, the overall value of One Health Research.
So, obviously I haven't done this work on my own,
and there's quite a long list of people here
that I'd like to acknowledge.
Various individuals that worked in the lab
in the vet school with me.
Faculty collaborators on campus, and one of the great things
about UW Madison is it's a very rich academic environment where
there's lots of opportunity to talk to interesting people
about your work.
And I've also been lucky enough to have support from a number of
organizations across the country.
I'd also like to acknowledge some of the financial support
that my lab's had to support this work.
And, in particular, I'd like to thank these individuals who have
made substantial contributions to the work over the last year.
And Lauren Baker has just recently graduated from vet
school in May and is now studying for a PhD in my lab.
And so she's going to be a big player in this work
that I've described over the next year or so.
All right, that's all I have.
So, I think there may be time for some questions.
[applause]