June 18, 2025 • 23 mins
Genetic prediction technology is revolutionizing how we understand disease risk in our pets, yet companion animal medicine lags behind similar advances in humans and production animals. Why? And what does this mean for veterinary medicine?
In this fascinating conversation with Dr. Peter Muir and Dr. Mehdi Momen, we explore the emerging science of polygenic risk scores – statistical tools that can predict an animal's likelihood of developing complex conditions based on their genetic makeup. Using cruciate ligament rupture in dogs as their primary example, our guests explain how conditions often mistaken as simple injuries actually have significant genetic components. With heritability estimated at 40% for this condition in Labrador Retrievers, the potential for accurate genetic prediction is substantial.
The challenges, however, are equally significant. Dog breeds show remarkable genetic diversity, meaning risk factors that predict disease in one breed may not transfer to another. As Dr. Muir notes, Greyhounds – despite being among the most athletic dogs – rarely suffer cruciate ligament ruptures, highlighting the breed-specific nature of genetic risk. Combined with limited funding and smaller datasets compared to human genomics research, these factors have slowed progress.
Yet the future looks promising. Advanced technologies, artificial intelligence, and multi-omics approaches are enhancing prediction accuracy. Unlike diagnostic tests, polygenic risk scores serve as preventive tools, allowing owners to modify their pets' lifestyle before problems develop – "not scary, just caring," as Dr. Momen eloquently puts it. These advances could transform veterinary practice, requiring future veterinarians to become more versed in bioinformatics and computational science.
Want to understand how genetic testing might help your pet live a healthier life? Subscribe to Veterinary Vertex for more cutting-edge discussions at the intersection of clinical practice and scientific discovery.
AJVR article: https://doi.org/10.2460/ajvr.25.01.0018
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
You're listening to Veterinary Vertex, a podcast of
the AVMA Journals.
In this episode we chat aboutthe prospects, opportunities and
challenges of polygenic riskscore prediction of complex
diseases in companion animalswith our guests Mehdi Momin and
Peter Muir.
Speaker 2 (00:18):
Welcome listeners.
I'm Editor-in-Chief LisaFortier, and I'm joined by
Associate Editor Sarah Wright.
Today we have Peter and Mendyjoining us.
Peter, my longtime friend andcolleague, thank you for all
you've done for our journals andfor being with us here today.
Speaker 3 (00:32):
Thank you for the invitation to be part of this
meeting.
Speaker 4 (00:36):
Thank you, Lisa and Sarah, for inviting us.
It's our pleasure to share ourexperience and our knowledge
about Polygyic risk scoreprediction with you and your
audience.
Speaker 1 (00:48):
Awesome, let's dive right in.
So, peter, your AJBR articlediscusses the unique challenges
and future opportunities thathinder the broader adoption of
polygenic risk score riskprediction in companion animals
compared to humans andproduction animals.
Please share with our listenersthe background on this article.
Speaker 3 (01:06):
Okay, thank you for the question.
Yeah, as an academic smallanimal surgeon, I've had a
longstanding interest incruciate ligament rupture in
dogs going back for a long time,and as that work unfolded, we
acquired a sort of bigger andbigger interest in the genetic
(01:27):
contribution to the disease.
So we started to do agenome-wide association study in
2014.
And those projects take a while, so it wasn't until 2017 that
the first paper from my lab waspublished on that topic, but
(01:48):
already, even in that very firstpaper, we'd started to do some
analysis about the geneticcontribution to the disease and
how that could potentially beused to predict cases from
controls cases from controls.
(02:10):
And so here we are today, someyears later, with a much more
sort of sophisticatedunderstanding of the topic in
general about genetic riskprediction for common, complex
or polygenic diseases.
Speaker 1 (02:20):
Yeah, definitely interesting article.
My in-laws dog actually justtore her CCL, so we're looking
to have surgery for her soon, sothat's something that's super
applicable to lots of pet ownersas well.
So, mehdi, what are theimportant take-home messages
from this AJBR article?
Speaker 4 (02:35):
Yeah, that's a good question.
Several months ago, before westarted to finalize the paper, I
discussed with Peter.
We know there are lots ofresearch about the polygenic
crystal score prediction inlivestock animals and also in
humans.
So also there are severalstudies in companion animals.
(02:59):
Peter, we have some experiencefrom before.
But also we can right now writea paper to bring more attention
to hologenic risk score, howthis quantity can be used in
practical for the riskstratification in companion
animal, like dogs, dogs.
(03:29):
So one thing is that I toldPeter we need to discuss about
the differences between breedsin terms of the RISC scores and
how we can optimize our models,how we can develop a model.
So I can say this articlebrings several different things
to readers.
One thing is same as humanproduction animal polygenic risk
(03:50):
score can be used in companionanimal as well for risk
stratification.
But we have a huge diversity indog population, for example, in
pets.
So we need to consider this.
We need to develop our modelaccurately predict polygenic
risk score as just dog'spopulation, for example, or
(04:12):
other pet population, so thisquantity can be used for
personalized veterinary care.
This is what brings thesepapers to readers.
Speaker 2 (04:24):
That's great, peter.
Back to you so many questions.
This will be multi-part.
So you mentioned the start ofthis was a GWAS study.
I've never done a GWAS studyand I can't imagine the amount
of data, so part of mymulti-part question is are you
still iterating that data?
What sparked your interest inpolygenic risk scores and why
(04:47):
are companion animals so farbehind humans and production
animals?
Speaker 3 (04:53):
Okay, yes, thank you for the question.
Yes, our data set is continuingto grow and we're continuing to
work actively on this.
So already, like in our lab, asmany other um uh investigators
are working, is there,essentially, labs are um
building up that to some degree,their own biobank, and I think
(05:15):
one of the challenges for thefuture will be how to figure out
ways of sharing biobank databetween labs or institutions,
because because, for sure, inthis genomics general field,
what's possible or questionsthat you can answer are
definitely related to themagnitude of the data set that
(05:38):
you have access to.
My interest in polygenic riskscores really originated, as I
mentioned, in looking as aclinician, looking at animals
with orthopedic problems andparticularly dogs with crucial
ligament rupture, where, prettyquickly, any um clinician who
works with some breeders orworks with a lot of um uh
(06:02):
trainers or field trial dogs andthat type of thing, you come to
realize pretty quickly thatthere's more going on with this
condition than just accidentalinjury.
And so, as a sort of clinician,clinician, scientist, then
obviously you're starting to askquestions well, what is
actually really going on withthis very common problem?
(06:23):
And so that's really what droveour interest in this topic area
.
Speaker 2 (06:32):
And why do you think companion animal is lagging
behind the use of polygenic riskscores in production and humans
?
Speaker 3 (06:40):
I think the biggest issue or challenge is investment
in the field.
If large data sets are neededto sort of really accelerate the
science of this topic, then atsome level that needs investment
through grant funding orinvestment through veterinary
(07:03):
schools etc.
And I think that one of thechallenges has been to figure
out sources of funding that cansupport impactful work on new
sort of big problems.
And I think the other otherthing is still that that some of
the work is still in an earlyphase, so it's definitely an
(07:29):
area where production animalscience is ahead of humans and
humans and production animalscience is ahead of companion
animals, and in that sense Iinclude like horses, as in
companion animals, as well aslike dogs and cats.
But it is starting to changeand move forward, moving forward
a fair bit, and I think thepromise for the future is pretty
(07:51):
bright a fair bit and I thinkthe promise for the future is
pretty bright.
Speaker 2 (07:58):
That's great, makes total sense.
You are one of, if not the keyopinion leader in this area, but
every time we write amanuscript we're surprised by
something which also excites usand keeps us investigating.
What from this articlesurprised you?
Speaker 3 (08:07):
I think one of the things that we've learned, which
we talked a little bit about inthis review article, is the
idea that multi-ancestryprediction is sort of a big
challenge or a big problem.
And back to crucioligamentrupture we know clinically and
have known for a long time thatthis is a condition that's
(08:27):
common in multiple differentbreeds of dog, and our academic
papers have published on a smallnumber of breeds, but
principally the LabradorRetriever, because it's the most
common until recently breed inthe US which is commonly
affected with this condition.
And so that was the sort ofreason for focusing on the
(08:49):
Labrador Retriever in thebeginning.
But we know from ongoing workthat we're continuing to pursue
that it is quite challenging todo predictions across different
breeds of domesticated animal oressentially populations of
different ancestry, and we thinkthat that can be overcome with
(09:12):
some more research and morefunding.
But it is a challenge andessentially it boils down to
this point about geneticheterogeneity that although
cruciate ligament rupture, as aprototypical example, is quite
heritable, it's quite common indifferent breeds of dogs.
There's heterogeneity in thegenetic contribution in the
(09:35):
different breeds and so a dataset that works well for
prediction in one breed will notnecessarily work well for
prediction in another breed, andso solutions to that sort of
scientific challenge is stillneeded.
Speaker 1 (09:49):
Sounds like a lot of future work, which actually
leads me really well into mynext question.
So, mehdi, what are the nextsteps for research in this topic
?
Speaker 4 (09:57):
That's a very good question.
As you know, when we'repredicting a polygenic risk
score for an individual, we'realways trying to increase
accuracy of prediction.
So we want to have the mostaccurate quantity as we can to
stratify risk across differentindividuals low risk, high risk
(10:21):
and medium risk.
So one thing can improve ourprediction accuracy is a
well-optimized referencepopulation.
We're always trying to have atransferability of our polygenic
risk score across differentbreeds, so we need to have a
reference population composed ofmany different breeds.
(10:41):
When we estimate SNP effects,this SNP effect could be
representative of all breeds,representative of all breeds.
So one thing could be improvinga good reference population,
establishing a referencepopulation.
Another thing is that withadvances in genomic technology,
(11:04):
we can have many layers of omics, information, genomics,
transcriptomics and epigenomics.
Another thing is that how wecan use these multiple layers of
information, genomicinformation, to fit to our model
and improve the accuracy.
So I think we need to thinkabout this area as well and
(11:29):
provide more information, moreinput for our models to improve
the prediction accuracies.
Speaker 1 (11:35):
Do you think AI could help with that at all?
Speaker 4 (11:38):
Yeah, that's a really good question.
As you know, when we have aprediction model polygeneric
risk score prediction model wehave input and we have some
exploratory variables.
I believe AI can play a role inboth sides of this equation.
We have input.
(11:59):
Some AI technologies canprovide accurate input.
When we have accurate input, wehave accurate prediction.
So AI can play a role forproviding us the most accurate
input or phenotypes.
On the other side, we haveexploratory variables, so they
(12:20):
have patterns.
Ai models have shown they havea high ability to recognize the
pattern of data, for example,nonlinear patterns.
So I believe AI can be used fordetecting this pattern, to
(12:40):
predict or empower our model forprediction.
Speaker 1 (12:45):
Very cool and for those of you just joining us,
we're discussing polygenic riskscores with our guests Peter and
Maddy.
Speaker 2 (12:56):
Peter, I didn't get a chance to look, but I'm
estimating you're close to, orover 300 peer-reviewed
manuscripts and tons of grants.
A very successful clinicianscientist.
How do you get it done?
What do you have for tips andtricks to sit down and cross
that finish line?
Speaker 3 (13:10):
Work hard.
I think certainly theuniversity environment here has
been very supportive for me interms of the work that I've had,
and some of thisinterdisciplinary research has
been very reliant on robustcollaboration with others and
(13:30):
that's certainly been a theme inmy work and very much so in
graduate students or traineesthat have worked in my lab over
time.
So I think in the current eraof science moving forward, good
teamwork, I think, and goodinterdisciplinary collaborations
(13:52):
are going to continue to bevery pivotal.
Speaker 2 (13:55):
Yeah, nothing excited me more when I was still in
academia than working acrossdisciplines.
It just really opened your mind.
I remember a physicist said onetime one of his students was up
drawing a cartilage and I waslike, oh no, no, you don't
understand.
Articular cartilage is reallycomplex, like the pretty glycans
and the collagen.
And he looked at me and he said, lisa, I model the earth.
(14:18):
And I was like, oh right, it'sreally respectful for different
and it just excites you so it'seasier to sit down and really
get through a manuscript.
But well done, I mean, you'rean amazing human being and
individual.
Speaker 3 (14:33):
I think one of the global topics for work in this
field is this idea that ingeneral, veterinary students are
not very exposed to computerscience or bioinformatics.
And I think that's certainlytrue at UW-Madison, but I
(14:53):
suspect that it's generally truein many other veterinary
schools.
And I think I'm certainly trueat UW-Madison, but I suspect
that it's generally true in manyother veterinary schools.
And I think I'm certainly likeprojecting into the future
development of thinking overwhat a curriculum should be, an
ideal curriculum should be forveterinary students.
I think that's an area thatdefinitely needs some more
reflection of.
Veterinarians are going to havebigger roles and bigger
(15:16):
exposure to sort of large datasets, and computer science and
bioinformatics are going tocontinue to play a very
important role in likeveterinary medicine just in
general.
I will hope your dean is notlistening or you'll end up on
the curriculum redesigncommittee listening, you'll end
(15:36):
up on the Curriculum RedesignCommittee, so that's certainly
been a theme for us in terms ofyou know how we think about
things here.
Speaker 1 (15:40):
So, peter and Mehdi, this next set of questions is
going to be really important forour listeners.
The first one is going to berevolving around the
veterinarian's perspective.
So what is one piece ofinformation the veterinarian
should know about polygenic riskscores?
Speaker 3 (15:53):
Great.
Well, thank you for thatquestion.
So I think one of the importantthings to recognize,
particularly for these diseases,is that there's an intrinsic
risk between the heritability ofa disease or trait and the
ability to predict it usinggenetic information.
(16:15):
And the easiest way to thinkabout that is for binary traits,
ie like a case in control.
And so back to the work that wedid with crucial ligament
rupture in dogs.
All of that initial work wasdone using the binary trait and
each dog was either a case orcontrol, and obviously, as a
(16:37):
clinician, the next Labradorthat walks into your office, if
you flip a coin and call it as acase or control, you're going
to be right half the time.
So heritability essentiallyhighlights the potential for a
predictive genetic test.
(16:57):
So, for example, in thescenario where the heritability
of crucioligamen rupture in theLabrador retriever was estimated
as 40% or 0.4, then with anideal setup, genetic risk
testing should be 90% accurate,because 0.5 and 0.4 is 0.9.
(17:19):
And so I think the take-homemessage for veterinarians is
that there's a strong linkagebetween genetic risk prediction
and heritability, and so oftenthe place that this work starts
for a new disease or conditionis to estimate the heritability.
Anything maybe can add to thata bit more.
Speaker 4 (17:43):
Yeah, one thing I should mention here is about the
PRS value.
Prs value actually is not adiagnosis tool, it's just a
stratification tool.
So how we can recognize or usethis value.
(18:04):
Our dog is at high risk, mediumrisk or low risk, so before any
clinical signs emerge, beforeany clinical signs emerge, so we
can use this value to manage toadjust, to change the lifestyle
for our pets.
I remember when we publishedour genetic test for the first
(18:28):
time and we announced throughour Facebook page somebody wrote
I am skating to get this testfor my dog.
I want, I am going to saypolygenic risk score values for
any disease for your pet is notscary, it's just caring.
It helps you to help your dog.
(18:50):
It navigates you through thechanging or adjusting your pet
in terms of the daily activity.
Speaker 1 (18:58):
So, on the other side of the relationship, what's one
thing clients should know aboutthis topic?
Speaker 3 (19:03):
Yeah, that's a great question.
So I would say still it's.
There's still a lack ofrecognition in general for the
intrinsic like geneticcontribution to like common
diseases, and by, in general,like common diseases or
conditions are polygenic interms of the genetic
(19:25):
contribution.
And so even today, we're stillregularly working in the clinic
with owners who have a dog withcruciate ligament rupture, where
they still have the perspectivethat the dog was playing ball
in the garden a few days ago andbecame lame and developed the
(19:46):
condition and so in their mindit's an injury situation, injury
situation on, whereas thereality is, um, there might have
been some uh activityassociated with with the rupture
event, but the underlying umreason the problem arose is as
(20:07):
because of intrinsic likegenetic disease.
Um, in, obviously, just again,using that as an example, we
have this paradoxical scenariowhere greyhounds are the fastest
, most athletic dog you couldpossibly come across and as a
breed, they're heavily protectedagainst the risk of crucial
(20:29):
ligament rupture.
So I think that's still thething that owners should be
aware of in terms of orthopedic,common orthopedic diseases in
general, but cruciate ligamentrupture in particular.
Speaker 2 (20:44):
Yeah.
But you know, when you look atthe Frenchie you can tell people
all you want about high riskfactor diseases, and it's the
same in horses.
You're like please don't buythat.
And the client's like oh, Ialready love it, it's too late.
Speaker 3 (20:59):
Yeah, no, people love Labrador Retrievers or French
Bulldogs, and not every.
I mean.
Greyhounds are great dogs, butnot everybody wants one.
Speaker 2 (21:08):
Yeah, my daughter got two miniature Bernadettles.
I was like you didn't ask andof course they have GI problems
and you're just like you couldhave asked your mother.
She could have told you that,but it was too late.
Well, thank you both Reallyfascinating work and Peter, for
all you've contributed toespecially AJVR, but JAVMA as
well and really supporting ourjournals.
We really appreciate it.
(21:28):
As we wind down, we like to aska little more of a fun question
.
So, peter, for you and if youhave it, you can show us what is
the oldest or most interestingitem on your desk or in your
desk drawer.
Speaker 3 (21:41):
The oldest item I have is my diploma from the
University of Bristol, so inJune I will be celebrating 40
years as a veterinarian.
Speaker 2 (21:52):
Very good.
Congratulations, Mehdi, for you.
What is your favorite animalfact?
Speaker 4 (21:58):
Oh, one thing for me.
The fun fact is that white dogshave 300 million olfactory
receptors.
Both humans have only 6 millionolfactory receptor.
Both humans have only 6 millionolfactory receptors.
This is just fencing for me.
Speaker 2 (22:20):
I did not know that, that's a good fact for me, too.
Speaker 1 (22:23):
I was at a big dinner with the Labrador and I can
attest the Labrador smelled thefood before the humans did and
tried to find it, so itdefinitely makes sense.
Thank you so much, peter andMaddy.
I really appreciate your timejust being here today sharing
your findings with our listeners, and also for sharing your
article, too, with AJBR.
Speaker 3 (22:40):
Yeah, well, thank you for the invitation.
I've enjoyed the discussion.
Speaker 4 (22:45):
Yeah, thank you for inviting us and it was a
pleasure.
Speaker 1 (22:49):
And to our listeners.
You can read Peter and Maddy'sarticle on AJBR.
I'm Sarah Wright with Visa40A.
Be on the lookout for nextweek's episode and don't forget
to leave us a rating and reviewon Apple Podcasts or whatever
platform you listen to.
You're listening to Veterinary Vertex, a podcast of
the AVMA Journals.
In this episode we chat aboutthe prospects, opportunities and
challenges of polygenic riskscore prediction of complex
diseases in companion animalswith our guests Mehdi Momin and
Peter Muir.
Speaker 2 (00:18):
Welcome listeners.
I'm Editor-in-Chief LisaFortier, and I'm joined by
Associate Editor Sarah Wright.
Today we have Peter and Mendyjoining us.
Peter, my longtime friend andcolleague, thank you for all
you've done for our journals andfor being with us here today.
Speaker 3 (00:32):
Thank you for the invitation to be part of this
meeting.
Speaker 4 (00:36):
Thank you, Lisa and Sarah, for inviting us.
It's our pleasure to share ourexperience and our knowledge
about Polygyic risk scoreprediction with you and your
audience.
Speaker 1 (00:48):
Awesome, let's dive right in.
So, peter, your AJBR articlediscusses the unique challenges
and future opportunities thathinder the broader adoption of
polygenic risk score riskprediction in companion animals
compared to humans andproduction animals.
Please share with our listenersthe background on this article.
Speaker 3 (01:06):
Okay, thank you for the question.
Yeah, as an academic smallanimal surgeon, I've had a
longstanding interest incruciate ligament rupture in
dogs going back for a long time,and as that work unfolded, we
acquired a sort of bigger andbigger interest in the genetic
(01:27):
contribution to the disease.
So we started to do agenome-wide association study in
2014.
And those projects take a while, so it wasn't until 2017 that
the first paper from my lab waspublished on that topic, but
(01:48):
already, even in that very firstpaper, we'd started to do some
analysis about the geneticcontribution to the disease and
how that could potentially beused to predict cases from
controls cases from controls.
(02:10):
And so here we are today, someyears later, with a much more
sort of sophisticatedunderstanding of the topic in
general about genetic riskprediction for common, complex
or polygenic diseases.
Speaker 1 (02:20):
Yeah, definitely interesting article.
My in-laws dog actually justtore her CCL, so we're looking
to have surgery for her soon, sothat's something that's super
applicable to lots of pet ownersas well.
So, mehdi, what are theimportant take-home messages
from this AJBR article?
Speaker 4 (02:35):
Yeah, that's a good question.
Several months ago, before westarted to finalize the paper, I
discussed with Peter.
We know there are lots ofresearch about the polygenic
crystal score prediction inlivestock animals and also in
humans.
So also there are severalstudies in companion animals.
(02:59):
Peter, we have some experiencefrom before.
But also we can right now writea paper to bring more attention
to hologenic risk score, howthis quantity can be used in
practical for the riskstratification in companion
animal, like dogs, dogs.
(03:29):
So one thing is that I toldPeter we need to discuss about
the differences between breedsin terms of the RISC scores and
how we can optimize our models,how we can develop a model.
So I can say this articlebrings several different things
to readers.
One thing is same as humanproduction animal polygenic risk
(03:50):
score can be used in companionanimal as well for risk
stratification.
But we have a huge diversity indog population, for example, in
pets.
So we need to consider this.
We need to develop our modelaccurately predict polygenic
risk score as just dog'spopulation, for example, or
(04:12):
other pet population, so thisquantity can be used for
personalized veterinary care.
This is what brings thesepapers to readers.
Speaker 2 (04:24):
That's great, peter.
Back to you so many questions.
This will be multi-part.
So you mentioned the start ofthis was a GWAS study.
I've never done a GWAS studyand I can't imagine the amount
of data, so part of mymulti-part question is are you
still iterating that data?
What sparked your interest inpolygenic risk scores and why
(04:47):
are companion animals so farbehind humans and production
animals?
Speaker 3 (04:53):
Okay, yes, thank you for the question.
Yes, our data set is continuingto grow and we're continuing to
work actively on this.
So already, like in our lab, asmany other um uh investigators
are working, is there,essentially, labs are um
building up that to some degree,their own biobank, and I think
(05:15):
one of the challenges for thefuture will be how to figure out
ways of sharing biobank databetween labs or institutions,
because because, for sure, inthis genomics general field,
what's possible or questionsthat you can answer are
definitely related to themagnitude of the data set that
(05:38):
you have access to.
My interest in polygenic riskscores really originated, as I
mentioned, in looking as aclinician, looking at animals
with orthopedic problems andparticularly dogs with crucial
ligament rupture, where, prettyquickly, any um clinician who
works with some breeders orworks with a lot of um uh
(06:02):
trainers or field trial dogs andthat type of thing, you come to
realize pretty quickly thatthere's more going on with this
condition than just accidentalinjury.
And so, as a sort of clinician,clinician, scientist, then
obviously you're starting to askquestions well, what is
actually really going on withthis very common problem?
(06:23):
And so that's really what droveour interest in this topic area
.
Speaker 2 (06:32):
And why do you think companion animal is lagging
behind the use of polygenic riskscores in production and humans
?
Speaker 3 (06:40):
I think the biggest issue or challenge is investment
in the field.
If large data sets are neededto sort of really accelerate the
science of this topic, then atsome level that needs investment
through grant funding orinvestment through veterinary
(07:03):
schools etc.
And I think that one of thechallenges has been to figure
out sources of funding that cansupport impactful work on new
sort of big problems.
And I think the other otherthing is still that that some of
the work is still in an earlyphase, so it's definitely an
(07:29):
area where production animalscience is ahead of humans and
humans and production animalscience is ahead of companion
animals, and in that sense Iinclude like horses, as in
companion animals, as well aslike dogs and cats.
But it is starting to changeand move forward, moving forward
a fair bit, and I think thepromise for the future is pretty
(07:51):
bright a fair bit and I thinkthe promise for the future is
pretty bright.
Speaker 2 (07:58):
That's great, makes total sense.
You are one of, if not the keyopinion leader in this area, but
every time we write amanuscript we're surprised by
something which also excites usand keeps us investigating.
What from this articlesurprised you?
Speaker 3 (08:07):
I think one of the things that we've learned, which
we talked a little bit about inthis review article, is the
idea that multi-ancestryprediction is sort of a big
challenge or a big problem.
And back to crucioligamentrupture we know clinically and
have known for a long time thatthis is a condition that's
(08:27):
common in multiple differentbreeds of dog, and our academic
papers have published on a smallnumber of breeds, but
principally the LabradorRetriever, because it's the most
common until recently breed inthe US which is commonly
affected with this condition.
And so that was the sort ofreason for focusing on the
(08:49):
Labrador Retriever in thebeginning.
But we know from ongoing workthat we're continuing to pursue
that it is quite challenging todo predictions across different
breeds of domesticated animal oressentially populations of
different ancestry, and we thinkthat that can be overcome with
(09:12):
some more research and morefunding.
But it is a challenge andessentially it boils down to
this point about geneticheterogeneity that although
cruciate ligament rupture, as aprototypical example, is quite
heritable, it's quite common indifferent breeds of dogs.
There's heterogeneity in thegenetic contribution in the
(09:35):
different breeds and so a dataset that works well for
prediction in one breed will notnecessarily work well for
prediction in another breed, andso solutions to that sort of
scientific challenge is stillneeded.
Speaker 1 (09:49):
Sounds like a lot of future work, which actually
leads me really well into mynext question.
So, mehdi, what are the nextsteps for research in this topic
?
Speaker 4 (09:57):
That's a very good question.
As you know, when we'repredicting a polygenic risk
score for an individual, we'realways trying to increase
accuracy of prediction.
So we want to have the mostaccurate quantity as we can to
stratify risk across differentindividuals low risk, high risk
(10:21):
and medium risk.
So one thing can improve ourprediction accuracy is a
well-optimized referencepopulation.
We're always trying to have atransferability of our polygenic
risk score across differentbreeds, so we need to have a
reference population composed ofmany different breeds.
(10:41):
When we estimate SNP effects,this SNP effect could be
representative of all breeds,representative of all breeds.
So one thing could be improvinga good reference population,
establishing a referencepopulation.
Another thing is that withadvances in genomic technology,
(11:04):
we can have many layers of omics, information, genomics,
transcriptomics and epigenomics.
Another thing is that how wecan use these multiple layers of
information, genomicinformation, to fit to our model
and improve the accuracy.
So I think we need to thinkabout this area as well and
(11:29):
provide more information, moreinput for our models to improve
the prediction accuracies.
Speaker 1 (11:35):
Do you think AI could help with that at all?
Speaker 4 (11:38):
Yeah, that's a really good question.
As you know, when we have aprediction model polygeneric
risk score prediction model wehave input and we have some
exploratory variables.
I believe AI can play a role inboth sides of this equation.
We have input.
(11:59):
Some AI technologies canprovide accurate input.
When we have accurate input, wehave accurate prediction.
So AI can play a role forproviding us the most accurate
input or phenotypes.
On the other side, we haveexploratory variables, so they
(12:20):
have patterns.
Ai models have shown they havea high ability to recognize the
pattern of data, for example,nonlinear patterns.
So I believe AI can be used fordetecting this pattern, to
(12:40):
predict or empower our model forprediction.
Speaker 1 (12:45):
Very cool and for those of you just joining us,
we're discussing polygenic riskscores with our guests Peter and
Maddy.
Speaker 2 (12:56):
Peter, I didn't get a chance to look, but I'm
estimating you're close to, orover 300 peer-reviewed
manuscripts and tons of grants.
A very successful clinicianscientist.
How do you get it done?
What do you have for tips andtricks to sit down and cross
that finish line?
Speaker 3 (13:10):
Work hard.
I think certainly theuniversity environment here has
been very supportive for me interms of the work that I've had,
and some of thisinterdisciplinary research has
been very reliant on robustcollaboration with others and
(13:30):
that's certainly been a theme inmy work and very much so in
graduate students or traineesthat have worked in my lab over
time.
So I think in the current eraof science moving forward, good
teamwork, I think, and goodinterdisciplinary collaborations
(13:52):
are going to continue to bevery pivotal.
Speaker 2 (13:55):
Yeah, nothing excited me more when I was still in
academia than working acrossdisciplines.
It just really opened your mind.
I remember a physicist said onetime one of his students was up
drawing a cartilage and I waslike, oh no, no, you don't
understand.
Articular cartilage is reallycomplex, like the pretty glycans
and the collagen.
And he looked at me and he said, lisa, I model the earth.
(14:18):
And I was like, oh right, it'sreally respectful for different
and it just excites you so it'seasier to sit down and really
get through a manuscript.
But well done, I mean, you'rean amazing human being and
individual.
Speaker 3 (14:33):
I think one of the global topics for work in this
field is this idea that ingeneral, veterinary students are
not very exposed to computerscience or bioinformatics.
And I think that's certainlytrue at UW-Madison, but I
(14:53):
suspect that it's generally truein many other veterinary
schools.
And I think I'm certainly trueat UW-Madison, but I suspect
that it's generally true in manyother veterinary schools.
And I think I'm certainly likeprojecting into the future
development of thinking overwhat a curriculum should be, an
ideal curriculum should be forveterinary students.
I think that's an area thatdefinitely needs some more
reflection of.
Veterinarians are going to havebigger roles and bigger
(15:16):
exposure to sort of large datasets, and computer science and
bioinformatics are going tocontinue to play a very
important role in likeveterinary medicine just in
general.
I will hope your dean is notlistening or you'll end up on
the curriculum redesigncommittee listening, you'll end
(15:36):
up on the Curriculum RedesignCommittee, so that's certainly
been a theme for us in terms ofyou know how we think about
things here.
Speaker 1 (15:40):
So, peter and Mehdi, this next set of questions is
going to be really important forour listeners.
The first one is going to berevolving around the
veterinarian's perspective.
So what is one piece ofinformation the veterinarian
should know about polygenic riskscores?
Speaker 3 (15:53):
Great.
Well, thank you for thatquestion.
So I think one of the importantthings to recognize,
particularly for these diseases,is that there's an intrinsic
risk between the heritability ofa disease or trait and the
ability to predict it usinggenetic information.
(16:15):
And the easiest way to thinkabout that is for binary traits,
ie like a case in control.
And so back to the work that wedid with crucial ligament
rupture in dogs.
All of that initial work wasdone using the binary trait and
each dog was either a case orcontrol, and obviously, as a
(16:37):
clinician, the next Labradorthat walks into your office, if
you flip a coin and call it as acase or control, you're going
to be right half the time.
So heritability essentiallyhighlights the potential for a
predictive genetic test.
(16:57):
So, for example, in thescenario where the heritability
of crucioligamen rupture in theLabrador retriever was estimated
as 40% or 0.4, then with anideal setup, genetic risk
testing should be 90% accurate,because 0.5 and 0.4 is 0.9.
(17:19):
And so I think the take-homemessage for veterinarians is
that there's a strong linkagebetween genetic risk prediction
and heritability, and so oftenthe place that this work starts
for a new disease or conditionis to estimate the heritability.
Anything maybe can add to thata bit more.
Speaker 4 (17:43):
Yeah, one thing I should mention here is about the
PRS value.
Prs value actually is not adiagnosis tool, it's just a
stratification tool.
So how we can recognize or usethis value.
(18:04):
Our dog is at high risk, mediumrisk or low risk, so before any
clinical signs emerge, beforeany clinical signs emerge, so we
can use this value to manage toadjust, to change the lifestyle
for our pets.
I remember when we publishedour genetic test for the first
(18:28):
time and we announced throughour Facebook page somebody wrote
I am skating to get this testfor my dog.
I want, I am going to saypolygenic risk score values for
any disease for your pet is notscary, it's just caring.
It helps you to help your dog.
(18:50):
It navigates you through thechanging or adjusting your pet
in terms of the daily activity.
Speaker 1 (18:58):
So, on the other side of the relationship, what's one
thing clients should know aboutthis topic?
Speaker 3 (19:03):
Yeah, that's a great question.
So I would say still it's.
There's still a lack ofrecognition in general for the
intrinsic like geneticcontribution to like common
diseases, and by, in general,like common diseases or
conditions are polygenic interms of the genetic
(19:25):
contribution.
And so even today, we're stillregularly working in the clinic
with owners who have a dog withcruciate ligament rupture, where
they still have the perspectivethat the dog was playing ball
in the garden a few days ago andbecame lame and developed the
(19:46):
condition and so in their mindit's an injury situation, injury
situation on, whereas thereality is, um, there might have
been some uh activityassociated with with the rupture
event, but the underlying umreason the problem arose is as
(20:07):
because of intrinsic likegenetic disease.
Um, in, obviously, just again,using that as an example, we
have this paradoxical scenariowhere greyhounds are the fastest
, most athletic dog you couldpossibly come across and as a
breed, they're heavily protectedagainst the risk of crucial
(20:29):
ligament rupture.
So I think that's still thething that owners should be
aware of in terms of orthopedic,common orthopedic diseases in
general, but cruciate ligamentrupture in particular.
Speaker 2 (20:44):
Yeah.
But you know, when you look atthe Frenchie you can tell people
all you want about high riskfactor diseases, and it's the
same in horses.
You're like please don't buythat.
And the client's like oh, Ialready love it, it's too late.
Speaker 3 (20:59):
Yeah, no, people love Labrador Retrievers or French
Bulldogs, and not every.
I mean.
Greyhounds are great dogs, butnot everybody wants one.
Speaker 2 (21:08):
Yeah, my daughter got two miniature Bernadettles.
I was like you didn't ask andof course they have GI problems
and you're just like you couldhave asked your mother.
She could have told you that,but it was too late.
Well, thank you both Reallyfascinating work and Peter, for
all you've contributed toespecially AJVR, but JAVMA as
well and really supporting ourjournals.
We really appreciate it.
(21:28):
As we wind down, we like to aska little more of a fun question
.
So, peter, for you and if youhave it, you can show us what is
the oldest or most interestingitem on your desk or in your
desk drawer.
Speaker 3 (21:41):
The oldest item I have is my diploma from the
University of Bristol, so inJune I will be celebrating 40
years as a veterinarian.
Speaker 2 (21:52):
Very good.
Congratulations, Mehdi, for you.
What is your favorite animalfact?
Speaker 4 (21:58):
Oh, one thing for me.
The fun fact is that white dogshave 300 million olfactory
receptors.
Both humans have only 6 millionolfactory receptor.
Both humans have only 6 millionolfactory receptors.
This is just fencing for me.
Speaker 2 (22:20):
I did not know that, that's a good fact for me, too.
Speaker 1 (22:23):
I was at a big dinner with the Labrador and I can
attest the Labrador smelled thefood before the humans did and
tried to find it, so itdefinitely makes sense.
Thank you so much, peter andMaddy.
I really appreciate your timejust being here today sharing
your findings with our listeners, and also for sharing your
article, too, with AJBR.
Speaker 3 (22:40):
Yeah, well, thank you for the invitation.
I've enjoyed the discussion.
Speaker 4 (22:45):
Yeah, thank you for inviting us and it was a
pleasure.
Speaker 1 (22:49):
And to our listeners.
You can read Peter and Maddy'sarticle on AJBR.
I'm Sarah Wright with Visa40A.
Be on the lookout for nextweek's episode and don't forget
to leave us a rating and reviewon Apple Podcasts or whatever
platform you listen to.
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