Veterinary Vertex
Veterinary Vertex is a weekly podcast that takes you behind the scenes of the clinical and research discoveries published in the Journal of the American Veterinary Medical Association (JAVMA) and the American Journal of Veterinary Research (AJVR). Tune in to learn about cutting-edge veterinary research and gain in-depth insights you won’t find anywhere else. Come away with knowledge you can put to use in your own practice – along with a healthy dose of inspiration to remind you what you love about veterinary medicine.
Veterinary Vertex
Polygenic Risk Score Prediction of Complex Diseases in Companion Animals
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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You're listening to Veterinary Vertex, a podcast of the AVMA Journals. In this episode we chat about the prospects, opportunities and challenges of polygenic risk score prediction of complex diseases in companion animals with our guests Mehdi Momin and Peter Muir.
Speaker 2:Welcome listeners. I'm Editor-in-Chief Lisa Fortier, and I'm joined by Associate Editor Sarah Wright. Today we have Peter and Mendy joining us. Peter, my longtime friend and colleague, thank you for all you've done for our journals and for being with us here today.
Speaker 3:Thank you for the invitation to be part of this meeting.
Speaker 4:Thank you, Lisa and Sarah, for inviting us. It's our pleasure to share our experience and our knowledge about Polygyic risk score prediction with you and your audience.
Speaker 1:Awesome, let's dive right in. So, peter, your AJBR article discusses the unique challenges and future opportunities that hinder the broader adoption of polygenic risk score risk prediction in companion animals compared to humans and production animals. Please share with our listeners the background on this article.
Speaker 3:Okay, thank you for the question. Yeah, as an academic small animal surgeon, I've had a longstanding interest in cruciate ligament rupture in dogs going back for a long time, and as that work unfolded, we acquired a sort of bigger and bigger interest in the genetic contribution to the disease. So we started to do a genome-wide association study in 2014. And those projects take a while, so it wasn't until 2017 that the first paper from my lab was published on that topic, but already, even in that very first paper, we'd started to do some analysis about the genetic contribution to the disease and how that could potentially be used to predict cases from controls cases from controls. And so here we are today, some years later, with a much more sort of sophisticated understanding of the topic in general about genetic risk prediction for common, complex or polygenic diseases.
Speaker 1:Yeah, definitely interesting article. My in-laws dog actually just tore her CCL, so we're looking to have surgery for her soon, so that's something that's super applicable to lots of pet owners as well. So, mehdi, what are the important take-home messages from this AJBR article?
Speaker 4:Yeah, that's a good question. Several months ago, before we started to finalize the paper, I discussed with Peter. We know there are lots of research about the polygenic crystal score prediction in livestock animals and also in humans. So also there are several studies in companion animals. Peter, we have some experience from before. But also we can right now write a paper to bring more attention to hologenic risk score, how this quantity can be used in practical for the risk stratification in companion animal, like dogs, dogs.
Speaker 4:So one thing is that I told Peter we need to discuss about the differences between breeds in terms of the RISC scores and how we can optimize our models, how we can develop a model. So I can say this article brings several different things to readers. One thing is same as human production animal polygenic risk score can be used in companion animal as well for risk stratification. But we have a huge diversity in dog population, for example, in pets. So we need to consider this. We need to develop our model accurately predict polygenic risk score as just dog's population, for example, or other pet population, so this quantity can be used for personalized veterinary care. This is what brings these papers to readers.
Speaker 2:That's great, peter. Back to you so many questions. This will be multi-part. So you mentioned the start of this was a GWAS study. I've never done a GWAS study and I can't imagine the amount of data, so part of my multi-part question is are you still iterating that data? What sparked your interest in polygenic risk scores and why are companion animals so far behind humans and production animals?
Speaker 3:Okay, yes, thank you for the question.
Speaker 3:Yes, our data set is continuing to grow and we're continuing to work actively on this.
Speaker 3:So already, like in our lab, as many other um uh investigators are working, is there, essentially, labs are um building up that to some degree, their own biobank, and I think one of the challenges for the future will be how to figure out ways of sharing biobank data between labs or institutions, because because, for sure, in this genomics general field, what's possible or questions that you can answer are definitely related to the magnitude of the data set that you have access to. My interest in polygenic risk scores really originated, as I mentioned, in looking as a clinician, looking at animals with orthopedic problems and particularly dogs with crucial ligament rupture, where, pretty quickly, any um clinician who works with some breeders or works with a lot of um uh trainers or field trial dogs and that type of thing, you come to realize pretty quickly that there's more going on with this condition than just accidental injury. And so, as a sort of clinician, clinician, scientist, then obviously you're starting to ask questions well, what is actually really going on with this very common problem? And so that's really what drove our interest in this topic area.
Speaker 2:And why do you think companion animal is lagging behind the use of polygenic risk scores in production and humans?
Speaker 3:I think the biggest issue or challenge is investment in the field. If large data sets are needed to sort of really accelerate the science of this topic, then at some level that needs investment through grant funding or investment through veterinary schools etc. And I think that one of the challenges has been to figure out sources of funding that can support impactful work on new sort of big problems. And I think the other other thing is still that that some of the work is still in an early phase, so it's definitely an area where production animal science is ahead of humans and humans and production animal science is ahead of companion animals, and in that sense I include like horses, as in companion animals, as well as like dogs and cats. But it is starting to change and move forward, moving forward a fair bit, and I think the promise for the future is pretty bright a fair bit and I think the promise for the future is pretty bright.
Speaker 2:That's great, makes total sense. You are one of, if not the key opinion leader in this area, but every time we write a manuscript we're surprised by something which also excites us and keeps us investigating. What from this article surprised you?
Speaker 3:I think one of the things that we've learned, which we talked a little bit about in this review article, is the idea that multi-ancestry prediction is sort of a big challenge or a big problem.
Speaker 3:And back to crucioligament rupture we know clinically and have known for a long time that this is a condition that's common in multiple different breeds of dog, and our academic papers have published on a small number of breeds, but principally the Labrador Retriever, because it's the most common until recently breed in the US which is commonly affected with this condition.
Speaker 3:And so that was the sort of reason for focusing on the Labrador Retriever in the beginning. But we know from ongoing work that we're continuing to pursue that it is quite challenging to do predictions across different breeds of domesticated animal or essentially populations of different ancestry, and we think that that can be overcome with some more research and more funding. But it is a challenge and essentially it boils down to this point about genetic heterogeneity that although cruciate ligament rupture, as a prototypical example, is quite heritable, it's quite common in different breeds of dogs. There's heterogeneity in the genetic contribution in the different breeds and so a data set that works well for prediction in one breed will not necessarily work well for prediction in another breed, and so solutions to that sort of scientific challenge is still needed.
Speaker 1:Sounds like a lot of future work, which actually leads me really well into my next question. So, mehdi, what are the next steps for research in this topic?
Speaker 4:That's a very good question. As you know, when we're predicting a polygenic risk score for an individual, we're always trying to increase accuracy of prediction. So we want to have the most accurate quantity as we can to stratify risk across different individuals low risk, high risk and medium risk. So one thing can improve our prediction accuracy is a well-optimized reference population. We're always trying to have a transferability of our polygenic risk score across different breeds, so we need to have a reference population composed of many different breeds. When we estimate SNP effects, this SNP effect could be representative of all breeds, representative of all breeds. So one thing could be improving a good reference population, establishing a reference population. Another thing is that with advances in genomic technology, we can have many layers of omics, information, genomics, transcriptomics and epigenomics. Another thing is that how we can use these multiple layers of information, genomic information, to fit to our model and improve the accuracy. So I think we need to think about this area as well and provide more information, more input for our models to improve the prediction accuracies.
Speaker 1:Do you think AI could help with that at all?
Speaker 4:Yeah, that's a really good question. As you know, when we have a prediction model polygeneric risk score prediction model we have input and we have some exploratory variables. I believe AI can play a role in both sides of this equation. We have input. Some AI technologies can provide accurate input. When we have accurate input, we have accurate prediction. So AI can play a role for providing us the most accurate input or phenotypes. On the other side, we have exploratory variables, so they have patterns. Ai models have shown they have a high ability to recognize the pattern of data, for example, nonlinear patterns. So I believe AI can be used for detecting this pattern, to predict or empower our model for prediction.
Speaker 1:Very cool and for those of you just joining us, we're discussing polygenic risk scores with our guests Peter and Maddy.
Speaker 2:Peter, I didn't get a chance to look, but I'm estimating you're close to, or over 300 peer-reviewed manuscripts and tons of grants. A very successful clinician scientist. How do you get it done? What do you have for tips and tricks to sit down and cross that finish line?
Speaker 3:Work hard. I think certainly the university environment here has been very supportive for me in terms of the work that I've had, and some of this interdisciplinary research has been very reliant on robust collaboration with others and that's certainly been a theme in my work and very much so in graduate students or trainees that have worked in my lab over time. So I think in the current era of science moving forward, good teamwork, I think, and good interdisciplinary collaborations are going to continue to be very pivotal.
Speaker 2:Yeah, nothing excited me more when I was still in academia than working across disciplines. It just really opened your mind. I remember a physicist said one time one of his students was up drawing a cartilage and I was like, oh no, no, you don't understand. Articular cartilage is really complex, like the pretty glycans and the collagen. And he looked at me and he said, lisa, I model the earth. And I was like, oh right, it's really respectful for different and it just excites you so it's easier to sit down and really get through a manuscript. But well done, I mean, you're an amazing human being and individual.
Speaker 3:I think one of the global topics for work in this field is this idea that in general, veterinary students are not very exposed to computer science or bioinformatics. And I think that's certainly true at UW-Madison, but I suspect that it's generally true in many other veterinary schools. And I think I'm certainly true at UW-Madison, but I suspect that it's generally true in many other veterinary schools. And I think I'm certainly like projecting into the future development of thinking over what a curriculum should be, an ideal curriculum should be for veterinary students. I think that's an area that definitely needs some more reflection of. Veterinarians are going to have bigger roles and bigger exposure to sort of large data sets, and computer science and bioinformatics are going to continue to play a very important role in like veterinary medicine just in general.
Speaker 3:I will hope your dean is not listening or you'll end up on the curriculum redesign committee listening, you'll end up on the Curriculum Redesign Committee, so that's certainly been a theme for us in terms of you know how we think about things here.
Speaker 1:So, peter and Mehdi, this next set of questions is going to be really important for our listeners. The first one is going to be revolving around the veterinarian's perspective. So what is one piece of information the veterinarian should know about polygenic risk scores?
Speaker 3:Great. Well, thank you for that question. So I think one of the important things to recognize, particularly for these diseases, is that there's an intrinsic risk between the heritability of a disease or trait and the ability to predict it using genetic information. And the easiest way to think about that is for binary traits, ie like a case in control. And so back to the work that we did with crucial ligament rupture in dogs.
Speaker 3:All of that initial work was done using the binary trait and each dog was either a case or control, and obviously, as a clinician, the next Labrador that walks into your office, if you flip a coin and call it as a case or control, you're going to be right half the time. So heritability essentially highlights the potential for a predictive genetic test. So, for example, in the scenario where the heritability of crucioligamen rupture in the Labrador retriever was estimated as 40% or 0.4, then with an ideal setup, genetic risk testing should be 90% accurate, because 0.5 and 0.4 is 0.9. And so I think the take-home message for veterinarians is that there's a strong linkage between genetic risk prediction and heritability, and so often the place that this work starts for a new disease or condition is to estimate the heritability. Anything maybe can add to that a bit more.
Speaker 4:Yeah, one thing I should mention here is about the PRS value. Prs value actually is not a diagnosis tool, it's just a stratification tool. So how we can recognize or use this value. Our dog is at high risk, medium risk or low risk, so before any clinical signs emerge, before any clinical signs emerge, so we can use this value to manage to adjust, to change the lifestyle for our pets. I remember when we published our genetic test for the first time and we announced through our Facebook page somebody wrote I am skating to get this test for my dog. I want, I am going to say polygenic risk score values for any disease for your pet is not scary, it's just caring. It helps you to help your dog. It navigates you through the changing or adjusting your pet in terms of the daily activity.
Speaker 1:So, on the other side of the relationship, what's one thing clients should know about this topic?
Speaker 3:Yeah, that's a great question.
Speaker 3:So I would say still it's.
Speaker 3:There's still a lack of recognition in general for the intrinsic like genetic contribution to like common diseases, and by, in general, like common diseases or conditions are polygenic in terms of the genetic contribution. And so even today, we're still regularly working in the clinic with owners who have a dog with cruciate ligament rupture, where they still have the perspective that the dog was playing ball in the garden a few days ago and became lame and developed the condition and so in their mind it's an injury situation, injury situation on, whereas the reality is, um, there might have been some uh activity associated with with the rupture event, but the underlying um reason the problem arose is as because of intrinsic like genetic disease. Um, in, obviously, just again, using that as an example, we have this paradoxical scenario where greyhounds are the fastest, most athletic dog you could possibly come across and as a breed, they're heavily protected against the risk of crucial ligament rupture. So I think that's still the thing that owners should be aware of in terms of orthopedic, common orthopedic diseases in general, but cruciate ligament rupture in particular.
Speaker 2:Yeah. But you know, when you look at the Frenchie you can tell people all you want about high risk factor diseases, and it's the same in horses. You're like please don't buy that. And the client's like oh, I already love it, it's too late.
Speaker 3:Yeah, no, people love Labrador Retrievers or French Bulldogs, and not every. I mean. Greyhounds are great dogs, but not everybody wants one.
Speaker 2:Yeah, my daughter got two miniature Bernadettles. I was like you didn't ask and of course they have GI problems and you're just like you could have asked your mother. She could have told you that, but it was too late. Well, thank you both Really fascinating work and Peter, for all you've contributed to especially AJVR, but JAVMA as well and really supporting our journals. We really appreciate it. As we wind down, we like to ask a little more of a fun question. So, peter, for you and if you have it, you can show us what is the oldest or most interesting item on your desk or in your desk drawer.
Speaker 3:The oldest item I have is my diploma from the University of Bristol, so in June I will be celebrating 40 years as a veterinarian.
Speaker 2:Very good. Congratulations, Mehdi, for you. What is your favorite animal fact?
Speaker 4:Oh, one thing for me. The fun fact is that white dogs have 300 million olfactory receptors. Both humans have only 6 million olfactory receptor. Both humans have only 6 million olfactory receptors. This is just fencing for me.
Speaker 2:I did not know that, that's a good fact for me, too.
Speaker 1:I was at a big dinner with the Labrador and I can attest the Labrador smelled the food before the humans did and tried to find it, so it definitely makes sense. Thank you so much, peter and Maddy. I really appreciate your time just being here today sharing your findings with our listeners, and also for sharing your article, too, with AJBR.
Speaker 3:Yeah, well, thank you for the invitation. I've enjoyed the discussion.
Speaker 4:Yeah, thank you for inviting us and it was a pleasure.
Speaker 1:And to our listeners. You can read Peter and Maddy's article on AJBR. I'm Sarah Wright with Visa40A. Be on the lookout for next week's episode and don't forget to leave us a rating and review on Apple Podcasts or whatever platform you listen to.
