Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever

IntroductionCanine cruciate ligament rupture (CR) is a common, complex, polygenic, orthopaedic disease in dogs that results in serious financial burden and patient morbidity even in the face of surgical correction. The goal of this study was to evaluate the clinical utility of CR polygenic risk scor...

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Main Authors: Benjamin Miranda, Mehdi Momen, Susannah J. Sample, Peter Muir
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Veterinary Science
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Online Access:https://www.frontiersin.org/articles/10.3389/fvets.2025.1625953/full
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author Benjamin Miranda
Mehdi Momen
Susannah J. Sample
Peter Muir
author_facet Benjamin Miranda
Mehdi Momen
Susannah J. Sample
Peter Muir
author_sort Benjamin Miranda
collection DOAJ
description IntroductionCanine cruciate ligament rupture (CR) is a common, complex, polygenic, orthopaedic disease in dogs that results in serious financial burden and patient morbidity even in the face of surgical correction. The goal of this study was to evaluate the clinical utility of CR polygenic risk score (PRS) prediction models using genome-wide SNP data from a large reference population of Labrador Retriever dogs.MethodsUsing 10-fold cross-validation and an independent validation population, we assessed Bayesian and machine learning models with and without covariates using both genome-wide SNPs as well as genic SNPs. Models were tuned by optimizing numbers of CR risk SNPs selected by genome-wide association and adjusting posterior probability thresholds to maximize prediction accuracy.ResultsModels that included clinical covariates (sex, neuter status, age, weight, withers height, as well as the first 10 principal components from the genetic relationship matrix) universally yielded higher accuracy up to 88.5% compared to 77% without covariates. Prediction accuracy for some models was reduced when only genic SNPs were used suggesting SNPs in non-coding regions could influence the CR disease risk.DiscussionOur results confirm that PRS models provide sufficient predictive accuracy for clinical application in veterinary medicine and offer a viable, early-life screening tool for personalized care and selective breeding to reduce CR incidence in high-risk breeds. Our results further confirm that CR is a complex polygenic disease in which genome-wide risk SNPs influence disease pathogenesis.
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spelling doaj-art-e1fa83da86604c779baa2fecd8f337c52025-08-26T11:12:19ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692025-08-011210.3389/fvets.2025.16259531625953Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador RetrieverBenjamin MirandaMehdi MomenSusannah J. SamplePeter MuirIntroductionCanine cruciate ligament rupture (CR) is a common, complex, polygenic, orthopaedic disease in dogs that results in serious financial burden and patient morbidity even in the face of surgical correction. The goal of this study was to evaluate the clinical utility of CR polygenic risk score (PRS) prediction models using genome-wide SNP data from a large reference population of Labrador Retriever dogs.MethodsUsing 10-fold cross-validation and an independent validation population, we assessed Bayesian and machine learning models with and without covariates using both genome-wide SNPs as well as genic SNPs. Models were tuned by optimizing numbers of CR risk SNPs selected by genome-wide association and adjusting posterior probability thresholds to maximize prediction accuracy.ResultsModels that included clinical covariates (sex, neuter status, age, weight, withers height, as well as the first 10 principal components from the genetic relationship matrix) universally yielded higher accuracy up to 88.5% compared to 77% without covariates. Prediction accuracy for some models was reduced when only genic SNPs were used suggesting SNPs in non-coding regions could influence the CR disease risk.DiscussionOur results confirm that PRS models provide sufficient predictive accuracy for clinical application in veterinary medicine and offer a viable, early-life screening tool for personalized care and selective breeding to reduce CR incidence in high-risk breeds. Our results further confirm that CR is a complex polygenic disease in which genome-wide risk SNPs influence disease pathogenesis.https://www.frontiersin.org/articles/10.3389/fvets.2025.1625953/fullcruciate ligament rupturedoggenome-wide association studygenomic predictionpolygenic risk score predictionLabrador Retriever
spellingShingle Benjamin Miranda
Mehdi Momen
Susannah J. Sample
Peter Muir
Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
Frontiers in Veterinary Science
cruciate ligament rupture
dog
genome-wide association study
genomic prediction
polygenic risk score prediction
Labrador Retriever
title Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
title_full Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
title_fullStr Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
title_full_unstemmed Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
title_short Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
title_sort accuracy of genome enabled polygenic risk score prediction of cruciate ligament rupture risk in the labrador retriever
topic cruciate ligament rupture
dog
genome-wide association study
genomic prediction
polygenic risk score prediction
Labrador Retriever
url https://www.frontiersin.org/articles/10.3389/fvets.2025.1625953/full
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