{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T22:37:08Z","timestamp":1792190228326,"version":"4.4.0"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T00:00:00Z","timestamp":1738454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100001458","name":"AKC (American Kennel Club) Canine Health Foundation","doi-asserted-by":"publisher","award":["03069"],"award-info":[{"award-number":["03069"]}],"id":[{"id":"10.13039\/100001458","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>This study investigates the efficacy of various genomic prediction models\u2014Genomic Best Linear Unbiased Prediction (GBLUP), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP)\u2014in predicting genomic breeding values (gEBVs). The phenotypic data include three binary health traits (anodontia, distichiasis, oral papillomatosis) and one behavioral trait (distraction) in a population of guide dogs. These traits impact the potential for success in guide dogs and are therefore routinely characterized but were chosen based on differences in heritability and case counts specifically to assess gEBV model performance. Utilizing a dataset from The Seeing Eye organization, which includes German Shepherds (n = 482), Golden Retrievers (n = 239), Labrador Retrievers (n = 1188), and Labrador and Golden Retriever crosses (n = 111), we assessed model performance within and across different breeds, trait heritability, case counts, and SNP marker densities. Our results indicate that no significant differences were found in model performance across varying heritabilities, case counts, or SNP densities, with all models performing similarly. Given its lack of need for parameter optimization, GBLUP was the most efficient model. Distichiasis showed the highest overall predictive performance, likely due to its higher heritability, while anodontia and distraction exhibited moderate accuracy, and oral papillomatosis had the lowest accuracy, correlating with its low heritability. These findings underscore that lower density SNP datasets can effectively construct gEBVs, suggesting that high-cost, high-density genotyping may not always be necessary. Additionally, the similar performance of all models indicates that simpler models like GBLUP, which requires less fine tuning, may be sufficient for genomic prediction in canine breeding programs. The research highlights the importance of standardized phenotypic assessments and carefully constructed reference populations to optimize the utility of genomic selection in canine breeding programs.<\/jats:p>","DOI":"10.3390\/ani15030408","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T08:47:57Z","timestamp":1738572477000},"page":"408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs"],"prefix":"10.3390","volume":"15","author":[{"given":"Joseph A.","family":"Thorsrud","sequence":"first","affiliation":[{"name":"Department of Animal Sciences, College of Agriculture and Life Sciences, Cornell University, 201 Morrison Hall, 507 Tower Road, Ithaca, NY 14853, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katy M.","family":"Evans","sequence":"additional","affiliation":[{"name":"The Seeing Eye Inc., 1 Seeing Eye Wy, Morristown, NJ 07960, USA"},{"name":"School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Loughborough LE12 5RD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyle C.","family":"Quigley","sequence":"additional","affiliation":[{"name":"The Seeing Eye Inc., 1 Seeing Eye Wy, Morristown, NJ 07960, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishnamoorthy","family":"Srikanth","sequence":"additional","affiliation":[{"name":"Department of Animal Sciences, College of Agriculture and Life Sciences, Cornell University, 201 Morrison Hall, 507 Tower Road, Ithaca, NY 14853, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8299-0447","authenticated-orcid":false,"given":"Heather J.","family":"Huson","sequence":"additional","affiliation":[{"name":"Department of Animal Sciences, College of Agriculture and Life Sciences, Cornell University, 201 Morrison Hall, 507 Tower Road, Ithaca, NY 14853, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Worboys, M., Strange, J.-M., and Pemberton, N. 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