Genetic testing predicts appearance but not behavior in dogs

K Kathryn A. Lord (Genomics and Computational Biology, University of Massachusetts Chan Medical School) V Vista Sohrab (Genomics and Computational Biology, UMass Chan Medical School) K Kasia Bryc (Medical and Population Genetics Program, Broad Institute of MIT and Harvard) M Michelle E. White (Medical and Population Genetics Program, Broad Institute of MIT and Harvard) B Brittney Kenney (Genomics and Computational Biology, UMass Chan Medical School) K Kathleen Morrill Pirovich (Genomics and Computational Biology, UMass Chan Medical School) F Frances L. Chen (Genomics and Computational Biology, UMass Chan Medical School) E Elinor K. Karlsson (Genomics and Computational Biology, University of Massachusetts Chan Medical School)

Abstract

Genetic tests for behavioral and personality traits in dogs are now being marketed to pet owners, but their predictive accuracy has not been validated. To evaluate the reliability of such tests, we analyzed data from Darwin’s Ark, a community science initiative that includes over 3,000 dogs with both genetic data and individual-level behavioral phenotypes. None of the candidate variants had significant associations or predictive power for behavioral traits as previously reported. However, we found strong associations with aesthetic traits that differentiate breeds, such as height, leg length, and ear shape. Our results suggest that earlier studies using breed-average phenotypes, rather than individually measured phenotypes, were confounded by population structure. Behavior in dogs is polygenic and complex, and thus cannot be accurately predicted using tests that consider only a few genetic variants. Furthermore, behavior in dogs is only moderately heritable, and environmental influences inherently limit the potential accuracy of genomic predictions. Developing meaningful, accurate genetic predictions for complex traits that can improve dog health and welfare will require very large cohorts of individually phenotyped dogs.

Article Details

Volume / Issue Vol. 122, Issue 48
Published December 02, 2025
ISSN 0027-8424
Publisher National Academy of Sciences

Authors (8)

K

Kathryn A. Lord

Genomics and Computational Biology, University of Massachusetts Chan Medical School

V

Vista Sohrab

Genomics and Computational Biology, UMass Chan Medical School

K

Kasia Bryc

Medical and Population Genetics Program, Broad Institute of MIT and Harvard

M

Michelle E. White

Medical and Population Genetics Program, Broad Institute of MIT and Harvard

B

Brittney Kenney

Genomics and Computational Biology, UMass Chan Medical School

K

Kathleen Morrill Pirovich

Genomics and Computational Biology, UMass Chan Medical School

F

Frances L. Chen

Genomics and Computational Biology, UMass Chan Medical School

E

Elinor K. Karlsson

Genomics and Computational Biology, University of Massachusetts Chan Medical School