GOdogs

University of Cambridge Metabolic Research Laboratories

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Institute of Metabolic Science, University of Cambridge.

CWG2020

This online appendix shows extended preliminary data in support of Eleanor Raffan’s application for a Dogs Trust Canine Welfare Grant.

Appendix (1) Evidence of the Utility of Measures of Eating Behaviour

The methods described in Box 1 of the application were applied to measure eating behaviour in retriever dogs with the POMC mutation (Raffan et al, 2016, ref. 7). A manuscript is in preparation. Download a pdf showing the data here.

Appendix (2) Comparison of owner-reported and experimental measures of eating behaviour.

For the retriever POMC experiments, we collected owner-reported measures of eating behaviour using the Dog Obesity Risk Assessment (DORA) Questionnaire and experimental data on food intake and food-motivation from the same dogs. Owner-reported and experimental measures were highly correlated but there are a minority of dogs for which owner-reported and experimental measures are discordant. These data reinforce that (a) owner-reported measures are valid for use in large data sets where experimental measures would not be practical, and (b) experimental measures are indicated for studies of smaller groups where a more precise measure is required.

The figure above shows there is a strong association (p = 0.001) between the time focussed on inaccessible food (in test described in Box 1 main application) and DORA questionnaire owner-reported food motivation. However, some owners reported dogs to be highly food-motivated at home but this was not reflected by high interest during the inaccessible food test (represented by points on top left of graph).

Appendix (3) Further details related to Figure 2 in main application.

Figure 2 from main application is repeated here with an extended figure legend.

Figure 2. Successful GWAS of complex traits in populations of mixed pure and cross breed dogs.
(a) Obesity loci were mapped in multiple breeds by applying breed-average obesity prevalence estimates to individual level genotypes in publicly available data using an approach similar to that applied by Plassais et al18. Breed obesity prevalence estimates were generated through collaboration with Dr Carina Salt (WALTHAM™ Petcare Science Institute, Mars Incorporated) who used the electronic medical record database of Banfield Pet Hospitals®, incorporating data from approximately 2.6 million dogs. For a single preventative care visit per dog information extracted included sex, neuter status, BCS, and breed. BCS was measured on a 5-point scale by veterinarians based on a combination of visual and haptic clues. Modelling was performed to generate a predicted probability for BCS class by breed using methods described previously (Salt, 2019)3. SNP genotyping data (170k array) from 4-10 dogs of 39 breeds deposited publicly by Hayward et al (2016) were extracted for the GWAS shown which applied mixed models incorporating a genetic relationship matrix, implemented in GEMMA. Top loci were also significant in an independent replication cohort that used genotypes deposited in relation to Parker et al (2017). This approach demonstrates that it is possible to discover obesity loci of genome-wide significance in a study incorporating multiple breeds but (because it uses breed-average cf individual level phenotype) is not as powerful or precise as the methods proposed in this application.*

(b) GWAS for body condition score in 123 pugs, and 244 Labradors (main application Fig 1). ER has already performed successful GWAS in these breeds, demonstrating that despite the confounding effect of owner management on obesity, genetic loci associated with BCS can be identified even using the 220k SNP array, by applying linear mixed models with relevant covariates (implemented in GenABEL).

(c) The Affymetrix 712K SNP array was used to map known (blue) and novel (red) height loci (owner-reported on a 5 pt scale) in 374 dogs of different pedigree and cross-breeds, showing that complex traits can be successfully mapped in a cohort including pure and cross-breed dogs. (Unpublished data courtesy of Dr Elinor Karlsson).  

*We additionally investigated whether the effect of the top obesity GWAS risk alleles found in the multi-breed obesity GWAS was mediated by eating behaviour. Breed-average food motivation score was calculated from the GOdogs database of >11,000 responses to the DORA questionnaire. Polygenic risk score was calculated as sum(effect size at top seven risk loci*risk allele count). Polygenic risk score was strongly associated with breed-average food-motivation score (p = 10^-16). The supplementary figure below shows breed-average PRS against breed-average food-motivation, indicating that a large part of genetic risk is mediated by altering eating behaviour, consistent with other species. This remains to be tested on PRS from a GWAS performed with individual level data (as proposed here).

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