Investigation into the risks for gastrointestinal signs in dogs

Lead Research Organisation: University of Edinburgh
Department Name: The Roslin Institute

Abstract

The identification of environmental influences and interactions on the development of disease are critical to determine the basis of complex genetic based traits. To date, large and rigorous population based epidemiological studies documenting the incidence and prevalence of diseases in dog breeds, or the environmental influences that affect them, have not been performed.

The Dogslife project (www.dogslife.ac.uk) was launched in 2010 to collate a cohort of Labrador Retrievers for the longitudinal study of health and disease. To date there are over 5,800 dogs enrolled in the project, with data collected on over 16,000 illness episodes, and with dogs followed for up to five years of their life. The collection of questionnaire data, digital uploads, and genetic information through biological samples submitted by participating dog owners, provide a unique resource of environmental and genetic information for epidemiological investigation.

Gastrointestinal (GI) signs are by far the most common illnesses reported by owners of pet dogs. Preliminary analysis of the Dogslife cohort has revealed that frequently GI signs do not result in the requirement for a veterinary presentation, yet they remain a significant cause of morbidity to the pet and owner. These are virtually unreported in the scientific literature, because of the over-reliance on veterinary healthcare records for collating information on such phenotypes. Over half of dogs develop vomiting and/or diarrhoea clinical signs in the first year of life, with approximately one third of dogs developing recurrent episodes. Although a number of infectious causes are known to cause GI signs, specific diagnoses are rare and the aetiology of the majority of cases remains unknown. There is an urgent need to identify the environmental and infectious risk factors for these cases, particularly with regard to potentially modifiable risks that could be used to reduce the high prevalence of GI signs.

Longitudinal evaluation of dogs participating in the Dogslife cohort affords the possibility to assess risks predating the development of GI signs, and to repeat risk evaluations through through life. This PhD project will evaluate the database information recorded of dogs and their exposures during the project and provide a doctoral training in quantitative epidemiology, genomics and microbiome analysis. During the 4-year studentship the successful candidate will evaluate existing and prospectively collected environmental, host genetic, microbial and environmental datasets for the infectious and non-infectious risks associated with the development of GI signs in the Dogslife cohort as they age, and the interactions between them. Microbiome (16S) sequencing, high-density SNP genotyping and novel virus screens will be used to ascertain infectious and genetic risks for the development of GI signs. Nested case-control studies will be included for low-frequency phenotypes, and whole genome sequencing may be used to explore the genetic basis of selected disease traits and their interaction with environmental risk factors.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/M010996/1 01/10/2015 31/03/2024
1801023 Studentship BB/M010996/1 12/09/2016 30/04/2021
 
Title Instruction videos for Dog Owners 
Description Instructions for how to use DNA and feacal collection kits for dog owners involved in University research. 
Type Of Art Film/Video/Animation 
Year Produced 2018 
Impact Other researchers can now use our videos to explain how to effectively collect these samples. 
URL https://www.ed.ac.uk/vet/services/small-animals/services/microbiome
 
Description We have developed novel data cleaning methods for cohort/longitduinal studies which have been oublished in a scientific journal called PLOS ONE. We have collected lots of data about dog health which will give us insights about how to keep them healthy.
Exploitation Route Our methodolgies are expected to be very useful for other epidemioligical researchers
Sectors Agriculture, Food and Drink,Healthcare,Other

URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228154
 
Description Our findings are used to inform veterinary healthcare decisions by owners, breeders and veterinary surgeons. We publish our results on the Dogslife website and inform our participants of all our findings.
First Year Of Impact 2010
Sector Healthcare,Other
Impact Types Societal,Economic

 
Description Birrell-Gray Travelling Scholarship
Amount £500 (GBP)
Organisation University of Edinburgh 
Sector Academic/University
Country United Kingdom
Start 11/2018 
End 11/2018
 
Title Novel data cleaning algorithm for growth data 
Description All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cut-offs and logic rules was designed. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains. 
Type Of Material Improvements to research infrastructure 
Year Produced 2020 
Provided To Others? Yes  
Impact Only just published, but we anticipate it will make an impact on researchers in academia and industry who deal with a range of growth data in different fields. 
URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228154
 
Title Dogslife height and weight data-the first 7 years of the cohort, 2010-2017 [dataset]. 
Description Dogslife is a longitudinal, online study of the health of pedigree UK Kennel Club registered Labrador Retrievers in the UK. Recruitment to Dogslife began in 2010 and continues into 2019. After registration, owners are prompted to complete regular online questionnaires on the Dogslife website about their dog's morphology, lifestyle and illness incidences. Data for this study were collected during the first 7 years of Dogslife from July 2010 to June 2017, via routine online reporting. Dog owners were asked to measure their dogs' weight every time they filled out the questionnaire; monthly for the first 12 months of the dog's life and every 3 months thereafter. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Impact Other researchers can use our data 
URL https://datashare.is.ed.ac.uk/handle/10283/3352
 
Description Banfield data sharing 
Organisation Waltham Centre for Pet Nutrition
Country United Kingdom 
Sector Private 
PI Contribution Used Banfield data to contribute towards some research we were doing on data cleaning
Collaborator Contribution Banfield gave us data
Impact My paper as linked above https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228154. Disciplines are epidemiology, data science, biology, statistics
Start Year 2018
 
Description Data sharing with Vet Compass 
Organisation Royal Veterinary College (RVC)
Country United Kingdom 
Sector Academic/University 
PI Contribution Used Vet Compass Data to compare to our cohort (dogslife) to help vaildate the findings of each study
Collaborator Contribution Provided us with data and inputed on the outcomes of the work
Impact Multidisciplinary - involves biology, epidemiology and data science
Start Year 2019
 
Description SAVSNET data sharing 
Organisation University of Liverpool
Department School of Veterinary Science Liverpool
Country United Kingdom 
Sector Academic/University 
PI Contribution Used SAVSNET data to contribute towards some research we were doing on data cleaning
Collaborator Contribution SAVSNET gave us data
Impact My paper as linked above https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228154. Disciplines are epidemiology, data science, biology, statistics
Start Year 2018
 
Description Dogslife Newsletter 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Study participants or study members
Results and Impact A newsletter was written to inform the current participants in our cohort (Dogslife) about a research paper that had been published as a consequence of them volunteering to give us data. It's purpose was to keep the participants aware of our appreciation for them giving us data and to explain the research using terminology that ia more accessible to the general public.
Year(s) Of Engagement Activity 2020
 
Description Roslin Institute Open Day 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact The general public attended Roslin open day. My particular activity involved setting up a stall that explained the differences between cause and correlation and talked about risk odds ratios, bias in reporting in the media etc.
Year(s) Of Engagement Activity 2017
 
Description Vogrie Country Park dog show 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact Stall at a dog show where we had some science activities and explained genetics and our research to the general public.
Year(s) Of Engagement Activity 2016