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.
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.
People |
ORCID iD |
Dylan Clements (Primary Supervisor) |
Publications
Woolley C
(2020)
Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data
in PLOS ONE
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 |