Automated phenotyping to accurately infer functional variants in clinical genetics

Lead Research Organisation: University of Oxford
Department Name: Physiology Anatomy and Genetics

Abstract

Rare diseases are numerous - so much so that as a group they are very common. One person in 17 has a rare genetic disorder, but most fail to receive a genetic diagnosis. Diagnosing the genetic cause of a disorder, even when there are only a handful of cases in the world, represents the first of many steps in finding effective treatments. Even though new genetic tests promise to assist in diagnosing some of these patients, the tests are expensive and currently only available to a few people in the wealthiest countries.

For the past 65 years expert clinical doctors have been matching a diagnosis to patients based on facial features and follow up clinical tests. We are developing algorithms through which a computer will learn and apply these skills objectively.

Identifying patients with the same genetic disorders allows comparisons to be made between them. In turn, this can improve estimates of how the disease might progress and allow direct therapeutic benefits, for instance by showing which symptoms are caused by the genetic disorder and which symptoms might be caused by other clinical issues that can be treated.

Using the latest research in computer vision and machine learning the algorithm automatically analyses patient photographs and finds their place in "Clinical Face Phenotype Space" (CFPS). Patients that share a specific dysmorphic disease or syndrome, will cluster together in CFPS. The CFPS model is created and shaped using ordinary, family album photos and accounts for variations between images that are not disease relevant (such as lighting, image quality, background, pose, age, gender, ethnicity, and facial expression).

In the present application we seek funding to develop methods for clinical geneticists to query CFPS for clinically relevant information. The work will develop means by which a patient's similarity to other patient groups can be visualised, explored and tested through robust statistical modelling.

Furthermore we will make it possible to overlay a patient's DNA with CFPS to identify disease causing mutations. This will improve our understanding of how rare diseases disrupt the normal functioning of the body, and in turn influence decisions in treatment strategies.

A clinician should, in future, be able to take a smartphone picture of a patient and query CFPS to quickly find out which genetic disease the person might have. For diseases unknown to medical science, CFPS will find if there are any other patients around the world that might have the same disease.

CFPS will learn from our faces to help diagnose rare diseases.

Technical Summary

We will pursue three primary methodological approaches required to allow us to leverage CFPS together with genotype data to aid clinical diagnoses.
1. Develop methods to allow clinicians to explore and interpret data in CFPS.
We will build on the foundation of the CFPS pipeline to return clinically meaningful data interpretations. This will require a 2nn based and Bayesian Risk framework for estimating the probability that two faces show similarity. Using dimensionality reduction approaches such as Stochastic Neighbour Embedding we will create navigation and visualisation software to explore informative vectors in CFPS. We will develop a likelihood based method for assessing consistency within groups to detect endophenotype stratification within syndromes.

2. Infer causative genotype-phenotype associations by integrating functional gene networks within the CFPS.
Next, we will develop methods to integrate DNA variation into CFPS. This will allow the automatic analysis of craniofacial phenotypes to predict the likelihood of functional pathways being causally disrupted in syndromes. We will use functional genetic pathway information to improve the predictive power of CFPS for clinical genetics and, vice versa, we will also use CFPS to enhance the power of functional gene networks in computational biology. To explore relationships between facial similarities (CFPS) and gene functional similarities we will use machine learning to alter the structure of informative vectors in CFPS to create a honed Functional Genetic CFPS (fgCFPS).

3. Create a robust statistical framework to infer perturbations in functional genetic pathways from facial phenotypes.
Finally, we will develop methods for querying fgCFPS to aid the understanding of complex genetic disorders with unknown aetiology. From the new fgCFPS we will develop methods that assign likelihood estimates for the contributions of functional pathways to syndromes where causative genetics are unknown or complex.

Planned Impact

This project is highly collaborative both within the UK and internationally. This is also translational and interdisciplinary science, criss+crossing the fields of computer vision, computational biology, and clinical genetics. Consequently it provides several direct pathways to impact. The most important of these is that our work will aid in the clinical diagnosis worldwide of the genetics of rare disorders. We have established and will be expanding an international network of collaborations to apply and develop our approach using patient data. We expect this project in 3 years' time to have progressed to being in the early stages of implementation in clinical genetics counselling, initially in the UK and Europe and then elsewhere.

In the space of three years the following directions will lead to measurable impacts:

The methods to interrogate CFPS together with genotype will add statistical power to the inference of genetic diagnoses. In collaboration with clinical sequencing efforts our approach will be applied to patient image data to add value to these existing datasets and on-going projects. This will result in research participants receiving diagnoses that otherwise they would not.

As part of the larger research effort we will set up partnerships with patient support groups for syndromes with identified genetic origins. We will present the science and the project as a means of public engagement, and offer the opportunity to contribute photos (under an informed consent model) for the further refinement of the CFPS. This is public engagement of the best and most interactive kind, which can be measured by the number of images of individuals (>10,000) and syndromes (>200) that we hope to collect over the coming years.

We will expand collaborations to additional clinical geneticists across the world. For clinicians the practical utility would be to evaluate the approach as a means of narrowing down hypotheses for the clinical diagnosis of known syndromes. Their results will also directly feed back to the project, as they will expand the number of images of clinically defined syndromes that will iteratively improve the CFPS - the more people that use it the better it gets.

We anticipate that extending contacts within the MRC Units in The Gambia and Uganda would be a beneficial means of geographically widening our research to efficiently test and apply CFPS in the future.

Publications

10 25 50
 
Title Fair Unsupervised GitHub 
Description Model and code to create fair representations from unsupervised representations 
Type Of Material Computer model/algorithm 
Year Produced 2018 
Provided To Others? Yes  
Impact Presented at NeurIPS workshop in Montreal. Code available to research community 
URL https://github.com/Nellaker-group/FairUnsupervisedRepresentations
 
Title Minerva&Me public participation in research website 
Description Minerva and Me is a secure public participation in research website through which people can choose to share some of their family photographs to help drive research forward. This is currently restricted within the domain of computational phenotyping from facial images to help the diagnosis of rare diseases but has been built to be modular and expandable. The study is fully research ethics approved and relies fundamentally on a dynamic consent model for participant information and continuing data control. Minerva&Me is intended to allow controlled access for other researchers, but this will always be under the dynamic and partcipant centric data control model (so to a degree the data sharing is not ours to determine). Any such additional projects or efforts will need to add extra data control options to the website and will be overseen by the Minerva&Me Advisory Board (which has legal, data security, clinical and patient representatives). 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? No  
Impact This has been build with broad engagement of patient representative groups as a framework for public participation in research with personally identifiable data such as facial photographs. 
URL https://www.minervaandme.com
 
Title Toward GitHub 
Description Placental histology cellular phenoytyping model 
Type Of Material Computer model/algorithm 
Year Produced 2018 
Provided To Others? Yes  
Impact Was published in Medical Imaging Deep Learning conference Amsterdam. Models and project are leading to followup projects and funding applciations. Models being used by other groups and organisations around the world. 
URL https://github.com/Nellaker-group/TowardsDeepPhenotyping
 
Description Minerva Consortium 
Organisation Baylor College of Medicine
Country United States 
Sector Hospitals 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Charité - University of Medicine Berlin
Country Germany 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Charles University
Department School of Medicine Charles Prague
Country Czech Republic 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Freeman Hospital
Country United Kingdom 
Sector Hospitals 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Great Ormond Street Hospital (GOSH)
Country United Kingdom 
Sector Hospitals 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Hospital for Sick Children, Toronto
Country Canada 
Sector Hospitals 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Imagine Institute
Country France 
Sector Hospitals 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation King Edward Memorial Hospital
Country Australia 
Sector Hospitals 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Manchester University NHS Foundation Trust
Country United Kingdom 
Sector Public 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Oregon Health and Science University
Country United States 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation Radboud University Nijmegen Medical Center
Country Netherlands 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation The Garvan Institute for Medical Research
Country Australia 
Sector Charity/Non Profit 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation The Jackson Laboratory
Country United States 
Sector Charity/Non Profit 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation The Wellcome Trust Sanger Institute
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation University of Cambridge
Department Cambridge Institute for Medical Research (CIMR)
Country United Kingdom 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation University of Cape Town
Country South Africa 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation University of Edinburgh
Country United Kingdom 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation University of Melbourne
Department Murdoch Children's Research Centre
Country Australia 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation University of Oxford
Department Weatherall Institute of Molecular Medicine (WIMM)
Country United Kingdom 
Sector Public 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017
 
Description Minerva Consortium 
Organisation University of Washington
Country United States 
Sector Academic/University 
PI Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Collaborator Contribution We launched the Minerva Consortium at the start of 2017 to enable the development of computational phenotyping from clinical imaging, specifically to aid the diagnosis of rare and developmental diseases. The MC structure is intended to coordinate research efforts and patient data sets for projects in an open academic collaboration.
Impact Newly launched, no outputs yet.
Start Year 2017