Automated phenotyping to accurately infer functional variants in clinical genetics

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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.