Automated phenotyping to accurately infer functional variants in clinical genetics

罕见病有很多种，以至于作为一个群体，它们非常常见。每17人中就有一人患有罕见的遗传疾病，但大多数人都没有接受基因诊断。诊断疾病的遗传原因，即使世界上只有少数病例，也是找到有效治疗方法的第一步。尽管新的基因测试有望帮助诊断其中一些患者，但这些测试价格昂贵，目前仅适用于最富裕国家的少数人。在过去的65年里，专家临床医生一直根据面部特征和后续临床测试将诊断与患者相匹配。我们正在开发算法，通过这些算法，计算机将客观地学习和应用这些技能。识别具有相同遗传疾病的患者可以在他们之间进行比较。反过来，这可以改善对疾病可能进展的估计，并提供直接的治疗益处，例如通过显示哪些症状是由遗传疾病引起的，哪些症状可能是由其他可以治疗的临床问题引起的。该算法利用计算机视觉和机器学习的最新研究，自动分析患者照片，并在“临床面部表型空间”（CFPS）中找到它们的位置。共享特定畸形疾病或综合征的患者将聚集在CFPS中。CFPS模型是使用普通的家庭相册照片创建和成形的，并考虑了与疾病无关的图像之间的变化（例如照明、图像质量、背景、姿势、年龄、性别、种族和面部表情）。这项工作将开发一种方法，通过这种方法，可以通过强大的统计模型可视化，探索和测试患者与其他患者群体的相似性。此外，我们将有可能用CFPS覆盖患者的DNA，以识别致病突变。这将提高我们对罕见疾病如何破坏身体正常功能的理解，进而影响治疗策略的决策。未来，临床医生应该能够通过智能手机拍摄患者的照片并查询CFPS，以快速找出患者可能患有的遗传疾病。对于医学上未知的疾病，CFPS会发现世界上是否有其他患者可能患有相同的疾病。CFPS会从我们的面部学习，以帮助诊断罕见疾病。

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.