Identifying New Disease Genes & Mechanisms for Musculoskeletal Disorders in 100K Genomes Project using Bioinformatics, Phenotyping & Machine Learning

影响骨骼或肌肉发育的遗传疾病虽然个别罕见，但总体上很常见。为患者及其家属提供基因诊断对于结束漫长的诊断过程非常重要。对于他们的临床医生来说，这可能会为提供正确的治疗提供信息。了解这些罕见的肌肉骨骼（MSK）疾病的遗传基础也可能提供对常见MSK疾病的见解，这是英国数百万人残疾和生活质量受损的主要原因。过去，罕见MSK疾病的遗传诊断依赖于已知基因的测序面板来确定致病基因，但由于许多疾病基因尚未被识别，这种基于面板的测序的诊断率很低。随着技术的进步和成本的降低，对患者的整个基因组（其DNA的完整补充）进行测序已经成为可能。此外，可以从基因组序列数据中查询许多类型的遗传变异，不仅包括那些涉及单个碱基对的遗传变异，还包括更复杂的基因组片段的复制、缺失或转位，以及基因之间区域的变异——内含子。人们越来越认识到这些区域在调节基因表达方面发挥着重要作用，但我们对其临床意义的了解却相当少。因此，对患者基因组进行分析以确定致病变异仍然存在许多挑战。认识到这种基因组测序方法的潜力，英国启动了一项国家项目（100KGP），在10万名患者中识别致病变异，目的是改善对这些患者的诊断，从而可能为他们的个性化治疗提供信息。由英国基因组公司运营的这些患者的测序现在已经完成，据估计，到目前为止，已经发现了四分之一的罕见疾病患者的诊断。解决其余的这些情况将需要研究团体的强烈努力来调查上述不同的变体类型。本提案旨在为专注于肌肉骨骼和相关发育疾病患者的努力做出贡献。我们将使用现有的GeL算法和我们自己的生物信息学工具来分析基因组序列数据，以确保我们已经调查了所有可能的变异，然后采用各种遗传策略来评估这些基因是否具有潜在的致病性。除了GeL项目已经收集的数据外，我们总是需要额外的临床或x射线数据。然而，这通常在医疗记录中可用，因此我们已经确定了涉及临床医生和患者自己的检索途径。我们成立了一个临床多学科小组，以便讨论病例，并将利用临床放射学评估方面的专业知识，确保对x射线数据进行系统分析。我们还会要求患者提供自我报告的数据，因为我们从其他研究中得知，患者非常善于记住他们骨折的部位和时间。最后，我们将看到机器学习或“人工智能”是否可以帮助我们识别这些庞大而复杂的数据集中无法通过人工检查识别的模式。我们预计这些努力将帮助我们在100KGP中为更多的患者提供诊断，然后可以在100KGP中为其他疾病提供诊断，为更多的患者提供遗传诊断。

Genetic disorders which affect the development of the skeleton or muscles are collectively common, even if individually rare. Providing a genetic diagnosis for the patients and their families is important for ending what is often a lengthy diagnostic odyssey. For their clinicians, it may inform provision of the correct treatment. Understanding the genetic basis of these rare musculoskeletal (MSK) disorders may also provide insights into common MSK disorders, which are a major cause of disability and impairment of quality of life for millions of people in the UK.In the past, genetic diagnosis of rare MSK diseases has relied on sequencing panels of known genes to identify the causative gene, but the diagnostic yield of such panel-based sequencing is low because many disease genes have not yet been identified.With technological improvements and cost reductions, sequencing of patients' entire genomes (the full complement of their DNA) has become a possibility. Furthermore, many types of genetic variants can be interrogated from genome sequence data, not just those involving single base pairs, but also more complex duplications, deletions or transpositions of segments of the genome as well as variants in the regions between genes - the introns. These regions have increasingly been recognised to play important roles in regulating gene expression but we have considerably less understanding about their clinical significance.Interrogation of patients' genomes to identify the disease-causing variants therefore still presents many challenges. Recognising the potential of this genome sequencing approach, the UK launched a national programme (100KGP) to identify pathogenic variants in 100,000 patients, with the aim of improving diagnoses for these patients that might also inform their personalised treatment. Run by Genomics England, sequencing of these patients is now complete and it is estimated that diagnoses have been found for a quarter of the rare disease patients so far. Solving the rest of these cases will require intense effort on behalf of the research community to investigate the different variant types described above.This proposal aims to contribute to that effort focusing on patients with musculoskeletal and related developmental conditions. We will use both existing GeL algorithms and our own bioinformatics tools to analyse the genome sequence data to ensure we have investigated all possible variants, and then employ a variety of genetic strategies to assess whether the genes are potentially pathogenic. We invariably need additional clinical or x-ray data to that already collected by the GeL programme. However, this is often available in medical records so we have identified routes to retrieving this which involve clinicians and patients themselves. We have established a clinical multi-disciplinary team to enable discussion of cases, and will employ expertise in clinical radiology assessments to ensure systematic analysis of x-ray data. We will also ask patients to provide us with self-reported data, as we know from other research studies that patients are very good at remembering which bones they have broken and when. Finally, we will see if machine learning or 'artificial intelligence' can help us identify patterns in these vast and complex datasets which could not be identified by our manual inspection.We anticipate that these efforts will help us provide diagnoses for many more patients in the 100KGP and can then be adopted for other diseases in the 100KGP providing genetic diagnoses for many more patients.