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性疾病是共同的，即使个别罕见。为患者及其家人提供基因诊断对于结束通常漫长的诊断之旅至关重要。对于他们的临床医生，它可以告知提供正确的治疗。了解这些罕见的肌肉骨骼（MSK）疾病的遗传基础还可能为常见的肌肉骨骼（MSK）疾病提供见解，这些疾病是导致英国数百万人残疾和生活质量受损的主要原因。过去，罕见肌肉骨骼（MSK）疾病的基因诊断依赖于已知基因的测序组来识别致病基因，但是这种基于基因组的测序诊断率很低，因为许多疾病基因还没有被识别出来。随着技术的进步和成本的降低，对病人的整个基因组（他们DNA的完整序列）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，不仅是涉及单碱基对的那些，而且还包括基因组片段的更复杂的复制、缺失或易位以及基因之间区域（内含子）中的变异。这些区域在调控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还相当少。因此，通过询问患者的基因组来识别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计划（100KGP），以确定10万名患者的致病性变异，旨在改善这些患者的诊断，这也可能为他们的个性化治疗提供信息。由Genomics England运营，这些患者的测序现已完成，据估计，到目前为止，已发现四分之一的罕见疾病患者的诊断。解决其余的这些情况下，将需要大量的努力，代表研究界调查上述不同的变异类型。本提案旨在促进这一努力，重点关注患者的肌肉骨骼和相关的发展条件。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，以确保我们已经研究了所有可能的变异，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计划已经收集的数据之外，我们总是需要额外的临床或X射线数据。然而，这通常是在医疗记录中，所以我们已经确定了路线，以检索这涉及临床医生和患者自己。我们已成立一个临床多学科团队，以便对病例进行讨论，并将利用临床放射学评估方面的专业知识，确保对X光数据进行系统分析。我们还将要求患者向我们提供自我报告的数据，因为我们从其他研究中了解到，患者非常善于记住他们骨折的骨头以及何时骨折。最后，我们将看看机器学习或“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的模式，这些模式是我们手动检查无法识别的。我们预计这些努力将帮助我们为100 KGP中的更多患者提供诊断，然后可以用于100 KGP中的其他疾病，为更多患者提供遗传诊断。

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.