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

识别新的疾病基因

• 批准号: MR/W01761X/1
• 负责人: Jenny Taylor
• 金额: $ 126.64万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW01761X%2F1
• 关键词: Identifying; New; Disease; Genes; Mechanisms

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.

影响骨骼或肌肉发育的遗传性疾病虽然个别罕见，但总体上很常见。为患者及其家人提供基因诊断对于结束通常漫长的诊断过程非常重要。对于临床医生来说，它可以为提供正确的治疗提供信息。了解这些罕见的肌肉骨骼 (MSK) 疾病的遗传基础还可以深入了解常见的 MSK 疾病，这些疾病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 疾病的基因诊断依赖于已知基因的测序面板来识别致病基因，但这种基于面板的测序的诊断率较低，因为许多疾病 基因尚未被识别。随着技术的进步和成本的降低，对患者的整个基因组（其 DNA 的完整序列）进行测序已成为可能。此外，可以从基因组序列数据中询问许多类型的遗传变异，不仅涉及单个碱基对，还包括基因组片段的更复杂的重复、缺失或转位以及基因之间区域（内含子）的变异。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，通过询问患者基因组来识别致病变异仍然面临许多挑战。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)，以识别 100,000 名患者的致病变异，目的是改善对这些患者的诊断，这也可能为他们的个性化治疗提供信息。由英国基因组公司运营的这些患者的测序现已完成，估计迄今为止已有四分之一的罕见病患者得到诊断。解决其余这些病例将需要研究界付出巨大努力来调查上述不同的变异类型。该提案旨在为关注肌肉骨骼和相关发育状况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致病性。除了 GeL 计划已收集的数据外，我们总是需要额外的临床或 X 射线数据。然而，这通常可以在医疗记录中找到，因此我们已经确定了检索此信息的途径，其中涉及临床医生和患者本身。我们建立了临床多学科团队来进行病例讨论，并将利用临床放射学评估方面的专业知识来确保对 X 射线数据进行系统分析。我们还会要求患者向我们提供自我报告的数据，正如我们从其他研究中了解到的那样，患者非常善于记住他们骨折的部位和时间。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，而这些模式是我们的手动检查无法识别的。我们预计这些努力将帮助我们为 100KGP 中的更多患者提供诊断，然后可用于 100KGP 中的其他疾病，为更多患者提供基因诊断。

项目成果

Conclusion of diagnostic odysseys due to inversions disrupting GLI3 and FBN1.

• DOI: 10.1136/jmg-2022-108753
• 发表时间: 2023-05
• 期刊: Journal of medical genetics
• 影响因子: 4

Continuous Patient State Attention Models

连续患者状态注意力模型

• DOI: 10.1101/2022.12.23.22283908
• 发表时间: 2022
• 作者: Chauhan V

Variable skeletal phenotypes associated with biallelic variants in PRKG2.

prkg2中与双重变体相关的可变骨骼表型。

• DOI: 10.1136/jmedgenet-2021-108027
• 发表时间: 2022-10
• 期刊: Journal of medical genetics
• 影响因子: 4

European Achondroplasia Forum guiding principles for the detection and management of foramen magnum stenosis.

• DOI: 10.1186/s13023-023-02795-2
• 发表时间: 2023-07-27
• 期刊: Orphanet journal of rare diseases
• 影响因子: 3.7

Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling

用于异质疾病进展建模的输入输出混合隐马尔可夫模型

• DOI: 10.1109/bhi56158.2022.9926903
• 发表时间: 2022
• 作者: Ceritli T