Assessing the pathogenicity, penetrance and expressivity of monogenic disease variants using large-scale population-based cohorts

使用大规模人群队列评估单基因疾病变异的致病性、外显率和表达性

• 批准号: MR/T00200X/1
• 负责人: Caroline Wright
• 金额: $ 82.62万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT00200X%2F1
• 关键词: Assessing; pathogenicity; penetrance; expressivity; monogenic

Interpreting the medical consequences of genetic variants in individuals is currently extremely challenging. Incorrect interpretation leads to massive overdiagnosis of genetic conditions, resulting in inappropriate treatment of individuals and increased healthcare costs due to unnecessary follow-on tests. Unfortunately, inaccurate genetic variant interpretation is a critical and growing problem because whole genome sequencing is becoming widespread throughout biomedical science and clinical medicine, replacing the standard clinical "disease-first" approach to diagnosis with a faster but less specific "DNA-first" approach. In addition, there has been a substantial increase in direct-to-consumer genetic testing resulting in numerous errors with major clinical implications. We aim to improve the interpretation of rare genetic variants by harnessing a uniquely powerful combination of newly available high-quality genetic data coupled with detailed clinical results on over half a million individuals.There are three main reasons for the incorrect interpretation of genetic variants, caused by historical gaps in the evidence base. First, many genetic variants that have been claimed to cause rare genetic diseases do not, often because the original evidence is now outdated and the variants have since been shown to be too common in the population to cause disease. Second, variants that cause inherited genetic diseases are identified by studying highly-selected, small groups of patients and families with a specific condition; this leads to the conclusion that every individual with the variant will get the condition, which in many cases is untrue. Third, the highly selected nature of the original discovery cohorts means that the complete set of disease symptoms caused by a particular genetic variant is unknown, and can be biased by family history and confounded by other familial diseases.We aim to address this evidence-gap by using newly available large-scale genome-wide sequencing datasets. We will focus on two examples of diseases caused by single rare genetic variants in one of hundreds of specific genes, where we have specific expertise and access to appropriate large-scale disease cohorts. We will compare the prevalence of disease-causing variants in these cohorts to that in a large-scale population cohort. Specifically, we will use datasets from UK Biobank (~500,000 participants), the Exeter-based monogenic diabetes cohort (~15,000 cases), and the UK-wide Deciphering Developmental Disorders Study (~13,500 cases). This enormous collection of high-resolution genetic data coupled with detailed clinical information is unparalleled and uniquely powerful. We will include evaluation of all rare variants linked with these genetic diseases, from the smallest (single base) to the largest (whole chromosome) changes. Based on our prior work, we anticipate producing robust estimates of how likely an individual with a particular disease-causing variant is to develop disease, and to expand and refine the disease symptoms associated with many rare genetic variants. We also expect to refute previous erroneous genetic causes of disease in the literature. Finally, we will test the hypothesis that differences in common genetic factors between the cohorts are responsible for disparities in disease occurrence and severity. This work will inform genetic variant interpretation in the clinic, reduce genetic overdiagnosis particularly from incidental findings, and facilitate the implementation of precision medicine. Our findings will have a direct impact on patients and families affected by genetic diseases, as well as members of the public undergoing genetic testing, and will provide novel insights about the nature of monogenic disease.

解释个体遗传变异的医学后果目前极具挑战性。错误的解释导致遗传状况的大规模过度诊断，导致对个体的治疗不当，并且由于不必要的后续测试而增加了医疗费用。不幸的是，不准确的遗传变异解释是一个至关重要的问题，因为整个基因组测序在整个生物医学科学和临床医学过程中都变得广泛，取代了标准的临床“疾病优先”诊断方法，以更快但更具体的“ DNA-FIRS”方法。此外，直接到消费者的基因检测已经大幅增加，导致许多错误具有重大临床意义。我们旨在通过利用新近可用的高质量遗传数据的独特强大组合以及对超过半百万个人的详细临床结果来改善稀有遗传变异的解释。有三个主要原因是对遗传变异的不正确解释，这是由于证据库中的历史差距而引起的。首先，许多据称引起罕见遗传疾病的遗传变异通常不会因为原始证据已经过时了，并且此后已证明这种变体在人群中非常普遍，无法引起疾病。其次，通过研究具有特定状况的高度选择的患者和家庭来鉴定引起遗传疾病的变体。这得出的结论是，每个具有变体的人都会得到这种状况，在许多情况下，这是不真实的。第三，原始发现人群的高度选择的性质意味着，由特定遗传变异型引起的完整疾病症状是未知的，并且可能因家族史而偏见，并被其他家族疾病混淆。我们旨在通过使用新近可用的大型全基因组测序数据集来解决这一证据隙。我们将重点关注由数百个特定基因之一中的单个稀有遗传变异引起的疾病的两个例子，在该基因中，我们具有特定的专业知识并获得适当的大规模疾病同龄人。我们将比较这些队列中引起疾病​​的变体的流行与大规模人群中的流行率。具体来说，我们将使用来自英国生物库（约500,000名参与者），基于埃克塞特的单基因糖尿病队列（约15,000例）的数据集和英国范围内的解密性发育障碍研究（约13,500例）。这种高分辨率的遗传数据和详细的临床信息的巨大收集是无与伦比的，并且具有独特的功能。我们将包括对与这些遗传疾病相关的所有稀有变体的评估，从最小（单碱基）到最大（整个染色体）变化。根据我们的先前工作，我们预计会产生强大的估计，即患有特定疾病变异的人的可能性是发展疾病的可能性，并扩大与许多罕见遗传变异相关的疾病症状。我们还期望在文献中反驳以前的错误遗传原因。最后，我们将检验以下假设：同类群之间常见遗传因素的差异是导致疾病发生和严重程度的差异。这项工作将为临床中的遗传变异解释提供依据，减少遗传过度诊断，尤其是在偶然发现中，并促进精确医学的实施。我们的发现将直接影响受遗传疾病影响的患者和家庭，以及接受基因检测的公众，并将提供有关单基因疾病性质的新见解。

项目成果

Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels.

• DOI: 10.1186/s12920-023-01454-6
• 发表时间: 2023-02-28
• 期刊: BMC medical genomics
• 影响因子: 2.7

Estimating diagnostic noise in panel-based genomic analysis

估计基于面板的基因组分析中的诊断噪声

• DOI: 10.1101/2022.03.18.22272595
• 发表时间: 2022
• 作者: Beaumont R

Prevalence of Fabry disease-causing variants in the UK Biobank.

• DOI: 10.1136/jmg-2022-108523
• 发表时间: 2023-04
• 期刊: Journal of medical genetics
• 影响因子: 4

Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance

与单基因疾病相关的基因中预测的功能丧失变异的聚类可以解释不完全外显率

• DOI: 10.1101/2023.10.11.23296535
• 发表时间: 2023
• 作者: Beaumont R

Large-scale blood mitochondrial genome-wide study provides novel insights into mitochondrial disease-related traits

• DOI: 10.1101/2023.06.12.23291273
• 发表时间: 2023-06-13
• 期刊: medRxiv
• 作者: Cannon，S.;Hall，T.;Patel，K. A.
• 通讯作者: Patel，K. A.