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variants that have been claimed to cause rare genetic diseases do not, often because the original evidence is now outdated and the variants have since been shown to be too common in the population to cause disease.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。不正确的解释会导致对遗传病的大量过度诊断，导致对个人的不适当治疗，并由于不必要的后续检测而增加医疗费用。不幸的是，不准确的遗传变异解释是一个关键且日益严重的问题，因为全基因组测序在生物医学科学和临床医学中越来越普遍，用更快但特异性较低的“dna优先”方法取代了标准的临床“疾病优先”诊断方法。此外，直接面向消费者的基因检测也大幅增加，导致许多具有重大临床意义的错误。我们的目标是通过利用新获得的高质量遗传数据与超过50万人的详细临床结果的独特强大组合来改善罕见遗传变异的解释。对遗传变异的错误解释主要有三个原因，这是由证据基础上的历史空白造成的。首先，许多被认为会导致罕见遗传病的基因变异并不会导致罕见遗传病，这通常是因为最初的证据现在已经过时，而且这些变异在人群中已经被证明太常见而不会导致疾病。第二，通过对具有特定病症的少数患者和家庭进行研究，确定导致遗传性遗传病的变异；由此得出的结论是，每个有这种变异的人都会患上这种疾病，但在很多情况下，这是不正确的。第三，原始发现队列的高度选择性意味着由特定遗传变异引起的全套疾病症状是未知的，并且可能受到家族史的偏见和其他家族疾病的混淆。我们的目标是通过使用新获得的大规模全基因组测序数据集来解决这一证据差距。我们将重点关注由数百个特定基因中的一个的单一罕见遗传变异引起的疾病的两个例子，在这些例子中，我们有专门的专业知识并可以获得适当的大规模疾病队列。我们将比较这些队列中致病变异的患病率与大规模人群队列中的患病率。具体来说，我们将使用来自英国生物银行（约500,000名参与者）、埃克塞特单基因糖尿病队列（约15,000例）和英国范围内的解码发育障碍研究（约13,500例）的数据集。这个庞大的高分辨率遗传数据集与详细的临床信息相结合，是无与伦比的，也是独一无二的。我们将包括与这些遗传疾病相关的所有罕见变异的评估，从最小的（单碱基）到最大的（整个染色体）变化。基于我们之前的工作，我们期望对具有特定致病变异的个体发展疾病的可能性进行可靠的估计，并扩展和完善与许多罕见遗传变异相关的疾病症状。我们也希望反驳以前在文献中错误的疾病遗传原因。最后，我们将检验这一假设，即群体之间共同遗传因素的差异是导致疾病发生和严重程度差异的原因。这项工作将为临床中的遗传变异解释提供信息，减少遗传过度诊断，特别是偶然发现，并促进精准医学的实施。我们的发现将对受遗传疾病影响的患者和家庭以及接受基因检测的公众成员产生直接影响，并将提供有关单基因疾病本质的新见解。

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

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

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

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

用于小框内插入缺失分类的计算机致病性预测工具的评估

• DOI: 10.1101/2022.10.27.22281598
• 发表时间: 2022
• 作者: Cannon S