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基于 4577 个分子特征明确的家系,鉴定因果变异时陷阱的诊断意义。

Diagnostic implications of pitfalls in causal variant identification based on 4577 molecularly characterized families.

机构信息

Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia.

Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.

出版信息

Nat Commun. 2023 Aug 29;14(1):5269. doi: 10.1038/s41467-023-40909-3.

DOI:10.1038/s41467-023-40909-3
PMID:37644014
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10465531/
Abstract

Despite large sequencing and data sharing efforts, previously characterized pathogenic variants only account for a fraction of Mendelian disease patients, which highlights the need for accurate identification and interpretation of novel variants. In a large Mendelian cohort of 4577 molecularly characterized families, numerous scenarios in which variant identification and interpretation can be challenging are encountered. We describe categories of challenges that cover the phenotype (e.g. novel allelic disorders), pedigree structure (e.g. imprinting disorders masquerading as autosomal recessive phenotypes), positional mapping (e.g. double recombination events abrogating candidate autozygous intervals), gene (e.g. novel gene-disease assertion) and variant (e.g. complex compound inheritance). Overall, we estimate a probability of 34.3% for encountering at least one of these challenges. Importantly, our data show that by only addressing non-sequencing-based challenges, around 71% increase in the diagnostic yield can be expected. Indeed, by applying these lessons to a cohort of 314 cases with negative clinical exome or genome reports, we could identify the likely causal variant in 54.5%. Our work highlights the need to have a thorough approach to undiagnosed diseases by considering a wide range of challenges rather than a narrow focus on sequencing technologies. It is hoped that by sharing this experience, the yield of undiagnosed disease programs globally can be improved.

摘要

尽管进行了大规模的测序和数据共享工作,但以前确定的致病性变异仅占孟德尔疾病患者的一部分,这突出表明需要准确识别和解释新的变异。在一个包含 4577 个分子特征明确的家族的大型孟德尔队列中,遇到了许多变体识别和解释具有挑战性的情况。我们描述了涵盖表型(例如新的等位基因疾病)、家系结构(例如表现为常染色体隐性表型的印迹疾病)、定位映射(例如消除候选自身纯合子区间的双重重组事件)、基因(例如新的基因-疾病关联)和变体(例如复杂的复合遗传)的挑战类别。总体而言,我们估计至少会遇到这些挑战之一的概率为 34.3%。重要的是,我们的数据表明,仅解决非测序相关的挑战,就可以预期诊断率提高约 71%。事实上,通过将这些经验教训应用于一组 314 例临床外显子或基因组报告呈阴性的病例,我们可以确定其中 54.5%的可能的因果变异。我们的工作强调,需要通过考虑广泛的挑战,而不是狭隘地关注测序技术,对未确诊的疾病采取全面的方法。希望通过分享这一经验,可以提高全球未确诊疾病计划的诊断率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/5d77045b2383/41467_2023_40909_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/20c09a5a85b5/41467_2023_40909_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/32a6ddaf28f2/41467_2023_40909_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/bbbc8af0950d/41467_2023_40909_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/31c9aac6139d/41467_2023_40909_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/5d77045b2383/41467_2023_40909_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/20c09a5a85b5/41467_2023_40909_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/32a6ddaf28f2/41467_2023_40909_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/bbbc8af0950d/41467_2023_40909_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/31c9aac6139d/41467_2023_40909_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e917/10465531/5d77045b2383/41467_2023_40909_Fig5_HTML.jpg

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