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荷兰基因组诊断实验室通过共享数据,加速和改进了变异解释,提高了准确性。

Dutch genome diagnostic laboratories accelerated and improved variant interpretation and increased accuracy by sharing data.

机构信息

Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.

Genomics Coordination Center & Department of Genetics, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands.

出版信息

Hum Mutat. 2019 Dec;40(12):2230-2238. doi: 10.1002/humu.23896. Epub 2019 Sep 3.

DOI:10.1002/humu.23896
PMID:31433103
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6900155/
Abstract

Each year diagnostic laboratories in the Netherlands profile thousands of individuals for heritable disease using next-generation sequencing (NGS). This requires pathogenicity classification of millions of DNA variants on the standard 5-tier scale. To reduce time spent on data interpretation and increase data quality and reliability, the nine Dutch labs decided to publicly share their classifications. Variant classifications of nearly 100,000 unique variants were catalogued and compared in a centralized MOLGENIS database. Variants classified by more than one center were labeled as "consensus" when classifications agreed, and shared internationally with LOVD and ClinVar. When classifications opposed (LB/B vs. LP/P), they were labeled "conflicting", while other nonconsensus observations were labeled "no consensus". We assessed our classifications using the InterVar software to compare to ACMG 2015 guidelines, showing 99.7% overall consistency with only 0.3% discrepancies. Differences in classifications between Dutch labs or between Dutch labs and ACMG were mainly present in genes with low penetrance or for late onset disorders and highlight limitations of the current 5-tier classification system. The data sharing boosted the quality of DNA diagnostics in Dutch labs, an initiative we hope will be followed internationally. Recently, a positive match with a case from outside our consortium resulted in a more definite disease diagnosis.

摘要

每年,荷兰的诊断实验室都使用下一代测序(NGS)对数千人进行遗传性疾病的分析。这需要在标准的 5 级分类尺度上对数百万个 DNA 变体进行致病性分类。为了减少数据解释所花费的时间,提高数据质量和可靠性,9 家荷兰实验室决定公开分享他们的分类。将近 100,000 个独特变体的变体分类被编目并在集中的 MOLGENIS 数据库中进行比较。当分类意见一致时,由多个中心分类的变体被标记为“共识”,并与 LOVD 和 ClinVar 国际共享。当分类意见相左(LB/B 与 LP/P)时,被标记为“冲突”,而其他非共识观察结果则被标记为“无共识”。我们使用 InterVar 软件对我们的分类进行了评估,与 ACMG 2015 指南进行了比较,显示总体一致性为 99.7%,仅相差 0.3%。荷兰实验室之间或荷兰实验室与 ACMG 之间的分类差异主要存在于外显率低或发病晚的基因中,这突显了当前 5 级分类系统的局限性。数据共享提高了荷兰实验室的 DNA 诊断质量,我们希望这一举措能在国际上得到效仿。最近,与我们联盟外的一个病例的匹配结果导致了更明确的疾病诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d3d/6900155/08922cd81841/HUMU-40-2230-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d3d/6900155/c1623d7f7892/HUMU-40-2230-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d3d/6900155/5d9264027ce8/HUMU-40-2230-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d3d/6900155/08922cd81841/HUMU-40-2230-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d3d/6900155/c1623d7f7892/HUMU-40-2230-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d3d/6900155/5d9264027ce8/HUMU-40-2230-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d3d/6900155/08922cd81841/HUMU-40-2230-g003.jpg

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本文引用的文献

1
Proposition of adjustments to the ACMG-AMP framework for the interpretation of MEN1 missense variants.对 ACMG-AMP 框架进行调整以解释 MEN1 错义变异的建议。
Hum Mutat. 2019 Jun;40(6):661-674. doi: 10.1002/humu.23746. Epub 2019 Mar 28.
2
MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians.MOLGENIS 研究:面向非生物信息学家的高级生物信息学数据软件。
Bioinformatics. 2019 Mar 15;35(6):1076-1078. doi: 10.1093/bioinformatics/bty742.
3
ClinGen's RASopathy Expert Panel consensus methods for variant interpretation.
通过数据推动医疗保健发展:BETTER项目对分布式分析的愿景。
Front Med (Lausanne). 2024 Oct 2;11:1473874. doi: 10.3389/fmed.2024.1473874. eCollection 2024.
4
Comprehensive EHMT1 variants analysis broadens genotype-phenotype associations and molecular mechanisms in Kleefstra syndrome.全面的 EHMT1 变异分析拓宽了 Kleefstra 综合征的基因型-表型关联和分子机制。
Am J Hum Genet. 2024 Aug 8;111(8):1605-1625. doi: 10.1016/j.ajhg.2024.06.008. Epub 2024 Jul 15.
5
SpadaHC: a database to improve the classification of variants in hereditary cancer genes in the Spanish population.SpadaHC:一个提高西班牙人群中遗传性癌症基因变异分类的数据库。
Database (Oxford). 2024 Jul 4;2024. doi: 10.1093/database/baae055.
6
Disrupted protein interaction dynamics in a genetic neurodevelopmental disorder revealed by structural bioinformatics and genetic code expansion.结构生物信息学和遗传密码扩展揭示了遗传神经发育障碍中的蛋白质相互作用动力学紊乱。
Protein Sci. 2024 Apr;33(4):e4953. doi: 10.1002/pro.4953.
7
Genome sequencing as a generic diagnostic strategy for rare disease.基因组测序作为一种罕见病的通用诊断策略。
Genome Med. 2024 Feb 14;16(1):32. doi: 10.1186/s13073-024-01301-y.
8
Computational prediction of human deep intronic variation.人类深内含子变异的计算预测。
Gigascience. 2022 Dec 28;12. doi: 10.1093/gigascience/giad085. Epub 2023 Oct 25.
9
Molecular Analysis and Reclassification of NSD1 Gene Variants in a Cohort of Patients with Clinical Suspicion of Sotos Syndrome.对临床疑似 Sotos 综合征患者队列中的 NSD1 基因突变进行分子分析和重新分类。
Genes (Basel). 2023 Jan 22;14(2):295. doi: 10.3390/genes14020295.
10
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Am J Hum Genet. 2022 Nov 3;109(11):1960-1973. doi: 10.1016/j.ajhg.2022.10.006.
ClinGen 的 RASopathy 专家小组用于变异解释的共识方法。
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Genet Med. 2018 Mar;20(3):351-359. doi: 10.1038/gim.2017.218. Epub 2018 Jan 4.
5
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6
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Hum Genet. 2017 Jun;136(6):665-677. doi: 10.1007/s00439-017-1779-6. Epub 2017 Mar 27.
7
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Am J Hum Genet. 2017 Feb 2;100(2):267-280. doi: 10.1016/j.ajhg.2017.01.004. Epub 2017 Jan 26.
8
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Nature. 2016 Aug 18;536(7616):285-91. doi: 10.1038/nature19057.
9
GENOMICS. A federated ecosystem for sharing genomic, clinical data.基因组学。一个用于共享基因组和临床数据的联合生态系统。
Science. 2016 Jun 10;352(6291):1278-80. doi: 10.1126/science.aaf6162.
10
The Ensembl Variant Effect Predictor.Ensembl变异效应预测器。
Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.