清晰
医学遗传学
计算机科学
相关性(法律)
文档
基因检测
自然语言处理
数据挖掘
生物信息学
计算生物学
机器学习
情报检索
人工智能
医学
遗传学
生物
基因
程序设计语言
内科学
生物化学
政治学
法学
作者
Michael T. Parsons,Miguel de la Hoya,Marcy E. Richardson,Emma Tudini,Michael G. Anderson,Windy Berkofsky‐Fessler,Sandrine M. Caputo,Raymond C. Chan,Melissa C. Cline,Bing Feng,Cristina Fortuño,E. Gómez,Johanna Hadler,Susan Hiraki,Megan Holdren,Claude Houdayer,Kathleen S. Hruska,Paul A. James,Rachid Karam,Huei San Leong
出处
期刊:Cold Spring Harbor Laboratory - medRxiv
日期:2024-01-23
被引量:3
标识
DOI:10.1101/2024.01.22.24301588
摘要
Abstract The ENIGMA research consortium ( https://enigmaconsortium.org/ ) develops and applies methods to determine clinical significance of variants in Hereditary Breast and Ovarian Cancer genes. An ENIGMA BRCA1/2 classification sub-group, originally formed in 2016 as a ClinGen external expert panel, evolved into a ClinGen internal Variant Curation Expert Panel (VCEP) to align with Federal Drug Administration recognized processes for ClinVar contributions. The VCEP reviewed American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) classification criteria for relevance to interpreting BRCA1 and BRCA2 variants. Statistical methods were used to calibrate evidence strength for different data types. Pilot specifications were tested on 40 variants, and documentation revised for clarity and ease-of-use. The original criterion descriptions for 13 evidence codes were considered non-applicable or overlapping with other criteria. Scenario of use was extended or re-purposed for eight codes. Extensive analysis and/or data review informed specification descriptions and weights for all codes. Specifications were applied to pilot variants with pre-existing ClinVar classification as follows: 13 Uncertain Significance or Conflicting, 14 Pathogenic and/or Likely Pathogenic, and 13 Benign and/or Likely Benign. Review resolved classification for 11/13 Uncertain Significance or Conflicting variants, and retained or improved confidence in classification for the remaining variants. Alignment of pre-existing ENIGMA research classification processes with ACMG/AMP classification guidelines highlighted several gaps in both the research processes and the baseline ACMG/AMP criteria. Calibration of evidence types was key to justify utility and strength of different evidence types for gene-specific application. The gene-specific criteria demonstrated value for improving ACMG/AMP-aligned classification of BRCA1 and BRCA2 variants.
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