Suppr超能文献

用于预测人类疾病错义变异隐性遗传的集成和共识方法。

Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease.

作者信息

Petrazzini Ben O, Balick Daniel J, Forrest Iain S, Cho Judy, Rocheleau Ghislain, Jordan Daniel M, Do Ron

机构信息

The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard, Medical School, Boston, MA, USA.

出版信息

Cell Rep Methods. 2024 Dec 16;4(12):100914. doi: 10.1016/j.crmeth.2024.100914. Epub 2024 Dec 9.

Abstract

Mode of inheritance (MOI) is necessary for clinical interpretation of pathogenic variants; however, the majority of variants lack this information. Furthermore, variant effect predictors are fundamentally insensitive to recessive-acting diseases. Here, we present MOI-Pred, a variant pathogenicity prediction tool that accounts for MOI, and ConMOI, a consensus method that integrates variant MOI predictions from three independent tools. MOI-Pred integrates evolutionary and functional annotations to produce variant-level predictions that are sensitive to both dominant-acting and recessive-acting pathogenic variants. Both MOI-Pred and ConMOI show state-of-the-art performance on standard benchmarks. Importantly, dominant and recessive predictions from both tools are enriched in individuals with pathogenic variants for dominant- and recessive-acting diseases, respectively, in a real-world electronic health record (EHR)-based validation approach of 29,981 individuals. ConMOI outperforms its component methods in benchmarking and validation, demonstrating the value of consensus among multiple prediction methods. Predictions for all possible missense variants are provided in the "Data and code availability" section.

摘要

遗传模式(MOI)对于致病性变异的临床解释至关重要;然而,大多数变异缺乏这一信息。此外,变异效应预测因子对隐性疾病基本不敏感。在此,我们介绍了MOI-Pred,这是一种考虑了MOI的变异致病性预测工具,以及ConMOI,这是一种整合了来自三个独立工具的变异MOI预测的共识方法。MOI-Pred整合了进化和功能注释,以产生对显性和隐性致病性变异均敏感的变异水平预测。MOI-Pred和ConMOI在标准基准测试中均展现出了先进的性能。重要的是,在基于29981名个体的真实世界电子健康记录(EHR)验证方法中,这两种工具的显性和隐性预测分别在患有显性和隐性疾病的致病性变异个体中得到了富集。ConMOI在基准测试和验证中优于其组成方法,证明了多种预测方法之间达成共识的价值。“数据和代码可用性”部分提供了所有可能错义变异的预测结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/515a/11704621/3271827722a6/fx1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验