根据推断人类特征的能力对计算变异效应预测器进行标杆分析。

IF 12.3 1区 生物学 Q1 Agricultural and Biological Sciences
Daniel R Tabet, Da Kuang, Megan C Lancaster, Roujia Li, Karen Liu, Jochen Weile, Atina G Coté, Yingzhou Wu, Robert A Hegele, Dan M Roden, Frederick P Roth
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引用次数: 0

摘要

背景:计算变异效应预测器为解释人类遗传变异提供了一种可扩展且日益可靠的方法,但循环性和偏倚问题限制了以往评估和比较预测器的方法。未被用于预测器训练的基因分型和表型参与者的群体级队列有助于对现有方法进行无偏见的基准测试。我们利用一组已报告的罕见变异负担关联的人类基因-性状关联,评估了英国生物库和 "我们所有人 "队列中 24 个计算变异效应预测因子与相关人类性状的相关性:结果:在根据英国生物库和 "我们所有人 "参与者的罕见错义变异推断人类特征方面,AlphaMissense优于所有其他预测因子。在这两个队列中,计算变异效应预测因子的总体排名显示出显著的正相关性:我们描述了一种评估计算变异效应预测因子的方法,它避免了以往评估的局限性。这种方法适用于未来的预测因子,并能继续为个人和临床遗传学的预测因子选择提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Benchmarking computational variant effect predictors by their ability to infer human traits.

Background: Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts.

Results: AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation.

Conclusion: We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

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来源期刊
Genome Biology
Genome Biology BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
25.50
自引率
3.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Genome Biology is a leading research journal that focuses on the study of biology and biomedicine from a genomic and post-genomic standpoint. The journal consistently publishes outstanding research across various areas within these fields. With an impressive impact factor of 12.3 (2022), Genome Biology has earned its place as the 3rd highest-ranked research journal in the Genetics and Heredity category, according to Thomson Reuters. Additionally, it is ranked 2nd among research journals in the Biotechnology and Applied Microbiology category. It is important to note that Genome Biology is the top-ranking open access journal in this category. In summary, Genome Biology sets a high standard for scientific publications in the field, showcasing cutting-edge research and earning recognition among its peers.
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