Missense variants pathogenicity annotation from homologous proteins.

Gabriel Ruiz-Alías, Sergi Soldevila, Xavier Altafaj, Arnau Cordomí, Mireia Olivella
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Abstract

Motivation: High-throughput DNA sequencing has revealed millions of single nucleotide variants (SNVs) in the human genome, with a small fraction linked to disease. The effect of missense variants, which alter the protein sequence, is particularly challenging to interpret due to the scarcity of clinical annotations and experimental information. While using conservation and structural information, current prediction tools still struggle to predict variant pathogenicity. In this study, we explored the pathogenicity of homologous missense variants-variants in equivalent positions across homologous proteins-focusing on proteins involved in autosomal dominant diseases.

Results: Our analysis of 2976 pathogenic and 17 555 non-pathogenic homologous variants demonstrated that pathogenicity can be extrapolated with 95% accuracy within a family, or up to 98% for closer homologs. Remarkably, the evaluation of 27 commonly used mutation predictor methods revealed that they were not fully capturing this biological feature. To facilitate the exploration of homologous variants, we created HomolVar, a web server that computationally predicts the pathogenesis of missense variants using annotations from homologous variants, freely available at https://rarevariants.org/HomolVar. Overall, these findings and the accompanying tool offer a robust method for predicting the pathogenicity of unannotated variants, enhancing genotype-phenotype correlations, and contributing to diagnosing rare genetic disorders.

Availability and implementation: HomolVar is freely available at https://rarevariants.org/HomolVar.

同源蛋白的错义变异致病性注释。
动机:高通量DNA测序揭示了人类基因组中数百万的单核苷酸变异(snv),其中一小部分与疾病有关。由于缺乏临床注释和实验信息,改变蛋白质序列的错义变异的影响尤其具有挑战性。虽然使用保守性和结构信息,但目前的预测工具仍然难以预测变异的致病性。在这项研究中,我们探讨了同源错义变异的致病性——同源蛋白中相等位置的变异——重点关注与常染色体显性疾病有关的蛋白质。结果:我们对2976个致病性和17555个非致病性同源变异的分析表明,在一个家族中推断致病性的准确率为95%,对于更接近的同源变异,推断致病性的准确率高达98%。值得注意的是,对27种常用突变预测方法的评估显示,它们没有完全捕捉到这种生物学特征。为了促进同源变异的探索,我们创建了HomolVar,这是一个web服务器,可以使用同源变异的注释来计算预测错义变异的发病机制,可以在https://rarevariants.org/HomolVar免费获得。总的来说,这些发现和伴随的工具提供了一个强大的方法来预测未注释的变异的致病性,增强基因型-表型相关性,并有助于诊断罕见的遗传疾病。可获得性:HomolVar可在https://rarevariants.org/HomolVar.Supplementary免费获得信息;补充数据可在Bioinformatics在线获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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