Debora S. Marks研究组揭示了人类疾病遗传学的全蛋白质组模型。2025年11月24日出版的《自然—遗传学》杂志发表了这项成果。

为了解决这一知识缺口，该课题组人员开发了popEVE，这是一种结合进化和人类种群数据的深度生成模型，用于在蛋白质组范围内估计变异缺失。popEVE实现了最先进的性能，而没有高估deleterothem变体的负担，并在严重发育障碍队列中识别了442个基因的变体，包括123个新的候选基因。这些基因在功能上与已知的疾病基因相似，它们的变异通常局限于关键区域。值得注意的是，popEVE可以优先考虑仅以儿童外显子组为主题的可能的遗传变异，即使没有亲代测序也可以进行诊断。这项工作为罕见病变异解释提供了一个可推广的框架，特别是在单例病例中，并展示了校准的、进化知情的临床基因组学评分模型的实用性。popEVE是一个蛋白质组深度生成模型，用于识别和预测错义突变的致病性。

研究人员表示，错义变异由于其微妙和环境依赖的影响，仍然是遗传解释的一个挑战。虽然目前的预测模型在已知的疾病基因中表现良好，但它们的评分并没有在蛋白质组中进行校准，限制了通用性。

附：英文原文

Title: Proteome-wide model for human disease genetics

Author: Orenbuch, Rose, Shearer, Courtney A., Kollasch, Aaron W., Spinner, Aviv D., Hopf, Thomas, van Niekerk, Lood, Franceschi, Dinko, Dias, Mafalda, Frazer, Jonathan, Marks, Debora S.

Issue&Volume: 2025-11-24

Abstract: Missense variants remain a challenge in genetic interpretation owing to their subtle and context-dependent effects. Although current prediction models perform well in known disease genes, their scores are not calibrated across the proteome, limiting generalizability. To address this knowledge gap, we developed popEVE, a deep generative model combining evolutionary and human population data to estimate variant deleteriousness on a proteome-wide scale. popEVE achieves state-of-the-art performance without overestimating the burden of deleterious variants and identifies variants in 442 genes in a severe developmental disorder cohort, including 123 novel candidates. These genes are functionally similar to known disease genes, and their variants often localize to critical regions. Remarkably, popEVE can prioritize likely causal variants using only child exomes, enabling diagnosis even without parental sequencing. This work provides a generalizable framework for rare disease variant interpretation, especially in singleton cases, and demonstrates the utility of calibrated, evolution-informed scoring models for clinical genomics. popEVE is a proteome-wide deep generative model to identify and predict pathogenicity of missense mutations causing genetic disorders.

DOI: 10.1038/s41588-025-02400-1

Source: https://www.nature.com/articles/s41588-025-02400-1