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VarPPUD:从一组优先级的强有力的候选变体中精确定位诊断变体

Rui Yin^1,2, Alba Gutiérrez-Sacristán¹,

¹Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.

PLoS computational biology

|September 22, 2025

概括

一种名为VarPPUD的新工具可以在罕见情况下更好地识别致病基因变异. 它的性能优于现有方法,有助于对具有挑战性的未诊断病例进行诊断.

科学领域:

遗传学是一个遗传学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

罕见和超罕见的遗传疾病影响全球人口的很大一部分.
这些疾病的准确诊断受到特定致病基因变异的挑战所阻碍.
目前的in silico变异病原性预测工具正在与复杂的未诊断病例作斗争.

研究的目的:

开发和验证VarPPUD,这是一种用于区分致病性遗传变异与其他有害变异的新型计算工具.
提高在具有挑战性的未诊断病例中变异性病原性预测的准确性,特别是未诊断疾病网络 (UDN) 中的病例.
提供一种更可靠的方法来识别病因变异,减少对广泛的手工分析和实验验证的需求.

主要方法:

VarPPUD是使用UDN病例的优先变体开发的,包括基因,氨基酸和核酸水平的特征.
该工具的性能通过对 UDN 案例的持久子集进行交叉验证来评估.
验证包括使用基于GAN的框架生成的合成数据和临时保留的UDN患者队列 (2022-2024年).

主要成果:

在具有挑战性的UDN案例中,VarPPUD实现了79.3%的交叉验证准确率和77.5%的精度.
与九种最先进的病原性预测工具相比,该工具的准确度平均提高了18.6%,精度提高了23.4%.
VarPPUD表现出强大的通用性,在新累积的病例中表现良好,在2021年后评估,没有再培训.

结论:

在复杂的遗传疾病中,VarPPUD显著提高了区分真实致病变异与其他有害变异的能力.
该工具在未被诊断的疾病中对变异性病原性预测的现有方法进行了大幅改进.
VarPPUD的特征重要性分析为新发现的致病变体的特征提供了洞察力,有助于未来的研究.

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