通过整合基因表达数据确定与疾病相关的罕见变异的优先次序

Hanmin Guo, Alexander Eckehart Urban, Wing Hung Wong
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引用次数: 0

摘要

摘要 罕见变异占人类基因变异的绝大多数,与常见变异相比,罕见变异可能对人类疾病产生更大的有害影响。在此,我们提出了载体统计法,这是一种通过整合基因表达数据来优先排序与疾病相关的罕见变异的统计框架。通过量化罕见变异对基因表达的影响,carrier statistic 可以优先选择那些对患病患者有较大功能影响的罕见变异。通过模拟研究和对真实多组学数据集的分析,我们证明了载体统计法适用于样本量有限(几百个)的研究,而且灵敏度大大高于现有的罕见变异关联方法。在阿尔茨海默病中的应用揭示了 15 个基因中有 16 个罕见变异具有极高的载体统计量。我们还发现,与健康人相比,患病病人的优先基因中罕见变异极多。载体统计方法可应用于各种罕见变异类型,并可适用于其他 Omics 数据模式,为研究复杂疾病的分子机制提供了一个强大的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prioritizing disease-related rare variants by integrating gene expression data
Abstract Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.
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