Quantifying concordant genetic effects of de novo mutations on multiple disorders.
Quantifying concordant genetic effects of de novo mutations on multiple disorders.
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量化从头突变对多种疾病的一致遗传影响。
DOI：
10.7554/elife.75551
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发表时间：
2022-06-06
期刊：
影响因子：
7.7
通讯作者：
Lu, Qiongshi
中科院分区：
文献类型：
--
作者：
Guo, Hanmin;Hou, Lin;Shi, Yu;Jin, Sheng Chih;Zeng, Xue;Li, Boyang;Lifton, Richard P.;Brueckner, Martina;Zhao, Hongyu;Lu, Qiongshi
对数以万计的父母-先证者三人组进行的外显子组测序发现了许多有害的从头突变（DNM）以及许多疾病的相关风险基因。最近的研究表明，多种疾病中 DNM 的共享基因和通路得到了丰富。然而，现有的分析策略仅关注对多种疾病达到统计显着性的基因，并且在每项研究中都需要大量的三重样本。因此，由于多基因性和不完全外显率，这些方法无法描述遗传共享的完整景观。在这项工作中，我们引入了 EncoreDNM，这是一种新颖的统计框架，用于量化两种以外显子组中 DNM 一致富集为特征的疾病之间的共享遗传效应。 EncoreDNM 利用全外显子组、摘要级 DNM 数据（包括在单一疾病分析中未达到统计显着性的基因）来评估两种疾病之间的整体和注释划分的遗传共享。将 EncoreDNM 应用于九种疾病的 DNM 数据，我们发现了丰富的成对富集相关性，特别是在不耐受致病突变的基因和在胎儿组织中高表达的基因中。这些结果表明 EncoreDNM 改进了当前的分析方法，并且可能在 DNM 研究中具有广泛的应用。
Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.