Abstract：
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.
摘要：
对数以万计的亲本-先证者三重奏的外显子组测序已经鉴定出许多有害的从头突变(DNM)和涉及许多疾病的风险基因。最近的研究表明，共有的基因和途径在多种疾病中富含DNM。然而,现有的分析策略仅关注对多种疾病具有统计学意义的基因，并且在每项研究中需要大量的三重样本。因此,由于多源性和不完整的外显率，这些方法无法表征遗传共享的全部景观。在这项工作中,我们介绍EncoreDNM，一种新的统计框架，用于量化两种疾病之间的共同遗传效应，其特征是外显子组中DNM的一致富集。EncoreDNM利用全外显子组，汇总级DNM数据，包括在单一疾病分析中没有达到统计学意义的基因，评估两种疾病之间的总体和注释分区遗传共享。将EncoreDNM应用于九种疾病的DNM数据，我们确定了丰富的成对富集相关性，特别是对致病突变不耐受的基因和在胎儿组织中高表达的基因。这些结果表明，EncoreDNM改进了当前的分析方法，并可能在DNM研究中具有广泛的应用。
