Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease.
Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease.
复制标题
传播和新生变异的综合建模确定了先天性心脏病的新风险基因。
DOI：
10.15302/j-qb-021-0248
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发表时间：
2021-06
影响因子：
3.1
通讯作者：
Zhao, Hongyu
中科院分区：
文献类型：
--
作者：
Li, Mo;Zeng, Xue;Wet, Chentian;Jin, Sheng Chih;Dong, Weilai;Brueckner, Martina;Lifton, Richard;Lu, Qiongshi;Zhao, Hongyu
全外显子组测序（WES）研究已经在先天性心脏病（CHD）先显子中发现了多个富集新生突变（dnm）的基因。然而，由于冠心病的病因异质性和每个基因的低突变率，仅基于dnm的风险基因识别在统计上仍然具有挑战性。在本文中，我们介绍了一个层次贝叶斯框架的基因水平关联测试，联合分析新发和罕见的传播变异。通过对多种类型的遗传变异、基因水平注释和来自大群体队列的参考数据的综合建模，我们的方法准确地表征了新生和传播变异的预期频率，与仅基于dnm的分析相比，显示出更高的统计能力。我们的方法应用于2645例冠心病先证父母三人组的WES数据，鉴定出15个重要基因，其中一半是新的基因，为冠心病的遗传基础提供了新的见解。这些结果显示了对疾病基因发现的传播和新生变异进行综合分析的力量。
Whole-exome sequencing (WES) studies have identified multiple genes enriched for de novo mutations (DNMs) in congenital heart disease (CHD) probands. However, risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene. In this manuscript, we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted variants. Through integrative modeling of multiple types of genetic variants, gene-level annotations, and reference data from large population cohorts, our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only. Applied to WES data of 2,645 CHD proband-parent trios, our method identified 15 significant genes, half of which are novel, leading to new insights into the genetic bases of CHD. These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.
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影响因子：
30.8
作者：
Krumm, Niklas;Turner, Tychele N.;Baker, Carl;Vives, Laura;Mohajeri, Kiana;Witherspoon, Kali;Raja, Archana;Coe, Bradley P.;Stessman, Holly A.;He, Zong-Xiao;Leal, Suzanne M.;Bernier, Raphael;Eichler, Evan E.
通讯作者：
Eichler, Evan E.
影响因子：
64.8
作者：
Kong A;Frigge ML;Masson G;Besenbacher S;Sulem P;Magnusson G;Gudjonsson SA;Sigurdsson A;Jonasdottir A;Jonasdottir A;Wong WS;Sigurdsson G;Walters GB;Steinberg S;Helgason H;Thorleifsson G;Gudbjartsson DF;Helgason A;Magnusson OT;Thorsteinsdottir U;Stefansson K
通讯作者：
Stefansson K
影响因子：
64.8
作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
通讯作者：
MacArthur, Daniel G
影响因子：
4.5
作者：
Chahrour MH;Yu TW;Lim ET;Ataman B;Coulter ME;Hill RS;Stevens CR;Schubert CR;ARRA Autism Sequencing Collaboration;Greenberg ME;Gabriel SB;Walsh CA
通讯作者：
Walsh CA
影响因子：
64.8
作者：
Garg, V;Muth, AN;Srivastava, D
通讯作者：
Srivastava, D