M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
复制标题
DOI：
10.1371/journal.pgen.1009849
复制
发表时间：
2021-11
期刊：
影响因子：
4.5
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Xie Y;Li M;Dong W;Jiang W;Zhao H
最近的研究表明，基于新生突变（dnm）的发现，多种早发性疾病具有共同的风险基因。因此，我们可以利用一种性状的信息来提高统计能力，以识别另一种性状的基因。然而，能够从多个性状中联合分析dna的方法很少。在本研究中，我们开发了一个名为M-DATA （Multi-trait framework for De novo mutation Association Test with Annotations）的框架，通过整合多个相关性状及其功能注释的数据来提高关联分析的统计能力。利用来自多种疾病的dna数量，我们开发了一种基于期望最大化算法的方法，既可以推断两种疾病之间的关联程度，又可以估计每种疾病的基因关联概率。我们将我们的方法应用于一个联合分析先天性心脏病（CHD）和自闭症数据的案例研究。我们的方法能够通过联合分析鉴定出23个冠心病基因，其中包括12个新基因，这大大超过了单性状分析，从而为冠心病病因学提供了新的见解。随着新一代测序技术的发展，可以识别具有有害影响的种系突变，如新生突变（dnm），以帮助发现先天性心脏病（CHD）等早期发病疾病的遗传原因。然而，由于招募和测序样本的成本较高，DNM研究的样本量较小，并且DNM的罕见性导致其发生率较低，因此统计能力仍然受到限制。与其他疾病的DNM分析相比，考虑到冠心病的遗传异质性，DNM分析更具挑战性。最近的研究表明，基于dnm的发现，早发性神经发育疾病和冠心病之间存在共同的疾病机制。目前，能够对多性状的DNM数据进行联合分析的方法很少。因此，我们开发了一个框架来同时识别DNM数据中多个性状的风险基因。将该方法应用于冠心病和自闭症的案例研究中，证明了与单性状分析相比，该方法在识别风险基因方面的能力有所提高。我们的研究结果为冠心病的病因学以及冠心病与自闭症之间的共同病因机制提供了新的见解。
Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology. With the development of new generation sequencing technology, germline mutations such as de novo mutations (DNMs) with deleterious effects can be identified to aid in discovering the genetic causes for early on-set diseases such as congenital heart disease (CHD). However, the statistical power is still limited by the small sample size of DNM studies due to the high cost of recruiting and sequencing samples, and the low occurrence of DNMs given its rarity. Compared to DNM analyses for other diseases, it is even more challenging for CHD given its genetic heterogeneity. Recent research has suggested shared disease mechanisms between early-onset neurodevelopmental diseases and CHD based on findings from DNMs. Currently, there are few methods that can jointly analyze DNM data on multiple traits. Therefore, we develop a framework to identify risk genes for multiple traits simultaneously for DNM data. The new method is applied to CHD and autism as a case study to demonstrate its improved power in identifying risk genes compared with single-trait analyses. Our results lead to new insights on the disease etiology of CHD, and the shared etiological mechanisms between CHD and autism.
登录
查看更多内容
影响因子：
4.5
作者：
Albert M;Schmitz SU;Kooistra SM;Malatesta M;Morales Torres C;Rekling JC;Johansen JV;Abarrategui I;Helin K
通讯作者：
Helin K
从 NPdenovo 数据库中发现的四种神经精神疾病共有具有从头突变的基因
影响因子：
11
作者：
Li J;Cai T;Jiang Y;Chen H;He X;Chen C;Li X;Shao Q;Ran X;Li Z;Xia K;Liu C;Sun ZS;Wu J
通讯作者：
Wu J
影响因子：
30.8
作者：
Coe, Bradley P.;Stessman, Holly A. F.;Eichler, Evan E.
通讯作者：
Eichler, Evan E.
影响因子：
9.8
作者：
Cheng, Hanyin;Dharmadhikari, Avinash V.;Lyon, Gholson J.
通讯作者：
Lyon, Gholson J.
影响因子：
30.8
作者：
Kircher, Martin;Witten, Daniela M.;Jain, Preti;O'Roak, Brian J.;Cooper, Gregory M.;Shendure, Jay
通讯作者：
Shendure, Jay