Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.
Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.
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DOI：
10.1002/ajmg.c.31330
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发表时间：
2012-05-15
影响因子：
3.1
通讯作者：
Ma'ayan, Avi
中科院分区：
文献类型：
--
作者：
Kou, Yan;Betancur, Catalina;Xu, Huilei;Buxbaum, Joseph D.;Ma'ayan, Avi
关键词：
自闭症谱系障碍（ASD）是一组相关的神经发育障碍，具有显著的综合患病率（约1%）和高遗传性。已经发现了数十个与ASD高风险相关的个体罕见基因和位点，它们与智力残疾（ID）的基因有广泛的重叠。然而，研究表明，可能还有数百个基因有待鉴定。廉价的大规模平行核苷酸测序的出现可以揭示遗传性复杂疾病的遗传基础，包括ASD和ID。然而，全外显子组测序（WES）和全基因组测序（WGS）提供了丰富的资源，其中出现了许多候选变异。人们一直认为，自闭症谱系障碍和自闭症谱系障碍的遗传变异将集中在参与不同途径和蛋白质复合物的基因上。因此，基于额外的功能信息（如蛋白质相互作用或与特定规范或经验途径或其他属性的关联）对候选基因进行优先排序的计算方法可能是有用的。在这项研究中，我们应用了几种监督学习方法，根据已知的ASD和ID疾病基因列表，对ASD或ID疾病基因候选基因进行优先排序。我们实现了两个基于网络的分类器和一个基于属性的分类器，以表明我们可以对这些神经发育障碍的已知基因进行排序和分类，并预测新的基因。我们还表明，ID和ASD共享干扰重叠突触调节子网络的共同途径。我们还表明，小鼠基因敲除中与神经元表型相关的特征可以帮助对神经发育基因进行分类。我们的方法可以广泛应用于其他疾病，有助于优先考虑基于WES和WGS的疾病基因发现中出现的新发现的遗传变异。
Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (~1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein-protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS.
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影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
影响因子：
3
作者：
Berger SI;Posner JM;Ma'ayan A
通讯作者：
Ma'ayan A
DOI：
10.1093/bioinformatics/btp026
发表时间：
2009-03-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Lachmann A;Ma'ayan A
通讯作者：
Ma'ayan A
影响因子：
5.8
作者：
Lachmann, Alexander;Xu, Huilei;Ma'ayan, Avi
通讯作者：
Ma'ayan, Avi
影响因子：
14.9
作者：
Chatr-aryamontri, Andrew;Ceol, Arnaud;Palazzi, Luisa Montecchi;Nardelli, Giuliano;Schneider, Maria Victoria;Castagnoli, Luisa;Cesareni, Gianni
通讯作者：
Cesareni, Gianni