DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics.
DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics.
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DOI：
10.1186/2040-2392-5-22
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发表时间：
2014-03-06
期刊：
影响因子：
6.2
通讯作者：
Roeder K
中科院分区：
文献类型：
--
作者：
Liu L;Lei J;Sanders SJ;Willsey AJ;Kou Y;Cicek AE;Klei L;Lu C;He X;Li M;Muhle RA;Ma'ayan A;Noonan JP;Sestan N;McFadden KA;State MW;Buxbaum JD;Devlin B;Roeder K
在自闭症谱系障碍（ASD）先证者中发现的从头功能丧失（dnLoF）突变是其未受影响的兄弟姐妹的两倍。同一基因中多个独立的dnLoF突变暗示该基因存在风险，因此为ASD遗传学提供了一条系统的，尽管艰巨的前进道路。使用额外的非遗传数据可能会提高识别ASD基因的能力。为了加速ASD基因的搜索，我们开发了一种新的算法DAWN来模拟两种数据：外显子组测序的罕见变异和中期胎儿前额叶和运动-体感新皮质中的基因共表达，这是风险的关键联系。该算法将集成数据转换为隐马尔可夫随机场，其中图结构由基因共表达确定，并且将这些相互关系与特定于节点的观察（即基因身份、表达、遗传数据和对风险的估计影响）相结合。利用目前可用的遗传数据和基因共表达的特定发育时间段，DAWN确定了127个可能影响风险的基因，以及一组可能的ASD子网络。利用已发表的靶向重测序结果进行的验证实验证明了其在可靠预测ASD基因方面的功效。DAWN还成功预测了已知的ASD基因，这些基因不包括在用于创建模型的遗传数据中。验证研究表明，DAWN是有效的预测ASD基因和子网络，利用遗传和基因表达数据。研究结果表明，神经突延伸和神经元分支是ASD的危险因素。在新出现的ASD序列数据和来自其他大脑区域和组织的基因表达数据上使用DAWN可能会识别新的ASD基因。DAWN也可用于其他复杂疾病，以识别这些疾病中的基因和子网络。
De novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes. To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk. Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model. Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.
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影响因子：
9.8
作者：
Feuk, Lars;Kalervo, Aino;Hannula-Jouppi, Katariina
通讯作者：
Hannula-Jouppi, Katariina
影响因子：
14.9
作者：
Blake JA;Bult CJ;Eppig JT;Kadin JA;Richardson JE;Mouse Genome Database Group
通讯作者：
Mouse Genome Database Group
影响因子：
3
作者：
Berger SI;Posner JM;Ma'ayan A
通讯作者：
Ma'ayan A
影响因子：
3.5
作者：
Anney R;Klei L;Pinto D;Almeida J;Bacchelli E;Baird G;Bolshakova N;Bölte S;Bolton PF;Bourgeron T;Brennan S;Brian J;Casey J;Conroy J;Correia C;Corsello C;Crawford EL;de Jonge M;Delorme R;Duketis E;Duque F;Estes A;Farrar P;Fernandez BA;Folstein SE;Fombonne E;Gilbert J;Gillberg C;Glessner JT;Green A;Green J;Guter SJ;Heron EA;Holt R;Howe JL;Hughes G;Hus V;Igliozzi R;Jacob S;Kenny GP;Kim C;Kolevzon A;Kustanovich V;Lajonchere CM;Lamb JA;Law-Smith M;Leboyer M;Le Couteur A;Leventhal BL;Liu XQ;Lombard F;Lord C;Lotspeich L;Lund SC;Magalhaes TR;Mantoulan C;McDougle CJ;Melhem NM;Merikangas A;Minshew NJ;Mirza GK;Munson J;Noakes C;Nygren G;Papanikolaou K;Pagnamenta AT;Parrini B;Paton T;Pickles A;Posey DJ;Poustka F;Ragoussis J;Regan R;Roberts W;Roeder K;Roge B;Rutter ML;Schlitt S;Shah N;Sheffield VC;Soorya L;Sousa I;Stoppioni V;Sykes N;Tancredi R;Thompson AP;Thomson S;Tryfon A;Tsiantis J;Van Engeland H;Vincent JB;Volkmar F;Vorstman JA;Wallace S;Wing K;Wittemeyer K;Wood S;Zurawiecki D;Zwaigenbaum L;Bailey AJ;Battaglia A;Cantor RM;Coon H;Cuccaro ML;Dawson G;Ennis S;Freitag CM;Geschwind DH;Haines JL;Klauck SM;McMahon WM;Maestrini E;Miller J;Monaco AP;Nelson SF;Nurnberger JI Jr;Oliveira G;Parr JR;Pericak-Vance MA;Piven J;Schellenberg GD;Scherer SW;Vicente AM;Wassink TH;Wijsman EM;Betancur C;Buxbaum JD;Cook EH;Gallagher L;Gill M;Hallmayer J;Paterson AD;Sutcliffe JS;Szatmari P;Vieland VJ;Hakonarson H;Devlin B
通讯作者：
Devlin B
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