A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates.
A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates.
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DOI：
10.3389/fgene.2020.500064
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发表时间：
2020
影响因子：
3.7
通讯作者：
Han S
中科院分区：
文献类型：
--
作者：
Lin Y;Afshar S;Rajadhyaksha AM;Potash JB;Han S
自闭症谱系障碍(ASD)是一种复杂的神经发育疾病，具有很强的遗传学基础。新基因突变在ASD中的作用已经得到了很好的证实，但到目前为止涉及的一系列基因仍然远未完成。目前的研究采用了一种基于机器学习的方法，使用来自人脑时空基因表达模式的特征、基因水平的约束度量和其他基因变异特征来预测ASD风险基因。通过我们的预测模型确定的基因丰富了独立的ASD风险基因集，并倾向于在ASD大脑中下调表达，特别是在额叶和顶叶皮质。排名最高的基因不仅包括那些先前有很强证据表明参与ASD的基因(例如NBEA、HERC1和TCF20)，而且还表明了潜在的新候选基因，如参与蛋白质泛素化的MYCBP2和CAND1。我们还表明，我们的方法在对经过精选的ASD候选基因进行排名方面优于最先进的评分系统。我们预测的风险基因的基因本体丰富分析揭示了与ASD明显相关的生物学过程，包括神经元信号、神经发生和染色质重塑，但也强调了可能支持ASD的其他潜在机制，如与蛋白质降解相关的RNA选择性剪接和泛素化途径的调节。我们的研究表明，人脑时空基因表达模式和基因水平的约束度量可以帮助预测ASD风险基因。我们的基因排序系统为ASD候选基因的优先排序提供了有用的资源。
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be down-expressed in ASD brains, especially in frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, NBEA, HERC1, and TCF20), but also indicated potentially novel candidates, such as, MYCBP2 and CAND1, which are involved in protein ubiquitination. We also showed that our method outperformed state-of-the-art scoring systems for ranking curated ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.
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影响因子：
16.2
作者：
Iossifov I;Ronemus M;Levy D;Wang Z;Hakker I;Rosenbaum J;Yamrom B;Lee YH;Narzisi G;Leotta A;Kendall J;Grabowska E;Ma B;Marks S;Rodgers L;Stepansky A;Troge J;Andrews P;Bekritsky M;Pradhan K;Ghiban E;Kramer M;Parla J;Demeter R;Fulton LL;Fulton RS;Magrini VJ;Ye K;Darnell JC;Darnell RB;Mardis ER;Wilson RK;Schatz MC;McCombie WR;Wigler M
通讯作者：
Wigler M
影响因子：
64.8
作者：
Iossifov, Ivan;O'Roak, Brian J.;Sanders, Stephan J.;Ronemus, Michael;Krumm, Niklas;Levy, Dan;Stessman, Holly A.;Witherspoon, Kali T.;Vives, Laura;Patterson, Karynne E.;Smith, Joshua D.;Paeper, Bryan;Nickerson, Deborah A.;Dea, Jeanselle;Dong, Shan;Gonzalez, Luis E.;Mandell, Jeffrey D.;Mane, Shrikant M.;Murtha, Michael T.;Sullivan, Catherine A.;Walker, Michael F.;Waqar, Zainulabedin;Wei, Liping;Willsey, A. Jeremy;Yamrom, Boris;Lee, Yoon-ha;Grabowska, Ewa;Dalkic, Ertugrul;Wang, Zihua;Marks, Steven;Andrews, Peter;Leotta, Anthony;Kendall, Jude;Hakker, Inessa;Rosenbaum, Julie;Ma, Beicong;Rodgers, Linda;Troge, Jennifer;Narzisi, Giuseppe;Yoon, Seungtai;Schatz, Michael C.;Ye, Kenny;McCombie, W. Richard;Shendure, Jay;Eichler, Evan E.;State, Matthew W.;Wigler, Michael
通讯作者：
Wigler, Michael
影响因子：
3.7
作者：
Chen YA;Tripathi LP;Dessailly BH;Nyström-Persson J;Ahmad S;Mizuguchi K
通讯作者：
Mizuguchi K
影响因子：
6.8
作者：
Duda, Marlena;Zhang, Hongjiu;Guan, Yuanfang
通讯作者：
Guan, Yuanfang
影响因子：
4.4
作者：
BONACICH, P
通讯作者：
BONACICH, P