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A deep learning based scoring system for prioritizing susceptibility variants for mental disorders

机译：基于深度学习的评分系统，用于对精神障碍的易感性变量进行优先排序

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Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders in personal genomes. Here, we developed a computational tool: a deep learning based scoring system (ncDeepBrain) to analyze whole genome/exome sequencing data on personal genomes by integrating contributions from coding, non-coding, structural variants, known brain expression quantitative trait locus (eQTLs), and enhancer/promoter peaks from PsychENCODE. The input is whole-genome variants and the output is prioritized list of variants that may be of relevance to the phenotypes. For population studies, our method can help prioritize novel variants that are associated with disease susceptibility; for individual patients, our method can help identify variants with major effect sizes for mental disorders.
机译：据报道，许多罕见和常见的遗传变异，包括SNP和CNV，都与精神障碍有关，但仍有待发现。然而，尽管有大量的高通量基因组学数据，但缺乏整合的方法来系统地对赋予个人基因组中的精神疾病易感性的变体进行优先级排序。在这里，我们开发了一种计算工具：一种基于深度学习的评分系统（ncDeepBrain），可通过整合编码，非编码，结构变体，已知脑表达定量性状基因座（eQTL）的贡献来分析个人基因组上的整个基因组/外显子组测序数据，以及PsychENCODE中的增强子/启动子峰值。输入是全基因组变体，输出是可能与表型相关的变体的优先列表。对于人群研究，我们的方法可以帮助确定与疾病易感性相关的新变异的优先级。对于个别患者，我们的方法可以帮助确定对精神障碍有重大影响的变异体。

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