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Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism

机译：联盟博弈论有助于识别与自闭症相关的非编码变体

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Studies on autism spectrum disorder (ASD) have amassed substantial evidence for the role of genetics in the disease’s phenotypic manifestation. A large number of coding and non-coding variants with low penetrance likely act in a combinatorial manner to explain the variable forms of ASD. However, many of these combined interactions, both additive and epistatic, remain undefined. Coalitional game theory (CGT) is an approach that seeks to identify players (individual genetic variants or genes) who tend to improve the performance—association to a disease phenotype of interest—of any coalition (subset of co-occurring genetic variants) they join. This method has been previously applied to boost biologically informative signal from gene expression data and exome sequencing data but remains to be explored in the context of cooperativity among non-coding genomic regions. We describe our extension of previous work, highlighting non-coding chromosomal regions relevant to ASD using CGT on alteration data of 4595 fully sequenced genomes from 756 multiplex families. Genomes were encoded into binary matrices for three types of non-coding regions previously implicated in ASD and separated into ASD (case) and unaffected (control) samples. A player metric, the Shapley value, enabled determination of individual variant contributions in both sets of cohorts. A total of 30 non-coding positions were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Cross-study analyses revealed that a subset of mutated non-coding regions (all of which are in human accelerated regions (HARs)) and related genes are involved in biological pathways or behavioral outcomes known to be affected in autism, suggesting the importance of single nucleotide polymorphisms (SNPs) within HARs in ASD. These findings support the use of CGT in identifying hidden yet influential non-coding players from large-scale genomic data, to better understand the precise underpinnings of complex neurodevelopmental disorders such as autism.
机译：自闭症谱系障碍（ASD）的研究已积累了大量证据，证明遗传学在该疾病的表型表现中的作用。具有低渗透性的大量编码和非编码变体可能以组合方式起作用，以解释ASD的可变形式。但是，这些加成和上位性结合的相互作用中有许多尚不确定。联盟博弈论（CGT）是一种方法，旨在识别倾向于改善其加入的任何联盟（共生遗传变异子集）的性能（与目标疾病表型相关）的参与者（个体遗传变体或基因）。 。该方法先前已被应用来增强来自基因表达数据和外显子组测序数据的生物学信息信号，但在非编码基因组区域之间的合作性背景下仍有待探索。我们描述了我们先前工作的扩展，重点介绍了使用CGT对来自756个多重家族的4595个全序基因组的变化数据使用AGT进行的与ASD相关的非编码染色体区域。基因组被编码成二进制矩阵，用于先前与ASD有关的三种类型的非编码区域，并分为ASD（病例）和不受影响的（对照）样品。玩家指标Shapley值可以确定两组同类群组中的各个变量。总共发现30个非编码位置的球员得分显着提高，并且可能代表了ASD遗传协调的重要贡献者。交叉研究分析显示，突变的非编码区（均位于人类加速区（HARs）中）和相关基因的一部分参与了已知受自闭症影响的生物途径或行为结局，这提示了单一研究的重要性ASD中HAR内的核苷酸多态性（SNP）。这些发现支持使用CGT从大规模基因组数据中识别隐藏但有影响力的非编码参与者，以更好地了解复杂的神经发育障碍（例如自闭症）的确切基础。

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