Liu Qiao, Chen Chen, Gao Annie, Tong Hang Hang, Xie Lei
Biochemistry, The Graduate Center, The City University of New York, New York, United States.
School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, United States.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2017 Nov;2017:2177-2182. doi: 10.1109/BIBM.2017.8217995. Epub 2017 Dec 18.
It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer's disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.
揭示DNA变异在复杂表型中的因果效应是一项重大挑战。尽管统计技术可以在全基因组关联研究（GWAS）中建立基因型与表型之间的相关性，但当变异罕见时，它们往往失效。新兴的基于网络的关联研究旨在解决统计分析中的这一缺点，但主要应用于编码变异。越来越多的证据表明，非编码变异在复杂疾病的病因中起关键作用。然而，很少有计算工具可用于研究罕见非编码变异对表型的影响。在这里，我们开发了一个多尺度建模的变异-功能-网络框架VariFunNet来应对这些挑战。VariFunNet首先预测由非编码变异导致的分子相互作用的功能变异。然后，我们将与功能变异相关的基因纳入组织特异性基因网络，并识别将功能变异传递到分子表型的子网。最后，我们量化子网的功能意义，并对非编码变异与表型的关联进行优先级排序。我们已将VariFunNet应用于研究罕见非编码变异对阿尔茨海默病（AD）的因果效应。在排名前21位的因果非编码变异中，有16个直接得到现有证据的支持。其余5个新变异会失调多个下游生物学过程，所有这些过程都与AD的病理学相关。此外，我们提出了可能调节多种AD相关途径的潜在新药物靶点。这些发现可能为发现预防、诊断和治疗AD的新生物标志物和疗法提供新线索。我们的结果表明，多尺度建模是研究因果基因型-表型关联的一种潜在强大方法。
