VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in Genome-Wide Association Studies: applied to Alzheimer's Disease.
VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in Genome-Wide Association Studies: applied to Alzheimer's Disease.
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DOI：
10.1109/bibm.2017.8217995
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
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作者：
Liu Q;Chen C;Gao A;Tong HH;Xie L;Alzheimer’s Disease Neuroimaging Initiative
揭示DNA变异在复杂表型中的因果效应是一个巨大的挑战。虽然统计技术可以在全基因组关联研究中建立基因类型和表型之间的相关性，但当变异很少时，统计技术往往会失败。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陷，但主要应用于编码变体。越来越多的证据表明，非编码变异体在复杂疾病的病因学中发挥着关键作用。然而，很少有计算工具可用于研究罕见的非编码变体对表型的影响。在这里，我们开发了一个多尺度建模从变量到功能到网络的框架VariFunNet来应对这些挑战。VariFunNet首先预测分子相互作用的功能变异，这些变异是由非编码变体引起的。然后，我们将与功能变异相关的基因整合到组织特有的基因网络中，并识别将功能变异传递到分子表型的子网络。最后，我们量化了子网络的功能含义，并对非编码变体与表型的关联进行了优先排序。我们已经应用VariFunNet来研究罕见的非编码变体在阿尔茨海默病(AD)中的因果效应。在排名前21位的因果非编码变体中，有16个直接得到了现有证据的支持。剩下的5个新变异体失调了多个下游生物学过程，所有这些都与AD的病理相关。此外，我们提出了潜在的新药靶点，可能调节导致AD的不同途径。这些发现可能为发现新的生物标记物和治疗AD的预防、诊断和治疗提供新的线索。我们的结果表明，多尺度建模是研究因果基因-表型关联的一种潜在的有效方法。
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.