Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
Nucleic Acids Res. 2022 Feb 22;50(3):1335-1350. doi: 10.1093/nar/gkac024.
In the last years, many studies were able to identify associations between common genetic variants and complex diseases. However, the mechanistic biological links explaining these associations are still mostly unknown. Common variants are usually associated with a relatively small effect size, suggesting that interactions among multiple variants might be a major genetic component of complex diseases. Hence, elucidating the presence of functional relations among variants may be fundamental to identify putative variants' interactions. To this aim, we developed Polympact, a web-based resource that allows to explore functional relations among human common variants by exploiting variants' functional element landscape, their impact on transcription factor binding motifs, and their effect on transcript levels of protein-coding genes. Polympact characterizes over 18 million common variants and allows to explore putative relations by combining clustering analysis and innovative similarity and interaction network models. The properties of the network models were studied and the utility of Polympact was demonstrated by analysing the rich sets of Breast Cancer and Alzheimer's GWAS variants. We identified relations among multiple variants, suggesting putative interactions. Polympact is freely available at bcglab.cibio.unitn.it/polympact.
在过去的几年中,许多研究能够确定常见遗传变异与复杂疾病之间的关联。然而,解释这些关联的机制生物学联系在很大程度上仍然未知。常见变异通常与相对较小的效应大小相关,这表明多个变异之间的相互作用可能是复杂疾病的主要遗传成分。因此,阐明变异之间存在功能关系对于识别潜在的变异相互作用可能是至关重要的。为此,我们开发了 Polympact,这是一个基于网络的资源,通过利用变异的功能元件景观、它们对转录因子结合基序的影响以及它们对蛋白质编码基因转录水平的影响,允许探索人类常见变异之间的功能关系。Polympact 描述了超过 1800 万个常见变异,并通过组合聚类分析和创新的相似性和相互作用网络模型来探索潜在的关系。研究了网络模型的特性,并通过分析丰富的乳腺癌和阿尔茨海默病 GWAS 变异集来证明了 Polympact 的实用性。我们确定了多个变异之间的关系,表明存在潜在的相互作用。Polympact 可在 bcglab.cibio.unitn.it/polympact 免费获取。