Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany.
BMC Genomics. 2012 Sep 18;13:490. doi: 10.1186/1471-2164-13-490.
Genome-wide association studies (GWAS) have provided a large set of genetic loci influencing the risk for many common diseases. Association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. However, GWA data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases.
Here, we describe a new approach to systematically prioritize and interpret available GWA data. We focus on the analysis of joint and disjoint genetic determinants across diseases. Using network analysis, we show that variant-based approaches are superior to locus-based analyses. In addition, we provide a prioritization of disease loci based on network properties and discuss the roles of hub loci across several diseases. We demonstrate that, in general, agonistic associations appear to reflect current disease classifications, and present the potential use of effect sizes in refining and revising these agonistic signals. We further identify potential branching points in disease etiologies based on antagonistic variants and describe plausible small-scale models of the underlying molecular switches.
The observation that a surprisingly high fraction (>15%) of the SNPs considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. Genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further.
全基因组关联研究（GWAS）提供了一大组影响许多常见疾病风险的遗传基因座。关联研究通常在单个群体中分析一个特定的特征，而不考虑特征之间潜在的表型和遗传相关性。然而，GWA 数据可以有效地用于识别具有类似或相反效果的重叠基因座，这些重叠基因座对不同的疾病有影响。
在这里，我们描述了一种系统地优先考虑和解释现有 GWA 数据的新方法。我们专注于分析跨疾病的联合和不联合遗传决定因素。使用网络分析，我们表明基于变体的方法优于基于基因座的分析。此外，我们根据网络属性对疾病基因座进行了优先级排序，并讨论了枢纽基因座在多种疾病中的作用。我们表明，一般来说，激动型关联似乎反映了当前的疾病分类，并且提出了在精炼和修改这些激动型信号时使用效应大小的潜在用途。我们进一步根据拮抗变体确定了疾病病因中的潜在分支点，并描述了潜在的分子开关的小规模模型。
令人惊讶的是，我们研究中考虑的 SNP 中有很大一部分（>15%）与相关和不相关的疾病既具有激动作用又具有拮抗作用，这表明影响人类疾病病因和进展的分子机制在某种程度上是相互关联的。两种疾病之间的遗传重叠也表明了受影响实体在特定致病途径中的重要性，应进一步研究。
