Type-2 diabetes-associated variants with cross-trait relevance: Post-GWAs strategies for biological function interpretation
Type-2 diabetes-associated variants with cross-trait relevance: Post-GWAs strategies for biological function interpretation
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DOI：
10.1016/j.ymgme.2017.03.004
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发表时间：
2017-05-01
影响因子：
3.8
通讯作者：
Allebrandt, Karla V.
中科院分区：
文献类型：
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作者：
Frau, Francesca;Crowther, Daniel;Allebrandt, Karla V.
2 型糖尿病 (T2D) 的全基因组关联研究 (GWA) 已成功识别出许多具有强大关联信号的基因座。然而，显然需要 GWA 后策略来了解这些变异的作用机制和临床相关性。几种合并症与 T2D 的关联表明这些表型有一个共同的病因，并使疾病的治疗变得复杂。在这项研究中，我们重点关注这些关系背后的遗传学，利用系统基因组学来识别与 T2D 和其他 12 种性状相关的遗传变异。 GWA 研究从大型 GWA 荟萃分析中获得了血糖特征、肥胖、冠状动脉疾病和血脂的成对比较汇总统计数据。我们使用网络医学方法来利用有关已识别基因和具有交叉性状效应的变异的实验信息来解释生物功能。我们鉴定了一组具有交叉性状效应的 38 个遗传变异，这些变异指向与 T2D 及其合并症相关的主要基因网络。我们根据 T2D 相关基因显示的相关性状数量以及显示其与疾病病因学关系的实验证据对 T2D 相关基因进行优先排序。在这项研究中，我们展示了系统基因组学和网络医学方法如何揭示 GWA 的发现，将发现转化为更具治疗相关性的背景。 (C) 2017 年作者。由爱思唯尔公司出版
Genome-wide association studies (GWAs) for type 2 diabetes (T2D) have been successful in identifying many loci with robust association signals. Nevertheless, there is a clear need for post-GWAs strategies to understand mechanism of action and clinical relevance of these variants. The association of several comorbidities with T2D suggests a common etiology for these phenotypes and complicates the management of the disease. In this study, we focused on the genetics underlying these relationships, using systems genomics to identify genetic variation associated with T2D and 12 other traits. GWAs studies summary statistics for pairwise comparisons were obtained for glycemic traits, obesity, coronary artery disease, and lipids from large consortia GWAs meta-analyses. We used a network medicine approach to leverage experimental information about the identified genes and variants with cross traits effects for biological function interpretation. We identified a set of 38 genetic variants with cross traits effects that point to a main network of genes that should be relevant for T2D and its comorbidities. We prioritized the T2D associated genes based on the number of traits they showed association with and the experimental evidence showing their relation to the disease etiology. In this study, we demonstrated how systems genomics and network medicine approaches can shed light into GWAs discoveries, translating findings into a more therapeutically relevant context. (C) 2017 The Authors. Published by Elsevier Inc.