论文标题
基于网络的基因优先次序基因组关联研究基因座基因座
Network Based Approach to Gene Prioritization at Genome-Wide Association Study Loci
论文作者
论文摘要
动机：全基因组关联研究（GWAS）成功地确定了数千个遗传风险基因座的复杂性状和疾病。这些GWAS基因座大多数都位于基因组的调节区域和每个GWAS风险基因座所产生其效果的基因。已经提出了许多利用生物学数据源的计算方法来识别GWAS基因座的推定休闲基因。但是，这些方法可以改进。结果：我们介绍了关系最大化方法，这是一种密集的模块搜索方法，可通过通过将GWAS数据从GWAS数据集成到基因共处网络中的关联信号而得出的候选子网络来识别GWAS基因座的假定因果基因。我们在慢性阻塞性肺部疾病GWAS中使用我们的方法。我们对关系最大化方法对公认的基线的性能进行了广泛的比较研究。
Motivation: Genome-wide association studies (GWAS) have successfully identified thousands of genetic risk loci for complex traits and diseases. Most of these GWAS loci lie in regulatory regions of the genome and the gene through which each GWAS risk locus exerts its effects is not always clear. Many computational methods utilizing biological data sources have been proposed to identify putative casual genes at GWAS loci; however, these methods can be improved upon. Results: We present the Relations-Maximization Method, a dense module searching method to identify putative causal genes at GWAS loci through the generation of candidate sub-networks derived by integrating association signals from GWAS data into the gene co-regulation network. We employ our method in a chronic obstructive pulmonary disease GWAS. We perform an extensive, comparative study of Relations-Maximization Method's performance against well-established baselines.
