PDF(Pubmed)
Abstract：
BACKGROUND: Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.
RESULTS: We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of \'seed\' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.
RESULTS: We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of \'seed\' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.
摘要：
背景：全基因组关联研究（GWAS）已经能够大规模分析遗传变异在人类疾病中的作用。尽管方法上取得了令人印象深刻的进步，当GWAS缺乏统计学功效时,后续的临床解释和应用仍然具有挑战性.近年来,然而,使用分子网络的信息扩散算法已经导致了对疾病基因的丰富见解。
结果：我们概述了在将网络传播方法应用于GWAS汇总统计时至关重要的设计选择和缺陷。我们从文献中强调总体趋势，并提出了基准实验，以扩展这些见解，选择三种疾病和五种分子网络作为案例研究。我们验证了，如果GWAS汇总统计具有足够的质量，则使用基于GWASP值的基因水平评分比选择未通过相关P值加权的一组“种子”疾病基因具有优势。除此之外，网络的大小和密度被证明是需要考虑的重要因素。最后,我们探索了几种集成方法，并表明组合多个网络可以改善网络传播方法。
结果：我们概述了在将网络传播方法应用于GWAS汇总统计时至关重要的设计选择和缺陷。我们从文献中强调总体趋势，并提出了基准实验，以扩展这些见解，选择三种疾病和五种分子网络作为案例研究。我们验证了，如果GWAS汇总统计具有足够的质量，则使用基于GWASP值的基因水平评分比选择未通过相关P值加权的一组“种子”疾病基因具有优势。除此之外，网络的大小和密度被证明是需要考虑的重要因素。最后,我们探索了几种集成方法，并表明组合多个网络可以改善网络传播方法。
