PGA: post-GWAS analysis for disease gene identification.
PGA: post-GWAS analysis for disease gene identification.
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PGA：疾病基因鉴定的 GWAS 后分析。
DOI：
10.1093/bioinformatics/btx845
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Zhang,ZhengdongD
中科院分区：
文献类型：
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作者：
Lin,Jhih-Rong;Jaroslawicz,Daniel;Cai,Ying;Zhang,Quanwei;Wang,Zhen;Zhang,ZhengdongD
虽然全基因组关联研究（GWAS）是识别疾病相关变异的有力方法，但它并没有直接解决这些遗传关联信号背后的生物学机制。在这里，我们提出了PGA，一个基于Perl和Java的程序后GWAS分析，预测可能的疾病基因GWAS报告的变体列表。PGA采用命令行界面设计，在识别疾病候选基因时结合了基因组和eQTL数据，并使用基因网络和本体论数据，根据它们与所讨论疾病的关系强度对它们进行评分。可用性和实施http：zdzlab.einstein.yu.edu/1/pga.htmlSupplementary信息补充数据可在Bioinformaticsonline上获得。
SummaryAlthough the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question.Availability and implementationhttp://zdzlab.einstein.yu.edu/1/pga.htmlSupplementary informationSupplementary data are available atBioinformaticsonline.