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首页> 美国卫生研究院文献>Genomics Informatics >Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS
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Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS

机译：全基因组关联研究结果的两种基因集分析方法的性能比较：GSA-SNP与i-GSEA4GWAS

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摘要

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.
机译：基因组分析（GSA）可用于解释生物学机制方面的全基因组关联研究（GWAS）结果。我们在相同的输入和参数设置下，比较了接受单核苷酸多态性（SNP）的GWAS p值或其基因逐基因汇总的两种不同GSA实现的性能，即GSA-SNP和i-GSEA4GWAS。使用来自韩国2型糖尿病GWAS研究的两组p值进行GSA运行：分别是原始基因型数据集和估算基因型数据集的259,188个SNP和1,152,947个SNP。当使用基因本体论术语作为基因集时，i-GSEA4GWAS对未估算和估算的数据集分别产生了283和1070个命中。另一方面，GSA-SNP分别报告两个数据集的94和38个命中。京都基因与基因组百科全书（KEGG）基因集也观察到了类似的趋势，但程度较小。由于算法中的缩放步骤，i-GSEA4GWAS对估算数据集的大量命中可能是伪像。归因于数据集的GSA-SNP命中次数减少可能是由于它依赖Z统计量这一事实，该统计量对关联背景水平的变化很敏感。建议对GSA结果进行明智的评估，也许基于多个程序。

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