Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS
Genomics & Informatics
; : 123-127, 2012.
Article
em En
| WPRIM
| ID: wpr-57571
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WPRO
ABSTRACT
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.
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Assunto principal:
Genoma
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Artefatos
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Polimorfismo de Nucleotídeo Único
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Diabetes Mellitus Tipo 2
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Estudo de Associação Genômica Ampla
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Genótipo
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Mãos
Idioma:
En
Revista:
Genomics & Informatics
Ano de publicação:
2012
Tipo de documento:
Article