Full metadata record
DC Field | Value | Language |
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dc.citation.endPage | 127 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 123 | - |
dc.citation.title | GENOMICS & INFORMATICS | - |
dc.citation.volume | 10 | - |
dc.contributor.author | Kwon, Ji-sun | - |
dc.contributor.author | Kim, Jihye | - |
dc.contributor.author | Nam, Dougu | - |
dc.contributor.author | Kim, Sangsoo | - |
dc.date.accessioned | 2023-12-22T04:43:29Z | - |
dc.date.available | 2023-12-22T04:43:29Z | - |
dc.date.created | 2013-07-17 | - |
dc.date.issued | 2012-09 | - |
dc.description.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. | - |
dc.identifier.bibliographicCitation | GENOMICS & INFORMATICS, v.10, no.2, pp.123 - 127 | - |
dc.identifier.issn | 1598-866X | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/3684 | - |
dc.language | 영어 | - |
dc.publisher | 한국유전체학회 | - |
dc.title | Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.identifier.kciid | ART001677139 | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | kci_candi | - |
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