• Title/Summary/Keyword: GSA-SNP

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Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS

  • Kwon, Ji-Sun;Kim, Ji-Hye;Nam, Doug-U;Kim, Sang-Soo
    • Genomics & Informatics
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    • v.10 no.2
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    • pp.123-127
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    • 2012
  • 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.

Gene-set based genome-wide association analysis for the speed of sound in two skeletal sites of Korean women

  • Kwon, Ji-Sun;Kim, Sangsoo
    • BMB Reports
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    • v.47 no.6
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    • pp.348-353
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    • 2014
  • The speed of sound (SOS) value is an indicator of bone mineral density (BMD). Previous genome-wide association (GWA) studies have identified a number of genes, whose variations may affect BMD levels. However, their biological implications have been elusive. We re-analyzed the GWA study dataset for the SOS values in skeletal sites of 4,659 Korean women, using a gene-set analysis software, GSA-SNP. We identified 10 common representative GO terms, and 17 candidate genes between these two traits (PGS < 0.05). Implication of these GO terms and genes in the bone mechanism is well supported by the literature survey. Interestingly, the significance levels of some member genes were inversely related, in several gene-sets that were shared between two skeletal sites. This implies that biological process, rather than SNP or gene, is the substantial unit of genetic association for SOS in bone. In conclusion, our findings may provide new insights into the biological mechanisms for BMD.

Gene Set Analyses of Genome-Wide Association Studies on 49 Quantitative Traits Measured in a Single Genetic Epidemiology Dataset

  • Kim, Jihye;Kwon, Ji-Sun;Kim, Sangsoo
    • Genomics & Informatics
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    • v.11 no.3
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    • pp.135-141
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    • 2013
  • Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP) genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO) terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait ($p_{corr}$ < 0.05). Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.