Dataset Information

Pathway-based analysis of primary biliary cirrhosis genome-wide association studies.

ABSTRACT: Genome-wide association studies (GWAS) have successfully identified several loci associated with primary biliary cirrhosis (PBC) risk. Pathway analysis complements conventional GWAS analysis. We applied the recently developed linear combination test for pathways to datasets drawn from independent PBC GWAS in Italian and Canadian subjects. Of the Kyoto Encyclopedia of Genes and Genomes and BioCarta pathways tested, 25 pathways in the Italian dataset (449 cases, 940 controls) and 26 pathways in the Canadian dataset (530 cases, 398 controls) were associated with PBC susceptibility (P<0.05). After correcting for multiple comparisons, only the eight most significant pathways in the Italian dataset had FDR <0.25 with tumor necrosis factor/stress-related signaling emerging as the top pathway (P=7.38 × 10??, FDR=0.18). Two pathways, phosphatidylinositol signaling and hedgehog signaling, were replicated in both datasets (P<0.05), and subjected to two additional complementary pathway tests. Both pathway signals remained significant in the Italian dataset on modified gene set enrichment analysis (P<0.05). In both GWAS, variants nominally associated with PBC were significantly overrepresented in the phosphatidylinositol pathway (Fisher exact P<0.05). These results point to established and novel pathway-level associations with inherited predisposition to PBC that, on further independent replication and functional validation, may provide fresh insights into PBC etiology.

SUBMITTER: Kar SP

PROVIDER: S-EPMC3780793 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Pathway-based analysis of primary biliary cirrhosis genome-wide association studies.

Kar S P SP Seldin M F MF Chen W W Lu E E Hirschfield G M GM Invernizzi P P Heathcote J J Cusi D D Gershwin M E ME Siminovitch K A KA Amos C I CI

Genes and immunity 20130207 3

Genome-wide association studies (GWAS) have successfully identified several loci associated with primary biliary cirrhosis (PBC) risk. Pathway analysis complements conventional GWAS analysis. We applied the recently developed linear combination test for pathways to datasets drawn from independent PBC GWAS in Italian and Canadian subjects. Of the Kyoto Encyclopedia of Genes and Genomes and BioCarta pathways tested, 25 pathways in the Italian dataset (449 cases, 940 controls) and 26 pathways in th ...[more]

PMID: 23392275

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Project description:BACKGROUND: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. RESULTS: We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA. CONCLUSIONS: The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.

Dataset Information

Pathway-based analysis of primary biliary cirrhosis genome-wide association studies.

Publications

Pathway-based analysis of primary biliary cirrhosis genome-wide association studies.

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