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SNP-based pathway enrichment analysis for genome-wide association studies.

Weng L, Macciardi F, Subramanian A, Guffanti G, Potkin SG, Yu Z, Xie X - BMC Bioinformatics (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of California, Irvine, USA.

ABSTRACT

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.

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A diagram of procedures involved in SNP set enrichment analysis (SSEA).
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Figure 1: A diagram of procedures involved in SNP set enrichment analysis (SSEA).

Mentions: To perform SNP-based pathway enrichment analysis of GWAS data, we developed a JAVA based software package called SNP Set Enrichment Analysis (SSEA) by extending the original GSEA code. SSEA consists of four procedures as outlined in Figure 1: 1) calculating the P-value of the association of each SNP to a trait of interest, 2) selecting representative SNPs for each gene using an adaptive SNP combination method, calculating the average number of representative SNPs for genes in each pathway and reselecting SNPs for gene in each pathway, 3) ranking all selected SNPs by their P-values and testing if the SNPs from a pathway are enriched with high ranks, and 4) calculating the FDR of the discovered pathways. See Methods for details.


SNP-based pathway enrichment analysis for genome-wide association studies.

Weng L, Macciardi F, Subramanian A, Guffanti G, Potkin SG, Yu Z, Xie X - BMC Bioinformatics (2011)

A diagram of procedures involved in SNP set enrichment analysis (SSEA).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3102637&req=5

Figure 1: A diagram of procedures involved in SNP set enrichment analysis (SSEA).
Mentions: To perform SNP-based pathway enrichment analysis of GWAS data, we developed a JAVA based software package called SNP Set Enrichment Analysis (SSEA) by extending the original GSEA code. SSEA consists of four procedures as outlined in Figure 1: 1) calculating the P-value of the association of each SNP to a trait of interest, 2) selecting representative SNPs for each gene using an adaptive SNP combination method, calculating the average number of representative SNPs for genes in each pathway and reselecting SNPs for gene in each pathway, 3) ranking all selected SNPs by their P-values and testing if the SNPs from a pathway are enriched with high ranks, and 4) calculating the FDR of the discovered pathways. See Methods for details.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of California, Irvine, USA.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus