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

Abstract

BackgroundRecently 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.ResultsWe 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.ConclusionsThe 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.

Tissues, Metabolic Pathways and Genes of Key Importance in Lactating Dairy Cattle

“…After identifying a QTL region, however, it may still be difficult to determine which variants in the region truly affect the trait. Knowledge of the physiology of the trait and of relevant metabolic pathways can be valuable in this regard, as it can highlight genes in the QTL region of potential importance [164]. One of the aims of this review is to facilitate discovery of the mechanisms underlying QTL associated with milk production and fertility-related traits, through highlighting tissues, pathways and genes that are known to play important roles in lactation.…”

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

“…The GO biological process category is also collected to find the biological processes of selected SNPs. Compared with recently published studies, the functional module data used in the proposed method are increased in terms of both the number of resources and the number of gene sets [28,40,41]. Whereas recent GSEAs used gene sets in the range of 200-1,000, primarily from KEGG, GO, MSigDB and BioCarta, collected gene set data for the expanded GSEA contained 3,663 functional modules from 11 resources.…”

Integrated Genome-Wide Pathway Association Analysis with INTERSNP

“…We note that idea just described is related to a recently described approach to gene set analysis [31] that models the joint association of independent, representative eigenSNPs within a gene. In addition, multiple SNPs within one gene are also considered by Weng et al [22] with a Kolmogorov-Smirnov-type test [24] .…”

“…Currently, a couple of main PAA approaches exist, among them methods by Wang et al [13] , O'Dushlaine et al [14] , Holmans et al [8] , Chai et al [15] , Medina et al [16] , Elbers et al [17] , De la Cruz et al [18] , Peng et al [19] , Luo et al [20] , Nam et al [21] , Weng et al [22] , and Wang et al [23] . All approaches have in common that they formalize a pathway as a set of genes, which is then formalized as a set of SNPs, namely the set of SNPs located within or in proximity of the gene.…”

“…For this reason, we will present an implementation of established scores such as the SNP ratio, the Gene Ratio (ALIGATOR) or Fisher-type scores. While we are able to implement several standard methods and to include them in our simulation study, it was not possible to evaluate other important approaches including GESBAP [16] , GSA-SNP [21] or SSEA [22] , whose implementations rely on either graphical user interface or web application.…”