Strategies and issues in the detection of pathway enrichment in genome-wide association studies

Abstract

A fundamental question in human genetics is the degree to which the polygenic character of complex traits derives from polymorphism in genes with similar or with dissimilar functions. The many genome-wide association studies now being performed offer an opportunity to investigate this, and although early attempts are emerging, new tools and modeling strategies still need to be developed and deployed. Towards this goal we implemented a new algorithm to facilitate the transition from genetic marker lists (principally those generated by PLINK) to pathway analyses of representational gene sets in either threshold or threshold-free downstream applications (e.g. DAVID, GSEA-P, and Ingenuity Pathway Analysis). This was applied to several large genome-wide association studies covering diverse human traits that included type 2 diabetes, Crohn’s disease, and plasma lipid levels. Validation of this approach was obtained for plasma HDL levels, where functional categories related to lipid metabolism emerged as the most significant in two independent studies. From analyses of these samples we highlight and address numerous issues related to this strategy, including appropriate gene based correction statistics, the utility of imputed vs. non imputed marker sets, and the apparent enrichment of pathways due solely to the positional clustering of functionally related genes. The latter in particular emphasizes the importance of studies that directly tie genetic variation to functional characteristics of specific genes. The software freely provided that we have called ProxyGeneLD may resolve an important bottleneck in pathway-based analyses of genome-wide association data. This has allowed us to identify at least one replicable case of pathway enrichment but also to highlight functional gene clustering as a potentially serious problem that may lead to spurious pathway findings if not corrected for.

Molecular pathways involved in neuronal cell adhesion and membrane scaffolding contribute to schizophrenia and bipolar disorder susceptibility

“…9,12 In this study, we used the SNP ratio test (SRT) 10 for the following reasons. The SRT is similar to approaches using methods based on gene set enrichment analysis 9 in that it tests for enrichment of statistically associated SNPs in a pathway using empirical P-values.…”

Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease

“…2 This is a relatively new approach, stemming from the application of GO-based analyses to gene expression data, 3 but despite promise only a handful of replicated cases of pathway enrichment have emerged. 4,5 One of the critical issues in enabling this strategy is to convert with high fidelity the single-nucleotide polymorphism (SNP) lists from genome-wide platforms to the list of the genes they represent. Toward this end, we developed a software program implemented in Perl, using as input genome-wide SNP results (primarily from PLINK 6 ), that considers linkage disequilibrium (LD) across regions of significance that corrects for the inflation of significance due to gene length.…”

“…Toward this end, we developed a software program implemented in Perl, using as input genome-wide SNP results (primarily from PLINK 6 ), that considers linkage disequilibrium (LD) across regions of significance that corrects for the inflation of significance due to gene length. 5 In brief, our software automates the process of converting genome-wide SNP lists to gene lists, beginning with the retrieval of LD structures in analogous populations with denser genotyping data (that is, HapMap). When a group of markers are in high LD in HapMap (we use an r 2 40.8 threshold), they are tied to a 'proxy cluster' treating it as a single signal.…”

“…An illustration of the algorithm can be found in our earlier paper. 5 Here, we have applied this program to a genome-wide association study in a French Alzheimer disease (AD) case-control sample.…”

“…A common problem with analyses of this nature is the false appearance of enrichment due to chromosomal clustering of functionally related genes. 5 For this particular analysis, all genes contributing to enrichment were located in distinct genomic loci (also shown in Table 1), with the closest genes being several megabases apart. However, there were also a few cases of ontology categories that could be dismissed because of positional clustering, the most prominent being 'cytokine activity' due to an enrichment of interferon genes that are located in tight genomic proximity (not shown).…”