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This example illustrates how to perform enrichment and prioritization analysis of GWAS summary statistics based on variational Bayes (VB) inference of RSS-BVSR model. This example consists of:
The following figure provides a schematic overview of the method.
As shown above, RSS fits two models for the enrichment and prioritization analysis.
Baseline model (\(M_0\)): SNPs across the whole genome are equally likely to be associated with the phenotype of interest.
Enrichment model (\(M_1\)): SNPs “inside” a gene set are more likely (i.e. “enriched”) to be associated with a target phenotype than remaining SNPs.
If the gene set is truly enriched, then the observed GWAS data should show more support for the enrichment over baseline model, that is, yielding a larger Bayes factor (BF).
In addition to identifying enrichments, RSS also automatically prioritizes loci within an enriched set by comparing the posterior distributions of genetic effects (\(\beta\)) under \(M_0\) and \(M_1\). Here we summarize the posterior of beta as \(P_1\), the posterior probability that at least one SNP in a locus is trait-associated. Differences between \(P_1\) estimated under \(M_0\) and \(M_1\) reflect the influence of enrichment on genetic associations, which can help identify new trait-associated genes.
The key difference between RSS and previous work, notably, Carbonetto and Stephens (2013), is that RSS uses publicly available GWAS summary data, rather than individual-level genotypes and phenotypes. To perform similar analysis of GWAS individual-level data, please see https://github.com/pcarbo/bmapathway.
To reproduce results of Example 5, please use scripts in the directory
example5. Before running the scripts, please make sure the VB subroutines of RSS are installed. Please find installation instructions here. It is advisable to go through the simulated example (Part A) before diving into the real data example (Part B).