Sequential Sentinel SNP Regional Association Plots (SSS‐RAP): An Approach for Testing Independence of SNP Association Signals Using Meta‐Analysis Data

Genome‐Wide Association Studies (GWAS) frequently incorporate meta‐analysis within their framework. However, conditional analysis of individual‐level data, which is an established approach for fine mapping of causal sites, is often precluded where only group‐level summary data are available for analysis. Here, we present a numerical and graphical approach, “sequential sentinel SNP regional association plot” (SSS‐RAP), which estimates regression coefficients (beta) with their standard errors using the meta‐analysis summary results directly. Under an additive model, typical for genes with small effect, the effect for a sentinel SNP can be transformed to the predicted effect for a possibly dependent SNP through a 2×2 2‐SNP haplotypes table. The approach assumes Hardy–Weinberg equilibrium for test SNPs. SSS‐RAP is available as a Web‐tool (http://apps.biocompute.org.uk/sssrap/sssrap.cgi). To develop and illustrate SSS‐RAP we analyzed lipid and ECG traits data from the British Women's Heart and Health Study (BWHHS), evaluated a meta‐analysis for ECG trait and presented several simulations. We compared results with existing approaches such as model selection methods and conditional analysis. Generally findings were consistent. SSS‐RAP represents a tool for testing independence of SNP association signals using meta‐analysis data, and is also a convenient approach based on biological principles for fine mapping in group level summary data.