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Sequential Sentinel SNP Regional Association Plots (SSS-RAP): An Approach for Testing Independence of SNP Association Signals Using Meta-Analysis Data.
MedLine Citation:
PMID:  23278391     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
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.
Authors:
Jie Zheng; Tom R Gaunt; Ian N M Day
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Annals of human genetics     Volume:  77     ISSN:  1469-1809     ISO Abbreviation:  Ann. Hum. Genet.     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-01-02     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0416661     Medline TA:  Ann Hum Genet     Country:  England    
Other Details:
Languages:  eng     Pagination:  67-79     Citation Subset:  IM    
Copyright Information:
© 2012 Blackwell Publishing Ltd/University College London.
Affiliation:
Bristol Genetic Epidemiology Laboratories, Department of Social Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
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