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