Copyright © 2008 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 82, Issue 4, 859-872, 03 April 2008
doi:10.1016/j.ajhg.2008.01.016
Article
Bayesian Meta-Analysis of Genetic Association Studies with Different Sets of Markers
Claudio Verzilli2, 10, Tina Shah1, 10, Juan P. Casas2, Juliet Chapman2, Manjinder Sandhu3, Sally L. Debenham3, Matthijs S. Boekholdt4, Kay Tee Khaw3, Nicholas J. Wareham5, Richard Judson6, Emelia J. Benjamin7, Sekar Kathiresan7, Martin G. Larson7, Jian Rong7, Reecha Sofat1, Steve E. Humphries8, Liam Smeeth2, Gianpiero Cavalleri9, John C. Whittaker2, 
, 
and Aroon D. Hingorani1
1 Centre for Clinical Pharmacology, University College London, London WC1E 6JF, UK
2 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
3 Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
4 Department of Cardiology, Academic Medical Center, Amsterdam 1100 DD, Netherlands
5 MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
6 Genaissance Pharmaceuticals, New Haven, CT 06511, USA
7 Framingham Heart Study, Framingham, MA 01702-5827, USA
8 Centre for Cardiovascular Genetics, University College London, London WC1E 6JF, UK
9 Molecular and Cellular Therapeutics, RCSI Research Institute Royal College of Surgeons in Ireland, Dublin 2, Ireland
Corresponding author10 These authors contributed equally to this work.
Abstract
Robust assessment of genetic effects on quantitative traits or complex-disease risk requires synthesis of evidence from multiple studies. Frequently, studies have genotyped partially overlapping sets of SNPs within a gene or region of interest, hampering attempts to combine all the available data. By using the example of C-reactive protein (CRP) as a quantitative trait, we show how linkage disequilibrium in and around its gene facilitates use of Bayesian hierarchical models to integrate informative data from all available genetic association studies of this trait, irrespective of the SNP typed. A variable selection scheme, followed by contextualization of SNPs exhibiting independent associations within the haplotype structure of the gene, enhanced our ability to infer likely causal variants in this region with population-scale data. This strategy, based on data from a literature based systematic review and substantial new genotyping, facilitated the most comprehensive evaluation to date of the role of variants governing CRP levels, providing important information on the minimal subset of SNPs necessary for comprehensive evaluation of the likely causal relevance of elevated CRP levels for coronary-heart-disease risk by Mendelian randomization. The same method could be applied to evidence synthesis of other quantitative traits, whenever the typed SNPs vary among studies, and to assist fine mapping of causal variants.
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