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. 2008 Jul 25;4(7):e1000130.
doi: 10.1371/journal.pgen.1000130.

Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies

Clive J Hoggart  1 , John C Whittaker, Maria De Iorio, David J Balding
Affiliations

Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies

Clive J Hoggart et al. PLoS Genet. .

Abstract

Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re-sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Logarithms of NEG and DE densities.
Fixed to have the same density at the origin.
Figure 2
Figure 2. Main simulation study.
Histograms of the number of selected SNPs tagging (at r 2>0.05) each causal SNP for (A) NEG and (B) ATT analyses.
Figure 3
Figure 3. GWA simulation.
(A) locations of the ten causal variants (vertical blue line) on the 20 Mb chromosome; also shown are the SNPs selected by NEG (red dots), and the SNPs with ATT p-value 5×10−7 (black dots) plotted against −log10 (p-value). (B) and (C) show zooms of two sub-intervals of (A).
Figure 4
Figure 4. Re-sequencing simulation.
Histograms of the maximum r 2 for each selected SNP with a causal variant for (A) NEG and (B) ATT analyses.
See this image and copyright information in PMC

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