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. 2015 Apr 17;11(4):e1004219.
doi: 10.1371/journal.pcbi.1004219. eCollection 2015 Apr.

MAGMA: generalized gene-set analysis of GWAS data

Christiaan A de Leeuw  1 , Joris M Mooij  2 , Tom Heskes  3 , Danielle Posthuma  4
Affiliations

MAGMA: generalized gene-set analysis of GWAS data

Christiaan A de Leeuw et al. PLoS Comput Biol. .

Abstract

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Comparison of gene analysis results for different test-statistics.
Gene -log10 p-values from the CD data gene analysis in MAGMA for three different gene test-statistics, comparing analyses using (A) the mean χ 2 statistic with the top χ 2 statistic, (B) the mean χ 2 statistic and the PC regression model and (C) the top χ 2 statistic and the PC regression model. P-values below 10–8 are truncated to 10–8 (grey points) to preserve the visibility of the other points.
Fig 2
Fig 2. Comparison of self-contained gene-set analysis results.
Gene set—log10 p-values from the CD data self-contained gene-set analysis for MAGMA and PLINK. Panel (A) shows the PLINK-avg (no pruning) results compared with the MAGMA-main analysis, panel (B) the PLINK-prune results compared with the MAGMA-main analysis and (C) the two PLINK analyses compared to each other. P-values below 10–8 are truncated to 10–8 (grey points) to preserve the visibility of the other points.
Fig 3
Fig 3. Comparison of competitive gene-set analysis results.
Gene set -log10 p-values from the CD data competitive gene-set analysis for MAGMA, ALIGATOR, INRICH and MAGENTA. Results for ALIGATOR and INRICH are shown for each for the SNP p-value cutoff that yielded the highest observed power (0.01 and 0.0001 respectively), MAGENTA at the advised 5th percentile cutoff. P-values for gene sets not evaluated by one of the methods are shown in grey. The shown correlations are for the -log10 p-values for gene-sets evaluated by both methods.
Fig 4
Fig 4. Comparison of competitive gene-set analysis results at different SNP cut-offs.
Comparison of gene set -log10 p-values from the CD data competitive gene-set analysis at different SNP p-value cut-offs for ALIGATOR (top row), INRICH (middle row) and MAGENTA (bottom row). The highest cut-off on the horizontal axis is compared to each of the lower cut-offs. P-values for gene sets not evaluated at the lower cut-off are shown in grey. The shown correlations are for the -log10 p-values for gene-sets evaluated at both cut-offs. Horizontal and vertical grey dotted lines demarcate the p = 0.05 nominal significance threshold.
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