Permutation-based approaches do not adequately allow for linkage disequilibrium in gene-wide multi-locus association analysis

Escott‐Price, Valentina; Schmidt, Karl Michael; Vedernikov, Alexey; Craddock, Nicholas John; Holmans, Peter Alan

doi:10.1038/ejhg.2012.8

Cited by 20 publications

(18 citation statements)

References 22 publications

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“…However, one concern with most existing methods is that they first summarize associations per marker before aggregating them to genes or gene sets. As demonstrated by Moskvina et al this makes the statistical power strongly dependent on local linkage disequilibrium (LD) [ 14 ], and also reduces power to detect associations dependent on multiple markers.…”

Section: Introductionmentioning

confidence: 99%

MAGMA: Generalized Gene-Set Analysis of GWAS Data

et al. 2015

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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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Section: Introductionmentioning

confidence: 99%

MAGMA: Generalized Gene-Set Analysis of GWAS Data

et al. 2015

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“…An established class of gene-based methods applies multiple regression analysis in which each SNP is coded as a covariate (Chapman & Whittaker, 2008;Clayton, Chapman, & Cooper, 2004;Moskvina et al, 2012;Wason & Dudbridge, 2012). A multi-SNP global statistic is constructed to represent a gene and test the global null hypothesis where the degrees of freedom (df) correspond to the number of SNPs.…”

Section: Introductionmentioning

confidence: 99%

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

Yoo

Sun

Poirier

et al. 2016

Genetic Epidemiology

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By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.

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“…Existing GSA methods often demonstrate low power [8] especially in situations where only a few genes within the gene-set are associated with the phenotype [12] . Additionally, in the presence of correlation between variants or genes due to linkage disequilibrium (LD), many existing methods cannot control the type-I error [14] . Resampling-based p-value calculation [15] can be used, but these approaches are computationally very expensive and hence can reduce the applicability of the method, especially for large datasets.…”

Section: Introductionmentioning

confidence: 99%

A powerful subset-based gene-set analysis method identifies novel associations and improves interpretation in UK Biobank

Dutta

VandeHaar

Fritsche

et al. 2019

Preprint

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A test of association between the phenotype and a set of genes within a biological pathway can be complementary to single variant or single gene association analysis and provide further insights into the genetic architecture of complex phenotypes. Although multiple methods exist to perform such a gene-set analysis, most have low statistical power when only a small fraction of the genes are associated with the phenotype. Further, since existing methods cannot identify possible genes driving association signals, interpreting results of such association in terms of the underlying genetic mechanism is challenging. Here, we introduce Gene-set analysis Association Using Sparse Signals (GAUSS), a method for gene-set association analysis with GWAS summary statistics. In addition to providing a p-value for association, GAUSS identifies the subset of genes that have the maximal evidence of association and appears to drive the association. Using pre-computed correlation structure among test statistics from a reference panel, the p-value calculation is substantially faster compared to other permutation or simulationbased approaches. Our numerical experiments show that GAUSS can increase power over several existing methods while controlling type-I error under a variety of association models.Through the analysis of summary statistics from the UK Biobank data for 1,403 phenotypes, we show that GAUSS is scalable and can identify associations across many phenotypes and genesets.

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Permutation-based approaches do not adequately allow for linkage disequilibrium in gene-wide multi-locus association analysis

Cited by 20 publications

References 22 publications

MAGMA: Generalized Gene-Set Analysis of GWAS Data

MAGMA: Generalized Gene-Set Analysis of GWAS Data

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

A powerful subset-based gene-set analysis method identifies novel associations and improves interpretation in UK Biobank

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