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Power considerations for ? inflation factor in meta-analyses of genome-wide association studies.

ABSTRACT: The genomic control (GC) approach is extensively used to effectively control false positive signals due to population stratification in genome-wide association studies (GWAS). However, GC affects the statistical power of GWAS. The loss of power depends on the magnitude of the inflation factor (?) that is used for GC. We simulated meta-analyses of different GWAS. Minor allele frequency (MAF) ranged from 0·001 to 0·5 and ? was sampled from two scenarios: (i) random scenario (empirically-derived distribution of real ? values) and (ii) selected scenario from simulation parameter modification. Adjustment for ? was considered under single correction (within study corrected standard errors) and double correction (additional ? corrected summary estimate). MAF was a pivotal determinant of observed power. In random ? scenario, double correction induced a symmetric power reduction in comparison to single correction. For MAF 1·2 and MAF >5%. Our results provide a quick but detailed index for power considerations of future meta-analyses of GWAS that enables a more flexible design from early steps based on the number of studies accumulated in different groups and the ? values observed in the single studies.

SUBMITTER: Georgiopoulos G

PROVIDER: S-EPMC6865175 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Power considerations for λ inflation factor in meta-analyses of genome-wide association studies.

Georgiopoulos Georgios G Evangelou Evangelos E

Genetics research 20160519

The genomic control (GC) approach is extensively used to effectively control false positive signals due to population stratification in genome-wide association studies (GWAS). However, GC affects the statistical power of GWAS. The loss of power depends on the magnitude of the inflation factor (λ) that is used for GC. We simulated meta-analyses of different GWAS. Minor allele frequency (MAF) ranged from 0·001 to 0·5 and λ was sampled from two scenarios: (i) random scenario (empirically-derived di ...[more]

PMID: 27193946

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Project description:IntroductionGene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher's inverse ?2-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.Simulation and powerWe investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon's rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.ApplicationWe applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 "transmembrane transporter activity" as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 "acetylcholine receptor activity" but only when not corrected for multiple testing (all GSA-methods applied; p ? 0.02).

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Power considerations for ? inflation factor in meta-analyses of genome-wide association studies.

Publications

Power considerations for λ inflation factor in meta-analyses of genome-wide association studies.

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