Increasing power of GWAS: pathway and meta-analysis

Abstract

Genome-wide association study (GWAS) has been generally underpowered because of the heavy multiple-testing correction. Pathway-based analysis in genome-wide association study (GWAS) is being widely used to overcome this problem and uncover novel multi-genic functional associations. However, many pathway-based methods exhibited low powers and were also affected by free parameters. We present the novel method GSA-SNP2 for pathway enrichment analysis for GWAS p-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. Another solution to increase the power of GWAS is meta-analysis. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have a nonzero effect. However, specific experimental conditions or genetic heterogeneity can lead to random statistic in each study. Here, we show that power of conventional meta-analysis methods rapidly decreases as increasing numbers of random statistics are included. The classical Fisher’s method, however, exhibits relatively high power that is robust to addition of random statistics. We demonstrate that degrees of freedom used for each study have a large effect on the robustness of methods and propose the use of a novel weighted Fisher’s method that is superior to original Fisher’s method, both with or without random statistics. We also propose another robust method based on joint distribution of ordered p-values. Simulation analyses for t-test and genome-wide association study (GWAS) demonstrated that our proposed methods, when a small number of studies have nonzero effects, outperformed existing p-value combining methods and also compared favorably with state-of-the-art methods for GWAS.