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• W2068135320 abstract "<i>Objectives:</i> Pathway association analysis (PAA) tests for an excess of moderately significant SNPs in genes from a common pathway. <i>Methods:</i> We present a Monte-Carlo simulation framework that allows to formulate the main ideas of existing PAA approaches using a self-contained rather than a competitive null hypothesis. A stand-alone implementation in INTERSNP makes time-consuming communication with standard GWAS software redundant. By additional parallelization with the OpenMP API, we achieve a reduction in running time for PAA by orders of magnitude, making a power simulation study for PAA feasible. Our approach properly accounts for linkage disequilibrium and is robust with respect to residual λ inflation. <i>Results:</i> We demonstrate that under simple, realistic disease models, PAA can actually strongly outperform the GWAS single-marker approach. PAA methods that make use of the strength of the SNP association (GenGen, Fisher’s combination test), in general, perform better than ratio-based methods (ALIGATOR, SNP ratio), whereas the relative performance of gene-based scoring (ALIGATOR, GenGen) and pathway-based scoring (SNP ratio, Fisher’s combination test) depends on the architecture of the assumed disease model. Finally, we present a new PAA score that models independent signals from the same gene in a regression framework and show that it is a reasonable compromise that combines the advantages of existing ideas." @default.
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• W2068135320 date "2012-01-01" @default.
• W2068135320 modified "2023-09-26" @default.
• W2068135320 title "Integrated Genome-Wide Pathway Association Analysis with INTERSNP" @default.
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• W2068135320 doi "https://doi.org/10.1159/000336196" @default.
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