Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association\nstudies (GWAS) across genes and pathways is a strategy to improve statistical power and\ngain biological insight. Here, we present Pascal (Pathway scoring algorithm), a powerful\ntool for computing gene and pathway scores from SNP-phenotype association summary\nstatistics. For gene score computation, we implemented analytic and efficient numerical\nsolutions to calculate test statistics. We examined in particular the sum and the maximum of\nchi-squared statistics, which measure the strongest and the average association signals\nper gene, respectively. For pathway scoring, we use a modified Fisher method, which offers\nnot only significant power improvement over more traditional enrichment strategies, but\nalso eliminates the problem of arbitrary threshold selection inherent in any binary membership\nbased pathway enrichment approach.We demonstrate the marked increase in power\nby analyzing summary statistics from dozens of large meta-studies for various traits. Our\nextensive testing indicates that our method not only excels in rigorous type I error control,\nbut also results in more biologically meaningful discoveries.
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