Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics

PLoS Comput Biol. 2016 Jan 25;12(1):e1004714. doi: 10.1371/journal.pcbi.1004714. eCollection 2016 Jan.


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

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Computational Biology / methods*
  • Genome-Wide Association Study / methods*
  • HapMap Project
  • Humans
  • Phenotype
  • Polymorphism, Single Nucleotide / genetics*
  • Software

Grant support

The CoLaus study was and is supported by research grants from GlaxoSmithKline(, the Faculty of Biology and Medicine of Lausanne, and the Swiss National Science Foundation( (grants 33CSCO-122661, 33CS30-139468 and 33CS30-148401). ZK received financial support from the Leenaards Foundation(, the Swiss Institute of Bioinformatics( and the Swiss National Science Foundation (31003A-143914, 51RTP0_151019). SB received funding from the Swiss Institute of Bioinformatics, the Swiss National Science Foundation (grant FN 310030_152724 / 1) and through the SysGenetiX project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.