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

Abstract

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.

Left–Right Asymmetry of Maturation Rates in Human Embryonic Neural Development

“…We tested the hypothesis that genes that showed lateralized expression in the embryonic spinal cord or hindbrain were enriched for association signals with schizophrenia by running the PASCAL software (http://www2. unil.ch/cbg/index.php?title=Pascal) (35). For this, we defined one gene set comprising the 681 genes that showed lateralization (toward either the right or the left) in the embryonic…”

“…This way, positive enrichment in the GSEA results would indicate that members of a particular gene set, on average, show relatively higher expression on the right side of the tissue, and negative enrichment would indicate relatively higher expression on the left side. GSEA reports a familywise error rate, which is a conservative measure to correct for multiple testing (35). To identify gene sets that were lateralized in both spinal cord and hindbrain, we selected gene sets that had an absolute normalized enrichment score (as calculated by GSEA software) exceeding 2.0 in both tissues.…”

Biological and clinical insights from genetics of insomnia symptoms

“…Gene-based analysis was performed using PASCAL 20 . Tissue enrichment analysis was conducted using FUMA 51 .…”

Identifying communities from multiplex biological networks by randomized optimization of modularity

“…The communities identified by the different challengers were evaluated according to the associations of their member genes with GWAS data, following the PASCAL tool 4 . The procedure leverages the SNP-based p -value statistics obtained from 180 GWAS datasets, covering common diseases and traits.…”

“…The challenge has been divided into two sub-challenges, to identify communities either i) from six biological networks independently, or ii) from all these networks jointly. The clustering approaches proposed by the participants are assessed regarding their capacity to reveal disease communities , defined as communities significantly associated with genes implicated in diseases in GWAS studies 3, 4 . The challengers proposed various strategies and clustering approaches, including kernel clustering, random walks or modularity optimization.…”