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

David Lamparter,Sven Bergmann,Zoltán Kutalik,Daniel Marbach,Rico Rueedi,Jennifer Listgarten

doi:10.1371/journal.pcbi.1004714

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.

Highlights

Genome-wide association studies (GWAS) have linked a large number of common genetic variants to various phenotypes
Pathway analysis tools integrate signals from multiple single nucleotide polymorphism (SNP) at various positions in the genome in order to map associated genomic regions to well-established pathways, i.e., sets of genes known to act in concert
This is generally carried out in two steps: first, individual SNPs are mapped to genes and their association p-values are combined into gene scores; second, genes are grouped into pathways and their gene scores are combined into pathway scores

Summary

Introduction

Genome-wide association studies (GWAS) have linked a large number of common genetic variants to various phenotypes. High-powered meta-analyses have revealed tens to hundreds of single nucleotide polymorphisms (SNPs) with robust associations. Pathway analysis aims to provide insight into the biological processes involved by aggregating the association signal observed for a collection of SNPs into a pathway level signal. This is generally carried out in two steps: first, individual SNPs are mapped to genes and their association p-values are combined into gene scores; second, genes are grouped into pathways and their gene scores are combined into pathway scores. Existing tools vary in the methods used for each step and the strategies employed to correct for correlation due to linkage disequilibrium

Methods

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Discussion

Conclusion