This paper introduces the compendium of cell type- and tissue-specific gene regulatory networks based on FANTOM5 data and the analysis of disease-associated genetic variants that disturb them.
Abstract. Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type- and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity between transcription factors, enhancers, promoters, and genes. Integration with 37 genome-wide association studies (GWASs) shows that disease-associated genetic variants — including variants that do not reach genome-wide significance — often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues.
This paper introduces Pascal (Pathway Scoring Algorithm), a fast and accurate tool for gene and pathway scoring from SNP-based summary statistics. See also the Pascal website.
Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. (PDF)
Lamparter D*, Marbach D*, Rueedi R, Kutalik Z, and Bergmann S.
PLoS Computational Biology, 12, e1004714, 2016. (PubMed)
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.