Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics
By David Lamparter, Daniel Marbach, Rico Rueedi, Zoltán Kutalik, Sven Bergmann. PLoS Comput. Biol., 2016
322 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5809f6658c4b8edb70fa2e5d0333609b58c2e207
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In Genetics
In Molecular Biology
- Gustavo A. Stolovitzky
- J. Sáez-Rodríguez
- Lenore J. Cowen
- Sven Bergmann
- Zoltán Kutalik
- Daniel Marbach
- David Lamparter
- Donna K. Slonim
Citations in the corpus
- webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study (2021, unknown): “First, gene-based P -values are calculated by the Pathway scoring algo-rithm (Pascal) (46).”
- Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, methodology): “Compared to alternative gene scoring methods that rely on Monte Carlo simulations, Pascal is about 100 times faster and more precise (Lamparter et al., 2016).”
- Functional mapping and annotation of genetic associations with FUMA (2017, methodology): “For instance, VEGAS performs permutation based simulation 33,34 , MAGMA employs multiple linear regression 35 and Pascal computes sum and maximum of chi-squared statistics 36 to obtain gene-based P -values.”