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Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726509/ https://www.ncbi.nlm.nih.gov/pubmed/26808494 http://dx.doi.org/10.1371/journal.pcbi.1004714 |
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author | Lamparter, David Marbach, Daniel Rueedi, Rico Kutalik, Zoltán Bergmann, Sven |
author_facet | Lamparter, David Marbach, Daniel Rueedi, Rico Kutalik, Zoltán Bergmann, Sven |
author_sort | Lamparter, David |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4726509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-47265092016-02-03 Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics Lamparter, David Marbach, Daniel Rueedi, Rico Kutalik, Zoltán Bergmann, Sven PLoS Comput Biol Research Article 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. Public Library of Science 2016-01-25 /pmc/articles/PMC4726509/ /pubmed/26808494 http://dx.doi.org/10.1371/journal.pcbi.1004714 Text en © 2016 Lamparter et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Lamparter, David Marbach, Daniel Rueedi, Rico Kutalik, Zoltán Bergmann, Sven Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics |
title | Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics |
title_full | Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics |
title_fullStr | Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics |
title_full_unstemmed | Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics |
title_short | Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics |
title_sort | fast and rigorous computation of gene and pathway scores from snp-based summary statistics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726509/ https://www.ncbi.nlm.nih.gov/pubmed/26808494 http://dx.doi.org/10.1371/journal.pcbi.1004714 |
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