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SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci

Summary: We created a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tis...

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Detalles Bibliográficos
Autores principales: Slowikowski, Kamil, Hu, Xinli, Raychaudhuri, Soumya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147889/
https://www.ncbi.nlm.nih.gov/pubmed/24813542
http://dx.doi.org/10.1093/bioinformatics/btu326
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author Slowikowski, Kamil
Hu, Xinli
Raychaudhuri, Soumya
author_facet Slowikowski, Kamil
Hu, Xinli
Raychaudhuri, Soumya
author_sort Slowikowski, Kamil
collection PubMed
description Summary: We created a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. Availability and implementation: http://broadinstitute.org/mpg/snpsea Contact: soumya@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-41478892014-09-02 SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci Slowikowski, Kamil Hu, Xinli Raychaudhuri, Soumya Bioinformatics Applications Notes Summary: We created a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. Availability and implementation: http://broadinstitute.org/mpg/snpsea Contact: soumya@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2014-09-01 2014-05-10 /pmc/articles/PMC4147889/ /pubmed/24813542 http://dx.doi.org/10.1093/bioinformatics/btu326 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Slowikowski, Kamil
Hu, Xinli
Raychaudhuri, Soumya
SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
title SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
title_full SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
title_fullStr SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
title_full_unstemmed SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
title_short SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
title_sort snpsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147889/
https://www.ncbi.nlm.nih.gov/pubmed/24813542
http://dx.doi.org/10.1093/bioinformatics/btu326
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