Dataset Information
SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci.
Ontology highlight
ABSTRACT: Unlabelled
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.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Slowikowski K
PROVIDER: S-EPMC4147889 | biostudies-literature | 2014 Sep
REPOSITORIES: biostudies-literature
Publications
SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci.
Bioinformatics (Oxford, England) 20140510 17
<h4>Unlabelled</h4>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 ...[more]
