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

ABSTRACT: 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.http://broadinstitute.org/mpg/snpsea.Supplementary data are available at Bioinformatics online.

SUBMITTER: Slowikowski K

PROVIDER: S-EPMC4147889 | biostudies-other | 2014 Sep

REPOSITORIES: biostudies-other

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

Slowikowski Kamil K Hu Xinli X Raychaudhuri Soumya S

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]

PMID: 24813542

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