Slowikowski, KamilHu, XinliRaychaudhuri, Soumya2014-10-012014Slowikowski, Kamil, Xinli Hu, and Soumya Raychaudhuri. 2014. “SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci.” Bioinformatics 30 (17): 2496-2497. doi:10.1093/bioinformatics/btu326. http://dx.doi.org/10.1093/bioinformatics/btu326.1367-4803http://nrs.harvard.edu/urn-3:HUL.InstRepos:12987235Summary: 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.en-USJournal Article2014-10-0110.1093/bioinformatics/btu326