SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci
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CitationSlowikowski, 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.
AbstractSummary: 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: email@example.com Supplementary information: Supplementary data are available at Bioinformatics online.
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