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

Kamil Slowikowski; Xinli Hu; Soumya Raychaudhuri

doi:10.1093/bioinformatics/btu326

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

Bioinformatics. 2014 Sep 1;30(17):2496-7. doi: 10.1093/bioinformatics/btu326. Epub 2014 May 10.

Authors

Kamil Slowikowski¹, Xinli Hu², Soumya Raychaudhuri¹

Affiliations

¹ Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston MA 02215, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston MA 02215, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA.
² Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston MA 02215, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA.

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.

Availability and implementation: http://broadinstitute.org/mpg/snpsea.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Erythroid Cells / metabolism
Genetic Loci
Genomics
Humans
Linkage Disequilibrium*
Polymorphism, Single Nucleotide*
Risk

Abstract

Publication types

MeSH terms

Grants and funding