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

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.

PMID: 24813542
PMCID: PMC4147889
DOI: 10.1093/bioinformatics/btu326

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

Kamil Slowikowski et al. Bioinformatics. 2014.

. 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.

PMID: 24813542
PMCID: PMC4147889
DOI: 10.1093/bioinformatics/btu326

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.

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Figures

**Fig. 1.**
Empirical P-values for specificity to each condition. 25 of 79 tissues (Gene Atlas) are shown. Adjacent: Pearson correlation coefficients for pairs of expression profiles ordered by hierarchical clustering with UPGMA

See this image and copyright information in PMC

References

1. Botstein D, et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 2000;25:25–29. - PMC - PubMed
1. Elbers CC, et al. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet. Epidemiol. 2009;33:419–431. - PubMed
1. Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–1073. - PMC - PubMed
1. Holden M, et al. GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies. Bioinformatics. 2008;24:2784–2785. - PubMed
1. Hu X, et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 2011;89:496–506. - PMC - PubMed

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

Affiliations

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

Authors

Affiliations

Abstract

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Molecular Biology Databases

Research Materials