SNPsea: an algorithm to identify cell types and pathways affected by risk loci
Overview
SNPsea is an algorithm to identify cell types and pathways likely to be affected by risk loci. It requires a list of SNP identifiers and a matrix of genes and conditions.
Genome-wide association studies (GWAS) have discovered multiple genomic loci associated with risk for different types of disease. SNPsea provides a simple way to determine the types of cells influenced by genes in these risk loci.
Suppose disease-associated alleles influence a small number of pathogenic cell types. We hypothesize that genes with critical functions in those cell types are likely to be within risk loci for that disease. We assume that a gene's specificity to a cell type is a reasonable indicator of its importance to the unique function of that cell type.
First, we identify the genes in linkage disequilibrium (LD) with the given trait-associated SNPs and score the gene set for specificity to each cell type. Next, we define a null distribution of scores for each cell type by sampling random SNP sets matched on the number of linked genes. Finally, we evaluate the significance of the original gene set's specificity by comparison to the null distributions: we calculate an exact permutation p-value.
SNPsea is a general algorithm. You may provide your own:
- Continuous gene matrix with gene expression profiles (or other values).
- Binary gene annotation matrix with presence/absence 1/0 values.
We provide three expression matrices and one annotation matrix.
The columns of the matrix may be tissues, cell types, GO annotation codes, or other conditions. Continuous matrices must be normalized before running SNPsea: columns must be directly comparable to each other.
Citation
If you benefit from this method, please cite:
Slowikowski, K et al. SNPsea: an algorithm to identify pathways, tissues, and conditions influenced by risk loci. Bioinformatics (2014). doi:10.1093/bioinformatics/btu326
For additional information, please see:
Hu, X et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. The American Journal of Human Genetics 89, 496-506 (2011). PubMed
Downloads
| Documentation: | HTML PDF Epub |
| Executable (Linux 64): | https://github.com/slowkow/snpsea/releases |
| Data: | http://dx.doi.org/10.6084/m9.figshare.871430 |
| Source code: | https://github.com/slowkow/snpsea/ |
Kamil Slowikowski wrote the code in Soumya Raychaudhuri's lab at the Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, and the Broad Institute.
 |
 |
 |