Home Page: http://www.broadinstitute.org/mpg/snpsea
Documentation: HTML | PDF | Epub
Executable: snpsea-v1.0.3.tar.gz
Data: SNPsea_data_20140520.zip
License: GNU GPLv3
If you benefit from this method, please cite:
Slowikowski, K. et al. SNPsea: an algorithm to identify cell types, tissues, and pathways affected by risk loci. Bioinformatics (2014). doi:10.1093/bioinformatics/btu326
See the first description of the algorithm and additional examples here:
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
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 you with three expression matrices and one annotation matrix. See the Data section of the Manual.
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.
The heatmap shows Pearson correlation coefficients between pairs of tissue expression profiles. The blue bars show p-values. Statistically significant p-values cross the Bonferroni multiple testing threshold (black line).
We identified BM-CD71+Early Erythroid as the cell type with most significant enrichment (P < 2e-7) for cell type-specific gene expression relative to 78 other tissues in the Gene Atlas (Su et al. 2004).
SNPsea tested the genes in linkage disequilibrium (LD) with 45 input SNPs associated with count of red blood cells (P <= 5e-8 in Europeans) (Harst et al. 2012). For each of the 79 cell types in the Gene Atlas, we tested a maximum of 1e7 null SNP sets where each null SNP was matched to an input SNP on the number of genes in LD.
We ran SNPsea like this:
options=(
--snps Red_blood_cell_count-Harst2012-45_SNPs.gwas
--gene-matrix GeneAtlas2004.gct.gz
--gene-intervals NCBIgenes2013.bed.gz
--snp-intervals TGP2011.bed.gz
--null-snps Lango2010.txt.gz
--out out
--slop 10e3
--threads 8
--null-snpsets 0
--min-observations 100
--max-iterations 1e7
)
snpsea ${options[*]}
# Time elapsed: 2 minutes 36 seconds
# Create the figure shown above:
snpsea-barplot outPlease submit an issue to report bugs or ask questions.
Please contribute bug fixes or new features with a pull request to this repository.