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https://github.com/slowkow/snpsea

:bar_chart: Identify cell types and pathways affected by genetic risk loci.
https://github.com/slowkow/snpsea

algorithm bioinformatics enrichment gene gene-sets gwas risk-loci tissue

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:bar_chart: Identify cell types and pathways affected by genetic risk loci.

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README 

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

========================================================================================



**Home Page:** 



**Documentation:** [HTML] | [PDF] | [Epub]



**Executable:** [snpsea-v1.0.3.tar.gz][exec]



**Data:** [SNPsea_data_20140520.zip][data]



**License:** [GNU GPLv3][license]



Citation

--------



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][Slowikowski2014]



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][Hu2011]



[Hu2011]: http://www.ncbi.nlm.nih.gov/pubmed/21963258

[Slowikowski2014]: http://bioinformatics.oxfordjournals.org/content/30/17/2496.full



Description

-----------



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:



1. Continuous gene matrix with gene expression profiles (or other values).

2. Binary gene annotation matrix with presence/absence 1/0 values.



We provide you with three expression matrices and one annotation matrix. See

the [Data][manualdata] section of the [Manual][HTML].



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.



Example

-------



![SNPsea results for RBC count-associated SNPs in the Gene Atlas.][example]



[example]: https://github.com/slowkow/snpsea/blob/master/docs/figures/Red_blood_cell_count-Harst2012-45_SNPs-GeneAtlas2004-single-pvalues_barplot.png



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][Su2004]).



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][Harst2012]). 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.



[Harst2012]: http://www.ncbi.nlm.nih.gov/pubmed/23222517

[Su2004]: http://www.ncbi.nlm.nih.gov/pubmed/15075390



We ran SNPsea like this:



```bash

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 out

```



Contributing

------------



Please [submit an issue][issues] to report bugs or ask questions.



Please contribute bug fixes or new features with a [pull request][pull] to this repository.



[issues]: https://github.com/slowkow/snpsea/issues

[pull]: https://help.github.com/articles/using-pull-requests/



[license]: https://github.com/slowkow/snpsea/blob/master/LICENSE



[exec]: https://github.com/slowkow/snpsea/archive/v1.0.3.tar.gz

[data]: http://dx.doi.org/10.6084/m9.figshare.871430



[HTML]: http://snpsea.readthedocs.org/en/latest/

[manualdata]: http://snpsea.readthedocs.org/en/latest/data.html

[PDF]: https://readthedocs.org/projects/snpsea/downloads/pdf/latest/

[Epub]: https://readthedocs.org/projects/snpsea/downloads/epub/latest/