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  • Post GWAS Interpretation

    I’m interpreting GWAS data for a project, from the data I extracted 3 lead SNPs and created LD plots for each using haploview. Now that I have the locations/bp coordinates for each haplotype block, what would be the next step?
    My guess would be to search databases for variants which may exist within these blocks which may not have been included in the study. Would it be reasonable to search databases for each SNP associated with these blocks to see if there is a potential functional SNP? Where would I do such a thing? Is this the right train of thought? Any advice / suggested resources would be greatly appreciated.

  • #2
    You can find SNPs in your region in the dbSNP database and ClinVar databases at NCBI, e.g. https://www.ncbi.nlm.nih.gov/clinvar/

    ClinVar will include SNPs that have been described in the literature. The dbSNP database at NCBI is pretty comprehensive of all human known SNPs.

    You can also view all this information on a genome browser that has variation data available. NCBI's GDV includes the information at NCBI.

    I've created a session with some SNP tracks that are available to activate for this browser. The "Clinical, dbSNP" track has NCBI's ClinVar data. I also added a track showing GWAS results from NHGRI, which could also point you to SNPs that were previously detected as possibly functional SNPs.

    Here's a sample set of tracks zoomed in at BRCA2.


    You can navigate to another region or gene using the search box on the left side of the page. Also, there are more tracks available if you browse or search the Tracks menu near the right side of the page. For instance, there are more SNP tracks under the Variation section and more GWAS tracks in the Phenotype and Disease section.

    Hope this helps!

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    • #3
      I just remembered the Variation Viewer browser is even better because it includes a table at the bottom where you can filter information about SNPs.

      Explore genetic variations and their clinical significance. Search known variants by gene, phenotype or location - or upload and compare your own data.

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