Dear community,
I am planning on conducting a GWAS analysis with two groups of patients differing in binary characteristics. As this cohort naturally is very rare, our sample size is limited to a total of approximately 1500 participants (low number for GWAS). Therefore, we were thinking on studying associations between pre-selected genes that might be phenotypically relevant to our outcome. As there exist no pre-data/arrays that studied similiar outcomes in a different patient cohort, we need to identify regions of interest bioinformatically.
1) Do you know any tools that might help me harvest genetic information for known pathways involved in relevant cell-functions and allow me to downscale my number of SNPs whilst still preserving the exploratory character of the study design? e.g. overall thrombocyte function, endothelial cell function, immune function etc.
2) Alternatively: are there bioinformatic ways (AI etc.) that circumvent the problem of multiple testing in GWAS studies and would allow me to robustly explore my dataset for associations even at lower sample sizes (n < 1500 participants)?
Thank you very much in advance!
Kind regards,
Michael Eigenschink
I think you can decrease number of SNPs by doing a comparison with publicly available genomes of human and relative organisms. The genomic comparison with online data of up to 10000 genomes of human can help to select highly significant SNPs.
for the second part of your problem, you can try vcf2gwas pipeline, that is very easy to run as a docker image
- Badges
- Science method