Closed garyzhubc closed 3 years ago
for fine mapping you should be looking at a small region of the genome... eg 1000s or 10k SNPs might be typical. So the short answer is that you need to break up the file into smaller regions
Hi @stephens999 ,
I am having the same problem as @garyzhubc , I am interested in a region that is a couple of megabases long, and I am working with a cohort of 500k. How should I proceed with loading data into R?
How do you recommend chunking the genotypic data? Should I go by the number of SNPs or by fixed ranges? Should my ranges overlap? What happens if SNPs from overlapping regions end up in different confidence sets?
Thank you!
Best, probalica
@probalica19 you can partition the genome to non-overlapping chunks with negligible LD between these chunks. See for example https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731402/. That partitions the European genome into 1700 chunks.
For large cohorts we suggest using sufficient statistics susieR::susie_suff_stat
to reduce the complexity from a factor of N
(500K) down to P
. P
should generally be smaller than N
when you focus on common variants fine-mapping.
What to do with a 2500G pgen file that cannot fit into R all at once? The experiment on the paper did the analysis on a much smaller number of SNPs. Susie is asking for an R matrix but it will be too big to be loaded.