Comments (5)
Indeed, the framework is similar to the one in LDSC, and we recommend using a similar preprocessing framework as for LDSC.
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I am working from the example into using the code with my data. I downloaded 1000 G, removed biallelics, double entries, set an all european panel. I set a MAF at .01 and I turned it into a bed file and computed allele frequencies.
then I tried to run step 2
#run pcgc_sync.py to collect annotations details
python pcgc_sync.py
--annot-chr baselineLD_v2.2/baselineLD.
--frqfile-chr 1000G/1000G.EUR.QC.
--out baselineLD_v2.2/baselineLD
And this happens
Traceback (most recent call last):
File "/projects/b1137/Diabetic_retinopathy/GeneticCorrelation/S-PCGC/S-PCGC/pcgc_sync.py", line 140, in
df_sync, df_overlap, df_overlap_common = pcgc_sync2(args)
File "/projects/b1137/Diabetic_retinopathy/GeneticCorrelation/S-PCGC/S-PCGC/pcgc_sync.py", line 90, in pcgc_sync2
df_sync, df_overlap, df_overlap_common = collect_annotations_chr_info(annot_fname, df_frq, min_annot)
File "/projects/b1137/Diabetic_retinopathy/GeneticCorrelation/S-PCGC/S-PCGC/pcgc_sync.py", line 36, in collect_annotations_chr_info
raise ValueError('Not all SNPs in the annotations file have MAFs')
ValueError: Not all SNPs in the annotations file have MAFs
I sm decreasing my MAF filtering, but there is only so much I am willing to reduce the MAF and believe the R^2. Is there a way to tell the code to accept that it can't match all of them and live with it?
from s-pcgc.
I really only want to compute heritability and genetic correlations at this point, without exploring (yet) annotations.
from s-pcgc.
Hi, the problem is not that some SNPs have low MAFs. The problem is that some SNPs have no MAF information. Hence, S-PCGC doesn't know how to handle these SNPs. If you don't think these SNPs are important, you're welcome to remove them from the annotation files all together...
Hope this helps, please let me know if not!
from s-pcgc.
Closing this issue, feel free to reopen if needed. Thanks!
from s-pcgc.
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