Closed complexgenome closed 2 years ago
Hi @weizhouUMICH
Sorry to be a nag on this. Can you please help here?
Hi @Sanjeev,
For step 1, you don;t need all imputed markers. The markers in Step 1 are for constructing GRM and the number of markers only needs to be larger than the independent samples in the data set. Please check the question 4 of FAQ on this page https://github.com/weizhouUMICH/SAIGE/wiki/Genetic-association-tests-using-SAIGE
https://github.com/weizhouUMICH/SAIGE/wiki/Genetic-association-tests-using-SAIGE Thanks, Wei
On Tue, Jun 15, 2021 at 5:26 PM Sanjeev @.***> wrote:
Hi @weizhouUMICH https://github.com/weizhouUMICH
Sorry to be a nag on this. Can you please look into this?
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@weizhouUMICH
Thank you. I understand for the GRM small number of markers are fine.
However, I am fitting null model for gene-based
tests. it needs SNPs (30 randomly) for different MACs (1-20).
--isCateVarianceRatio=TRUE
I got this from your reply at:
https://github.com/weizhouUMICH/SAIGE/issues/226#issuecomment-662220726
Hi @weizhouUMICH
PLINK data don't have reliable SNPs with lower allele frequency.
I extract TOPMED imputation imputed SNPs (80% RSQ, MAF <=1%). The number of SNPs is ~35 million. When I try to construct kinship I get error for the bad alloc. I am using node with 65G memory.
nbyte: 1809
nbyte: 1782
reserve: 77072023552
M: 43201809, N: 7234
size of genoVecofPointers: 40
bad_alloc caught1: std::bad_alloc
Can you please guide on on how to select rare markers for gene-based association test model fitting?
Sorry fo the late reply! We have just released a new version 1.0.0. It has substantial computational efficiency improvements for both Step 1 and Step 2 for single-variant and set-based tests and clearer log output. We have created a new program github page https://github.com/saigegit/SAIGE with the documentation provided https://saigegit.github.io/SAIGE-doc/ The program will be maintained by multiple SAIGE developers there. The docker image has been updated. Please feel free to try the version 1.0.0 and report issues if any.
Thanks! Wei
Hi @weizhouUMICH
I have plink file for autosomal CHRs with 42,788,581 SNPs. These are high quality SNPs from TOPMED imputation output. I try to run null model using these data I get error for
I would like to run gene-based analysis, where saige needs to fit model using different MAC as categorical variables. Size of PLINK .bim and .bed file is ~75G.
I run analysis on node requesting with 50G memory.
Is there a way I can resolve this? Can I limit SNPs until say, 5% MAF only for this step?
best,