Post by cp179 on Oct 29, 2015 17:24:29 GMT
Hello,
I have an imputed file whose first lines look like the following:
SNP Al1 Al2 Freq1 MAF Quality Rsq
1:787844 4 2 0.744554774657859 0.255445225342141 NA 0.10237
1:790758:GTA_G 6 7 0.733329959008047 0.266670040991953 NA 0.14147
But when I try to prune based on Rsq, it does not prune anything. For example, see this bolded below:
> gcta64 --dosage-mach DATAchr1c.dose.gz DATAchr1c.info.processed.gz --imput-rsq 0.3 --maf 0.01 --make-grm --out test
Analysis started: Thu Oct 29 10:08:19 2015
Options:
--dosage-mach-gz DATAchr1c.dose.c1.gz DATAchr1c.info.processed.gz
--imput-rsq 0.3
--maf 0.01
--make-grm
--out chr1_trimmed
Note: This is a multi-thread program. You could specify the number of threads by the --thread-num option to speed up the computation if there are multiple processors in your machine.
Reading map file of the imputed dosage data from [DATAchr1c.info.processed.gz].
1616437 SNPs to be included from [DATAchr1c.info.processed.gz].
Reading dosage data from [DATAchr1c.dose.c1.gz] in individual-major format (Note: may use huge RAM).
(Imputed dosage data for 5120 individuals detected).
Imputed dosage data for 5120 individuals are included from [DATAchr1c.dose.c1.gz].
Pruning SNPs with imputation Rsq > 0.3 ...
After pruning for imputation Rsq > 0.3, there are 1616437 SNPs (0 SNPs with imputation Rsq < 0.3).
Calculating allele frequencies ...
Pruning SNPs with MAF > 0.01 ...
After pruning SNPs with MAF > 0.01, there are 709572 SNPs (906865 SNPs with MAF < 0.01).
Recoding genotypes (individual major mode) ...
Any help would be greatly appreciated...
charles patlo
I have an imputed file whose first lines look like the following:
SNP Al1 Al2 Freq1 MAF Quality Rsq
1:787844 4 2 0.744554774657859 0.255445225342141 NA 0.10237
1:790758:GTA_G 6 7 0.733329959008047 0.266670040991953 NA 0.14147
But when I try to prune based on Rsq, it does not prune anything. For example, see this bolded below:
> gcta64 --dosage-mach DATAchr1c.dose.gz DATAchr1c.info.processed.gz --imput-rsq 0.3 --maf 0.01 --make-grm --out test
Analysis started: Thu Oct 29 10:08:19 2015
Options:
--dosage-mach-gz DATAchr1c.dose.c1.gz DATAchr1c.info.processed.gz
--imput-rsq 0.3
--maf 0.01
--make-grm
--out chr1_trimmed
Note: This is a multi-thread program. You could specify the number of threads by the --thread-num option to speed up the computation if there are multiple processors in your machine.
Reading map file of the imputed dosage data from [DATAchr1c.info.processed.gz].
1616437 SNPs to be included from [DATAchr1c.info.processed.gz].
Reading dosage data from [DATAchr1c.dose.c1.gz] in individual-major format (Note: may use huge RAM).
(Imputed dosage data for 5120 individuals detected).
Imputed dosage data for 5120 individuals are included from [DATAchr1c.dose.c1.gz].
Pruning SNPs with imputation Rsq > 0.3 ...
After pruning for imputation Rsq > 0.3, there are 1616437 SNPs (0 SNPs with imputation Rsq < 0.3).
Calculating allele frequencies ...
Pruning SNPs with MAF > 0.01 ...
After pruning SNPs with MAF > 0.01, there are 709572 SNPs (906865 SNPs with MAF < 0.01).
Recoding genotypes (individual major mode) ...
Any help would be greatly appreciated...
charles patlo
