I have successfully run Step 1 computing the GRM on the fly. However, when I try using a sparse GRM computed through SAIGE (Step 0), the code initially runs but then gets stuck on the line "use sparse kinship to fit the model".
Any tips would be much appreciated.
See experts from the log below:
sparse GRM will be used to fit the NULL model and nThreads is set to 1
Leave-one-chromosome-out is not applied
24810 samples have genotypes
sex are categorical covariates
formula is y_binary~sex+age
24810 samples have non-missing phenotypes
24810 samples are in the sparse GRM
24810 samples who have non-missing phenotypes are also in the sparse GRM
24810 samples will be used for analysis
[...]
qr transformation has been performed on covariates
colnames(data.new) is y_binary minus1 sexMale sexUnknown age
out.transform$Param.transform$qrr: 4 4
[1] "isCovariateOffset=TRUE, so fixed effects coefficients won't be estimated."
extract sparse GRM
[1] 27851446
set elements in the sparse GRM <= 0 to zero
[1] 27851446
24810 samples have been used to fit the glmm null model
Setting up sparse GRM using step_0_relatednessCutoff_0.05_1000_randomMarkersUsed.sparseGRM.mtx and step_0_relatednessCutoff_0.05_1000_randomMarkersUsed.sparseGRM.mtx.sampleIDs.txt
Dimension of the sparse GRM is 24810 24810
2 locationMat.n_rows
27851446 locationMat.n_cols
27851446 valueVec.n_elem
geno.g_minMACVarRatio 20
geno.g_maxMACVarRatio -1
Markers in the Plink file with MAF < 0.01 will be removed before constructing GRM
Markers in the Plink file with missing rate > 0.15 will be removed before constructing GRM
y_binary is a binary trait
[1] "formula.new"
y_binary ~ 1
<environment: 0xa561be8>
[1] "head(data.new)"
y_binary minus1 sexMale sexUnknown age covoffset
1 0 -1 -1.0599135 -8.538737e-18 1.1771875 0.07386317
2 0 -1 -1.0599135 -1.665592e-16 -1.0781962 -0.38528577
3 0 -1 -1.0599135 -1.159836e-18 -1.1224195 -0.39428869
4 0 -1 -1.0599135 -2.417142e-18 -1.4319819 -0.45730913
5 0 -1 0.9434732 -1.200045e-02 0.3630953 0.22223307
6 0 -1 0.9434732 -1.200045e-02 -0.8309314 -0.02084578
glm:
Call: glm(formula = formula.new, family = binomial, data = data.new,
offset = covoffset)
Coefficients:
(Intercept)
-0.0005768
Degrees of Freedom: 24809 Total (i.e. Null); 24809 Residual
Null Deviance: 33990
Residual Deviance: 33990 AIC: 33990
Start fitting the NULL GLMM
user system elapsed
61.884 46.398 20.396
user system elapsed
66.039 50.184 20.555
[1] "Start reading genotype plink file here"
nbyte: 6203
nbyte: 6203
reserve: 6205000
M: 1000, N: 24810
setgeno mark1
setgeno mark2
42 markers with MAF >= 0.01 and missing rate <= 0.15
time: 264.405
[1] "Genotype reading is done"
inital tau is 1 0.1
use sparse kinship to fit the model
use sparse kinship to fit the model
Tau:
[1] 1.0 0.1
Fixed-effect coefficients:
[,1]
[1,] 0.007855873
use sparse kinship to fit the model
use sparse kinship to fit the model
use sparse kinship to fit the model
I have successfully run Step 1 computing the GRM on the fly. However, when I try using a sparse GRM computed through SAIGE (Step 0), the code initially runs but then gets stuck on the line "use sparse kinship to fit the model".
Any tips would be much appreciated.
See experts from the log below: