*******************************************************************
* Genome-wide Complex Trait Analysis (GCTA)
* version 1.94.0 beta Linux
* (C) 2010-present, Jian Yang, The University of Queensland
* Please report bugs to Jian Yang <jian.yang.qt@gmail.com>
*******************************************************************
Analysis started at 13:18:34 UTC on Tue Jun 14 2022.
Hostname: ip-10-0-201-15

Accepted options:
--pheno ozht.phen
--qcovar ozht.prs4.qcovar
--covar ozht.covar
--grm ozGRM
--out ozhtPRS4
--reml
--reml-est-fix

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 IDs of the GRM from [ozGRM.grm.id].
1956 IDs are read from [ozGRM.grm.id].
Reading the GRM from [ozGRM.grm.bin].
GRM for 1956 individuals are included from [ozGRM.grm.bin].
Reading phenotypes from [ozht.phen].
Non-missing phenotypes of 1895 individuals are included from [ozht.phen].
Reading quantitative covariate(s) from [ozht.prs4.qcovar].
6 quantitative covariate(s) of 1933 individuals are included from [ozht.prs4.qcovar].
Reading discrete covariate(s) from [ozht.covar].
1 discrete covariate(s) of 1933 individuals are included from [ozht.covar].

6 quantitative variable(s) included as covariate(s).
1 discrete variable(s) included as covariate(s).
1894 individuals are in common in these files.

Performing  REML analysis ... (Note: may take hours depending on sample size).
1894 observations, 8 fixed effect(s), and 2 variance component(s)(including residual variance).
Calculating prior values of variance components by EM-REML ...
Updated prior values: 43.7703 34.2567
logL: -4806.73
Running AI-REML algorithm ...
Iter.	logL	V(G)	V(e)	
1	-4747.20	59.39007	17.62675	
2	-4645.59	69.66570	11.06386	
3	-4589.65	75.95222	8.57561	
4	-4565.86	80.17987	7.41804	
5	-4555.48	83.20819	6.77879	
6	-4550.58	85.42239	6.38956	
7	-4548.16	87.04814	6.13836	
8	-4546.94	88.24021	5.97017	
9	-4546.31	89.11175	5.85481	
10	-4545.99	91.12224	5.60027	
11	-4545.65	91.39066	5.57795	
12	-4545.64	91.41736	5.57477	
13	-4545.64	91.42062	5.57436	
Log-likelihood ratio converged.

Calculating the logLikelihood for the reduced model ...
(variance component 1 is dropped from the model)
Calculating prior values of variance components by EM-REML ...
Updated prior values: 68.84960
logL: -4970.79105
Running AI-REML algorithm ...
Iter.	logL	V(e)	
1	-4935.55	68.81912	
2	-4935.55	68.79829	
3	-4935.55	68.75324	
4	-4935.55	68.75327	
5	-4935.55	68.75327	
Log-likelihood ratio converged.

Summary result of REML analysis:
Source	Variance	SE
V(G)	91.420622	3.882391
V(e)	5.574364	0.358485
Vp	96.994986	3.839481
V(G)/Vp	0.942529	0.004480

Sampling variance/covariance of the estimates of variance components:
1.507296e+01	-2.299303e-01	
-2.299303e-01	1.285118e-01	
Estimatesof fixed effects:

Source	Estimate	SE
mean	168.212392	8.737739
X_2	-0.112576	0.022412
X_3	2.572887	483.807226
X_4	412.614297	292.132395
X_5	48.578056	188.894702
X_6	-57.183480	178.965315
X_7	2.801184	0.629326
X_8	-9.528049	0.565490

Summary result of REML analysis has been saved in the file [ozhtPRS4.hsq].

Analysis finished at 13:18:42 UTC on Tue Jun 14 2022
Overall computational time: 7.53 sec.