*******************************************************************
* 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 13:18:19 UTC on Tue Jun 14 2022.
Hostname: ip-10-0-201-15

Accepted options:
--pheno ozht.phen
--qcovar ozht.prs2.qcovar
--covar ozht.covar
--grm ozGRM
--out ozhtPRS2
--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.prs2.qcovar].
6 quantitative covariate(s) of 1933 individuals are included from [ozht.prs2.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:  43.828 34.2361
logL: -4807.36
Running AI-REML algorithm ...
Iter.	logL	V(G)	V(e)	
1	-4747.62	59.53572	17.48921	
2	-4644.39	69.76967	10.98529	
3	-4588.49	76.01234	8.53453	
4	-4564.96	80.21423	7.39286	
5	-4554.71	83.22495	6.76154	
6	-4549.88	85.42585	6.37683	
7	-4547.49	87.04118	6.12843	
8	-4546.29	88.22501	5.96209	
9	-4545.67	89.09008	5.84799	
10	-4545.36	91.08456	5.59628	
11	-4545.02	91.34835	5.57435	
12	-4545.01	91.37438	5.57124	
13	-4545.01	91.37754	5.57085	
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: 69.17535
logL: -4974.01940
Running AI-REML algorithm ...
Iter.	logL	V(e)	
1	-4939.88	69.14531	
2	-4939.88	69.12478	
3	-4939.88	69.08038	
4	-4939.88	69.08041	
5	-4939.88	69.08041	
Log-likelihood ratio converged.

Summary result of REML analysis:
Source	Variance	SE
V(G)	91.377539	3.879798
V(e)	5.570853	0.358140
Vp	96.948392	3.837078
V(G)/Vp	0.942538	0.004478

Sampling variance/covariance of the estimates of variance components:
1.505283e+01	-2.289649e-01	
-2.289649e-01	1.282644e-01	
Estimatesof fixed effects:

Source	Estimate	SE
mean	168.417530	8.736724
X_2	-0.112660	0.022406
X_3	15.076693	483.651444
X_4	406.821756	292.025107
X_5	54.259660	188.838053
X_6	-65.133325	178.945021
X_7	3.286590	0.720470
X_8	-9.516487	0.565417

Summary result of REML analysis has been saved in the file [ozhtPRS2.hsq].

Analysis finished at 13:18:27 UTC on Tue Jun 14 2022
Overall computational time: 7.57 sec.