*******************************************************************
* 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 13:18:11 UTC on Tue Jun 14 2022.
Hostname: ip-10-0-201-15

Accepted options:
--pheno ozht.phen
--qcovar ozht.prs1.qcovar
--covar ozht.covar
--grm ozGRM
--out ozhtPRS1
--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.prs1.qcovar].
6 quantitative covariate(s) of 1933 individuals are included from [ozht.prs1.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: 43.9528 34.2896
logL: -4810.94
Running AI-REML algorithm ...
Iter.	logL	V(G)	V(e)	
1	-4751.85	59.84011	17.40093	
2	-4647.03	70.13324	10.91462	
3	-4590.77	76.39449	8.49173	
4	-4567.32	80.61353	7.36394	
5	-4557.13	83.63826	6.74011	
6	-4552.33	85.84919	6.35988	
7	-4549.96	87.47110	6.11437	
8	-4548.77	88.65891	5.94998	
9	-4548.16	89.52614	5.83726	
10	-4547.85	91.52381	5.58866	
11	-4547.51	91.78332	5.56727	
12	-4547.51	91.80849	5.56429	
13	-4547.51	91.81150	5.56392	
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: 69.78139
logL: -4980.18681
Running AI-REML algorithm ...
Iter.	logL	V(e)	
1	-4948.05	69.75216	
2	-4948.05	69.73219	
3	-4948.05	69.68899	
4	-4948.05	69.68902	
5	-4948.05	69.68902	
Log-likelihood ratio converged.

Summary result of REML analysis:
Source	Variance	SE
V(G)	91.811500	3.895050
V(e)	5.563916	0.357498
Vp	97.375416	3.852776
V(G)/Vp	0.942861	0.004449

Sampling variance/covariance of the estimates of variance components:
1.517142e+01	-2.276706e-01	
-2.276706e-01	1.278046e-01	
Estimatesof fixed effects:

Source	Estimate	SE
mean	169.095957	8.764314
X_2	-0.113977	0.022456
X_3	-17.551325	484.849288
X_4	410.861461	292.698935
X_5	51.660560	189.243241
X_6	-41.123137	179.306181
X_7	3.182480	0.808135
X_8	-9.535405	0.566582

Summary result of REML analysis has been saved in the file [ozhtPRS1.hsq].

Analysis finished at 13:18:19 UTC on Tue Jun 14 2022
Overall computational time: 7.57 sec.