*******************************************************************
* 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 13:24:19 UTC on Tue Jun 14 2022.
Hostname: ip-10-0-201-15

Accepted options:
--pheno ozht.phen
--qcovar ozht.prs5.qcovar
--covar ozht.covar
--grm ozGRM
--out ozhtPRS5
--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.prs5.qcovar].
6 quantitative covariate(s) of 1933 individuals are included from [ozht.prs5.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: 43.7455 34.2568
logL: -4806.24
Running AI-REML algorithm ...
Iter.	logL	V(G)	V(e)	
1	-4746.69	59.33900	17.65774	
2	-4645.51	69.62249	11.07936	
3	-4589.55	75.91415	8.58302	
4	-4565.71	80.14304	7.42229	
5	-4555.30	83.17158	6.78155	
6	-4550.39	85.38582	6.39149	
7	-4547.97	87.01165	6.13978	
8	-4546.74	88.20385	5.97127	
9	-4546.12	89.07556	5.85569	
10	-4545.79	91.08660	5.60067	
11	-4545.45	91.35558	5.57830	
12	-4545.44	91.38236	5.57510	
13	-4545.44	91.38565	5.57470	
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: 68.71671
logL: -4969.43466
Running AI-REML algorithm ...
Iter.	logL	V(e)	
1	-4933.73	68.68605	
2	-4933.73	68.66510	
3	-4933.73	68.61979	
4	-4933.73	68.61982	
5	-4933.73	68.61982	
Log-likelihood ratio converged.

Summary result of REML analysis:
Source	Variance	SE
V(G)	91.385646	3.881152
V(e)	5.574698	0.358524
Vp	96.960344	3.838207
V(G)/Vp	0.942505	0.004483

Sampling variance/covariance of the estimates of variance components:
1.506334e+01	-2.300251e-01	
-2.300251e-01	1.285397e-01	
Estimatesof fixed effects:

Source	Estimate	SE
mean	168.446708	8.737619
X_2	-0.113225	0.022407
X_3	-9.622067	483.773546
X_4	412.972237	292.081569
X_5	48.675267	188.861598
X_6	-57.801962	178.935760
X_7	2.763790	0.613928
X_8	-9.544225	0.565300

Summary result of REML analysis has been saved in the file [ozhtPRS5.hsq].

Analysis finished at 13:24:25 UTC on Tue Jun 14 2022
Overall computational time: 6.32 sec.