*******************************************************************
* 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 13:18:27 UTC on Tue Jun 14 2022.
Hostname: ip-10-0-201-15

Accepted options:
--pheno ozht.phen
--qcovar ozht.prs3.qcovar
--covar ozht.covar
--grm ozGRM
--out ozhtPRS3
--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.prs3.qcovar].
6 quantitative covariate(s) of 1933 individuals are included from [ozht.prs3.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: 43.7761 34.2641
logL: -4806.95
Running AI-REML algorithm ...
Iter.	logL	V(G)	V(e)	
1	-4747.50	59.39239	17.64269	
2	-4646.02	69.67637	11.07406	
3	-4590.07	75.97320	8.58117	
4	-4566.24	80.20843	7.42150	
5	-4555.84	83.24244	6.78120	
6	-4550.93	85.46101	6.39135	
7	-4548.50	87.09011	6.13976	
8	-4547.28	88.28472	5.97131	
9	-4546.65	89.15818	5.85577	
10	-4546.33	91.17320	5.60084	
11	-4545.98	91.44246	5.57847	
12	-4545.97	91.46926	5.57527	
13	-4545.97	91.47254	5.57487	
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: 68.88252
logL: -4971.07558
Running AI-REML algorithm ...
Iter.	logL	V(e)	
1	-4935.95	68.85209	
2	-4935.95	68.83129	
3	-4935.94	68.78631	
4	-4935.94	68.78634	
5	-4935.94	68.78634	
Log-likelihood ratio converged.

Summary result of REML analysis:
Source	Variance	SE
V(G)	91.472544	3.884525
V(e)	5.574867	0.358533
Vp	97.047411	3.841601
V(G)/Vp	0.942555	0.004479

Sampling variance/covariance of the estimates of variance components:
1.508953e+01	-2.300927e-01	
-2.300927e-01	1.285462e-01	
Estimatesof fixed effects:

Source	Estimate	SE
mean	168.639132	8.743245
X_2	-0.112417	0.022418
X_3	-16.914727	484.031778
X_4	417.248000	292.245842
X_5	47.967643	188.946734
X_6	-54.884823	179.008572
X_7	2.898535	0.664494
X_8	-9.535677	0.565604

Summary result of REML analysis has been saved in the file [ozhtPRS3.hsq].

Analysis finished at 13:18:34 UTC on Tue Jun 14 2022
Overall computational time: 7.50 sec.