--mlma in GWAS analysis not printing output file

I've been trying to run a GWAS using mlma. Everything seems to be running just fine, but the .mlma results/output file isn't printing. Has anyone else had this problem or know how to sort it out? Much appreciated! 

Here's what I've tried running: 


...Desktop\gcta_1.93.0beta_win\bin>gcta64 --mlma --bfile SexAverage --grm SexAverage --pheno phenotypes.phen --mpheno 3 --qcovar QuantitativeCovars.txt --covar QualitativeCovars.txt --out SexAvg_Weight --thread-num 10



*******************************************************************

* Genome-wide Complex Trait Analysis (GCTA)

* version 1.93.0 beta Windows

* (C) 2010-2019, The University of Queensland

* Please report bugs to Jian Yang <jian.yang@uq.edu.au>

*******************************************************************

Analysis started at 13:36:10 E. Australia Standard Time on Sat Dec 14 2019.

Hostname: DESKTOP-63TLD7E



Accepted options:

--mlma

--bfile SexAverage

--grm SexAverage

--pheno phenotypes.phen

--mpheno 3

--qcovar QuantitativeCovars.txt

--covar QualitativeCovars.txt

--out SexAvg_FinalWeight

--thread-num 10



Note: the program will be running on 10 threads.



Reading PLINK FAM file from [SexAverage.fam].

388 individuals to be included from [SexAverage.fam].

Reading PLINK BIM file from [SexAverage.bim].

6163 SNPs to be included from [SexAverage.bim].

Reading PLINK BED file from [SexAverage.bed] in SNP-major format ...

Genotype data for 388 individuals and 6163 SNPs to be included from [SexAverage.bed].

Reading phenotypes from [phenotypes.phen].

There are 3 traits specified in the file [phenotypes.phen].

Trait #3 is included for analysis.

Non-missing phenotypes of 388 individuals are included from [phenotypes.phen].

Reading quantitative covariates from [QuantitativeCovars.txt].

1 quantitative covariate(s) of 388 individuals read from [QuantitativeCovars.txt].

Reading discrete covariate(s) from [QualitativeCovars.txt].

2 discrete covariate(s) of 388 individuals are included from [QualitativeCovars.txt].

Reading IDs of the GRM from [SexAverage.grm.id].

388 IDs read from [SexAverage.grm.id].

Reading the GRM from [SexAverage.grm.bin].

GRM for 388 individuals are included from [SexAverage.grm.bin].

388 individuals are in common in these files.

1 quantitative variable(s) included as covariate(s).

2 discrete variable(s) included as covariate(s).



Performing MLM association analyses (including the candidate SNP) ...



Performing REML analysis ... (Note: may take hours depending on sample size).

388 observations, 5 fixed effect(s), and 2 variance component(s)(including residual variance).

Calculating prior values of variance components by EM-REML ...

Updated prior values: 4344 3230.73

logL: -1831.3

Running AI-REML algorithm ...

Iter. logL V(G) V(e)

1 -1767.86 0.01200 2329.28419 (1 component(s) constrained)

2 -1721.27 69.31397 2293.10807

3 -1715.98 153.35614 2240.14772

4 -1712.09 245.03480 2183.30920

5 -1709.47 335.67052 2130.90710

6 -1707.84 417.45025 2087.22250

7 -1706.92 485.46322 2053.48065

8 -1706.44 652.63501 1975.78749

9 -1705.99 661.34882 1979.93340

10 -1705.99 660.66039 1980.46434

11 -1705.99 660.75143 1980.40128

Log-likelihood ratio converged.

Calculating allele frequencies ...



Running association tests for 6163 SNPs ...


...Desktop\gcta_1.93.0beta_win\bin>