# Statistical Analysis Execution Time¶

GWAS analysis of imputed markers is computationally intensive. While it is feasible to run such analyses on some simple models like linear and logistic regression, more complex models like Cox regression and mixed linear models require more computing power or specialized implementations.

We have optimized the mixed linear model analysis to significantly decrease
computation time. Using a two-step approach (as described by Sikorska *et al.*,
2015 [doi: 10.1038/ejhg.2015.1]), the
execution time is comparable to a simple linear regression. Prior to
optimization, the analysis of chromosome 2 was performed in 53 hours for 33
sub-analysis with 6 threads each (which corresponds to 198 threads).

The following figure shows the execution time for a typical imputation analysis of chromosome 2, imputed for 5,045 samples. Chromosome 2 was composed a total of 1,170,797 loci, where 961,019 were of sufficient quality, and 528,932 had a MAF higher than 1%. The black dashed line is the execution time for Plink.

Note

On some installation, when executing the analysis with *n* threads,
*OPENBLAS* automatically uses all the CPUs for each thread, such that the
load quickly increases to *n* times the number of CPUs. Such high load slows
down the analysis considerably.

To avoid this, always export the following environment variable and specify
the total number of threads using the `--nb-process`

option.

```
export OPENBLAS_NUM_THREADS=1
```

We are planning to optimize the Cox’s proportional hazard regression in the near future.