I know logistic mixed model is not possible, but is it possible to analyse case-control traits using a linear model and then transform the effect size (like BOLT-LMM)?

When I try I get the following error:

```
/databricks/python/lib/python3.7/site-packages/hail/stats/linear_mixed_model.py:492: RuntimeWarning: divide by zero encountered in log
neg_log_reml = (np.linalg.slogdet(xdx)[1] - logdet_d + self._dof * np.log(sigma_sq)) / 2
/databricks/python/lib/python3.7/site-packages/scipy/optimize/optimize.py:1767: RuntimeWarning: invalid value encountered in double_scalars
r = (xf - nfc) * (fx - ffulc)
Exception: failed to fit log_gamma: optimum within 0.001 of upper bound.
```