Hello!

I am running a simple burden test using `hl.logistic_regression_rows()`

on a matrix table `mt_burden`

containing my genomic and covariate data. `mt_burden`

contains the following:

**row fields:**

`gene_symbol`

: str

**column fields:**

`pheno.is_case`

: int32 (coded 0/1),

`pheno.age_at_enrollment`

: int32,

`pheno.pca_features`

: array (list of 10 PCs)

**entry_fields:**

`ind_het`

: bool (carrier status)

The following call runs as expected for a simple burden test without an interaction term, returning the effect size estimates for the `ind_het`

covariate for each gene of interest in `gene_symbol`

:

```
covariates = [1.0, mt_burden.pheno.age_at_enrollment,
mt_burden.pca_features[1], ..., mt_burden.pca_features[10]]
log_reg = hl.logistic_regression_rows(
test = 'wald',
y = mt_burden.pheno.is_case,
x = mt_burden.ind_het,
covariates = covariates,
max_iterations = 50
)
```

I would like to run a similar analysis, but including a age\_at\_enrollment * ind\_het interaction term as a covariate. I can form this with the followingâ€¦

`mt_burden = mt_burden.annotate_entries(het_times_age = mt_burden.ind_het * mt_burden.pheno.age_at_enrollment)`

â€¦but then am running into a few problems:

- Since
`het_times_age`

is an entry field,`hl.logistic_regression_rows`

is throwing an error when this is included in the list of covariates. I see this thread, but was wondering if there was any update on this functionality! - More generally, I would sometimes like the logistic regression to return beta estimates for more than one covariate. I see that
`hl.logistic_regression_rows`

only accepts a single`<float64>`

value for`x`

, and it throws an error when I try and pass a list of covariates. Is there a way of accomplishing this?

Sorry for the trouble and thanks so much for your help!

Best,

John