Polygenic risk score calculation

Hail Query & hailctl
1

Hello there!
I already did linear regression, and get a corresponded matrix table for further polygenic score calculation per individual. My MatrixTable has the next structure:

mt.describe()

----------------------------------------
Global fields:
    None
----------------------------------------
Column fields:
    's': str
----------------------------------------
Row fields:
    'locus': locus<GRCh37>
    'cvas': struct {
        beta: str, 
        pval: str
    }
----------------------------------------
Entry fields:
 'GT': call

Then I want to sum over all variants and calculate (n_non_ref_counts * beta) for each individual.

mt = mt.annotate_rows(whole_dataset = 1)
mts = mt.group_rows_by(summary_stat = mt.whole_dataset)\
   .aggregate(risk_score = mt.GT.n_alt_alleles()*hl.float(mt.cvas.beta))

but I get next to an error:

ExpressionException: scope violation: 'GroupedMatrixTable.aggregate_entries' expects an expression indexed by ['column']
    Found indices ['column', 'row'], with unexpected indices ['row']. Invalid fields:
        'GT' (indices ['column', 'row'])
        'cvas' (indices ['row'])
    'GroupedMatrixTable.aggregate_entries' supports aggregation over axes ['column', 'row'], so these fields may appear inside an aggregator function.
2

I think this:

   .aggregate(risk_score = mt.GT.n_alt_alleles()*hl.float(mt.cvas.beta))

should be:

   .aggregate(risk_score = hl.agg.sum(mt.GT.n_alt_alleles()*hl.float(mt.cvas.beta)))

Does that seem right?

3

That’s worked. Thank you!