I just ran the following on SMA.sav file in SAS. The data file can be accessed here.

Proc Logistic Data = sma descending;
Where Age=1;
class Genotype Treatment / param=ref;
Model sma_bin = Genotype Treatment Genotype * Treatment / CLodds=both firth;
oddsratio Genotype;
oddsratio Treatment;
run;


I'm confused in relation to the following Firth logistic regression outcome. There appears to be a counter-intuitive result.
One that emerges if you look at the 100% stacked columns, and then look at what the logistic regression is telling us.

1. The columns tell us that a combination of T1OE and NMN (the Genotype = 1, Treatment = 1 situation) REDUCES our risk of being entered into the higher smooth muscle actin category (orange), relative to the 0, 0 situation - WT and Veh, the last column (See Fig 1)
2. But the logistic regression (see Fig 2) tells us that the combination of T1OE and NMN (the 1 values of the binary variables), results in a higher risk of being entered into the elevated smooth muscle actin category (orange). If we exponentiate the beta estimate, we get something like 20x odds ratio of being entered into the orange. This seems to be telling us the opposite, since going by the visual trend, having both these conditions lowers our risk of being entered into the orange, relative to when we had neither of these conditions. How can I reconcile this? Can this be right or am I misunderstanding something here?

Fig 1: 100% stacked columns showing visual trends

Fig 2: SAS Output