The most likely explanation is that the Biomarker is a suppressor variable. A suppressor variable is correlated with another predictor variable in such a way that the predictor is significant when both are entered into a model, but not when it is entered alone. Unfortunately, suppression is just one of those statistical phenomena that aren't very intuitive. This website is fairly long, but very clear and includes a discussion of all the relevant issues with a section on suppressor variables at the end. I also found this American Statistician paper, which is specific to suppressor variables. I haven't read it yet, but it looks quite good.
Another possibility is that the Biomarker is not a suppressor, but it accounts for enough of the residual variance in your response variable (glucose), that the weaker gene - glucose relationship becomes significant. Remember that 'significance' is assessed by the relationship between the variability that a predictor accounts for, and the residual variability. If the Biomarker accounts for a good deal of what would otherwise be residual variability, but consumes only, for example, 1 degree of freedom, this could increase the power of your analysis with respect to the gene. Under this interpretation, you would have simply needed more data to resolve the gene - glucose relationship, but there might not be any correlation between the gene and the Biomarker.
In neither case would it be correct to call this a spurious correlation. A spurious correlation is when there is a zero-order correlation between two variables, but no direct relationship. The classic situation is where two variables A and B are both caused by a third variable, C, but otherwise have no direct connection. A real-world example I once heard is that when the economy speeds up, it enhances both the birth rate and steel production, but that there is no direct connection between them.
An interaction is a third, distinct concept. An interaction obtains when you would describe a situation using the word 'depends'. For instance, if someone asked what is the effect of taking the birth control pill, you might say:
It depends, for women, it suppresses ovulation and so reduces the
chance of pregnancy. But for men, since they don't ovulate, it has no
(I acknowledge that this is a rather forced example.)