The same will hold true for GLM. DAG are non-parametric in the sense that they don't place any restrictions on the distributions of $\mathbf{S}$ in their relation to $X$ or $Y$. When using parametric regression models, you assume that the models are flexible enough to capture the true density. You can do this with linear regression if the outcome is Gaussian, or with a GLM that has the appropriate distributions for $Y$.
Consider the following example. There is a single continuous variable $S$ which is a common cause of both a binary treatment $X$ and binary outcome $Y$ (i.e. $S$ is a confounder in this context). The causal diagram makes no assertion on the relationship between $S$ and $Y$. We assume that DAG satisfies Markov factorization, so that we can write the density for the DAG as
$$f(S, X, Y) = f(Y|X, S)f(X|S)f(S)$$
Note that says nothing about the particular functional forms of $f(Y|X, S)$. In fact, the described DAG is consistent with all of the following models
$$ \Pr(Y|X,S) = \beta_0 + \beta_1 X + \beta_2 S$$
$$ \Pr(Y|X,S) = \beta_0 + \beta_1 X + \beta_2 S + \beta_4 S^2$$
$$ \Pr(Y|X,S) = expit(\beta_0 + \beta_1 X + \beta_2 X S^2 + \beta_4 S^5)$$
in addition to nonparametric structural equation models (SEM). Nonparametric SEM are actually the math underlying the causal DAGs we write.
In the case of a continuous $Y$, the DAG makes no restrictions on the distribution for the errors of the outcome. When using parametric models the important part is selecting an appropriate distribution, something DAGs cannot tell you.
