We study lower bounds on the Fisher information of Gaussian location mixtures and their applications to estimation problems. First, for a discrete mixing distribution, we derive a universal positive-semidefinite lower bound on the Fisher information matrix in terms of the Rényi entropy of order two. For a mixing distribution supported on $K$ points, this bound scales as $\log(K)/K^2$, improving by a logarithmic factor on the support-size dependence obtained from Stam's inequality. We then develop dimension-aware trace bounds using classical Sobolev inequalities. These bounds are expressed in terms of Sibson's $α$-mutual information and yield the scaling $K^{-2/n}$ in dimension $n$. A complementary bound involving the $α$-mutual information of order $1/2$ is obtained using the sharp Young convolution inequality. The derived inequalities are further specialized to rotationally invariant mixing distributions supported on discrete spherical shells. In this setting, an intrinsic-dimensional bound on Sibson mutual information leads to lower bounds on the Fisher information in terms of the number of occupied shells. As applications, we study least favorable priors for bounded Gaussian mean estimation. In the scalar case, we show that the least favorable prior on $[-R,R]$ has at least $Ω(R\sqrt{\log R})$ support points and that each of its probability masses is of order at most $1/R$. In the vector case, we show that the number of occupied shells grows at least linearly with $R$. Finally, Brown's identity and the I-MMSE relationship are used to translate the Fisher-information inequalities into upper bounds on the MMSE and mutual information.

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