Table 1.

Summary of the model-wise (tree-based) prior distributions on proportions^a,b and total phenotypic variance

Additive (Model A, gi = ai)		Additive and dominance (Model AD, ${\mathit{g}}_{\mathit{i}}={\mathit{a}}_{\mathit{i}}+{\mathit{d}}_{\mathit{i}}$ )	Additive and nonadditive (Model ADX, ${\mathit{g}}_{\mathit{i}}={\mathit{a}}_{\mathit{i}}+{\mathit{d}}_{\mathit{i}}+{\mathit{x}}_{\mathit{i}}$ )
Default	Expert	Expert	Expert
A-tree: $\begin{array}{cc}{p}_{\frac{\mathrm{g}}{\mathrm{g}+\mathrm{e}}}\hfill & \sim \text{Dirichlet}\left(2\right)\hfill \\ {\sigma }_{\mathrm{P}}^2\hfill & \sim \text{Jeffrey}{\mathrm{s}}^{\prime }\hfill \end{array}$	A-tree*: $\begin{array}{cc}{p}_{\frac{\mathrm{g}}{\mathrm{g}+\mathrm{e}}}\hfill & \sim {\text{PC}}_0\left(R_{\frac{\mathrm{g}}{\mathrm{g}+\mathrm{e}}}\right)\hfill \\ {\sigma }_{\mathrm{P}}^2\hfill & \sim \text{Jeffrey}{\mathrm{s}}^{\prime }\hfill \end{array}$	AD-tree*: $\begin{array}{cc}{p}_{\frac{\mathrm{g}}{\mathrm{g}+\mathrm{e}}}\hfill & \sim {\text{PC}}_0\left(R_{\frac{\mathrm{g}}{\mathrm{g}+\mathrm{e}}}\right)\hfill \\ p_{\frac{\mathrm{a}}{\mathrm{g}}}\hfill & \sim {\text{PC}}_{\mathrm{M}}\left(R_{\frac{\mathrm{a}}{\mathrm{g}}}\right)\hfill \\ {\sigma }_{\mathrm{P}}^2\hfill & \sim \text{Jeffrey}{\mathrm{s}}^{\prime }\hfill \end{array}$	ADX-tree*: $\begin{array}{cc}{p}_{\frac{\mathrm{g}}{\mathrm{g}+\mathrm{e}}}\hfill & \sim {\text{PC}}_0\left(R_{\frac{\mathrm{g}}{\mathrm{g}+\mathrm{e}}}\right)\hfill \\ p_{\frac{\mathrm{a}}{\mathrm{g}}}\hfill & \sim {\text{PC}}_{\mathrm{M}}\left(R_{\frac{\mathrm{a}}{\mathrm{g}}}\right)\hfill \\ p_{\frac{\mathrm{d}}{\mathrm{d}+\mathrm{x}}}\hfill & \sim {\text{PC}}_{\mathrm{M}}\left(R_{\frac{\mathrm{d}}{\mathrm{d}+\mathrm{x}}}\right)\hfill \\ {\sigma }_{\mathrm{P}}^2\hfill & \sim \text{Jeffrey}{\mathrm{s}}^{\prime }\hspace{0.17em}\hfill \end{array}$

^a $p\sim {\text{PC}}_0(R)$ describes a PC prior for a variance proportion that has median equal to R and a preference for the variance proportion being equal to 0.

^b $p\sim {\text{PC}}_{\mathrm{M}}(R)$ describes a PC prior for a variance proportion with median R and a preference for the variance proportion being equal to the median R, with 75% probability in $[\text{logit}(R)-1,\text{logit}(R)+1]$ around the median on logit-scale.