Corrigendum

Corrigendum

In the article by G. de los Campos, H. Naya, D. Gianola, J. Crossa, A. Legarra, E. Manfredi, K. Weigel, and J. M. Cotes (GENETICS 182: 375–385) entitled “Predicting Quantitative Traits with Regression Models for Dense Molecular Markers and Pedigree,” File S1 of the supporting information contains a mistake in the fully conditional density of the residual variance. This mistake has been corrected in the new version of File S1 that is available online at http://www.genetics.org/content/182/1/375/suppl/DC1. Importantly, the R-program used for analysis in the article, which is provided in File S2 (BRL.rda), as well as the BLR program of R (http://cran.r-project.org/web/packages/BLR/index.html), do not suffer from that mistake. We thank Rohan Fernando and Rob Tempelman for pointing out the mistake in File S1.

Corrected text now shown on page 5SI (first 3 lines):

$$\begin{array}{l}p\left({\sigma }_{\epsilon }^2|\mathit{else}\right)\propto \left[{\displaystyle \prod _{i=1}^{n}N\left(y_i|\mu +{{\mathbf{x}}^{\prime }}_{ri}{\mathbf{\beta }}_r+{{\mathbf{x}}^{\prime }}_{li}{\mathbf{\beta }}_l+u_i,{\sigma }_{\epsilon }^2\right)}\right]\left[{\displaystyle \prod _{j=1}^{p}N\left({\beta }_{lj}|0,{\sigma }_{\epsilon }^2{\tau }_j^2\right)}\right]{\chi }^{-2}\left({\sigma }_{\epsilon }^2|S_{\epsilon },d{f}_{\epsilon }\right)\\ \propto \left[{\displaystyle \prod _{i=1}^{n}N\left({\epsilon }_i|{\sigma }_{\epsilon }^2\right)}\right]\left[{\displaystyle \prod _{j=1}^{p}N\left({\tilde{\beta }}_{lj}|0,{\sigma }_{\epsilon }^2\right)}\right]{\chi }^{-2}\left({\sigma }_{\epsilon }^2|S_{\epsilon },d{f}_{\epsilon }\right)\\ ={\chi }^{-2}\left({\sigma }_{\epsilon }^2|S=S_{\epsilon }+\mathbf{\epsilon }\prime \mathbf{\epsilon }+\tilde{\mathbf{\beta }}\prime \tilde{\mathbf{\beta }},df=d{f}_{\epsilon }+n+p\right)\end{array}$$ [14]

where $\mathbf{\epsilon }=\left({\epsilon }_1,..,{\epsilon }_n\right)\mathbf{\prime }=\mathbf{y}-1\mu -{\mathbf{X}}_r{\mathbf{\beta }}_r-{\mathbf{X}}_l{\mathbf{\beta }}_l-\mathbf{u}$, ${\tilde{\beta }}_{lj}=\left(\frac{{\beta }_{lj}}{{\tau }_j}\right)$ and $\tilde{\mathbf{\beta }}=\left({\tilde{\beta }}_{l1},\dots ,{\tilde{\beta }}_{lp}\right)\mathbf{\prime }.$