Rodrigo R. Amadeu, Luis Felipe V. Ferrão, Ivone de Bem Oliveira, Juliana Benevenuto, Jeffrey B. Endelman, Patricio R. Munoz. Crop Sci. 60:656-665 (2020). https://doi.org/10.1002/csc2.20075.

Under the Materials and Methods section and Statistical Analyses subsection the mathematical notation has been corrected.

Where it reads:

Using a similar notation to Enciso-Rodriguez et al. (2018), in the A-BRR and A-BayesB models the genotypic effect of the individual i is represented as

$$g_i=\sum _{m=1}^{97,314}{\mathit{X}}_a{\mathrm{\alpha }}_m$$

where ${\mathrm{\alpha }}_m$ is the additive effect of the m-th SNP. In the A+D-BRR and A+D-BayesB models,

$$g_i=\sum _{m=1}^{97,314}{\mathit{X}}_a{\mathrm{\alpha }}_m+\sum _{m=1}^{97,314}{\mathit{X}}_d{\mathrm{\beta }}_m$$

where ${\mathrm{\beta }}_m$ is the dominance effect of the m-th SNP. For Gnr-BRR and Gnr-BayesB models,

$$g_i={\sum }_{m^{\prime }=1}^{M^{\prime }}{\mathit{X}}_g{\mathrm{\gamma }}_m,$$

where ${\mathrm{\gamma }}_{m^{\prime }}$ is the general effect of $m^{\prime }$-th dummy variable created based on each genotype class. $M^{\prime }$ is the total of the dummy variables, and X is the genotypic matrix based on marker information parametrized as additive (X_α), dominance (X_β), or total genetic (X_γ) as illustrated in Table 1.

Please read:

Using a similar notation to Enciso-Rodriguez et al. (2018), in the A-BRR and A-BayesB models the genotypic effect of the individual i is represented as

$$\mathit{g}={\mathit{X}}_{\mathrm{\alpha }}\mathit{\alpha }$$

where α is the vector of the additive effects of all the SNPs. In the A+D-BRR and A+D-BayesB models,

$$\mathit{g}={\mathit{X}}_{\mathrm{\alpha }}\mathit{\alpha }+{\mathit{X}}_{\mathrm{\beta }}\mathit{\beta }$$

where β is the vector of dominance effects of all the SNPs. For Gnr-BRR and Gnr-BayesB models,

$$\mathit{g}={\mathit{X}}_{\mathrm{\gamma }}\mathit{\gamma },$$

where γ is the vector of the general effect of all the dummy variable created based on each genotype class of the SNPs. X_α and X_β has dimensions equal number of individuals × number of markers, X_γ has dimensions equal the number of individuals × number of dummy variables depending upon the SNP information (e.g. if all SNPs have the presence of the five possible dosages, number of columns of X_γ is equal number of markers times 5). X_α, X_β, and X_γ parametrizations are illustrated in the Table 1.

中文翻译：

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更正：优势效应对同源四倍体基因组预测的影响

Rodrigo R. Amadeu、Luis Felipe V. Ferrão、Ivone de Bem Oliveira、Juliana Benevenuto、Jeffrey B. Endelman、Patricio R. Munoz。作物科学。60:656-665 (2020)。https://doi.org/10.1002/csc2.20075。

在材料和方法部分和统计分析小节下，数学符号已更正。

哪里写着：

使用与 Enciso-Rodriguez 等人类似的符号。(2018)，在 A-BRR 和 A-BayesB 模型中，个体i的基因型效应表示为

$$G_{\mathrm{一世}}=\sum _{米=1}^{97,314}{\mathit{X}}_{\mathrm{一种}}{\mathrm{\alpha }}_{米}$$

在哪里 ${\mathrm{\alpha }}_{米}$是第m个 SNP的累加效应。在 A+D-BRR 和 A+D-BayesB 模型中，

$$G_{\mathrm{一世}}=\sum _{米=1}^{97,314}{\mathit{X}}_{\mathrm{一种}}{\mathrm{\alpha }}_{米}+\sum _{米=1}^{97,314}{\mathit{X}}_d{\mathrm{\beta }}_{米}$$

在哪里 ${\mathrm{\beta }}_{米}$是第m个 SNP的优势效应。对于 Gnr-BRR 和 Gnr-BayesB 模型，

$$G_{\mathrm{一世}}={\sum }_{{米}^{\prime }=1}^{{米}^{\prime }}{\mathit{X}}_G{\mathrm{\gamma }}_{米},$$

在哪里 ${\mathrm{\gamma }}_{{米}^{\prime }}$ 是一般效果 ${米}^{\prime }$-th 基于每个基因型类创建的虚拟变量。 ${米}^{\prime }$是虚拟变量的总和，X是基于标记信息的基因型矩阵，参数化为加性 ( X _α )、优势 ( X _β ) 或总遗传 ( X _γ )，如表 1 所示。

请阅读：

使用与 Enciso-Rodriguez 等人类似的符号。(2018)，在 A-BRR 和 A-BayesB 模型中，个体i的基因型效应表示为

$$\mathit{G}={\mathit{X}}_{\mathrm{\alpha }}\mathit{\alpha }$$

其中 α 是所有 SNP 的累加效应的向量。在 A+D-BRR 和 A+D-BayesB 模型中，

$$\mathit{G}={\mathit{X}}_{\mathrm{\alpha }}\mathit{\alpha }+{\mathit{X}}_{\mathrm{\beta }}\mathit{\beta }$$

其中β是所有 SNP 的优势效应向量。对于 Gnr-BRR 和 Gnr-BayesB 模型，

$$\mathit{G}={\mathit{X}}_{\mathrm{\gamma }}\mathit{\gamma },$$

其中γ是基于 SNP 的每个基因型类别创建的所有虚拟变量的一般效应的向量。X _α和X _{β 的}维度等于个体数量 × 标记数量，X _{γ 的}维度等于个体数量 × 虚拟变量数量，具体取决于 SNP 信息（例如，如果所有 SNP 都存在五种可能的剂量，则数量X _γ的列数等于标记数乘以 5)。X _α、X _β和X _γ参数化如表 1 所示。