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Correction: Impact of dominance effects on autotetraploid genomic prediction
Crop Science ( IF 2.0 ) Pub Date : 2021-04-19 , DOI: 10.1002/csc2.20500


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 = m = 1 97 , 314 X a α m
where α m is the additive effect of the m-th SNP. In the A+D-BRR and A+D-BayesB models,
g i = m = 1 97 , 314 X a α m + m = 1 97 , 314 X d β m
where β m is the dominance effect of the m-th SNP. For Gnr-BRR and Gnr-BayesB models,
g i = m = 1 M X g γ m ,
where γ m is the general effect of m -th dummy variable created based on each genotype class. M is the total of the dummy variables, and X is the genotypic matrix based on marker information parametrized as additive (Xα), dominance (Xβ), or total genetic (Xγ) 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
g = X α α
where α is the vector of the additive effects of all the SNPs. In the A+D-BRR and A+D-BayesB models,
g = X α α + X β β
where β is the vector of dominance effects of all the SNPs. For Gnr-BRR and Gnr-BayesB models,
g = X γ γ ,
where γ is the vector of the general effect of all the dummy variable created based on each genotype class of the SNPs. Xα and Xβ has dimensions equal number of individuals × number of markers, Xγ 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 Xγ is equal number of markers times 5). Xα, Xβ, and Xγ parametrizations are illustrated in the Table 1.


中文翻译:

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 一世 = = 1 97 , 314 X 一种 α
在哪里 α 是第m个 SNP的累加效应。在 A+D-BRR 和 A+D-BayesB 模型中,
G 一世 = = 1 97 , 314 X 一种 α + = 1 97 , 314 X d β
在哪里 β 是第m个 SNP的优势效应。对于 Gnr-BRR 和 Gnr-BayesB 模型,
G 一世 = = 1 X G γ ,
在哪里 γ 是一般效果 -th 基于每个基因型类创建的虚拟变量。 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
G = X α α
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,
G = X α α + X β β
其中β是所有 SNP 的优势效应向量。对于 Gnr-BRR 和 Gnr-BayesB 模型,
G = X γ γ ,
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 αX 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 αX βX γ参数化如表 1 所示。
更新日期:2021-06-05
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