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Genomic Selection with Fixed-effect Markers Improves the Prediction Accuracy for Capsaicinoid Contents in Capsicum annuum
Horticulture Research ( IF 8.7 ) Pub Date : 2022-09-13 , DOI: 10.1093/hr/uhac204 Geon Woo Kim 1 , Ju-Pyo Hong 1 , Hea-Young Lee 1 , Jin-Kyung Kwon 1 , Dong-Am Kim 2 , Byoung-Cheorl Kang 1
Horticulture Research ( IF 8.7 ) Pub Date : 2022-09-13 , DOI: 10.1093/hr/uhac204 Geon Woo Kim 1 , Ju-Pyo Hong 1 , Hea-Young Lee 1 , Jin-Kyung Kwon 1 , Dong-Am Kim 2 , Byoung-Cheorl Kang 1
Affiliation
Capsaicinoids provide chili peppers (Capsicum spp.) with their characteristic pungency. Several structural and transcription factor genes are known to control capsaicinoid contents in pepper. However, many other genes also regulating capsaicinoid contents remain unknown, making it difficult to develop pepper cultivars with different levels of capsaicinoids. Genomic selection (GS) uses genome-wide random markers (including many in undiscovered genes) for a trait to improve selection efficiency. In this study, we predicted the capsaicinoid contents of pepper breeding lines using several GS models trained with genotypic and phenotypic data from a training population. We used a core collection of 351 Capsicum accessions and 96 breeding lines as training and testing populations, respectively. To obtain the optimal number of single nucleotide polymorphism (SNP) markers for GS, we tested various numbers of genome-wide SNP markers based on linkage disequilibrium. We obtained the highest mean prediction accuracy (0.550) for different models using 3,294 SNP markers. Using this marker set, we conducted GWAS and selected 25 markers that were associated with capsaicinoid biosynthesis genes and quantitative trait loci for capsaicinoid contents. Finally, to develop more accurate prediction models, we obtained SNP markers from GWAS as fixed-effect markers for GS, where 3,294 genome-wide SNPs were employed. When four to five fixed-effect markers from GWAS were used as fixed effects, the RKHS and RR-BLUP models showed accuracies of 0.696 and 0.689, respectively. Our results lay the foundation for developing pepper cultivars with various capsaicinoid levels using GS for capsaicinoid contents.
中文翻译:
固定效应标记的基因组选择提高了辣椒中辣椒素含量的预测准确性
类辣椒素为辣椒 (Capsicum spp.) 提供其特有的刺激性。已知几种结构和转录因子基因可控制辣椒中的类辣椒素含量。然而,许多其他调节类辣椒素含量的基因仍然未知,因此很难开发出具有不同类辣椒素含量的辣椒品种。基因组选择 (GS) 使用全基因组随机标记(包括许多未发现的基因)来提高选择效率。在这项研究中,我们使用几个 GS 模型预测了辣椒育种品系的辣椒素含量,这些模型使用来自训练群体的基因型和表型数据进行训练。我们分别使用了 351 个辣椒种质和 96 个育种系的核心集合作为训练和测试种群。为了获得 GS 的单核苷酸多态性 (SNP) 标记的最佳数量,我们基于连锁不平衡测试了各种数量的全基因组 SNP 标记。我们使用 3,294 个 SNP 标记获得了不同模型的最高平均预测准确度 (0.550)。使用这个标记集,我们进行了 GWAS 并选择了 25 个与辣椒素生物合成基因和辣椒素含量的数量性状位点相关的标记。最后,为了开发更准确的预测模型,我们从 GWAS 获得了 SNP 标记作为 GS 的固定效应标记,其中使用了 3,294 个全基因组 SNP。当使用来自 GWAS 的四到五个固定效应标记作为固定效应时,RKHS 和 RR-BLUP 模型的准确度分别为 0.696 和 0.689。
更新日期:2022-09-13
中文翻译:
固定效应标记的基因组选择提高了辣椒中辣椒素含量的预测准确性
类辣椒素为辣椒 (Capsicum spp.) 提供其特有的刺激性。已知几种结构和转录因子基因可控制辣椒中的类辣椒素含量。然而,许多其他调节类辣椒素含量的基因仍然未知,因此很难开发出具有不同类辣椒素含量的辣椒品种。基因组选择 (GS) 使用全基因组随机标记(包括许多未发现的基因)来提高选择效率。在这项研究中,我们使用几个 GS 模型预测了辣椒育种品系的辣椒素含量,这些模型使用来自训练群体的基因型和表型数据进行训练。我们分别使用了 351 个辣椒种质和 96 个育种系的核心集合作为训练和测试种群。为了获得 GS 的单核苷酸多态性 (SNP) 标记的最佳数量,我们基于连锁不平衡测试了各种数量的全基因组 SNP 标记。我们使用 3,294 个 SNP 标记获得了不同模型的最高平均预测准确度 (0.550)。使用这个标记集,我们进行了 GWAS 并选择了 25 个与辣椒素生物合成基因和辣椒素含量的数量性状位点相关的标记。最后,为了开发更准确的预测模型,我们从 GWAS 获得了 SNP 标记作为 GS 的固定效应标记,其中使用了 3,294 个全基因组 SNP。当使用来自 GWAS 的四到五个固定效应标记作为固定效应时,RKHS 和 RR-BLUP 模型的准确度分别为 0.696 和 0.689。