Genomic Selection for Prediction of Fruit-Related Traits in Pepper (Capsicum spp.).
Genomic Selection for Prediction of Fruit-Related Traits in Pepper (Capsicum spp.).
复制标题
用于预测胡椒中水果相关性状的基因组选择（辣椒属）。
DOI：
10.3389/fpls.2020.570871
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发表时间：
2020
影响因子：
5.6
通讯作者：
Kang BC
中科院分区：
文献类型：
--
作者：
Hong JP;Ro N;Lee HY;Kim GW;Kwon JK;Yamamoto E;Kang BC
辣椒（Capsicum spp.）与果实相关的性状是品质的关键决定因素。这些性状是由数量遗传的基因控制的，标记辅助选择（MAS）已被证明是不够有效的。在这里，我们评估了基因组选择的潜力，其中训练群体的基因型和表型数据用于预测测试群体的表型，仅基因型数据，用于预测辣椒果实相关性状。我们测量了5个果实性状（果长，果形，果宽，果重，果皮厚度）在351个加入辣椒核心收集，包括229辣椒annuum，48辣椒，48辣椒chinense，25辣椒frutescens，和1辣椒chacoense在4年在两个不同的位置和基因分型测序这些加入。在整个核心种质中，考虑到其遗传距离和性不亲和性，我们只纳入了302个C。annum complex（229 C. annuum、48 C. chinense和25 C. frutescens）进行进一步分析。我们使用表型和基因型数据来研究基因组预测模型、标记密度和群体结构的影响。在10个基因组预测方法测试，再生核希尔伯特空间（RKHS）产生的预测精度最高（测量预测值和观测值之间的相关性）的性状，与准确度为0.75，0.73，0.84，0.83，和0.82的果实长度，果形，果宽，果重，果皮厚度，分别。总体而言，预测精度与果实性状的标记数量呈正相关。我们测试了我们的基因组选择模型在一个单独的人口重组自交系来源于两个亲本系的核心收集。尽管训练群体和测试群体之间的遗传多样性差异很大，我们得到了适度的预测精度为0.32，0.34，0.50，和0.48果实长度，果形，果宽，和果实重量，分别。这种对果实相关性状的基因组选择的使用表明了核心收集和基因组选择作为作物改良工具的潜在用途。
Pepper (Capsicum spp.) fruit-related traits are critical determinants of quality. These traits are controlled by quantitatively inherited genes for which marker-assisted selection (MAS) has proven insufficiently effective. Here, we evaluated the potential of genomic selection, in which genotype and phenotype data for a training population are used to predict phenotypes of a test population with only genotype data, for predicting fruit-related traits in pepper. We measured five fruit traits (fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness) in 351 accessions from the pepper core collection, including 229 Capsicum annuum, 48 Capsicum baccatum, 48 Capsicum chinense, 25 Capsicum frutescens, and 1 Capsicum chacoense in 4 years at two different locations and genotyped these accessions using genotyping-by-sequencing. Among the whole core collection, considering its genetic distance and sexual incompatibility, we only included 302 C. annum complex (229 C. annuum, 48 C. chinense, and 25 C. frutescens) into further analysis. We used phenotypic and genotypic data to investigate genomic prediction models, marker density, and effects of population structure. Among 10 genomic prediction methods tested, Reproducing Kernel Hilbert Space (RKHS) produced the highest prediction accuracies (measured as correlation between predicted values and observed values) across the traits, with accuracies of 0.75, 0.73, 0.84, 0.83, and 0.82 for fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness, respectively. Overall, prediction accuracies were positively correlated with the number of markers for fruit traits. We tested our genomic selection models in a separate population of recombinant inbred lines derived from two parental lines from the core collection. Despite the large difference in genetic diversity between the training population and the test population, we obtained moderate prediction accuracies of 0.32, 0.34, 0.50, and 0.48 for fruit length, fruit shape, fruit width, and fruit weight, respectively. This use of genomic selection for fruit-related traits demonstrates the potential use of core collections and genomic selection as tools for crop improvement.
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影响因子：
5.4
作者：
Ben Chaim, A;Grube, RC;Paran, I
通讯作者：
Paran, I
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
--
DOI：
10.1007/s13253-015-0222-5
发表时间：
2015-12-01
影响因子：
1.4
作者：
de los Campos, Gustavo;Veturi, Yogasudha;Perez-Rodriguez, Paulino
通讯作者：
Perez-Rodriguez, Paulino
DOI：
10.1534/g3.118.200273
发表时间：
2018-07-02
期刊：
G3 (Bethesda, Md.)
影响因子：
--
作者：
Enciso-Rodriguez F;Douches D;Lopez-Cruz M;Coombs J;de Los Campos G
通讯作者：
de Los Campos G
影响因子：
4.6
作者：
Akdemir, Deniz;Isidro-Sanchez, Julio
通讯作者：
Isidro-Sanchez, Julio