Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values.
Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values.
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DOI：
10.1007/s00122-021-03988-8
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Goldman IL
中科院分区：
文献类型：
--
作者：
Brainard SH;Ellison SL;Simon PW;Dawson JC;Goldman IL
胡萝卜市场等级的主要表型决定因素--根的大小和形状--主要受加性控制，但也受高度多基因的遗传控制。胡萝卜根的大小和形状不仅是产量的主要决定因素，也是市场等级的主要决定因素。这些定量表型在历史上一直难以客观评估，因此市场类别的主观视觉评估仍然是进行这些性状选择的主要方法。然而，数字图像分析的进步，最近有可能的高通量量化的大小和形状属性。因此，现在可以利用现代遗传分析方法来研究根形态的遗传控制。为此，本研究利用全基因组关联分析（GWAS）和基因组估计育种值（GEBV），并证明了市场级的组件是高度多基因性状，可能在许多小效应QTL的影响下。相对较大比例的加性遗传方差的许多组件表型支持高预测能力的GEBV，在潜在的市场类性状的平均预测能力为0.67。GWAS确定了多个QTL的四个表型组成的市场类：长度，长宽比，最大宽度，和根填充，以前未表征的性状，代表胡萝卜根形状的大小无关的部分。通过将数字图像分析与GWAS和GEBV相结合，本研究在我们对胡萝卜商品级遗传控制的理解方面取得了新的进展。胡萝卜市场类的基因组选择的直接实际效用和可行性也进行了描述，并提供了具体的指导方针，训练人口的设计。在线版本包含补充材料，可通过10.1007/s 00122 -021-03988-8获得。
The principal phenotypic determinants of market class in carrot—the size and shape of the root—are under primarily additive, but also highly polygenic, genetic control. The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided. The online version contains supplementary material available at 10.1007/s00122-021-03988-8.
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影响因子：
5.8
作者：
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