使用基于图像的方法和传统实验室方法测量小麦谷粒物理品质时,不同的 QTL 是小麦谷粒物理品质的基础

JSFA reports Pub Date : 2024-04-15 DOI:10.1002/jsf2.192
Livinus Emebiri, Shane Hildebrand
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引用次数: 0

摘要

小麦(Triticum aestivum L.)的销售价值部分取决于谷物的物理特性,因为它们直接或间接地影响着磨粉性能和烘焙质量。这些特征在过去都是人工测量的,但现在,数字图像分析(DIA)越来越多地被用来取代缓慢的表型系统。我们测量了 142 个在田间生长两年的小麦双倍单倍体的谷粒物理品质,并使用定量性状位点(QTL)绘图方法发现:(1) 对于小麦粒重,使用 DIA 提供的遗传信息与使用传统表型方法获得的遗传信息基本一致,两种方法的遗传率估计值相同。大多数检测到的 QTL 在传统表型方法和数字表型方法之间是一致的;(2) 然而,在百粒重(HLW)和干瘪粒百分比(SCR)的 QTL 分析中出现了更复杂的结构。不同方法的遗传率估计值相差高达 0.24,更重要的是,这两个性状的许多检测到的 QTL 都是方法特异性的;(3)尽管方法特异性,但确定的 QTL 映射到已知含有谷物物理性状基因的基因组区域。对于这些性状,使用传统方法进行表型时,遗传率估计值比数字图像分析大,这表明需要进一步改进数字图像分析,使其与传统表型方法更好地相关联。本文受版权保护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Differential QTL underlie wheat grain physical quality when measured using image-based versus traditional laboratory methods

Differential QTL underlie wheat grain physical quality when measured using image-based versus traditional laboratory methods

Background

The marketing value of wheat (Triticum aestivum L.) is determined, in parts, by the grain's physical characteristics, owing to which they directly (or indirectly) influence milling performance and baking quality. These characteristics have been manually measured in the past, but now, digital image analysis (DIA) is being increasingly used to replace the slow phenotyping system. Here, we asked whether this could lead to the identification of the same or different genes when compared to the traditional phenotyping methods.

Results

We measured grain physical quality on 142 wheat doubled haploids grown in the field over 2 years, and in using the quantitative trait locus (QTL) mapping approach we found that (1) for wheat grain weight, the use of DIA provided genetic information that mostly conformed to those obtained using the traditional phenotyping methods, with heritability estimates that were identical across both methods. Majority of the QTL detected were consistent between the traditional versus digital phenotyping methods; (2) a more complex architecture, however, arose from QTL analyses of hectoliter mass (HLM) and percentage of shriveled grains (SCR). The estimates for heritability varied by as much as 0.24 across methods and, more significantly, many of the detected QTL for both traits were method-specific; (3) though method-specific, identified QTL was mapped to genomic regions known to harbor genes for grain physical traits.

Conclusions

Thousand-grain weight (TGW) is robust to a phenotyping method, but a different genetic system underlies HLM and SCR, when these were measured using traditional versus digital image analysis. For these traits, heritability estimates were larger when phenotyped using traditional methods relative to digital image analysis, suggesting that further refinements are required to better correlate digital image analysis with the traditional phenotyping methods.

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