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高光谱和全基因组关联分析当地泰国籼稻叶片磷素状况。

Hyperspectral and genome-wide association analyses of leaf phosphorus status in local Thai indica rice.

机构信息

Faculty of Science, Department of Botany, Center of Excellence in Environment and Plant Physiology, Chulalongkorn University, Bangkok, Thailand.

Faculty of Science, Program in Biotechnology, Chulalongkorn University, Bangkok, Thailand.

出版信息

PLoS One. 2022 Apr 20;17(4):e0267304. doi: 10.1371/journal.pone.0267304. eCollection 2022.

Abstract

Phosphorus (P) is an essential mineral nutrient and one of the key factors determining crop productivity. P-deficient plants exhibit visual leaf symptoms, including chlorosis, and alter spectral reflectance properties. In this study, we evaluated leaf inorganic phosphate (Pi) contents, plant growth and reflectance spectra (420-790 nm) of 172 Thai rice landrace varieties grown hydroponically under three different P supplies (overly sufficient, mildly deficient and severely deficient conditions). We reported correlations between Pi contents and reflectance ratios computed from two wavebands in the range of near infrared (720-790 nm) and visible energy (green-yellow and red edge) (r > 0.69) in Pi-deficient leaves. Artificial neural network models were also developed which could classify P deficiency levels with 85.60% accuracy and predict Pi content with R2 of 0.53, as well as highlight important waveband sections. Using 217 reflectance ratio indices to perform genome-wide association study (GWAS) with 113,114 SNPs, we identified 11 loci associated with the spectral reflectance traits, some of which were also associated with the leaf Pi content trait. Hyperspectral measurement offers a promising non-destructive approach to predict plant P status and screen large germplasm for varieties with high P use efficiency.

摘要

磷(P)是一种必需的矿物质营养元素,也是决定作物生产力的关键因素之一。缺磷的植物会表现出明显的叶片症状,包括黄化,并改变光谱反射特性。在这项研究中,我们评估了 172 种泰国水稻地方品种在水培条件下,在三种不同磷供应(过度充足、轻度缺乏和严重缺乏)下的叶片无机磷(Pi)含量、植物生长和反射光谱(420-790nm)。我们报告了 Pi 缺乏叶片中近红外(720-790nm)和可见能量(绿-黄和红边)范围内两个波段的反射率比值与 Pi 含量之间的相关性(r>0.69)。我们还开发了人工神经网络模型,该模型可以以 85.60%的准确率对 P 缺乏水平进行分类,预测 Pi 含量的 R2 为 0.53,并突出重要的波段部分。使用 217 个反射率比值指数与 113114 个 SNP 进行全基因组关联研究(GWAS),我们鉴定了 11 个与光谱反射特性相关的位点,其中一些位点也与叶片 Pi 含量特性相关。高光谱测量提供了一种有前途的非破坏性方法,可以预测植物的 P 状态,并筛选具有高 P 利用效率的品种的大量种质资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bf3/9020724/63b75e5542c7/pone.0267304.g001.jpg

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