Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome.
Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome.
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单变量和多变量 QTL 分析揭示水稻离子组中矿物质元素之间的协方差
DOI：
10.3389/fgene.2021.638555
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发表时间：
2021
影响因子：
3.7
通讯作者：
Huang XY
中科院分区：
文献类型：
--
作者：
Liu H;Long SX;Pinson SRM;Tang Z;Guerinot ML;Salt DE;Zhao FJ;Huang XY
稻米为世界上一半的人口提供了超过五分之一的每日热量，并且是必需矿物质营养素和有毒元素的主要膳食来源。稻米通常缺乏一些必需的营养素，但在某些情况下可能含有不安全的有毒元素。确定控制水稻矿质营养和有毒微量金属（离子组）含量的数量性状基因座（QTL），将有助于开发营养改良的水稻品种。然而，QTL分析传统上认为每个元素单独考虑没有考虑它们的相互关系。在这项研究中，我们进行了主成分分析（PCA）和多变量QTL分析，以确定控制水稻离子组中的矿质元素之间的协方差的遗传位点。对水稻重组自交系（RIL）群体的全基因组进行了重测序，并对不同生长条件下RIL群体的籽粒、地上部和根系中16种元素的含量进行了单变量和多变量QTL分析。通过对单个元素浓度作为单独性状的分析，共鉴定出167个独特的元素QTL，其中53个QTL控制单个环境/组织内元素浓度的协方差（PC-QTL），152个QTL决定不同环境/组织间元素的协方差（aPC-QTL）。结果表明，在这些QTL簇中，OsHMA 4和OsNRAMP 5等候选基因被定位，其中OsHMA 4和OsNRAMP 5为元件QTL、PC-QTL和aPC-QTL。同时鉴定基本QTL和PC QTL将有助于克隆潜在的致病基因和解析水稻离子组的复杂调控。
Rice provides more than one fifth of daily calories for half of the world’s human population, and is a major dietary source of both essential mineral nutrients and toxic elements. Rice grains are generally poor in some essential nutrients but may contain unsafe levels of some toxic elements under certain conditions. Identification of quantitative trait loci (QTLs) controlling the concentrations of mineral nutrients and toxic trace metals (the ionome) in rice will facilitate development of nutritionally improved rice varieties. However, QTL analyses have traditionally considered each element separately without considering their interrelatedness. In this study, we performed principal component analysis (PCA) and multivariate QTL analyses to identify the genetic loci controlling the covariance among mineral elements in the rice ionome. We resequenced the whole genomes of a rice recombinant inbred line (RIL) population, and performed univariate and multivariate QTL analyses for the concentrations of 16 elements in grains, shoots and roots of the RIL population grown in different conditions. We identified a total of 167 unique elemental QTLs based on analyses of individual elemental concentrations as separate traits, 53 QTLs controlling covariance among elemental concentrations within a single environment/tissue (PC-QTLs), and 152 QTLs which determined covariation among elements across environments/tissues (aPC-QTLs). The candidate genes underlying the QTL clusters with elemental QTLs, PC-QTLs and aPC-QTLs co-localized were identified, including OsHMA4 and OsNRAMP5. The identification of both elemental QTLs and PC QTLs will facilitate the cloning of underlying causal genes and the dissection of the complex regulation of the ionome in rice.
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影响因子：
16.6
作者：
Huang XY;Deng F;Yamaji N;Pinson SR;Fujii-Kashino M;Danku J;Douglas A;Guerinot ML;Salt DE;Ma JF
通讯作者：
Ma JF
影响因子：
7
作者：
Huang, Xuehui;Feng, Qi;Han, Bin
通讯作者：
Han, Bin
影响因子：
11.8
作者：
Li, Gang;Sun, Guo-Xin;Zhu, Yong-Guan
通讯作者：
Zhu, Yong-Guan
影响因子：
5.1
作者：
Korshunova, YO;Eide, D;Pakrasi, HB
通讯作者：
Pakrasi, HB
影响因子：
3.8
作者：
Bouchet, S.;Bertin, P.;Charcosset, A.
通讯作者：
Charcosset, A.