Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome

Abstract

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.

Relationships Among Arsenic-Related Traits, Including Rice Grain Arsenic Concentration and Straighthead Resistance, as Revealed by Genome-Wide Association

“…The most commonly identified StHD QTL was qStHD8-2, which co-located with QTL previously identified in four different populations (Pan et al, 2012;Li et al, 2016;Murugaiyan et al, 2019). When the RMC QTL for grain-As were compared with previously reported As QTL determined in various biparental mapping or GWA populations (Zhang et al, 2008;Norton et al, 2012a;Norton et al, 2014;Zhang et al, 2014;Norton et al, 2019;Liu et al, 2021), nine of the 15 RMC grain-As GWA-QTL coincided with a previously reported grain-As locus (Table 4). Four of the 11 RMC QTL for P, six of 18 for S, one of 19 for Ca, and 2 of 9 for Cu were validated by known transporter genes or previous QTL studies (Zhang et al, 2014;Liu et al, 2021) (Supplementary Table S3).…”

“…When the RMC QTL for grain-As were compared with previously reported As QTL determined in various biparental mapping or GWA populations (Zhang et al, 2008;Norton et al, 2012a;Norton et al, 2014;Zhang et al, 2014;Norton et al, 2019;Liu et al, 2021), nine of the 15 RMC grain-As GWA-QTL coincided with a previously reported grain-As locus (Table 4). Four of the 11 RMC QTL for P, six of 18 for S, one of 19 for Ca, and 2 of 9 for Cu were validated by known transporter genes or previous QTL studies (Zhang et al, 2014;Liu et al, 2021) (Supplementary Table S3). Because alternate alleles became very rare in the smaller subpopulations, the table presents the results based on the entire RMC panel when the QTL was significant there.…”

“…Altering the Lsi1 transporter to reduce the root uptake of As may seem enticing, but alterations in this protein have been detrimental, substantially decreasing plant growth and grain yield as well as grain-As (Ma et al, 2008). Furthermore, of the quantitative trait loci (QTL) for grain-As reported to date (Norton et al, 2010;Norton et al, 2012a;Norton et al, 2014;Zhang et al, 2014;Yang et al, 2018;Frouin et al, 2019;Norton et al, 2019;Fernández-Baca et al, 2021;Liu et al, 2021), none have encompassed the Lsi1 locus, indicating that the wide natural variation observed for grain-As (3-to 100-fold differences reported by Norton et al (2012b), Pinson et al (2015), and Duan et al (2017) is not caused by mutation in the Lsi1 gene. In contrast, grain-As QTL have co-located with the Lsi2 gene (Norton et al, 2019), which impacts the root-to-shoot transfer of Si and As (Ma et al, 2008), and to the ABCC1 gene (Norton et al, 2019) which reduces transport of As to grains by increasing vacuolar sequestration (Song et al, 2014).…”

Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants

“…This makes the application of latent phenotyping more challenging for basic biological questions than for prediction. Despite these challenges, latent phenotyping approaches have been used to successfully identify genetic loci linked with tomato fruit and leaves (Chitwood et al., 2012; Li, Frank, et al., 2018; Wang et al., 2019), rice grains (Iwata et al., 2015), the maize, rice, and soybean ionomes (Chu et al., 2016; Fikas et al., 2019; Liu et al., 2021), the Brassica defensive metabolome (D'Oria et al., 2021; Katz et al., 2021), oat seed fatty acid concentrations (Carlson et al., 2019), inflorescence development in maize and sorghum (Leiboff & Hake, 2019; Rice et al., 2020), carrot shoot and roots (Turner et al., 2018), response to drought in Setaria (Ubbens et al., 2020), and strawberry fruit shape (Nagamatsu et al., 2021). Some of these successful examples have relied on prior information from preceding univariate analyses to validate the loci discovered using latent phenotyping and to aid their interpretation of those newly discovered loci (Fikas et al., 2019; Katz et al., 2021; Ubbens et al., 2020).…”

Socio-Technical Changes for Sustainable Rice Production: Rice Husk Amendment, Conservation Irrigation, and System Changes

“…The carryover effect of these shifts into the soil microbial populations is an ongoing area of research (Fernández-Baca et al, 2021b). Genetic approaches are also used to decrease root to shoot translocation of Cd (Ueno et al, 2009;Yan et al, 2019), and to identify genes associated with uptake of multiple essential elements in different growth conditions (Zhang et al, 2014;Liu et al, 2021), locations (Norton et al, 2014), and varieties (Pinson et al, 2015).…”