Novel QTL Associated with Shoot Branching Identified in Doubled Haploid Rice (Oryza sativa L.) under Low Nitrogen Cultivation

Abstract

Shoot branching is considered as an important trait for the architecture of plants and contributes to their growth and productivity. In cereal crops, such as rice, shoot branching is controlled by many factors, including phytohormones signaling networks, operating either in synergy or antagonizing each other. In rice, shoot branching indicates the ability to produce more tillers that are essential for achieving high productivity and yield potential. In the present study, we evaluated the growth and development, and yield components of a doubled haploid population derived from a cross between 93-11 (P1, indica) and Milyang352 (P2, japonica), grown under normal nitrogen and low nitrogen cultivation open field conditions. The results of the phenotypic evaluation indicated that parental lines 93-11 (P1, a high tillering indica cultivar) and Milyang352 (P2, a low tillering japonica cultivar) showed distinctive phenotypic responses, also reflected in their derived population. In addition, the linkage mapping and quantitative trait locus (QTL) analysis detected three QTLs associated with tiller number on chromosome 2 (qTNN2-1, 130 cM, logarithm of the odds (LOD) 4.14, PVE 14.5%; and qTNL2-1, 134 cM, LOD: 6.05, PVE: 20.5%) and chromosome 4 (qTN4-1, 134 cM, LOD 3.92, PVE 14.5%), with qTNL2-1 having the highest phenotypic variation explained, and the only QTL associated with tiller number under low nitrogen cultivation conditions, using Kompetitive Allele-Specific PCR (KASP) and Fluidigm markers. The additive effect (1.81) of qTNL2-1 indicates that the allele from 93-11 (P1) contributed to the observed phenotypic variation for tiller number under low nitrogen cultivation. The breakthrough is that the majority of the candidate genes harbored by the QTLs qTNL2-1 and qTNN4-1 (here associated with the control of shoot branching under low and normal nitrogen cultivation, respectively), were also proposed to be involved in plant stress signaling or response mechanisms, with regard to their annotations and previous reports. Therefore, put together, these results would suggest that a possible crosstalk exists between the control of plant growth and development and the stress response in rice.

QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line

“…The Fluidigm SNP genotyping system has automated polymerase chain reaction (PCR) and integrated fluidic circuit (IFC) technology, which automatically mixes PCR reagents through microfluidic channel networks. The indica-japonica SNP assays, based on the Fluidigm system developed in the previous study [16], have been applied to various genetic analyses and molecular breeding, such as bulked segregant analysis (BSA) [17], genetic diversity analysis [18,19], QTL analysis [20][21][22][23], and background profiling [24][25][26]. This Fluidigm SNP marker set has provided a faster and more cost-effective tool than other high-throughput SNP genotyping systems for primary analysis during molecular breeding using inter-subspecific populations, to date.…”

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

“…Most important plant traits are quantitative in nature and are influenced by multiple quantitative trait locus (QTLs). The effects of these QTLs on the expression of quantitative trait has been considered in various species such as maize [ 8 ], barley [ 9 ], rice [ 10 , 11 ], oilseed rape [ 12 , 13 , 14 ], wheat [ 15 ] and sugar beet [ 16 ]. Many loci showing cumulative small effects are often rejected in standard QTL analyses [ 17 , 18 ].…”

Flooding tolerance in the major rice weed Echinochloa crus-galli

“…Within the Oryzoideae, 103 DEOs were commonly regulated in both genotypes, while 25 DEOs (including 19 annotated ones) were present or differentially regulated only in O. sativa FR13A. In this latter group, we noticed a few DEOs upregulated during submergence and downregulated upon recovery including the "pseudo-response regulator" (PRR) which is a family of transcriptional repressors of the circadian clock expression genes (Nakamichi et al, 2010;Toda et al, 2019), the "phytosulfokines precursor" (PSK) which are thought to have a role in growth and development and in adaptation to biotic and abiotic stresses (Sauter et al, 2015) and the "OsFBDUF13" thought to be involved in the control of shoot branching under low nitrogen cultivation (Kwon et al, 2021). "Enoyl-CoA isomerase" (ECI), involved in the beta-oxidation of unsaturated fatty acids was downregulated during submergence.…”