Genome-Wide Association Analysis Dissects the Genetic Basis of the Grain Carbon and Nitrogen Contents in Milled Rice

Abstract

Background: Carbon (C) and nitrogen (N) are two fundamental components of starch and protein, which are important determinants of grain yield and quality. The food preferences of consumers and the expected end-use of grains in different rice-growing regions require diverse varieties that differ in terms of the grain N content (GNC) and grain C content (GCC) of milled rice. Thus, it is important that quantitative trait loci (QTLs)/genes with large effects on the variation of GNC and GCC are identified in breeding programs. Results: To dissect the genetic basis of the variation of GNC and GCC in rice, the Dumas combustion method was used to analyze 751 diverse accessions regarding the GNC, GCC, and C/N ratio of the milled grains. The GCC and GNC differed significantly among the rice subgroups, especially between Xian/Indica (XI) and Geng/Japonica (GJ). Interestingly, in the GJ subgroup, the GNC was significantly lower in modern varieties (MV) than in landraces (LAN). In the XI subgroup, the GCC was significantly higher in MV than in LAN. One, six, and nine QTLs, with 55 suggestively associated single nucleotide polymorphisms, were detected for the GNC, GCC, and C/N ratio in three panels during a single-locus genome-wide association study (GWAS). Three of these QTLs were also identified in a multi-locus GWAS. We screened 113 candidate genes in the 16 QTLs in gene-based haplotype analyses. Among these candidate genes, LOC_Os01g06240 at qNC-1.1, LOC_Os05g33300 at qCC-5.1, LOC_Os01g04360 at qCN-1.1, and LOC_Os05g43880 at qCN-5.2 may partially explain the significant differences between the LAN and MV. These candidate genes should be cloned and may be useful for molecular breeding to rapidly improve the GNC, GCC, and C/N ratio of rice. Conclusions: Our findings represent valuable information regarding the genetic basis of the GNC and GCC and may be relevant for enhancing the application of favorable haplotypes of candidate genes for the molecular breeding of new rice varieties with specific grain N and C contents.

Dissecting of the Deterioration in Eating Quality for Erect Panicle (Ep) Type High Yield Japonica Super Rice in Northest China

“…The nitrogen element content of the samples was analyzed with a vario MACRO cube (Elementar Co., Hanau, Germany), which is based on the Dumas combustion method. The operation and parameter setting were according to Tang et al ( 2019 ). The agronomic nitrogen use efficiency and apparent nitrogen recovery efficiency were calculated according to Chen et al ( 2018 ).…”

Development of SNP Markers from GWAS for Selecting Seed Coat and Aleurone Layers in Brown Rice (Oryza sativa L.)

“…Brach et al [ 42 ] performed a GWAS analysis using high-quality re-sequencing data for 10 eating and cooking quality (ECQs), in a core collection consisting of 227 non-glutinous genetic resources and four derived resources. In addition, diverse resources, consisting of 751 rice accessions from the 3000 Rice Genomes Project [ 43 ], were analyzed through GWAS, to dissect the genetic basis of GNC and GCC mutations, which are important determinants of grain yield and quality [ 44 ]. In this study, GWAS was performed using high-quality resequencing data from a 294 rice core collection and phenotypic data, and using a staining method with verified significance.…”

Genome-Wide Association Mapping Reveals Novel Putative Gene Candidates Governing Reproductive Stage Heat Stress Tolerance in Rice

“…Several SNP arrays with varying densities already exist in rice enabling high-throughput genotyping, which include 44 K ( Zhao et al, 2011 ), 6 K ( Yu et al, 2014 ), 50 K ( Singh et al, 2015 ), 700 K ( McCouch et al, 2016 ), and 7 K ( Thomson et al, 2017 ). GWAS is routinely utilized for mapping biotic ( Li et al, 2019 ; Hada et al, 2020 ), abiotic ( Rohilla et al, 2020 ; Yuan et al, 2020 ), and grain quality ( Misra et al, 2019 ; Tang et al, 2019 )-related attributes in rice. Even though vast genetic variation was reported for RSHT in rice, it has not been properly utilized for identifying MTAs through a GWAS.…”