Unfavourable environmental conditions, including soil salinity, lead to decreased rice (Oryza sativa L.) productivity, especially at the reproductive stage. In this study, we examined 30 rice varieties, which revealed significant differences in the photosynthetic performance responses under salt stress conditions during the reproductive stage, which ultimately affected yield components after recovery. In rice with a correlation between net photosynthetic rate (PN) and intercellular CO2 concentration (Ci) under salt stress, PN was found to be negatively correlated with filled grain number after recovery. Applying stringent criteria, we identified 130,317 SNPs and 15,396 InDels between two "high-yield rice" varieties and two "low-yield rice" varieties with contrasting photosynthesis and grain yield characteristics. A total of 2,089 genes containing high- and moderate-impact SNPs or InDels were evaluated by gene ontology (GO) enrichment analysis, resulting in over-represented terms in the apoptotic process and kinase activity. Among these genes, 262 were highly expressed in reproductive tissues, and most were annotated as receptor-like protein kinases. These findings highlight the importance of variations in signaling components in the genome and these loci can serve as potential genes in rice breeding to produce a variety with salt avoidance that leads to increased yield in saline soil.

Background

Salt stress, a major plant environmental stress, is a critical constraint for rice productivity. Dissecting the genetic loci controlling salt tolerance in rice for improving productivity, especially at the flowering stage, remains challenging. Here, we conducted a genome-wide association study (GWAS) of salt tolerance based on exome sequencing of the Thai rice accessions.

Results

Photosynthetic parameters and cell membrane stability under salt stress at the flowering stage; and yield-related traits of 104 Thai rice (Oryza sativa L.) accessions belonging to the indica subspecies were evaluated. The rice accessions were subjected to exome sequencing, resulting in 112,565 single nucleotide polymorphisms (SNPs) called with a minor allele frequency of at least 5%. LD decay analysis of the panel indicates that the average LD for SNPs at 20 kb distance from each other was 0.34 (r²), which decayed to its half value (~ 0.17) at around 80 kb. By GWAS performed using mixed linear model, two hundred loci containing 448 SNPs on exons were identified based on the salt susceptibility index of the net photosynthetic rate at day 6 after salt stress; and the number of panicles, filled grains and unfilled grains per plant. One hundred and forty six genes, which accounted for 73% of the identified loci, co-localized with the previously reported salt quantitative trait loci (QTLs). The top four regions that contained a high number of significant SNPs were found on chromosome 8, 12, 1 and 2. While many are novel, their annotation is consistent with potential involvement in plant salt tolerance and in related agronomic traits. These significant SNPs greatly help narrow down the region within these QTLs where the likely underlying candidate genes can be identified.

Conclusions

Insight into the contribution of potential genes controlling salt tolerance from this GWAS provides further understanding of salt tolerance mechanisms of rice at the flowering stage, which can help improve yield productivity under salinity via gene cloning and genomic selection.

INTRODUCTION: Owing to advances in high-throughput genome sequencing, QTL-Seq mapping of salt tolerance traits is a major platform for identifying soil-salinity tolerance QTLs to accelerate marker-assisted selection for salt-tolerant rice varieties. We performed QTL-BSA-Seq in the seedling stage of rice from a genetic cross of the extreme salt-sensitive variety, IR29, and Jao Khao (JK), a Thai salt-tolerant variety. METHODS: A total of 462 F2 progeny grown in soil and treated with 160 mM NaCl were used as the QTL mapping population. Two high- and low-bulk sets, based on cell membrane stability (CMS) and tiller number at the recovery stage (TN), were equally sampled. The genomes of each pool were sequenced, and statistical significance of QTL was calculated using QTLseq and G prime (G) analysis, which is based on calculating the allele frequency differences or Δ(SNP index). RESULTS: Both methods detected the overlapping interval region, wherein CMS-bulk was mapped at two loci in the 38.41-38.85 Mb region with 336 SNPs on chromosome 1 (qCMS1) and the 26.13-26.80 Mb region with 1,011 SNPs on chromosome 3 (qCMS3); the Δ(SNP index) peaks were -0.2709 and 0.3127, respectively. TN-bulk was mapped at only one locus in the overlapping 38.26-38.95 Mb region on chromosome 1 with 575 SNPs (qTN1) and a Δ(SNP index) peak of -0.3544. These identified QTLs in two different genetic backgrounds of segregating populations derived from JK were validated. The results confirmed the colocalization of the qCMS1 and qTN1 traits on chromosome 1. Based on the CMS trait, qCMS1/qTN1 stably expressed 6%-18% of the phenotypic variance in the two validation populations, while qCMS1/qTN1 accounted for 16%-20% of the phenotypic variance in one validation population based on the TN trait. CONCLUSION: The findings confirm that the CMS and TN traits are tightly linked to the long arm of chromosome 1 rather than to chromosome 3. The validated qCMS-TN1 QTL can be used for gene/QTL pyramiding in marker-assisted selection to expedite breeding for salt resistance in rice at the seedling stage.