Soil salinity is a serious menace in rice production threatening global food security. Rice responses to salt stress involve a series of biological processes, including antioxidation, osmoregulation or osmoprotection, and ion homeostasis, which are regulated by different genes. Understanding these adaptive mechanisms and the key genes involved are crucial in developing highly salt-tolerant cultivars. In this review, we discuss the molecular mechanisms of salt tolerance in rice—from sensing to transcriptional regulation of key genes—based on the current knowledge. Furthermore, we highlight the functionally validated salt-responsive genes in rice.

Excessive amounts of metal are toxic and severely affect plant growth and development. Understanding the genetic control of metal tolerance is crucial to improve rice resistance to Fe, Zn, and Al toxicity. The multi-parent advanced generation inter-cross (MAGIC) populations were genotyped using a 55 K rice SNP array and screened at the seedling stage for Fe, Zn, and Al toxicity using a hydroponics system. Association analysis was conducted by implementing a mixed linear model (MLM) for each of the five MAGIC populations double cross DC1 (founders were SAGC-08, HHZ5-SAL9-Y3-Y1, BP1976B-2-3-7-TB-1-1, PR33282-B-8-1-1-1-1-1), double cross DC2 (founders of double cross were FFZ1, CT 16658-5-2-2SR-2-3-6MP, IR 68, IR 02A127), eight parents population 8way (founders were SAGC-08, HHZ5-SAL9-Y3-Y1, BP1976B-2-3-7-TB-1-1, PR33282-B-8-1-1-1-1-1, FFZ1, CT 16658-5-2-2SR-2-3-6MP, IR 68, IR 02A127), DC12 (DC1+DC2) and rice multi-parent recombinant inbred line population RMPRIL (DC1+DC2+8way). A total of 21, 30, and 21 QTL were identified for Fe, Zn, and Al toxicity tolerance, respectively. For multi tolerance (MT) as Fe, Zn, and Al tolerance-related traits, three genomic regions, MT1.1 (chr.1: 35.4–36.3 Mb), MT1.2 (chr.1: 35.4–36.3 Mb), and MT3.2 (chr.3: 35.4-36.2 Mb) harbored QTL. The chromosomal regions MT2.1 (chr.2: 2.4–2.8 Mb), MT2.2 (chr.2: 24.5–25.8 Mb), MT4 (chr.4: 1.2 Mb Mb), MT8.1 (chr.8: 0.7–0.9 Mb), and MT8.2 (chr.8: 2.2–2.4 Mb) harbored QTL for Fe and Zn tolerance, while MT2.3 (chr.2: 30.5–31.6 Mb), MT3.1 (chr.3: 12.5–12.8 Mb), and MT6 (chr.6: 2.0–3.0 Mb) possessed QTL for Al and Zn tolerance. The chromosomal region MT9.1 (chr.9: 14.2–14.7 Mb) possessed QTL for Fe and Al tolerance. A total of 11 QTL were detected across different MAGIC populations and 12 clustered regions were detected under different metal conditions, suggesting that these genomic regions might constitute valuable regions for further marker-assisted selection (MAS) in breeding programs.

A number of studies reported major genes/QTLs for rice grain shapes, chalkiness and starch physicochemical properties. For these finely mapped QTLs or cloned genes to make an impact in practical breeding, it is necessary to test their effects in different genetic backgrounds. In this study, two hundred nineteen markers for 20 starch synthesis genes, 41 fine mapped grain shape and related traits QTLs/genes, and 54 chalkiness QTLs/genes plus 15 additional markers and a large indica population of 375 advanced lines were used to identify marker-trait associations under 6 environments that can be used directly in breeding for grain quality traits. The significant associations detected by the QK model were used to declare the usefulness of the targeted genes/QTLs. A total of 65 markers were detected associations with grain quality trait at least in one environment. More phenotypic variations could be explained by haplotype than single marker, as exemplified by the starch biosynthesising genes. GBSSI was the major gene for AC and explained up to 55 % of the phenotypic variation, which also affected GC and accounted up to 11.31 % of the phenotypic variation. SSIIa was the major gene for chalkiness and explained up to 17 and 21 % of variation of DEC and PGWC, respectively. In addition, RMw513 and RM18068 were associated with DEC in 6 environments as well. Four markers (RGS1, RM15206, RMw513 and Indel1) tightly linked to GS3, gw5, and qGL7-2 were the most important ones for grain shapes. Allelic combinations between SSIIa and RMw513 revealed more variations in DEC. The validated markers for genes/QTLs with major effects could be directly used in breeding for grain quality via marker-assisted selection. Creating desirable allelic combinations by gene pyramiding might be an effective approach for the development of high quality breeding lines in rice.

Three new rice ( Oryza sativa L.) multiparent advanced generation intercross (MAGIC) populations were developed using eight elite indica rice varieties from different breeding programs. These three populations were two recombinant inbred line (RIL) populations derived from two 4‐way crosses, DC1 and DC2, and one RIL population derived from an 8‐way cross. These populations were genotyped using an Illumina Infinium rice 6K SNP chip. The potential of the three MAGIC populations in identifying marker–trait associations was demonstrated using the plant height (PH) and heading date (HD) measured in 2014. A population of 248 IRRI breeding lines and a population of 323 Chinese breeding lines were also included to compare genetic diversity and linkage disequilibrium (LD) pattern. Our study discovered that (i) the 8‐way population had a higher gene diversity than the DC1, DC2, and IRRI populations; (ii) all three MAGIC populations showed no clear population structure; (iii) LD decayed to r ² < 0.2 at about 2.5, 2.5, 1.25, 1.75, and 4.0 Mb for the DC1, DC2, 8‐way, IRRI, and Chinese populations, respectively; and (iv) the 8‐way population was more powerful than the DC1, DC2, and IRRI populations on QTL identification. The association analysis identified two and three QTL for PH and HD, respectively. Four of the five QTL had peak markers close to known genes. A novel QTL for PH was identified on chromosome 12 using the 8‐way population. Therefore, our study suggests that the three new MAGIC populations are valuable resources for QTL identification.