Advances in Sensing, Response and Regulation Mechanism of Salt Tolerance in Rice.

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.

Genome-Wide Association Study of Grain Size Traits in Indica Rice Multiparent Advanced Generation Intercross (MAGIC) Population.

Rice grain size plays a crucial role in determining grain quality and yield. In this study, two multiparent advanced generation intercross (MAGIC) populations, DC1 and BIM, were evaluated for grain size across three environments and genotyped with 55K array-based SNP detection and genotype-by-sequencing (GBS), respectively, to identify QTLs and SNPs associated with grain length, grain width, grain length-width ratio, grain thickness, and thousand grain weight. A total of 18 QTLs were identified for the five grain size-related traits and explained 6.43-63.35% of the total phenotypic variance. Twelve of these QTLs colocalized with the cloned genes, GS3, GW5/qSW5, GW7/GL7/SLG7, and GW8/OsSPL16, of which the first two genes showed the strongest effect for grain length and grain width, respectively. Four potential new genes were also identified from the QTLs, which exhibited both genetic background independency and environment stability and could be validated in future studies. Moreover, the significant SNP markers identified are valuable for direct utilization in marker-assisted breeding to improve rice grain size.

Using Heading date 1 preponderant alleles from indica cultivars to breed high-yield, high-quality japonica rice varieties for cultivation in south China.

Heading date 1 (Hd1) is an important gene for the regulation of flowering in rice, but its variation in major cultivated rice varieties, and the effect of this variation on yield and quality, remains unknown. In this study, we selected 123 major rice varieties cultivated in China from 1936 to 2009 to analyse the relationship between the Hd1 alleles and yield-related traits. Among these varieties, 19 haplotypes were detected in Hd1, including two major haplotypes (H8 and H13) in the japonica group and three major haplotypes (H14, H15 and H16) in the indica group. Analysis of allele frequencies showed that the secondary branch number was the major aimed for Chinese indica breeding. In the five major haplotypes, SNP316 (C-T) was the only difference between the two major japonica haplotypes, and SNP495 (C-G) and SNP614 (G-A) are the major SNPs in the three indica haplotypes. Association analysis showed that H16 is the most preponderant allele in modern cultivated Chinese indica varieties. Backcrossing this allele into the japonica variety Chunjiang06 improved yield without decreasing grain quality. Therefore, our analysis offers a new strategy for utilizing these preponderant alleles to improve yield and quality of japonica varieties for cultivation in the southern areas of China.

QTL Identification for Cooking and Eating Quality in indica Rice Using Multi-Parent Advanced Generation Intercross (MAGIC) Population.

Association mapping using a multi-parent advanced generation intercross (MAGIC) population provides a promising tool in genetic dissection of rice cooking and eating quality (CEQ). In this study, QTLs were identified for ten physicochemical properties related to CEQ using 508 F6 MAGIC lines. The whole population and eight founder lines were genotyped with 6K Illumina Infinium HD Assay. All traits had high heritability estimates and showed a large genetic variation in the MAGIC population. Highly significant phenotypic correlations were present between traits. AC was significantly positively correlated with PKT, TV, FV, SBV, PKT, and RT but significantly negatively correlated with GC and BDV. Seventeen QTLs were identified for all traits. GBSSI locus was hosted or closely to nine QTLs, qAC6, qGC6.1, qPKT6.1, qPKV6, qBDV6.1, qTV6.1, qFV6, qSBV6, and qRT6, suggesting that GBSSI impacts the overall CEQ. Another locus closed to SSIIa, located at 6.99 Mb, affects five traits, GC, PKT, BDV, SBV, and PT. The identified QTLs revealed small to modest effects where the highest percentage of phenotypic variance explained was 17.18%. These QTLs are directly relevant and useful in breeding for CEQ in indica rice. These results also confirmed that QTL mapping via association mapping using a MAGIC population is a powerful method in genetic analysis of complex traits.

Association Mapping of Ferrous, Zinc, and Aluminum Tolerance at the Seedling Stage in Indica Rice using MAGIC Populations.

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.

Characterization of Three Rice Multiparent Advanced Generation Intercross (MAGIC) Populations for Quantitative Trait Loci Identification.

Three new rice ( L.) multiparent advanced generation intercross (MAGIC) populations were developed using eight elite 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 < 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.