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Salinity and alkalinity seriously threaten global rice production. As a consequence, elucidation of the genetic basis of salinity and alkalinity tolerance is crucial for rice breeding. To identify genetic marker loci/QTLs associated with salinity and alkalinity tolerance, we performed association mapping based on different statistical models using 347 rice accessions from all over the world. Salinity tolerance for all traits was evaluated on saline land along the seashore of Hebei, while alkalinity tolerance at the seedling stage was assessed in the alkaline soil of paddy fields in Jilin. A total of 148 SSRs were used for genotyping. Within the entire population, 64.1 % of SSR locus pairs were in significant linkage disequilibrium (LD) (P

Cold tolerance at booting stage is one of the major determinants for a stable yield of rice (Oryza sativa L.) in many high elevation or high latitude regions. Understanding the genetic basis of cold tolerance is crucial for the improvement of cold tolerance through breeding. In this study, association mapping was performed in 347 rice accessions worldwide with different statistical models in order to identify the genetic marker loci/QTL associated with cold tolerance traits at the booting stage. The evaluation of cold tolerance for all the traits was conducted under natural low temperature in Yunnan and cold water irrigation in Jilin. The 148 SSRs were used for the genotyping. Population structure analysis identified three main subpopulations for the accessions that corresponded to major geographic origins. The relative kinship analysis revealed a weak or no relationship for most of the individual pairs. Model comparisons indicated that the Q+K model controlling both population structure (Q) and the relative kinship (K) was performed better than other models in association mapping. In total, 24 markers were identified that were significantly associated with cold tolerance, including five markers in Yunnan and 19 markers in Jilin. Moreover, RM282, RM252, RM335 and RM6824 were identified in multiple environments or years. Many of these identified markers were located either in or nearby the regions where the QTLs have been reported for cold tolerance at booting stage. These results highlighted the targeted regions for future studies and might be subsequently used in breeding programs to trace and select the useful alleles by MAS.

Using an F 2:3 population with 200 lines derived from a cross between the japonica rice ( Oryza sativa L.) varieties Gaochan 106 and Changbai 9, quantitative trait loci (QTLs) for root number, root length, seedling height, and their relative alkaline damage rates at early seedling stage were identified under 0.15% Na 2 CO 3 alkaline stress. A total of 26 QTLs associated with these traits were detected on chromosomes 1, 5, 6, 7, 8, 9, and 11, consisting of 4 QTLs for root number, 5 QTLs for relative alkaline damage rate of root number, 6 QTLs for root length, 2 QTLs for relative alkaline damage rate of root length, 5 QTLs for seedling height, and 4 QTLs for relative alkaline damage rate of seedling height. QTLs qRN6-1 , qRN11 , qRRN11-2 , qRRL11-2 , qSH1 , qSH11-2 , qRSH5 , and qRSH6-2 accounted for 29.91, 13.42, 23.86, 21.06, 15.81, 16.53, 29.89, and 34.63% of the phenotypic variations, respectively. These QTLs, except for qRN6-1 with the marker flanking distance of 19.0 cM, were located with relatively large marker flanking distances over 26.3 cM. The positive alleles at 13 loci originated from the tolerant parent Changbai 9, and the positive alleles at other loci were from the sensitive parent Gaochan 106. Partial dominance and over dominance were the main effects at these loci.

The quantitative trait loci (QTLs) for the dead leaf rate (DLR) and the dead seedling rate (DSR) at the different rice growing periods after transplanting under alkaline stress were identified using an F(2:3) population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F(3) lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent "Gaochan106". The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.