Yaling Chen

Rapid Identification of Major QTLs Associated with Rice Grain Weight and Their Utilization

To uncover the genetics of rice grain weight, we constructed an RIL population derived from a cross between a large grain accession M201 and a small size variety JY293. Specific Locus Amplified Fragment Sequencing (SLAF-Seq) technology was used to genotype two bulked DNA pools made from individual DNA of the heaviest 30 lines and the lightest 30 lines according to the 1000 grain weight (TGW). Bulked segregant analysis (BSA) was used to identify SLAFs strongly associated with TGW. Two marker-intensive regions at 24,600,000–24,850,000 bp and 25,000,000–25,350,000 bp on chromosome 3 were identified tightly related to the TGW. Then a linkage map of chromosome 3 was constructed with SSR markers and some SLAF derived single nucleotide polymorphisms (SNPs). Quantitative trait locus (QTL) mapping for TGW, grain length, grain width, and grain thickness revealed one major QTL in the second hot-region and two other minor QTLs for grain weight. These three QTLs displayed hierarchical effects on grain length and grain weight in order of qTGW3.2 (qGL3) qTGW3.1 (GS3) qTGW3.3. Multiple comparisons of means among the eight combinations of 3 QTLs revealed that the lines with two of three QTLs deriving from M201 displayed a large grain weight phenotype (TGW 40.2g, average data of three years) and lines with both qTGW3.1 and qTGW3.3 alleles from M201 (42.5g) had similar grain weight to the qTGW3.2 (40.8g) alone. Two strategies with similar effectiveness were proposed to improve grain weight by marker-assisted selection (MAS). One is to introduce the novel qTGW3.2 allele alone, and the other is to pyramid qTGW3.1 and qTGW3.3 alleles together. One new allele of GS3 (39 bp deletion in intron 1) and two SNPs in coding sequence of qGL3 identified in this study from M201 are useful in pyramiding elite alleles for molecular breeding for improvement of rice yield.

Association Mapping of Quantitative Trait Loci for Mineral Element Contents in Whole Grain Rice (Oryza sativa L.).

Mineral elements in brown rice grain play an important role in human health. In this study, variations in the content of iron (Fe), zinc (Zn), selenium (Se), cadmium (Cd), and lead (Pb) in 378 accessions of brown rice were investigated, and association mapping was used to detect the quantitative trait loci (QTLs) responsible for the variation. Among seven subpopulations, the mean values of Zn and Cd in the japonica group were significantly higher than in the indica groups. The population structure accounted for from 5.7% (Se) to 22.1% (Pb) of the total variation. Correlation analyses showed that Pb was positively correlated with the other minerals (P < 0.001) except for Se. For the five mineral elements investigated, 20 QTLs, including some previously reported and new candidate loci, were identified. Particularly, three cases of QTL colocalization, i.e. Cd and Pb on chromosome 5, Zn and Pb on chromosome 7, and Se and Pb on chromosome 11, were observed. This study suggested that the identified markers could feasibly be used to enhance desired micronutrients while reducing the heavy metal content in whole rice grain by marker-assisted selection (MAS).

Genotype × Environment Interactions for Agronomic Traits of Rice Revealed by Association Mapping

Abstract Agronomic traits are important determinants to rice yield, which are controlled by complex genetic factors as well as genotype by environment (G × E) interaction effects. The G × E effects for agronomic traits of rice have been dissected with various approaches, but not with the current available approach, the association studies. In this study, a total of 32 655 single nucleotide polymorphisms were used to carry out associations with 14 agronomic traits among 20 rice accessions in two environments. The G × E interaction effects for all the agronomic traits were at highly significant levels (P

Genetic diversity of amylose content and RVA pasting parameters in 20 rice accessions grown in Hainan, China.

Starch physicochemical properties determine the eating and cooking quality of rice. The genetic diversity in the apparent amylose content (AAC) and pasting viscosity parameters of 20 geographically diverse rice accessions were investigated. It was found that AAC and pasting viscosities differed widely among different accessions, but each accession performed relatively stably across two environments. Analysis of variance (ANOVA) indicated that all traits were predominantly controlled by genotypic variance, but the genotype×environment interaction effects were also significant except for AAC and PT. Significant correlations were found for each parameter between 2years (P<0.001). Association mapping identified a total of 22 main-effect quantitative trait loci (QTLs) responsible for all traits except for CPV. This study showed that starch physicochemical properties of rice were highly stable and mainly controlled by genetic factors, and gave insight into the molecular improvement of eating quality using marker assisted breeding with the identified QTLs/genes.

Analysis of Genotype, Environment, and Their Interaction Effects on the Phytochemicals and Antioxidant Capacities of Red Rice (Oryza sativa L.)

Fourteen red rice varieties were planted in two locations during summer (Hangzhou) and winter (Hainan) to study the effect of genotype and environment on the phytochemicals and antioxidant capacities of rice grain. B-type proanthocyanidins in red rice were detected by LC-MS/MS and quantified by using the vanillin assay. Analysis of variance showed that total phenolic content (TPC), total flavonoid content (TFC) and 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacity were mainly affected by environmental factors, which accounted for more than 60% of the total variance. However, total proanthocyanidin content (TPAC) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity were equally affected by both genotype and environment. The genotype × environment effects were significant for all traits. The pairwise correlations among TPC, TFC, TPAC, ABTS, and DPPH were also significant (r > 0.900, P < 0.001). Principal component analysis identified the genotypes tha...

Analysis of genotype × environment interactions for polyphenols and antioxidant capacity of rice by association mapping.

Uncovering the genetic basis of polyphenol content and antioxidant activity traits in rice accessions is important to improve the nutritional quality of whole grain rice and to ameliorate the increasing nutrition problem of the rice-eating population. In this study, 20 diverse rice accessions were planted in Hainan province, China, for 2 years to investigate the effects of genotype, environment, and their interactions on total phenolic (TPC), flavonoid (TFC), proanthocyanidins content (TPAC), ABTS, and DPPH radical scavenging activity by association mapping. Analyses of variance (ANOVA) showed that TPC, TFC, TPAC, ABTS, and DPPH were mainly affected by genetic variance, accounting for >94% of the total variance. The interaction between genotype × environment (G × E) was also highly significant (P < 0.001). Red-pericarp rice accumulates proanthocyanidins, which had significantly higher TPC, TFC, ABTS, and DPPH than white-pericarp rice. The correlation coefficient among these parameters were highly significant (r > 0.96; P < 0.001). Twenty-three putative unique loci were identified by association mapping. Five loci were close to previously identified genes or quantitative trait loci (QTLs). Among them, qPAC7-3 identified for TPAC in 2011 was near to the brown pericarp and seed coat (Rc) gene, and the locus at the qPC4/qFC4/qPAC4/qACA4/qACD4 cluster on chromosome 4 detected in two environments was near to a transcriptional activator A (Ra) gene. Some loci were identified in only one environment, indicating that these QTLs were sensitive to environment. This study provides a primary SNP-resource for further identification of genes responsible for polyhenol contents and antioxidant activity in rice whole grains.

Variation in mineral elements in grains of 20 brown rice accessions in two environments

Twenty rice accessions were planted in Hainan province, China, for 2 years to investigate the effects of genotype, environment, and their interactions on the Ca, Mg, Na, K, Fe, Zn, and Cu contents in brown rice. Analysis of variance showed that the Ca, Na and K were mainly affected by the genotypic variance, whereas the Fe, Zn and Cu were mainly affected by the environment variance. The genotype × environment interaction effects for Mg, Na, Zn, and Cu were highly significant (P < 0.001), though it only accounted for a small proportion of the total variation (0.5-16.3%). The correlation analyses showed that Mg was significantly positively correlated with K, Fe, and Zn. A total of 9 and 8 single nucleotide polymorphism (SNP) loci were identified in 2011 and 2012, respectively, which were strongly associated with for Ca, Cu, K, Na, and Zn.

QTL mapping for rice grain quality: a strategy to detect more QTLs within sub-populations

Wx is considered to be the most important gene controlling eating and cooking qualities and pasting properties in rice (Oryza sativa L.). In this study, a recombinant inbred line population derived from indica rice parents differing in apparent amylose content (AAC) was used to detect quantitative trait loci (QTLs) for ten grain quality parameters for rice quality improvement. QTL mapping was performed on the whole population and on two sub-populations based on Wx genotypes. A total of 29 QTLs were found in the whole population. Ten QTLs for 7 traits were detected in the two sub-populations, four of which (qPRO3.1, qPV9, qHPV9, and qCS7) were also detected in the whole population, whereas the other six were QTLs with minor effects that might be covered by the Wx locus. Besides the Wx locus with the largest effect on AAC and most pasting properties, there were another six QTL clusters contributing to grain quality located on chromosomes 2, 3, 5, 6 and 9. It was also found that some QTLs for peak viscosity, breakdown and consistency were closely linked to rice grain shape related QTLs on chromosome 3. A QTL cluster on chromosome 9 for peak viscosity, hot paste viscosity and cold paste viscosity was detectable in the whole population, which was close to the isoamylase 3 (ISA3) locus. A QTL cluster for both peak time and pasting temperature on chromosome 6 was near to the starch synthase I locus, and was potentially a new QTL with minor effect for peak time and pasting temperature. These findings will promote better understanding of the genetic regulation of rice eating and cooking qualities.

Association mapping of quantitative trait loci for yield-related agronomic traits in rice (Oryza sativa L.)

Abstract High yield in rice mainly depends on large grain weight, ideal plant architecture and proper flowering time adapting to various geographic regions. To help achieve higher yield, phenotype variations of heading date (HD), plant architecture and grain shape in a panel of 416 rice accessions were investigated in this study. A total of 143 markers including 100 simple sequence repeat (SSR) markers and 43 gene-tagged markers were employed in association mapping to detect quantitative trait loci (QTL) responsible for these variations. Among the 7 subpopulations, POP5 in japonica group showed the largest values of HD and grain width (GW), but the smallest values of grain length (GL) and grain length to width ratio (GLW). Among the six indica groups, POP7 had the largest values of HD, GL, GLW, and 1 000-grain weight (TGW). A total of 27 QTLs were detected underlying these phenotypic variations in single year, while 12 of them could be detected in 2006 and 2007. GS3 marker was closely associated with GL, GW and GLW, and widely distributed in different groups. The starch synthesis related gene markers, SSI, SSIIa, SBE1, AGPL4, and ISA1, were linked to plant height (PH), panicle length (PL), flag leaf length (FLL), GW, and GLW. The SSR markers, RM267, RM340 and RM346, were linked to at least two traits. Therefore, these new markers will probably be used to improve rice grain yield or plant architecture when performing marker-assisted selection of proper alleles.