QTL analysis of eating quality and cooking process of rice using a new RIL population derived from a cross between Minghui 63 and Khao Dawk Mali105

Abstract The cooking and eating quality of the rice grain is one of the most serious problems in many rice producing areas of the world. In this study, QTL analysis was performed for rice cooking and eating quality using a new recombinant inbred line (RIL) population derived from a cross between Minghui 63 (MH63), the Chinese best male sterility restorer in the hybrid rice programs, and Khao Dawk Mali105 (KDML105), the Thai jasmine rice, known as the best quality rice. The traits analyzed included amylose content (AC), gel consistency (GC), alkali spreading value (ASV), and 13 parameters from the viscosity profile. Comparison of the QTLs identified revealed 11 QTL clusters for these traits distributed on six chromosomes. The QTLs for the traits in the same class often clustered into the same chromosomal regions. A total of 29 QTLs were identified for 16 traits (or parameters) in the two years at P≤0.01 level. Our results clearly showed that the QTL cluster (six QTLs) corresponding to the Wx locus controlled six of the viscosity parameters such as BAtime-time needed from initial viscosity increase to peak viscosity (PKV), hot paste viscosity (HPV), final viscosity (FV), setback viscosity (SB) and consistency viscosity (CS), and had no effect on AC, GC, and ASV. The QTL cluster (13 QTLs) corresponding to the Alk locus played a role in ASV, GC, AC and all of the viscosity parameters except for PKV, FV and CS. In this study both AC and GC were not influenced by the Wx gene region. Our study investigated QTL analysis for the seven parameters of the viscosity profile, namely, Atemp, Atime, Btemp, Btime, BAtime, V95, and FV. Most of the QTLs previously found for these parameters on chromosome 6 in the Wx and Alk loci and on chromosome 5 and chromosome 7 were confirmed in the present study. Furthermore, new minor and major QTLs were also mapped on the chromosomes 5, 6, 7, 8, 11 and 12 for these parameters. However, we noted the instability of some of these QTLs across the environments and their small phenotypic variation value. Further investigation of these new QTLs or locus could bring more specific and comprehensive and probably complete information about them. Keywords: QTL, Recombinant inbred line, Rice quality, SSR markers, Viscosity profile. Abbreviations: AC-amylose content; Add-additive effect; Alk-alkali gene locus; Atemp-pasting temperature; Atime-pasting time; BAtime-time needed from initial viscosity increase to PKV; BD-breakdown viscosity; Btemp-peak temperature; Btime-peak time; Chrs-chromosome; CPV-cool paste viscosity; CS-consistency viscosity; FV-final viscosity at 40°C; GC-gel consistency; GTgelatinization temperature; HPV-hot paste viscosity; KDML105-Kkao Dawk Mali105; MH63-Minghui 63; PKV-peak viscosity ; QTL-quantitative trait loci; RIL-recombinant inbred lines; RVA-rapid visco analyzer; SB-setback viscosity; SD-standard deviation; SSR-simple sequence repeats; V95-viscosity at 95°C; Var%-phenotypic variation percentage; Wx-waxy gene locus

Molecular characterization and genetic diversity of different genotypes of Oryza sativa and Oryza glaberrima

Background: Availability of related rice species is critical for rice breeding and improvement. Two distinct species of domesticated rice exist in the genus Oryza: Oryza sativa (Asian rice) and Oryza glaberrima (African rice). New rice for Africa (NERICA) is derived from interspecific crosses between these two species. Molecular profiling of these germplasms is important for both genetics and breeding studies. We used 30 polymorphic SSR markers to assess the genetic diversity and molecular fingerprints of 53 rice genotypes of O. sativa, O. glaberrima, and NERICA. Results: In total, 180 alleles were detected. Average polymorphism information content and Shannon's information index were 0.638 and 1.390, respectively. Population structure and neighbor-joining phylogenetic tree revealed that 53 genotypes grouped into three distinct subpopulations conforming to the original three groups, except three varieties (IR66417, WAB450-4, MZCD74), and that NERICA showed a smaller genetic distance from O. sativa genotypes (0.774) than from O. glaberrima genotypes (0.889). A molecular fingerprint map of the 53 accessions was constructed with a novel encoding method based on the SSR polymorphic alleles. Ten specific SSR markers displayed different allelic profiles between the O. glaberrima and O. sativa genotypes. Conclusions: Genetic diversity studies revealed that 50 rice types were clustered into different subpopulations whereas three genotypes were admixtures. Molecular fingerprinting and 10 specific markers were obtained to identify the 53 rice genotypes. These results can facilitate the potential utilization of sibling species in rice breeding and molecular classification of O. sativa and O. glaberrima germplasms

Molecular characterization and genetic diversity of different genotypes of Oryza sativa and Oryza glaberrima

Background: Availability of related rice species is critical for rice breeding and improvement. Two distinct species of domesticated rice exist in the genus Oryza: Oryza sativa (Asian rice) and Oryza glaberrima (African rice). New rice for Africa (NERICA) is derived from interspecific crosses between these two species. Molecular profiling of these germplasms is important for both genetics and breeding studies. We used 30 polymorphic SSR markers to assess the genetic diversity and molecular fingerprints of 53 rice genotypes of O. sativa, O. glaberrima, and NERICA. Results: In total, 180 alleles were detected. Average polymorphism information content and Shannon's information index were 0.638 and 1.390, respectively. Population structure and neighbor-joining phylogenetic tree revealed that 53 genotypes grouped into three distinct subpopulations conforming to the original three groups, except three varieties (IR66417, WAB450-4, MZCD74), and that NERICA showed a smaller genetic distance from O. sativa genotypes (0.774) than from O. glaberrima genotypes (0.889). A molecular fingerprint map of the 53 accessions was constructed with a novel encoding method based on the SSR polymorphic alleles. Ten specific SSR markers displayed different allelic profiles between the O. glaberrima and O. sativa genotypes. Conclusions: Genetic diversity studies revealed that 50 rice types were clustered into different subpopulations whereas three genotypes were admixtures. Molecular fingerprinting and 10 specific markers were obtained to identify the 53 rice genotypes. These results can facilitate the potential utilization of sibling species in rice breeding and molecular classification of O. sativa and O. glaberrima germplasms. Keywords: African rice, Asian rice, Fingerprinting, Food security, Genetic relationship, Microsatellite markers, Molecular profiling, Phylogenetic tree, Polymorphic alleles, Rice breeding, SS

Genetic Diversity and Phenotypic Variation in an Introgression Line Population Derived from an Interspecific Cross between <i>Oryza glaberrima</i> and <i>Oryza sativa</i>

<div><p>The introduction of closely related species genomic fragments is an effective way to enrich genetic diversity and creates new germplasms in crops. Here, we studied the genetic diversity of an introgression line (IL) population composed of 106 ILs derived from an interspecific tetra cross between <i>O</i>. <i>glaberrima</i> and <i>O</i>. <i>sativa</i> (RAM3/Jin23B//Jin23B///YuetaiB). The proportion of <i>O</i>. <i>glaberrima</i> genome (PGG) in the ILs ranged from 0.3% to 36.7%, with an average value of 12.32% which is close to the theoretically expected proportion. A total of 250 polymorphic alleles were amplified by 21 AFLP primer combinations with an average of 12 alleles per primer. Population structure analysis revealed that the IL population can be divided into four genetically distinct subpopulations. Both principal component analysis and neighbor-joining tree analysis showed that ILs with a higher PGG displayed greater genetic diversity. Canonical discriminant analysis identified six phenotypic traits (plant height, yield per plant, filled grain percentage, panicle length, panicle number and days to flowering) as the main discriminatory traits among the ILs and between the subpopulations and showed significant phenotypic distances between subpopulations. The effects of PGG on phenotypic traits in the ILs were estimated using a linear admixed model, which showed a significant positive effect on grain yield per plant (0.286±0.117), plant height (0.418 ± 0.132), panicle length (0.663 ± 0.107), and spikelet number per panicle (0.339 ± 0.128), and a significant negative effect on filled grain percentage (-0.267 ± 0.123) and days to flowering (-0.324 ± 0.075). We found that an intermediate range (10% − 20%) of PGG was more effective for producing ILs with favorable integrated agronomic traits. Our results confirm that construction of IL population carrying <i>O</i>. <i>glaberrima</i> genomic fragments could be an effective approach to increase the genetic diversity of <i>O</i>. <i>sativa</i> genome and an appropriate level of PGG could facilitate pyramiding more favorable genes for developing more adaptive and productive rice.</p></div

Pairwise Pearson’s correlations between PGG or FVL and phenotypic traits in ILs.

<p>Pairwise Pearson’s correlations between PGG or FVL and phenotypic traits in ILs.</p

Phenotypic traits in ILs with different PGGs.

<p>1: PGG < 10%, 2: 10% ≤ PGG < 20%, 3: 20% ≤ PGG < 30%, 4: 30% ≤ PGG < 40%.</p

Comparison of two matrixes of pairwise DIST distances between ILs based on AFLP markers and phenotypic traits.

<p>Lower left (a): Matrix of pairwise DIST distances between ILs based on AFLP markers; (b): The structure of the IL population; Upper right (c): The PGG in ILs; (d): Matrix of pairwise DIST distances between ILs based on phenotypic traits.</p

PGG and frequency of variable loci (FVL) in the ILs.

<p>PGG and frequency of variable loci (FVL) in the ILs.</p

Effect and significance of PGG on phenotypic traits.

<p>Effect and significance of PGG on phenotypic traits.</p

Estimated population structures of the IL population with <i>K</i> = 2 (a) and <i>K</i> = 4 (b).

<p>The ILs were sorted according to the Q vector of cluster 1(a) and the max Q vectors in each subpopulation (b).</p