QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

Liu, Qiangming; Jiang, Jianhua; Niu, Fuan; He, Ying-jun; Hong, Delin

doi:10.1016/s1672-6308(13)60105-5

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…A number of QTLs affecting grain size traits have been mapped previously. For GL, eleven of the fourteen QTLs (qGL2-1, qGL2-2, qGL3-2, qGL3-3, qGL3-4, qGL3-5, qGL4, qGL8-1, qGL8-2, qGL10, and qGL12) were located in the same regions or close to previously reported [12,[64][65][66][67][68]. The known grain size gene, qGL3/GL3.1/OsPPKL1, was located in the region of qGL3-3 [25][26][27].…”

Section: Compared With Previously Identified Qtls or Genessupporting

confidence: 78%

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice (Oryza sativa L.)

Leng¹,

Wang²,

Wang³

et al. 2022

Phyton

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Grain size traits are critical agronomic traits which directly determine grain yield, but the genetic bases of these traits are still not well understood. In this study, a total of 154 chromosome segment substitution lines (CSSLs) population derived from a cross between a japonica variety Koshihikari and an indica variety Nona Bokra was used to investigate grain length (GL), grain width (GW), length-width ratio (LWR), grain perimeter (GP), grain area (GA), and thousand grain weight (TGW) under four environments. QTL mapping analysis of six grain size traits was performed by QTL IciMapping 4.2 with an inclusive composite interval mapping (ICIM) model. A total of 64 QTLs were identified for these traits, which mapped to chromosomes 1, 2, 3, 4, 6, 7, 8, 10, 11, and 12 and accounted for 1.6%-27.1% of the total phenotypic variations. Among these QTLs, thirty-six loci were novel and seven QTLs were identified under four environments. One locus containing the known grain size gene, qGL3/ GL3.1/OsPPKL1, also have been found. Moreover, five pairs of digenic epistatic interactions were identified except for GL and GP. These findings will facilitate fine mapping of the candidate gene and QTL pyramiding to genetically improve grain yield in rice.

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Section: Compared With Previously Identified Qtls or Genessupporting

confidence: 78%

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice (Oryza sativa L.)

Leng¹,

Wang²,

Wang³

et al. 2022

Phyton

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“… Zhao et al (2016) detected 140 QTLs controlling chalkiness by reciprocal cross between Teqing and Lemont in nine environments. Liu et al (2013) detected 15 QTLs controlling chalkiness by MCIM, ICIM, and MIMR three methods. Li et al (2004) detected two QTLs controlling chalkiness by an interspecific backcross population derived from cultivated Asian ( O. sativa L.) and African ( O. glaberrima S.) rice.…”

Section: Discussionmentioning

confidence: 99%

Genetic Dissection of qPCG1 for a Quantitative Trait Locus for Percentage of Chalky Grain in Rice (Oryza sativa L.)

Zhu

Zhang

et al. 2018

Front. Plant Sci.

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Rice is a pivotal cereal crop that provides the staple food for more than half of the world’s population. Along with improvements in the standard of living, people not only pay attention to the grain yield but also to the grain quality. Chalkiness is one of the most important index of grain quality. In this study, qPCG1, a QTL for percentage of chalky grain, was mapped in an interval with a physical distance about 139 kb on chromosome 1 by residual heterozygous line (RHL) method. qPCG1 was incomplete dominant and the additive effect plays a major role and explained 6.8–21.9% of phenotypic variance within the heterogeneous region on chromosome 1. The effect of allele from Zhonghui9308 was decreasing the percentage of chalky grains (PCG). Microscope observation results indicated that there are great differences in the shape, structure and arrangement of starch granule between the chalky part and transparent part. Analysis of starch physicochemical properties showed that the total starch content, amylose content and chain length distribution of amylopectin changed while the protein contents were not apparently affected with the changed chalkiness. qPCG1 had little influence on main agronomic traits and it might be useful in rice breeding for it did not bring negative effect on grain yield while reducing the chalkiness.

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“…If epistatic effects of the genes are not taken into account, the associations for a single gene might be inaccurate or misleading, thereby hindering the development of FMs and causing inconsistency in FM application ( Brenner et al, 2013 ). Epistatic effects among the QTLs controlling quality traits have been reported ( Lee and Koh, 2010 ; Anuradha et al, 2012 ; Liu et al, 2013 ); therefore, researchers should discern the epistatic effects of the genes influencing a trait in quality rice breeding.…”

Section: Challenges In the Development And Application Of Fms In Breementioning

confidence: 99%

Review of functional markers for improving cooking, eating, and the nutritional qualities of rice

et al. 2015

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After yield, quality is one of the most important aspects of rice breeding. Preference for rice quality varies among cultures and regions; therefore, rice breeders have to tailor the quality according to the preferences of local consumers. Rice quality assessment requires routine chemical analysis procedures. The advancement of molecular marker technology has revolutionized the strategy in breeding programs. The availability of rice genome sequences and the use of forward and reverse genetics approaches facilitate gene discovery and the deciphering of gene functions. A well-characterized gene is the basis for the development of functional markers, which play an important role in plant genotyping and, in particular, marker-assisted breeding. In addition, functional markers offer advantages that counteract the limitations of random DNA markers. Some functional markers have been applied in marker-assisted breeding programs and have successfully improved rice quality to meet local consumers’ preferences. Although functional markers offer a plethora of advantages over random genetic markers, the development and application of functional markers should be conducted with care. The decreasing cost of sequencing will enable more functional markers for rice quality improvement to be developed, and application of these markers in rice quality breeding programs is highly anticipated.

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QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

Cited by 10 publications

References 12 publications

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice (Oryza sativa L.)

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice (Oryza sativa L.)

Genetic Dissection of qPCG1 for a Quantitative Trait Locus for Percentage of Chalky Grain in Rice (Oryza sativa L.)

Review of functional markers for improving cooking, eating, and the nutritional qualities of rice

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