Fine mapping of grain length QTLs on chromosomes 1 and 7 in Basmati rice (Oryza sativa L.)

Singh, Rakesh; Singh, Ashok Kumar; Sharma, Tilak Raj; Singh, Anil Kumar; Singh, Nagendra

doi:10.1007/s13562-011-0080-3

Cited by 42 publications

(27 citation statements)

References 29 publications

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“…In the region of 22.3–22.8 Mb on chromosome 7, a QTL cluster ( qGL7, qGLWR7 , and qGW7.3 ) was detected in the region of a fine mapped QTL ( qGRL7.1 ) affecting GL, GW, and GLWR (Singh et al, 2012). Our haplotype analysis suggested five candidates for this QTL, including Os07g0563700 (IKI3 family protein), Os07g0563800 (a GTPase-activating protein), Os07g0564000 (Conserved hypothetical protein), Os07g0564100 (a UDP-glucuronosyl / UDP-glucosyltransferase family protein) and Os07g0564150 (a hypothetical gene).…”

Section: Discussionmentioning

confidence: 99%

“…To date, many genes governing grain shape have been identified and cloned, such as GW2 (Song et al, 2007), GIF1 (Wang et al, 2008), qSW5 (Shomura et al, 2008), GS3 (Mao et al, 2010), GS5 (Li et al, 2011), qGL3 (Zhang et al, 2012), GW8 (Wang S. et al, 2012), GS6 (Sun et al, 2013), GS2 (Hu et al, 2015), GL7 / GW7 (Wang S. et al, 2015; Wang Y. et al, 2015), OsMAPK6 (Liu S. et al, 2015), and GLW7 (Si et al, 2016). Besides these genes, many QTL affecting grain size have been identified through linkage mapping and association studies (Zhao et al, 2011; Singh et al, 2012; Zhang W. et al, 2013; Yang et al, 2014; Liu D. et al, 2015; Qiu et al, 2015; Edzesi et al, 2016; Feng et al, 2016), and some of them have been fine mapped such as GW1-1 and qGRL1.1 (Singh et al, 2012), GW3 and GW6 (Guo et al, 2009), qGL-7 (Bai et al, 2010), qGRL7.1 (Singh et al, 2012). For grain chalkiness, only one gene, Chalk5 is cloned (Li et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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New Candidate Genes Affecting Rice Grain Appearance and Milling Quality Detected by Genome-Wide and Gene-Based Association Analyses

et al. 2017

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Appearance and milling quality are two crucial properties of rice grains affecting its market acceptability. Understanding the genetic base of rice grain quality could considerably improve the high quality breeding. Here, we carried out an association analysis to identify QTL affecting nine rice grain appearance and milling quality traits using a diverse panel of 258 accessions selected from 3K Rice Genome Project and evaluated in two environments Sanya and Shenzhen. Genome-wide association analyses using 22,488 high quality SNPs identified 72 QTL affecting the nine traits. Combined gene-based association and haplotype analyses plus functional annotation allowed us to shortlist 19 candidate genes for seven important QTL regions affecting the grain quality traits, including two cloned genes (GS3 and TUD), two fine mapped QTL (qGRL7.1 and qPGWC7) and three newly identified QTL (qGL3.4, qGW1.1, and qGW10.2). The most likely candidate gene(s) for each important QTL were also discussed. This research demonstrated the superior power to shortlist candidate genes affecting complex phenotypes by the strategy of combined GWAS, gene-based association and haplotype analyses. The identified candidate genes provided valuable sources for future functional characterization and genetic improvement of rice appearance and milling quality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Candidate Genes Affecting Rice Grain Appearance and Milling Quality Detected by Genome-Wide and Gene-Based Association Analyses

et al. 2017

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“…GS3 functions as a negative regulator for grain size which is a major QTL for GL and TGW and a minor QTL for GW and GT [16]. The qGL7.2 (S07_22844850) and qTGW7.2 (S07_22684516) were located in the region harboring the previously fine mapped QTL affecting GL, GW and GLWR, qGRL7.1 (22127.4Kb-24526.7Kb) [6]. The qGLWR7.1 (S07_25383179) was located in the region of Srs1 (25381698-25389547) affecting grain size [23].…”

Section: Gwas Is Effectivementioning

confidence: 98%

“…Many genes/QTL for rice grain appearance quality and milling quality traits have been reported in the last decades. The GW1-1 and GW1-2 [5], qGRL1.1 [6], GS2 [7], GW3 and GW6 [8], qGL-4b [9], qPGWC-7 [10] qGL-7 [11], qGRL7.1 [6], gw8.1 [12], gw9.1 [13], tgw11 [14] have been fine mapped. The GW2 [15], GS3 [16], qGL-3 [17], qSW5 [18], GS5 [19], Chalk5 [20], TGW6 [21], GW6a [22], SRS1 [23], GL7/GW7 [24,25], GW8 [26] and CycT1;3 [27] have been cloned.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide Association Study of Grain Appearance and Milling Quality in a Worldwide Collection of Indica Rice Germplasm

Fang¹,

Yingguo²,

Wu³

et al. 2018

Acta Agronomica Sinica

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Grain appearance quality and milling quality are the main determinants of market value of rice. Breeding for improved grain quality is a major objective of rice breeding worldwide. Identification of genes/QTL controlling quality traits is the prerequisite for increasing breeding efficiency through marker-assisted selection. Here, we reported a genome-wide association study in indica rice to identify QTL associated with 10 appearance and milling quality related traits, including grain length, grain width, grain length to width ratio, grain thickness, thousand grain weight, degree of endosperm chalkiness, percentage of grains with chalkiness, brown rice rate, milled rice rate and head milled rice rate. A diversity panel consisting of 272 indica accessions collected worldwide was evaluated in four locations including Hangzhou, Jingzhou, Sanya and Shenzhen representing indica rice production environments in China and genotyped using genotyping-by-sequencing and Diversity Arrays Technology based on next-generation sequencing technique called DArTseq™. A wide range of variation was observed for all traits in all environments. A total of 16 different association analysis models were compared to determine the best model for each trait-environment combination. Association mapping based on 18,824 high quality markers yielded 38 QTL for the 10 traits. Five of the detected QTL corresponded to known genes or fine mapped QTL. Among the 33 novel QTL identified, qDEC1.1 (qGLWR1.1), qBRR2.2 (qGL2.1), qTGW2.1 (qGL2.2), qGW11.1 (qMRR11.1) and qGL7.1 affected multiple traits with relatively large effects and/or were detected in multiple environments. The research provided an insight of the genetic architecture of rice grain quality and important information for mining genes/QTL with large effects within indica accessions for rice breeding.

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“…Most recent literature has focused on new SNP technologies, but by far the most common systems in use by public sector breeding programs are traditional SSRs. These are widely employed in biparental mapping studies such as QTL mapping and fine-mapping (Bradbury et al 2005, Weng et al 2008, Singh et al 2012, Ghimire et al 2012, Swamy et al 2013, Yadaw et al 2013, Yang et al 2016, but have also been used in cross-population meta-analyses (Swamy et al 2013) and allelic diversity assessments (Mohammadi-Nejad et al 2008, Singh et al 2015. Given their high throughput nature SNPs and GBS are the platforms of choice for strategies requiring high sample volumes and/or marker densities, such as genomic selection and genome-wide association studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Criteria for evaluating molecular markers: Comprehensive quality metrics to improve marker-assisted selection

Platten

Cobb

Zantua

2018

Preprint

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Metrics are presented which fully quantify the association of a marker with a target QTL. Evaluating markers with these metrics results in improved reliability of markers and therefore MAS projects.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/249987 doi: bioRxiv preprint first posted online Jan. 19, 2018; 2 AbstractDespite strong interest over many years, the usage of quantitative trait loci in plant breeding has often failed to live up to expectations. A key weak point in the utilisation of QTLs is the "quality" of markers used during marker-assisted selection (MAS): unreliable markers result in variable outcomes, leading to a perception that MAS products fail to achieve reliable improvement. Most reports of markers used for MAS focus on markers derived from the mapping population. There are very few studies that examine the reliability of these markers in other genetic backgrounds, and critically, no metrics exist to describe and quantify this reliability. To improve the MAS process, this work proposes five core metrics that fully describe the reliability of a marker. These metrics give a comprehensive and quantitative measure of the ability of a marker to correctly classify germplasm, particularly against a background of high allelic diversity. Markers that score well on these metrics will have far higher reliability in breeding, and deficiencies in specific metrics give information on circumstances under which a marker may not be reliable. The metrics are applicable across different marker types and platforms, allowing an objective comparison of the performance of different markers irrespective of the platform. Evaluating markers using these metrics demonstrates that trait-specific markers consistently outperform markers designed for other purposes. These metrics also provide a superb set of criteria for designing superior marker systems for a target QTL, enabling the selection of an optimal marker set before committing to design.

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Fine mapping of grain length QTLs on chromosomes 1 and 7 in Basmati rice (Oryza sativa L.)

Cited by 42 publications

References 29 publications

New Candidate Genes Affecting Rice Grain Appearance and Milling Quality Detected by Genome-Wide and Gene-Based Association Analyses

New Candidate Genes Affecting Rice Grain Appearance and Milling Quality Detected by Genome-Wide and Gene-Based Association Analyses

Genome-wide Association Study of Grain Appearance and Milling Quality in a Worldwide Collection of Indica Rice Germplasm

Criteria for evaluating molecular markers: Comprehensive quality metrics to improve marker-assisted selection

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