Identification and validation of QTLs controlling multiple traits in sorghum

Wang, Hailian; Zhang, Huawen; Du, Ruiying; Chen, Gui-Ling; Bin, Liu; Yang, Yanbing; Qin, Ling; Cheng, Er-Ying; Qiang, Liu; Guan, Yanan

doi:10.1071/cp15239

Cited by 14 publications

(7 citation statements)

References 36 publications

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“…6 ; qPH7 and qPH9 ). Comparing these QTLs with those reported previously using the QTL Atlas 50 , both QC7 and QC9 were supported by numerous previous reports of plant height- and panicle length-related QTLs in sorghum 27 , 51 – 57 (Table S1 ). In sorghum, four major loci controlling plant height, Dw1 , Dw2 , Dw3 , and Dw4 , have been extensively characterized and reported that their recessive alleles reduce internode length 6 .…”

Section: Resultssupporting

confidence: 88%

Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghum

Takanashi

Shichijo

Sakamoto

et al. 2021

Sci Rep

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Although spikelet-related traits such as size of anther, spikelet, style, and stigma are associated with sexual reproduction in grasses, no QTLs have been reported in sorghum. Additionally, there are only a few reports on sorghum QTLs related to grain size, such as grain length, width, and thickness. In this study, we performed QTL analyses of nine spikelet-related traits (length of sessile spikelet, pedicellate spikelet, pedicel, anther, style, and stigma; width of sessile spikelet and stigma; and stigma pigmentation) and six grain-related traits (length, width, thickness, length/width ratio, length/thickness ratio, and width/thickness ratio) using sorghum recombinant inbred lines. We identified 36 and 7 QTLs for spikelet-related traits and grain-related traits, respectively, and found that most sorghum spikelet organ length- and width-related traits were partially controlled by the dwarf genes Dw1 and Dw3. Conversely, we found that these Dw genes were not strongly involved in the regulation of grain size. The QTLs identified in this study aid in understanding the genetic basis of spikelet- and grain-related traits in sorghum.

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Section: Resultssupporting

confidence: 88%

Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghum

Takanashi

Shichijo

Sakamoto

et al. 2021

Sci Rep

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“…QTL clusters controlling PH, TB, STI‐TB, SFW and JW were consistently mapped in the marker interval of Xcup19‐SB4177 on chromosome 7 under salt stress condition (Figure ). In previous normal field condition, QTLs for PH, TB and SFW were always identified to have a positive effect from L‐Tian (Guan et al, ; Wang et al, ). Dw3 , a putative gene controlling PH in sorghum, was reported to be physically located at approximately 58,610,896–58,618,660 bp on chromosome 7 (Mace & Jordan, ) and was found only between Xcup19‐SB4177.…”

Section: Discussionmentioning

confidence: 99%

“…Phenotypic values of PH, SD, TB, SFW, JW and Brix under normal and salt stress conditions in 2014 and 2016 were considered as replications utilized for statistical analysis using Minitab version 17.3.1 (Minitab). Linkage groups (LGs) were constructed using Mapmaker/EXP3.0 (Lincoln, Daly, & Lander, ) and detailed information for the RILs genetic map with 181 simple sequence repeats (SSR) was described in previous studies (Wang et al, , ). Phenotypes of PH, SD, TB, SFW, JW and Brix under salt stress conditions and STI‐PH, STI‐SD, STI‐TB, STI‐SFW, STI‐JW and STI‐Brix based on the pooled data in 2014 and 2016 were utilized to analyse QTLs using a composite interval mapping (CIM) module of Cartographer 2.5 (Wang, Basten, & Zeng, ).…”

Section: Methodsmentioning

confidence: 99%

QTL analysis of salt tolerance in Sorghum bicolor during whole‐plant growth stages

Wang

Liu

et al. 2020

Plant Breeding

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To study the salt tolerance genetics of sorghum, 181 recombinant inbred lines (RILs) were used to locate quantitative trait loci (QTLs) underlying salt stress adaptability. Six traits, namely, plant height (PH), stem diameter (SD), total biomass (TB), stem fresh weight (SFW), juice weight (JW) and Brix, were investigated under normal and salt stress conditions in two years. A total of 53 QTLs for the six traits under both conditions and their corresponding salt tolerance index (STI) were detected and phenotypic variation explained (PVE) ranged from 4.16% to 20.42%. Six of the QTLs, qTB6, qSFW9, qJW9, qBrix2, qBrix10 and qSTI‐Brix9, were the main effect QTLs controlling salt tolerance and had a PVE more than 10%. qSFW9 and qJW9 colocalized in the same marker interval as SB5069‐UGSM18 and had PVEs of 17.70% and 14.20%, respectively, with positive effects from L‐Tian. QTL clusters controlling PH, TB, STI‐TB, SFW and JW were consistently mapped in the marker interval of Xcup19‐SB4177 on chromosome 7. These locations might serve as target sites for marker‐assisted selection (MAS) in improving salt tolerance of sorghum.

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“…For QTL confirmation, one scheme is to detect QTLs in early generations like F 2 and F 2:3 , and then confirmed in advanced generations from the same cross. Such approach have been successfully conducted in rice 9 , sweet sorghum 10 , soybean 11 , cucumber 12 . And the early generations with beneficial effect would be served as new breeding materials for cultivating new varieties 7 .…”

Section: Introductionmentioning

confidence: 99%

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Zhao

2019

Sci Rep

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Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

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Identification and validation of QTLs controlling multiple traits in sorghum

Cited by 14 publications

References 36 publications

Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghum

Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghum

QTL analysis of salt tolerance in Sorghum bicolor during whole‐plant growth stages

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

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