Identification and QTL mapping of Z550, a rice backcrossed inbred line with increased grains per panicle

Wang, Shiming; Cui, Guoqing; Wang, Hui; Ma, Fuying; Xia, Saisai; Li, Yunfeng; Yang, Zhenglin; Ling, Yinghua; Zhang, Changwei; He, Guanghua; Zhao, Fangming

doi:10.1016/s2095-3119(18)61996-3

Cited by 5 publications

(5 citation statements)

References 18 publications

(26 reference statements)

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“…qGP8 was also previously reported associated with seed germination under osmotic stress detected in the RM6208-RM8264 marker interval [38]. However, the same QTL was detected in linked marker RM7027 associated with the grain number per panicle [39]. These inferences support our study that the four QTLs are linked to seed germination and yield.…”

Section: Discussionsupporting

confidence: 90%

QTL Mapping and Candidate Gene Analysis for Seed Germination Response to Low Temperature in Rice

Kim

Jan

Park

et al. 2022

IJMS

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Low temperature is a serious threat to the seed emergence of rice, which has become one of the main limiting factors affecting rice production in the world. It is of great significance to find the candidate genes controlling low-temperature tolerance during seed germination and study their functions for breeding new rice cultivars with immense low-temperature tolerance during seed germination. In the current experiment, 120 lines of the Cheongcheong Nagdong Double Haploid (CNDH) population were used for quantitative trait locus (QTL) analysis of low-temperature germinability. The results showed a significant difference in germination under low different temperature (LDT) (15 °C, 20 °C) conditions. In total, four QTLs were detected on chromosome 3, 6, and 8. A total of 41 genes were identified from all the four QTLs, among them, 25 genes were selected by gene function annotation and further screened through quantitative real-time polymerase chain reaction (qRT-PCR). Based on gene function annotation and level of expression under low-temperature, our study suggested the OsGPq3 gene as a candidate gene controlling viviparous germination, ABA and GA signaling under low-temperature. This study will provide a theoretical basis for marker-assisted breeding and lay the basis for further mining molecular mechanisms of low-temperature germination tolerance in rice.

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Section: Discussionsupporting

confidence: 90%

QTL Mapping and Candidate Gene Analysis for Seed Germination Response to Low Temperature in Rice

Kim

Jan

Park

et al. 2022

IJMS

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“…Previously, Pang et al (2017) and Feng et al (2018) reported QTLs in the population, generated by crossing Ils developed from local rice varieties. Similarly, QTLs were reported in the F 2 population derived from a cross between a back cross inbred line (japonica × indica) and a japonica cultivar Z550 (Wang et al, 2018). Various studies reported QTLs for yield traits in rice using primary mapping populations like F 2:3 (Sabouri et al, 2009;Zhang et al, 2010;Kim et al, 2014;Tian et al, 2015;Biswas et al, 2017;Kumar et al, 2019) and F 2:4 (Rabiei et al, 2015;Verma et al, 2017;Jeon et al, 2018;Kumar et al, 2019).…”

Section: Introductionmentioning

confidence: 90%

“…There are many reports on mapping yield QTLs using wild species ( Swamy and Sarla, 2008 ; Swamy et al, 2011 , 2014 ; Wickneswari et al, 2012 ; Ma et al, 2016 ; Bhatia et al, 2018 ). Back cross inbred lines are useful for mapping QTLs and gene pyramiding ( Bhatia et al, 2018 ; Wang et al, 2018 ). In addition, QTL pyramiding using introgression lines (ILs) is an effective method in molecular breeding for complex traits ( Feng et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…Various studies reported QTLs for yield traits in rice using primary mapping populations like F 2:3 ( Sabouri et al, 2009 ; Zhang et al, 2010 ; Kim et al, 2014 ; Tian et al, 2015 ; Biswas et al, 2017 ; Kumar et al, 2019 ) and F 2:4 ( Rabiei et al, 2015 ; Verma et al, 2017 ; Jeon et al, 2018 ; Kumar et al, 2019 ). Crossing of two wild-derived back cross inbred lines generate hybrid lines which can closely resemble the parental lines ( Wang et al, 2018 ) with combination of improved traits. We selected these two high yielding BILs with contrasting yield contributing traits to develop F 2:3:4 populations with the objective of mapping consistent and precise QTLs for yield-related traits across three generations.…”

Section: Introductionmentioning

confidence: 99%

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Mapping of QTLs for Yield Traits Using F2:3:4 Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara

et al. 2022

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Wild introgressions play a crucial role in crop improvement by transferring important novel alleles and broadening allelic diversity of cultivated germplasm. In this study, two stable backcross alien introgression lines 166s and 14s derived from Swarn/Oryza nivara IRGC81848 were used as parents to generate populations to map quantitative trait loci (QTLs) for yield-related traits. Field evaluation of yield-related traits in F2, F3, and F4 population was carried out in normal irrigated conditions during the wet season of 2015 and dry seasons of 2016 and 2018, respectively. Plant height, tiller number, productive tiller number, total dry matter, and harvest index showed a highly significant association to single plant yield in F2, F3, and F4. In all, 21, 30, and 17 QTLs were identified in F2, F2:3, and F2:4, respectively, for yield-related traits. QTLs qPH6.1 with 12.54% phenotypic variance (PV) in F2, qPH1.1 with 13.01% PV, qTN6.1 with 10.08% PV in F2:3, and qTGW6.1 with 15.19% PV in F2:4 were identified as major effect QTLs. QTLs qSPY4.1 and qSPY6.1 were detected for grain yield in F2 and F2:3 with PV 8.5 and 6.7%, respectively. The trait enhancing alleles of QTLs qSPY4.1, qSPY6.1, qPH1.1, qTGW6.1, qTGW8.1, qGN4.1, and qTDM5.1 were from O. nivara. QTLs of the yield contributing traits were found clustered in the same chromosomal region. qTGW8.1 was identified in a 2.6 Mb region between RM3480 and RM3452 in all three generations with PV 6.1 to 9.8%. This stable and consistent qTGW8.1 allele from O. nivara can be fine mapped for identification of causal genes. From this population, lines C212, C2124, C2128, and C2143 were identified with significantly higher SPY and C2103, C2116, and C2117 had consistently higher thousand-grain weight values than both the parents and Swarna across the generations and are useful in gene discovery for target traits and further crop improvement.

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“…Therefore, pyramiding these elite QTLs by marker‐assisted selection will certainly benefit rice high‐yield breeding. Most QTL studies have involved indica / japonica populations in which a large variation in panicle size was observed (S. M. Wang et al, 2019; Xie et al, 2019; Zhang et al, 2021). Although a number of QTLs for panicle structure have been detected, a few QTL/gene have been cloned, such as MOC1 , LAX1 , LAX2 , GN1a/OsCKX2 , DEP1 , SP1 and APO1 (http://www.ricedata.cn/gene/).…”

Section: Introductionmentioning

confidence: 99%

Identification of the major quantitative trait locus cluster qGNPS4.1 for grain number and panicle structure in rice (Oryza sativa)

Jin

et al. 2022

Plant Breeding

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Panicle structure trait is one of the most important factors affecting yield. Exploring novel regulation genes and clarifying the genetic mechanism have an important meaning to the improvement of rice yield. A recombinant inbred line (RIL) derived from the cross between 'Longdao5' (Japonica) and 'Zhongyouzao8' (Indica) was used to identify quantitative trait loci (QTLs) for grain number and panicle structure traits under multiple environments. Seventy-seven QTLs were detected, 21 or 20 of which were stably and reliably detected under two or more than three ecological environments, respectively. These QTLs of the grain number and panicle structure (qGNPS) clustered on chromosomes 1, 4, 5 and 10 with pleiotropic genetic overlap effect and stable expression, and then, the major QTL clusters qGNPS4.1 and qGNPS10 were unreported. The novel stable expression major QTL cluster qGNPS4.1 control with grain number and secondary branch number was detected on chromosome 4 under multiple environments. Using the segregating RHL-F 2 and F 2:3 population, we narrowed the location of qGNPS4.1 to a 214.94-kb region. These results will lay the foundation for the major QTL clone and also will enrich the molecular markerassisted selection of grain number and panicle structure for molecular breeding in rice.

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Identification and QTL mapping of Z550, a rice backcrossed inbred line with increased grains per panicle

Cited by 5 publications

References 18 publications

QTL Mapping and Candidate Gene Analysis for Seed Germination Response to Low Temperature in Rice

QTL Mapping and Candidate Gene Analysis for Seed Germination Response to Low Temperature in Rice

Mapping of QTLs for Yield Traits Using F2:3:4 Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara

Identification of the major quantitative trait locus cluster qGNPS4.1 for grain number and panicle structure in rice (Oryza sativa)

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