Construction of genetic linkage map and identification of QTLs related to agronomic traits in maize using DNA transposon-based markers

Ramekar, Rahul Vasudeo; Jin, Kyu; Park, Kyong‐Cheul; Roy, Neha Samir; Kim, Nam‐Soo; Lee, Ju Kyong

doi:10.1270/jsbbs.18017

Cited by 7 publications

(4 citation statements)

References 63 publications

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“…In the present study, a linkage map was constructed corresponding to the 10 chromosomes of the maize genome using 1,386 markers, spanning 2076 cM in length. The length of the linkage map constructed in this study was shorter than those reported by Ramekar et al [ 26 ], Wang et al [ 28 ], and Ertiro et al [ 60 ] but longer than those reported by Samayoa et al [ 61 ], Zhao et al [ 62 ] and Badu-Apraku et al [ 39 ]. The differences between our findings and those of earlier researchers could be attributed to the number of markers used and the type and size of the mapping populations.…”

Section: Discussioncontrasting

confidence: 59%

“…During the past few years, the recent advances in molecular marker technologies have facilitated the construction of high-density genetic linkage maps and detection of novel QTL associated with quantitative traits in segregating populations and the characterization of the map positions in the genome of crop plants [ 26 – 28 ]. In maize, studies on QTL identification for complex traits have focused mainly on abiotic stresses such as drought [ 29 – 31 ] and low soil nitrogen [ 32 , 33 ] and good progress has been made.…”

Section: Introductionmentioning

confidence: 99%

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Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation

et al. 2020

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Striga is an important biotic factor limiting maize production in sub-Saharan Africa and can cause yield losses as high as 100%. Marker-assisted selection (MAS) approaches hold a great potential for improving Striga resistance but requires identification and use of markers associated with Striga resistance for adequate genetic gains from selection. However, there is no report on the discovery of quantitative trait loci (QTL) for resistance to Striga in maize under artificial field infestation. In the present study, 198 BC 1 S 1 families obtained from a cross involving TZEEI 29 ( Striga resistant inbred line) and TZEEI 23 ( Striga susceptible inbred line) plus the two parental lines were screened under artificial Striga- infested conditions at two Striga -endemic locations in Nigeria in 2018, to identify QTL associated with Striga resistance indicator traits, including grain yield, ears per plant, Striga damage and number of emerged Striga plants. Genetic map was constructed using 1,386 DArTseq markers distributed across the 10 maize chromosomes, covering 2076 cM of the total genome with a mean spacing of 0.11 cM between the markers. Using composite interval mapping (CIM), fourteen QTL were identified for key Striga resistance/tolerance indicator traits: 3 QTL for grain yield, 4 for ears per plant and 7 for Striga damage at 10 weeks after planting (WAP), across environments. Putative candidate genes which encode major transcription factor families WRKY, bHLH, AP2-EREBPs, MYB, and bZIP involved in plant defense signaling were detected for Striga resistance/tolerance indicator traits. The QTL detected in the present study would be useful for rapid transfer of Striga resistance/tolerance genes into Striga susceptible but high yielding maize genotypes using MAS approaches after validation. Further studies on validation of the QTL in different genetic backgrounds and in different environments would help verify their reproducibility and effective use in breeding for Striga resistance/tolerance.

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Section: Discussioncontrasting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation

et al. 2020

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“…In maize, SSR markers are considered the most suitable markers for constructing genetic linkage maps and evaluating QTLs due to their high allelic variation and co-dominance (Sabadin et al, 2008). In a recent study, (Ramekar et al, 2018) used 907 analytical markers, including MUTD, SCARs, SSRs, and SNPs, to construct a genetic map of the maize RIL population. Among them, the full length of the genetic map was 6248.2 cM, the average genetic distance between markers was 6.84 cM, and 24 traits related to grain yield and quality were mined.…”

Section: Zm00001d037590 and Zm00001d012321 Were Key Genes Of Cold Tol...mentioning

confidence: 99%

Map-based cloning and WGCNA uncover the key genes controlling cold tolerance in maize introgression lines seedlings

Zheng

Yang

et al. 2022

Preprint

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Summary Low-temperature chilling injury in early spring or late spring severely restricts maize production. Therefore, improving its temperature tolerance is one of the important goals of maize breeding. The wild maize relatives such as Z. perennis and T. dactyloides have strong cold tolerance and thus can be used to improve the cold tolerance of cultivated maize. In the previous study, the allohexaploid MTP ( Zea . Z. perennis and T. dactyloides ) was created as a genetic bridge material and the introgressed cross of the backbone maize inbred line to obtain the MTP-maize introgression line with strong cold tolerance. In this study, F 2:3 populations was developed of the cold-tolerant maize introgression line IB030 and the cold-sensitive maize introgression line IB021. A high-density genetic linkage map was constructed using SSR genetic markers and performed QTL mapping for related traits at 2°C, RNA-seq was performed using cold-tolerant introgression line IB030 and recurrent parent B73 (cold-sensitive) after low temperature stress at seedling stage. The key genes controlling REC, SDW and SFW of maize introgression line seedlings were located by forward genetics combined with WGCNA analysis strategy. Key genes function verification results showed that, Zm00001d037590 REC and Zm00001d012321 REC have the highest expression levels in introgression line IB030, followed by the wild type B73, and finally the EMS mutant after low temperature stress, these results initially concluded that key genes positively regulate the cold tolerance of maize, and also laid a theoretical and practical foundation for improvement of cultivated maize against low-temperature stress.

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“…Many population structures have been used for crop QTL detection and mapping. For example, backcross populations, F 2 populations, doubled haploid populations, recombinant inbred line populations, and near-isogenic line populations have all proven useful in identifying and con rming QTLs using various molecular marker systems (Choi et al 2019;Farre et al 2016;Park et al 2013;Ramekar et al 2018;Sa et al 2015, Tanksley andNelson 1996). However, for Perilla crop, QTL analysis using genetic maps of segregated populations is very di cult because various molecular markers, such as simple sequence repeat (SSR) markers for each chromosome, have not yet been developed for Perilla crop, in contrast with other major crops, such as rice, wheat and maize.…”

Section: ; Mackay 2001mentioning

confidence: 99%

Genetic variation and association mapping in the F2 population of the Perilla crop (Perilla frutescens L.) using new developed Perilla SSR markers

Kim

Jin

et al. 2021

Euphytica

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The transcriptome sequencing approach RNA-seq represents a powerful tool for transcriptional analysis and development of SSR markers for nonmodel crop. In the Perilla crop, analysis of the distribution of different repeat motifs showed that dinucleotide repeats were the most abundant (62.0%) type, followed by trinucleotide repeats (35.3%), with the two together comprising 97.3% of the eSSR repeats. In this study, we developed 39 new SSR primer sets by the transcriptome sequencing approach RNA-sEq. A total of 130 alleles were detected segregating in nine Perilla accessions with an average of 3.3 alleles per locus, ranging from 125 to 360 bp. The number of alleles per locus ranged from two to six. To detect SSR markers associated with morphological characteristics of Perilla crop, a total of 40 individuals from the F 2 population of Perilla were selected for association analysis based on their leaf-and plant-related characteristics. In an association analysis of 37 SSR markers and 9 leaf-and plantrelated traits in the 40 individuals of the F 2 population, we identi ed 12 and 11 SSR markers associated with leafrelated traits and plant-related traits, respectively. Therefore, the new Perilla SSR primers described in this study could be helpful in identifying genetic diversity and genetic mapping, designating important genes/QTLs for Perilla crop breeding programs, and allowing Perilla breeders to improve leaf and plant quality through markerassisted selection (MAS) breeding programs.

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Construction of genetic linkage map and identification of QTLs related to agronomic traits in maize using DNA transposon-based markers

Cited by 7 publications

References 63 publications

Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation

Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation

Map-based cloning and WGCNA uncover the key genes controlling cold tolerance in maize introgression lines seedlings

Genetic variation and association mapping in the F2 population of the Perilla crop (Perilla frutescens L.) using new developed Perilla SSR markers

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