Quantitative Trait Locus Analysis for Deep-Sowing Germination Ability in the Maize IBM Syn10 DH Population

Liu, Hongjun; Lin, Zhang; Wang, Jiechen; Li, Changsheng; Zeng, Xing; Xie, Shupeng; Zhang, Yongzhong; Liu, Sisi; Hu, Songlin; Wang, Jianhua; Lee, Michael; Lübberstedt, Thomas; Zhao, Guangyao

doi:10.3389/fpls.2017.00813

Cited by 46 publications

(43 citation statements)

References 42 publications

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“…e ∕ r + S 2 e × 100, where S 2 t : treatment variance; S 2 e : error variance; r: the number of replicates (Liu et al, 2017).…”

Section: Histograms Of Frequency Distributions Of Germination-relatedmentioning

confidence: 99%

“…The study of Qin, Zhao, Yang, Li, and Yang (2018) showed that it could improve the yield traits in the case of maize variety 'Zhongyu 3' by increasing sowing depth properly. However, the existing varieties of maize have poor deep-sowing tolerance during seed germination (Kalpana et al, 2015;Liu et al, 2017;Troyer, 1997;Yin et al, 2016;Zhao et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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QTL analysis of deep‐sowing tolerance during seed germination in the maize IBM Syn4 RIL population

Han

Shen

et al. 2020

Plant Breeding

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In arid or semi‐arid regions, deep‐sowing is an effective treasure to ensure seeds absorbing water from deep soil layer at present. However, the existing maize varieties have poor tolerance to deep‐sowing, which is attributed to that few genes are explored and utilised. In this study, 243 IBM Syn4 recombinant inbred lines (RIL) constructed with B73 and Mo17 as parents and 1,339 DNA markers evenly distributed in 10 chromosomes, were used for QTL analysis of deep‐sowing tolerance during seed germination. There were significant differences in germination‐related traits between the parental lines at 12.5 cm sowing depth. Among them, 7, 7, 5, 10 and 2 QTLs for emergence rate, seedling length, plumule length, mesocotyl length and coleoptile length were detected, respectively. These QTLs explained 2.75% to 10.49% of the phenotypic variance with LOD scores ranging from 2.50 to 8.27. In addition, 12 overlapping QTLs formed five QTL clusters on chromosomes 3, 5, 7 and 9. This study provides a basis for molecular marker‐assisted breeding and functional study in deep‐sowing germination of maize.

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“…e ∕ r + S 2 e × 100, where S 2 t : treatment variance; S 2 e : error variance; r: the number of replicates (Liu et al, 2017).…”

Section: Histograms Of Frequency Distributions Of Germination-relatedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QTL analysis of deep‐sowing tolerance during seed germination in the maize IBM Syn4 RIL population

Han

Shen

et al. 2020

Plant Breeding

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“…Vigorous and uniform seedlings are necessary for achieving a high yield in crop production. However, various abiotic stresses occurring after sowing impair seedling establishment ( Basnet et al, 2015 ; Liu et al, 2017 ). Maize ( Zea mays L.) is one of the most important crops, accounting for 40% of the world’s cereal food production ( Bouis and Welch, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

QTL Mapping in Three Connected Populations Reveals a Set of Consensus Genomic Regions for Low Temperature Germination Ability in Zea mays L.

Wang

et al. 2018

Front. Plant Sci.

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Improving seed vigor in response to cold stress is an important breeding objective in maize that allows early sowing. Using two cold tolerant inbred lines 220 and P9-10 and two susceptible lines Y1518 and PH4CV, three connected F2:3 populations were generated for detecting quantitative trait locus (QTL) related to seed low-temperature germination ability. At 10°C, two germination traits (emergence rate and germination index) were collected from a sand bed and three seedling traits (seedling root length, shoot length, and total length) were extracted from paper rolls. Significant correlations were found among all traits in all populations. Via single-population analysis, 43 QTL were detected with explained phenotypic variance of 0.62%∼39.44%. Seventeen QTL explained more than 10% phenotypic variance; of them sixteen (94.12%) inherited favorable alleles from the tolerant lines. After constructing a consensus map, three meta-QTL (mQTL) were identified to include at least two initial QTL from different populations. mQTL1-1 included seven initial QTL for both germination and seedling traits; with three explaining more than 30% phenotypic variance. mQTL2-1 and mQTL9-1 covered two to three initial QTL. The favorable alleles of the QTL within these three mQTL regions were all inherited from the tolerant line 220 and P9-10. These results provided a basis for cloning of genes underlying the mQTL regions to uncover the molecular mechanisms of maize cold tolerance during germination.

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“…Although there are a significant number of recombination events, generating this population is very labor-and time-intensive. Recently, an IBM-DH population was developed and used in a deep-sowing germination quantitative trait loci (QTL) study [14]. Compared with populations generated by DH induction in early generations, the IBM-DH population is relatively fast and cost-effective to generate, while enhancing QTL mapping resolution.…”

Section: Introductionmentioning

confidence: 99%

Recombination Pattern Characterization via Simulation Using Different Maize Populations

Ren

Gong

et al. 2020

IJMS

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Efficient recombination is critical to both plant breeding and gene cloning. However, almost all traditional recombination studies and genetic improvements require the slow and labor-intensive population construction process, and little is known about the recombination characteristics of populations of different types, generations, and origins. Here, we provide a simple and efficient simulation method for population construction based on doubled haploid (DH) and intermated B73 × Mo17 maize (IBM) populations to predict the recombination pattern. We found that the chromosomes had 0, 1, 2, and 3 recombination events that occurred at rates of 0.16, 0.30, 0.23, and 0.15, respectively, in the DH and the recombination rate of each chromosome in the IBM population ranged from 0 to 12.1 cM per 125 kb. Based on the observed recombination parameters, we estimated the number of recombination events and constructed the linkage maps of the simulated DH and recombination inbred line (RIL) populations. These simulated populations exhibited similar recombination patterns compared with the real populations, suggesting the feasibility of this simulation approach. We then compared the recombination rates of the simulated populations of different types (DH induced or self-crossed), generations, and origins (using the 8, 16, and 32 multiparent advanced generation intercross (MAGIC) populations), and suggested a rapid and cost-effective population construction procedure for breeders and geneticists, while maintaining an optimal recombination rate. This study offers a convenient method for optimizing the population construction process and has broader implications for other crop species, thereby facilitating future population studies and genetic improvement strategies.

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Quantitative Trait Locus Analysis for Deep-Sowing Germination Ability in the Maize IBM Syn10 DH Population

Cited by 46 publications

References 42 publications

QTL analysis of deep‐sowing tolerance during seed germination in the maize IBM Syn4 RIL population

QTL analysis of deep‐sowing tolerance during seed germination in the maize IBM Syn4 RIL population

QTL Mapping in Three Connected Populations Reveals a Set of Consensus Genomic Regions for Low Temperature Germination Ability in Zea mays L.

Recombination Pattern Characterization via Simulation Using Different Maize Populations

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