Quantitative trait locus analysis for ear height in maize based on a recombinant inbred line population

Li, Z Q; Zhang, H M; Wu, Xiling; Sun, Yu; Liu, X H

doi:10.4238/2014.january.21.13

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Plant architecture directly affects biomass in higher plants, and particularly influences grain yields in agricultural crops. The genetics of various aspects of maize ( Zea mays L.) plant architecture, a complicated agronomic trait that is mainly determined by plant height (PH), ear height (EH), and internode number (IN), have recently been extensively investigated [ 1 – 3 ]. These three components reflect the spatial conformation of the maize plant, which is closely correlated with biomass, lodging resistance, and tolerance of stress associated with high plant density.…”

Section: Introductionmentioning

confidence: 99%

Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines

et al. 2016

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BackgroundPlant architecture attributes, such as plant height, ear height, and internode number, have played an important role in the historical increases in grain yield, lodging resistance, and biomass in maize (Zea mays L.). Analyzing the genetic basis of variation in plant architecture using high density QTL mapping will be of benefit for the breeding of maize for many traits. However, the low density of molecular markers in existing genetic maps has limited the efficiency and accuracy of QTL mapping. Genotyping by sequencing (GBS) is an improved strategy for addressing a complex genome via next-generation sequencing technology. GBS has been a powerful tool for SNP discovery and high-density genetic map construction. The creation of ultra-high density genetic maps using large populations of advanced recombinant inbred lines (RILs) is an efficient way to identify QTL for complex agronomic traits.ResultsA set of 314 RILs derived from inbreds Ye478 and Qi319 were generated and subjected to GBS. A total of 137,699,000 reads with an average of 357,376 reads per individual RIL were generated, which is equivalent to approximately 0.07-fold coverage of the maize B73 RefGen_V3 genome for each individual RIL. A high-density genetic map was constructed using 4183 bin markers (100-Kb intervals with no recombination events). The total genetic distance covered by the linkage map was 1545.65 cM and the average distance between adjacent markers was 0.37 cM with a physical distance of about 0.51 Mb. Our results demonstrated a relatively high degree of collinearity between the genetic map and the B73 reference genome. The quality and accuracy of the bin map for QTL detection was verified by the mapping of a known gene, pericarp color 1 (P1), which controls the color of the cob, with a high LOD value of 80.78 on chromosome 1. Using this high-density bin map, 35 QTL affecting plant architecture, including 14 for plant height, 14 for ear height, and seven for internode number were detected across three environments. Interestingly, pQTL10, which influences all three of these traits, was stably detected in three environments on chromosome 10 within an interval of 14.6 Mb. Two MYB transcription factor genes, GRMZM2G325907 and GRMZM2G108892, which might regulate plant cell wall metabolism are the candidate genes for qPH10.ConclusionsHere, an ultra-high density accurate linkage map for a set of maize RILs was constructed using a GBS strategy. This map will facilitate identification of genes and exploration of QTL for plant architecture in maize. It will also be helpful for further research into the mechanisms that control plant architecture while also providing a basis for marker-assisted selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2555-z) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines

et al. 2016

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“…The genetic analysis of quantitative traits is difficult and complex in maize, and quantitative traits are affected by key genes and interacting networks of small-effect genes. Therefore, different studies have provided different results including quantitative trait loci (QTL) number, distribution, and genetic effects for one trait [ 6 , 7 ]. This lack of conformity may also be explained by the many differences in parental materials, segregation-population types, ecological conditions, genetic maps, analytical methods and phenotype evaluation [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Ploidy effect and genetic architecture exploration of stalk traits using DH and its corresponding haploid populations in maize

Meng

Liu

et al. 2016

BMC Plant Biol

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BackgroundDoubled haploid (DH) lines produced via in vivo haploid induction have become indispensable in maize research and practical breeding, so it is important to understand traits characteristics in DH and its corresponding haploids which derived from each DH lines. In this study, a DH population derived from Zheng58 × Chang7-2 and a haploid population, were developed, genotyped and evaluated to investigate genetic architecture of eight stalk traits, especially rind penetrometer resistance (RPR) and in vitro dry matter digestion (IVDMD), which affecting maize stalk lodging-resistance and feeding values, respectively.ResultsPhenotypic correlation coefficients ranged from 0.38 to 0.69 between the two populations for eight stalk traits. Heritability values of all stalk traits ranged from 0.49 to 0.81 in the DH population, and 0.58 to 0.89 in the haploid population. Quantitative trait loci (QTL) mapping study showed that a total of 47 QTL for all traits accounting for genetic variations ranging from 1.6 to 36.5 % were detected in two populations. One or more QTL sharing common region for each trait were detected between two different ploidy populations. Potential candidate genes predicated from the four QTL support intervals for RPR and IVDMD were indirectly or directly involved with cellulose and lignin biosynthesis, which participated in cell wall formation. The increased expression levels of lignin and cellulose synthesis key genes in the haploid situation illustrated that dosage compensation may account for genome dosage effect in our study.ConclusionsThe current investigation extended understanding about the genetic basis of stalk traits and correlations between DH and its haploid populations, which showed consistence and difference between them in phenotype, QTL characters, and gene expression. The higher heritabilities and partly higher QTL detection power were presented in haploid population than in DH population. All of which described above could lay a preliminary foundation for genetic architecture study with haploid population and may benefit selection in haploid-stage to reduce cost in DH breeding.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0742-3) contains supplementary material, which is available to authorized users.

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“…These findings are in close correspondence with the earlier observation of WIETHOLTER ( 2008) who observed an average of 2.7 alleles per locus in 23 SSR loci. LI et al (2014) observed 2.45 average numbers of alleles based on 11 SSR loci. Similarly, molecular diversity analysis of 27 maize inbred lines based on 10 SSR markers resulted in 23 polymorphic alleles with an average of 2.3 alleles per locus (ABDEL-RAHMAN et al, 2016).…”

Section: Genotyping Of Mapping Populationmentioning

confidence: 97%

“…Several researchers have reported that yield contributing traits usually exhibit stable QTLs across environments (MESSMER et al, 2009;LIU et al, 2014;ZHANG et al, 2017). Till today various researchers carried out QTL analysis for various yield traits, namely those related to ear morphology (EL, ED) (MENDES-MOREIRA et al, 2015;CHEN et al, 2016;SU et al, 2017), KR/E (VELDBOOM and LEE, 1994;AUSTIN and LEE, 1996), K/R (CHEN et al, 2016;SU et al, 2017), TW (CHEN et al, 2016;PAN et al, 2017;SU et al, 2017;ZHAO et al, 2018) and GY/P (VEIGA et al, 2012;CHEN et al, 2016;SU et al, 2017;NIKOLIC et al, 2018;RIBEIRO et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Identification of QTLS for yield and contributing traits in maize-teosinte derived bils under diseased-stressed and control conditions

et al. 2021

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In maize, grain yield is the most important trait having a complex inheritance pattern. Yield contributing traits are more stable and have higher heritability than yield. Therefore, the present study was conducted to identify quantitative trait loci (QTLs) associated with grain yield and its components by using simple sequence repeat (SSR) markers. A population of 169 BC1F5 lines was derived from the crossing between maize inbred line DI-103 and teosinte-parviglumis was utilized for genotyping and phenotyping. In diseased stressed condition (E1), ear length (EL), ear diameter (ED), kernel rows per ear (KR/E), kernels per row (K/R), test weight (TW), and grain yield per plant (GY/P) had 7, 6, 7, 4, 6 and 5 QTLs whereas, in controlled condition (E2) 5, 2, 5, 4, 5 and 3 QTLs were detected for enlisted characters, respectively. Consistent QTLs across the environments were detected for 5 of the 6 investigated traits and number of QTLs were EL (2), ED (1), KR/E (3), TW (1), and GY/P (1) whereas, for K/R none of the QTLs were common between E1 and E2. By mapping analysis, we have identified genomic regions associated with two traits in a manner that was consistent with phenotypic correlations among traits, supporting either pleiotropy or tight linkage among QTLs. Three co-localized QTLs were identified between grain yield and contributing traits. Notably umc1720-linked QTL at bin 4.10 was simultaneously responsible for GY and EL, ED, KR/E, K/R; umc1215-linked QTL at bin 6.03 was simultaneously responsible for GY and ED, KR/E, K/R, TW; umc1279-linked QTL was responsible for GY and ED, TW. The findings suggest that the chromosomal region containing co-localized QTLs governing multiple yields associated traits are potential targets for selection. In addition for 6 studied traits, 44 superior lines were identified, and along with both the parents i.e. maize (DI-103) and teosinte they were clustered in 11 groups. Therefore, lines clustered independently can be utilized in a hybridization programme for the accumulation of yield contributing traits for yield maximization.

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Quantitative trait locus analysis for ear height in maize based on a recombinant inbred line population

Cited by 10 publications

References 16 publications

Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines

Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines

Ploidy effect and genetic architecture exploration of stalk traits using DH and its corresponding haploid populations in maize

Identification of QTLS for yield and contributing traits in maize-teosinte derived bils under diseased-stressed and control conditions

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