Detecting QTLs for plant architecture traits in cucumber (Cucumis sativus L.)

Li, Xiao Zun; Yuan, Xiao Jun; Jiang, Su; Pan, Jun; Deng, Si Li; Wang, Gang; He, Huan; Wu, Ai Zhong; Zhu, Linghua; Koba, Takato; Cai, Run

doi:10.1270/jsbbs.58.453

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…There is only one QTL for cucumber stem diameter that has been reported. Li [ 10 ] detected two QTLs related to cucumber stem diameter, qMSD-1 (Chr.1: 630,399 bp) on chromosome 1 between markers OP-V20 and CSFR12, and qMSD-7 (Chr.7: 20,536,944 bp) on chromosome 7 between markers e23M15a and e23M15b. The QTL on Chr.1, qMSD-1 , was far from the gSD1.1 locus (physical position 15,337,811 bp) detected on Chr.1 in this study, suggesting that the eight loci we detected are all novel loci.…”

Section: Discussionmentioning

confidence: 99%

“…At present, there are few studies on the inheritance and gene mapping of stem diameter in cucumber. Li [ 10 ] used the F 2:3 populations constructed by S94 (North China type) and S06 (European greenhouse type) as parents to map the QTLs of the cucumber stem diameter, and detected two loci— qMSD-1 and qMSD-7 , with a heritability of 8.5%. It is difficult to develop molecular markers closely linked with stem diameter that can be used for molecular-assisted breeding.…”

Section: Introductionmentioning

confidence: 99%

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Genome-Wide Association Studies Reveal Candidate Genes Related to Stem Diameter in Cucumber (Cucumis sativus L.)

Yang

Dong

Han

et al. 2022

Genes

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The stem diameter, an important agronomic trait, affects cucumber growth and yield. However, no genes responsible for cucumber stem diameter have been identified yet. In this study, the stem diameter of 88 cucumber core germplasms were measured in spring 2020, autumn 2020 and autumn 2021, and a genome-wide association study (GWAS) was carried out based on the gene sequence and stem diameter of core germplasms. A total of eight loci (gSD1.1, gSD2.1, gSD3.1, gSD3.2, gSD4.1, gSD5.1, gSD5.2, and gSD6.1) significantly associated with cucumber stem diameter were detected. Of these, five loci (gSD1.1, gSD2.1, gSD3.1, gSD5.2, and gSD6.1) were repeatedly detected in two or more seasons and were considered as robust and reliable loci. Based on the linkage disequilibrium sequences of the associated SNP loci, 37 genes were selected. By further investigating the five loci via analyzing Arabidopsis homologous genes and gene haplotypes, five genes (CsaV3_1G028310, CsaV3_2G006960, CsaV3_3G009560, CsaV3_5G031320, and CsaV3_6G031260) showed variations in amino acid sequence between thick stem lines and thin stem lines. Expression pattern analyses of these genes also showed a significant difference between thick stem and thin stem lines. This study laid the foundation for gene cloning and molecular mechanism study of cucumber stem development.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Studies Reveal Candidate Genes Related to Stem Diameter in Cucumber (Cucumis sativus L.)

Yang

Dong

Han

et al. 2022

Genes

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“…development, cyclin, transcript factor and phytohormone pathways responsible for cell organization (Pierre-Jerome et al, 2018). In cucurbit crops, the research on stem diameter has been mainly focused on genetic analysis and QTL mapping (Li et al, 2008;Rao et al, 2021), and there is no relevant study in Luffa. In present study, we identified a significant qID2 associated with main stem internode diameter on chromosome 9, which explained 11% of the phenotypic variation (Figure 2; Supplementary Table S6).…”

Section: Laccrwn3 Regulating the Internode Diameter Variation Of Luffamentioning

confidence: 99%

QTL mapping reveals candidate genes for main agronomic traits in Luffa based on a high-resolution genetic map

Liu¹,

Gan

Luo³

et al. 2022

Front. Plant Sci.

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Luffa is an important medicinal and edible vegetable crop of Cucurbitaceae. Strong heterosis effects and strikingly complementary characteristics were found between the two domesticated Luffa cultivars, Luffa acutangula and Luffa cylindrica. To explore the genetic basis underlying their important agronomic traits, we constructed the first interspecific high-density genetic linkage map using a BC1 population of 110 lines derived from a cross between S1174 (Luffa acutangula) and P93075 (Luffa cylindrica). The map spanned a total of 2246.74 cM with an average distance of 0.48 cM between adjacent markers. Thereafter, a large-scale field-based quantitative trait loci (QTLs) mapping was conducted for 25 important agronomic traits and 40 significant genetic loci distributed across 11 chromosomes were detected. Notably, a vital QTL (qID2) located on chromosome 9 with a minimum distance of 23 kb was identified to be responsible for the internode diameter and explained 11% of the phenotypic variation. Lac09g006860 (LacCRWN3), encoding a nuclear lamina protein involved in the control of nuclear morphology, was the only gene harbored in qID2. Sequence alignment showed completely different promoter sequences between the two parental alleles of LacCRWN3 except for some nonsynonymous single nucleotide polymorphisms (SNPs) in exons, and the expression level in thick-stem P93075 was distinctively higher than that in thin-stem S1174. According to the natural variation analysis of a population of 183 inbred lines, two main haplotypes were found for LacCRWN3: the P93075-like and S1174-like, with the former haplotype lines exhibiting significantly thicker internode diameters than those of the latter haplotype lines. It showed that LacCRWN3, as the only CRWN3 gene in Cucurbitaceae, was the most likely candidate gene regulating the internode diameter of Luffa. Our findings will be beneficial for deciphering the molecular mechanism of key phenotypic traits and promoting maker-assisted breeding in Luffa.

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“…The software also provides the potentially superior QTL design for improving complex traits using the main QTL effects and the QTL × environments interaction effects [28]. Because of the advantages of using MCIM to analyze digenic epistasis and QTL × environment interactions, and the user-friendly software that is now available, the MCIM approach has been widely applied to the analyses of the QTLs for complex traits in various plant experimental populations such as wheat [29][30][31][32][33][34], barley [35], soybean [36][37][38][39], cucumber [40], cabbage [41], cotton [42], rapeseed [43,44] and rice [45][46][47][48], as well as in Drosophila [49].…”

Section: Qtl Mappingmentioning

confidence: 99%

Statistical approaches in QTL mapping and molecular breeding for complex traits

Zhu

2012

Chin. Sci. Bull.

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Most of the important agronomic traits in crops, such as yield and quality, are complex traits affected by multiple genes with gene × gene interaction as well as gene × environment interaction. Understanding the genetic architecture of complex traits is a long-term task for quantitative geneticists and plant breeders who wish to design efficient breeding programs. Conventionally, the genetic properties of traits can be revealed by partitioning the total variation into variation components caused by specific genetic effects. With recent advances in molecular genotyping and high-throughput technology, the unraveling of the genetic architecture of complex traits by analyzing quantitative trait locus (QTL) has become possible. The improvement of complex traits has also been achieved by pyramiding individual QTL. In this review, we describe some statistical methods for QTL mapping that can be used to analyze QTL × QTL interaction and QTL × environment interaction, and discuss their applications in crop breeding for complex traits. The most important economic traits in crops are quantitative in nature. The genetic variation of quantitative traits is usually controlled by minor-effect genes and the environments as well as by epistatic effects between different genes and by interactions between genes and environments. Because of their complicated genetic architecture, quantitative traits are usually referred to as complex trait. The genetic architecture includes knowledge about the numbers and positions of the genes in the chromosomes, the magnitude of their effects, and the contributions of additive, dominance and epistatic effects [1].Most plant breeders and quantitative geneticists have long been interested in uncovering the genetic architecture of yield and other complex traits [2]. Understanding genetic architecture can provide insights into how the architecture is translated into genetic variation and selection response [3]. Based on diallele cross experiments, the genetic architecture of a trait has been analyzed by decomposing the trait variation into additive, dominance, and epistasis variances and studying their interaction variances with the environments [4][5][6][7]. The total genetic covariance between two traits can also be dissected into genetic covariate components. With the increasing availability of molecular markers, the quantitative genetics theory of complex traits and marker-assisted selection has undergone major advances. It is now possible to connect marker variations with phenotypic variations, and to dissect genetic architecture of traits into individual genetic variants. The selection of traits can also be achieved using individual quantitative trait loci (QTLs). In this review, we discuss some of the statistical methods used for QTL mapping of experimental populations and examine their applications in marker-assisted selection (MAS) for improving complex traits.

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Detecting QTLs for plant architecture traits in cucumber (Cucumis sativus L.)

Cited by 9 publications

References 25 publications

Genome-Wide Association Studies Reveal Candidate Genes Related to Stem Diameter in Cucumber (Cucumis sativus L.)

Genome-Wide Association Studies Reveal Candidate Genes Related to Stem Diameter in Cucumber (Cucumis sativus L.)

QTL mapping reveals candidate genes for main agronomic traits in Luffa based on a high-resolution genetic map

Statistical approaches in QTL mapping and molecular breeding for complex traits

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