Expression quantitative trait loci infer the regulation of isoflavone accumulation in soybean (Glycine max L. Merr.) seed

Wang, Yan; Han, Yingpeng; Teng, Weili; Zhao, Xue; Li, Yongguang; Wu, Lijia; Li, Dongmei; Li, Wenbin

doi:10.1186/1471-2164-15-680

Cited by 28 publications

(27 citation statements)

References 78 publications

(101 reference statements)

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“…Since, biochemical pathways and enzyme protein sequences are known for some of seed composition traits, candidate gene identification is much easier for these traits resulting in the development of several functional markers in soybean i.e., oleic acid content, isoflavone content, and raffinose content etc. (Dierking and Bilyeu, 2010; Pham et al, 2010, 2011; Wang et al, 2014). However, for agronomic and yield related traits, biochemical pathways are not well-characterized and therefore QTL mapping is the major strategy for identification of underlying genes.…”

Section: Difficulties Challenges and Potential Errors In Qtlomics Amentioning

confidence: 98%

“…In soybean, isoflavones are critical factor in defense against pathogens (Benhamou and Nicole, 1999; Subramanian et al, 2004), in promoting nodulation by rhizobia (Subramanian et al, 2006, 2007) and in changing or adjusting the microbial population around plant roots (Lozovaya et al, 2004). Several QTLs have been mapped for three species of isoflavones i.e., genistein ( GEN ), daidzein ( DAI ), glycitein ( GLY ), and total of isoflavones ( TOT ) (Primomo et al, 2005; Zeng et al, 2009; Gutierrez-Gonzalez et al, 2010, 2011; Wang et al, 2014). Gutierrez-Gonzalez et al (2010, 2011) identified and validated a major QTL in two RILs derived from low isoflavone lines Magellan and Essex, and high isoflavone line PI 437654.…”

Section: Qtlomics: Qtls To Genes In Soybeanmentioning

confidence: 99%

“…The study identified number of putative candidate genes underlying several of the main-effect and epistatic QTLs. Since there is possibility of trans-acting regulatory elements responsible for the phenotype, Wang et al (2014) used expression QTL (eQTL) analysis combined with phenotypic QTL analyses identifying a total of 33 eQTLs, of them five eQTL intervals were overlapped with phenotypic QTLs (pQTLs). A total of 11 candidate genes were identified within the overlapped eQTL and pQTL genomic regions (Table 2; Wang et al, 2014).…”

Section: Qtlomics: Qtls To Genes In Soybeanmentioning

confidence: 99%

“…Since there is possibility of trans-acting regulatory elements responsible for the phenotype, Wang et al (2014) used expression QTL (eQTL) analysis combined with phenotypic QTL analyses identifying a total of 33 eQTLs, of them five eQTL intervals were overlapped with phenotypic QTLs (pQTLs). A total of 11 candidate genes were identified within the overlapped eQTL and pQTL genomic regions (Table 2; Wang et al, 2014). Identification of causative genetic variants within these genes and structural variations in flanking regions is required for development of haplotype based molecular markers facilitating manipulation of isoflavone content in soybean.…”

Section: Qtlomics: Qtls To Genes In Soybeanmentioning

confidence: 99%

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QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding

et al. 2016

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Food legumes play an important role in attaining both food and nutritional security along with sustainable agricultural production for the well-being of humans globally. The various traits of economic importance in legume crops are complex and quantitative in nature, which are governed by quantitative trait loci (QTLs). Mapping of quantitative traits is a tedious and costly process, however, a large number of QTLs has been mapped in soybean for various traits albeit their utilization in breeding programmes is poorly reported. For their effective use in breeding programme it is imperative to narrow down the confidence interval of QTLs, to identify the underlying genes, and most importantly allelic characterization of these genes for identifying superior variants. In the field of functional genomics, especially in the identification and characterization of gene responsible for quantitative traits, soybean is far ahead from other legume crops. The availability of genic information about quantitative traits is more significant because it is easy and effective to identify homologs than identifying shared syntenic regions in other crop species. In soybean, genes underlying QTLs have been identified and functionally characterized for phosphorous efficiency, flowering and maturity, pod dehiscence, hard-seededness, α-Tocopherol content, soybean cyst nematode, sudden death syndrome, and salt tolerance. Candidate genes have also been identified for many other quantitative traits for which functional validation is required. Using the sequence information of identified genes from soybean, comparative genomic analysis of homologs in other legume crops could discover novel structural variants and useful alleles for functional marker development. The functional markers may be very useful for molecular breeding in soybean and harnessing benefit of translational research from soybean to other leguminous crops. Thus, soybean crop can act as a model crop for translational genomics and breeding of quantitative traits in legume crops. In this review, we summarize current status of identification and characterization of genes underlying QTLs for various quantitative traits in soybean and their significance in translational genomics and breeding of other legume crops.

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Section: Difficulties Challenges and Potential Errors In Qtlomics Amentioning

confidence: 98%

Section: Qtlomics: Qtls To Genes In Soybeanmentioning

confidence: 99%

Section: Qtlomics: Qtls To Genes In Soybeanmentioning

confidence: 99%

Section: Qtlomics: Qtls To Genes In Soybeanmentioning

confidence: 99%

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QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding

et al. 2016

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“…, Wang et al. ). However, conventional QTL analysis methods could not show the actions of these genes directly.…”

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confidence: 99%

Dynamic quantitative trait loci underlies isoflavone accumulation in soybean seed

Han

Zhao

et al. 2016

Plant Breeding

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Most of quantitative trait loci (QTL) underlying soybean seed isoflavone contents were derived from the harvest stage of plant development, which uncover the genetic effects that were expressed in earlier seed developmental stages. The aim of this study was to detect conditional QTL associated with isoflavone accumulation during the entire seed development. A total of 112 recombinant inbred lines developed from the cross between 'Zhongdou 27' (higher seed isoflavone content) and 'Jiunong 20' (lower seed isoflavone content) were used for the conditional QTL analysis of daidzein (DZ), genistein (GT), glycitein (GC) and total isoflavone (TI) accumulations through composite interval mapping with mixed genetic model. The results indicated that the number and type of QTL and their additive effects for individual and total isoflavone accumulations were different among R3 to R8 developmental stages. Three unconditional QTL and six conditional QTL for DZ, four unconditional QTL and five conditional QTL for GT, six unconditional QTL and five conditional QTL for GC, six unconditional QTL and seven conditional QTL for TI were identified at different developmental stages, respectively. Unconditional and conditional QTL that affect individual and total isoflavone accumulations exhibited multiple expression patterns, implying that some QTL are active for long period and others are transient. Two genomic regions, Satt144-Satt569 in linkage group F (LG F; chromosome 13, chr 13) for DZ, GC, GT and TI accumulations and Satt540-Sat_240 in LG M (chr 07) for TI and GC accumulations, were found to significantly affect individual and total isoflavone accumulations in multiple developmental stages, suggesting that the accumulation of soybean seed isoflavones is governed by time-dependent gene expression.

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Two Decades of QTL Mapping of Isoflavone in Soybean Seed

Kassem

2021

Soybean Seed Composition

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Expression quantitative trait loci infer the regulation of isoflavone accumulation in soybean (Glycine max L. Merr.) seed

Cited by 28 publications

References 78 publications

QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding

QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding

Dynamic quantitative trait loci underlies isoflavone accumulation in soybean seed

Two Decades of QTL Mapping of Isoflavone in Soybean Seed

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