QTL Mapping of Isoflavone, Oil and Protein Contents in Soybean (Glycine max L. Merr.)

Liang, Hui-zhen; Yu, Yan; Wang, Shufeng; Lian, Yun; Wang, Ting-feng; Wei, Yanli; Gong, Pengtao; Liu, Xueyi; Xuanjun, Fang; Zhang, Mengchen

doi:10.1016/s1671-2927(09)60197-8

Cited by 76 publications

(65 citation statements)

References 16 publications

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“…Oil and protein contents were negatively correlated, which agrees with the literature (Liang et al, 2010;Rodrigues et al, 2010). These correlations were high, with small variation (r = -0.80** to -0.92**), revealing the degree of genetic association between the characters and explaining the difficulties for simultaneous selection.…”

Section: Resultssupporting

confidence: 89%

Biometric analysis of protein and oil contents of soybean genotypes in different environments

Rodrigues

Arruda

Cruz

et al. 2014

Pesq. agropec. bras.

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The objective of this work was to identify by biometric analyses the most stable soybean parents, with higher oil or protein contents, cultivated at different seasons and locations of the state of Minas Gerais, Brazil. Forty-nine genotypes were evaluated in the municipalities of Viçosa, Visconde do Rio Branco, and São Gotardo, in the state of Minas Gerais, from 2009 to 2011. Protein and oil contents were analyzed by infrared spectrometry using a FT-NIR analyzer. The effects of genotype, environment, and genotype x environment interaction were significant. The BARC-8 soybean genotype is the best parent to increase protein contents in the progenies, followed by BR 8014887 and CS 3032PTA276-3-4. Selection for high oil content is more efficient when the crossings involve the Suprema, CD 01RR8384, and A7002 genotypes, which show high mean phenotypic values, wide adaptability, and greater stability to environmental variation.

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Section: Resultssupporting

confidence: 89%

Biometric analysis of protein and oil contents of soybean genotypes in different environments

Rodrigues

Arruda

Cruz

et al. 2014

Pesq. agropec. bras.

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“…QPor3-1 showed a similar position and smaller interval than the QTL detected by Lee et al (1996). QPor6-1 and QPor6-2 detected in this study were in a hot region on chromosome 06, where similar results were shown by Palomeque et al (2009), Liang et al (2010 and Rossi et al (2013). QPor7-1 and QPor7-2 were consistently located on Gm07 in this study; this region was also identified as a hot region by Eskandari et al (2013).…”

Section: Main Effects Of the Stable Additive Qtl Analysissupporting

confidence: 81%

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

Pan

Han

et al. 2016

Mol Breeding

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The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %.Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive maineffect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.

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“…No significant correlation between protein content and seed isoflavone content was observed in the combined environment analysis; however, five of eight individual environments (WST15, CHT15, CHT16, ROD15 and HOU15) showed significant negative correlations, and one environment (WST16) showed a significant positive correlation between the two traits. The negative relationship between protein and isoflavones has been frequently reported (Liang et al., ; Morrison et al., ; Primomo et al., ; Smallwood et al., ), although no correlation has also been observed (Charron et al., ). Although Primomo et al.…”

Section: Discussionmentioning

confidence: 94%

Identification of quantitative trait loci for seed isoflavone concentration in soybean (Glycine max) against soybean cyst nematode stress

et al. 2018

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Isoflavones are plant secondary metabolites produced in soybean (Glycine max), which provide plant defense against pathogens and are beneficial to human health. Soybean cyst nematode (SCN) is a major yield‐limiting pest in most soybean‐producing area across the world. Traits, seed isoflavones and SCN resistance are quantitative in nature, and their phenotypic evaluations are expensive. Quantitative trait loci (QTL) underlying the two traits will be helpful to develop SCN‐resistant lines with elevated isoflavones using marker‐assisted‐selection (MAS). This study aims to identify isoflavones and SCN‐related QTL in a soybean population consisting of 109 RILs, which was developed from a cross between two commercial soybean cultivars viz. ‘RCAT1004’ and ‘DH4202’ and grown in four non‐SCN and SCN‐infested fields during 2015 and 2016. While single marker ANOVA identified 10 QTL for isoflavones and five for SCN (p < 0.01), simple interval and multiple QTL mappings identified four QTL associated with isoflavones (LOD ≥ 2.2). These results contribute to a better understanding of the genetics of the two traits and provide molecular markers that can be used in MAS to facilitate developing SCN‐resistant soybeans with increased isoflavones.

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QTL Mapping of Isoflavone, Oil and Protein Contents in Soybean (Glycine max L. Merr.)

Cited by 76 publications

References 16 publications

Biometric analysis of protein and oil contents of soybean genotypes in different environments

Biometric analysis of protein and oil contents of soybean genotypes in different environments

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

Identification of quantitative trait loci for seed isoflavone concentration in soybean (Glycine max) against soybean cyst nematode stress

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