Quantitative trait loci underlying soybean seed tocopherol content with main additive, epistatic and QTL × environment effects

Liu, Huancheng; Cao, Guanglu; Wu, Depeng; Jiang, Zhenfeng; Han, Yingpeng; Li, Wenbin

doi:10.1111/pbr.12534

Cited by 9 publications

(11 citation statements)

References 39 publications

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“…The GWAS based on natural populations was an alternative method of linkage analysis and was widely used in the analysis of important crop traits (Rakotoson et al, 2021;Yuan et al, 2021). Compared with linkage analysis, the range of phenotypic variation in single analysis was increased by GWAS, which was due to the high natural variation caused by the accumulation of historical reorganization events in the natural population (Liu et al, 2017). The development of genome sequencing and SNP genotyping technology has promoted the applicability of the GWAS in soybean research; thus, it was particularly important to select an appropriate model for effective and accurate mapping according to research needs (Kim et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM

Jiang

Zhou³

et al. 2023

Front. Plant Sci.

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IntroductionSoybean cyst nematode (SCN, Heterodera glycines Ichinohe) is an important disease affecting soybean yield in the world. Potential SCN-related QTLs and QTL-by-environment interactions (QEIs) have been used in SCN-resistant breeding.MethodsIn this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTLs and QEIs for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7 in 156 different soybean cultivars materials.Results and discussionThe results showed that 53 QTLs were detected in single environment analysis; 36 QTLs and 9 QEIs were detected in multi-environment analysis. Based on the statistical screening of the obtained QTLs, we obtained 10 novel QTLs and one QEI which were different from the previous studies. Based on previous studies, we identified 101 known genes around the significant/suggested QTLs and QEIs. Furthermore, used the transcriptome data of SCN-resistant (Dongnong L-10) and SCN-susceptible (Suinong 14) cultivars, 10 candidate genes related to SCN resistance were identified and verified by Quantitative real time polymerase chain reaction (qRT-PCR) analysis. Haplotype difference analysis showed that Glyma.03G005600 was associated with SCN HG Type 0 and HG Type 1.2.3.5.7 resistance and had a haplotype beneficial to multi-SCN-race resistance. These results provide a new idea for accelerating SCN disease resistance breeding.

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Section: Discussionmentioning

confidence: 99%

Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM

Jiang

Zhou³

et al. 2023

Front. Plant Sci.

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“…In recent years, a variety of new methods have been proposed, with the rapid development of computing technology and sequencing technology ( Wang et al., 2016 ; Huang et al., 2018 ; Xiao et al., 2021 ; Li et al., 2022a ). Although this propelled much of the practicability of GWAS, it is particularly important to select the appropriate sequencing method and suitable model for improving the positioning efficiency according to the research needs ( Liu et al, 2017 ; Kim et al, 2021 ). For this study, we adopted six models (GLM, MLM, CMLM, BLINK, FarmCPU, and 3VmrMLM), to conduct GWAS of Toc content in soybean seeds.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide association studies reveal novel QTLs, QTL-by-environment interactions and their candidate genes for tocopherol content in soybean seed

Miao²,

Liu³

et al. 2022

Front. Plant Sci.

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Genome-wide association studies (GWAS) is an efficient method to detect quantitative trait locus (QTL), and has dissected many complex traits in soybean [Glycine max (L.) Merr.]. Although these results have undoubtedly played a far-reaching role in the study of soybean biology, environmental interactions for complex traits in traditional GWAS models are frequently overlooked. Recently, a new GWAS model, 3VmrMLM, was established to identify QTLs and QTL-by-environment interactions (QEIs) for complex traits. In this study, the GLM, MLM, CMLM, FarmCPU, BLINK, and 3VmrMLM models were used to identify QTLs and QEIs for tocopherol (Toc) content in soybean seed, including δ‐Tocotrienol (δ‐Toc) content, γ‐Tocotrienol (γ‐Toc) content, α‐Tocopherol (α‐Toc) content, and total Tocopherol (T-Toc) content. As a result, 101 QTLs were detected by the above methods in single-environment analysis, and 57 QTLs and 13 QEIs were detected by 3VmrMLM in multi-environment analysis. Among these QTLs, some QTLs (Group I) were repeatedly detected three times or by at least two models, and some QTLs (Group II) were repeatedly detected only by 3VmrMLM. In the two Groups, 3VmrMLM was able to correctly detect all known QTLs in group I, while good results were achieved in Group II, for example, 8 novel QTLs were detected in Group II. In addition, comparative genomic analysis revealed that the proportion of Glyma_max specific genes near QEIs was higher, in other words, these QEIs nearby genes are more susceptible to environmental influences. Finally, around the 8 novel QTLs, 11 important candidate genes were identified using haplotype, and validated by RNA-Seq data and qRT-PCR analysis. In summary, we used phenotypic data of Toc content in soybean, and tested the accuracy and reliability of 3VmrMLM, and then revealed novel QTLs, QEIs and candidate genes for these traits. Hence, the 3VmrMLM model has broad prospects and potential for analyzing the genetic structure of complex quantitative traits in soybean.

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“…Shaw et al (2017) [15] found nine and five QTLs, by single marker analyses and interval mapping, respectively, for α-Toc contents in a cross with OAC Bayfield and a low α-Toc OAC Shire across three locations over 2 yrs, of which the QTL tagged by Satt117 (Chr15) had the largest effect, accounting for up to 32% of the phenotypic variation. Liu et al (2017) [13] reported a total of 18 QTLs for α-Toc contents in an RIL population of a cross with the Chinese high α-Toc local variety Beifeng 9, of which four QTLs in Chr15 had stable and significant additive effects across six environments. These studies similarly detected QTLs in Chr15, although the candidate genes remained undetermined.…”

Section: Discussionmentioning

confidence: 99%

Identification of quantitative trait loci for increased α-tocopherol biosynthesis in wild soybean using a high-density genetic map

et al. 2019

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BackgroundSoybean is one of the most important crop sources of tocopherols (Toc). However, the content of α-Toc, an isoform with the highest vitamin E activity in humans, is low in most cultivars. With the aim of broadening genetic variability, we performed quantitative trait locus (QTL) analysis for a high seed α-Toc trait detected in a wild soybean and characterized the sequence polymorphisms and expression profiles of γ-tocopherol methyltransferase (γ-TMT) genes as potential candidates.ResultsA recombinant inbred line population was developed from a cross between the low α-Toc breeding line TK780 and the high α-Toc wild accession B04009. The α-Toc content in seeds correlated strongly with the ratio of α-Toc to γ-Toc contents. QTL analysis using a high-density map constructed with 7710 single nucleotide polymorphisms (SNPs) generated by restriction site-associated DNA sequencing detected six QTLs involved in α-Toc biosynthesis. Of these, three in chromosomes (Chr) 9, 11, and 12 produced consistent effects during a 2-year trial. B04009 allele at QTLs in Chr9 and Chr12 and TK780 allele at the QTL in Chr11 each promoted the conversion of γ-Toc to α-Toc, which elevated the seed α-Toc content. SNPs and indels were detected between the parents in three γ-TMT genes (γ-TMT1, γ-TMT2, and γ-TMT3) co-located in the QTLs in Chr9 and Chr12, of which some existed in the cis-regulatory elements associated with seed development and functions. In immature cotyledons, γ-TMT3 was expressed at higher levels in B04009 than TK780, irrespective of two thermal conditions tested, whereas the expression of γ-TMT2 was markedly upregulated under higher temperatures, particularly in B04009.ConclusionsWe identified QTLs consistently controlling α-Toc biosynthesis in wild soybean seeds in 2-year trials. The QTL on Chr9 had been previously identified in soybean, whereas the QTLs on Chr11 and Chr12 were novel. Further molecular dissections and characterization of the QTLs may facilitate the use of high α-Toc alleles from wild soybean in soybean breeding and an understanding of the molecular mechanisms underlying α-Toc biosynthesis in soybean seeds.

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Quantitative trait loci underlying soybean seed tocopherol content with main additive, epistatic and QTL × environment effects

Cited by 9 publications

References 39 publications

Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM

Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM

Genome-wide association studies reveal novel QTLs, QTL-by-environment interactions and their candidate genes for tocopherol content in soybean seed

Identification of quantitative trait loci for increased α-tocopherol biosynthesis in wild soybean using a high-density genetic map

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