Novel QTL and Meta-QTL Mapping for Major Quality Traits in Soybean

Isoflavone, protein, and oil are the most important quality traits in soybean. Since these phenotypes are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify their complex and unclear genetic background. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and a high-density linkage map based on whole-genome resequencing to map novel QTL and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with a high logarithm of odd (LOD) scores were identified through composite interval mapping (CIM), including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of the gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1,034 QTL collected from previous studies. For the first time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL, respectively, related to oil and protein were obtained as well. The results promote the understanding of the biosynthesis and regulation of isoflavones, protein, and oil at molecular levels, and facilitate the construction of molecular modular for great quality traits in soybean.

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Mapping QTLs for protein and oil content in soybean by removing the influence of related traits in a four-way recombinant inbred line population

The Journal of Agricultural Science ◽

10.1017/s0021859620000040 ◽

2019 ◽

Vol 157 (9-10) ◽

pp. 659-675 ◽

Cited By ~ 2

Author(s):

Xiyu Li ◽

Hong Xue ◽

Kaixin Zhang ◽

Wenbin Li ◽

Yanlong Fang ◽

...

Keyword(s):

Qtl Mapping ◽

Recombinant Inbred ◽

Oil Content ◽

Molecular Breeding ◽

Recombinant Inbred Lines ◽

Interval Mapping ◽

Inbred Lines ◽

Additive Effects ◽

Linkage Maps ◽

Trait Locus

AbstractProtein content (PC) and oil content (OC) are important breeding traits of soybean [Glycine max (L.) Merr.]. Quantitative trait locus (QTL) mapping for PC and OC is important for molecular breeding in soybean; however, the negative correlation between PC and OC influences the accuracy of QTL mapping. In the current study, a four-way recombinant inbred lines (FW-RILs) population comprising 160 lines derived from the cross (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19) was planted in eight different environments and PC and OC measured. Conditional and unconditional QTL analyses were carried out by interval mapping (IM) and inclusive complete IM based on linkage maps of 275 simple sequences repeat markers in a FW-RILs population. This analysis revealed 59 unconditional QTLs and 52 conditional QTLs among the FW-RILs. An analysis of additive effects indicated that the effects of 13 protein QTLs were not related to OC, whereas OC affected the expression of 13 and eight QTLs either partially or completely, respectively. Eight QTLs affecting OC were not influenced by PC, whereas six and 26 QTLs were partially and fully affected by PC, respectively. Among the QTLs detected in the current study, two protein QTLs and five oil QTLs had not been previously reported. These findings will facilitate marker-assisted selection and molecular breeding of soybean.

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GWAS Discovery Of Candidate Genes for Yield-Related Traits in Peanut and Support from Earlier QTL Mapping Studies

Genes ◽

10.3390/genes10100803 ◽

2019 ◽

Vol 10 (10) ◽

pp. 803 ◽

Cited By ~ 1

Author(s):

Wang ◽

Yan ◽

Li ◽

Zhao ◽

...

Keyword(s):

Qtl Mapping ◽

Candidate Genes ◽

Genome Wide Association Study ◽

Gene Annotation ◽

Genotyping By Sequencing ◽

Nucleotide Polymorphisms ◽

Market Demand ◽

Oil Crops ◽

A Genome ◽

Genomic Regions

Peanut (Arachis hypogaea L.) is one of the most important oil crops worldwide, and its yet increasing market demand may be met by genetic improvement of yield related traits, which may be facilitated by a good understanding of the underlying genetic base of these traits. Here, we have carried out a genome-wide association study (GWAS) with the aim to identify genomic regions and the candidate genes within these regions that may be involved in determining the phenotypic variation at seven yield-related traits in peanut. For the GWAS analyses, 195 peanut accessions were phenotyped and/or genotyped; the latter was done using a genotyping-by-sequencing approach, which produced a total of 13,435 high-quality single nucleotide polymorphisms (SNPs). Analyses of these SNPs show that the analyzed peanut accessions can be approximately grouped into two big groups that, to some extent, agree with the botanical classification of peanut at the subspecies level. By taking this genetic structure as well as the relationships between the analyzed accessions into consideration, our GWAS analyses have identified 93 non-overlapping peak SNPs that are significantly associated with four of the studied traits. Gene annotation of the genome regions surrounding these peak SNPs have found a total of 311 unique candidate genes. Among the 93 yield-related-trait-associated SNP peaks, 12 are found to be co-localized with the quantitative trait loci (QTLs) that were identified by earlier related QTL mapping studies, and these 12 SNP peaks are only related to three traits and are almost all located on chromosomes Arahy.05 and Arahy.16. Gene annotation of these 12 co-localized SNP peaks have found 36 candidates genes, and a close examination of these candidate genes found one very interesting gene (arahy.RI9HIF), the rice homolog of which produces a protein that has been shown to improve rice yield when over-expressed. Further tests of the arahy.RI9HIF gene, as well as other candidate genes especially those within the more confident co-localized genomic regions, may hold the potential for significantly improving peanut yield.

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Gene–environment interactions in complex diseases: genetic models and methods for QTL mapping in multiple half-sib populations

Genetics Research ◽

10.1017/s0016672306008391 ◽

2006 ◽

Vol 88 (2) ◽

pp. 119-131 ◽

Cited By ~ 6

Author(s):

HAJA N. KADARMIDEEN ◽

YONGJUN LI ◽

LUC L. G. JANSS

Keyword(s):

Qtl Mapping ◽

Scale Effects ◽

High Standard ◽

Interval Mapping ◽

Mapping Method ◽

Genetic Models ◽

Gene Environment ◽

Trait Locus ◽

Outbred Populations ◽

Binary Scale

An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15–100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20–80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.

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Neighbor QTL: an interval mapping method for quantitative trait loci underlying plant neighborhood effects

10.1101/2020.05.20.089474 ◽

2020 ◽

Author(s):

Yasuhiro Sato ◽

Kazuya Takeda ◽

Atsushi J. Nagano

Keyword(s):

Qtl Mapping ◽

Neighborhood Effects ◽

Quantitative Trait ◽

Recombinant Inbred Lines ◽

Insect Herbivory ◽

R Package ◽

Interval Mapping ◽

Mapping Method ◽

Neighbor Effects ◽

Genotype Probabilities

AbstractPhenotypes of sessile organisms, such as plants, rely not only on their own genotype but also on the genotypes of neighboring individuals. Previously, we incorporated such neighbor effects into a single-marker regression using the Ising model of ferromagnetism. However, little is known about how to incorporate neighbor effects in quantitative trait locus (QTL) mapping. In this study, we propose a new method for interval QTL mapping of neighbor effects, named “Neighbor QTL”. The algorithm of neighbor QTL involves the following: (i) obtaining conditional self-genotype probabilities with recombination fraction between flanking markers, (ii) calculating neighbor genotypic identity using the self-genotype probabilities, and (iii) estimating additive and dominance deviation for neighbor effects. Our simulation using F2 and backcross lines showed that the power to detect neighbor effects increased as the effective range became smaller. The neighbor QTL was applied to insect herbivory on Col × Kas recombinant inbred lines of Arabidopsis thaliana. Consistent with previous evidence, the pilot experiment detected a self QTL effect on the herbivory at GLABRA1 locus. We also observed a weak QTL on chromosome 4 regarding neighbor effects on the herbivory. The neighbor QTL method is available as an R package (https://cran.r-project.org/package=rNeighborQTL), providing a novel tool to investigate neighbor effects in QTL studies.

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Genomic Locus Modulating IOP in the BXD RI Mouse Strains

10.1101/202937 ◽

2017 ◽

Author(s):

Rebecca King ◽

Ying Li ◽

Jiaxing Wang ◽

Felix L. Struebing ◽

Eldon E. Geisert

Keyword(s):

Candidate Genes ◽

Quantitative Trait ◽

Significant Quantitative Trait Locus ◽

Interval Mapping ◽

Mouse Strains ◽

Genomic Locus ◽

Quantitative Trait Analysis ◽

Significant Locus ◽

Trait Locus ◽

Different Strains

AbstractPurposeIntraocular pressure (IOP) is the primary risk factor for developing glaucoma. The present study examines genomic contribution to the normal regulation of IOP in the mouse.MethodsThe BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP from 532 eyes from 33 different strains. The IOP data will be subjected to conventional quantitative trait analysis using simple and composite interval mapping along with epistatic interactions to define genomic loci modulating normal IOP.ResultsThe analysis defined one significant quantitative trait locus (QTL) on Chr.8 (100 to 106 Mb). The significant locus was further examined to define candidate genes that modulate normal IOP. There are only two good candidate genes within the 6 Mb over the peak, Cdh8 (Cadherin 8) and Cdh11 (Cadherin 11). Expression analysis on gene expression and immunohistochemistry indicate that Cdh11 is the best candidate for modulating the normal levels of IOP.ConclusionsWe have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL that are two potential candidates for modulating IOP with the most likely gene being Cdh11.

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Construction of a High-density Genetic Map and Identification of Candidate Genes for Resistance to Fusarium Wilt based on the Resequencing of Recombinant Inbred Line Population in Gossypium Barbadense

10.21203/rs.3.rs-139530/v1 ◽

2021 ◽

Author(s):

Wanli Han ◽

Jieyin Zhao ◽

Xiaojuan Deng ◽

Aixing Gu ◽

Duolu Li ◽

...

Keyword(s):

Candidate Genes ◽

Genetic Map ◽

Fusarium Wilt ◽

Recombinant Inbred ◽

Gene Annotation ◽

Recombinant Inbred Lines ◽

Fiber Quality ◽

Gossypium Barbadense ◽

High Density ◽

Pcr Analysis

Abstract Background: Resistance to Fusarium wilt (FW) is of great significance for increasing the yield of Gossypium barbadense. Most published genetic studies on G. barbadense focus on yield and fiber quality traits, while there are few reports on resistance to FW. Results: To understand the genetic basis of cotton resistance to FW, this study used 110 recombinant inbred lines (RILs) of G. barbadense obtained from the parental materials Xinhai 14 and 06-146, and Nannong was used to construct a high-density genetic linkage map. The high-density genetic map was based on the resequencing of 933,845 single-nucleotide polymorphism (SNP) markers, and 3627 bins covering 2483.17 cM were finally obtained. The collinearity matched the physical map. A total of 9 QTLs for FW resistance were identified, each QTL explained 4.27-14.92% of the observed phenotypic variation, and qFW-Dt3-1 was identified in at least two environments. According to gene annotation information from multiple databases, promoter homeopathic elements and transcriptome data, 10 candidate genes were screened in a stable QTL interval. qRT-PCR analysis showed that the GOBAR_DD06292 gene was differentially expressed in the roots of the two parents under FW stress and exhibited the same expression trend in the G. barbadense resource materials.Conclusions: These results indicate the importance of the GOBAR_DD06292 gene in FW resistance in G. barbadense and lay a molecular foundation for the analysis of the molecular mechanism of FW in G. barbadense.

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QTL mapping of resistance to powdery mildew race 1 in a recombinant inbred line population of melon

Plant Disease ◽

10.1094/pdis-12-20-2643-re ◽

2021 ◽

Author(s):

Sandra Branham ◽

Chandrasekar S Kousik ◽

Mihir Mandal ◽

Patrick Wechter

Keyword(s):

South Carolina ◽

Powdery Mildew ◽

Qtl Mapping ◽

Candidate Genes ◽

Inbred Line ◽

Recombinant Inbred Line ◽

Recombinant Inbred ◽

Missense Mutations ◽

Race 1 ◽

Whole Genome Resequencing

Powdery mildew, caused by the fungus Podosphaera xanthii, is one of the most important diseases of melon. While there are several pathogenic races of P. xanthii, race 1 is the predominant race in South Carolina and the U.S. We used a densely genotyped recombinant inbred line melon population for traditional QTL mapping, to identify two major (qPx1-5 and qPx1-12) and two minor (qPx1-4 and qPx1-10) QTLs (named according to race – chromosome number) associated with resistance to P. xanthii race 1. QTL mapping of disease severity in multiple tissues (hypocotyl, cotyledons, true leaves and stems) identified the same genetic basis of resistance in all tissue types. Whole-genome resequencing of the parents was used for marker development across the major QTLs and functional annotation of SNPs for candidate gene analysis. KASP markers were tightly linked to the QTL peaks of qPx1-5 (pm1-5_25329892, pm1-5_25461503 and pm1-5_25625375) and qPx1-12 (pm1-12_22848920 and pm1-12_22904659) in the population and will enable efficient marker-assisted introgression of powdery mildew resistance into improved germplasm. Candidate genes were identified in both major QTL intervals that encode putative R genes with missense mutations between the parents. The candidate genes provide targets for future breeding efforts and a fundamental examination of resistance to powdery mildew in melon.

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Identification of QTLs Associated with Two-seed Pod Length and Width in Soybean

Legume Research - An International Journal ◽

10.18805/lr-615 ◽

2021 ◽

Author(s):

D.P. Shan ◽

J.G. Xie ◽

Y. Yu ◽

R. Zhou ◽

Z.L. Cui ◽

...

Keyword(s):

Candidate Genes ◽

Gene Annotation ◽

Soybean Seed ◽

Chromosome 17 ◽

Substitution Lines ◽

Epistatic Effects ◽

Chromosome Segment Substitution Lines ◽

Trait Locus ◽

Segment Substitution ◽

Pod Length

Background: Two-seed pod length and width (TSPL and TSPW, respectively) are the traits underlying seed size, which is an important factor influencing soybean yield. Methods: A population comprising 213 chromosome segment substitution lines from a cross between ‘Suinong14’ (SN14) and ZYD00006 was used for a quantitative trait locus (QTL) analysis. The QTLs were identified on the basis of the phenotypes from 2016 to 2019. Additionally, IciMapping 4.2 was used to analyze the phenotypic and genetic data. Genes were annotated using the KEGG and Phytozome databases. Result: Five QTLs for TSPL and four QTLs for TSPW were identified. One QTL on chromosome 17 was detected for TSPL in 2017 and 2018 as well for TSPW in 2018 and 2019. Analyses of the additive × additive epistatic effects of QTLs revealed six stable loci pairs for epistatic effects on the two traits. On the basis of an alignment of the parental gene sequences and the gene annotation information, Glyma.04G188800, Glyma.11G164700, Glyma.13G132700, Glyma.17G156100 and Glyma.13G133200 were selected as candidate genes for TSPL, whereas Glyma.13G174400, Glyma.13G174700, Glyma.16G012500, Glyma.17G156100, Glyma.19G161700 and Glyma.19G161800 were selected as candidate genes for TSPW. These results may be relevant for future attempts to modify soybean seed traits.

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QTL mapping in outbred populations: successes and challenges

Physiological Genomics ◽

10.1152/physiolgenomics.00127.2013 ◽

2014 ◽

Vol 46 (3) ◽

pp. 81-90 ◽

Cited By ~ 29

Author(s):

Leah C. Solberg Woods

Keyword(s):

Qtl Mapping ◽

Candidate Genes ◽

Complex Traits ◽

Full Genome Sequence ◽

Backcross Population ◽

Animal Populations ◽

Resolution Mapping ◽

Trait Locus ◽

Genomic Regions ◽

Outbred Populations

Quantitative trait locus (QTL) mapping in animal populations has been a successful strategy for identifying genomic regions that play a role in complex diseases and traits. When conducted in an F2 intercross or backcross population, the resulting QTL is frequently large, often encompassing 30 Mb or more and containing hundreds of genes. To narrow the locus and identify candidate genes, additional strategies are needed. Congenic strains have proven useful but work less well when there are multiple tightly linked loci, frequently resulting in loss of phenotype. As an alternative, we discuss the use of highly recombinant outbred models for directly fine-mapping QTL to only a few megabases. We discuss the use of several currently available models such as the advanced intercross (AI), heterogeneous stocks (HS), the diversity outbred (DO), and commercially available outbred stocks (CO). Once a QTL has been fine-mapped, founder sequence and expression QTL mapping can be used to identify candidate genes. In this regard, the large number of alleles found in outbred stocks can be leveraged to identify causative genes and variants. We end this review by discussing some important statistical considerations when analyzing outbred populations. Fine-resolution mapping in outbred models, coupled with full genome sequence, has already led to the identification of several underlying causative genes for many complex traits and diseases. These resources will likely lead to additional successes in the coming years.

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