scholarly journals Pod and seed trait QTL identification to assist breeding for peanut market preferences

2019 ◽  
Author(s):  
Carolina Chavarro ◽  
Ye Chu ◽  
Corley Holbrook ◽  
Thomas Isleib ◽  
David Bertioli ◽  
...  
Keyword(s):  
Seed Weight ◽  
Chromosomal Rearrangements ◽  
Snp Array ◽  
Phenotypic Variance ◽  
Linkage Groups ◽  
Single Linkage ◽  
Size Index ◽  
Peanut Genome ◽  
Qtl Identification ◽  
Variance Explained

ABSTRACTAlthough seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explorethe distribution of recombination and potential chromosomal rearrangements. These traits were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

scholarly journals Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

2020 ◽  
Vol 10 (7) ◽  
pp. 2297-2315 ◽  
Author(s):  
Carolina Chavarro ◽  
Ye Chu ◽  
Corley Holbrook ◽  
Thomas Isleib ◽  
David Bertioli ◽  
...  
Keyword(s):  
Linkage Map ◽  
Seed Weight ◽  
Chromosomal Rearrangements ◽  
Snp Array ◽  
Phenotypic Variance ◽  
Linkage Groups ◽  
Single Linkage ◽  
Size Index ◽  
Peanut Genome ◽  
Variance Explained

Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explore the distribution of recombination and potential chromosomal rearrangements. This linkage map was then used to identify Quantitative Trait Loci (QTL) for seed and pod traits that were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

scholarly journals QTL identification for seed weight and size based on a high-density SLAF-seq genetic map in peanut (Arachis hypogaea L.)

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Shengzhong Zhang ◽  
Xiaohui Hu ◽  
Huarong Miao ◽  
Ye Chu ◽  
Fenggao Cui ◽  
...  
Keyword(s):  
Genetic Map ◽  
Seed Weight ◽  
Genome Wide Association Study ◽  
Bulked Segregant Analysis ◽  
High Density ◽  
Width Ratio ◽  
Phenotypic Variance ◽  
Phenotypic Data ◽  
Cultivated Peanut ◽  
Peanut Genome

Abstract Background The cultivated peanut is an important oil and cash crop grown worldwide. To meet the growing demand for peanut production each year, genetic studies and enhanced selection efficiency are essential, including linkage mapping, genome-wide association study, bulked-segregant analysis and marker-assisted selection. Specific locus amplified fragment sequencing (SLAF-seq) is a powerful tool for high density genetic map (HDGM) construction and quantitative trait loci (QTLs) mapping. In this study, a HDGM was constructed using SLAF-seq leading to identification of QTL for seed weight and size in peanut. Results A recombinant inbred line (RIL) population was advanced from a cross between a cultivar ‘Huayu36’ and a germplasm line ‘6–13’ with contrasting seed weight, size and shape. Based on the cultivated peanut genome, a HDGM was constructed with 3866 loci consisting of SLAF-seq and simple sequence repeat (SSR) markers distributed on 20 linkage groups (LGs) covering a total map distance of 1266.87 cM. Phenotypic data of four seed related traits were obtained in four environments, which mostly displayed normal distribution with varied levels of correlation. A total of 27 QTLs for 100 seed weight (100SW), seed length (SL), seed width (SW) and length to width ratio (L/W) were identified on 8 chromosomes, with LOD values of 3.16–31.55 and explaining phenotypic variance (PVE) from 0.74 to 83.23%. Two stable QTL regions were identified on chromosomes 2 and 16, and gene content within these regions provided valuable information for further functional analysis of yield component traits. Conclusions This study represents a new HDGM based on the cultivated peanut genome using SLAF-seq and SSRs. QTL mapping of four seed related traits revealed two stable QTL regions on chromosomes 2 and 16, which not only facilitate fine mapping and cloning these genes, but also provide opportunity for molecular breeding of new peanut cultivars with improved seed weight and size.

scholarly journals GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jisu Shin ◽  
Sang Hong Lee
Keyword(s):  
Complex Traits ◽  
Error Rates ◽  
Type I ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Summary Statistics ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Scale Data ◽  
Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

Patterns of genome duplication within the Brassica napus genome

Genome ◽  
2003 ◽  
Vol 46 (2) ◽  
pp. 291-303 ◽  
Author(s):  
I A.P Parkin ◽  
A G Sharpe ◽  
D J Lydiate
Keyword(s):  
Brassica Napus ◽  
Genome Duplication ◽  
Chromosomal Rearrangements ◽  
Centric Fusion ◽  
Linkage Groups ◽  
Gross Chromosomal Rearrangements ◽  
A Genome ◽  
C Genome ◽  
Homologous Locus ◽  
Duplicate Loci

The progenitor diploid genomes (A and C) of the amphidiploid Brassica napus are extensively duplicated with 73% of genomic clones detecting two or more duplicate sequences within each of the diploid genomes. This comprehensive duplication of loci is to be expected in a species that has evolved through a polyploid ancestor. The majority of the duplicate loci within each of the diploid genomes were found in distinct linkage groups as collinear blocks of linked loci, some of which had undergone a variety of rearrangements subsequent to duplication, including inversions and translocations. A number of identical rearrangements were observed in the two diploid genomes, suggesting they had occurred before the divergence of the two species. A number of linkage groups displayed an organization consistent with centric fusion and (or) fission, suggesting this mechanism may have played a role in the evolution of Brassica genomes. For almost every genetically mapped locus detected in the A genome a homologous locus was found in the C genome; the collinear arrangement of these homologous markers allowed the primary regions of homoeology between the two genomes to be identified. At least 16 gross chromosomal rearrangements differentiated the two diploid genomes during their divergence from a common ancestor.Key words: genome evolution, Brassicaeae, polyploidy, homoeologous linkage groups.

scholarly journals Identification of quantitative trait loci for leaf traits in rice

Genetika ◽  
2016 ◽  
Vol 48 (2) ◽  
pp. 643-652 ◽  
Author(s):  
Baoyan Jia ◽  
Xinhua Zhao ◽  
Yang Qin ◽  
Muhammad Irfan ◽  
Tae-Heon Kim ◽  
...  
Keyword(s):  
Recombinant Inbred Lines ◽  
Leaf Traits ◽  
Rice Cultivar ◽  
Interval Mapping ◽  
Mapping Method ◽  
Japonica Rice ◽  
Phenotypic Variance ◽  
Simple Sequence ◽  
Variance Explained ◽  
Map Distance

A recombinant inbred lines (RILs) population of 90 lines were developed from a subspecies cross between an indica type cultivar, ?Cheongcheong?, and a japonica rice cultivar, ?Nagdong? was evaluated for leaf traits in 2009. A genetic linkage map consisting of 154 simple sequence repeat (SSR) markers was constructed, covering 1973.6 cM of 12 chromosomes with an average map distance of 13.9 cM between markers. By composite interval mapping method a total of 19 QTLs were identified for the leaf traits on 5 chromosomes (Chr.1, Chr.3, Chr.6, Chr.8 and Chr.11). The percentage of phenotypic variance explained by each QTL varied from 8.1% to 29.4%. Five pleiotropic effects loci were identified on chromosomes 1,6.

Detection of a Major QTL and Development of KASP Markers for Seed Weight by Combining QTL-seq, QTL-mapping and RNA-seq in Peanut

2021 ◽  
Author(s):  
Zhihui Wang ◽  
Liying Yan ◽  
Yuning Chen ◽  
Xin Wang ◽  
Dongxin Huai ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Seed Development ◽  
Seed Weight ◽  
Molecular Mechanisms ◽  
Large Contribution ◽  
Phenotypic Variance ◽  
Rna Seq ◽  
Late Stages ◽  
Kasp Markers

Abstract Seed weight is a major target of peanut breeding as an important component of seed yield. However, relatively little is known about QTLs and candidate genes associated with seed weight in peanut. In this study, three major QTLs on chromosomes A05, B02 and B06 were determined by applying NGS-based QTL-seq approach for a RIL population. These three QTL regions have been successfully narrowed down through newly developed SNP and SSR markers based on traditional QTL mapping. Among these three QTL regions, qSWB06.3 exhibited stable expression with large contribution to phenotypic variance across all environments. Furthermore, RNA-seq were applied for early, middle and late stages of seed development, and differentially expression genes (DEGs) were identified in ubiquitin-proteasome pathway, serine/threonine protein pathway and signal transduction of hormones and transcription factors. Notably, DEGs at early stage were majorly related to regulating cell division, whereas DEGs at middle and late stages were mainly associated with cell expansion during seed development. Through integrating SNP variation, gene expression and functional annotation, candidate genes related to seed weight in qSWB06.3 were predicted and distinct expression pattern of those genes were exhibited using qRT-PCR. In addition, KASP-markers in qSWB06.3 were successfully validated in diverse peanut varieties and the alleles of parent Zhonghua16 in qSWB06.3 was associated with high seed weight. This suggested that qSWB06.3 was reliable and the markers in qSWB06.3 could be deployed in marker-assisted breeding to enhance seed weight. This study provided insights into the understanding of genetic and molecular mechanisms of seed weight in peanut.

scholarly journals Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jian Ma ◽  
Han Zhang ◽  
Shuiqin Li ◽  
Yaya Zou ◽  
Ting Li ◽  
...  
Keyword(s):  
Genetic Map ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Snp Array ◽  
Kernel Weight ◽  
Phenotypic Variance ◽  
Kernel Length ◽  
Kasp Marker ◽  
Kernel Traits ◽  
Major Qtl

Abstract Background Kernel length (KL), kernel width (KW) and thousand-kernel weight (TKW) are key agronomic traits in wheat breeding. Chuannong16 (‘CN16’) is a commercial cultivar with significantly longer kernels than the line ‘20828’. To identify and characterize potential alleles from CN16 controlling KL, the previously developed recombinant inbred line (RIL) population derived from the cross ‘20828’ × ‘CN16’ and the genetic map constructed by the Wheat55K SNP array and SSR markers were used to perform quantitative trait locus/loci (QTL) analyses for kernel traits. Results A total of 11 putative QTL associated with kernel traits were identified and they were located on chromosomes 1A (2 QTL), 2B (2 QTL), 2D (3 QTL), 3D, 4A, 6A, and 7A, respectively. Among them, three major QTL, QKL.sicau-2D, QKW.sicau-2D and QTKW.sicau-2D, controlling KL, KW and TKW, respectively, were detected in three different environments. Respectively, they explained 10.88–18.85%, 17.21–21.49% and 10.01–23.20% of the phenotypic variance. Further, they were genetically mapped in the same interval on chromosome 2DS. A previously developed kompetitive allele-specific PCR (KASP) marker KASP-AX-94721936 was integrated in the genetic map and QTL re-mapping finally located the three major QTL in a 1- cM region flanked by AX-111096297 and KASP-AX-94721936. Another two co-located QTL intervals for KL and TKW were also identified. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified QTL. Significant relationships between kernel traits and spikelet number per spike and anthesis date were detected and discussed. Conclusions Three major and stably expressed QTL associated with KL, KW, and TKW were identified. A KASP marker tightly linked to these three major QTL was integrated. These findings provide information for subsequent fine mapping and cloning the three co-localized major QTL for kernel traits.

scholarly journals Detection of Quantitative Trait Loci (QTL) Associated with the Fruit Morphology of Tomato

Genes ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1117
Author(s):  
Pragya Adhikari ◽  
James McNellie ◽  
Dilip R. Panthee
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Recombinant Inbred Lines ◽  
Tomato Fruit ◽  
Snp Array ◽  
Fruit Shape ◽  
Phenotypic Variance ◽  
Fruit Morphology ◽  
Trait Loci ◽  
Shape And Size

Tomato (Solanum lycopersicum L.) is the second most-consumed vegetable in the world. The market value and culinary purpose of tomato are often determined by fruit size and shape, which makes the genetic improvement of these traits a priority for tomato breeders. The main objective of the study was to detect quantitative trait loci (QTL) associated with the tomato fruit shape and size. The use of elite breeding materials in the genetic mapping studies will facilitate the detection of genetic loci of direct relevance to breeders. We performed QTL analysis in an intra-specific population of tomato developed from a cross between two elite breeding lines NC 30P × NC-22L-1(2008) consisting of 110 recombinant inbred lines (RIL). The precision software Tomato Analyzer (TA) was used to measure fruit morphology attributes associated with fruit shape and size traits. The RIL population was genotyped with the SolCAP 7720 SNP array. We identified novel QTL controlling elongated fruit shape on chromosome 10, explaining up to 24% of the phenotypic variance. This information will be useful in improving tomato fruit morphology traits.

Multilevel Twin Models: Geographical Region as a Third Level Variable

2020 ◽  
Author(s):  
Zenab Tamimy ◽  
Sofieke T. Kevenaar ◽  
Jouke Jan Hottenga ◽  
Michael D. Hunter ◽  
Eveline L. de Zeeuw ◽  
...  
Keyword(s):  
Multilevel Model ◽  
Phenotypic Variance ◽  
Environmental Variance ◽  
Twin Model ◽  
Snp Data ◽  
Genome Wide ◽  
Twin Models ◽  
Regional Clustering ◽  
Level Variable ◽  
Variance Explained

AbstractThe classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region did no longer explain variation in height. Our results suggest that the phenotypic variance explained by region actually represent ancestry effects on height.

scholarly journals QTL Mapping and Candidate Gene Analysis for Pod Shattering Tolerance in Soybean (Glycine max)

Plants ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1163
Author(s):  
Jeong-Hyun Seo ◽  
Beom-Kyu Kang ◽  
Sanjeev K. Dhungana ◽  
Jae-Hyeon Oh ◽  
Man-Soo Choi ◽  
...  
Keyword(s):  
Growth Stages ◽  
Phenotypic Variance ◽  
Potential Candidate ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Reproductive Process ◽  
Pod Shattering ◽  
Selection For ◽  
Elite Cultivar ◽  
Variance Explained

Pod shattering is an important reproductive process in many wild species. However, pod shattering at the maturing stage can result in severe yield loss. The objectives of this study were to discover quantitative trait loci (QTLs) for pod shattering using two recombinant inbred line (RIL) populations derived from an elite cultivar having pod shattering tolerance, namely “Daewonkong”, and to predict novel candidate QTL/genes involved in pod shattering based on their allele patterns. We found several QTLs with more than 10% phenotypic variance explained (PVE) on seven different chromosomes and found a novel candidate QTL on chromosome 16 (qPS-DS16-1) from the allele patterns in the QTL region. Out of the 41 annotated genes in the QTL region, six were found to contain SNP (single-nucleotide polymorphism)/indel variations in the coding sequence of the parents compared to the soybean reference genome. Among the six potential candidate genes, Glyma.16g076600, one of the genes with known function, showed a highly differential expression levels between the tolerant and susceptible parents in the growth stages R3 to R6. Further, Glyma.16g076600 is a homolog of AT4G19230 in Arabidopsis, whose function is related to abscisic acid catabolism. The results provide useful information to understand the genetic mechanism of pod shattering and could be used for improving the efficiency of marker-assisted selection for developing varieties of soybeans tolerant to pod shattering.

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