Dataset Information

Association Mapping in Scandinavian Winter Wheat for Yield, Plant Height, and Traits Important for Second-Generation Bioethanol Production.

ABSTRACT: A collection of 100 wheat varieties representing more than 100 years of wheat-breeding history in Scandinavia was established in order to identify marker-trait associations for plant height (PH), grain yield (GY), and biomass potential for bioethanol production. The field-grown material showed variations in PH from 54 to 122 cm and in GY from 2 to 6.61 t ha(-1). The release of monomeric sugars was determined by high-throughput enzymatic treatment of ligno-cellulosic material and varied between 0.169 and 0.312 g/g dm for glucose (GLU) and 0.146 and 0.283 g/g dm for xylose (XYL). As expected, PH and GY showed to be highly influenced by genetic factors with repeatability (R) equal to 0.75 and 0.53, respectively, while this was reduced for GLU and XYL (R = 0.09 for both). The study of trait correlations showed how old, low-yielding, tall varieties released higher amounts of monomeric sugars after straw enzymatic hydrolysis, showing reduced recalcitrance to bioconversion compared to modern varieties. Ninety-three lines from the collection were genotyped with the DArTseq(®) genotypic platform and 5525 markers were used for genome-wide association mapping. Six quantitative trait loci (QTLs) for GY, PH, and GLU released from straw were mapped. One QTL for PH was previously reported, while the remaining QTLs constituted new genomic regions linked to trait variation. This paper is one of the first studies in wheat to identify QTLs that are important for bioethanol production based on a genome-wide association approach.

SUBMITTER: Bellucci A

PROVIDER: S-EPMC4660856 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association Mapping in Scandinavian Winter Wheat for Yield, Plant Height, and Traits Important for Second-Generation Bioethanol Production.

Bellucci Andrea A Torp Anna Maria AM Bruun Sander S Magid Jakob J Andersen Sven B SB Rasmussen Søren K SK

Frontiers in plant science 20151126

A collection of 100 wheat varieties representing more than 100 years of wheat-breeding history in Scandinavia was established in order to identify marker-trait associations for plant height (PH), grain yield (GY), and biomass potential for bioethanol production. The field-grown material showed variations in PH from 54 to 122 cm and in GY from 2 to 6.61 t ha(-1). The release of monomeric sugars was determined by high-throughput enzymatic treatment of ligno-cellulosic material and varied between 0 ...[more]

PMID: 26635859

Similar Datasets

Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

Project description:Identification of genes for yield components, plant height (PH), and yield-related physiological traits and tightly linked molecular markers is of great importance in marker-assisted selection (MAS) in wheat breeding. In the present study, 246 F8 RILs derived from the cross of Zhou 8425B/Chinese Spring were genotyped using the high-density Illumina iSelect 90K single nucleotide polymorphism (SNP) assay. Field trials were conducted at Zhengzhou and Zhoukou of Henan Province, during the 2012-2013 and 2013-2014 cropping season under irrigated conditions, providing data for four environments. Analysis of variance (ANOVA) of agronomic and physiological traits revealed significant differences (P < 0.01) among RILs, environments, and RILs × environments interactions. Broad-sense heritabilities of all traits including thousand kernel weight (TKW), PH, spike length (SL), kernel number per spike (KNS), spike number/m(2) (SN), normalized difference in vegetation index at anthesis (NDVI-A) and at 10 days post-anthesis (NDVI-10), SPAD value of chlorophyll content at anthesis (Chl-A) and at 10 days post-anthesis (Chl-10) ranged between 0.65 and 0.94. A linkage map spanning 3609.4 cM was constructed using 5636 polymorphic SNP markers, with an average chromosome length of 171.9 cM and marker density of 0.64 cM/marker. A total of 866 SNP markers were newly mapped to the hexaploid wheat linkage map. Eighty-six QTL for yield components, PH, and yield-related physiological traits were detected on 18 chromosomes except 1D, 5D, and 6D, explaining 2.3-33.2% of the phenotypic variance. Ten stable QTL were identified across four environments, viz. QTKW.caas-6A.1, QTKW.caas-7AL, QKNS.caas-4AL, QSN.caas-1AL.1, QPH.caas-4BS.2, QPH.caas-4DS.1, QSL.caas-4AS, QSL.caas-4AL.1, QChl-A.caas-5AL, and QChl-10.caas-5BL. Meanwhile, 10 QTL-rich regions were found on chromosome 1BS, 2AL (2), 3AL, 4AL (2), 4BS, 4DS, 5BL, and 7AL exhibiting pleiotropic effects. These QTL or QTL clusters are tightly linked to SNP markers, with genetic distances to the closest SNPs ranging from 0 to 1.5 cM, and could serve as target regions for fine mapping, candidate gene discovery, and MAS in wheat breeding.

Project description:Uncovering the genetic basis of yield-related traits is important for molecular improvement of wheat cultivars. In this study, a genome-wide association study was conducted using the wheat 55K genotyping assay and a diverse panel of 384 wheat genotypes. The accessions used included 18 founder parents and 15 widely grown cultivars with annual maximum acreages of over 667,000 ha, and the remaining materials were elite cultivars and breeding lines from several major wheat ecological areas of China. Field trials were conducted in five major wheat ecological regions of China over three consecutive years. A total of 460 significant loci were detected for eight yield-related traits. Forty-five superior alleles distributed over 31 loci for which differences in phenotypic values grouped by single nucleotide polymorphism (SNP) reached significant levels (P < 0.05) in nine or more environments, were detected; some of these loci were previously reported. Eleven of the 31 superior allele loci on chromosomes 4A, 5A, 3B, 5B, 6B, 7B, 5D, and 7D had pleiotropic effects. For example, AX-95152512 on 5D was simultaneously related to increased grain weight per spike (GWS) and decreased plant height (PH); AX-109860828 on 5B simultaneously led to a high 1,000-kernel weight (TKW) and short PH; and AX-111600193 on 4A was simultaneously linked to a high TKW and GWS, and short PH. The favorable alleles in each accession ranged from 2 to 30 with an average of 16 at the thirty-one loci in the population, and six accessions (Zhengzhou683, Suzhou7829, Longchun7, Ningmai6, Yunmai35 and Zhen7630) contained more than 27 favorable alleles. A significant association between the number of favorable alleles and yield was observed (r = 0.799, p < 0.0001), suggesting that pyramiding multiple QTL with marker-assisted selection may effectively increase yield of wheat. Furthermore, distribution of superior alleles in founder parents and widely grown cultivars was also discussed here. This study is useful for marker-assisted selection for yield improvement and dissecting the genetic mechanism of important cultivars in wheat.

Project description:BackgroundGenomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.ResultsGenomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64-70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between - 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was "superior" to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.ConclusionsOur results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

Dataset Information

Association Mapping in Scandinavian Winter Wheat for Yield, Plant Height, and Traits Important for Second-Generation Bioethanol Production.

Publications

Association Mapping in Scandinavian Winter Wheat for Yield, Plant Height, and Traits Important for Second-Generation Bioethanol Production.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets