Dataset Information

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.

ABSTRACT: With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data. This study undertook an extensive analysis of 907 elite winter barley (Hordeum vulgare L.) lines across multiple environments from two breeding companies. Six genomic prediction models were evaluated to demonstrate the effect of using pedigree and marker information individually and in combination, as well their interactions with the environment. Each model was evaluated using three cross-validation schemes that allows the prediction of newly developed lines (lines that have not been evaluated in any environment), prediction of new or unobserved years, and prediction of newly developed lines in unobserved years. The results showed that the best prediction model depends on the cross-validation scheme employed. In predicting newly developed lines in known environments, marker information had no advantage to pedigree information. Predictions in this scenario also benefited from including genotype-by-environment interaction in the models. However, when predicting lines and years not observed previously, marker information was superior to pedigree data. Nonetheless, such scenarios did not benefit from the addition of genotype-by-environment interaction. A combination of pedigree-based and marker-based information produced a similar or only marginal improvement in prediction ability. It was also discovered that combining populations from the different breeding programs to increase training population size had no advantage in prediction.

SUBMITTER: Ankamah-Yeboah T

PROVIDER: S-EPMC7227446 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.

Ankamah-Yeboah Theresa T Janss Lucas Lodewijk LL Jensen Jens Due JD Hjortshøj Rasmus Lund RL Rasmussen Søren Kjærsgaard SK

Frontiers in plant science 20200508

With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the p ...[more]

PMID: 32457780

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Project description:Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT's research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are publicly available as Supporting Information.

Project description:Key messageExploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs.

Project description:Malnutrition has emerged as one of the major health problems worldwide. Traditional yellow maize has low provitamin-A (proA) content and its genetic base in proA biofortification breeding program of subtropics is extremely narrow. To diversify the proA rich germplasm, 10 elite low proA inbreds were crossed with a proA rich donor (HP702-22) having mutant crtRB1 gene. The F2 populations derived from these crosses were genotyped using InDel marker specific to crtRB1. Severe marker segregation distortion was observed. Seventeen crtRB1 inbreds developed through marker-assisted pedigree breeding and seven inbreds generated using marker-assisted backcross breeding were characterized using 77 SSRs. Wide variation in gene diversity (0.08 to 0.79) and dissimilarity coefficient (0.28 to 0.84) was observed. The inbreds were grouped into three major clusters depicting the existing genetic diversity. The crtRB1-based inbreds possessed high β-carotene (BC: 8.72μg/g), β-cryptoxanthin (BCX: 4.58μg/g) and proA (11.01μg/g), while it was 2.35μg/g, 1.24μg/g and 2.97μg/g in checks, respectively. Based on their genetic relationships, 15 newly developed crtRB1-based inbreds were crossed with five testers (having crtRB1 gene) using line × tester mating design. 75 experimental hybrids with crtRB1 gene were evaluated over three locations. These experimental hybrids possessed higher BC (8.02μg/g), BCX (4.69μg/g), proA (10.37μg/g) compared to traditional hybrids used as check (BC: 2.36 μg/g, BCX: 1.53μg/g, proA: 3.13μg/g). Environment and genotypes × environment interaction had minor effects on proA content. Both additive and dominance gene action were significant for proA. The mean proportion of proA to total carotenoids (TC) was 44% among crtRB1-based hybrids, while 11% in traditional hybrids. BC was found to be positively correlated with BCX (r = 0.68) and proA (r = 0.98). However, no correlation was observed between proA and grain yield. Several hybrids with >10.0 t/ha grain yield with proA content >10.0 μg/g were identified. This is the first comprehensive study on development of diverse proA rich maize hybrids through marker-assisted pedigree breeding approach. The findings provides sustainable and cost-effective solution to alleviate vitamin-A deficiency.

Dataset Information

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.

Publications

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.

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