ArticleG3 (Bethesda, Md.)2016
Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium.
Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Genomic prediction of agronomic traits in a switchgrass (Panicum virgatum L.) half-sib progeny panel evaluated across multiple environments.The plant genome · 2026Article
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 2025Article
- Genomic prediction in a backcross population using relationship matrices.Archives animal breeding · 2025Article
- Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genetic loci associated with winter survivorship in diverse lowland switchgrass populations.The plant genome · 2021Article
- High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (G3 (Bethesda, Md.) · 2020Article
- Advancing crested wheatgrass [Agropyron cristatum (L.) Gaertn.] breeding through genotyping-by-sequencing and genomic selection.PloS one · 2020Article
- Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits.G3 (Bethesda, Md.) · 2019Article
- Genomic Prediction for Winter Survival of Lowland Switchgrass in the Northern USA.G3 (Bethesda, Md.) · 2019Article
- Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample.G3 (Bethesda, Md.) · 2019Article
- Breeding progress and preparedness for mass-scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar.Global change biology. Bioenergy · 2019Article
- Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations.BMC plant biology · 2018Article
- Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Extensive Genetic Diversity is Present within North American Switchgrass Germplasm.The plant genome · 2018Article
- Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Genomic Prediction of Complex Phenotypes Using Genic Similarity Based Relatedness Matrix.Frontiers in genetics · 2018Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS) is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families' parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.
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