Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium

摘要: Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS) attractive technology generate rapid switchgrass, meet the goals of substantial displacement petroleum use with biofuels near future. In this study, we empirically assessed prediction procedures for genomic two different populations, consisting 137 110 half-sib families tested locations United States three agronomic traits: dry matter yield, plant height, heading date. Marker data were produced families’ parents by exome capture sequencing, generating up 141,030 polymorphic markers available genomic-location annotation information. We evaluated that varied not only learning schemes models, also way preprocessed account redundancy marker More complex generally significantly more accurate than simplest procedure, likely due limited population sizes. Nevertheless, highly significant gain accuracy was transforming through correlation matrix. Our results suggest marker-data transformations and, generally, linkage disequilibrium among markers, offer valuable opportunities improving GS. Some accuracies should motivate implementation GS switchgrass breeding programs.