Genome‐wide association and genomic prediction for yield and component traits ofMiscanthus sacchariflorus

Abstract

Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic‐enabled breeding tools, like marker‐assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of Miscanthus breeding. The present study conducted genome‐wide association (GWA) and genomic prediction of biomass yield and 14 yield‐components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single‐nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield‐component traits in M. sacchariflorus that may facilitate marker‐assisted breeding for biomass yield. Genomic prediction accuracy for the yield‐related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus. This study indicated that MAS and genomic prediction will likely be beneficial for conducting population‐improvement of M. sacchariflorus.

Genotype by environment model predictive ability in Miscanthus

“…Five environmental variables related to temperature, precipitation, and latitude were measured at each environment where the Msi trials were grown (Clark et al., 2019b), while a more comprehensive set of 14 environmental variables assessing temperature, precipitation, and diurnal range were quantified at the Msa trials (Njuguna, Clark, Anzoua, et al., 2023). All phenotypic and environmental variables are available in the Illinois data bank locations provided in the Data Avaialbility Statement (Clark et al., 2019b; Njuguna, Clark, Anzoua, et al., 2023; Njuguna, Clark, Lipka, et al., 2023). For both Msi and Msa, the trait that we analyzed for this study was biomass yield.…”

“…In their analysis of Msi, Clark et al., (2019b) considered a total of 14 traits, including biomass yield, basal circumference, culm dry weight, and the diameter of the topmost internode. A set of 14 similar traits were evaluated across the Msa trials (Njuguna, Clark, Lipka, et al., 2023). Five environmental variables related to temperature, precipitation, and latitude were measured at each environment where the Msi trials were grown (Clark et al., 2019b), while a more comprehensive set of 14 environmental variables assessing temperature, precipitation, and diurnal range were quantified at the Msa trials (Njuguna, Clark, Anzoua, et al., 2023).…”