Training Population Optimization for Genomic Selection in Miscanthus

Abstract

<jats:p><jats:italic>Miscanthus</jats:italic> is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies <jats:italic>Miscanthus</jats:italic> × <jats:italic>giganteus</jats:italic> (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species <jats:italic>M. sinensis</jats:italic> (Msi) and <jats:italic>M. sacchariflorus</jats:italic> (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F<jats:sub>2</jats:sub> panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F<jats:sub>2</jats:sub> panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F<jats:sub>2</jats:sub> panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.</jats:p>

Genomic and pedigree‐based predictive ability for quality traits in tea (Camellia sinensis (L.) O. Kuntze)

“…Lozada et al (2019) observed a positive correlation between TPS and prediction accuracy for yield and agronomic traits in soft red winter wheat. Similar results were also reported by Zhang et al (2017b) in maize and Olatoye et al (2020) in Miscanthus (grass). From our results for cross-validations, an optimal number of genotypes (* 80 % of the entire population) should be included in the training panel to achieve improved predictions in tea.…”

Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview

“…Other studies also stressed the importance of considering an other way to construct the TRS by random sampling (Lorenz and Smith, 2015 ; He et al, 2016 ; Cericola et al, 2017 ; Neyhart et al, 2017 ; Norman et al, 2018 ; de Bem Oliveira et al, 2020 ; Olatoye et al, 2020 ), clustering approaches (Akdemir et al, 2015 ; Isidro et al, 2015 ; Bustos-Korts et al, 2016 ; Rincent et al, 2017 ; Norman et al, 2018 ; Guo et al, 2019 ; Sarinelli et al, 2019 ; Adeyemo et al, 2020 ), by using different levels of relatedness between TRS and TS (Lorenz and Smith, 2015 ; Berro et al, 2019 ; Roth et al, 2020 ) or by using other alternatives algorithms to CD-mean and PEV-mean such as different design matrix algorithm (Akdemir and Isidro-Sánchez, 2019 ), estimated theoretical accuracy (EthAcc) (Mangin et al, 2019 ), upper bound reliability (Yu et al, 2020 ), or the Fast and Unique Representative Subset Selection (FURS) (Guo et al, 2014 ). A criterion that is derived directly from Pearson's correlation between GEBVs and phenotypic values of the TS derived from the GBLUP model showed higher predictive ability than CD and PEV (Ou and Liao, 2019 ).…”

TrainSel: An R Package for Selection of Training Populations

“…While the usefulness of optimal training set (TRS) in GS is clearly supported by the literature (Rincent et al, 2012;Akdemir et al, 2015;Isidro et al, 2015;Lorenz and Smith, 2015;He et al, 2016;Cericola et al, 2017;Neyhart et al, 2017;Norman et al, 2018;Guo et al, 2019;Mangin et al, 2019;de Bem Oliveira et al, 2020;Olatoye et al, 2020;Yu et al, 2020;Kadam et al, 2021), the flexible and efficient software tools for implementing them have been limited. Indeed, only a few software tools such as STPGA (Akdemir, 2017) and TSDFGS (Ou and Liao, 2019) are available for public use.…”