Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine

Abstract

Background Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. Results MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.

Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa

“…If a regression coefficient is equal to 1, there is no bias. A regression coefficient greater or smaller than 1 indicates inflated or deflated predictions, respectively [ 48 ]. The training and testing populations were split randomly into 80% and 20% genotypes (5-fold cross-validation).…”

Genomic prediction of resistance to Hymenoscyphus fraxineus in common ash (Fraxinus excelsior L.) populations

“…Genome‐wide association studies (GWAS) are a robust method for identifying potential causal genes or genomic regions that are associated with plant phenotypic variation (Ingvarsson & Street, 2011; Kruglyak, 2008). Several studies have successfully identified single‐nucleotide polymorphisms (SNPs) that are associated with resistances to abiotic and biotic stresses, including drought and disease resistance, in various plant species (Beaulieu et al., 2020; Bouvet et al., 2020; Cappa et al., 2022; Laverdière et al., 2022; Lenz et al., 2020; Muchero et al., 2018; Mukrimin et al., 2018; Soro et al., 2022; Stocks et al., 2019; Vázquez‐Lobo et al., 2017; Wang et al., 2019; Westbrook et al., 2020). The utilization of GWAS results can expedite the conventional breeding process and enable genotype‐based selection (Neale & Kremer, 2011).…”

Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce

“…In GS, a population in which individuals are both phenotyped and genotyped is used to train a model, which is then used to predict the genetic merit of young genotyped, but unphenotyped, offspring. Selections from a large number of genotyped candidates should translate into higher selection intensities, while preserving genetic diversity in improved varieties, or allowing for efficient multi-trait selection strategies ( Bouvet et al., 2020 ; Lenz et al., 2020b ; Bousquet et al., 2021 ; Cappa et al., 2022 ).…”

“…Changing environmental conditions and forest product markets are putting pressure on tree breeding programs to rapidly deliver adapted planting stock with superior end-use quality attributes. Hence, accelerating breeding for improved reforestation material is becoming essential for traits related to wood quality ( Hassegawa et al., 2020 ), biotic stress resistance ( Beaulieu et al., 2020 ; Lenz et al., 2020b ; Westbrook et al., 2020 ; Mphahlele et al., 2021 ; Gamal El-Dien et al., 2022 ), and resilience to abiotic stress such as drought ( Bouvet et al., 2020 ; Depardieu et al., 2020 ; Cappa et al., 2022 ; Laverdière et al., 2022 ; Soro et al., 2022 ). GS can hence play an active role in climate change mitigation strategies and provide more flexibility to tree breeders.…”