ArticleBMC genomics2022
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.
Article in BMC genomics, 2022. 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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17 citing papers in PubMed, 24 citations in OpenAlex.
- Research on multi-trait genome association study method based on Shannon information entropy.BMC bioinformatics · 2026Article
- Breeding for climate adaptation: genetic variation and genomic selection for drought response in Scots pine.BMC genomics · 2026Article
- A haplotype-layered GWAS identifies a multi-trait grain mold resistance hub on sorghum chromosome 5.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Bayesian networks and structural equation models reveal genetic causal relationships between productivity, defense, and climate-adaptability traits in interior lodgepole pine.G3 (Bethesda, Md.) · 2026Article
- Multi-Year Phenotypic Assessment and Genetic Selection in Progeny Trials ofPlants (Basel, Switzerland) · 2026Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- AI-Powered Plant Science: Transforming Forestry Monitoring, Disease Prediction, and Climate Adaptation.Plants (Basel, Switzerland) · 2025Review
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- Short- and Long-Term Growth Response to Multiple Drought Episodes: Evidence of Genetic Adaptation in a Conifer Species.Ecology and evolution · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Genomic and Epigenomic Influences on Resilience across Scales: Lessons from the Responses of Fish to Environmental Stressors.Integrative and comparative biology · 2024Review
- Genomic prediction of resistance toEvolutionary applications · 2024Article
- Enhancing genomic association studies in slash pine through close-range UAV-based morphological phenotyping.Forestry research · 2024Article
- Genomic selection in forest trees comes to life: unraveling its potential in an advanced four-generationFrontiers in plant science · 2024Article
- Identifying genetic variation associated with environmental gradients and drought-tolerance phenotypes in ponderosa pine.Ecology and evolution · 2023Article
- Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce.Frontiers in plant science · 2023Article
- Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa.Horticulture research · 2023Article
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15 authors at 5 institutions in 4 countries.
Funding
Abstract
backgroundGenomic 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.
resultsMT-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.
conclusionsThe 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.
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