Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce
type: Dataset
distribution: Landing page for the dataset
description: Genomic (SNP data) and phenotype information (measurements of spring frost tolerance, bud burst and height) of the manuscript "Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce" are included in attached files. See README for more details about the files. Abstract Background Warming spring temperatures are expected to increase the risk of frost damage to emerging buds of Norway spruce by advancing their spring phenology and increasing the likelihood of damaging stochastic frost event. Current methods for assessing spring frost tolerance rely on field phenotyping after frost, which limits the ability to measure basal frost tolerance due to the sporadic nature of frost events, thereby limiting large-scale sampling. Quantitative phenotyping data are essential for genomic selection (GS) approaches that combine genomic information with phenotypic data to predict complex traits. Results In this study, we introduce a field sampling method to assess basal frost tolerance in Norway spruce using an ion leakage assay, and evaluate the effectiveness of multivariate GS models for predicting spring frost tolerance. We phenotyped 38 families across three consecutive springs at a predetermined early bud-burst stage to assess frost damage. The narrow-sense heritability of spring frost tolerance was estimated at 0.23. A strong genetic correlation between bud burst and frost damage (-0.63) improved prediction accuracy when using a multivariate model that included bud burst as an assisting trait. Additionally, we found that optimizing prediction models by increasing the number of clones in the training set further improved the accuracy of frost tolerance predictions. The observed genetic correlation between basal frost tolerance and bud burst suggests that early-flushing genotypes exhibit higher tolerance to spring frost, providing a new trait to consider in the Norway spruce breeding programs against frost damage. Conclusion The nove…
modified: 2025-04-11T00:00:00Z
issued: 2025-01-17T00:00:00Z
publisher: Swedish University of Agricultural Sciences
theme: HEAL
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Tuuli Aro
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Zhi-Quiang Chen
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Mari Suontama
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Biyue Tan
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Henrik Hallingbäck
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Harry X Wu
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Vaughan Hurry
    type: Agent
    type: PrivateIndividual(s)
keyword: Tree improvement (incl. selection and breeding)

Landing page for the dataset
type: Distribution
title: Landningssida för datasetet @sv
access URL: Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce
availability: stable

Swedish University of Agricultural Sciences
type: Agent
name: Sveriges lantbruksuniversitet @sv
type: Academia-ScientificOrganisation
source: 02yy8x990