ArticleThe plant genome2022
Integrating genome-wide association mapping of additive and dominance genetic effects to improve genomic prediction accuracy in Eucalyptus.
Article in The plant genome, 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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Who cites it
17 citing papers in PubMed.
- Integrating multi-model GWAS prior information enhances genomic prediction of cold tolerance traits in Populus simonii.BMC plant biology · 2026Article
- Genomic prediction of wild-derived powdery mildew resistance for strawberry (Fragaria × ananassa) pre-breeding.Heredity · 2026Article
- Integrating GWAS-guided markers preselection with genomic selection enhances prediction of pulpwood-related traits in slash pine (Pinus elliottii Englem.).BMC plant biology · 2026Article
- Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation.Frontiers in plant science · 2026Article
- Genome-wide association study reveals genomic regions associated withFrontiers in plant science · 2026Article
- Quantifying genetic and genotypic gain gaps in Eucalyptus: the hidden cost of ignoring inbreeding and dominance.Heredity · 2025Article
- Genomic selection with GWAS-identified QTL markers enhances prediction accuracy for quantitative traits in poplar (Populus deltoides).Communications biology · 2025Article
- Increased genomic predictive ability in mango using GWAS-preselected variants and fixed-effect SNPs.Frontiers in plant science · 2025Article
- Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection inFrontiers in genetics · 2024Article
- Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Molecular breeding : new strategies in plant improvement · 2023Article
- Comparing DNA isolation methods for forest trees: quality, plastic footprint, and time-efficiency.Plant methods · 2023Article
- Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce.BMC genomics · 2023Article
- Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection.International journal of molecular sciences · 2022Review
- Genomic selection in tropical perennial crops and plantation trees: a review.Molecular breeding : new strategies in plant improvement · 2022Review
- Integrating genome-wide association mapping of additive and dominance genetic effects to improve genomic prediction accuracy in Eucalyptus.The plant genome · 2022Article
- Genetic dissection of natural variation in oilseed traits of camelina by whole-genome resequencing and QTL mapping.The plant genome · 2021Article
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2 authors.
Funding
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Abstract
Genome-wide association studies (GWAS) is a powerful and widely used approach to decipher the genetic control of complex traits. Still, a significant challenge for dissecting quantitative traits in forest trees is statistical power. This study uses a population consisting of 1,123 samples derived from two successive generations of crosses between Eucalyptus grandis (W. Hill) and E. urophylla (S.T. Blake). All samples have been phenotyped for growth and wood property traits and genotyped using the EuChip60K chip, yielding 37,832 informative single nucleotide polymorphisms (SNPs). We use multi-locus GWAS models to assess additive and dominance effects to identify markers associated with growth and wood property traits in the eucalypt hybrids. Additive and dominance association models identified 78 and 82 significant SNPs across all traits, respectively, which captured between 39 and 86% of the genomic-based heritability. We also used SNPs identified from the GWAS and SNPs using less stringent significance thresholds to evaluate predictive abilities in a genomic selection framework. Genomic selection models based on the top 1% SNPs captured a substantially greater proportion of the genetic variance of traits compared with when we used all SNPs for model training. The prediction ability of estimated breeding values improved significantly for all traits when using either the top 1% SNPs or SNPs identified using a relaxed p value threshold (p < 10
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