Tree Genetics and Molecular Breeding 2024, Vol.14, No.1, 12-21 http://genbreedpublisher.com/index.php/tgmb 16 wood property traits, particularly in major timber species such as Eucalyptus, poplar, and various coniferous trees (Table 1) (Du et al., 2018). Porth et al. (2013) conducted a genome-wide association study on 334 unrelated black cottonwood (Populus trichocarpa) individuals and discovered 141 significant single nucleotide polymorphisms (SNPs) associated with cell wall traits. Furthermore, Resende et al. (2017) applied regional heritability mapping (RHM) to 768 hybrid Eucalyptus trees, showing that complex traits in Eucalyptus are controlled by multiple rare-effect alleles. These studies revealed the potential of GWAS in elucidating wood composition and wood property traits. Another research direction is to explore the effects of rare alleles and major structural variations on GWAS. For example, Fahrenkrog et al. (2017) conducted a GWAS on wood composition traits in 391 unrelated eastern cottonwood (Populus deltoides) individuals and discovered multiple low-frequency SNPs associated with bioenergy traits. The applications of GWAS have also extended to coniferous tree species, despite their typically large genomes. For instance, Uchiyama et al. (2013) conducted a genome-wide association study on 367 unrelated Japanese cedar (Cryptomeria japonica D. Don) individuals and discovered six new QTLs associated with wood property traits and the number of male strobili. These studies demonstrate that GWAS is a powerful tool for unveiling the genetic basis of tree traits and providing valuable information for future molecular breeding. However, this field still faces challenges, including optimizing GWAS design, exploring the effects of rare alleles, and developing high-throughput phenotyping technologies. Table 1 Summary of Genome-wide Association Studies (GWAS) of wood property traits in main timber species (Adopted from Du et al., 2018) Phenotype Species Sample size No. of markers Method Reference Growth and wood properties Eucalyptus globulus 303 7 680 [Diversity Array Technology markers (DArT)] General linear model (GLM) and unified mixed model (UMM) Cappa et al., 2013 Wood density, stiffness, microfibril angle, and ring width Picea glauca 1694 7 434 (SNPs) Mixed linear model (MLM) Lamara et al., 2016 16 wood chemistry/ ultrastructure traits Populus trichocarpa 334 29 233 (SNPs) GLM Porth et al., 2013 Lignin percentage, Lignin S:G ratio, 5-carbon sugars, and 6-carbon sugars Populus deltoides 391 334 679 (consensus SNPs), 185 526 (Common SNPs), 76 804 (functional SNPs) Single-variant and multiple-variant associations on GLM Fahrenkrog et al., 2017 Basic wood density (BWD), bleached pulp, pulp yield (SPY), and pulp bleaching content Eucalyptus grandis × Eucalyptus urophylla 768 24 806 (SNPs) GWAS and regional heritability mapping Resende et al., 2017 17 wood-quality traits Norway spruce 517 178 101 (SNPs) Multilocus LASSO Penalized regression Baison et al., 2018 Seven wood properties Populus tomentosa 435 5 482 (InDels) MLM and Kempthorne model Gong et al., 2017 Note: Diversity Array Technology (DArT) markers (Adopted from Du et al., 2018) 4.2 Identification of genetic markers related to important tree traits One important application of Genome-wide Association Studies (GWAS) in tree breeding is the identification of genetic markers associated with economically and ecologically important traits. These traits include growth rate, wood quality, stress tolerance (such as drought resistance and pest resistance), among others. For example, Bai et
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