State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
Key Laboratory of National Forestry and Grassland Administration on Plant Ex situ Conservation, Beijing Floriculture Engineering Technology Research Centre, Beijing Botanical Garden, Beijing, 100093, China.
BMC Genomics. 2024 Jan 2;25(1):11. doi: 10.1186/s12864-023-09891-4.
Genomic dissection of genetic effects on desirable traits and the subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additive and non-additive effects (dominance, imprinting and epistasis) were evaluated for nine valuable traits related to growth, wood physical and chemical properties, and competitive ability using three pedigree-based and four Genomics-based Best Linear Unbiased Predictions (GBLUP) models and used to determine the genetic model. The predictive ability (PA) of two genomic prediction methods, GBLUP and Reproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classified into two types based on different quantitative genetic architectures: for type I, including wood chemical properties and Pilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive ability and acoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominance and imprinting showed low to moderate contributions (< 36.26%). GBLUP was more suitable for traits of type I (PAs = 0.37-0.39 vs. 0.14-0.25), and RKHS was more suitable for traits of type II (PAs = 0.23-0.37 vs. 0.07-0.23). Non-additive effects make no meaningful contribution to the enhancement of PA of GBLUP method for all traits. These findings enhance our current understanding of the architecture of quantitative traits and lay the foundation for the development of genomic selection strategies in Japanese larch.
对遗传效应进行基因组解析，以获得理想性状，并随后利用基因组选择，这为加速林木树种的遗传改良速度带来了巨大的希望。在这项研究中，在试验点对 66 个日本落叶松（Larix kaempferi (Lam.) Carrière）开放授粉家系的 661 株后代树木进行了采样。利用基于 3 个系谱和 4 个基因组最佳线性无偏预测（GBLUP）模型，评估了 9 个与生长、木材物理和化学性质以及竞争能力相关的有价值性状的加性和非加性效应（显性、印迹和上位性）的贡献，并用于确定遗传模型。比较了两种基因组预测方法（GBLUP 和复制核希尔伯特空间（RKHS））的预测能力（PA）。根据不同的数量遗传结构，可将这些性状分为两类：对于第 I 类，包括木材化学性质和 Pilodyn 穿透性，加性效应是变异的主要来源（38.20-67.46%）；对于第 II 类，包括生长、竞争能力和声波速度，上位性起着重要作用（50.76-91.26%）。显性和印迹的作用较小（<36.26%）。GBLUP 更适合第 I 类性状（PA=0.37-0.39 与 0.14-0.25），而 RKHS 更适合第 II 类性状（PA=0.23-0.37 与 0.07-0.23）。非加性效应对提高 GBLUP 方法的 PA 没有明显的贡献。这些发现增强了我们对数量性状结构的理解，并为日本落叶松基因组选择策略的发展奠定了基础。
