基于基因组选择提升尾叶桉选择效率研究

Genomic selection can increase the efficiency of selection and genetic gain per generation in tree breeding, which is valuable in application. In this research, we will use half-sib families from natural population and full-sib families as experiment materials, develop SNPs covering the whole genomic regions by high-throughout sequencing, obtain precise phenotypic data of growth, wood density and wood component. Based on these genetic and phenotypic data, we calculate the genomic estimate breeding values(GEBV) using least square, best linear unbiased prediction，Genomic-BLUP，Bayes LASSO，Bayes A，Bayes B methods. The accuracy of statistical methods/material component/marker density combinations were tested by cross-validation to select the best genomic selection system of Eucalyptus urophylla. In this research, we will apply the genomic selection method in the breeding of E. urophylla. Aiming to prove the important economic traits of growth, wood density and wood component, we intend to improve the selection efficiency and provide robust reference for other tree species breeding.

基因组选择研究对于提升林木育种选择精度和提升遗传增益具有重要的理论意义和应用价值。本项目以尾叶桉自然群体内单株和杂交子代作为研究材料，利用高通量测序技术获取覆盖尾叶桉全基因组的大量分子标记，并利用精准表型测定获取参试样本的生长量和木材组成等表型数据，以此为基础，利用最小二乘法、最佳线性无偏估计（BLUP）、Genomic-BLUP、Bayes LASSO，Bayes A和Bayes B等计算方法估算各标记的基因组估计育种值（GEBV），并对研究中不同的试验群体组成和不同的分子标记数量的全基因组选择准确性进行评价，最终构建最优尾叶桉全基因组选择体系。本项目将基因组选择方法首次引入桉树的育种研究，针对尾叶桉生长、木材密度和木材成分等重要经济表型，进行更高效的选育，为尾叶桉的选育提供新的模式，并为其他树种的遗传选育提供重要的参照。