红锥生长性状的全基因组选择与优良子代早期评选  

Genomic Selection for Growth Traits and Early Selection of Superior Progeny in Castanopsis hystrix

作　　者：魏瑞研 张卫华[2] 徐放[2] 林元震 Wei Ruiyan;Zhang Weihua;Xu Fang;Lin Yuanzhen(College of Forestry and Landscape Architecture,South China Agricultural University,Guangzhou 510642;Guangdong Academy of Forestry,Guangzhou 510642)

机构地区：[1]华南农业大学林学与风景园林学院,广州510642 [2]广东省林业科学研究院,广州510520

出　　处：《林业科学》2024年第12期83-91,共9页Scientia Silvae Sinicae

基　　金：广东省重点领域研发计划项目(2020B020215002);国家重点研发计划项目(2016YFD0600606)。

摘　　要：【目的】开展红锥全基因组选择(genomic selection,GS)研究和优良子代早期评选,对快速选育优良新品种具有重要意义。【方法】以红锥全分布区内226个无性系和覆盖23个半同胞家系的479株子代为试验材料,通过高通量重测序获取基因型分型数据,针对生长性状开展GS研究,采用5折交叉验证法评估5种GS模型和10种SNPs数量对GS预测准确性的影响;基于GS预测模型估计候选群体的基因组估计育种值(genomic estimated breeding value,GEBV),采用布雷金多性状评定法进行优良子代个体的早期综合评选。【结果】训练群体中,胸径性状的变异系数为22.73%,大于树高(17.13%),它们之间呈极显著正相关(r=0.63,P<0.001);树高和胸径在种源间均存在极显著差异(P<0.001)。参考群体和候选群体经重测序分型、数据质控后,每个个体得到790877个SNPs,这些SNPs较均匀地分布在红锥基因组上。基于基因组最佳无偏估计(genomic best linear unbiased prediction,GBLUP)模型,训练群体树高和胸径的广义遗传力分别为0.52和0.48,不同标记SNPs数量对遗传力估计影响很小。在五种GS模型中,树高GS预测准确性最高的是Bayes B模型(0.21),胸径则是Bayes ridge regression(BRR)模型(0.06);贝叶斯模型预测准确性要优于GBLUP模型,但它们之间差异不显著。对于10种SNPs数量,在0.5~5 K阶段,GS预测准确性先升高,随后达到平台期。树高性状调用Bayes B模型,胸径性状调用BRR模型,对红锥候选群体各性状GEBV使用布雷金多性状综合评定法评选出15株优良子代个体,树高、胸径GEBV均值分别比参考群体均值提高了7.0%和5.2%。这些子代个体具体为4438、4468、4407、4388、4052、4461、4390、4389、4410、4399、4460、4467、4212、4044、4459和4020,主要来自F5和F29两个家系。【结论】本研究建立了红锥的GS预测模型,并依据候选群体GEBV进行了优良个体的早期评选,为后续红锥优良新品种的快速�【Objective】This study aims to perform the genome selection(GS)for growth traits and the early selection of superior progeny in Castanopsis hystrix,which has great significance for rapid breeding of new superior varieties of C.hystrix.【Method】In this study,226 clones in the main distribution area and 479 progenies over 23 half-sib families were used as experimental materials.Genotyping datasets were obtained by high-throughput re-sequencing technology,and GS studies were conducted on the growth traits.The effects of 5 different GS models and 10 different numbers of SNPs on GS prediction accuracy were assessed using 5-fold cross-validation.Then,the genomic estimated breeding values(GEBV)of candidate populations were estimated based on the GS model,and early selection of superior progeny individuals was implemented by the Breggin multi-trait evaluation method.【Result】The coefficient of variation of DBH trait in the training population was 22.73%,and greater than that of height trait(17.13%),and there was a significantly positive correlation between them(r=0.63,P<0.001).There were significant differences in both height and DBH among provenances(P<0.001).After re-sequencing and data quality control,790877 SNPs were obtained for each individual in the reference population and candidate population,and these SNPs were uniformly distributed in the C.hystrix genome.Based on the Genomic Best Linear Unbiased Prediction(GBLUP)model,the broadsense heritability of height and DBH in the training population was 0.52 and 0.48,respectively,and the number of SNPs with different markers had little effect on the heritability estimation.Among the five GS models,Bayes B model had the highest GS prediction accuracy(0.21)for height,while Bayes ridge regression(BRR)model had the highest GS prediction accuracy(0.06)for DBH.The prediction accuracy of Bayes models was higher than that of GBLUP model,but the difference was not significant.For 10 different numbers of SNPs,the prediction accuracy of GS first increased during 0.5-5 K

关 键 词：红锥 基因组选择 生长性状 早期选择 SNP 

分 类 号：S722.5[农业科学—林木遗传育种]