Li Zhengcao, Gao Ning, Martini Johannes W R, Simianer Henner
Animal Breeding and Genetics Group, Department of Animal Sciences, Center for Integrated Breeding Research, University of Göttingen, Göttingen, Germany.
State Key Laboratory of Biocontrol, Guangzhou Higher Education Mega Center, School of Life Science, Sun Yat-sen University, Guangzhou, China.
Front Genet. 2019 Feb 25;10:126. doi: 10.3389/fgene.2019.00126. eCollection 2019.
Gene expression profiles potentially hold valuable information for the prediction of breeding values and phenotypes. In this study, the utility of transcriptome data for phenotype prediction was tested with 185 inbred lines of for nine traits in two sexes. We incorporated the transcriptome data into genomic prediction via two methods: GTBLUP and GRBLUP, both combining single nucleotide polymorphisms (SNPs) and transcriptome data. The genotypic data was used to construct the common additive genomic relationship, which was used in genomic best linear unbiased prediction (GBLUP) or jointly in a linear mixed model with a transcriptome-based linear kernel (GTBLUP), or with a transcriptome-based Gaussian kernel (GRBLUP). We studied the predictive ability of the models and discuss a concept of "omics-augmented broad sense heritability" for the multi-omics era. For most traits, GRBLUP and GBLUP provided similar predictive abilities, but GRBLUP explained more of the phenotypic variance. There was only one trait (olfactory perception to Ethyl Butyrate in females) in which the predictive ability of GRBLUP (0.23) was significantly higher than the predictive ability of GBLUP (0.21). Our results suggest that accounting for transcriptome data has the potential to improve genomic predictions if transcriptome data can be included on a larger scale.
基因表达谱可能为预测育种值和表型提供有价值的信息。在本研究中，利用185个自交系针对两个性别的九个性状测试了转录组数据用于表型预测的效用。我们通过两种方法将转录组数据纳入基因组预测：GTBLUP和GRBLUP，这两种方法都结合了单核苷酸多态性（SNP）和转录组数据。利用基因型数据构建共同的加性基因组关系，其用于基因组最佳线性无偏预测（GBLUP），或与基于转录组的线性核（GTBLUP）或基于转录组的高斯核（GRBLUP）一起用于线性混合模型。我们研究了模型的预测能力，并讨论了多组学时代的“组学增强广义遗传力”概念。对于大多数性状，GRBLUP和GBLUP提供了相似的预测能力，但GRBLUP解释了更多的表型变异。只有一个性状（雌性对丁酸乙酯的嗅觉感知），其中GRBLUP的预测能力（0.23）显著高于GBLUP的预测能力（0.21）。我们的结果表明，如果能够更大规模地纳入转录组数据，考虑转录组数据有可能改善基因组预测。
