Table_1_Integrating Gene Expression Data Into Genomic Prediction.pdf

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 Drosophila melanogaster 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（Gene expression profiles）或许可为育种值与表型的预测提供极具价值的信息。本研究以185株黑腹果蝇（Drosophila melanogaster）近交系为研究材料，针对雌雄两性共9个性状，探究了转录组数据（transcriptome data）在表型预测中的应用效能。我们通过GTBLUP与GRBLUP两种方法，将转录组数据整合至基因组预测框架中，两种方法均结合了单核苷酸多态性（single nucleotide polymorphisms, SNPs）与转录组数据。具体而言，研究中利用基因型数据构建通用加性基因组关系矩阵，该矩阵既可单独用于基因组最佳线性无偏预测（genomic best linear unbiased prediction, GBLUP），也可与基于转录组的线性核（对应GTBLUP方法）或基于转录组的高斯核（对应GRBLUP方法）共同纳入线性混合模型（linear mixed model）。我们分析了各模型的预测能力，并探讨了面向多组学时代的“组学增强型广义遗传力”概念。就绝大多数性状而言，GRBLUP与GBLUP的预测能力相近，但GRBLUP可解释更多的表型方差。仅在1个性状——雌性个体对丁酸乙酯（Ethyl Butyrate）的嗅觉感知——中，GRBLUP的预测能力（0.23）显著高于GBLUP的预测能力（0.21）。本研究结果表明，若能实现转录组数据的大规模整合，将有望提升基因组预测的整体效能。