Presently, multi-omics data (e.g., genomics, transcriptomics, proteomics, and metabolomics) are available to improve genomic predictors. Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level. Therefore, using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction. In this study, simultaneously using whole-genome sequencing (WGS) and gene expression level data, four strategies for single-nucleotide polymorphism (SNP) preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel. Using genomic best linear unbiased prediction (GBLUP) with complete WGS data, the prediction accuracies were 0.208 ± 0.020 (0.181 ± 0.022) for the startle response and 0.272 ± 0.017 (0.307 ± 0.015) for starvation resistance in the female (male) lines. Compared with GBLUP using complete WGS data, both GBLUP and the genomic feature BLUP (GFBLUP) did not improve the prediction accuracy using SNPs preselected from complete WGS data based on the results of genome-wide association studies (GWASs) or transcriptome-wide association studies (TWASs). Furthermore, by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus (eQTL) mapping of all genes, only the startle response had greater accuracy than GBLUP with the complete WGS data. The best accuracy values in the female and male lines were 0.243 ± 0.020 and 0.220 ± 0.022, respectively. Importantly, by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS, both GBLUP and GFBLUP resulted in great accuracy and small bias of genomic prediction. Compared with the GBLUP using complete WGS data, the best accuracy values represented increases of 60.66% and 39.09% for the starvation resistance and 27.40% and 35.36% for startle response in the female and male lines, respectively. Overall, multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction. The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction.

中文翻译：

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多组学数据辅助的基因组特征标记预选可提高基因组预测的准确性

当前，多组学数据（例如，基因组学，转录组学，蛋白质组学和代谢组学）可用于改善基因组预测因子。Omics数据不仅为基因组预测提供了新的数据层，而且在有机表型和基因组变异之间架起了一座桥梁，而桥梁无法在基因组序列水平上轻易捕获。因此，使用多组学数据选择特征标记是提高基因组预测准确性的可行策略。在这项研究中，同时使用全基因组测序（WGS）和基因表达水平数据，在果蝇遗传参考小组中研究了四种单核苷酸多态性（SNP）预选策略，以进行基因组预测。使用具有完整WGS数据的基因组最佳线性无偏预测（GBLUP），预测精度为0.208±0.020（0.181±0。022）对于雌性（雄性）系的惊吓反应和0.272±0.017（0.307±0.015）的抗饥饿性。与使用完整WGS数据的GBLUP相比，GBLUP和基因组特征BLUP（GFBLUP）均不能提高基于全基因组关联研究（GWASs）或转录组关联研究的结果从完整WGS数据中预先选择的SNP的预测准确性（TWAS）。此外，通过使用基于所有基因的表达定量性状基因座（eQTL）定位结果从WGS数据中预先选择的SNP，只有完整的WGS数据的惊吓反应比GBLUP的准确性更高。雌性和雄性品系的最佳准确度值分别为0.243±0.020和0.220±0.022。重要的，通过使用基于TWAS重要基因的eQTL定位结果进行预选的SNP，GBLUP和GFBLUP均导致了较高的准确性，并且基因组预测的偏差较小。与使用完整的WGS数据的GBLUP相比，雌性和雄性品系的最佳抗饥饿性分别为60.66％和39.09％，惊吓反应分别为27.40％和35.36％。总体而言，多组学数据可以协助基因组特征预选并改善基因组预测的性能。从这项研究中获得的新知识将丰富多组学在基因组预测中的应用。雌性和雄性品系的抗饥饿性分别为09％，惊吓反应的27.40％和35.36％。总体而言，多组学数据可以协助基因组特征预选并改善基因组预测的性能。从这项研究中获得的新知识将丰富多组学在基因组预测中的应用。雌性和雄性品系的抗饥饿性分别为09％和惊吓反应的27.40％和35.36％。总体而言，多组学数据可以协助基因组特征预选并改善基因组预测的性能。从这项研究中获得的新知识将丰富多组学在基因组预测中的应用。