Dataset of Duroc×Erhualian F₂ pig population

<b>Background:</b>The development of multi-omics has increased the likelihood of further improving genomic prediction (GP) of complex traits. Gene expression data can directly reflect the genotype effect, and thus they are widely used for GP. Generally, the gene expression data are integrated into multiple random effect model as independent data layers or used to replace genotype data for genomic prediction. In this study, we integrated pedigree, genotype, and gene expression data into single-step method and investigated the effects of this integration on prediction accuracy.<b>Results: </b>The single-step method integrating genotype and gene expression data effectively improved genomic prediction accuracy of three complex traits in the Drosophila melanogaster genetic reference panel (DGRP) dataset. In addition, single-step method also improved the prediction accuracy of more than 90% of the 54 traits in Duroc×Erhualian F2pig population dataset. On average, the prediction accuracy of the single-step method integrating gene expression data was 27.0% and 9.5% higher than that of the pedigree-based best linear unbiased prediction (ABLUP) and genome-based best linear unbiased prediction (GBLUP), when the weighting factor (w) was set as 0, and it was 4.3% higher than that of the single-step best linear unbiased prediction (ssBLUP) under different wvalues.<b>Conclusions:</b>Overall, the analyses of two datasets confirmed that integration of gene expression data into single-step method could effectively improve genomic prediction accuracy. Our findings enrich the application of multi-omics data to genomic prediction and provide valuable reference for integrating multi-omics data into genetic evaluation model, which will contribute to genetic improvement.

研究背景：多组学（multi-omics）技术的发展提升了进一步优化复杂性状基因组预测（genomic prediction, GP）的可能性。基因表达数据可直接反映基因型效应，因此被广泛应用于基因组预测。通常，基因表达数据会作为独立数据层整合入多随机效应模型，或替代基因型数据用于基因组预测。本研究将系谱、基因型与基因表达数据整合至单步（single-step）方法中，并探究该整合策略对预测精度的影响。研究结果：将基因型与基因表达数据整合的单步方法，可有效提升黑腹果蝇遗传参考面板（Drosophila melanogaster genetic reference panel, DGRP）数据集内3种复杂性状的基因组预测精度。此外，该单步方法还可提升杜洛克×二花脸F₂猪群体数据集54个性状中超90%性状的预测精度。当加权因子（weighting factor, w）设为0时，整合基因表达数据的单步方法的预测精度平均较基于系谱的最佳线性无偏预测（ABLUP）与基于基因组的最佳线性无偏预测（GBLUP）分别提升27.0%与9.5%；在不同加权因子取值下，其预测精度较单步最佳线性无偏预测（ssBLUP）平均提升4.3%。研究结论：综上，两项数据集的分析证实，将基因表达数据整合至单步方法可有效提升基因组预测精度。本研究成果丰富了多组学数据在基因组预测中的应用，为将多组学数据整合至遗传评估模型提供了重要参考，将助力遗传改良工作。