Pan-genomic open reading frames (ORFs) potentially carry protein-coding gene or coding variant information in a population. In this study, we suggest that pan-genomic ORFs are promising to be utilized in estimation of heritability and genomic prediction. A Saccharomyces cerevisiae dataset with whole-genome SNPs, pan-genomic ORFs, and the copy numbers of those ORFs is used to test the effectiveness of ORF data as a predictor in three prediction models for 35 traits. Our results show that the ORF-based heritability can capture more genetic effects than SNP-based heritability for all traits. Compared to SNP-based genomic prediction (GBLUP), pan-genomic ORF-based genomic prediction (OBLUP) is distinctly more accurate for all traits, and the predictive abilities on average are more than doubled across all traits. For four traits, the copy number of ORF-based prediction(CBLUP) is more accurate than OBLUP. When using different numbers of isolates in training sets in ORF-based prediction, the predictive abilities for all traits increased as more isolates are added in the training sets, suggesting that with very large training sets the prediction accuracy will be in the range of the square root of the heritability. We conclude that pan-genomic ORFs have the potential to be a supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction.

泛基因组开放阅读框（ORF）可能在人群中携带蛋白质编码基因或编码变异信息。在这项研究中，我们建议泛基因组ORF有望用于遗传力的估计和基因组预测。一个酿酒酵母具有全基因组SNP，全基因组ORF以及这些ORF的拷贝数的数据集，用于在35个性状的三个预测模型中测试ORF数据作为预测因子的有效性。我们的结果表明，对于所有性状，基于ORF的遗传力比基于SNP的遗传力能捕获更多的遗传效应。与基于SNP的基因组预测（GBLUP）相比，基于全基因组ORF的基因组预测（OBLUP）对所有性状的准确性明显更高，并且平均预测能力在所有性状上均增加了一倍以上。对于四个特征，基于ORF的预测（CBLUP）的拷贝数比OBLUP更为精确。在基于ORF的预测中，在训练集中使用不同数量的分离株时，随着在训练集中添加更多分离株，对所有性状的预测能力均会提高，这表明训练集非常大时，预测精度将在遗传力的平方根范围内。我们得出的结论是，在评估遗传力和基因组预测时，泛基因组ORF可能是单核苷酸多态性的补充。