The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ~200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

从遗传和基因组数据中准确预测复杂性状表型的能力对于实施个性化医疗和精准农业至关重要；然而，目前大多数复杂性状的预测准确性较低。在这里，我们使用了黑腹果蝇遗传参考面板 (DGRP) 约 200 个自交系中的全基因组序列、深度 RNA 测序和三个数量性状的高质量表型数据，比较了三个数量性状的基因表达和基因型的预测准确性。复杂的特征。我们发现表达水平（女性和男性分别为r = 0.28 和 0.38）比基因型（女性和男性分别为r = 0.07 和 0.15）提供了更高的抗饥饿性预测准确度，对寒冷昏迷恢复的预测准确度相似（对于模型和性别均无效），并且惊吓反应的预测准确性较低（女性和男性基因型分别为r = 0.15 和 0.14；女性和男性转录本分别为r = 0.12 和 0.11）。包括基因型和表达水平的模型并未优于最佳的单组分模型。然而，当我们将基因本体论（GO）类别作为基因组变异和转录本的附加信息层时，所有三个性状的准确性都显着提高。我们发现这三个特征中的每一个都具有强烈的预测性 GO 术语，其中一些具有清晰合理的生物学解释。例如，对于女性的饥饿抵抗力，GO：0033500（转录本r = 0.39）和 GO：0032870（转录本r = 0.40）与碳水化合物稳态和细胞对激素刺激的反应（包括胰岛素受体信号通路）有关）， 分别。总之，这项研究表明，整合不同来源的信息提高了预测准确性，并有助于阐明三种果蝇复杂表型的遗传结构。