Recent literature on the differential role of genes within networks distinguishes core from peripheral genes. If previous works have shown contrasting features between them, whether such categorization matters for phenotype prediction remains to be studied. We measured 17 phenotypic traits for 241 cloned genotypes from a Populus nigra collection, covering growth, phenology, chemical and physical properties. We also sequenced RNA for each genotype and built co-expression networks to define core and peripheral genes. We found that cores were more differentiated between populations than peripherals while being less variable, suggesting that they have been constrained through potentially divergent selection. We also showed that while cores were overrepresented in a subset of genes statistically selected for their capacity to predict the phenotypes (by Boruta algorithm), they did not systematically predict better than peripherals or even random genes. Our work is the first attempt to assess the importance of co-expression network connectivity in phenotype prediction. While highly connected core genes appear to be important, they do not bear enough information to systematically predict better quantitative traits than other gene sets.

中文翻译：

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自然种群中的基因表达预测和网络支持全基因学说。

关于网络内基因差异作用的最新文献将核心与外围基因区分开。如果先前的作品在它们之间表现出鲜明对比的特征，那么这种分类对于表型预测是否重要仍然有待研究。我们测量了黑杨集合中241个克隆基因型的17个表型性状，涵盖了生长，物候，化学和物理特性。我们还对每种基因型的RNA进行了测序，并建立了共表达网络以定义核心和外围基因。我们发现，核心之间的差异远大于外围地区，而变化程度较小，这表明核心已经通过可能不同的选择受到限制。我们还表明，虽然核心在统计预测其表型能力的基因子集中过分代表（通过Boruta算法），但它们并没有系统地预测外围基因甚至随机基因。我们的工作是评估共表达网络连通性在表型预测中的重要性的首次尝试。尽管高度连接的核心基因似乎很重要，但它们没有足够的信息来系统地预测比其他基因组更好的定量性状。