BioForA, INRAE, ONF, Orléans, France.
BIOGECO, INRAE, Univ. Bordeaux, Cestas, France.
BMC Genomics. 2020 Jun 22;21(1):416. doi: 10.1186/s12864-020-06809-2.
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.
最近关于网络中基因差异作用的文献区分了核心基因和外围基因。如果之前的研究已经表明它们之间存在相反的特征，那么这种分类对于表型预测是否重要仍有待研究。
我们测量了来自欧洲黑杨 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。我们还对每个基因型进行了 RNA 测序，并构建了共表达网络来定义核心和外围基因。我们发现核心基因在种群之间的分化程度高于外围基因，而变异性较小，这表明它们受到了潜在的分歧选择的限制。我们还表明，虽然核心基因在一组因预测表型能力而被统计选择的基因（通过 Boruta 算法）中所占比例过高，但它们的预测能力并不比外围基因甚至随机基因系统地更好。
我们的工作首次尝试评估共表达网络连接性在表型预测中的重要性。虽然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集更好的定量性状。
