Gene expression predictions and networks in natural populations supports the omnigenic theory

BMC Genomics

Jean-Charles Leplé¹  Odile Rogier²  Leopoldo Sanchez²  Vincent Segura²  Marie-Claude Lesage-Descauses²  Aurélien Chateigner²  Véronique Jorge²  Ludivine Soubigou-Taconnat³  Véronique Brunaud³  Marie-Laure Martin-Magniette³  Christine Paysant-Le Roux³ 

[1] BIOGECO, INRAE, Univ. Bordeaux;BioForA, INRAE, ONF;Institute of Plant Sciences Paris-Saclay (IPS2), CNRS, INRAE, Université Paris-Sud, Université d’Evry, Université Paris-Saclay;

关键词: Core;    Peripheral;    Boruta;    Machine learning;    Populus nigra;   

DOI  :  10.1186/s12864-020-06809-2

来源: DOAJ

【 摘 要 】

Abstract Background 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. Results 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. Conclusion 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.

【 授权许可】

Unknown