Session: Parallel session 8 - Plant and Food OMICS

Integration of phenomic, proteomic, and genomic data into a multi-scale network unravels missing heritability for maize response to water deficit

Mélisande BLEIN-NICOLAS¹, Yacine DJABALI^1,2,3, Renaud RINCENT¹, Marie-Laure MARTIN^2,3,4

¹Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette, France
²Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
³Université de Paris Cité, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
⁴Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, Palaiseau, France

The evolution of maize yields under water deficit conditions is of particular concern in the context of climate change and human population growth. However, the multiplicity and versatility of drought-response mechanisms make the design of new drought-tolerant varieties a complex task that would greatly benefit from a better understanding of the genotype-phenotype relationship. To provide novel insight into this relationship, we aimed to build a multi-scale network linking genetic loci, proteins, and drought-responsive phenotypic traits, that best explained the variance observed for phenotypic traits.

To address this issue, we implemented an original systems biology method combining genome-wide association study (GWAS), network inference, and statistical modeling on a multi-omic dataset, including phenotypic, proteomic, and genomic data acquired from 254 maize hybrids grown under well-watered (WW) and water deficit (WD) conditions. This dataset was supplemented with plasticity indices calculated as the WD/WW log ratios for protein abundances and WD/WW ratios for phenotypic traits.

Using our method, we were able to build a multi-scale network that increased the explained portion of phenotypic variance by 20 points as compared to a classical GWAS (84% vs. 65%). The proteins in the network were involved in signaling, protein folding, and oxidation-reduction processes. Several of the loci involved in the network were located in genomic regions detected only by the protein analysis and strongly contributed to the variance of the phenotypic traits. These genomic regions provide a list of candidate genes, several of which can physically interact with the proteins involved in the network.

Overall, our results show that multi-omics data integration can be an efficient way to capture missing heritability for complex phenotypic traits and identify new candidate genes related to maize drought response.