(549c) Looking Beyond GWAS: Identifying Functional Roles of SNPs Using Metabolic Networks in Arabidopsis and Populus

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies (GWAS) identify the genetic background behind a trait by examining the associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are now common, GWAS often lacks the power required to uncover the relatively small effect sizes by many loci conferred by most genetic variants. An association between a genetic variant at a locus and a trait is also not directly informative with respect to the mechanisms that govern the variant being associated with the phenotype, making biological interpretation of the results a challenge. Here, we propose a complementary analysis (‘SNPeffect’) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect was used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of activating and inactivating SNPs present in the enzyme-coding regions of the genotypes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, first for a perennial woody tree. As expected, our results indicate that plant growth is a complex polygenic trait governed by carbon and energy partitioning. Growth-affecting SNPs were found to be primarily in amino-acid metabolism, glycolysis, TCA cycle, and energy metabolism. Faster-growing Arabidopsis genotypes were predicted to have higher fluxes through the protein metabolism pathways and employ the more energy efficient purine salvage pathway as opposed to de novo purine biosynthesis for generating AMP and GMP. In poplar, growth-determining SNPs with an upregulating role were found in genes belonging to cellulose and lignin biosynthesis, amino acid metabolism, and energy metabolism. This is in line with breeding strategies that target pathways governing carbon and energy partition. SNPeffect also identified putative growth-associated genes in both the species, distributed among pathways such as glycolysis, folate metabolism, and pyrimidine metabolism. These can serve as candidate genes for future studies.