Unraveling variant-to-function relationships by integrating SNP-specific gene expression networks and metabolic models

Abstract

ABSTRACTElucidating genotype-phenotype or variant-to-function relationships remains a challenge in genetics. For quantitative traits, causal SNPs act either additively or epistatically, resulting in complex interactions that are difficult to dissect molecularly. Here, we developed gene co-expression networks and genome-scale metabolic models for all combinations of causal SNPs of yeast sporulation efficiency, a quantitative trait, to determine how genetic interactions drive phenotypic variation. Analysis of expression networks identified SNP-SNP interaction-dependent changes in the connectivity of crucial metabolic regulators. These regulators revealed how some SNP combinations added to higher phenotypic value. Using IMAT and Localgini thresholding algorithm, we integrated the gene expression data of all SNP combinations in the Yeast genome-scale metabolic model, resulting in 16 SNP-specific metabolic models. Genome-scale differential flux analysis was used to identify differentially activated metabolic reactions across multiple metabolic pathways in each SNP-specific model. This analysis revealed causal reactions in six major metabolic pathways explaining the observed sporulation efficiency differences. Finally, the differential regulation of the pentose phosphate pathway in specific SNP combinations suggested autophagy as a pentose phosphate pathway-dependent compensatory mechanism for increasing sporulation efficiency. Our study presents a modelling framework that has the potential to discover causal metabolic pathways or reactions regulated by combinations of SNPs, providing insights into Genome-wide association studies (GWAS) variants of metabolic diseases.AUTHOR SUMMARYUnderstanding how genetic changes influence traits that vary in degree, such as human height, can be difficult to identify. In this study with yeast, we have developed an approach to understand how different genetic variations work together to affect a specific trait: sporulation efficiency. We used a network of genes that are co-expressed, or turned on and off together, to identify how different genetic variations interact with one another. We found that certain combinations of variations can make key genes in metabolic pathways more or less active, which can ultimately affect the overall efficiency of sporulation. To further understand how these genetic changes affect metabolism, we used a genome-scale model to simulate the metabolic pathways in yeast. By comparing the metabolic pathways of different genetic combinations, we identified specific reactions that were responsible for the observed changes in sporulation efficiency. Overall, this study provides a new way to understand how variations in our genes can affect our metabolism and could be particularly useful for identifying the genetic causes of metabolic diseases in humans.