Grant: Project Grant

A central goal in biology is to explain how variations in genomic DNA sequences cause diversity in animal characteristic or "traits", such as weight, height, or susceptibility to diseases. Correlating genomic mutations in individuals, specifically "single nucleotide polymorphisms" or SNPs, with their traits showed that many of such mutations across the genome affect traits. However, the molecular mechanism and causal origin of why many SNPs affect traits remain a major challenge. We propose a solution to this challenge, and provide evidence that it works, based on our recent studies showing that the effects of mutations to the organismal trait can be deciphered by projecting the mutation's effect to how proteins inside the cell interact with each other. Using yeast as a model, the method we developed is called protein-protein interaction (PPI) quantitative trait locus ("piQTL") mapping. In Aim 1, we will use piQTL to measure a spectrum of genome-to-environment relationships to establish patterns of interactions between an individual's SNPs and their response to a variety of environmental stresses. In Aim 2, we will study how these SNPs' effects trickle up through a hierarchy of increasingly integrated signals, from RNA encoded by genes to the proteins encoded by RNAs, to the protein-protein interactions that orchestrate all cellular processes. In Aim 3, we develop a practical way to perform piQTL mapping on all protein-protein interactions in a cell. There are now >400K distinct regions in the genome that are associated with >5,000 traits in humans, but there is no comprehensive way to understand how SNPs influence these traits. Our approach provides proof-of-principle and a roadmap for a new method of mutation-to-phenotype mapping that could reveal the functional and biochemical drivers of complex traits in humans, including those associated with diseases such as cancers and neurodegenerative disorders.