Mapping phenotypic variance in complex traits to genetic and non-genetic components using molecular data

Genetics only explain a small proportion of phenotypic variance, with common diseases typically having 10%-30% heritability (Loh et al. 2017 Nature Genetics). This project aims to explain the remaining 70%-90% of variance using molecular data. Past efforts have attributed genetic variance to expression data (Yao et al. 2020 Nature Genetics) and different tissues (Amariuta et al. 2023 Nature Genetics); yet limited attention is paid to the non-genetic variance.  We aim to develop methods to provide an unbiased estimate of the environment variance in complex traits that are mediated through molecular traits. Specifically, we are interested in the proportion of non-genetic variance that are mediated by gene expression, protein level, and metabolomics. We will utilize large-scale proteomic and metabolomic data that are linked to electronic health records to validate the model and provide the molecular explanation for common complexity traits.