Identifying functional genetic variation in humans requires sifting through hundreds of thousands of individual variants and linking them to the trait of interest. We often do not know whether a gene is functional in a tissue or specific cell. Machine-learning models have become valuable for such endeavors. Somepalli et al. developed a model they call FUGUE, which they used to map the tissue-specific expression of human disease–associated genes and their protein context and interactions. Interestingly, FUGUE revealed that tissue-relevant genes cluster on the genome within topologically associated domains. The authors supply prioritized gene lists for 30 human tissues for genes associated with heart disease, Alzheimer’s disease, cancer, and development.
PLoS Comput. Biol. 17, e1009194 (2021).