Context Dependent Perturbation of Allelic Expression Imbalance Reveals Novel Candidate Therapeutic Targets for Metabolic diseases

Abstract

Not all alleles express equally. Allelic imbalance (AI) denotes variations in gene expression based on the inherited versions, or alleles, of those genes. While AI has the potential to shed light on many complex diseases, its specific role in obesity and connection with gene regulation remains largely elusive. In this study, we delve into AI’s correlation with gene expression in obesity and its potential ramifications for susceptibility to associated health complications. By melding transcriptomic and epigenomic data through a colocalization approach, we discerned significant variations in the length and functional roles of both coding and noncoding differentially expressed genes across AI spectrums. Further, our examination unveiled DNaseI hypersensitive sites in close association with genetic markers identified in large-scale association studies (GWAS-identified SNPs) and polygenic risk variants, emphasizing the importance of length-dependent regulation and allelic interactions. These discoveries not only expand our understanding of the genetic underpinnings of obesity but also highlight new paths for personalized diagnostics and treatments. Our findings robustly advocate for intensified research for integration of rare pathogenic variants into the intricate interplay between genes and the environment.