NLM DIR Seminar Schedule

Abstract:

Deep learning has revolutionized genomics by enabling the extraction of complex patterns from large-scale biological datasets. We leveraged a CNN-based model to characterize the impact of genetic variants on enhancer activity in the human genome, focusing on two key contexts that I will discuss in my talk.
First, I will discuss how we used our model to quantify the genome wide impact of regulatory variants on enhancer turnover which has driven phenotypic divergence during past speciation events and continues to facilitate environmental adaptation. Specifically, we explored how the fate of the cis-regulatory landscape changes in the face of accumulating variants by addressing key questions, such as, what fraction of the genome undergoes regulatory turnover due to accumulating variants, and, are certain genes or transcription factor binding sites more susceptible to regulatory changes than others? We investigated regulatory turnover across three evolutionary contexts: 1) recent evolutionary history – variants arising from human-chimpanzee substitutions, 2) modern population variation – genetic diversity observed in present-day human populations, and 3) mutational susceptibility – the effect of random mutations on enhancer activity. I will highlight key findings of our study which broadly support a model of regulatory innovation within cell type–specific enhancers with potential to accommodate adaptive changes while preserving the core regulatory network that is driven by cell ubiquitous evolutionarily constrained enhancers.
In the second part, I will discuss how the model can enhance the identification of genetic variants associated with Polycystic Ovary Syndrome- a complex neuroendocrine disorder affecting millions of women worldwide. By predicting the regulatory impact of PCOS-associated variants, our model improves the prioritization of functional variants and provides new insights into the genetic basis of this disorder.