Friday, April 19, 2024
4/19/2024
Understanding the functional impact of genetic variation, particularly its impact on disease, remains a key challenge in human genetics. Most disease-associated genetic loci lie in regulatory regions of the human genome and are suspected to act by regulating the expression levels of nearby genes. Yet despite extensive efforts to characterize genetic regulatory effects on gene expression, most disease loci have not been explained by association with expression levels. Pervasive context-specificity of these regulatory effects introduces one key bottleneck contributing to this discrepancy. Detecting regulatory effects requires observation of gene expression in the specific cell states where the effect is active, and large-scale efforts have primarily focused on measuring expression levels in healthy adult tissues. Characterizing genetic regulation in increasingly diverse and dynamic cellular contexts will therefore reveal novel genetic regulatory effects which may help elucidate molecular mechanisms of disease loci. To analyze genetic regulation of gene expression in diverse cellular contexts we will leverage single-cell RNA-sequencing of embryoid bodies from multiple human donors. Embryoid bodies are three-dimensional aggregates of induced pluripotent stem cells that spontaneously differentiate into dozens of non-discrete cell types. Analysis of expression at the single-cell level will enable us to cut through the heterogeneity of these complex aggregates and map each cell to a unique position within a clearly defined differentiation landscape. Embryoid bodies thus offer a unified experimental framework for the study of diverse differentiation trajectories, accelerating the exploration of genetic regulation across the many cell states that may contribute to human disease. In this proposed research, I will develop novel computational and statistical tools that leverage the expansive and multifurcating landscape of cellular differentiation to improve our ability to resolve context-specific and dynamic genetic regulatory effects. I will develop a probabilistic model to compare gene expression dynamics between individuals, and a hypothesis testing framework to attribute inter-individual differences to genetic variation. I will validate the dynamic genetic regulatory effects we discover using in vitro and in vivo chromatin accessibility data. Finally, I will search for overlap between newly discovered regulatory variants and known disease loci to reveal novel insights into the target genes mediating genetic effects on common diseases.
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