Predicting the Impact of Genomic Variation on Cellular States

Project Summary
Linking genotype to phenotype by predicting the functional effects of genomic variation is crucial to realizing the
potential of genomic medicine. Over the past two decades, consortium efforts have characterized common and
rare population-scale genetic variation and functional gene regulatory elements across cell types. More recently,
single-cell technologies have enabled organism-scale surveys of molecular cell states. The availability of these
three data types means that the goal of general models to predict the effects of variants is finally within reach.
Currently, integrating these diverse biological data sets to build predictive models is difficult. While resources
such as RegulomeDB help researchers annotate variants with putative regulatory function, they often lack cell
type specificity and predict variant function in a general sense. Similarly, GTEx effectively links specific variants
to changes in gene expression, but these variants are primarily SNPs, and the predicted effects are mostly
pairwise interactions. Furthermore, previous efforts rely primarily on bulk measurements, with limited exploration
of the impact of genomic variation at the single-cell level. We propose quantitative shifts in cellular state as a
new paradigm for defining and predicting variant function. Single-cell transcriptomic and epigenomic data from
healthy individuals provide a reference atlas of cell states. By comparing cell state distributions against this
reference, we can identify quantitative shifts resulting from genetic variation and explore these deviations as
potential disease states. We will then build models to predict shifts in cell state by combining single-cell data with
background germline genetic variation, chromatin structure, and supporting functional data.

Grant Number: 5U01HG011952-05
NIH Institute/Center: NIH
Principal Investigator: Alan Boyle