Statistical methods and software for population-scale single-cell genetics
Single-cell gene expression assays offer exciting new possibilities for studying genetic regulation of gene expression in humans. We can move beyond linking variation in DNA to average gene expression level, as we do with expression quantitative trait locus (eQTL) mapping that assays gene expression from bulk cell populations. With gene expression at single-cell resolution we can start to think about: improving cell context- and cell state-specific eQTL mapping; finding interactions between genotype, cell state and gene expression; discovering dynamic QTLs where effects change over time, space or development; and we can think about studying variance of expression as a QTL phenotype in its own right. Early single-cell QTL studies (e.g. http://dx.doi.org/10.1101/630996) have begun to generate population-scale single-cell genetics datasets, but computational tools are lacking. This project will develop the necessary statistical methods and software ecosystem to enable the widespread studying of genetic regulation of gene expression at single-cell resolution.
Dr Davis McCarthy
Bioinformatics & cellular genomics
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