The Malleability of Gene Regulation in Healthy Individuals: Analyzing CRISPR-based Screens with Single-Cell RNA-Sequencing Readout across Genetic Backgrounds

Abstract

A question that has driven the field of quantitative genetics since its inception, is that of understanding the relationship between genetic variation and complex traits. In the last few decades, genome-wide association (GWAS) and quantitative trait studies have implicated thousands of genetic loci in various physical and molecular traits. However, where there was hope that traits could be explained by a single, causal variant driving a genetic condition, this is only rarely the case, with the majority of traits driven by the aggregation of common, low-effect variants causing changes in gene regulation. Most puzzlingly, even where there is a clear disease-causing mutation, different individuals can often cause a diverse range of disease phenotypes, from no discernable symptoms to severe disease. Deconvoluting the impact of high-effect variants, the combined effects of genetic background and the environment remains an important question with many practical implications. Existing methods for teasing apart the many contributing factors require specific data-sets and are infeasible for many rare diseases, nor can the effects of lifestyle and environment be completely distinguished. Recent technological advances in genome engineering, sequencing technology and cell line differentiation can be used to mitigate these challenges. However, these techniques have been limited in scale and do not account for variation in genetic background. In this thesis, I explore four CRISPR-based screens with single-cell RNA sequencing read-out (scRNA-seq) that together map out an evolution of experiments that start from conducting a proof-of-concept CRISPRi screen targeting tens of genes across two cell lines to one targeting thousands of genes pooled across tens of genetic backgrounds. In doing so, we establish experimental and power considerations for knocking down genes with variable essentiality across pooled cell lines, as well as a computational framework for interpreting mean and variation in transcriptomic consequences from gene knockdown. Work done during my PhD and presented in this thesis is the second genome-scale experiment of its kind and is the only study that accounts for the role of genetic background on gene function, establishing a necessary and fundamental building block for conducting population-scale genetic analyses.