A 2026 Crick PhD project with Greg Findlay.

Project background and description

Studying effects of genetic variants is a powerful means of probing biological mechanisms. Increasingly, knowledge of human variants can also be used to improve patient care through innovative therapies. However, it remains challenging to predict which mutations have molecular consequences impacting function, and consequently, to correctly identify human mutations underlying disease. Relatedly, while many CRISPR technologies are now entering the clinic with the potential to cure diseases long-thought to be incurable, for many potential applications it remains unclear how best to edit the human genome to produce therapeutic effects. 
Our lab develops high-throughput genome editing methods to ask how variants impact function [1]. We have developed and optimised CRISPR-based methods such as saturation genome editing (SGE) [2] and pooled prime editing [3] that allow us to quantify the effects of up to tens of thousands of variants in human cells simultaneously. Our data sets for variants in tumour suppressor genes such as BRCA1 and VHL [2, 4] are now widely used clinically to better identify patients at risk of cancer following genetic testing. Furthermore, we’ve recently demonstrated the power of such methods for discovering the genetic origins of rare disease [5].
We are now seeking to develop these state-of-the-art methods further by:  1. Scaling our approaches to address critical questions regarding gene regulation, and 2. Coupling our editing strategies to functional assays that afford greater phenotypic resolution across pathways and cell types. Our past work has often relied on growth-based readouts. This limits which variants can be studied and the types of phenotypes we can explore. Ultimately, we seek to amass quantitative data sets for hundreds of thousands of variants engineered across diverse human cell types. Such data will be critical for building models of how variant effects manifest across cells, tissues, and organs throughout human development, greatly enhancing our ability to diagnose and treat genetic disease.
The successful applicant will lead one or more projects in this area, learning our lab’s strategies for engineering variants at scale while also developing new ways of probing effects across cell types. For example, the project may include developing stem-cell based systems to assay variants before and after cell differentiation, or using single-cell RNA sequencing as a means of probing how variants impact mRNA levels and cell state. Within the supportive research environment of the lab, the student will be given autonomy to choose specific areas of biological interest. There will also be extensive opportunities for collaboration.

Ultimately, this research will lead to new methods that can be applied widely to many questions, while also generating new insights into fundamental questions in human genetics.

Candidate background

No specific experiences are absolutely required, though highly competitive candidates will have substantial, in-depth research experience in functional genomics, gene regulation, human genetics, computational biology, biotechnology, bioengineering or a related field.

Experience with coding and analysis of large-scale data sets will prove highly valuable, as will intensive wet-lab experience, for instance, developing and performing cellular and molecular assays. Some more specific beneficial experiences include: expertise in molecular biology, including cloning and generation of NGS libraries; expertise in cell culture, particularly working with stem cells or other in vitro models of disease; expertise in bioinformatics, for instance, writing scripts, running pipelines, analysing omics data, and performing statistical analyses. Attention to detail, respect for colleagues, and strong communication skills are highly valued.

Candidates should be highly motivated to learn, curious about human genomics, and eager to develop and optimise new experimental methods independently while still working as part of a larger team. It is expected that the successful applicant will perform both experimental work and data analysis as required by the project, with necessary training and support provided.

References