Advances in single cell RNA sequencing have enabled the population genetic analysis of gene expression and cell state abundance. The genetic analysis of millions of single cells across thousands of individuals has the potential to identify mutations that predispose to disease by elevating pathogenic cell states.
The Morgan lab has pioneered the algorithm Milo that identifies factors that alter the abundance of cell states between experimental conditions and pinpoints high resolution cell states that differ between health and disease [1]. Likewise, the Khamseh lab developed Stator that uses higher-order interactions amongst genes to identify cell states shared across cell types [2]. More recently, the Morgan lab has extended Milo to cell state quantitative trait loci mapping to identify genetic regulators of cell state abundance using mixed effect models [3]. However, there remains a significant computational bottleneck associated with analysing millions of single nucleotide polymorphisms (SNPs) across thousands of single cell states. Recent developments in whole-genome regression models have the potential to overcome these computational limitations [4] and unlock genome-wide cell state QTL mapping across millions of SNPs and thousands of individuals.
This PhD project will develop cutting-edge computational algorithms at the interface of single cell omics and statistical genetics to reveal the genetic regulation of cell state abundance. Integrating whole-genome regression models into the Milo and Stator frameworks will create the tools required to link genes to health via cell state function. The student will have the scope to develop strong quantitative skills across the Morgan and Khamseh labs, in single-cell omics analysis, statistical and causal inference and quantitative genetics.
Successful appointees will be based in both the Morgan (computational biology, University of Aberdeen) and Khamseh (Biomedical AI, University of Edinburgh) groups, providing cross-institutional opportunities to interact with biologists, clinicians, computer scientists and mathematicians.
The ideal student will have excellent problem solving and critical analysis skills, with a first degree or 2:1 from a quantitative subject such as computer science, statistics, physics, bioinformatics, genetics or engineering. Knowledge of genetics is desirable, but not essential as full training will be provided.
Informal enquiries contact Michael.morgan@abdn.ac.uk or ava.khamesh@ed.ac.uk
References
[1] Dann E, Henderson NC, Teichmann SA, Morgan MD, Marioni JC (2022) Nat Biotech 40(2), 245-253
[2] Jansma A, Yao Y, Wolfe J, Del Debbio L, Beentjes S, Ponting CP, Khamseh
A EMBO Molecular Systems Biology (in press)
doi.org/10.1101/2023.12.18.572232
[3] Kluzer A, Marioni JC, Morgan MD (2023) bioRxi,
doi.org/10.1101/2023.11.08.566176
[4] Mbatchou J, Barnard L, Backman J, et al. (2021) Nature Genetics 53, 1097-1103
Applicants should hold a minimum of a 2:1 UK Honours degree (or international equivalent) in a relevant subject. Those with a 2:2 UK Honours degree (or international equivalent) may be considered, provided they have (or are expected to achieve) a Distinction or Commendation at master’s level.
We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.
All students must meet the eligibility criteria as outlined in the UKRI guidance on funding for postgraduate training and development. This guidance should be read in conjunction with the Terms and conditions for training funding – UKRI.
Via the link on findAPhD.com: www.findaphd.com/phds/project/eastbio-mapping-dna-variants-to-cell-states-in-health-and-disease/?p180148