The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

To find a particular PI's email or look up other PI details, use the menu at the top of this page (PIs tab).

How social stratification in urban admixing populations shaped their genetic structure: novel insights from paleogenomics and deep learning
Assortative mating and sex bias modulate the configuration of human societies and shape their population structure. The cultural and socioeconomic context of the admixing society, such as the stratification of the distribution of wealth and power or racial segregation policies, have historically shifted the human mating behaviour from random mating. Recently, the geopolitical and economic scenario originated from the end of the colonial era, the globalization and the threat of climate change have triggered world-wide migrations towards the big metropolitan areas that gave rise to contemporaneous admixing societies and complex urban population structures. Similarly, the overlapped layers of ancient migrations and admixture processes that configured the European population structure during thousands of years have been shown to be sex-biased and might also be affected by assortative mating patterns within settlements [1,2]. This project aims to unveil the footprint of complex mating behaviors driven by cultural and social environments on the genetic structure of British and European populations of past and present. To solve these questions, the PhD candidate will integrate mathematical modelling with machine learning to analyse large-scale genomic and phenotypic data from over 500,000 individuals from UK Biobank and published modern and ancient genomes. The student will derive a mathematical model of how assortative mating and sex bias shaped local genomic ancestry in time [3]. She/he will then apply deep neural networks to infer such evolutionary parameters from longitudinal genomic data [4] and finally integrate these findings into the current phenotypic and cultural landscape of Britain [5] and Europe. We are looking for a PhD candidate interested in addressing interdisciplinary questions related to anthropology, population genetics and big data analysis. The student will be supervised by world-leading experts in human evolution, mathematical biology and machine learning applied to genomic data. We encourage students with broad scientific interests and keen attitude to computational science to apply and join our vibrant and diverse research group at the Imperial College of London. [1] Skoglund & Mathieson. Ancient genomics of modern humans: the first decade. Annual Review of Genomics and Human Genetics. 2018 [2] Goldber, Gunther, Rosenber & Jakobsson. Ancient X chromosomes reveal contrasting sex bias in Neolithic and Bronze Age Eurasian migrations. PNAS. 2017 [3] Goldberg, Verdu & Rosenberg. Autosomal Admixture Levels Are Informative About Sex Bias in Admixed Populations. Genetics. 2014 [4] Sheehan & Song. Deep Learning for Population Genetic Inference. Plos Computational Biology. 2016 [5] Brace et al. Ancient genomes indicate population replacement in Early Neolithic Britain. Nature Ecol and Evol. 2019
Matteo Fumagalli
Tom Thorne
Pontus Skoglund; The Crick Institute; London;
Development of mathematical theory, Computing, Quantitative data analysis
Tom Thorne
The student will develop skills of modelling in mathematical biology (mating systems and genetic segregation) as well as data science, by processing and integrating data from different sources (e.g. DNA sequencing machines and historical records). The student will also acquire a solid knowledge of the theory behind deep learning and be trained in basic of software engineering and programming.
While modern urban environments are characterised by high levels of stratification, most current approaches to understand genetic structure in time are based on the assumption of random mating. This project will provide a new perspective to this issue, by generating a novel theoretical framework using genetic and historical data.
By providing a theoretical framework for how assortative mating and sex bias shaped extant genomes, we can provide a tool to infer past mating preferences using contemporary data. These findings will shed light onto the fine-scale evolutionary mechanisms leading to population structure and diversification, even beyond humans populations.
There is an ongoing debate on cultural and social stratification in modern urban societies. The model developed here will help understand how the dynamics of past mating systems affected the current genetic and phenotypic heterogeneity of human populations.
From a methodological point of view, in evolutionary biology there is extensive research trying to integrate probabilistic modelling and predictive algorithms based on deep learning. As such, we foresee the scope for introducing either novel architectures or neural layers which can be exported to other fields outside of evolutionary biology.
This project will combine mathematical biology, anthropology, machine learning and bioinformatics. The student will be part of an excellent multidisciplinary environment, with Dr Tom Thorne being an expert in artificial intelligence, Dr Skoglund a world-leader in ancient DNA, and Dr Fumagalli a leading figure in evolutionary genetics.
Population genetics and evolution, Ecological/Evolutionary tools, technology & methods
Computing (e.g. python), modeling, data science (machine learning, Bayesian statistics) and principles of population genetics will be provided between University of Reading (from the Msc in Advanced Computer Science) and the close campus of Silwood Park. Further training will be given by attending workshops in genetic data analysis and large-scale computing.
Dept Computer Science, UoReading; Silwood Park campus, ICL
2019-05-30 08:23:20