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.

Does narcissism determine the type of faces we find attractive?
It is often said that opposites attract, however humans have been found to choose mates similar to themselves for a variety of traits. Surprisingly, facial appearance is one such trait, and people have been found to prefer mates who look similar to themselves. This is particularly interesting because the face is often the first thing people see when meeting someone, it is a visually distinguishing feature, and because the face contains information about relatedness and ancestry. Because facial morphology is heritable within and between human populations, a tendency towards assortative mating (choosing mates similar to oneself) is expected to have important evolutionary and social consequences for human populations, however these remain poorly understood and underappreciated. Specifically, face-dependent assortative mating is expected to maintain genetic and phenotypic diversity for face shape, and to maintain genetic substructure even when diverse groups of people live together. This, in turn, will have important social implications because ancestry groups have been historically discriminated against or privileged around the world. The first step in understanding the phenomenon of face-dependent assortative mating will be to determine the specific aspects of one’s own face and of other people’s faces that drive the attraction towards similarity. Face shapes differ along several axes of variation including ancestry (e.g. more or less African of European), sexual dimorphism (e.g. masculinity or femininity), or with respect to specific features (e.g. nose length, chin width, etc.), and it is not clear which of these axes drive facial preference assortment. This project will be the first to investigate the role that one’s own facial appearance plays on whom they find attractive. It will also be the first study of human mate preference and attractiveness to integrate both high-resolution 3D facial images with genomic data. The student will develop an experimental procedure for testing facial preferences that incorporates information about their own face shape and their own genetic information. To do so, a large 3D facial image database paired with genomic data will be constructed using data already available from the Hodgson and Shriver labs. The facial images will then be morphed along the axes of variation to be more or less similar to a test subject who will then rate the images for attractiveness. A machine learning approach will be used to key in on the specific aspects of facial morphology that the test subject uses to determine attractiveness. A genome wide association study will be performed to identify genetic variants involved in the aspects of face shape that are found to be assortative with respect to facial preference.
Jason Hodgson
Stefanos Zafeiriou
Mark Shriver, Dept. of Anthropology, Penn State University,
Computing, Quantitative data analysis, Evolutionary observations / data collection
Stefanos Zafeiriou
The project will involve developing a novel experimental procedure for investigating the aspects of face shape that drive individual preference. The method will account for the individual’s own face shape and genomic profile as well as those of the people they are rating. The project will require quantitative genetics, bioinformatics, statistical machine learning, and statistical image analysis.
This project will be the first to use machine learning of 3D facial morphology to understand the role of self face shape in determining what faces people find attractive. It will then determine the evolutionary impact of these preferences with a genomic approach.
This project evaluates the impact of sexual selection and non-random mating on influencing human population structure and adaptive evolution.
Effective public health requires an understanding of the genetic diversity of the population. This project will enable us to better predict the genetic diversity of populations and how they will change into the future.
Population genetic models used to understand human genetic diversity include the simplifying assumption of random mating. By quantifying the mechanism and impact of non-random mating, this project has the potential to force a reevaluation of our understanding of past population processes such as migration and admixture, as well as informing the future course of human evolution.
This project incorporates evolutionary biology, biological anthropology, population genetics, quantitative genetics, computer science, statistical image analysis, statistical machine learning, and statistical modelling.
Behavioural ecology, Population genetics and evolution
The student will be trained in human population genetics, quantitative genetics, human subjects research, machine learning, and 3D image analysis.
Silwood Park and South Kensington Campuses of Imperial College. The student will also be trained in human phenotyping at Penn State University.
2017-10-02 22:41:50