Computational Modelling Group

A composite likelihood approach to genome-wide data analyses.

Andrew Collins (Investigator), Jane Gibson, Ioannis Politopoulos

We describe composite likelihood-based analysis of a genome-wide breast cancer case-control sample by determining genome regions of fixed size on a linkage disequilibrium map which delimit comparable levels of linkage disequilibrium. Analysis of findings suggests further validation in more samples from other cohorts as well as the exploitation of novel computationally-intensive methods such as next-generation sequencing.

Genetic studies to characterise the role of genetic factors in early-onset breast cancer

Andrew Collins (Investigator), Rosanna Upstill-Goddard

Breast cancer is a highly heterogeneous disease, with many distinct subtypes. In the majority of breast cancer cases the causative genetic component is poorly characterised. This study aims to explore both rare and common mutations in early-onset breast cancer patients and the contribution of such variants to disease using a variety of analytic approaches.

Identifying variants in next generation sequencing data of 61 paediatric Inflammatory Bowel Disease patients

Sarah Ennis (Investigator), Gaia Andreoletti

This study aims to characterise the mutations of genes known to predispose Inflammatory bowel disease in 61 paediatric patients using next generation sequencing analysis. Our aim is to identify the relative impact of known genes in individual case presentations of disease and correlate matches with clinical manifestation.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Andrew Collins, Mahesan Niranjan, Reuben Pengelly (Investigators), Alejandra Vergara Lope

This iPhD project uses a Gaussian Bayesian Networks approaches framework through machine learning approach to predict which genes are involved in the development of different diseases.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Mahesan Niranjan, Andrew Collins, Reuben Pengelly (Investigators)

This PhD project uses a Monte Carlo molecular simulation processes approach to predict which genes are involved in the development of different diseases.

Simulating Sleeping Sickness: a two-host agent-based model

Jason Noble, Peter Atkinson (Investigators), Simon Alderton

Sleeping sickness is a vector-borne, parastic disease which affects millions of people across 36 sub-Saharan African countries. Using agent-based models, we aim to gain a greater understanding of the interactions between the tsetse fly vector and both animal and human hosts.

Building an accurate representation will allow the testing of local interventation scenarios including the closing of watering holes, and the selective spraying of cattle with insecticides.

The application of next-generation sequencing to unresolved familial disease

Andrew Collins, Sarah Ennis (Investigators), Jane Gibson, Reuben Pengelly

Next-generation sequencing (NGS) allows us to sequence individual patients cost-effectively, allowing us to enter a new era of genomic medicine. The level of genetic detail that we can access through these methods is unprecedented making it suitable for clinical molecular diagnostics.

Whole exome sequencing identifies novel FLNA mutation in familial Ebstein's anomaly

Jane Gibson, Andrew Collins, Sarah Ennis (Investigators), Gaia Andreoletti

We describe the application of whole-exome sequencing in a family in which eight people in three generations presented Ebstein's anomaly.