From gene to function: hunting for new treatment targets across Alzheimer's, Parkinson's and Huntington's disease.
Professor Julie Williams and colleagues at Cardiff University have discovered over 27 risk genes for dementia which implicate the innate immune response in determining a person’s susceptibility to developing Alzheimer’s disease. Building on their world-class expertise in genetics and immunology, the UK DRI at Cardiff University uses these discoveries as the starting point for understanding disease mechanisms and producing new therapies.
The team uses cellular and animal models to understand the function of risk genes implicated in two major areas of immunity: microglia cells and the complement system. They study the involvement of complement proteins in the loss of synapses and cell death in Alzheimer’s disease and describe how Alzheimer’s risk genes affect the production and activation of microglia in the brain.
An additional programme of work develops novel mathematical approaches to the study of dementia that will be shared with by all members of the UK DRI. This work is developing models for stratifying dementia risk and statistical tools for identifying patterns in large biological ‘omics’ data sets.
UK DRI at Cardiff University is located in the Hadyn Ellis building on the Innovation campus, with access to state of the art facilities within the Cardiff University Dementia Research Network.
About the role: In this role, you will work independently and as part of a multi-disciplinary team, to perform self-directed analyses using in house and publicly available datasets for high impact, peer-reviewed publications. Applicants should be knowledgeable and enthusiastic with the ability to multi-task and communicate effectively. Core tasks will include data analysis and management of genetic, genomic and related phenotypic data from dementia case-control and cohort samples, publicly available and in-house generated genomic human data. Collaborative working is a key component of this role meaning that communication skills will be vital, in order to cultivate international collaborations from a number of academic institutions. What we are looking for: We’d like to hear from you if you have a background in machine learning and biostatistics with proven experience of extensive large-scale data analyses and manipulation, including analysis of genome-wide association studies and genetic interactions. Expert knowledge of data manipulation in a UNIX/Linux environment and proficiency in high level programming languages such as Python and R Statistics is essential. Knowledge of techniques used in genetics and epidemiology, as well as experience in using software used for the analysis of genomic data such as PLINK, LDScore, is desirable. Reference No: 19246BR Closing date: 01-12-2024
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