Biostatistics and functional genomics in dementia
Award Number
UKDRI-3003Programme
Research grantStatus / Stage
ActiveDates
1 September 2017 -31 December 2100
Duration (calculated)
83 years 03 monthsFunder(s)
MRC (UKRI)Funding Amount
£773,306.00Funder/Grant study page
MRC UKRIContracted Centre
UK Dementia Research Institute at Cardiff UniversityPrincipal Investigator
Professor Valentina Escott-PricePI Contact
escottpricev@cardiff.ac.ukPI ORCID
0000-0003-1784-5483WHO Catergories
Tools and methodologies for interventionsUnderstanding risk factors
Understanding Underlying Disease
Disease Type
Dementia (Unspecified)CPEC Review Info
| Reference ID | 246 |
|---|---|
| Researcher | Reside Team |
| Published | 12/06/2023 |
Data
| Award Number | UKDRI-3003 |
|---|---|
| Status / Stage | Active |
| Start Date | 20170901 |
| End Date | 21001231 |
| Duration (calculated) | 83 years 03 months |
| Funder/Grant study page | MRC UKRI |
| Contracted Centre | UK Dementia Research Institute at Cardiff University |
| Funding Amount | £773,306.00 |
Abstract
The UK Dementia Research Institute (UK DRI) is an initiative funded by the Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. Funding details for UK DRI programmes will be added in 2019. This programme will bridge the gap between statistical genetic association and tractable biological mechanisms and dissect complex pathways using novel mathematical approaches Our aims are to 1. Integrate biological data with large genetic datasets to detect disease-relevant mechanisms 2. Apply non-linear multivariate mathematical approaches (sCCA, SVM, etc) for disease stratification 3. Develop novel mathematical approaches tailored to identifying patterns in non-linear multidimensional “omics” space 4. Share algorithms, data, software tools with all members of UK DRI via DPUK platform Outcomes: Successful completion of these aims will deliver insights into fundamental biology, provide the resources and reagents (in the form of actionable causal and protective mutations, implicated pathogenic pathways, patient strata, advanced robust methodology for data analyses) that will fuel mechanistic and translational research, deliver novel therapeutic targets, inform clinical practice and provide guidance on optimal analysis approaches for large “omics” datasets.
Aims
Our aims are to
1. Integrate biological data with large genetic datasets to detect disease-relevant mechanisms
2. Apply non-linear multivariate mathematical approaches (sCCA, SVM, etc) for disease stratification
3. Develop novel mathematical approaches tailored to identifying patterns in non-linear multidimensional “omics” space
4. Share algorithms, data, software tools with all members of UK DRI via DPUK platform