Genetic association studies have facilitated a remarkable range of discoveries improving our understanding of the underlying biology of diseases, and translation toward new therapeutics. Although large datasets such as UK Biobank can considerably improve the power of these studies, the difficulties of dealing with them are also increasing. One of the main results of the proposed research was to use the power of a large supercomputer to analyze and publish genetic association studies for 778 human traits and diseases, freely available for any researcher. To this end, a web service was developed to allow researchers to freely browse for these associations and compare them between traits. This tool simplifies the work of geneticists, epidemiologists, medical researchers, and pharmaceutical companies when looking for these associations, thus boosting the research process.
Please note that this research has been conducted under application 788 (PI: Albert Tenesa, title: 'Heritability of disease frequency').
The return has been archived under application 4939 to align with the participant identifiers included in the data files. Once downloaded the file extension should be changed to .7z, and extracted using 7zip software.
Estimation of the genetic correlation among human cancers and correlated intermediate traits and identification of pleiotropic cancer loci
Most diseases have a strong genetic component. To date scientist have identified a few of the genes that increase risk of disease (e.g. cancer), but many remain elusive. One reason for that is that scientists study one disease at a time. This is quite unsatisfactory because we know that some people tend to have multiple diseases either simultaneously or at different times, and that having one disease increases the risk of having another. For example, being overweight increases one?s risk of developing cancer. We believe that using statistical modelling of two or more diseases simultaneously could help in identifying more susceptibility genes and help constructing better models of risk prediction.
Our aims are to investigate to which degree different cancers are determined by the same genes; and to which degree cancer biomarkers and risk factors are determined by the same set of genes as cancer. For instance, we know that being overweight increases the risk of developing colorectal cancer, but we don?t know for sure if that link is at the genetic level (same genes make one overweight and more prone to cancer) or at the environment level (e.g. an unhealthy lifestyle makes one, both, overweight and prone to cancer). We request data on any cancer diagnosis, genetic information and biomarker and risk factor information on the whole UK biobank cohort (no samples are required). Our primary cancer sites of interest are: Breast, lung, colorectal, prostate, melanoma, non Hodgkin Lymphoma.
Our project will help to understand cancer aetiology and this will translate into better prevention policies and more effective cancer screening programs. It also has the potential of discovering new therapeutic targets aimed at combating cancer and offers the possibility of drug repositioning (using an available drug to treat a different condition).
|Lead investigator:||Professor Albert Tenesa|
|Lead institution:||University of Edinburgh|
1 related Return
|Return ID||App ID||Description||Archive Date|
|2501||4939||An atlas of genetic associations in UK Biobank||19 Oct 2020|
|2423||An atlas of genetic associations in UK Biobank||Canela-Xandri, O. et al||2018||Nature Genetics. 2018|