Evaluating and benchmarking Qatar Biobank data with the UK Biobank for various disease states
Approved Research ID: 67387
Approval date: January 25th 2021
Qatari population is one example of Middle Eastern populations with some special characteristics such as history of admixture, consanguinity, and also a high prevalence of some chronic diseases including Diabetes and Cardiovascular diseases. Qatar has embarked on an aggressive journey in precision medicine and we are collecting data and creating a national repository (biobank and clinical warehouse). We have multiple aims in understanding variants for a particular disease, how well learnings from literature that can find support in UK biobank can be translated to local Qatari population.
As an instance we are performing GWAS on Coronary Artery Disease (CAD) and for Type II Diabetes (T2D) using whole genome sequencing data which is part of the Qatar Cardiovascular Biorepository. The data consists of ~1000 Qatari CAD patients and ~6000 controls. In this project, we will dissect the genetic architecture of CHD in Qatar by analyzing the genetic data of the collected samples. The goal is to identify genetic risk factors involved in CHD, either known or new and specific to the Qatari population. We will use the UK Biobank as a "baseline" resource for reproducing results and to test Polygenic Risk Scores (PRSs). Here, one important question that needs to be addressed is: Could what we know from European and other population studies (as analyzed in UK Biobank) be translated to the Qatari population?
We are also working on the samples to define the landscape of cancer genetics in Qatar. The aim of the study is to to determine the prevalence of known pathogenic variants in Qatari population for four major cancers: Breast, Prostate, Colorectal, Ovarian, and Lung cancers, even though we plan to extend it further as well. For this we want to first perform a benchmark analysis with the UK Biobank data for pevalence, comparative analysis and testing for hypotheses.
QCRI is also leading the digital health efforts in a very large clinical trial looking into the prevention of diabetes type II. This project includes four clinical trials looking into different intervention to prevent type II diabetes in specific cohorts (e.g. pre-diabetes, gestational diabetes, early stage type II diabetes) where mobile and wearable technologies will be incorporated as core elements reinforcing behavioral change interventions. The actigraphy data along with the phenotype data in the UK biobank will be utilized to create models that can predict quality of sleep based on activity or various relationships or risks associated with lifestyle.