Principal Investigator: Professor Chirag Patel
Department: Biomedical Informatics
Harvard Medical School, Biomedical Informatics, 10 Shattuck Street, Boston MA 02115.Tags: 22881, diabetes, heart disease, mendelian
Funding body: National Institutes of Health (NIH)
1a: Many non-communicable diseases (e.g., Type 2 diabetes [T2D] and heart disease) are the most burdensome diseases in the world and are caused by the interplay between environmental exposures and inherited genetic factors. An omni-present exposure includes bacterial agents, such as Helicobacter Pylori (H.Pylori), and viral agents, such as human immunodeficiency virus (HIV). In this investigation, we aim to test whether genetic susceptibility to infection is associated with disease, such as T2D and heart disease (including stroke), and risk factors for these diseases such as body mass index (BMI), cholesterol, and inflammatory biomarkers.
1b: Our research is focused on deciphering the risk factors for type 2 diabetes, and heart disease. This focus is in-line with the UK Biobank’s vision of improving the prevention, diagnosis and treatment of a wide range of serious illnesses which include diabetes and heart disease.
1c: We will associate genetic variants for susceptibility of infection with common diseases including time to type 2 diabetes. We will also associate variants with body mass index, blood pressure, and other biomarker risk factors for diabetes such as hemoglobin A1C, cholesterol. First we will collect variants that have been previously associated with infectious disease in the GWAS catalog. Second, we will test, using GWAS arrays on the UK Biobank participants, associations between infectious disease SNPs with the time-to-disease.
1d: Full cohort.
Many non-communicable diseases (e.g., Type 2 diabetes [T2D] and heart disease ) are the most burdensome diseases in the world and are caused by the interplay between environmental exposures, such as air pollution and infectious disease, and inherited genetic factors. An omni-present exposure includes bacterial agents, such as Helicobacter Pylori (H.Pylori), and viral agents, such as human immunodeficiency virus (HIV). Other exposures include air pollution, both residential air and noise pollution. Further complicating the exposome includes behavior, such as physical activity and physical fitness. In this investigation, we aim to test whether genetic susceptibility to infection, pollution, and behavior is associated with disease, such as T2D and heart disease (including stroke), and risk factors for these diseases such as body mass index (BMI), cholesterol, and inflammatory biomarkers. In our previous proposal, we requested some continuous trait variables (such as laboratory biomarkers and geolocated pollution levels) available in UK Biobank; however, at the time, the accelerometer, pollution, and physiological clinical biomarkers, such as glucose, cholesterol, and hemoglobin A1C were not available. According to the website, the biomarker panels should be available by the second quarter of this year, making this application well-timed.
In our new request, we have requested raw accelerometer data. We will use this data to assess the variance of physical activity of individuals (as well as sedentary activity) in individuals and associate them with phenotypes as denoted in our proposal, including cardiometabolic disease. We will compare these associations, which have been well-established in the observational literature, with those that we have proposed in our investigation, infectious disease. We will seek assistance from our collaborators in the UK who have been working with complex accelerometer data. Second, in a separate study, we found genes whose expression changes due to physical activity intervention. In our UK Biobank investigation, we wish to test whether the genetic variants in these genes interact with raw physical activity measures measured during daily non-interventional exercise in association with cardiometabolic disease.
We also acknowledge accelerometer data to be complex. We do have data science capability on our team and authorized to use the data to understand and analyze these data (Masters and PhD-level bioinformatics and data scientist analysts). Further, we have also analyzed raw accelerometer data from iOS devices (iPhones) and correlated these with self-reported dynamic phenotypes (article in preparation). In this project, we converted raw accelerometer data to step count (and to sedentary activity), along with pulse rate, and associated these behaviors with self-reported daily fasting glucose.