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Approved research

Identify sub-population based on genetic risks shared between habit-forming behaviors and diseases

Principal Investigator: Mr Tatsuya Ando
Approved Research ID: 28404
Approval date: April 18th 2017

Lay summary

There are many patients with specific lifestyle that could potentially relate to their behavior problems; however, mechanisms linking habit-forming behaviors such as sleep, coffee assumption and eating behavior to diseases are poorly understood. We will examine the coheritability among clinical symptoms and habit-forming behaviors. Then, we will attempt to detect the sub-populations with the risk loci shared between habit-forming behaviors and diseases. Also, we will reveal the relationships brain functions/structures and brain regions/cell types expressing the genes close to the loci to understand disease mechanism. The purpose of the UK Biobank is to improve the prevention, diagnosis, and treatment of illness. As it proves that various habit-forming behaviors would associate with cardio-metabolic diseases, psychiatric disorders and all-cause mortality, relationship between diseases and behaviors is becoming a major public attention. By using the medical, cognitive, imaging and genetic data from UK Biobank, we believe that the genetic and lifestyle factors underlying the diseases could be identified, and it will be an important step towards improving prediction, prevention, and treatment for clinical symptoms and diseases. We will be looking for links between genetic variants and clinical symptom/habit-forming behaviors from questioners. We will use statistical methods to identify diseases sharing the genetic risk variants from UK Biobank with the behavior. Additionally, we will use UK Biobank cognitive function and brain imaging data in combination with public gene expression data sets that could be useful to identify the relevant brain region and cell types most likely associated with the genetic factors and eventually understand the mechanisms of the behavior and/or diseases. The full cohort We will need the genotype data and MRI scans of as many of these participants as possible.