Integration of regulatory elements and multi-trait genomic and phenomic data to improve the accuracy of risk prediction of human complex diseases/traits
Principal Investigator: Professor Tie-Lin Yang
Approved Research ID: 46387
Approval date: June 18th 2019
Complex diseases in humans, such as cancer, heart disease, and depression, are caused by a variety of genetic factors, surrounding environment, lifestyle, among other influences. The ability to accurately assess the risk of developing such diseases before onset is becoming increasingly important in clinical practice. However, we are still unable to accurately predict the risk for most complex diseases. One of the ways that we can potentially improve our understanding of genetic risk for complex diseases is by understanding how different diseases are genetically similar to each other. For example, type II diabetes (TIID) and increased body mass index (BMI) are two traits that are genetically similar, and it has been shown that higher BMI leads to an increase in risk for TIID. Therefore, considering genetic information of related diseases/traits can be a very helpful tool when assessing risk for a particular disease/trait of interest. In this project, we aim to improve the risk prediction of complex diseases by taking into account the genetic information of related diseases/traits. We believe that this can provide the basis for prevention and individualized treatment.