Statistical genetic pleiotropy modelling to identify drug repurposing targets
Approved Research ID: 60304
Approval date: January 11th 2021
Drug repurposing is to find new usage of existing drugs and is an efficient way to identify new therapeutic approaches with existing drugs. As an example, aspirin has been used for relieving pain via blocking cyclooxygenases, an inflammation-causing compound; and is repurposed to reduce risks of stroke via its effect on lowing platelets stickiness. In past, studies have identified tens of thousands of genetic variants and mutations that are associated with disease risks. Among them, several genetic variants and mutations are found to be associated with more than one disease. For example, a genetic variant in the FTO gene region is associated with both extreme obesity and melanoma risk; suggesting that this genetic variant may affect multiple different pathophysiological pathways under obesity and melanoma; and targeting FTO gene may be able to treat both obesity and melanoma. Thus, a drug was originally developed to treat one disease via targeting a certain gene and/or biological pathways may be used to treat other diseases, if these diseases share pathophysiological pathways. Such "genetic pleiotropy" phenomenon enables us to identify novel uses of existing drugs to treat different diseases. Recently, we have developed a statistical approach, empirical linear combination test statistics (eLC), to identify genetic variants/mutations with such genetic pleiotropy effects. The genetic pleiotropy effect is defined as a genetic variant/mutation independently influences multiple diseases.
In this study, we proposed to perform drug repurposing analysis with following steps: (1) We will perform association analyses using our eLC approach to identify genetic variants/mutations with genetic pleiotropy effects of multiple diseases. The disease and disease condition will be extracted from ICD-10 codes, image measurements, biomarker measurements, questionnaire, medical information and physical examinations in the UKBB data. (2) Once we identify genetic variants/mutations with genetic pleiotropy effects, we will predict targeted genes that affected by these genetic variants/mutations via functional annotations. (3) To identify new usage (indications) of existing drugs, the targeted genes will be mapped to druggable and chemistry databases to identify drugs compounds of the existing drugs.
If finished successfully, our findings will provide valuable information on new usage of existing drugs and potential novel therapeutic targets, which will contribute to the unmet medical needs and improve public health care.
This study would take two years for 3 full time staffs.