| Title: | Prioritising genetic findings for drug target identification and validation |
| Journal: | Atherosclerosis |
| Published: | 26 Jan 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38325120/ |
| DOI: | https://doi.org/10.1016/j.atherosclerosis.2024.117462 |
| URL: | http://www.atherosclerosis-journal.com/article/S0021915024000224/pdf |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 8863
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Abstract
The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation.</p>
5 Authors
- Nikita Hukerikar
- Aroon D Hingorani
- Folkert W Asselbergs
- Chris Finan
- Amand F Schmidt
1 Application
| Application ID | Title |
|---|---|
| 12113 | Harnessing associations of genetic and phenotypic traits for drug target validation, Ref: 12113. |
Enabling scientific discoveries that improve human health