4.7 Review

Advancing the use of genome-wide association studies for drug repurposing

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NATURE REVIEWS GENETICS
卷 22, 期 10, 页码 658-671

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NATURE PORTFOLIO
DOI: 10.1038/s41576-021-00387-z

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资金

  1. National Health and Medical Research Council (NHMRC) Senior Research Fellowship [1121474]
  2. University of Newcastle Faculty of Health and Medicine Gladys M Brawn Senior Fellowship
  3. Australian government

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Genome-wide association studies have provided important biological insights into complex diseases, offering opportunities for drug repurposing. Leveraging common variant genetics through approaches such as single-loci mapping to drug targets, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring, researchers aim to identify new treatment options more efficiently.
Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring. Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases. The authors review approaches that leverage GWAS to identify opportunities for repurposing existing drugs, including single-loci mapping to drug targets, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization and polygenic scoring.

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