Computational drug repositioning using big data from genetic studies

  • Wen Zhang Icahn School of Medicine at Mount Sinai, USA (US)
Keywords: Computational drug repositioning, big data, GWAS, genetic study

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This mini-review gives the development of computational drug repositioning using big data from perspective of genetic study. The reverse profile principle is utilized to reposition drug hits by investigating gene expression, genotyping and GWAS data. Several big data sets are introduced, which are remarkable references that utilized for the genetic studies. Relevant computational genetics methods and the developments are briefly described as well. This review aims to give insights into this area so as to raise more ideas as well as to attract more attentions for this ascendant field. With the development of information technology and engineering applications, prosperous results are highly expected.


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Subramanian A, Narayan R, Corsello SM, et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 2017; 171: 1473-1452.

GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

Fromer M, Roussos P, Sieberts SK, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19:1442–53.

Wang D, Liu S, Warrell J, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science. 2018; 14,362(6420). pii: eaat8464. doi: 10.1126/science.aat8464.

Franzén O, Ermel R, Cohain A, et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science. 2016;353:827–30.

Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, Mills GB, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.

Gusev A, Ko A, Shi H, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.

Barbeira AN, Dickinson SP, Bonazzola R, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018; 9:1825.

Gamazon ER, Wheeler HE, Shah KP, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47:1091–8.

So HC, Chau C KL, Chiu WT, et al. Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry. Nat Neurosci. 2017; 20:1342-1349.

Received 2019-06-18
Published 2019-05-20
How to Cite
W. Zhang, “Computational drug repositioning using big data from genetic studies”, J. Appl. Sci. Eng. Technol. Educ., vol. 1, no. 1, pp. 1-3, May 2019.