Multi-layered genetic approaches to identify approved drug targets

Abstract

Drugs targeting genes that harbor natural variations associated with the disease the drug is indicated for have increased odds to be approved. Various approaches have been proposed to identify likely causal genes for complex diseases, including gene-based genome-wide association studies (GWAS), rare variant burden tests in whole exome sequencing studies (Exome) or integration of GWAS with expression/protein quantitative trait loci (eQTL-GWAS/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 common clinical traits and benchmarked their ability to recover drug target genes defined using a combination of five drug databases. Across all traits, the top prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81 and 1.31 for the GWAS, eQTL-GWAS, Exome and pQTL-GWAS methods, respectively. We quantified the performance of these methods using the area under the receiver operating characteristic curve as metric, and adjusted for differences in testable genes and data origins. GWAS performed significantly better (54.3%) than eQTL (52.8%) and pQTL-GWAS (51.3%), but not significantly so against the Exome approach (51.7% vs 52.8% for GWAS restricted to UK Biobank data). Furthermore, our analysis showed increased performance when diffusing gene scores on gene networks. However, substantial improvements in the protein-protein interaction network may be due to circularity in the data generation process, leading to the node (gene) degree being the best predictor for drug target genes (OR = 8.7, 95% CI = 7.3-10.4) and warranting caution when applying this strategy. In conclusion, we systematically assessed strategies to prioritize drug target genes highlighting promises and potential pitfalls of current approaches.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by the Swiss National Science Foundation (310030_189147).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee/IRB of UK Biobank gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

Whole blood expression QTLs are from the eQTLGen eQTL meta-analysis and are available at https://www.eqtlgen.org/cis-eqtls.html. Tissue-wide expression QTLs are from the GTEx project and are available at https://gtexportal.org/home/datasets. Plasma protein QTLs are from the deCODE study and are available at https://www.decode.com/summarydata/. Summary statistics from whole exome gene burden tests are available in the GWAS Catalog (accession IDs are in Table S2). Genetic and phenotypic data from the UK Biobank Resource are available to approved researchers. GWAS summary statistics from the UK Biobank are available at http://www.nealelab.is/uk-biobank and https://pan.ukbb.broadinstitute.org. GWAS summary statistics from FinnGen are available at https://www.finngen.fi/en/access_results. GWAS summary statistics for multiple sclerosis (MS) are available by application from https://imsgc.net/?page_id=31. Full list of GWAS summary statistics used in this study is in Table S1-3, all of which are publicly available. UK10K individual-level data are available upon request (https://www.uk10k.org/data_access.html).

https://www.eqtlgen.org/cis-eqtls.html

https://gtexportal.org/home/datasets

https://www.decode.com/summarydata/

http://www.nealelab.is/uk-biobank

https://pan.ukbb.broadinstitute.org

https://www.finngen.fi/en/access_results

https://imsgc.net/?page_id=31

https://www.uk10k.org/data_access.html

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