A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis

Abstract

Genomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWASs) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI (N _mean = 1,329,242). Individual methodologies complementarily prioritized drugs and drug targets, which were systematically validated by referring previously known drug-disease relationships. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism and the interleukin-4 and interleukin-13 signaling pathway for gout. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analyses.

Keywords: Mendelian randomization; cross-population meta-analysis; gene prioritization; genetically regulated gene expression; genome-wide association study; genomics-driven drug discovery.

Graphical abstract

Figure 1

Overview of the genomics-driven drug discovery framework The framework consisted of three components. Each component utilizes the summary statistics of genome-wide association analyses and external resources to prioritize candidate drugs. GBMI, Global Biobank Meta-analysis Initiative; ATC, Anatomical Therapeutic Chemical Classification System; ICD-10, International Statistical Classification of Diseases and Related Health Problems; pQTL, protein quantitative trait loci; LD, linkage disequilibrium; MR, Mendelian randomization; IV, instrumental variable; GTEx, the Genotype-Tissue Expression project; TWAS, transcriptome-wide association study; LINCS, the Library of Integrated Network-based Cellular Signatures project.

Figure 2

Enrichment of prioritized drug-target genes in the disease-relevant medication categories (A) Overall enrichment of drug-target genes nominated by five gene prioritization tools and their omnibus results. The error bars represent 95% confidence intervals. (B and C) Enrichment of the prioritized drug-target genes in the disease-relevant ATC codes (B) and the disease-irrelevant ATC codes (C). The diseases are sorted in the descending order of the number of genome-wide significant loci determined in GBMI GWAS. (D) Enrichments for the omnibus results per disease and ATC code. OR, odds ratio; POAG, primary open-angle glaucoma; COPD, chronic obstructive pulmonary disease; VTE, venous thromboembolism; ThC, thyroid cancer; AAA, abdominal aortic aneurysm; HF, heart failure; IPF, idiopathic pulmonary fibrosis; UtC, uterine cancer; AcApp, acute appendicitis; HCM, hypertrophic cardiomyopathy; RA, rheumatoid arthritis; HAE, acute attacks of hereditary angioedema.

Figure 3

Endophenotype Mendelian randomization (A) Drug-target proteins with significant causal effects inferred by Mendelian randomization and with colocalization between GBMI GWAS and protein quantitative trait loci (pQTL). (B) LocusZoom plots showing colocalization between GWAS for VTE and pQTL for F11. The fine-mapped variants are shown with their rsID. Only the variants shared between GBMI GWAS and pQTL summary statistics are shown for visualization purposes. (C and D) Enrichment of the prioritized drug-target proteins in the disease-relevant ATC codes (B) and the disease-irrelevant ATC codes (C). The error bars represent 95% confidence intervals.

Figure 4

Negative correlation tests between genetically determined and compound-regulated gene expression profiles (A) Quantile-quantile plots of the negative correlation tests between genetically determined and compound-regulated gene expression profiles. Compounds with false discovery rates (FDR) < 0.05 are indicated by larger dots. The compound names are shown for at most three significant compounds, for visualization purposes. (B and C) Enrichment of the prioritized compounds in the disease-relevant ATC codes (B) and the disease-irrelevant ATC codes (C). No compound was prioritized for HF, IPF, stroke, UtC, and HCM (colored in gray). The error bars represent 95% confidence intervals, and the confidence interval was infinite for the compounds with FDR < 0.05 in (B).

Figure 5

Drug discovery nominated plausible candidate drugs and target genes for VTE Drug-target genes nominated by omnibus gene prioritization and proteins nominated by Mendelian randomization (MR) are highlighted in a Manhattan plot of GBMI GWAS for VTE. The compound with a significant negative correlation between genetically regulated and compound-regulated gene expression profiles is also shown.

Figure 6

The three drug discovery approaches complementarily prioritized drug targets for gout The genes prioritized for gout by the three drug discovery approaches were connected to each other if their protein-protein interaction scores were larger than 0.3. Out of the 50 prioritized genes, 43 genes formed a large cluster and shown in the figure. Line thickness represents the strength of protein-protein interaction. The prioritized genes were enriched in six pathways, and these pathways are overlayed on the gene network.