Network and pathway expansion of genetic disease associations identifies successful drug targets

Aidan Macnamara,Alex Gutteridge,Ali Amin Al Olama,Mark R Hurle,Cong Guo,Karsten B Sieber,Nikolina Nakic

doi:10.1038/s41598-020-77847-9

Aidan Macnamara, Alex Gutteridge + Show 5 more

Open Access

https://doi.org/10.1038/s41598-020-77847-9

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Abstract

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand–receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein–protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.

Highlights

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases
Several previous studies have applied the concepts of network propagation to disease association and identification of novel drug t argets[10,11], but none have directly and systematically addressed the question of whether such approaches can replicate the effect observed by Nelson et al, which is what we focus on in this study
Our negative control is a set of randomly chosen genes from the background set which we confirm to have no significant enrichment for successful drug targets (OR: 1; p = 0.8)

Summary

Introduction

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. The hypothesis of these studies is that genetic associations are more likely to be due to causal relationships between gene activity and disease risk compared with other forms genomic association analysis, such as transcriptomics This is due to the lack (in most common diseases outside cancers) of any known molecular mechanism for how the presence of disease could affect DNA sequence. The hypothesis behind the use of GWAS for disease gene identification is that with DNA sequence associations this reverse causality can be more confidently excluded in many cases To test this hypothesis, Nelson et al.[2] showed through analysis of historic drug discovery programs, that genes with a direct genetic link to a disease have comprised 2% of preclinical drug discovery programs, compared to 8.2% of approved drugs. There may be an absence of suitable genetic instruments or we lack the ability to confidently map disease association signals to their cis or trans effector genes

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