Identification of therapeutic targets from genetic association studies using hierarchical component analysis

Hao-Chih Lee,Pankaj Agarwal,Aparna A Divaraniya,Benjamin S Glicksberg,Christine E Becker,Osamu Ichikawa,Joel T Dudley

doi:10.1186/s13040-020-00216-9

Abstract

BackgroundMapping disease-associated genetic variants to complex disease pathophysiology is a major challenge in translating findings from genome-wide association studies into novel therapeutic opportunities. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues.ResultsWe present a novel strategy, called GWAS component analysis, for transferring disease associations from single-nucleotide polymorphisms to co-expression modules by stacking models trained using reference genome and tissue-specific gene expression data. Application of this method to genome-wide association studies of blood cell counts confirmed that it could detect gene sets enriched in expected cell types. In addition, coupling of our method with Bayesian networks enables GWAS components to be used to discover drug targets.ConclusionsWe tested genome-wide associations of four disease phenotypes, including age-related macular degeneration, Crohn’s disease, ulcerative colitis and rheumatoid arthritis, and demonstrated the proposed method could select more functional genes than S-PrediXcan, the previous single-step model for predicting gene-level associations from SNP-level associations.

Highlights

Genome-wide association studies (GWAS) seek to identify how genetic variations, typically represented as single-nucleotide polymorphisms (SNPs), contribute to variability in expression of phenotypic traits or diseases across the population
The coexpression modules determined by weighted correlation network analysis (WGCNA) are likely to reflect biological pathways and gene functions [20], and we sought to probe if these co-expression modules were linked to genetics
For a single tissue, ~ 2% WGCNA edges are overlapped with the multi-species co-expression network despite the overlap is very significant (Odds ratios range from 1.45 to 39.87, Supplementary Table 1)

Summary

Introduction

Genome-wide association studies (GWAS) seek to identify how genetic variations, typically represented as single-nucleotide polymorphisms (SNPs), contribute to variability in expression of phenotypic traits or diseases across the population. Studies have demonstrated the widelyspread SNP associations with tiny effect sizes can collectively contribute to a large portion of heritability for complex traits such as schizophrenia [12] and height [13]. These ubiquitous genetic signals across genome, acting directly on any genes, may propagate through interconnected gene regulatory network to affect functions of disease-related genes [14]. Studies have shown that hub genes, genes interacting with many other genes, are subject to negative evolutionary selection [15,16,17], hinting the potential of combing network topology with genetic signals in search of therapeutic targets. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues

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Discussion

Conclusion