Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment

Abstract

Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases.

Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases.

Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification.

Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.

Figure 1

Overview of the workflow used in this study. 1) Meta-GWAS of 256 diseases and disease traits (left) revealed Th differentiation (right) to be the pathway most enriched for GWAS genes. 2) Identification of disease modules in eight CD4⁺ T-cell-associated diseases (A, allergy; AML, acute myelogenous leukemia; ATL, adult T cell leukemia; CLL, chronic lymphocytic leukemia; HES, hypereosinophilic syndrome; MS, multiple sclerosis; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus). These modules partially overlapped and formed a pleiotropic module. The pleiotropic module is marked using a solid black circle. 3) The pleiotropic module was highly enriched for genes relevant to many diseases. 4) Prospective clinical studies of multiple sclerosis and seasonal allergic rhinitis showed that pleiotropic and disease-specific genes could stratify patients for individualized medication.

Figure 2

The pleiotropic module was enriched for GWAS genes, cancer genes, as well as therapeutic targets and biomarkers. (A) Schematic representation of the pleiotropic module. The inner circle represents nucleoplasmic genes and the outer non-nucleoplasmic genes according to Gene Ontology. Therapeutic targets are marked with squares, biomarkers with tilted squares, and hexagons represent both. Node colors code for GWAS genes (yellow), cancer genes (blue), both (green). For clarity we do not show the 7,144 interactions between the pleiotropic module genes. (B) Strongly positive correlations were found between the fraction of GWAS genes and cancer genes in the modules versus the number of disease modules. PCC, Pearson's correlation coefficient.

Figure 3

Classification of treatment response based on pleiotropic or disease-specific genes. (A) Glucocorticoid (GC) treatment of CD4⁺ T cells from patients with seasonal allergic rhinitis (SAR) had the largest effect on the expression of genes that participated in many disease modules. This effect was not observed following natazulimab treatment of CD4⁺ T cells from patients with multiple sclerosis (MS). The figure shows the correlation between the mean treatment ± standard error of the mean effect on mRNA expression measured by the squared student t-values between GC-treated and untreated cells and the number of disease modules a gene participated in. PCC, Pearson's correlation coefficient. (B) Pleiotropic or disease-specific genes accurately classified high and low responders to treatment in SAR and MS. The estimated probabilities (cross-validated) of a sample being a high responder (HR) based on the LASSO classifiers after drug treatment. The horizontal black line at 0.5 represent the classification border of HR and low responders (LR). The probability estimates of each group of patients are summarized into box-plots showing the median, inner quartile range, whiskers and outliers.