Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome

Abstract

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.

Fig 1. Manhattan plot showing the MR relationships between gene expression changes and schizophrenia risk.

Chromosomal position is on the x-axis and p-value for the Wald ratio estimate (-log10 scaled) is on the y-axis. The prioritised genes which passed the multiple correction threshold (solid red line) and colocalization analysis (PP₄>70%) are annotated as diamonds and labelled with the gene name. FAM86B3P and FAM85B genes on chromosome 8 and AC243562.2, GOLGA2P7 and NMB genes and FURIN and FES genes on chromosome 15 are located close together (in LD with each other), challenging attribution of the causal gene at these loci.

Fig 2. Manhattan plots showing the MR relationships between gene expression changes and Alzheimer’s disease and Parkinson’s disease outcomes.

Chromosomal position is on the x-axis and p-value for the Wald ratio estimate (-log10 scaled) is on the y-axis. The prioritised genes which passed the multiple correction threshold (solid red line) and colocalization analysis (PP₄>70%) are annotated as diamonds and labelled with the gene name. For Alzheimer’s disease the ZNF646, KAT8 and PRSS36 genes on chromosome 16 are in LD, and for Parkinson’s disease the GPNMB and KLHL7-DT genes on chromosome 7 and the STX4, AC135050.3 and HSD3B7 on chromosome 16 are in LD, challenging attribution of the causal gene at these loci.

Fig 3. Error bar plot (95% confidence interval around the eQTL effect is displayed) comparing gene expression effects across the 13 brain tissues available in GTEx version 7.

9 of our 47 prioritised genes showed statistical evidence of effect size heterogeneity across the brain tissues. STX4 and TSPAN14 genes showed evidence of differing direction of gene expression effect for certain brain tissues.