Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10⁻⁸ with slope 1) and rs34743896 (p = 5 × 10⁻¹⁰ with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.

Mapping the pleiotropic effect of genetic variation on biological processes and complex phenotypes is fundamental to extracting translational insight from genome-wide association studies (GWAS). Here we present The Human Genotype-Phenotype Map (GPMap), a repository of colocalizing genetic associations across 15,997 complex traits and 2.7 million molecular measurements, leveraging common and rare variants and cis -and trans -acting effects across disaggregated tissue types and single cell datasets to trace the complex pathways through which they act. We identify over 49.3 million colocalizing trait pairs, which aggregate into 97,393 colocalization groups, representing distinct pleiotropic variants based on shared genetic signals, with 55.8% of genome-wide significant disease-associated loci colocalizing with at least one molecular trait. This insight facilitates clustering of complex health and disease phenotypes based on genetic architecture, and the dissection of polygenic traits reflecting the composite impact of many underlying processes. We show that leveraging pleiotropic information can enhance the selection of genetic instruments for causal inference approaches and improves prediction of drug trial success. This open-source resource is available at https://gpmap.opengwas.io , with functionality for user GWAS upload.

It is often difficult to ascertain whether patient-reported side-effects are caused by a drug, and if so, through which mechanism. Adverse side-effects are the primary cause of antipsychotic drug discontinuation rather than poor efficacy. Using a novel method combining genetic and drug binding affinity data, we investigated evidence of causal mechanisms for 80 reported side-effects of 6 commonly prescribed antipsychotic drugs which together target 68 receptors. We analysed publicly available drug binding affinity data and genetic association data using Mendelian randomization and genetic colocalization to devise a representative ‘score’ for each combination of drug, side-effect, and receptor. We show that 36 side-effects are likely caused by drug action through 30 receptors, which are mainly attributable to off-target effects (26 off-target receptors underlying 39 side-effects). This method allowed us to distinguish which reported side-effects have evidence of causality. Of individual drugs, clozapine has the largest cumulative side-effect profile (Score = 57.5, SE = 11.2), and the largest number of side-effects (n = 36). We show that two well-known side-effects for clozapine, neutropenia and weight change, are underpinned by the action of GABA and CHRM3 receptors respectively. Our novel genetic approach can map side-effects to drugs and elucidate underlying mechanisms, which could potentially inform clinical practice, drug repurposing, and pharmacological development. Further, this method can be generalized to infer the on-target and off-target effects of drugs at any stage of the drug development pipeline.

Adverse side-effects are the primary cause of antipsychotic drug discontinuation rather than poor efficacy. It is often difficult to ascertain whether patient-reported side-effects are caused by a drug, and if so, through which mechanism. Using a novel genetic method we have devised, we investigated evidence of causality and mechanism for 105 reported side-effects of 6 commonly prescribed antipsychotic drugs which target 68 receptors. We used publicly available drug binding affinity data and GWAS summary statistics along with statistical methods including Mendelian randomization and genetic colocalization to devise a representative ‘score’ for each combination of drug, side-effect, and receptor. We show that 48 side-effects are likely caused by drug action through 30 receptors, which are mainly attributable to off-target effects (26 off-target receptors underlying 39 side effects). Of individual drugs, clozapine has the largest cumulative side effect profile (Score = 27.4, SE = 5.2), and the largest number of side-effects (n = 46). We show that two well-known side-effects for clozapine, neutropenia and weight change, are underpinned by the action of GABA and CHRM3 receptors respectively. Our novel genetic approach can map side-effects to drugs and elucidate underlying mechanisms, which could potentially inform clinical practice, drug repurposing, and pharmacological development.

Background: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. Methods: We performed genome-wide association studies (GWAS) for subsequent major adverse cardiovascular events (MACE) (N _cases =51,929, N _cntrl =39,980) and subsequent arterial ischemic stroke (AIS) N _cases =45,120, N _cntrl =46,789) after first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (pQTLs) to determine the effect of 1,463 plasma protein abundances on subsequent MACE using Mendelian randomization (MR). Results: Two variants were significantly associated with subsequent cardiovascular events: rs76472767 (OR=0.75, 95% CI = 0.64-0.85, p= 3.69x10 ^-08 ) with subsequent AIS and rs13294166 (OR=1.52, 95% CI = 1.37-1.67, p=3.77x10 ^-08 ) with subsequent MACE. Using MR, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 (effect OR= 0.77, 95% CI = 0.66-0.88, adj. p=0.05), and TNFRSF14 (effect OR=1.42, 95% CI = 1.24-1.60, adj. p=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. Conclusions: We found evidence that two proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.