Challenges and coping experiences in caring for children with Autism Spectrum Disorder (ASD) in central region, Ghana.

Major depression (MD) is a leading cause of years lived with disability globally []. Current pharmacological treatments are ineffective for many patients, with only one-third reaching remission after first-line antidepressants and 28–40% classified as having treatment-resistant depression [, ]. Currently approved drugs target a narrow range of therapeutic mechanisms, with most antidepressants acting on monoaminergic neurotransmitter systems []. The lack of novel pharmacological treatments for MD highlights the pressing need for innovative strategies to facilitate efficient drug discovery. Human genetics offers a promising avenue to guide the development of pharmacological treatments. Drugs acting on targets with supporting human genetic evidence are 2.6 times more likely to succeed in drug discovery pipelines and progress into clinical practice than those without [–]. This is reflected in recent FDA approvals: 63% of drugs approved in the past decade had genetic support []. Given that genes encode proteins, which constitute over 90% of drug targets, genetic methods can identify disease-associated protein targets for repurposing existing drugs or novel development [, ]. To accelerate discovery and repurposing, an efficient strategy involves focusing on the ‘druggable genome’ rather than all protein-coding genes, reducing the multiple testing burden [–]. The druggable genome comprises genes encoding proteins targeted by drugs that are already approved, those at the clinical stage of drug development, or those potentially druggable based on experimental evidence []. Mendelian randomisation (MR) enables scanning of the druggable genome by using genetic variants as instruments to estimate the effect of altering each drug target’s activity on disease risk []. For each target protein, genetic variants selected as instruments lie near the protein-encoding gene (-region) []. These instruments are associated with individual variation in a molecular trait in a given tissue, including changes in protein values or mRNA expression []. -MR leverages data from genome-wide association studies (GWAS) to obtain the effects of each selected variant on both the exposure (target protein activity) and the outcome (MD). To infer exposure-outcome causal effects, MR assumes that variant effects on the outcome operate through the exposure and are independent of confounders []. Under these assumptions, significant MR estimates suggest modulating the corresponding proteins may influence disease processes, identifying candidate targets for that outcome [, ]. MR has key advantages for identifying drug targets. First, the random assortment of genetic variants at conception mimics treatment allocation in randomised controlled trials, strengthening causal inference of target-outcome effects []. Second, MR provides effect directions to infer whether pharmacological activation or inhibition of a target mitigates disease risk []. Effect directions can be combined with pharmacological data on existing drugs to identify target-compound pairs with aligned mechanisms, aiding repurposing [, ].