The Pharmacogenomics Journal

Introduction: The application of pharmacogenomics (PGx) in pediatrics is limited by the lack of age-oriented interpretation approaches, as algorithms developed for adults do not account for ontogenetic changes in the activity of drug-metabolizing enzymes and transport proteins. The aim of this study was to evaluate the clinical applicability of pharmacogenomic data in Russian children, assess the concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes, and develop recommendations for the generation of age-oriented PGx reports. Methods: We analyzed whole-exome sequencing (WES) data from 524 pediatric patients and 635 newborns, filtering pharmacogenomic annotations according to PharmGKB/ClinPGx evidence levels (1A-2B) and the presence of the 'Pediatrics' tag. The concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes was assessed in newborns. In a pediatric subgroup of 100 patients, a retrospective analysis of medical records was performed to evaluate the structure of pharmacotherapy and the frequency of adverse drug reactions (ADRs). A 'PGx-ADR-cost' database was created, and the relative population burden index was calculated for 27 gene-variant-drug-ADR associations. Results: Clinically relevant annotations (requiring drug avoidance or dose modification) accounted for only 5% of all initial pharmacogenomic annotations in both cohorts; 67.6% (pediatric cohort) and 67.2% (neonatal cohort) of these were related to alleles with altered function. Concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes in newborns was observed in only 5 of 14 (35.71%) gene-drug pairs. ADRs were identified in 21% of the 100 pediatric patients; however, only two cases could be explained by high-evidence PharmGKB/ClinPGx annotations. Ranking by relative population burden identified UGT1A1*28-irinotecan-induced neutropenia and HLA-A*31:01-carbamazepine-induced severe cutaneous reactions as priority associations. Conclusions: Age represents a critical factor in the interpretation of pharmacogenomic data in children, as current approaches to PGx reporting do not adequately incorporate the ontogenetic context. We propose a pediatric PGx interpretation model that includes mandatory reporting of patient age, ontogenetic adjustment, evidence-level stratification, and multidisciplinary clinical assessment. Prospective validation is required to confirm the clinical utility of the proposed approach.

Background. Pharmacogenetic (PGx) testing can guide drug prescribing but remains limited by the genomic assay used. Genotyping arrays are widely implemented yet limited to predefined variants, whereas low-pass whole-genome sequencing (LP-WGS) is not constrained by fixed probe design and may provide broader PGx variant availability after imputation. Methods. We compared Illumina Global Screening Array (GSA) v3 with ~1x LP-WGS for PGx profiling in 500 hospital biobank participants with electronic health record evidence of exposure to pharmacogenetically actionable drugs and reported adverse drug reactions. Concordance was evaluated genome-wide, at 20 actionable pharmacogenes for PharmCAT-derived star alleles and metabolizer phenotypes, and for HLA alleles. Results. Genome-wide concordance between imputed array and LP-WGS data was high (median 99.63%; interquartile range, 99.59%-99.64%). For pharmacogenetically relevant variants, LP-WGS captured a larger fraction, particularly rare alleles absent from the array data, whilst maintaining high concordance at shared sites. Predicted phenotype concordance exceeded 98% for most genes, although gene-specific differences in phenotype classification were observed. LP-WGS reduced missing phenotype assignments for selected loci, particularly CYP2C19 and NAT2, by improving resolution of star-allele structure. However, in structurally complex or incompletely characterized genes such as CYP2C9 and CYP2D6, broader variant recovery increased indeterminate classifications rather than consistently improving clinical interpretability. For HLA loci, concordance varied by imputation strategy, with SNP2HLA performing marginally better utilizing the GSA array compared to the LP-WGS approach. Conclusions. Overall, LP-WGS provides broader variant coverage and improved resolution for selected pharmacogenes but did not resolve all clinically important loci. These findings support further evaluation of LP-WGS as a scalable PGx screening approach, especially where long-term genomic data reuse is a priority.

Sub-Saharan African populations carry pharmacogenomic alleles poorly represented in the European-derived reference panels underlying most clinical genotyping tools. We present a curated, machine-readable catalog of nine actionable alleles across six pharmacogenes (CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP3A5, NAT2) with African-specific frequency ranges, functional annotations, and evidence levels derived from reanalysis of 661 high-coverage whole-genome sequences across seven 1000 Genomes Project African populations. Direct comparison against PharmCAT v3.4.0 shows that CYP2D6 produces zero diplotype calls (0/661 samples callable) due to monomorphic reference positions absent from standard variant-only VCF output, a known limitation whose consequences for African allele carriers had not been reported. afripharmagen's reduced-position strategy identifies 243 CYP2D617 and 134 CYP2D629 carriers from the same input. For CYP2B6, CYP2C9, CYP2C19, and NAT2, both tools show concordance of 95-100%. Frequency gradients (CYP2B66: 30-50%; CYP2D617: 15-35% in West Africa; CYP3A5*1: 60-95%) translate directly into prescribing risk for efavirenz, tramadol, tacrolimus, and isoniazid. Pharmacogenomic decision support in African settings must incorporate population-specific allele definitions and input-format-aware strategies.

Pharmacogenomics (PGx) can improve safety and effectiveness of commonly dispensed medicines, but its value at the population level depends on how often clinically actionable PGx phenotypes co-occur with the medicines they affect. We assessed this co-occurrence in a cross-sectional analysis of Our Future Health (OFH), a new UK national biobank, by applying Pharmacogenomics Clinical Annotation Tool (PharmCAT v3.1.1) to imputed genotypes from 738,531 participants across 17 pharmacogenes with established PGx prescribing guidelines. Every participant had at least one actionable PGx phenotype, with a mean of 6.1 (SD 1.3). The number of actionable PGx phenotypes was similar across genetically inferred ancestry groups, although the pharmacogenes contributing to that count differed between groups. Using linked primary care dispensing records, 36.8% (95% CI 36.7-36.9) had been dispensed at least one medicine between April 2018 and June 2025 matched to a gene for which they carried an actionable PGx phenotype. Co-occurrence rose with age, ranging from 43.7% to 58.9% across ancestry groups among those aged [≥]70 years. Participants carried an actionable PGx phenotype for a mean of 13.8 (SD 6.5) of the 33 medicines dispensed in English primary care with PGx prescribing guidance, of which a mean of 0.6 (SD 1.0) had been dispensed. Co-occurrence was concentrated in a few widely dispensed classes, principally proton-pump inhibitors and antidepressants acting through CYP2C19 and statins through SLCO1B1. These findings highlight opportunities to optimise treatment for a large proportion of patients receiving routine medications and identify where pre-emptive PGx testing could have the greatest clinical benefit.

Objectives: To assess how patients with non-valvular atrial fibrillation (NVAF) receiving oral anticoagulants are managed in routine primary care practice in Spain. Methods: This observational, descriptive study included patients with NVAF treated with oral anticoagulants for at least 6 months before enrolment and managed in primary care settings in Spain. The Barthel and ACTS questionnaires were administered to evaluate functional autonomy and treatment satisfaction, respectively. Results: A total of 1,901 patients were included: 428 received vitamin K antagonists (VKAs) and 1,473 direct oral anticoagulants (DOACs). Compared with patients receiving DOACs, those treated with VKAs were significantly older and had a higher prevalence of more hypertension. Mean treatment duration was 7.6 years for VKAs and 3.8 years for DOACs. Among patients receiving VKAs, 56.2% and 59.0% achieved good anticoagulation control according to the direct and Rosendaal methods, respectively. Patients in the DOAC group reported greater satisfaction across several domains, including perceived treatment benefits, lower impact on daily life, and overall positive treatment effect. Event incidence rates (per 1,000 person-years) were higher with DOACs than with VKAs for stroke (1.75; 95% CI 1.05-2.92), ischemic stroke (1.78; 95% CI 1.06-3.00), acute myocardial infarction (1.64; 95% CI 0.97-2.79), and major bleeding (3.68; 95% CI 1.81-7.46). Conclusions: In routine primary care practice in Spain, patient profiles and treatment duration varied by oral anticoagulant type. These differences may partly explain the higher rates of stroke and major bleeding observed with DOACs versus VKAs, despite greater treatment satisfaction among patients receiving DOACs.

Accurately predicting the effects of pharmacogenomic variants is essential for the development of personalized therapeutic strategies, as genetic variability can influence drug response differently across patients. Here, we assessed several computational approaches using a dataset of pharmacogenomic variants with either clinical annotations or functional characterization by deep mutational scanning, compiled from the literature, with an additional focus on CYP2C9, a clinically relevant drug-metabolizing enzyme. Our results show that, despite recent methodological advances, substantial room for improvement remains. In particular, current methods struggle to distinguish gain-of-function variants associated with increased drug clearance and fast-metabolizer phenotypes from neutral variants, whereas loss-of-function variants that reduce drug clearance are predicted more accurately. The integration of structural and evolutionary information appears to be a key strategy for improving performance, with the coevolution-based StructureDCA method achieving the highest accuracy compared with classical genetic variant-effect predictors and recent deep learning approaches, including the pathogenic-variant predictor AlphaMissense and general protein language model-based methods. Finally, our results indicate that computational models can complement in vitro experiments in clinical variant interpretation, as StructureDCA predictions showed better agreement with clinically annotated phenotypes than large-scale deep mutational scanning data in several cases.

Since copy number variations (CNVs) in pharmacogenes can cause significant alterations in drug metabolism, their reliable detection is of high importance both for large-scale studies and personalized medicine. Whole-genome sequencing, and specifically long-read sequencing, is the gold standard for CNV detection. Despite increasing availability of these technologies, genotyping arrays are still widely used as cost-effective alternatives in biobank and clinical settings, yet calling CNVs based on array intensity signals is challenging due to low base pair resolution. In this work, we developed a neural network model, nnCNV, to predict deletions in the CYP2C19 pharmacogene region from array intensity signals. We compared our method to the most widely used algorithm, PennCNV, and demonstrated better performance reaching 100% accuracy in the test dataset. Furthermore, we predicted probe-by-probe CYP2C19 deletion coordinates for all Estonian Biobank samples using nnCNV and PennCNV, and validated these predictions using an identity-by-descent (IBD) sharing method, which also demonstrated superior nnCNV performance. For the deletion samples with conflicting PennCNV and nnCNV predictions, we performed PCR analysis for validation, which showed 97% precision for nnCNV compared to 23% for PennCNV. Finally, we assessed the gradient-based feature importance maps and showed that nnCNV utilizes signal intensity information not only from deletion probes, but also from probes in flanking regions. Our results demonstrate that long-range information, which cannot be utilized by hidden Markov models, can improve CNV calling.

Persons with epilepsy (PWE) experience high rates of psychiatric comorbidity, yet the population-scale pharmacoepidemiology of psychiatric medication (PM) use alongside antiseizure medications (ASMs) has not been previously characterized. Using Epic Cosmos, a federated electronic health record network spanning >300 million patients across >2,000 hospital systems, we examined patterns of ASM and PM co-prescriptions in PWE (ICD- 10 G40.x) every year between 2018-2025. PM prescriptions were similarly assessed in patients with asthma (J45.x). We found that despite the introduction of several newer ASMs, the overall prescribing landscape remained stable, with little change in the relative use of individual ASMs over time. Compared with asthma patients, PWE were more likely to receive prescriptions for opiates, antidepressant and antipsychotic medications across the age spectrum. 17-year-old or younger PWE were more likely to receive ADHD/stimulant medications, whereas adults and older adults exhibited a shift toward cognitive enhancing agents. We did not observe a preponderance of PM co- prescribing with any specific ASM or ASM class. Together, these results provide a population-scale, age-specific survey of psychiatric medication co-prescriptions in epilepsy, establishing a framework to monitor surrogate markers of psychiatric comorbidity and to support pharmacovigilance of potential drug-drug interactions.

Motivation: Genetically supported drug targets are an estimated 2.6 times more likely to succeed clinically, yet a disease association alone does not show which protein to target, in which direction, or what other effects may follow. This currently requires chaining separate tools. Results: drugMR is a Nextflow pipeline that, from GWAS and pQTL summary statistics, runs cis-Mendelian randomisation, colocalisation, SMR-HEIDI with bulk and/or single-cell QTLs, three-trait colocalisation and a phenome-wide screen for adverse effects and repurposing. Datasets are added through a manifest, every step runs in containers on a laptop, cluster or cloud, and results are explored in a dashboard. Applied to schizophrenia with brain pQTLs, drugMR tested 2,019 proteins in 4 h 19 min and prioritised 58 proteogenomic targets, 22 with multi-omic support; 4 had potential adverse effects. Availability and implementation: drugMR: https://github.com/guillermocomesanacimadevila/drugMR; documentation: https://guillermocomesanacimadevila.github.io/drugMR/. Demo data: https://doi.org/10.5281/zenodo.22917384.

Preeclampsia (PE) is a hypertensive pregnancy-specific disorder and a leading cause of maternal and fetal mortality. A common feature of PE placentas and maternal plasma is dyslipidemia, or abnormal lipid levels, which can increase oxidative stress and endothelial dysfunction. However, the precise transcriptional, post-transcriptional, and epigenetic mechanisms underlying these abnormalities remain poorly characterized. Identifying such changes may clarify disease mechanisms and identify lipid-related PE biomarkers. We conducted a large-scale meta-analysis integrating public placental datasets from NCBI GEO, comprising four DNA methylation (DNAm) datasets (n = 172), three RNA-sequencing datasets (n = 92), and an independent RNA microarray validation cohort (n =146). We evaluated differential DNAm (limma), gene expression (DESeq2), transcript-level shifts (Swish), and alternative splicing (rMATS) in PE versus control placentas, with all analyses stratified by fetal sex via an interaction term model. We also performed placental cell-type deconvolution to quantify PE-associated cell-type proportion changes. Our results demonstrated that lipid-related regulation changes in PE placentas occur primarily at the gene and transcript level, with DNAm showing no changes. We also identified significant isoform switching in PE that were undetected by differential gene expression analysis, and primarily driven by alternative transcription initiation and termination sites rather than alternative splicing. A subset of these isoform switches mapped to pathways dysregulated in PE and were predicted to cause functional protein changes. An interaction term model identified several sex-specific differentially expressed genes (DEGs) in PE, including a subset of male-specific downregulated genes involved in oxidative metabolism. However, many of the remaining sex-specific DEGs across both sexes were previously uncharacterized in the literature. These findings suggest that transcriptional and isoform-level regulation play a role in PE-associated dyslipidemia, with certain regulatory pathways displaying fetal sex-specific patterns. Highlights- Preeclampsia-associated dyslipidemia manifests at the gene and transcript level - Reciprocal isoform switches were missed by standard gene-level analyses - Alternative transcript initiation and termination drove isoform switching - Sex-interaction modeling identified sex-specific transcriptional shifts in PE

Computational safety evaluation of phytochemical libraries commonly relies on models trained on experimentally tested compounds and applied to much larger and chemically diverse collections. An important but often overlooked question is whether predicted compounds are adequately represented within the experimental chemical space underlying these models. Here, we examined this question for the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database using experimentally characterized datasets for three anti-target endpoints: hERG blockade, cytochrome P450 (CYP) inhibition, and pregnane X receptor (PXR) induction. After structure standardization, 14,956 unique IMPPAT compounds were compared with 8,877 hERG, 4,905 CYP, and 4,138 PXR reference compounds using consensus nearest-neighbor similarity based on Morgan and atom-pair fingerprints. At a Tanimoto similarity threshold of 0.80, only 0.92%, 0.30%, and 0.46% of IMPPAT compounds had close measured analogs in the hERG, CYP, and PXR datasets, respectively. Relative to the internal nearest-neighbor distributions of the reference datasets, a typical IMPPAT compound lay near the 1st percentile, indicating substantial chemical-space separation. Compounds with close experimental analogs also showed distinct molecular characteristics, with descriptor-based logistic regression yielding cross-validated AUC values of 0.77-0.80. Plant-level enrichment was markedly reduced after excluding widely distributed metabolites and was further restricted when the nearest measured analog was required to have pIC50 or pEC50 [≥] 5. The remaining signals were concentrated in a limited number of taxa, including Rauvolfia, Sinopodophyllum/Dysosma, Cinchona, and Catharanthus roseus. Overall, most IMPPAT phytochemicals lack close structural representation among currently measured anti-target compounds. Computational safety predictions for these molecules should therefore be interpreted as extrapolations and accompanied by explicit measures of structural support. Absence of a close experimental analog should be regarded as a gap in available evidence rather than evidence of safety.

PurposeTo describe the development of - and outcomes from - an institution-level initiative supporting access to innovative genetic medicines information and opportunities. MethodsWe developed and implemented a Genetic Medicines Patient Assessment Pathway for rare genetic conditions at a large public hospital, with multidisciplinary teams collaborating to evaluate a patients molecular and clinical amenability to experimental and/or to-be-developed genetic medicines (primarily nucleic acid therapeutics). A Genetic Medicines Consult Clinic operating in parallel offered no-cost specialized consultations and counseling to families regarding genetic medicines inquiries and Pathway outcomes. We retrospectively reviewed submissions, decisions, and outcomes over a >2-year period. ResultsA total of 63 submissions were reviewed in the Patient Assessment Pathway. Most submissions were classified as eligible or potentially eligible for at least one genetic medicines approach (42/63; 67%). Recommendations generated 87 discrete actions, such as outreach to potential collaborators (n = 28), offering participation in existing research studies (n = 23), or referral to the Consult Clinic (n = 19). Two patients have been approved for therapeutic program initiation or clinical trial enrolment. ConclusionFindings to date support feasibility and high uptake of a structured, multidisciplinary, medical genetics-led model for assessing patient-level opportunities for genetic medicines.

The majority of antidepressant and antipsychotic medications are underscored by their common weight-related adverse drug events; medication-induced weight gain is a central hindrance to psychiatric treatments because it impacts patient compliance and results in a cascade of secondary comorbidities with negative health outcomes. Here, we use network pharmacology approaches to characterize the multiple effects of drugs and explore the mechanisms by which drug-molecule interactions lead to adverse drug events. We first construct a robust interactome containing 17,293 proteins (network vertices) and 408,883 weighted interactions (network edges) and identify communities of interconnected vertices using a modularity optimization clustering algorithm. We link 33 antidepressants and 26 antipsychotics into this network via their drug-protein interactions and implement network measures of cosine similarity, shortest path distance, and connectivity significance to determine which communities are most proximal to the drugs. We identify a subnetwork of 33 (out of 536) communities enriched for genes associated with adiposity traits to test for connectivity significance to the drug vertices. Through an additional random walk analysis, we triangulate consistent links between mental health drugs and molecular pathways, including GPCR, PI3K-Akt, leptin, and insulin signaling, as well as adipogenesis. We highlight specific components within these curated pathways and map the crosstalk between them. While we demonstrate our approach in the context of mental health drugs and their weight-related side effects, our study design is broadly applicable to a wide range of drug classes, drug events, and traits of interest.

Objective: Whether a scheduled antiseizure medication (ASM) continues on schedule across the ICU-to-floor transfer has not been characterized. We quantified ASM administration-gap frequency across this transfer and compared it with gap frequency during matched non-transfer intervals in the same patient and drug. Methods: In this retrospective MIMIC-IV (version 3.1) cohort study, we identified epilepsy and status-epilepticus admissions with an ICU stay followed by floor transfer and a scheduled ASM order active at ICU departure. A gap was defined as an interval exceeding 1.5 times the expected dosing interval between the last ICU dose and first floor dose, or no further dose before discharge, and compared with a matched non-transfer control interval in the same patient and drug (paired McNemar test). A multivariable model evaluated six prespecified clinical predictors; sociodemographic variables were summarized descriptively. Results: Among 2,469 ASM transition-by-drug observations (1,583 admissions, 1,335 patients), an administration gap occurred in 251 (10.2%; 95% CI, 8.7%-11.7%). Gap frequency across the transfer exceeded frequency during matched non-transfer control intervals in the same patient and drug: a paired rate difference of 5.8 percentage points (95% CI, 4.4-7.1; 7.5% vs 1.7%; P = 7.3 x 10^-22) before the transfer and 6.4 percentage points (95% CI, 4.9-7.9; 8.9% vs 2.5%; P = 1.9 x 10^-23) after. Gap rates were similar for intravenous-available (9.9%) and oral-only (11.4%) drugs (rate difference, 1.5 percentage points; 95% CI, -1.6 to 4.5; P = .34). None of six prespecified predictors reached significance after correction. Significance: An antiseizure medication administration gap occurred in approximately 1 of every 10 drug-transition observations at the ICU-to-floor transfer, exceeding matched non-transfer gap rates by 5.8 to 6.4 percentage points. This transfer-associated excess, rather than any single medication or patient characteristic, supports a structured medication-continuity check.

Background and Objective: Prescribing multiple drugs simultaneously, known as polypharmacy, is increasingly common in managing chronic diseases such as diabetes and cardiovascular conditions. Studies show that roughly 40% of elderly patients are on five or more medications, putting them at significant risk of adverse drug-drug interactions (DDIs) [1]. While computational methods based on Graph Neural Networks (GNNs) have shown strong potential for predicting these interactions from a drugs molecular structure, most existing models are limited in two important ways: they rely on attention mechanisms that cannot adapt to the unique context of each atom in a molecule, and they only predict whether an interaction exists rather than how dangerous it might be. Methods: We propose HMVA-DDI, a two-stage deep learning framework that addresses both shortcomings. In the first stage, our model predicts whether two drugs will interact. If it detects an interaction, the second stage classifies it into one of three clinical severity levels: minor, moderate, or major. To better capture molecular structure, we replace the standard attention mechanism with a more powerful version called Graph Attention Network v2 (GATv2), which considers each atoms specific context when learning its representation. We also introduce a technique to prevent information loss in deeper network layers, and we tailor the input features separately for each prediction task. Results: Evaluated on two standard benchmarks, DrugBank 5.0.3 [2] and DDInter [3], HMVA-DDI achieves an AUROC of 0.9819 and AUPRC of 0.9759 on binary interaction detection, a marked improvement over the baseline. For severity classification, our best model variant (qcSvr4) reaches an overall AUROC of 0.9444, with a particularly large improvement in detecting low-severity (Minor) interactions, a historically difficult class. Conclusion: HMVA-DDI bridges the gap between computational drug research and clinical usefulness by not only predicting interactions accurately but also indicating interaction severity. This two-level system can help healthcare professionals prioritize warnings, reducing the alert fatigue problem where doctors are overwhelmed by too many non-critical notifications.

BACKGROUND Safe and effective antidepressant deprescribing strategies are needed in general practice where most antidepressant prescribing occurs. METHODS We conducted a pragmatic, cluster-randomised controlled trial in general practice to test invitation to general practitioner (GP) review combined with resources to inform shared decision-making and guide hyperbolic tapering for stopping antidepressants compared to usual care. Adults taking antidepressants for longer than 12 months were recruited from 26 Australian GP practices between March 2023 and November 2024, irrespective of their intention to stop or depression or anxiety symptom scores. The primary outcome was cessation at 12 months. Secondary outcomes included cessation at 6 months, and >75% dose reduction and depression, anxiety and withdrawal symptom scores at 6 and 12 months. RESULTS Overall, 483 patients were randomised. Mean age was 50 years; 73% were women; mean duration of antidepressant use was 14.1 years. Cessation at 12 months was observed in 32 of 215 (14.9%) intervention and 16 of 187 (8.6%) usual care patients (odds ratio (OR) = 1.95 [95%CI, 1.00 to 3.81]; p=0.050). Cessation at 6 months was observed in 11.7% intervention vs 4.8% usual care (OR = 2.68; 95%CI, 1.18 to 6.05), and >75% dose reduction at 12 months in 19.6% intervention vs 9.9% usual care (OR = 2.28; 95%CI, 1.20 to 4.31). Symptom scores were similar between groups. No adverse events were attributable to the intervention. CONCLUSIONS In general practice, invitation to GP antidepressant review combined with information and guidance on hyperbolic tapering can support cessation or dose reduction without causing adverse effects or relapse. Absolute cessation rates were modest but still meaningful given the high prevalence of long term antidepressant use. TRIAL REGISTRATION ANZCT registry identifier, ACTRN12622001379707p.

Background: Genetic and observational studies have identified metabolic factors potentially contributing to the risk of Major Depression (MD). However, studies to date have concentrated on common genetic variants, many of which are in linkage disequilibrium. This makes it difficult to localise variants to a single gene. To overcome this, we used short read whole-genome sequenced datasets (srWGS) to conduct a targeted rare variant association analysis of the FADS region, which has previously been implicated in metabolic effects on MD. Methods: Using srWGS data from 740,373 individuals from All of Us (AoU) and the UK Biobank (UKB) we tested for associations between rare variants in the FADS region and MD. Additionally we conducted association tests between rare FADS variants and five blood-based metabolites implicated by previous studies of MD. Finally, we applied Mendelian randomisation to identify causal effects of metabolites on MD using rare-variants. Results: We found FADS rare variants were significantly associated with all tested metabolites, but not with MD. Using Wald-Ratio MR we found a putative causal effect of Docosahexaenoic acid (DHA) on MD using a rare-variant instrument in the FADS2 gene. Conclusion: Our study represents the largest rare-variant analysis of MD to date. However, the lack of associations between FADS rare-variants and MD suggests our analysis is underpowered, or that genetic effects on MD are conferred substantially by higher frequency variants. Rare-variant MR suggested a causal effect of DHA on MD, and implicates the FADS2 gene.

Background Oral contraceptive (OC) use, particularly during adolescence, may increase depression risk in some individuals, but it remains unclear who is susceptible to mood-related side effects and who is not. We aimed to detect single nucleotide polymorphisms (SNPs) and genes that moderate the effect of OC use on depression in young adulthood using data from the UK Biobank. Methods N=202,243 participants were followed from birth to age 23.29 (SD: 2.58) years. We used Cox models for counting processes to test the association between OC use and incident depression in young adulthood, and conducted a genome-wide-by-drug-interaction study (GWDIS) of SNP by OC use interactions alongside a standard genome-wide association study (GWAS) on incident depression in young adulthood. Results Over half of all participants (57.5%) initiated OC and 1.0% received a depression diagnosis during follow up. OC initiators had a 20% higher hazard of incident depression than non-initiators (HR=1.20, 95% CI=1.04-1.37). No SNPs reached genome-wide significance in the GWDIS, though eight showed suggestive interaction signals (p<1e-5). At the gene level, FSIP1 (p=5.90e-5) and EHBP1 (p=6.47e-5) showed suggestive signals, but none passed the genome-wide threshold. No SNPs reached genome-wide significance in the GWAS. Conclusions We did not find evidence for genetic variants that moderate the association between OC initiation and depression. If such effects exist, they are likely to be small and polygenic, suggesting there is currently no solid basis for using genetic data for individualised contraception counselling concerning mood-based side effects.

Background: Preeclampsia (PE) is a hypertensive pregnancy disorder with contributions from both maternal and fetal genetic factors, but how both genomes regulate placental transcription remains incompletely understood. Results: We analyzed placental 5'-end RNA sequencing data from 59 pregnancies in the FINNPEC cohort and performed separate maternal and fetal cis-eQTL analyses using gene-level expression and promoter-level expression defined as Transcript Far 5' Ends (TFE). Genotype-by-PE interaction models were used to identify genetic effects that differ according to PE status. No gene-level interaction eQTLs remained significant after multiple-testing correction, although suggestive signals included LILRB2 in the maternal and LPCAT3 in the fetal analyses. In contrast, we identified two significant syntenic promoter-level interaction eQTLs: rs12504642-TFE61820 at the FHDC1 locus in the maternal analysis and rs1557150-TFE9032 at the PPIAP31 locus in the fetal analysis. Conclusion: These findings suggest that PE-dependent genetic interaction effects may be detectable at specific transcript promoter regions even when they are not captured at annotated gene levels.

Polygenic risk scores (PRS) are rapidly developing, enabling estimates of genetic risk for developing psychiatric disorders. Psychiatric PRS are also increasingly entering consumer and clinical settings. Yet, little is known about how adults with psychiatric disorders perceive their potential use. Here, we conducted semi-structured interviews with 29 adults with psychiatric disorders to examine their perspectives on hypothetically receiving psychiatric PRS for themselves and their current or future children. Key themes emerged around perceived utility, motivations and deterrents to genetic testing, interpretation of PRS, and anticipated responses to results. In particular, participants expressed hope that PRS could support self-understanding, inform better psychiatric care, and enable earlier recognition of difficulties for their children. Participants however raised multiple concerns, including those of privacy, stigma, discrimination, eugenics, and the risk of self-fulling prophecies. Together, these findings offer critical empirical evidence to inform guidance on the responsible use of psychiatric PRS in clinical and consumer settings.