Repositioning candidate gene approaches in the post-GWAS era: a clinical perspective from psychiatric pharmacogenetics

2025 Donald Seldin Lecture: Leveraging Diverse Population Genomics and Multiomics Integration for Gene Discovery of Cardiovascular and Kidney Diseases.

This review discusses the implications of frameworks leveraging genetic admixture and multiomics data for advancing gene discovery in cardiovascular and kidney disease research. By broadening gene discovery efforts to additional populations that have a disproportionately high risk of disease and leveraging genetic diversity in admixed populations, studies can identify population-enriched risk variants that traditionally have been missed in genome-wide association studies. The use of multiomics approaches, including the transcriptome, proteome, and metabolome, advances a mechanistic understanding of disease beyond associations. As single-cell omics technologies continue to improve, their integration into gene discovery may help uncover cell-type-specific regulatory pathways and more precise biological contexts. The full potential of these approaches depends on sustained investment in diverse, well-characterized omics data sets, methodological innovation in multiancestry statistical approaches, and interdisciplinary collaboration bridging genomics, epidemiology, and clinical medicine. These efforts will need to be translated into clinically actionable insights, including ancestry-informed risk stratification and targeted therapeutics, to improve outcomes for cardiovascular and kidney diseases.

Beyond Targeted Gene Panels: Whole-Exome Sequencing as a Strategic Platform for Precision Therapeutics in Alzheimer’s Disease

Alzheimer’s disease (AD) continues to be one of the greatest challenges in public health due to its multifactorial and heterogeneous nature, involving multiple physiological axes that encompass a large number of genetic, metabolic, vascular, and inflammatory interactions. In current clinical practice, medical specialties, mainly neurology and psychiatry, still rely on targeted gene panels for genetic evaluation. Although these panels remain effective for certain predefined hypotheses, their restricted and predefined nature limits the detection of the broader spectrum of genetic variation that may contribute to the complex biological interactions underlying neurodegeneration. Whole-exome sequencing (WES) is, from our clinic-based perspective, one of the most comprehensive genomic approaches currently available, since it allows the analysis of the ~19,500 protein-coding regions, and depending on the library approximately 5500 additional clinically relevant genomic loci, including splice sites, untranslated regions, long non-coding RNAs (lncRNAs), pseudogenes, regulatory elements and mitochondrial DNA (mtDNA). It enables the identification of pathogenic variants and variants of uncertain significance (VUS) under the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification frameworks. It also expands biological interpretation to variants conventionally classified as benign, which, when interpreted collectively, may contribute to pathway-level contextualization within the hypothesis-generating theoretical framework proposed in this review without implying pathogenicity, causal inference, or immediate clinical actionability. Additionally, WES enables the identification of secondary and incidental findings that may provide clinically relevant information beyond the primary phenotype, thereby supporting preventive surveillance and clinical risk management. This review analyzes the use of WES as a strategic platform for personalized decision-making in contemporary practice given the multifactorial and heterogeneous complexity of AD. It also addresses the complexities and limitations of the ACMG/AMP recommendations for filtering and classification of variants, the lack of standardization between reports and platforms, and the need for physician training, which constitute a great challenge for the translation of data to therapeutic decision-making. While the clinical utility of whole-exome sequencing (WES) in genetic diagnosis and precision medicine is well established, this review additionally proposes a hypothesis-generating theoretical framework whereby variants conventionally classified as benign or of uncertain significance may contribute to pathway-level biological contextualization in Alzheimer’s disease.

Pharmacogenetics and Pharmacogenomics as Enabling Technologies for Precision Drug Development and Personalized Therapy.

Population pharmacogenomics in Russia: insights from 6102 exomes and implications for genomic medicine

Personalized pharmacotherapy requires systematic consideration of genetic factors influencing drug efficacy and safety. The accumulation of large-scale whole-exome sequencing (WES) resources provides an opportunity to assess population frequencies of clinically significant pharmacogenetic variants; however, the applicability of exome-based pharmacogenomics across populations, particularly those underrepresented in existing reference datasets, requires further evaluation. A retrospective analysis of 6102 anonymized sequencing datasets obtained between 2020 and 2025 was performed using the DNBSEQ-G400 (MGI) platform and Agilent SureSelect Human All Exon v6/v7/v8 enrichment kits. SNV and indel detection, CNV analysis, high-resolution HLA typing, and diplotype assignment for key pharmacogenes were conducted. Pharmacogenomic annotations were derived from ClinPGx (formerly PharmGKB) (levels of evidence 1 A–2B), CPIC, and PharmVar. Haplotype phasing (SHAPEIT5), genotype imputation (IMPUTE5), and phased linkage disequilibrium analysis were performed using a reference panel of 814 Russian whole-genome sequences generated with the same bioinformatic workflow to assess the feasibility of reconstructing clinically relevant non-coding pharmacogenetic variants not captured by WES. WES reliably detected 33 of 34 Very Important Pharmacogenes (VIPs), allowing determination of allele frequencies, metabolizer statuses for 13 VIPs, and HLA diversity. The highest allelic and phenotypic variability was observed in CYP2D6 , CYP2C19 , and CYP2B6 . A total of 663 ClinPGx annotations were identified, predominantly related to drug metabolism (50.38%) and toxicity (29.56%), involving psychotropic drugs, anticoagulants, statins, opioid analgesics, antineoplastic agents, and immunosuppressants. However, at least 32 drugs require assessment of non-coding pharmacogenetic variants or accurate CYP2D6 copy number determination – both beyond standard WES capability. Phased haplotype analysis demonstrated that haplotype-based imputation was feasible for only a limited subset of clinically relevant non-coding pharmacogenetic variants, whereas the majority lacked informative exonic proxies required for accurate inference from WES data. These findings represent one of the largest reference resource to date of pharmacogenetically significant variant and HLA allele frequencies in the Russian population. The results confirm WES as a robust and scalable platform for population-level pharmacogenomic screening and broader clinical genomic applications, while demonstrating that haplotype-based imputation cannot fully overcome the intrinsic limitations of exome sequencing for comprehensive pharmacogenomic profiling. Selection of genomic testing strategies should balance diagnostic completeness, analytical complexity, cost, scalability, and long-term potential for reanalysis.

Assessing Computational Models for Pharmacogenomic Variant Interpretation

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.

The Polygenic Risk Score in Clinical Pharmacy: From Genomic Architecture to Precision Drug Therapy: A Narrative Review

Background: Precision medicine increasingly relies on polygenic risk scores (PRS) to predict drug efficacy, safety, and therapeutic variability. Traditional monogenic pharmacogenetics, focused on individual gene variants, explains only 20-40% of drug response variation, leaving substantial "missing heritability." Polygenic models aggregating thousands of genome-wide variants offer improved prediction, yet clinical implementation in pharmacy practice remains inconsistent and incomplete. Objective: This narrative review synthesizes evidence on PRS methodologies, clinical applications across therapeutic domains, the pharmacist's role in precision drug therapy, barriers to adoption, and emerging technologies, to provide a comprehensive synthesis for pharmacy educators, practitioners, and health system leaders. Methods: A targeted literature search of PubMed, Google Scholar, and bioRxiv (2010-2025) identified 90 peer-reviewed articles addressing PRS genomic principles, calculation and validation methodologies, clinical applications in cardiovascular (statins, clopidogrel), psychiatric (antidepressants, antipsychotics), and endocrine (metformin) pharmacotherapy, pharmacy implementation frameworks, and ethical and regulatory considerations. Articles were selected based on methodological rigor, applicability to clinical pharmacy practice, and contribution to understanding of PRS mechanisms, validation, or implementation. Results: Polygenic models capture cumulative genetic burden and demonstrate moderate to good predictive accuracy across therapeutic domains (AUC 0.60-0.75), exceeding monogenic approaches. Clinical applications are emerging but largely research-stage; few pragmatic trials assess PRS-guided pharmacotherapy effectiveness versus standard care. Pharmacists' competencies as genomic consultants are well-articulated conceptually but underdeveloped operationally. Major barriers include economic constraints (cost of testing, lack of reimbursement), educational gaps (insufficient pharmacist training), methodological concerns (ancestry bias, limited population-specific models), and incomplete regulatory frameworks and implementation infrastructure. Literature distribution shows emphasis on clinical applications (31%) and PRS methodology (24%), with limited evidence on pharmacy implementation (20%) or health economic impact. Conclusion: PRS represent a paradigm shift toward precision pharmacotherapy but require systematic workforce development, health system investment, policy reform, and implementation science evidence to realize clinical and equitable impact