Aug 2026· Frontiers in Genetics· Vol 17· 0 citations· 63 references
Medicine
TL;DR
It is proposed that the strategic integration of genome-wide analyses, sequencing, multi-omics data, and hypothesis-driven genetic studies may accelerate the clinical adoption of pharmacogenetics and contribute to more effective precision medicine in psychiatry.
Abstract
The clinical adoption of pharmacogenetics has advanced unevenly despite rapid progress in genomic discovery. Genome-wide association studies and next-generation sequencing have substantially improved our understanding of the polygenic and regulatory architecture of complex diseases, yet translating these insights into clinically actionable pharmacogenetic strategies remains challenging. In this opinion article, we argue that targeted genetic approaches, traditionally represented by candidate gene association studies, should be reconsidered within a modern genomic framework. While such approaches proved limited as discovery tools for complex disease risk, they may retain translational value when integrated with genome-wide evidence and multi-omics prioritization. In pharmacogenetics, pathway-focused investigations and targeted validation studies can help bridge the gap between large-scale genomic discovery and clinical implementation. Within this framework, pharmacokinetic pharmacogenes (e.g., CYP2D6, CYP2C19) and pharmacodynamic phenotypes (e.g., treatment response, antipsychotic-induced weight gain) represent fundamentally different translational challenges, differing in effect size, biological complexity, and clinical readiness. We propose that the strategic integration of genome-wide analyses, sequencing, multi-omics data, and hypothesis-driven genetic studies may accelerate the clinical adoption of pharmacogenetics and contribute to more effective precision medicine in psychiatry.
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.
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