Genomewide Association Studies in Pharmacogenomics

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Functional Genomics in Psoriasis

Psoriasis is an autoimmune cutaneous condition that significantly impacts quality of life and represents a burden on society due to its prevalence. Genome-wide association studies (GWASs) have pinpointed several psoriasis-related risk loci, underlining the disease's complexity. Functional genomics is paramount to unveiling the role of such loci in psoriasis and disentangling its complex nature. In this review, we aim to elucidate the main findings in this field and integrate our discussion with gold-standard techniques in molecular biology-i.e., Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-and high-throughput technologies. These tools are vital to understanding how disease risk loci affect gene expression in psoriasis, which is crucial in identifying new targets for personalized treatments in advanced precision medicine.

Pharmacogenomic allele coverage of genome-wide genotyping arrays: a comparative analysis

The use of genome-wide genotyping arrays in pharmacogenomics (PGx) research and clinical implementation applications is increasing but it is unclear which arrays are best suited for these applications. Here, we conduct a comparative coverage analysis of PGx alleles included on genome-wide genotyping arrays, with an emphasis on alleles in genes with PGx-based prescribing guidelines. Genomic manifest files for seven arrays including the Axiom Precision Medicine Diversity Array (PMDA), Axiom PMDA Plus, Axiom PangenomiX, Axiom PangenomiX Plus, Infinium Global Screening Array, Infinium Global Diversity Array (GDA) and Infinium GDA with enhanced PGx (GDA-PGx) Array, were evaluated for coverage of 523 star alleles across 19 pharmacogenes included in prescribing guidelines developed by the Clinical Pharmacogenetic Implementation Consortium and Dutch Pharmacogenomics Working Group. Specific attention was given to coverage of the Association of Molecular Pathology's Tier 1 and Tier 2 allele sets for CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, NUDT15, TPMT and VKORC1 . Coverage of the examined PGx alleles was highest for the Infinium GDA-PGx (88%), Axiom PangenomiX Plus (77%), Axiom PangenomiX (72%) and Axiom PMDA Plus (70%). Three arrays (Infinium GDA-PGx, Axiom PangenomiX Plus and Axiom PMDA Plus) fully covered the Tier 1 alleles and the Axiom PangenomiX array provided full coverage of Tier 2 alleles. In conclusion, PGx allele coverage varied by gene and array. A superior array for all PGx applications was not identified. Future comparative analyses of genotype data produced by these arrays are needed to determine the robustness of the reported coverage estimates.

Prediction models of persistent taxane-induced peripheral neuropathy among breast cancer survivors using whole-exome sequencing

Persistent taxane-induced peripheral neuropathy (TIPN) is highly prevalent among early-stage breast cancer survivors (ESBCS) and has detrimental effect on quality of life. We leveraged logistic regression models to develop and validate polygenic prediction models to estimate the risk of persistent PN symptoms in a training cohort and validation cohort taking clinical risk factors into account. Based on 337 whole-exome sequenced ESBCS two of five prediction models for individual PN symptoms obtained AUC results above 60% when validated. Using the model for numbness in feet (35 SNVs) in the test cohort, 73% survivors were correctly predicted. For tingling in feet (55 SNVs) 70% were correctly predicted. Both models included SNVs from the ADAMTS20, APT6V0A2, CCDC88C, CYP2C8, EPHA5, NR1H3, PSKH2/APTV0D2, and SCN10A genes. For cramps in feet, difficulty climbing stairs and difficulty opening a jar the validation was unsuccessful. Polygenic prediction models including clinical risk factors can estimate the risk of persistent taxane-induced numbness in feet and tingling in feet in ESBCS.

Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications

Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 740-26,669). Our findings at genome-wide significance level recover previously reported pharmacogenetic signals and also include novel associations for lipid response to statins (N = 26,669) near LDLR and ZNF800. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. Furthermore, we demonstrate that individuals with higher genetically determined low-density and total cholesterol baseline levels experience increased absolute, albeit lower relative biomarker reduction following statin treatment. In summary, we systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in over 50,000 UK Biobank and All of Us participants with EHR and identified clinically relevant genetic predictors for improved personalized treatment strategies.

Overnutrition and Lipotoxicity: Impaired Efferocytosis and Chronic Inflammation as Precursors to Multifaceted Disease Pathogenesis

Overnutrition, driven by the consumption of high-fat, high-sugar diets, has reached epidemic proportions and poses a significant global health challenge. Prolonged overnutrition leads to the deposition of excessive lipids in adipose and non-adipose tissues, a condition known as lipotoxicity. The intricate interplay between overnutrition-induced lipotoxicity and the immune system plays a pivotal role in the pathogenesis of various diseases. This review aims to elucidate the consequences of impaired efferocytosis, caused by lipotoxicity-poisoned macrophages, leading to chronic inflammation and the subsequent development of severe infectious diseases, autoimmunity, and cancer, as well as chronic pulmonary and cardiovascular diseases. Chronic overnutrition promotes adipose tissue expansion which induces cellular stress and inflammatory responses, contributing to insulin resistance, dyslipidemia, and metabolic syndrome. Moreover, sustained exposure to lipotoxicity impairs the efferocytic capacity of macrophages, compromising their ability to efficiently engulf and remove dead cells. The unresolved chronic inflammation perpetuates a pro-inflammatory microenvironment, exacerbating tissue damage and promoting the development of various diseases. The interaction between overnutrition, lipotoxicity, and impaired efferocytosis highlights a critical pathway through which chronic inflammation emerges, facilitating the development of severe infectious diseases, autoimmunity, cancer, and chronic pulmonary and cardiovascular diseases. Understanding these intricate connections sheds light on potential therapeutic avenues to mitigate the detrimental effects of overnutrition and lipotoxicity on immune function and tissue homeostasis, thereby paving the way for novel interventions aimed at reducing the burden of these multifaceted diseases on global health.

Precise Therapy Using the Selective Endogenous Encapsidation for Cellular Delivery Vector System

Interindividual variability in drug response is a major problem in the prescription of pharmacological treatments. The therapeutic effect of drugs can be influenced by human genes. Pharmacogenomic guidelines for individualization of treatment have been validated and used for conventional dosage forms. However, drugs can often target non-specific areas and produce both desired and undesired pharmacological effects. The use of nanoparticles, liposomes, or other available forms for drug formulation could help to overcome the latter problem. Virus-like particles based on retroviruses could be a potential envelope for safe and efficient drug formulations. Human endogenous retroviruses would make it possible to overcome the host immune response and deliver drugs to the desired target. PEG10 is a promising candidate that can bind to mRNA because it is secreted like an enveloped virus-like extracellular vesicle. PEG10 is a retrotransposon-derived gene that has been domesticated. Therefore, formulations with PEG10 may have a lower immunogenicity. The use of existing knowledge can lead to the development of suitable drug formulations for the precise treatment of individual diseases.

Genetic variation supports a causal role for valproate in prevention of ischemic stroke

BACKGROUND

Valproate is a candidate for ischemic stroke prevention due to its anti-atherosclerotic effects in vivo. Although valproate use is associated with decreased ischemic stroke risk in observational studies, confounding by indication precludes causal conclusions.

AIMS

We applied Mendelian randomization to determine whether genetic variants that influence seizure response among valproate users associate with ischemic stroke.

METHODS

We derived a genetic score for valproate response using genome-wide association data of seizure response after valproate intake from the Epilepsy Pharmacogenomics Consortium. We then tested this score among valproate users of the UK Biobank for association with incident and recurrent ischemic stroke using Cox proportional hazard models. As replication, we tested found associations in an independent cohort of valproate users of the Mass General Brigham Biobank.

RESULTS

Among 2150 valproate users (mean 56 years, 54% females), 82 ischemic strokes occurred over a mean 12 year follow-up. Higher valproate response genetic score was associated with higher serum valproate levels (+5.78 µg/ml per 1 standard deviation (SD), 95% confidence interval (CI) (3.45, 8.11)). After adjusting for age and sex, higher valproate response genetic score was associated with lower ischemic stroke risk (hazard ratio (HR) per 1 SD 0.73, 95% CI (0.58, 0.91)) with a halving of absolute risk in the highest compared to the lowest score tertile (4.8% vs 2.5%, p trend = 0.027). Among 194 valproate users with prevalent stroke at baseline, a higher valproate response genetic score was associated with lower recurrent ischemic stroke risk (HR per 1 SD 0.53, 95% CI (0.32, 0.86)) with reduced absolute risk in the highest compared to the lowest score tertile (3/51, 5.9% vs 13/71, 18.3%, p trend = 0.026). The valproate response genetic score was not associated with ischemic stroke among the 427,997 valproate non-users (p = 0.61), suggesting minimal pleiotropy. In 1241 valproate users of the Mass General Brigham Biobank with 99 ischemic stroke events over 6.5 years follow-up, we replicated our observed associations between the valproate response genetic score and ischemic stroke (HR per 1 SD 0.77, 95% CI (0.61, 0.97)).

CONCLUSION

These results demonstrate that a genetically predicted favorable seizure response to valproate is associated with higher serum valproate levels and reduced ischemic stroke risk among valproate users, providing causal support for valproate effectiveness in ischemic stroke prevention. The strongest effect was found for recurrent ischemic stroke, suggesting potential dual-use benefits of valproate for post-stroke epilepsy. Clinical trials will be required in order to identify populations that may benefit most from valproate for stroke prevention.

DATA ACCESS STATEMENT

UK Biobank participant data are available after approval of a research proposal. The weights of the used genetic scores are available in the Supplemental Tables.

Psychiatric Level 1A evidence pharmacogenomics in a Brazilian admixed cohort and global populations

Purpose: To compare minor allele frequencies (MAFs) of psychiatric drug response variants in a Brazilian admixed cohort with global populations and other Brazilian groups. Methods: PharmGKB MAFs were gathered from publicly available genetic datasets for Brazil and worldwide. Results: Among 146 variants in CYP2D6 and CYP2C19, 41 were present in Brazil, mostly rare (MAF <1%). 11 variants showed significant MAF differences with large effect sizes compared with global populations. CYP2C19*3 (rs4986893), CYP2C19*17 (rs12248560), CYP2D6*17 (rs28371706-A) and CYP2D6*29 (rs61736512) exhibited higher frequencies in Brazil, with the latter three also differing from other Brazilian groups. Conclusion: This study highlights significant pharmacogenomic diversity in Brazil and globally, underscoring the need for more research in personalized psychiatric drug therapy.

Pharmacogenomic knowledge and awareness among diverse patients treated with angiotensin converting enzyme inhibitors

We developed novel electronic phenotyping algorithms for the BioMe biobank data, which accurately identified angiotensin converting enzyme inhibitor (ACEi)-induced angioedema cases and controls. A survey was mailed to all 1075 patients and 91 were returned. Over a third reported that prescribing physicians had not discussed with them the concepts of interindividual drug response variability or adverse event risk, and 73% of patients were previously unaware of pharmacogenomics; however, most patients were interested in having pharmacogenomic testing. Moreover, 67% of patients indicated that pharmacogenomic testing would positively influence their medication compliance. In addition to identifying an innovative approach to define biobank cohorts for pharmacogenomic studies, these results indicate that patients are interested in pharmacogenomic testing, which could translate to improved adherence.

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Evolution of predictive risk factor analysis for chemotherapy-related toxicity

The causes of variation in toxicity to the same treatment regimen among seemingly similar patients remain largely unknown. There was tremendous optimism that the patient's germline genome would be strongly predictive of treatment-related toxicity and could be used to personalize treatment and improve therapeutic outcomes. However, there has been limited success in discovering robust pharmacogenetic predictors of treatment-related toxicity and even less progress in translating the few validated predictors into clinical practice. It is apparent that identification of toxicity predictors that can be used to predict and prevent treatment-related toxicity will require thinking beyond germline genomics. To that end, we propose an integrated biomarker discovery approach that recognizes that a patient's toxicity risk is determined by the cumulative effects of a broad range of "omic" and non-omic factors. This commentary describes the limited success in discovering and translating clinical and pharmacogenetic toxicity predictors into clinical practice. We illustrate the evolution of cancer toxicity biomarker discovery and translation through studies of taxane-induced peripheral neuropathy, which is one of the most common and debilitating side effects of cancer treatment. We then discuss the opportunities for discovering non-genomic (e.g., metabolomic, lipidomic, transcriptomic, proteomic, microbiomic, medical, behavioral, environmental) and integrated biomarkers that may be more strongly predictive of toxicity risk and the potential challenges with translating integrated biomarkers into clinical practice. This integrated biomarker discovery approach may circumvent some of the major limitations in toxicity biomarker science and move precision oncology treatment forward so that patients receive maximum treatment benefit with minimal toxicity.

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This review highlights 2 promising post-GWAS methods for PGx. First, we discuss the polygenic architecture of the PGx traits, especially for drug-induced liver injury. Experimental modelling using multiple donors' human primary hepatocytes and human liver organoids demonstrated the polygenic architecture of drug-induced liver injury susceptibility and found biological vulnerability in genetically high-risk tissue donors. Second, we discuss the challenges of interpreting the roles of variants in noncoding regions. Beyond methods involving expression quantitative trait locus analysis and massively parallel reporter assays, we suggest the use of in silico mutagenesis through machine learning methods to understand the roles of variants in transcriptional regulation. This review underscores the importance of these post-GWAS methods in providing critical insights into PGx, potentially facilitating drug development and personalized treatment.

Identification of Genetic Variation Influencing Metformin Response in a Multiancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP)

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not been replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in prediabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in the metformin (MET; n = 876) and placebo (PBO; n = 887) arms. Multiple linear regression assessed association with 1-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (P < 9 × 10-9). In the MET arm, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR = 0.07; MAFEUR = 0.002) was associated with an increase in percentage of glycated hemoglobin (per minor allele, β = 0.39 [95% CI 0.28, 0.50]; P = 2.8 × 10-12). rs145591055 near OMSR (MAF = 0.10 in American Indians) was associated with weight loss (kilograms) (per G allele, β = -7.55 [95% CI -9.88, -5.22]; P = 3.2 × 10-10) in the MET arm. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants [P(G×T) < 1.0 × 10-4]. Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in prediabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy.

Genetic variation supports a causal role for valproate in prevention of ischemic stroke

Valproate is a candidate for ischemic stroke prevention due to its anti-atherosclerotic effects in vivo. Although valproate use is associated with decreased ischemic stroke risk in observational studies, confounding by indication precludes causal conclusions. To overcome this limitation, we applied Mendelian randomization to determine whether genetic variants that influence seizure response among valproate users associate with ischemic stroke. We derived a genetic score for valproate response using genome-wide association data of seizure response after valproate intake from the Epilepsy Pharmacogenomics Consortium. We then tested this score among valproate users of the UK Biobank for association with incident and recurrent ischemic stroke using Cox proportional hazard models. Among 2,150 valproate users (mean 56 years, 54% females), 82 ischemic strokes occurred over a mean 12-year follow-up. Higher valproate response genetic score was associated with higher serum valproate levels (+5.78 μg/ml per one SD, 95% CI [3.45, 8.11]). After adjusting for age and sex, higher valproate response genetic score was associated with lower ischemic stroke risk (HR per one SD 0.73, [0.58, 0.91]) with a halving of absolute risk in the highest compared to the lowest score tertile (4.8% vs 2.5%, p-trend=0.027). Among 194 valproate users with prevalent stroke at baseline, a higher valproate response genetic score was associated with lower recurrent ischemic stroke risk (HR per one SD 0.53, [0.32, 0.86]) with reduced absolute risk in the highest compared to the lowest score tertile (3/51, 5.9% vs. 13/71, 18.3%, p-trend=0.026). The valproate response genetic score was not associated with ischemic stroke among the 427,997 valproate non-users (p=0.61), suggesting minimal pleiotropy. In an independent cohort of 1,241 valproate users of the Mass General Brigham Biobank with 99 ischemic stroke events over 6.5 years follow-up, we replicated our observed associations between the valproate response genetic score and ischemic stroke (HR per one SD 0.77, 95% CI: [0.61, 0.97]). These results demonstrate that a genetically predicted favorable seizure response to valproate is associated with higher serum valproate levels and reduced ischemic stroke risk among valproate users, providing causal support for valproate effectiveness in ischemic stroke prevention. The strongest effect was found for recurrent ischemic stroke, suggesting potential dual-use benefits of valproate for post-stroke epilepsy. Clinical trials will be required in order to identify populations that may benefit most from valproate for stroke prevention.

Multi-Omics Studies in Historically Excluded Populations: The Road to Equity

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Local Ancestry-Informed Candidate Pathway Analysis of Warfarin Stable Dose in Latino Populations

Accuracy of warfarin dose prediction algorithms may be improved by including data from diverse populations in genetic studies of dose variability. Here, we surveyed single nucleotide polymorphisms in vitamin K-related genetic pathways for association with warfarin dose requirements in two admixed Latino populations in standard-principal component adjusted and contemporary-local ancestry adjusted regression models. A total of five variants from vitamin K-related genes/pathways were associated with warfarin dose in both cohorts (P < 0.0125) in standard models. Local ancestry-adjusted analysis unveiled 35 associated variants with absolute effects ranging from β = 9.04 ( ±2.23) to 39.18 ( ±10.89) per ancestral allele in the discovery cohort and β = 6.47 (± 2.02) to 17.82 (± 6.83) in the replication cohort. Importantly, we demonstrate the technical validity of the Tractor model in cohorts with admixed ancestry from three founder populations and bring attention to the technical hurdles obstructing the inclusion of diverse, especially admixed, populations in pharmacogenomic research.

Pharmacogenomics: current status and future perspectives

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice - widely regarded as one of the initial steps in mainstreaming genomic medicine - is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS

MOTIVATION

Pharmacogenomics (PGx) research holds the promise for detecting association between genetic variants and drug responses in randomized clinical trials, but it is limited by small populations and thus has low power to detect signals. It is critical to increase the power of PGx genome-wide association studies (GWAS) with small sample sizes so that variant-drug-response association discoveries are not limited to common variants with extremely large effect.

RESULTS

In this article, we first discuss the challenges of PGx GWAS studies and then propose the adaptively weighted joint test (AWOT) and Cauchy Weighted jOint Test (CWOT), which are two flexible and robust joint tests of the single nucleotide polymorphism main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures are proposed to accurately calculate the joint test P-value. We evaluate AWOT and CWOT through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in PGx settings (i.e. with strong genotype-by-treatment interaction effects, but weak genotype main effects). We demonstrate the value of AWOT and CWOT by applying them to the PGx GWAS from the Bezlotoxumab Clostridium difficile MODIFY I/II Phase 3 trials.

AVAILABILITY AND IMPLEMENTATION

The R package COWT is publicly available on CRAN https://cran.r-project.org/web/packages/cwot/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring human biology with N-of-1 clinical trials

Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.

Precision Medicine in Antidepressants Treatment

Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.

Effects of Gabapentin and Pregabalin on Calcium Homeostasis: Implications for Physical Rehabilitation of Musculoskeletal Tissues

PURPOSE OF REVIEW

In this review, we discuss the mechanism of action of gabapentinoids and the potential consequences of long-term treatment with these drugs on the musculoskeletal system.

RECENT FINDINGS

Gabapentinoids, such as gabapentin (GBP) and pregabalin (PGB) were designed as antiepileptic reagents and are now commonly used as first-line treatment for neuropathic pain and increasingly prescribed off-label for other pain disorders such as migraines and back pain. GBP and PGB exert their analgesic actions by selectively binding the α₂δ₁ auxiliary subunit of voltage-sensitive calcium channels, thereby inhibiting channel function. Numerous tissues express the α₂δ₁ subunit where GBP and PGB can alter calcium-mediated signaling events. In tissues such as bone, muscle, and cartilage, α₂δ₁ has important roles in skeletal formation, mechanosensation, and normal tissue function/repair that may be affected by chronic use of gabapentinoids. Long-term use of gabapentinoids is associated with detrimental musculoskeletal outcomes, including increased fracture risk. Therefore, understanding potential complications is essential for clinicians to guide appropriate treatments.

On the Verge of Precision Medicine in Diabetes

The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Cytochrome P450 polymorphism: From evolution to clinical use

The cytochromes P450s can be divided in two groups, those of high importance for endogenous functions being evolutionary quite stable and those participating in detoxification of drugs and other xenobiotics having less important endogenous functions. In the latter group extensive genetic diversity has been allowed and in addition this is of high importance for survival in different environments. The genetic polymorphisms in these genes have evolved to some extent based on dietary restrictions and environmental factors and have not been subject of conservation due to less importance for survival. In cases of high dietary selection events, gene multiplication and amplification events have been seen. The different variants in genes encoding drug metabolizing enzymes can be used as genetic biomarkers (pharmacogenomic labels) for adjustment of drug treatment leading to less adverse drug reactions and better response. Indeed, this has improved the use of personalized medicine, although the missing heredity seen based on twin studies indicates that there are indeed many more genetic variants to be discovered before one can achieve a satisfactory relationship between genotype and phenotype with respect to drug metabolism and toxicity.

From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

A systematic review and analysis of the use of polygenic scores in pharmacogenomics

Polygenic scores (PGS) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGS for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n=30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGS were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g. schizophrenia-derived PGS for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGS, with between 3 and 6.6 million variants included in the PGS. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGS deposited on the PGS Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.

The need to shift pharmacogenetic research from candidate gene to genome-wide association studies

The primary research approach in pharmacogenetics has been candidate gene association studies (CGAS), but pharmacogenomic genome-wide association studies (GWAS) are becoming more common. We are now at a critical juncture when the results of those two research approaches, CGAS and GWAS, can be compared in pharmacogenetics. We analyzed publicly available databases of pharmacogenetic CGAS and GWAS (i.e., the Pharmacogenomics Knowledgebase [PharmGKB^®] and the NHGRI-EBI GWAS catalog) and the vast majority of variants (98%) and genes (94%) discovered in pharmacogenomic GWAS were novel (i.e., not previously studied CGAS). Therefore, pharmacogenetic researchers are not selecting the right candidate genes in the vast majority of CGAS, highlighting a need to shift pharmacogenetic research efforts from CGAS to GWAS.