Pharmacogenomics: Challenges and Future

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (5 September 2023) | Viewed by 29038

Special Issue Editors


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Guest Editor
Institute of Biomedical Research and Innovation (IRIB-CNR), Section of Catanzaro, 88100 Catanzaro, Italy
Interests: pharmacogenetics; biomarkers; cancer; medicine for drug addiction; alcoholism
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
Interests: cancer; integrative omics analysis; mirna therapeutics; pharmacogenomics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy
Interests: pharmacogenetics; pediatrics

Special Issue Information

Dear Colleagues,

Over the past years, the growth of pharmacogenomics (PGx) within the medical community has contributed to improving the practice of precision medicine. The genetic make-up influences interindividual variability in drugs response in terms of dosing, drug efficacy/toxicity, hypersensitivity reactions, drug resistance, and clinical outcome. The discovery of PGx biomarkers may lead to tailored prescription, with a major impact on healthcare costs. Until now, FDA recommendations are provided for over 200 drugs in several therapeutic areas, especially for cancer. However, PGx implementation is still limited, and several barriers need to be overcome. For this Special Issue, contributions from both experts and beginners in the PGx field are invited. We welcome reviews and original research articles covering many aspects of PGx, from the discovery of new PGx biomarkers to methodological strategies to increase PGx knowledge and implementation, including validation of PGx biomarkers for clinical translation. Moreover, studies on ethical, legal, and economic aspects and on the role of epigenetic and non-genetic factors are also welcome.

Dr. Mariamena Arbitrio
Dr. Maria Teresa Di Martino
Dr. Francesca Scionti
Guest Editors

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Keywords

  • ADME genes
  • Regulation in interindividual variability
  • Polymorphic variant
  • Single-Nucleotide polymorphism (SNP)
  • Rare variants and copy number variation (CNV)
  • Biomarkers
  • Pharmacokinetics (PK)/Pharmacodynamics (PD)
  • PGx genotyping strategies
  • PGx implementation
  • PGx discovery tools
  • PGx validation tools
  • Preemptive dose modulation

Published Papers (12 papers)

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Editorial

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4 pages, 154 KiB  
Editorial
Pharmacogenomics: Challenges and Future
by Mariamena Arbitrio
Genes 2024, 15(6), 714; https://doi.org/10.3390/genes15060714 - 30 May 2024
Viewed by 468
Abstract
Over the last few decades, the implementation of pharmacogenomics (PGx) in clinical practice has improved tailored drug prescriptions [...] Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)

Research

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11 pages, 1557 KiB  
Article
Genome-Wide Association Study of Beta-Blocker Survival Benefit in Black and White Patients with Heart Failure with Reduced Ejection Fraction
by Jasmine A. Luzum, Alessandra M. Campos-Staffico, Jia Li, Ruicong She, Hongsheng Gui, Edward L. Peterson, Bin Liu, Hani N. Sabbah, Mark P. Donahue, William E. Kraus, L. Keoki Williams and David E. Lanfear
Genes 2023, 14(11), 2019; https://doi.org/10.3390/genes14112019 - 28 Oct 2023
Cited by 1 | Viewed by 1368
Abstract
In patients with heart failure with reduced ejection fraction (HFrEF), individual responses to beta-blockers vary. Candidate gene pharmacogenetic studies yielded significant but inconsistent results, and they may have missed important associations. Our objective was to use an unbiased genome-wide association study (GWAS) to [...] Read more.
In patients with heart failure with reduced ejection fraction (HFrEF), individual responses to beta-blockers vary. Candidate gene pharmacogenetic studies yielded significant but inconsistent results, and they may have missed important associations. Our objective was to use an unbiased genome-wide association study (GWAS) to identify loci influencing beta-blocker survival benefit in HFrEF patients. Genetic variant × beta-blocker exposure interactions were tested in Cox proportional hazards models for all-cause mortality stratified by self-identified race. The models were adjusted for clinical risk factors and propensity scores. A prospective HFrEF registry (469 black and 459 white patients) was used for discovery, and linkage disequilibrium (LD) clumped variants with a beta-blocker interaction of p < 5 × 10−5, were tested for Bonferroni-corrected validation in a multicenter HFrEF clinical trial (288 black and 579 white patients). A total of 229 and 18 variants in black and white HFrEF patients, respectively, had interactions with beta-blocker exposure at p < 5 × 10−5 upon discovery. After LD-clumping, 100 variants and 4 variants in the black and white patients, respectively, remained for validation but none reached statistical significance. In conclusion, genetic variants of potential interest were identified in a discovery-based GWAS of beta-blocker survival benefit in HFrEF patients, but none were validated in an independent dataset. Larger cohorts or alternative approaches, such as polygenic scores, are needed. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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14 pages, 3431 KiB  
Article
An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics
by Marianna Milano, Giuseppe Agapito and Mario Cannataro
Genes 2023, 14(10), 1915; https://doi.org/10.3390/genes14101915 - 7 Oct 2023
Cited by 2 | Viewed by 1338
Abstract
Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types [...] Read more.
Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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16 pages, 2842 KiB  
Article
DNA Methylation as a Biomarker for Monitoring Disease Outcome in Patients with Hypovitaminosis and Neurological Disorders
by Olaia Martínez-Iglesias, Vinogran Naidoo, Lola Corzo, Rocío Pego, Silvia Seoane, Susana Rodríguez, Margarita Alcaraz, Adriana Muñiz, Natalia Cacabelos and Ramón Cacabelos
Genes 2023, 14(2), 365; https://doi.org/10.3390/genes14020365 - 30 Jan 2023
Cited by 4 | Viewed by 1754
Abstract
DNA methylation remains an under-recognized diagnostic biomarker for several diseases, including neurodegenerative disorders. In this study, we examined differences in global DNA methylation (5mC) levels in serum samples from patients during the initial- and the follow-up visits. Each patient underwent a blood analysis [...] Read more.
DNA methylation remains an under-recognized diagnostic biomarker for several diseases, including neurodegenerative disorders. In this study, we examined differences in global DNA methylation (5mC) levels in serum samples from patients during the initial- and the follow-up visits. Each patient underwent a blood analysis and neuropsychological assessments. The analysis of 5mC levels revealed two categories of patients; Group A who, during the follow-up, had increased 5mC levels, and Group B who had decreased 5mC levels. Patients with low Fe-, folate-, and vitamin B12- levels during the initial visit showed increased levels of 5mC after treatment when assessed during the follow-up. During the follow-up, 5mC levels in Group A patients increased after treatment for hypovitaminosis with the nutraceutical compounds Animon Complex and MineraXin Plus. 5mC levels were maintained during the follow-up in Group A patients treated for neurological disorders with the bioproducts AtreMorine and NeoBrainine. There was a positive correlation between 5mC levels and MMSE scores, and an inverse correlation between 5mC and ADAS-Cog scores. This expected correlation was observed in Group A patients only. Our study appears to indicate that 5mC has a diagnostic value as a biomarker across different pathologies. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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22 pages, 5686 KiB  
Article
A Python Clustering Analysis Protocol of Genes Expression Data Sets
by Giuseppe Agapito, Marianna Milano and Mario Cannataro
Genes 2022, 13(10), 1839; https://doi.org/10.3390/genes13101839 - 12 Oct 2022
Cited by 5 | Viewed by 5516
Abstract
Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to [...] Read more.
Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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11 pages, 1017 KiB  
Article
Effects of Genetic Polymorphisms of Cathepsin A on Metabolism of Tenofovir Alafenamide
by Soichiro Ito, Takeshi Hirota, Miyu Yanai, Mai Muto, Eri Watanabe, Yuki Taya and Ichiro Ieiri
Genes 2021, 12(12), 2026; https://doi.org/10.3390/genes12122026 - 20 Dec 2021
Cited by 2 | Viewed by 2412
Abstract
Cathepsin A (CatA) is important as a drug-metabolizing enzyme responsible for the activation of prodrugs, such as the anti-human immunodeficiency virus drug Tenofovir Alafenamide (TAF). The present study was undertaken to clarify the presence of polymorphisms of the CatA gene in healthy Japanese [...] Read more.
Cathepsin A (CatA) is important as a drug-metabolizing enzyme responsible for the activation of prodrugs, such as the anti-human immunodeficiency virus drug Tenofovir Alafenamide (TAF). The present study was undertaken to clarify the presence of polymorphisms of the CatA gene in healthy Japanese subjects and the influence of gene polymorphism on the expression level of CatA protein and the drug-metabolizing activity. Single-strand conformation polymorphism method was used to analyze genetic polymorphisms in healthy Japanese subjects. Nine genetic polymorphisms were identified in the CatA gene. The polymorphism (85_87CTG>-) in exon 2 was a mutation causing a deletion of leucine, resulting in the change of the leucine 9-repeat (Leu9) to 8-repeat (Leu8) in the signal peptide region of CatA protein. The effect of Leu8 on the expression level of CatA protein was evaluated in Flp-In-293 cells with a stably expressed CatA, resulting in the expression of CatA protein being significantly elevated in variant 2 with Leu8 compared with Leu9. Higher concentrations of tenofovir alanine (TFV-Ala), a metabolite of TAF, were observed in the Leu8-expressing cells than in the Leu9-expressing cells using LC/MS/MS. Our findings suggest that the drug metabolic activity of CatA is altered by the genetic polymorphism. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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15 pages, 2676 KiB  
Article
A Methodological Framework to Discover Pharmacogenomic Interactions Based on Random Forests
by Salvatore Fasola, Giovanna Cilluffo, Laura Montalbano, Velia Malizia, Giuliana Ferrante and Stefania La Grutta
Genes 2021, 12(6), 933; https://doi.org/10.3390/genes12060933 - 18 Jun 2021
Cited by 2 | Viewed by 1958
Abstract
The identification of genomic alterations in tumor tissues, including somatic mutations, deletions, and gene amplifications, produces large amounts of data, which can be correlated with a diversity of therapeutic responses. We aimed to provide a methodological framework to discover pharmacogenomic interactions based on [...] Read more.
The identification of genomic alterations in tumor tissues, including somatic mutations, deletions, and gene amplifications, produces large amounts of data, which can be correlated with a diversity of therapeutic responses. We aimed to provide a methodological framework to discover pharmacogenomic interactions based on Random Forests. We matched two databases from the Cancer Cell Line Encyclopaedia (CCLE) project, and the Genomics of Drug Sensitivity in Cancer (GDSC) project. For a total of 648 shared cell lines, we considered 48,270 gene alterations from CCLE as input features and the area under the dose-response curve (AUC) for 265 drugs from GDSC as the outcomes. A three-step reduction to 501 alterations was performed, selecting known driver genes and excluding very frequent/infrequent alterations and redundant ones. For each model, we used the concordance correlation coefficient (CCC) for assessing the predictive performance, and permutation importance for assessing the contribution of each alteration. In a reasonable computational time (56 min), we identified 12 compounds whose response was at least fairly sensitive (CCC > 20) to the alteration profiles. Some diversities were found in the sets of influential alterations, providing clues to discover significant drug-gene interactions. The proposed methodological framework can be helpful for mining pharmacogenomic interactions. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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Review

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20 pages, 1747 KiB  
Review
Polymorphisms and Pharmacogenomics of NQO2: The Past and the Future
by Elzbieta Janda, Jean A. Boutin, Carlo De Lorenzo and Mariamena Arbitrio
Genes 2024, 15(1), 87; https://doi.org/10.3390/genes15010087 - 10 Jan 2024
Cited by 2 | Viewed by 1731
Abstract
The flavoenzyme N-ribosyldihydronicotinamide (NRH):quinone oxidoreductase 2 (NQO2) catalyzes two-electron reductions of quinones. NQO2 contributes to the metabolism of biogenic and xenobiotic quinones, including a wide range of antitumor drugs, with both toxifying and detoxifying functions. Moreover, NQO2 activity can be inhibited by several [...] Read more.
The flavoenzyme N-ribosyldihydronicotinamide (NRH):quinone oxidoreductase 2 (NQO2) catalyzes two-electron reductions of quinones. NQO2 contributes to the metabolism of biogenic and xenobiotic quinones, including a wide range of antitumor drugs, with both toxifying and detoxifying functions. Moreover, NQO2 activity can be inhibited by several compounds, including drugs and phytochemicals such as flavonoids. NQO2 may play important roles that go beyond quinone metabolism and include the regulation of oxidative stress, inflammation, and autophagy, with implications in carcinogenesis and neurodegeneration. NQO2 is a highly polymorphic gene with several allelic variants, including insertions (I), deletions (D) and single-nucleotide (SNP) polymorphisms located mainly in the promoter, but also in other regulatory regions and exons. This is the first systematic review of the literature reporting on NQO2 gene variants as risk factors in degenerative diseases or drug adverse effects. In particular, hypomorphic 29 bp I alleles have been linked to breast and other solid cancer susceptibility as well as to interindividual variability in response to chemotherapy. On the other hand, hypermorphic polymorphisms were associated with Parkinson’s and Alzheimer’s disease. The I and D promoter variants and other NQO2 polymorphisms may impact cognitive decline, alcoholism and toxicity of several nervous system drugs. Future studies are required to fill several gaps in NQO2 research. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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37 pages, 599 KiB  
Review
Pharmacogenomics of Dementia: Personalizing the Treatment of Cognitive and Neuropsychiatric Symptoms
by Barbara Vuic, Tina Milos, Lucija Tudor, Matea Nikolac Perkovic, Marcela Konjevod, Gordana Nedic Erjavec, Vladimir Farkas, Suzana Uzun, Ninoslav Mimica and Dubravka Svob Strac
Genes 2023, 14(11), 2048; https://doi.org/10.3390/genes14112048 - 6 Nov 2023
Cited by 2 | Viewed by 2780
Abstract
Dementia is a syndrome of global and progressive deterioration of cognitive skills, especially memory, learning, abstract thinking, and orientation, usually affecting the elderly. The most common forms are Alzheimer’s disease, vascular dementia, and other (frontotemporal, Lewy body disease) dementias. The etiology of these [...] Read more.
Dementia is a syndrome of global and progressive deterioration of cognitive skills, especially memory, learning, abstract thinking, and orientation, usually affecting the elderly. The most common forms are Alzheimer’s disease, vascular dementia, and other (frontotemporal, Lewy body disease) dementias. The etiology of these multifactorial disorders involves complex interactions of various environmental and (epi)genetic factors and requires multiple forms of pharmacological intervention, including anti-dementia drugs for cognitive impairment, antidepressants, antipsychotics, anxiolytics and sedatives for behavioral and psychological symptoms of dementia, and other drugs for comorbid disorders. The pharmacotherapy of dementia patients has been characterized by a significant interindividual variability in drug response and the development of adverse drug effects. The therapeutic response to currently available drugs is partially effective in only some individuals, with side effects, drug interactions, intolerance, and non-compliance occurring in the majority of dementia patients. Therefore, understanding the genetic basis of a patient’s response to pharmacotherapy might help clinicians select the most effective treatment for dementia while minimizing the likelihood of adverse reactions and drug interactions. Recent advances in pharmacogenomics may contribute to the individualization and optimization of dementia pharmacotherapy by increasing its efficacy and safety via a prediction of clinical outcomes. Thus, it can significantly improve the quality of life in dementia patients. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
14 pages, 583 KiB  
Review
Pharmacogenomics: A Step forward Precision Medicine in Childhood Asthma
by Giuliana Ferrante, Salvatore Fasola, Velia Malizia, Amelia Licari, Giovanna Cilluffo, Giorgio Piacentini and Stefania La Grutta
Genes 2022, 13(4), 599; https://doi.org/10.3390/genes13040599 - 28 Mar 2022
Cited by 3 | Viewed by 2665
Abstract
Personalized medicine, an approach to care in which individual characteristics are used for targeting interventions and maximizing health outcomes, is rapidly becoming a reality for many diseases. Childhood asthma is a heterogeneous disease and many children have uncontrolled symptoms. Therefore, an individualized approach [...] Read more.
Personalized medicine, an approach to care in which individual characteristics are used for targeting interventions and maximizing health outcomes, is rapidly becoming a reality for many diseases. Childhood asthma is a heterogeneous disease and many children have uncontrolled symptoms. Therefore, an individualized approach is needed for improving asthma outcomes in children. The rapidly evolving fields of genomics and pharmacogenomics may provide a way to achieve asthma control and reduce future risks in children with asthma. In particular, pharmacogenomics can provide tools for identifying novel molecular mechanisms and biomarkers to guide treatment. Emergent high-throughput technologies, along with patient pheno-endotypization, will increase our knowledge of several molecular mechanisms involved in asthma pathophysiology and contribute to selecting and stratifying appropriate treatment for each patient. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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12 pages, 1571 KiB  
Review
Machine Learning: An Overview and Applications in Pharmacogenetics
by Giovanna Cilluffo, Salvatore Fasola, Giuliana Ferrante, Velia Malizia, Laura Montalbano and Stefania La Grutta
Genes 2021, 12(10), 1511; https://doi.org/10.3390/genes12101511 - 26 Sep 2021
Cited by 15 | Viewed by 2725
Abstract
This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of [...] Read more.
This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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Other

14 pages, 255 KiB  
Protocol
Genetic Association of Beta-Lactams-Induced Hypersensitivity Reactions: A Protocol for Systematic Review and Meta-Analysis
by Lalita Lumkul, Mati Chuamanochan, Surapon Nochaiwong, Mongkhon Sompornrattanaphan, Prapasri Kulalert, Mongkol Lao-araya, Pakpoom Wongyikul and Phichayut Phinyo
Genes 2022, 13(4), 681; https://doi.org/10.3390/genes13040681 - 13 Apr 2022
Cited by 2 | Viewed by 2364
Abstract
Beta-lactam (BL) antibiotics are among the drugs commonly related to hypersensitivity reactions. Several candidate gene studies and genome-wide association studies have reported associations of genetic variants and hypersensitivity reactions induced by BL antibiotics. However, the results were inconclusive. This protocol details a comprehensive [...] Read more.
Beta-lactam (BL) antibiotics are among the drugs commonly related to hypersensitivity reactions. Several candidate gene studies and genome-wide association studies have reported associations of genetic variants and hypersensitivity reactions induced by BL antibiotics. However, the results were inconclusive. This protocol details a comprehensive systematic review of genetic factors associated with BL-induced hypersensitivity. A systematic search of literature related to genetic associations of BL-induced hypersensitivity will be performed through PubMed, Medline, Scopus, EMBASE, Web of Science, CINAHL, and the Cochrane central register of Controlled Trials (CENTRAL) from their inception dates with no language restrictions. Two reviewers will independently screen, extract, and appraise the risk of bias. Frequencies of genetic variants that comply with Hardy–Weinberg equilibrium will be extracted and pooled. Genetic models will be applied to variant effect calculation as per allele and genotype analysis. Based on statistical heterogeneity among studies, common effect estimation (odds ratio) and its corresponding 95% confidence interval will be analyzed. Sensitivity and subgroup analyses will be performed to determine the robustness of eligible studies. This systematic review and meta-analysis will provide comprehensive evidence of genetic effects regarding BL-induced hypersensitivity. The findings will enlighten the determination of disease-related genotypes that would potentially reveal allergy profiling in patients. Full article
(This article belongs to the Special Issue Pharmacogenomics: Challenges and Future)
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