Machine learning approach yields epigenetic biomarkers of food allergy: A novel 13-gene signature to diagnose clinical reactivity

Environmental Factor Index (EFI): A Novel Approach to Measure the Strength of Environmental Influence on DNA Methylation in Identical Twins

BACKGROUND/OBJECTIVES

The dynamic interaction between genomic DNA, epigenetic modifications, and phenotypic traits was examined in identical twins. Environmental perturbations can induce epigenetic changes in DNA methylation, influencing gene expression and phenotypes. Although DNA methylation mediates gene-environment correlations, the quantitative effects of external factors on DNA methylation remain underexplored. This study aimed to quantify these effects using a novel approach.

METHODS

A cohort study was conducted on healthy monozygotic twins to evaluate the influence of environmental stimuli on DNA methylation. We developed the Environmental Factor Index (EFI) to identify methylation sites showing statistically significant changes in response to environmental stimuli. We analyzed the identified sites for associations with disorders, DNA methylation markers, and CpG islands.

RESULTS

The EFI identified methylation sites that exhibited significant associations with genes linked to various disorders, particularly cancer. These sites were overrepresented on CpG islands compared to other genomic features, highlighting their regulatory importance.

CONCLUSIONS

The EFI is a valuable tool for understanding the molecular mechanisms underlying disease pathogenesis. It provides insights into the development of preventive and therapeutic strategies and offers a new perspective on the role of environmental factors in epigenetic regulation.

The future of food allergy diagnosis

Food allergy represents an increasing global health issue, significantly impacting society on a personal and on a systems-wide level. The gold standard for diagnosing food allergy, the oral food challenge, is time-consuming, expensive, and carries risks of allergic reactions, with unpredictable severity. There is, therefore, an urgent need for more accurate, scalable, predictive diagnostic techniques. In this review, we discuss possible future directions in the world of food allergy diagnosis. We start by describing the current clinical approach to food allergy diagnosis, highlighting novel diagnostic methods recommended for use in clinical practice, such as the basophil activation test and molecular allergology, and go on to discuss tests that require more research before they can be applied to routine clinical use, including the mast cell activation test and bead-based epitope assay. Finally, we consider exploratory approaches, such as IgE glycosylation, IgG4, T and B cell assays, microbiome analysis, and plasma cytokines. Artificial intelligence is assessed for potential integrated interpretation of panels of diagnostic tests. Overall, a framework is proposed suggesting how combining established and emerging technologies can effectively enhance the accuracy of food allergy diagnosis in the future.

Prediction of pediatric peanut oral food challenge outcomes using machine learning

Background

Clinical testing, including food-specific skin and serum IgE level tests, provides limited accuracy to predict food allergy. Confirmatory oral food challenges (OFCs) are often required, but the associated risks, cost, and logistic difficulties comprise a barrier to proper diagnosis.

Objective

We sought to utilize advanced machine learning methodologies to integrate clinical variables associated with peanut allergy to create a predictive model for OFCs to improve predictive performance over that of purely statistical methods.

Methods

Machine learning was applied to the Learning Early about Peanut Allergy (LEAP) study of 463 peanut OFCs and associated clinical variables. Patient-wise cross-validation was used to create ensemble models that were evaluated on holdout test sets. These models were further evaluated by using 2 additional peanut allergy OFC cohorts: the IMPACT study cohort and a local University of Michigan cohort.

Results

In the LEAP data set, the ensemble models achieved a maximum mean area under the curve of 0.997, with a sensitivity and specificity of 0.994 and 1.00, respectively. In the combined validation data sets, the top ensemble model achieved a maximum area under the curve of 0.871, with a sensitivity and specificity of 0.763 and 0.980, respectively.

Conclusions

Machine learning models for predicting peanut OFC results have the potential to accurately predict OFC outcomes, potentially minimizing the need for OFCs while increasing confidence in food allergy diagnoses.

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

PURPOSE OF REVIEW

Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.

RECENT FINDINGS

We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.

Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review

The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.

Current state and prospects of artificial intelligence in allergy

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

Telemedicine beyond the pandemic: challenges in the pediatric immunology clinic

INTRODUCTION

Telemedicine and electronic medical records (EMRs) have revolutionized healthcare in recent years, offering numerous benefits that improve the delivery of care and the overall patient outcomes.

AREAS COVERED

Telemedicine allows providers to diagnose and treat patients remotely, often eliminating the need for face-to-face visits. Its benefits include improved access to care, convenience for patients, and reduced costs both for patients and providers. When used with remote patient monitoring and remote therapeutic monitoring, continuous care becomes possible. EMRs allow providers to store, access, and share patient information more efficiently than paper charts. The benefits of EMRs include improved patient safety, increased efficiency, and reduced costs.

EXPERT OPINION

The combination of telemedicine with EMRs makes it possible to envision the advent of computer-assisted diagnosis (CAD). This technology uses artificial intelligence and machine learning algorithms to analyze medical information including images, clinical and physiologic data, test results and remotely obtained information to support healthcare providers in making accurate diagnoses. By providing providers with what is essentially a second opinion, CAD systems can help prevent misdiagnoses and improve the quality of care. Such systems are not meant to replace healthcare providers, but rather to support them in making more informed and accurate diagnoses.

Epigenome alterations in food allergy: A systematic review of candidate gene and epigenome-wide association studies

OBJECTIVE

The aim of this study was to systematically review the evidence across studies that assessed DNA methylome variations in association with food allergy (FA).

DESIGN

A systematic review of the literature and meta-analysis were carried out within several databases. However, the risk of bias in the included articles was not evaluated.

DATA SOURCES

PubMed, Cochrane Database of Systematic Reviews, and Web of Science were used to search up to July 2022.

ELIGIBILITY CRITERIA

We included targeted and epigenome-wide association studies (EWASs) that assessed DNA methylome alterations in association with FA in adult or paediatric populations.

RESULTS

Among 366 publications, only 16 were retained, which were mainly focused on FA in children. Seven candidate gene-targeted studies found associations in Th1/Th2 imbalance (IL4, IL5, IL10, INFG, IL2 and IL12B genes), regulatory T cell function (FOXP3 gene), Toll-like receptors pathway (TLR2, CD14 genes) and digestive barrier integrity (FLG gene). Nine EWAS assessed the association with peanut allergy (n = 3), cow's milk allergy (n = 2) or various food allergens (n = 4). They highlighted 11 differentially methylated loci in at least two studies (RPS6KA2, CAMTA1, CTBP2, RYR2, TRAPPC9, DOCK1, GALNTL4, HDAC4, UMODL1, ZAK and TNS3 genes). Among them, CAMTA1 and RPS6KA2, and CTBP2 are involved in regulatory T cell function and Th2 cell differentiation, respectively. Gene-functional analysis revealed two enriched gene clusters involved in immune responses and protein phosphorylation. ChIP-X Enrichment Analysis 3 showed eight significant transcription factors (RXRA, ZBTB7A, ESR1, TCF3, MYOD1, CTCF, GATA3 and CBX2). Ingenuity Pathway Analysis identified canonical pathways involved, among other, in B cell development, pathogen-induced cytokine storm signalling pathway and dendritic cell maturation.

CONCLUSION

This review highlights the involvement of epigenomic alterations of loci in Th1/Th2 and regulatory T cell differentiation in both candidate gene studies and EWAS. These alterations provide a better insight into the mechanistic aspects in FA pathogenesis and may guide the development of epigenome-based biomarkers for FA.

Promises and challenges in pharmacoepigenetics

Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.

DNA Methylation Analysis Reveals Distinct Patterns in Satellite Cell-Derived Myogenic Progenitor Cells of Subjects with Spastic Cerebral Palsy

Spastic type cerebral palsy (CP) is a complex neuromuscular disorder that involves altered skeletal muscle microanatomy and growth, but little is known about the mechanisms contributing to muscle pathophysiology and dysfunction. Traditional genomic approaches have provided limited insight regarding disease onset and severity, but recent epigenomic studies indicate that DNA methylation patterns can be altered in CP. Here, we examined whether a diagnosis of spastic CP is associated with intrinsic DNA methylation differences in myoblasts and myotubes derived from muscle resident stem cell populations (satellite cells; SCs). Twelve subjects were enrolled (6 CP; 6 control) with informed consent/assent. Skeletal muscle biopsies were obtained during orthopedic surgeries, and SCs were isolated and cultured to establish patient-specific myoblast cell lines capable of proliferation and differentiation in culture. DNA methylation analyses indicated significant differences at 525 individual CpG sites in proliferating SC-derived myoblasts (MB) and 1774 CpG sites in differentiating SC-derived myotubes (MT). Of these, 79 CpG sites were common in both culture types. The distribution of differentially methylated 1 Mbp chromosomal segments indicated distinct regional hypo- and hyper-methylation patterns, and significant enrichment of differentially methylated sites on chromosomes 12, 13, 14, 15, 18, and 20. Average methylation load across 2000 bp regions flanking transcriptional start sites was significantly different in 3 genes in MBs, and 10 genes in MTs. SC derived MBs isolated from study participants with spastic CP exhibited fundamental differences in DNA methylation compared to controls at multiple levels of organization that may reveal new targets for studies of mechanisms contributing to muscle dysregulation in spastic CP.

diproperm: An R Package for the DiProPerm Test

High-dimensional low sample size (HDLSS) data sets frequently emerge in many biomedical applications. The direction-projection-permutation (DiProPerm) test is a two-sample hypothesis test for comparing two high-dimensional distributions. The DiProPerm test is exact, i.e., the type I error is guaranteed to be controlled at the nominal level for any sample size, and thus is applicable in the HDLSS setting. This paper discusses the key components of the DiProPerm test, introduces the diproperm R package, and demonstrates the package on a real-world data set.

Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine

The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.

Potential Biomarkers, Risk Factors and their Associations with IgE-mediated Food Allergy in Early Life: A Narrative Review

Food allergy affects the quality of life of millions of people worldwide and presents a significant psychological and financial burden for both national and international public health. In the past few decades, the prevalence of allergic disease has been on the rise worldwide. Identified risk factors for food allergy include family history, mode of delivery, variations in infant feeding practices, prior diagnosis of other atopic diseases such as eczema, and social economic status. Identifying reliable biomarkers which predict the risk of developing food allergy in early life would be valuable in both preventing morbidity and mortality and by making current interventions available at the earliest opportunity. There is also the potential to identify new therapeutic targets. This narrative review provides details on the genetic, epigenetic, dietary and microbiome influences upon the development of food allergy and synthesizes the currently available data indicating potential biomarkers. While there is a large body of research evidence available within each field of potential risk factors, there are very limited number of studies which span multiple methodological fields, for example including immunology, microbiome, genetic/epigenetic factors and dietary assessment. We recommend that further collaborative research with detailed cohort phenotyping is required to identify biomarkers, and whether these vary between at-risk populations and the wider population. The low incidence of oral food challenge confirmed food allergy in the general population, and the complexities of designing nutritional intervention studies will provide challenges for researchers to address in generating high quality, reliable and reproducible research findings.

STATEMENT OF SIGNIFICANCE

Food allergy affects the quality of life of millions of people worldwide and presents a significant psychological and financial burden for both national and international public health. Identifying reliable biomarkers which predict the risk of developing food allergy would be valuable in both preventing morbidity and mortality and by making current interventions available at the earliest opportunity. This review provides details on the genetic, epigenetic, dietary and microbiome influences upon the development of food allergy. This helps in identifying reliable biomarkers to predict the risk of developing food allergy, which could be valuable in both preventing morbidity and mortality and by making interventions available at the earliest opportunity.

Seeing the Forest for the Trees: Evaluating Population Data in Allergy-Immunology

A population-level study is essential for understanding treatment effects, epidemiologic phenomena, and health care best practices. Evaluating large populations and associated data requires an analytic framework, which is commonly used by statisticians, epidemiologists, and data scientists. This document will serve to provide an overview of these commonly employed methods in allergy and immunology research. We will draw upon recent examples from the allergy-immunology literature to contextualize discrete principles of relevance to population-level analysis that include statistical features of a study population, elements of statistical inference, regression analysis, and an overview of machine learning practices. Our intent is to guide the reader through a practical description of this important quantitative discipline and facilitate greater understanding about data and result display in the medical literature.

Leveraging clinical epigenetics in heart failure with preserved ejection fraction: a call for individualized therapies

Described as the 'single largest unmet need in cardiovascular medicine', heart failure with preserved ejection fraction (HFpEF) remains an untreatable disease currently representing 65% of new heart failure diagnoses. HFpEF is more frequent among women and associates with a poor prognosis and unsustainable healthcare costs. Moreover, the variability in HFpEF phenotypes amplifies complexity and difficulties in the approach. In this perspective, unveiling novel molecular targets is imperative. Epigenetic modifications-defined as changes of DNA, histones, and non-coding RNAs (ncRNAs)-represent a molecular framework through which the environment modulates gene expression. Epigenetic signals acquired over the lifetime lead to chromatin remodelling and affect transcriptional programmes underlying oxidative stress, inflammation, dysmetabolism, and maladaptive left ventricular remodelling, all conditions predisposing to HFpEF. The strong involvement of epigenetic signalling in this setting makes the epigenetic information relevant for diagnostic and therapeutic purposes in patients with HFpEF. The recent advances in high-throughput sequencing, computational epigenetics, and machine learning have enabled the identification of reliable epigenetic biomarkers in cardiovascular patients. Contrary to genetic tools, epigenetic biomarkers mirror the contribution of environmental cues and lifestyle changes and their reversible nature offers a promising opportunity to monitor disease states. The growing understanding of chromatin and ncRNAs biology has led to the development of several Food and Drug Administration approved 'epidrugs' (chromatin modifiers, mimics, anti-miRs) able to prevent transcriptional alterations underpinning left ventricular remodelling and HFpEF. In the present review, we discuss the importance of clinical epigenetics as a new tool to be employed for a personalized management of HFpEF.

Noninvasive and minimally invasive techniques for the diagnosis and management of allergic diseases

Allergic diseases of the (upper and lower) airways, the skin and the gastrointestinal tract, are on the rise, resulting in impaired quality of life, decreased productivity, and increased healthcare costs. As allergic diseases are mostly tissue-specific, local sampling methods for respective biomarkers offer the potential for increased sensitivity and specificity. Additionally, local sampling using noninvasive or minimally invasive methods can be cost-effective and well tolerated, which may even be suitable for primary or home care sampling. Non- or minimally invasive local sampling and diagnostics may enable a more thorough endotyping, may help to avoid under- or overdiagnosis, and may provide the possibility to approach precision prevention, due to early diagnosis of these local diseases even before they get systemically manifested and detectable. At the same time, dried blood samples may help to facilitate minimal-invasive primary or home care sampling for classical systemic diagnostic approaches. This EAACI position paper contains a thorough review of the various technologies in allergy diagnosis available on the market, which analytes or biomarkers are employed, and which samples or matrices can be used. Based on this assessment, EAACI position is to drive these developments to efficiently identify allergy and possibly later also viral epidemics and take advantage of comprehensive knowledge to initiate preventions and treatments.

Artificial intelligence in the diagnosis of pediatric allergic diseases

Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self-care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in improving data quality and processing and setting-up advanced algorithms to interpret the data. This review aimed to summarize current knowledge about AI and discuss its impact on the diagnostic framework of pediatric allergic diseases such as eczema, food allergy, and respiratory allergy, along with the future opportunities that AI research can offer in this medical area.

Genetics of Food Allergy

The risk factors for food allergy (FA) include both genetic variants and environmental factors. Advances using both candidate-gene association studies and genome-wide approaches have led to the identification of FA-associated genes involved in immune responses and skin barrier functions. Epigenetic changes have also been associated with the risk of FA. In this chapter, we outline current understanding of the genetics, epigenetics and the interplay with environmental risk factors associated with FA. Future studies of gene-environment interactions, gene-gene interactions, and multi-omics integration may help shed light on the mechanisms of FA, and lead to improved diagnostic and treatment strategies.

DNA Methylation Levels in Mononuclear Leukocytes from the Mother and Her Child Are Associated with IgE Sensitization to Allergens in Early Life

DNA methylation changes may predispose becoming IgE-sensitized to allergens. We analyzed whether DNA methylation in peripheral blood mononuclear cells (PBMC) is associated with IgE sensitization at 5 years of age (5Y). DNA methylation was measured in 288 PBMC samples from 74 mother/child pairs from the birth cohort ALADDIN (Assessment of Lifestyle and Allergic Disease During INfancy) using the HumanMethylation450BeadChip (Illumina). PBMCs were obtained from the mothers during pregnancy and from their children in cord blood, at 2 years and 5Y. DNA methylation levels at each time point were compared between children with and without IgE sensitization to allergens at 5Y. For replication, CpG sites associated with IgE sensitization in ALADDIN were evaluated in whole blood DNA of 256 children, 4 years old, from the BAMSE (Swedish abbreviation for Children, Allergy, Milieu, Stockholm, Epidemiology) cohort. We found 34 differentially methylated regions (DMRs) associated with IgE sensitization to airborne allergens and 38 DMRs associated with sensitization to food allergens in children at 5Y (Sidak p ≤ 0.05). Genes associated with airborne sensitization were enriched in the pathway of endocytosis, while genes associated with food sensitization were enriched in focal adhesion, the bacterial invasion of epithelial cells, and leukocyte migration. Furthermore, 25 DMRs in maternal PBMCs were associated with IgE sensitization to airborne allergens in their children at 5Y, which were functionally annotated to the mTOR (mammalian Target of Rapamycin) signaling pathway. This study supports that DNA methylation is associated with IgE sensitization early in life and revealed new candidate genes for atopy. Moreover, our study provides evidence that maternal DNA methylation levels are associated with IgE sensitization in the child supporting early in utero effects on atopy predisposition.

New Machine Learning Applications to Accelerate Personalized Medicine in Breast Cancer: Rise of the Support Vector Machines

Artificial intelligence, machine learning, health care robots, and algorithms for clinical decision-making are currently being sought after in diverse fields of clinical medicine and bioengineering. The field of personalized medicine stands to benefit from new technologies so as to harness the omics big data, for example, to individualize and accelerate cancer diagnostics and therapeutics in particular. In this overarching context, breast cancer is one of the most common malignancies worldwide with multiple underlying molecular etiologies and each subtype displaying diverse clinical outcomes. Disease stratification for breast cancer is, therefore, vital to its effective and individualized clinical care. The support vector machine (SVM) is a rising machine learning approach that offers robust classification of high-dimensional big data into small numbers of data points (support vectors), achieving differentiation of subgroups in a short amount of time. Considering the rapid timelines required for both diagnosis and treatment of most aggressive cancers, this new machine learning technique has important clinical and public applications and implications for high-throughput data analysis and contextualization. This expert review describes and examines, first, the SVM models employed to forecast breast cancer subtypes using diverse systems science data, including transcriptomics, epigenetics, proteomics, and radiomics, as well as biological pathway, clinical, pathological, and biochemical data. Then, we compare the performance of the present SVM and other diagnostic and therapeutic prediction models across the data types. We conclude by emphasizing that data integration is a critical bottleneck in systems science, cancer research and development, and health care innovation and that SVM and machine learning approaches offer new solutions and ways forward in biomedical, bioengineering, and clinical applications.

Genetics and epigenetics of allergy

PURPOSE OF REVIEW

Allergic diseases are prototypic examples for gene × environment-wide interactions. This review considers the current evidence for genetic and epigenetic mechanisms in allergic diseases and highlights barriers and facilitators for the implementation of these novel tools both for research and clinical practice.

RECENT FINDINGS

The value of whole-genome sequencing studies and the use of polygenic risk score analysis in homogeneous well characterized populations are currently being tested. Epigenetic mechanisms are known to play a crucial role in the pathogenesis of allergic disorders, especially through mediating the effects of the environmental factors, well recognized risk modifiers. There is emerging evidence for the immune-modulatory role of probiotics through epigenetic changes. Direct or indirect targeting of epigenetic mechanisms affect expression of the genes favouring the development of allergic diseases and can improve tissue biology. The ability to specifically edit the epigenome, especially using the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 technology, holds the promise of enhancing understanding of how epigenetic modifications function and enabling manipulation of cell phenotype for research or therapeutic purposes.

SUMMARY

Additional research in the role of genetic and epigenetic mechanisms in relation to allergic diseases' endotypes is needed. An international project characterizing the human epigenome in relation to allergic diseases is warranted.

Recent findings in the genetics and epigenetics of asthma and allergy

In asthma and allergy genetics, a trend towards a few main topics developed over the last 2 years. First, a number of studies have been published recently which focus on overlapping and/or very specific phenotypes: within the allergy spectrum but also reaching beyond, looking for common genetic traits shared between different diseases or disease entities. Secondly, an urgently needed focus has been put on asthma and allergy genetics in populations genetically different from European ancestry. This acknowledges that the majority of new asthma patients today are not white and asthma is a truly worldwide disease. In epigenetics, recent years have seen several large-scale epigenome-wide association studies (EWAS) being published and a further focus was on the interaction between the environment and epigenetic signatures. And finally, the major trends in current asthma and allergy genetics and epigenetics comes from the field of pharmacogenetics, where it is necessary to understand the susceptibility for and mechanisms of current asthma and allergy therapies while at the same time, we need to have scientific answers to the recent availability of novel drugs that hold the promise for a more individualized therapy.

Correction: Machine learning approach yields epigenetic biomarkers of food allergy: A novel 13-gene signature to diagnose clinical reactivity

[This corrects the article DOI: 10.1371/journal.pone.0218253.].