The nuts and bolts of omics for the clinical allergist

Journal Pre-proof The nuts and bolts of omics for the clinical allergist Yamini V. Virkud, M.D., Rachel S. Kelly, Ph.D., Caleb Wood, Jessica A. Lasky-Su, Sc.D. PII:

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Please cite this article as: Virkud YV, Kelly RS, Wood C, Lasky-Su JA, The nuts and bolts of omics for the clinical allergist, Annals of Allergy, Asthma and Immunology (2019), doi: https://doi.org/10.1016/ j.anai.2019.09.017. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

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Objective: Omics, aka multi-omics, is an emerging area of research that is advancing the use of personalized medicine in clinical practice and is therefore relevant for the practicing allergist. Data Sources: We performed a literature search of a selection of scientific findings in omics and allergy, including variants that may be important to allergy outcomes in the genome, transcriptome, metabolome, microbiome, epigenome, and exposome, among others. Study Selections: Basic science papers and review articles. Results: The use of multi-omic data in clinical practice is changing how clinicians treat their patients whereby more personalized approaches are becoming standard in medical practice and has the potential to transform the treatment of allergies. Conclusion: Multi-omic data are relevant and will become increasingly important for the clinical allergist.

1 1

Introduction: What is “omics”?

2

The term “omics” (aka multi-omics) refers to the usage of high-throughput technology to

3

characterize a set of molecules relevant to a field of biology, and has grown to encompass a

4

variety of “-omes” including the genome (DNA), transcriptome (RNA), proteome (proteins),

5

metabolome (metabolites), microbiome (microbiota), exposome (exposures), and many

6

others.1 In 1957, scientists discovered the central dogma of molecular biology that describes a

7

process whereby DNA in a cell is translated into RNA, which then transfers the information to

8

cellular organelles in order to make proteins essential for all cellular structure and function

9

[Figure 1].2, 3 Accordingly, most of the early scientific focus was on characterizing the genome,

10

transcriptome, and proteome. However, now there are 6 current large omic fields that are

11

being studied extensively today: genome (DNA), transcriptome (RNA), proteome (proteins),

12

epigenome (modification to DNA that alter expression), metabolome (metabolites),

13

microbiome (microbiota), exposome (exposures) [Figure 2].

14

“Systems biology” and “integrative omics” are two terms that refer to use of advanced

15

bioinformatics and sophisticated computational approaches to study interactions between

16

these -omes in explaining the pathophysiology of an organism, also known as the “interactome”

17

4

18

“Big Data”, it has only been in the last decade that technological advances have allowed for

19

rapid sequencing and characterization of molecules that previously took years to perform 1.

20

These improvements in efficiency and cost of sequencing omics datasets, paralleled with

21

development of advanced analytic techniques to process these complex datasets, have led to

22

increased study of omics in a variety of human diseases, including asthma, allergic rhinitis, food

. Because omics generally involves the use of very large data sets of information, often called

2 23

allergies, and immunodeficiencies. As scientists learn more about how changes in the

24

interactome predict outcomes in allergic and immunologic diseases, allergists/immunologists

25

may find themselves using omics technologies to help guide diagnosis and treatment of

26

individual patients, a field known as personalized medicine. We are already beginning to see

27

the first steps of personalized medicine in our field, as clinical immunologists begin to use

28

techniques such as whole exome sequencing, to better define the pathophysiology of primary

29

immunodeficiencies 5.

30

Personalized medicine in allergy/immunology

31

Personalized medicine (aka precision medicine) is a promising approach to studying disease

32

etiology, pathogenesis, progression and treatment, in order to obtain information that is

33

specific to an individual that can be utilized to identify the optimal treatment option for that

34

individual.6 Current diagnostic practices for allergists/immunologists include the use of skin

35

prick and intradermal testing, IgE measurements, pulmonary function testing, and a variety of

36

immunological testing (immunoglobulin levels, flow cytometry, tryptase, complement levels,

37

etc). However, many of these tests, particularly skin tests and IgE levels, are limited in their

38

diagnostic specificity and sensitivity for allergic diseases, and even further limited in their ability

39

to predict disease severity and/or treatment response.7 For example, two patients may both

40

have a positive wheal upon skin testing to peanut, yet one may develop an IgE mediated

41

allergic reaction upon peanut consumption while the other might not. Furthermore, the size of

42

the skin test or peanut-specific IgE level cannot sufficiently inform the patient about their

43

sensitivity to peanut or the severity of reaction they may experience. Scientific research is

44

currently focusing on studying various diseases to determine information that may be relevant

3 45

to inform these personalized approaches to treatment. One of the most promising sources of

46

information to be utilized for personalized approaches to many different disease outcomes,

47

including asthma and allergies, are multi-omics data that can now be generated in real time in a

48

timeframe and at a cost practical for clinical use. By researching these novel omics, scientists

49

may be able to identify factors in one’s specific chemical makeup to predict an individual risk

50

for the development of a disease and response to treatment. 1, 6

51

Below we review the omics data types that are commonly generated today and the clinical and

52

translational impact they may have on the development of personalized medicine.

53

Genomics

54

Of the omic fields, genomics, the comprehensive study of genetic variants, is the most

55

advanced, likely due to early focus on this field in the Human Genome Project.1 The Human

56

Genome Project was an effort started in 1990 to sequence the entirety of several volunteers’

57

DNA and was completed in April 2003.8 It should be noted, however, that the terminology “the

58

human genome” fails to capture the vast differences in individual genomes, due to base pair

59

nucleotide differences known as single nucleotide polymorphisms (SNPs). SNPs account for the

60

unique nature of individual’s DNA, with an average of 20 million differences out of 3.2 billion

61

base pairs, and thus, to explain the variety of manifestations of human disease, each person

62

must be sequenced individually.9 Based on early sequencing methods, this would not have been

63

feasible, as the Human Genome Project took 13 years and cost $2.7 billion dollars to sequence

64

a small number of genomes. In contrast with older techniques, “next generation sequencing”

65

(NGS) refers to a variety of modern technologies used to massively increase the speed,

66

efficiency, and accuracy of sequencing. With the development of NGS, an individual’s genome

4 67

can be sequenced in a few hours for less than $1000, and for several hundred dollars for a

68

limited sequencing of the coding DNA sequences, known as whole exome sequencing.8 The

69

speed of NGS allows samples to be analyzed fairly quickly, which means that DNA analysis can

70

be practical in a medical setting. By doing this, a doctor can receive the fully sequenced DNA of

71

a patient, and with proper annotation of genetic variants, might be able to use these SNPs to

72

diagnose or guide treatment for a specific disease.

73

Genomic sequencing is already proving useful in the diagnosis and management of allergic and

74

immunologic diseases. In a meta-analysis of studies performing NGS to diagnose primary

75

immunodeficiencies, they found that the proportion of patients with primary

76

immunodeficiencies that were genetically diagnosed ranged from 15-79% of the studies. Early

77

evidence is gathering for genetic biomarkers of allergic disease, including asthma,10 food

78

allergy,11 atopic dermatitis,12 allergic rhinitis,13 and drug allergy.13, 14 However, despite the

79

association of these genetic variants with allergic diseases, the impact on how the disease

80

manifests or varies is often unknown, and the effect of the individual variant is often small. As

81

a result, using genomic data by itself can often only provide an incomplete picture of the

82

individual patient’s personalized risk of disease.15

83

Transcriptomics

84

Transcriptomics involves the characterization of primarily messenger RNA (mRNA) transcript

85

expression levels, most commonly using techniques such as microarray chips or RNA-seq.1

86

Unlike DNA sequencing, transcriptomics provides a potentially more relevant glimpse of the

87

genes actively being transcribed to eventually form proteins. Until recently, early

88

transcriptomics involved the use of oligonucleotide microarrays, chips that contained a set of

5 89

relevant complementary DNA (cDNA) probes. RNA samples were added to bind or hybridize to

90

the matching cDNA probes, and the amount of bound RNA was measured to determine RNA

91

expression levels. While affordable, these techniques were limited by pre-selected cDNA

92

probes, and measurement of new or rare transcripts was not possible. Current advances in NGS

93

technology allows for RNA-sequencing (RNA-seq), where RNA is digested, rapidly sequenced,

94

and then matched up against reference genomes. While this alignment to identify RNA

95

sequences is more computationally intense, it allows better resolution of rare and unknown

96

RNA transcripts.16

97

In the field of allergy and immunology, early research is indicating that transcriptomic changes

98

can be associated with allergic disease phenotype.16-19 For example, one study examining

99

patients with asthma being treated with anti-IL13 therapies described the use of

100

transcriptomics to identify genes whose expression levels predicted improved treatment

101

response, and may potentially be used as a biomarker of therapeutic response.18 Another study

102

examined esophageal biopsies in patients with newly diagnosed eosinophilic esophagitis, and

103

found differential expression of genes in association with esophageal narrowing and with

104

fibrostenosis. These findings may guide the development of targeted treatment modalities and

105

may distinguish which patients are more likely to respond to such therapies.19

106

Epigenomics

107

Epigenomics is defined as the quantification and collective study of the modifications made to

108

DNA and histones, such as methyl tags used by the body to inhibit certain genes.1 While

109

mutations in DNA are the primary means by which individual differences are encoded, these

110

modifications to DNA can also be inherited. Types of DNA modification include the addition of

6 111

methyl or hydroxymethyl groups to the DNA which generally decrease expression of a gene

112

(DNA methylation or hydroxymethylation); the addition of acetyl, methyl, phosphorus or

113

ubiquitin tails to histones to alter transcription (histone modification), and the binding of small

114

noncoding RNAs, known as microRNAs (miRNA), that bind to and block translation of mRNA.16

115

Research in epigenomics has identified novel and heritable changes in DNA methylation,

116

including associations with maternal smoking and air pollution in asthma,20, 21 oral

117

immunotherapy in the development of tolerance in peanut allergy,22, as well as the

118

development of natural tolerance in cow’s milk allergy.23 Epigenetic changes have also been

119

identified in a variety of other allergic diseases including atopic dermatitis,24 allergic rhinitis,25

120

and aspirin-exacerbated respiratory disease.26

121

Proteomics

122

Proteomics is the global characterization of proteins and peptides that are either produced or

123

modified by an organism.1 As proteins are the key product of DNA and RNA and mediate most

124

cellular processes, it is reasonable to expect that proteomics might provide the most accurate

125

reflection of disease pathophysiology.27 However, a number of factors limit the feasibility of

126

high throughput efficient proteomic sequencing, including the complexity of protein structure,

127

the importance of 3-D conformation in mediating protein function, and the large range in

128

concentration. Mass spectrometry, chromatography, and gel electrophoresis are the most

129

common analysis tools at the moment for these set of chemicals, but these techniques can miss

130

key aspects of protein folding. Emerging research have used proteomics techniques to identify

131

subtypes of disease and also examine allergen protein structures. Proteomics approaches have

132

yielded new insights into a variety of allergens, including the allergenicity of milk protein in

7 133

baked milk products,28 identification of B-cell epitopes on fish allergen,29 novel allergens in

134

shrimp,30 as well as simulations of the effects of gastric digestion on peanut component

135

proteins.31 Several studies have begun to use proteomics to analyze subtypes of disease,

136

examining nasal secretions from patients with chronic rhinosinusitis,32, 33 as well as sputum

137

samples from subjects with asthma.34 Overall though, in relation to allergies and personalized

138

medicine, the field of proteomics is still developing and will likely require further advances in

139

proteomic sequencing technology.

140

Metabolomics

141

Metabolomics is the comprehensive study of all the small molecules, known as metabolites,

142

found within a biological specimen that include amino acids, fatty acids, carbohydrates, or

143

other products of cellular metabolic functions. Current technologies now allow a feasible, high-

144

throughput assessment of a large number metabolites resulting from genomic, transcriptomic,

145

and proteomic variability. Metabolomics provides the most integrated profile of biological

146

status reflecting the ‘net results’ of genetic and environmental interactions. 35-37 Compared with

147

other omics, that translation of quantified metabolites into the clinical setting is often very

148

straightforward, as measures of several metabolites are already utilized to diagnose (e.g. blood

149

glucose levels for diabetes) and assess the severity of (e.g. measures of cholesterol) disease.38

150

As technologies continue to expand the scope of measurable metabolites, the potential for

151

metabolites in personalized medicine continues to expand. While the research to identify

152

allergy-associated metabolites is just beginning, metabolites in urine, plasma and stool have all

153

been associated with allergic phenotypes, including intestinal sphingolipids, plasma and dietary

154

polyunsaturated fatty acids, and folate metabolites.39-42 Utilizing multiple metabolomes to

8 155

study allergic disease, particularly food allergies, represents a particular area for potential

156

breakthrough discoveries by providing biological insight into the process of food digestion

157

through the stool metabolome and the subsequent systemic effects in the circulation through

158

plasma/serum metabolomes.

159

Microbiome

160

Microbiomics encompasses the characterization of bacteria and microorganisms living on an

161

organism, and can include flora from a variety of sources including the gut, skin, and mucosal

162

surfaces.1 Early microbiome work relied on a methodology known as 16S sequencing, where

163

ribosomal RNA units from the bacteria were amplified and sequenced.1 These data were then

164

used to cluster species into groups known as OTUs, operational taxonomic units. More

165

recently, a technique known as shotgun sequencing has been developed, where all of the

166

microbial DNA is sequenced, allowing for greater distinction between species.1 Microbiome

167

research has explored dysbiosis, pathologic alterations in gut microbiota, in a number of allergic

168

diseases, including asthma and food allergies. Some genera of bacteria have been increased in

169

association with asthma, including Haemophilus, Moraxella, Neisseria, and Streptococcus.43

170

Furthermore, certain microbial signatures have been linked with specific features of asthma

171

pathophysiology, such as corticosteroid responsiveness and obesity-related asthma.43 In food

172

allergy, dysbiosis resulting in the loss of certain Clostridial species is thought to mediate a loss

173

of regulatory T cell activity that normally promotes the development of tolerance to allergens.44

174

These findings support the potential of microbiome based therapeutic strategies for food

175

allergies.

176

Exposome

9 177

The exposome represents the entirety of exposures that can influence an organism’s

178

development and pathophysiology.45 This term not only encompasses environmental

179

exposures, but can also include aspects like diet and behavioral risk factors that influence

180

disease.46 From an allergist’s perspective, it is fairly intuitive that exposures can influence

181

disease pathophysiology, whether it is the influence of environmental pollen in triggering

182

allergic rhinitis47 or the behavioral and diet effects of early peanut consumption in reducing risk

183

of peanut allergy.48 The field of exposomics is complicated by the wide variety of exposures

184

(biological, chemical, physical, or behavioral) and sources (air, diet, medicinal, soil, water, or

185

even commercial products we use).49 Furthermore, age, developmental stage, and route by

186

which these exposures are encountered can have dramatic effects on disease

187

pathophysiology,49 as seen with food allergy where cutaneous exposure can cause allergic

188

sensitization while oral exposure results in tolerance.50, 51 Measurement of the exposome can

189

encompass a wide variety of techniques, including air/indoor pollutants, allergen sampling,

190

examination of house dust, exposure to food contaminants, and many others.46 Despite these

191

complexities, the Practall consensus group from both the American Academy of Allergy,

192

Asthma, and Immunology and the European Academy of Allergy and Clinical Immunology have

193

identified a need for large-scale initiatives to allow for better characterization of the exposome

194

in the field of allergy.49

195

A number of studies have been performed on the influence of the exposome on allergic

196

disease, though literature searches of the term “exposome” are complicated by the fact that

197

many of these studies were published prior to the coining of the term. Regarding atopic

198

dermatitis, exposure to outdoor air pollutants, volatile organic compounds, and tobacco smoke

10 199

has been associated with worsening atopic dermatitis.52 The effects of pollutants, components

200

of tobacco smoke, bisphenol A, and phthalate metabolites, have also been studied in relation to

201

worsening lung function and asthma.49 Exposure to short chain fatty acids produced in the gut

202

can epigenetically modify DNA and are a mechanism through which diseases like asthma and

203

allergies can form.53 While complicated to measure and characterize, the exposome interacts

204

closely with the –omes described above, and is an essential part of allergic disease

205

development.

206

Integrative Omics

207

Although important insights into allergy and immunology have been gained by the study of

208

single omic data types, it is increasingly recognized the integration of multiple hierarchical

209

levels of omic data has potential to provide the most complete biological insight into allergy

210

pathobiology. Allergic disease affects whole body physiology, via inflammation, oxidative stress

211

and tissue remodeling, and as such is evident and measurable at all levels of the central dogma

212

of molecular biology and now extending well beyond, including the genome, the epigenome,

213

the transcriptome, the metabolome, the exposome, and the micobiome.54 Integrative omic

214

approaches are now utilized to evaluate multiple forms of omic data together and more

215

recently have become a focus in allergy-based research.42, 54, 55 Statistical methods to integrate

216

multiomic data are emerging and multiple review articles have summarized these approaches1,

217

56

218

in epidemiological studies. In the field of allergy and immunology multiomic integration is

219

starting to provide important insights into disease pathophysiology.37 Using both microbiome

220

and metabolomics data, a recent study identified that intestinal microbial-derived sphingolipids

and provide additional insight into important considerations when analyzing multiomic data

11 221

are inversely associated with food allergy.42 Other studies have integrated genetic and

222

metabolomic data using networks and identified important biological relationships between a

223

well-established asthma gene, ORMDL3, lipid metabolism, and asthma phenotypes.

224

It is widely accepted that allergic disease is highly heterogeneous in nature and results from

225

the complex interplay of multiple multiomic influences. Identifying these allergic “endotypes”,

226

i.e. subtypes defined by their functional or pathobiological mechanisms, is crucial as individuals

227

with allergy may appear phenotypically similar but have different underlying pathobiology and

228

respond differently to the same therapy.12 Treatment and management strategies based on

229

underlying pathobiological mechanism, rather than a ‘one-size-fits-all’ approach, may be more

230

effective in terms of improved outcomes and optimized use of healthcare resources. Omics

231

data provide a novel opportunity for endotyping that moves beyond clinical characteristics such

232

as eosinophil count, cytokine count, and IgE levels, particularly when combined with machine

233

learning clustering approaches.57 Utilizing multiomic data in conjunction with clustering

234

algorithms enables the identification of more homogeneous allergy endotypes that are more

235

likely to respond more similarly to specific more personalized treatment regimen. The

236

identification of allergy endotypes utilizing multiomic data, in conjunction with clinical and

237

demographic information, offers one of the most promising approaches creating effective

238

personalized treatments based on molecular data (Figure 3). While molecular endotypes have

239

been created using singular omics data, multiomic molecular endotypes is an emerging field.37,

240

58

241

the molecular endotypes in multiple, diverse populations and repeatedly demonstrate the

242

efficacy of endotype-specific treatment responses.

In order for allergy endotypes to be successfully utilized in the clinic, it is essential to validate

12 243

Limitations of Omics Research

244

As beneficial as omics data may be to personalized medicine, there are still important

245

limitations of these data. It is important to recognize that the precise generation of multiomic

246

data is rapidly evolving. While some fields, such as genetics, have entered an era where whole

247

genome sequencing is a reality, technologies to generate other omic datatypes are more

248

complex and continue to evolve. Other omics, such as the exposome, are inherently difficult to

249

define and measure comprehensively. The multiomic era also comes with the tremendous

250

challenge of “Big Data,” that requires sophisticated analytical techniques and high computing

251

power to identify the disease associated variants. The large amount of data increases the risk of

252

false positive findings, which then necessitates a significantly larger sample size. In rare

253

diseases, it can be difficult to develop sample sizes large enough to overcome these statistical

254

issues. Along with the large sample size, the technical expertise required to analyze these

255

complex data can also add to the cost of conducting robust multi-omics studies. Furthermore,

256

most cohorts have a limited amount of multiomic data; therefore, comprehensive studies that

257

utilize a diverse number of multiomic datatypes together rarely exist. As multiomic data

258

continue to be generated, our ability to identify and replicate allergy associated variants will

259

continue to improve. Simultaneously, statistical methods will continue to evolve and enable

260

more effective analyses with these data, leading to reliable personalized approaches to

261

treatment.

262

Conclusion

263

Multiomic data have great potential to improve our treatment of allergic disease though the

264

implementation of personalized medicine approaches that account for an individual’s

13 265

underlying biological makeup and history of environmental exposures. Continued scientific

266

research in epidemiological studies and clinical trials will likely identify and validate important

267

multiomic variants important for allergic disease and allergy endotypes that have important

268

implications on treatment. Following these discoveries, a critical step for the successful

269

implementation into the clinic is the ability to simply and inexpensively measure important

270

molecular variants for allergy among the patient population. As multiomic knowledge for

271

allergic disease continues to expand, the potential for personalized approach to allergy will

272

likely become a reality.

1

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Figure 1: Central Dogma of Molecular Biology. The “central dogma” of molecular biology illustrates the process of changing DNA to RNA to proteins to metabolites via transcription and translation. Figure 1 was created with Biorender.com.

Figure 2: Omes and Omics. Various types of omic data are commonly used in human medical research. The areas of omic research include the omic data generated via the central dogma: genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (small molecules including Amino Acids, Fatty Acids, Carbohydrates, Vitamins, Lipids, Nucleotides); however, new types of omic data have emerged, including the fields of epigenomics (methyl tags and histones), exposomics (allergens, toxins, diet, behavior, pollution, climate, radiation, medication), and microbiomics (bacteria and microorganisms). Figure 2 was created with Biorender.com. Figure 3: Omics and Personalized Medicine. As cost of high throughput sequencing decreases and computational methods to analyze these big datasets improve, we may be able to combine various omics datasets to analyze individuals. These omics data can analyzed for biomarkers (a single gene, transcript, epigenetic modification, protein, metabolite, bacteria, or exposure that is associated with disease), or signatures (a combination of many biomarkers), or networks (a complex mapping of many signatures accounting for the relationship between biomarkers within the signatures). These types of analyses will be able to guide diagnostic approaches and identify therapeutic targets tailored to the individual needs of each patient, thereby providing personalized medicine for a variety of allergy endotypes, and distinguishing those without allergy who do not require treatment. Figure 3 was created with Biorender.com.

DNA

Transcription (DNA RNA)

RNA

Translation (RNA Proteins)

Proteins

Processing & Degradation (Proteins Metabolites)

Metabolites

Genome

Epigenome

Transcriptome

Proteome

Metabolome

Microbiome

Exposures

Metabolites

DNA (genes)

Methyl Tags and Histones

RNA (transcripts)

Proteins

Genomics

Epigenomics

Transcriptomics

Proteomics

Exposome

(Amino Acids, Fatty Acids, Carbohydrates, Vitamins, Lipids, Nucleotides)

Bacteria and Microorganisms

(Allergens, Toxins, Diet, Behavior, Pollution, Climate, Radiation, Medicines)

Metabolomics

Microbiomics

Exposomics

Patients

Omic Datatypes

Bioinformatics Analysis

Personalized Medicine Allergy Endotypes

Genomics

Biomarker Epigenomics Signature

Transcriptomics

Proteomics Network

Metabolomics

Microbiomics Exposomics

Non-Allergic / No Treatment

Key Messages •

• •

•

The term “omics” (aka multi-omics) refers to the usage of high-throughput technology to characterize a set of molecules relevant to a field of biology, and has grown to encompass a variety of “-omes” including the genome (DNA), transcriptome (RNA), proteome (proteins), metabolome (metabolites), microbiome (microbiota), exposome (exposures) Integrative omics refers to the study of how different omics data interact to affect a condition or disease of interest. Personalized medicine is an approach to studying disease etiology, pathogenesis, progression and treatment, that utilizes information specific to an individual (for example, their genome or transcriptome) to identify the optimal treatment option for that individual. Endotype refers to a subtype of a disease that is defined by a distinct pathophysiologic mechanism, in contrast with a phenotype, which describes characteristics of a disease without implying a different underlying mechanism.

The nuts and bolts of omics for the clinical allergist Authors: Yamini V. Virkud M.D.1, 2 Rachel S. Kelly Ph.D.2, Caleb Wood*, Jessica A. Lasky-Su Sc.D.2 Affiliations: 1. Department of Pediatrics, Massachusetts General Hospital for Children and Harvard Medical School, Boston, MA, USA 2. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA * Indicates no academic degree Corresponding author: Jessica Lasky-Su Journal: Annals of Allergy, Asthma, and Immunology Address: 181 Longwood Avenue, Boston, MA 02115 Funding: This manuscript is supported by an R01 grants from the National Heart, Lung, and Blood Institute of the National Institutes (NHLBI) of Health to Brigham and Women’s Hospital, NHLBI 1R01HL123915-01 and R01HL141826. (JALS). YVV. was supported by a grant from the National Institute of Allergy and Infectious Diseases of the US National Institutes of Health (K23AI130408). RSK is supported by the grant from NHLBI (K01HL146980). Keywords: Allergy, personalized medicine, precision medicine, omics, microbiome, metabolomics, transcriptomics, genomics, exposome, computational biology, systems biology, endotype; gene expression Abbreviations: Clinical Trial or Protocol number associated with this study: N/A Running Title: Omics for the Clinical Allergist Manuscript Body Word Count: 3,283 Author Contributions: Yamini Virkud - literature review, writing the manuscript, figure generation Rachel S. Kelly - literature review, writing the manuscript, figure generation Caleb Wood - literature review, writing the manuscript, figure generation Jessica A. Lasky-Su - literature review, writing the manuscript, figure generation Conflict of Interest: None of the authors involved in this manuscript have any conflicts of interest relevant to this work