The Journal of Allergy and Clinical Immunology
Volume 126, Issue 2 , Pages 200-209, August 2010

Using genetics to predict the natural history of asthma?

  • John W. Holloway, PhD

      Affiliations

    • Division of Infection, Inflammation & Immunity, School of Medicine, University of Southampton, Southampton, United Kingdom
    • Division of Human Genetics, School of Medicine, University of Southampton, Southampton, United Kingdom
    • Corresponding Author InformationReprint requests: John W. Holloway, PhD, III Division, MP810, Southampton General Hospital, Southampton SO16 6YD, United Kingdom.
    • ,
  • Syed H. Arshad, MD, FRCP

      Affiliations

    • Division of Infection, Inflammation & Immunity, School of Medicine, University of Southampton, Southampton, United Kingdom
    • David Hide Asthma and Allergy Research Centre, Isle of Wight, United Kingdom
    • ,
  • Stephen T. Holgate, MD, DSc, FMed Sci

      Affiliations

    • Division of Infection, Inflammation & Immunity, School of Medicine, University of Southampton, Southampton, United Kingdom

Received 7 May 2010; received in revised form 3 June 2010; accepted 8 June 2010.

Article Outline

Clinical practice reminds us that there is considerable variability in the course of asthma over time. Treatment of patients with asthma would be considerably improved if one could accurately predict the likely course of disease over the life course. Recently, with the advent of the era of genome-wide association studies, there has been a monumental shift in our understanding of the genetic factors that underlie inherited susceptibility to asthma. Genes have been identified that modulate many aspects of the natural history of asthma, such as susceptibility to atopy, altered lung development, and susceptibility to more severe disease. Heritability studies have even suggested a role for genetic factors in remission of asthma. However, although the discovery of novel genetic factors underlying disease susceptibility has undoubtedly improved our understanding of disease pathogenesis, whether these advances have improved the ability to predict the natural history in individual patients is questionable, and the application of genetic testing to clinical practice remains some way off.

Key words: Asthma, genetic, prediction, natural history

Abbreviations used: ADAM-33, A disintegrin and metalloproteinase domain, AUC, Area under the receiver operating characteristic curve, BHR, Bronchial hyperresponsiveness, CAMP, Childhood Asthma Management Program, COPD, Chronic obstructive pulmonary disease, GWAS, Genome-wide association study, STAT-6, Signal transducer and activator of transcription 6

 

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Information for Category 1 CME Credit 

Credit can now be obtained, free for a limited time, by reading the review articles in this issue. Please note the following instructions.

Method of Physician Participation in Learning Process: The core material for these activities can be read in this issue of the Journal or online at the JACI Web site: www.jacionline.org. The accompanying tests may only be submitted online at www.jacionline.org. Fax or other copies will not be accepted.

Date of Original Release: August 2010. Credit may be obtained for these courses until July 31, 2012.

Copyright Statement: Copyright © 2010-2012. All rights reserved.

Overall Purpose/Goal: To provide excellent reviews on key aspects of allergic disease to those who research, treat, or manage allergic disease.

Target Audience: Physicians and researchers within the field of allergic disease.

Accreditation/Provider Statements and Credit Designation: The American Academy of Allergy, Asthma & Immunology (AAAAI) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The AAAAI designates these educational activities for a maximum of 1 AMA PRA Category 1 Credit™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

List of Design Committee Members: John W. Holloway, PhD, Syed H. Arshad, MD, FRCP, and Stephen T. Holgate, MD, DSc, FMed Sci

Activity Objectives

1.To provide evidence of genetic linkage in asthma.

2.To understand new genes related to asthma progression and severity.

3.To provide evidence of genetic and environmental differences between adult and childhood asthma.

4.To correlate predictors of the natural history of asthma.

Recognition of Commercial Support: This CME activity has not received external commercial support.

Disclosure of Significant Relationships with Relevant Commercial Companies/Organizations: J. W. Holloway has received research funding from Asthma, Allergy and Inflammation Research and the National Institute of Health Research (NIHR), United Kingdom. S. H. Arshad has received research support from the National Institutes of Health. S. T. Holgate has shares in Synairgen and has consulted for Novartis, MSD, Almiral, Synairgen, Altair, and Amgen.

Glossary

 

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ADAM-33 

A disintegrin and metalloproteinase domain–containing protein 33 (ADAM-33) is a member of the ADAM family, members of which are related to venom proteins. ADAM proteins are involved in cell adhesion, signaling, and activation of other proteins (TNF-α and defensins). ADAM-33 is specifically associated with asthma and bronchial hyperreactivity.

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AUC 

The area under a receiver operating characteristic curve (AUC) measures the accuracy (sensitivity and specificity) of a test. An AUC of 1.0 is a perfect test result, whereas an AUC of 0.5 is a test that fails to predict a disease state (no better than random).

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DPP10, ORMDL3 

Inactive dipeptidyl peptidase 10 belongs the family of serine proteases but has no protease activity. ORMDL3 is an endoplasmic reticulum–associated protein that might play a role in protein folding. Although the functions of these proteins in asthma remain unclear, single nucleotide polymorphisms of both genes have been associated with asthma by using GWASs and replicate genomic studies in various ethnic populations.

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FILAGGRIN, COL29A1 

Filaggrin is important for proper structure of the keratin cytoskeleton and proper skin formation. Filaggrin mutations cause ichthyosis and predispose to eczema and asthma. COL29A1 is an epidermal collagen that is a expressed in the skin, lung, and gastrointestinal tract.

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FUNCTIONAL POLYMORPHISM 

Single nucleotide polymorphisms with functional consequences, such as the creation or loss of a transcription factor binding site or enhancer site or change to amino acid sequence of the encoded protein.

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GENOTYPE RELATIVE RISK 

The odds ratio of having a disease when carrying the genetic risk variant (eg, a given single nucleotide polymorphism) versus the non–risk variant state.

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GSTM1, GSTT1 

Glutathione-S-transferase μ1 and glutathione-S-transferase θ1 gene deletion polymorphisms have been associated with pediatric asthma, especially in the context of lung function decrease caused by oxidative stress.

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GWAS 

A genome-wide association study (GWAS) examines genetic variation across the entire genome in the context of a disease state (cases vs control subjects).

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IL-4, IL-13 

IL-4 and IL-13 bind a common α chain on their receptors and therefore have some overlapping effects, such as on IgE production. IL-13 can function upstream of IL-5–induced signal transducer and activator of transcription 6 activation. IL-13 overexpression in animal models is sufficient to induce many of the features of asthmatic airway remodeling.

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IL-23 

IL-23 is important in the maintenance of the TH17 CD4+ T-cell phenotype, and its levels are increased in autoimmune states, such as inflammatory bowel disease. IL-23 is made by dendritic cells and promotes IL-17 production.

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IL-33 

IL-33 is an IL-1 family member that is produced by epithelial cells, smooth muscle cells, and fibroblasts. IL-33 binds the ST2 receptor and increases degranulation and cytokine production in IgE-sensitized mast cells. It is associated with anaphylaxis and severe asthma.

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RAD50 

A genome-wide association screen showed that asthmatic patients have multiple single nucleotide polymorphisms in the RAD50-IL13-IL4 region on 5q31.1. RAD-50 is a DNA repair protein.

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RISK ALLELE FREQUENCY 

The frequency of the presumed disease-inducing or disease-modifying allele in the general population. The expectation is that the allele frequency will be higher in subjects with disease (cases) compared with that seen in control subjects.

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STAT-6 

Many cellular responses to TH2 cytokines are modulated through activation of the transcription factor signal transducer and activator of transcription 6 (STAT-6; eg, induction of eotaxins). Mice deficient in STAT-6 can lack eosinophilia, airway hyperreactivity, and mucus production.

The Editors wish to acknowledge Seema Aceves, MD, PhD, for preparing this glossary.

Clinical practice reminds us that there is considerable variability in the course of asthma over time. Although the majority of asthma begins in childhood, a significant proportion of children or adolescents will outgrow their disease completely, with others experiencing long remissions with occasional relapses. For others, asthma becomes progressively worse, leading to lifelong morbidity and even mortality. Although much research has focused on understanding the risk factors for disease onset in patients with asthma, less is understood about the factors that predict the course of disease in an individual patient. Treatment of patients with asthma would be considerably improved if one could accurately predict the likely course of disease over the life course.

Four years ago in the pages of this journal, Charles Reed1 elegantly summarized current understanding of the natural history of asthma. He concluded that “future studies will also include correlations of the natural history of asthma with specific genotypes and the effect the environment has on the expression of these genotypes.” Four years on and with the advent of the era of genome-wide association studies (GWASs), there has been a monumental shift in our understanding of the genetic factors that underlie inherited susceptibility to asthma.2 Therefore it is appropriate to reflect on our understanding of the natural history of asthma and to assess whether we can use this wealth of genetic information to more accurately predict the course of disease to both aid in treatment decisions and provide meaningful information to patients and parents about the likely course of their disease.

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The natural history of asthma 

During the last 2 decades, longitudinal follow-up of cohorts has facilitated an increase in the understanding of the natural history of asthma. Those with the longest follow-up and contributing most to the natural history and progression of asthma have been summarized in Table I. Multiple phenotypes have been defined with distinct outcomes, including infant wheeze, childhood atopic and nonatopic asthma, and adult-onset asthma. It is commonly acknowledged that most asthma has its origins in early childhood. Early environmental exposures, such as infections, allergens, and pollutants, in those with genetic predisposition result in the development of asthma during childhood and even later in adult life.

Table I. Longitudinal cohorts investigating the natural history of asthma during childhood, adolescence, and early adult life by using objective assessments
LocationPopulationRecruitment yearAge at recruitmentCohort sizeData collection at age (y)Method of data collection
Melbourne Asthma StudyMelbourne, AustraliaCase-control19577 y4997, 10, 14, 21, 28, 35, and 42Questionnaires, SPT, PFT
Tucson Children's Respiratory StudyTucson, ArizUnselected1980-1984Birth12461, 2, 3, 6, 11, 13, 16, and 22Questionnaires, SPT, PFT, IgE, BHR
Dunedin Multidisciplinary CohortDunedin, New ZealandUnselected1972-19733 y10373, 5, 7, 9, 11, 13, 15, 18, 21, 26, and 32Questionnaires, SPT, PFT, IgE, BHR
Isle of Wight Whole Population Birth CohortIsle of Wight, United KingdomUnselected1989-1990Birth14561, 2, 4, 10, and 18Questionnaires, SPT, PFT, IgE, BHR
Multicenter Allergy StudyGermanyUnselected1990Birth1314Yearly assessments at 1-7 and then 13Questionnaires, SPT, PFT, IgE, BHR
Childhood Asthma Management ProgramUnited StatesAsthma1993-19945-12 y1041Yearly assessments (5-18)Questionnaires, PFT, IgE, BHR

BHR, Bronchial hyperresponsiveness; PFT, pulmonary function test; SPT, skin prick test.

Early childhood 

The natural history of asthma is quite variable, and in some it is characterized by repeated remission and relapse.3, 4 In the Dunedin cohort nearly half of the subjects had wheezed at some time during their life by the age of 26 years.5 Fifteen percent had persistent wheezing, whereas 12% relapsed after a remission. In early childhood recurrent wheeze is common, and in a minority (approximately 30%) it might herald the onset of atopic asthma.6 In others wheezing is usually transient, related to airway caliber and early-life viral infections, and it tends to improve.7 However, in the Tucson study those classified as early transient wheezers had lower lung function in early childhood (both in infancy and at 6 years), which persisted into adolescence.7 This was confirmed in the United Kingdom Isle of Wight cohort study, which also showed that although transient wheezers remain largely asymptomatic, lung function abnormalities can be detected later in childhood.8 In later childhood most children have atopic asthma, which is associated with other typical features, such as bronchial hyperresponsiveness (BHR) and airways obstruction, although nonatopic wheeze is not uncommon.9 The long-term outlook for children with nonatopic wheeze is excellent because most grow out of wheeze and retain normal lung function, whereas children with atopic wheeze tend to have persistent symptoms in adolescence and reduced lung function.10, 11

Adolescents and adults 

In the Tucson study the frequency of wheezing increased until age 11 years and then decreased. This is in contrast to the recent reports from the Isle of Wight birth cohort, in which wheeze frequency continued to increase until the age of 18 years.12 Asthma is more prevalent in young boys but less so in girls.13 This is associated with both boys growing out of asthma and girls acquiring nonatopic asthma during adolescence.14 In the European Community Respiratory Health Survey the incidence of asthma was reported to be higher in boys during childhood, but in adults incident asthma was higher in women and those with parental asthma and allergy. Young women also showed less remission of asthma, resulting in an overall sex reversal in asthma prevalence from a male-dominant disease during childhood to female dominance in adults.15 The longitudinal study of the US Childhood Asthma Management Program (CAMP) confirms that the same sex pattern is also true for BHR, which is more persistent and severe in postpubertal women.16 After the second decade, remission continues to occur in nearly half of the subjects, but new-onset asthma becomes rare. The reported incidence of adult-onset asthma is approximately 0.25% per year.17, 18 In a 9-year follow-up of 6,461 young adults (20-44 years of age), 140 (2.2%) had incident asthma (0.24% per year). A similar figure (0.22% per year) was reported by Settipane et al18 in a 23-year follow-up of college students. In many respects adult-onset asthma is a different disease from childhood-onset asthma because it is mostly nonatopic, with smoking and rhinitis being the major risk factors.19 Occupational asthma typifies an adult-onset asthma phenotype initiated by exposure to environmental allergens or chemicals.20 Genetic susceptibility, type and duration of exposure, and simultaneous exposure to pollutants, such as cigarette smoke, are major factors in the initiation and persistence of symptoms.21 Another important phenotype in adult-onset asthma is that associated with aspirin hypersensitivity with comorbid features of rhinitis, flushing, and nasal polyps.22

Progression 

Long-term studies have shown that those with mild intermittent asthma have normal lung function in adult life, but decreased spirometric results are observed in those with persistent and severe disease.23 This observation is supported by a large retrospective study, which showed that asthma severity is determined very early in its course.24 Most asthmatic patients have evidence of airway wall changes and remodeling beginning during childhood and at the inception of asthma.25, 26 Similarly, asthma-related persistent lung function abnormalities are evident in early childhood, but this is nonprogressive in most children,5, 27 although in some a progressive decrease in lung function is observed, which might be linked to severe airway inflammation and remodeling.28 In CAMP 26% of participants had a progressive loss of lung function (FEV1).28 Factors associated with this loss of lung function include frequent and severe exacerbations, younger age, male sex, smoking, atopy, and the presence of airway eosinophils, indicating persistent airway inflammation.28, 29 A worrying feature in some asthmatic patients with persistent symptoms is the development of fixed airway disease akin to those who have chronic obstructive pulmonary disease (COPD).30 In a study of nonsmoking asthmatic patients, sputum airway eosinophilia of 2% or greater was the only independent factor associated with fixed airflow obstruction.31 However, despite computed tomographic scans showing thicker airway walls, it is not clear whether persistence of asthma, irreversible airway disease, or progressive decrease in lung function is associated with particular structural changes as a consequence of inflammation or whether it is inherent to the persistent asthma phenotype from the outset.

Predictors 

Recurrent and frequent wheeze in early life predispose to the development of asthma in later childhood.32 In the Melbourne asthma study those with mild asthma generally continued to have mild asthma, and 60% were free of wheeze by the age of 42 years, whereas 79% of those with severe disease continued to wheeze well into adulthood, and most of these continued to have severe symptoms.33 Thus early onset of severe and persistent disease heralds chronic and more severe asthma across the life course. Another factor that determines asthma severity is the duration of disease. In childhood longer disease duration tracks with more persistent and severe disease,34 which in adult life might result in a progressive decrease in lung function over time.35 In the Dunedin cohort BHR and IgE-dependent sensitization to mite or cat allergens were predictors of persistent asthma.5 An interesting finding from the Tucson birth cohort is that IgE levels in infancy (but not at birth) are associated with persistence of wheeze.11 This was further investigated in the Multicentre Allergy Study, in which early-life (<3 years of age) exposure and sensitization to common perennial allergens, such as house dust mite, cat, and dog, were found to be major risk factors for the persistence of childhood wheeze and the development of BHR and airflow obstruction during childhood.10 Importantly, this study showed that atopy before the age of 5 years had poor predictability for asthma later in childhood, whereas after this age, atopy was a strong influence on the persistence of childhood asthma. This indicates the importance of environmental exposures early in life. This study also showed that wheezy children had asthma when they also had eczema in early childhood.36 Maternal asthma, atopy, and recurrent respiratory tract infections in early childhood were predictors of persistence of wheeze in the Isle of Wight birth cohort,8 whereas the European Community Respiratory Health Survey also reported an increased incidence of asthma in adults among those with recurrent respiratory tract infections during childhood.15 Remission was higher during childhood (43%) than adolescence (30%) or adulthood (15%) and lower if the parents had either asthma or allergy. Unfortunately, regular anti-inflammatory treatment did not induce remission during adolescence in the CAMP study.37 Thus predictors of persistent disease include disease severity (frequent and severe symptoms), heredity (parental asthma and allergy), allergic comorbidity (eg, eczema and rhinitis), and early BHR and atopy (positive skin test response and high serum total IgE level, Box 1).

Box 1. Risk factors for persistence of wheeze during childhood


•Maternal asthma

•Atopy (especially allergic sensitization to dust mite and cat)

•Frequent wheeze

•Poor lung function in early childhood

•Recurrent chest infections in early childhood

•Bronchial hyperresponsiveness

In summary, the 2 important phenotypes in childhood are transient wheeze during infancy and atopic asthma. Atopic asthma could be persistent from early childhood or of late onset. Nonatopic asthma is not uncommon in childhood9 but is more prevalent in adults, especially among women.5 Thus most asthma starts during the first decade of life, and as one goes through adolescence, it persists in some and improves in others. In nearly half of those in whom it improves, there is recurrence in adult life, whereas adult-onset asthma is rare. Factors associated with persistence of asthma into later childhood and adult life includes disease severity, presence of BHR, maternal asthma, and associated atopy and allergic disease. Despite these associations, accurate prediction of the natural history of asthma in terms of its progression (remission, persistence, or recurrence), severity, and long-term consequences, such as a decrease in lung function in any patient, is not yet possible. Could genetics help to improve the predictive capability?

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Genetic prediction of complex disease susceptibility 

The common perception regarding the goal of genetic studies of common complex diseases, such as asthma, is that it will provide opportunities for targeted preventative actions through better prediction of disease risk (ie, stratified medicine). However, despite the astonishing rate of discovery of genetic variants predisposing to common diseases since the advent of the GWAS,38 there has been considerable disappointment expressed in the low predictive power of the risk loci identified. In GWASs the predictive value of variation in any one gene is low, with a typical genotype relative risk of only 1.1 to 1.5.39 For example, for the first loci identified for asthma susceptibility using a GWAS approach, ORM1-like protein 3 (ORMDL3)/Gasdermin B (GSDMB) on chromosome 17q21, a meta-analysis of 5 published studies on rs7216389 in 9 populations resulted in an odds ratio for asthma of 1.44 (95% CI, 1.35-1.54).40 Although this undoubtedly reflects the complex interactions between different genetic and environmental factors underlying disease, it has led some to question the usefulness of both genetic approaches to disease prediction and of further research into genetic factors underlying common disease in general.41 However, this rather negative interpretation ignores the insights provided in disease pathophysiology revealed by the identification of previously unrecognized biological pathways underlying common diseases.42, 43 Examples include the role of the complement pathway in patients with age-related macular degeneration44 and autophagy and IL-23–related pathways in patients with Crohn disease.45

Given the relatively low effect sizes of risk alleles identified through GWASs, it is reasonable to question the usefulness of using this new genetic information to predict susceptibility to common diseases. Epidemiologists have been using multiple risk factors to predict disease risk since the 1960s,46 as exemplified by scores such as the Cambridge type 2 diabetes risk score47 and the QRISK equation for coronary heart disease.48 It is possible to use similar models to predict disease risk based on multiple genetic risk factors (Box 2). For example, simulation studies based on the use of 50 genes relevant for disease development demonstrated that the area under the receiver operating characteristic curve (AUC) of 0.8 can be reached if the genotype relative risk is 1.5 and the risk allele frequency is 10%.49 However, perhaps more realistic estimates based on relative risks per allele of 1.1 to 1.2 (typical for loci identified in GWASs) suggest that between 87 and 867 risk alleles would be needed to account for a sibling relative risk of 2.0 and 138 to 1,374 risk alleles would be needed to account for a sibling relative risk of 3.0.50

Box 2. Predicting disease by using genetic markers


•Genotype- or allele-specific odds ratios are odds ratios that refer to those who have a certain genotype compared with those who have another genotype. It is sometimes referred to as genotype relative risk. It is calculated as the ratio between the fraction (probability) with the risk variant (carriers) versus the non–risk variant (noncarriers) in the groups of affected patients versus control subjects. In reports of GWASs, an additive model of inheritance is often assumed, and the allele-specific odds ratio is reported.

•The AUC is a well-established measure for determining the efficacy of tests in correctly classifying diseased and nondiseased subjects.

•Receiver operating characteristic curves plot the true-positive rate (sensitivity) as a function of the true-negative rate (specificity) for different cutoff points.

•The larger the area under the curve, the better the diagnostic test. If the area is 1.0, the test will always correctly classify diseased and nondiseased subjects. If the area is 0.5, then the test has no power to classify diseased or nondiseased subjects.

Despite this, a number of studies have recently assessed the viability of predicting risk of disease based on a limited number of multilocus genetic markers identified through genome-wide approaches.51, 52, 53, 54, 55 Wacholder et al53 assessed the ability of 10 common genetic variants (with odds ratios of 1.05-1.25) to predict the risk of breast cancer in comparison with, and in combination with, traditional risk factors, such as age, reproductive history, and family history. They found that the AUC for a risk model with age, study, entry year and 4 traditional risk factors was 0.58, and the addition of 10 genetic variants increased the AUC to only 0.618. Similarly, a recent analysis of 20 genetic variants' ability to predict type 2 diabetes in comparison with 2 established risk models showed that a genetic score based on the number of risk alleles carried (range, 0-40; AUC, 0.54; 95% CI, 0.50-0.58) and a weighted genetic risk function did not effectively discriminate cases of diabetes.52 Both of the established clinical risk models, the Cambridge risk score (AUC, 0.72; 95% CI, 0.69-0.76) and the Framingham offspring risk score (AUC, 0.78; 95% CI, 0.75-0.82), led to better discrimination of cases than the genotype scores. Again, similar to the study of Wacholder et al53 in patients with breast cancer, adding genetic information to phenotype-based risk models did not improve discrimination and provided only a small improvement in the AUC. It is possible that as knowledge of the genetic factors underlying disease increases, the predictive power of genetic testing will increase sufficiently to enable its use in clinical decision making. A recent analysis of the maximum AUC achievable given the population prevalence and estimated heritability of a range of common diseases estimated that for asthma, a genetic test must explain only 29% of the heritability to achieve an AUC of 0.75, with the maximum AUC obtainable for asthma achieving 0.88 if all heritability was explained by the test.56

In conclusion, genetic risk scores for common diseases currently show relatively poor discrimination and add little to clinical risk scores that incorporate family history. Thus even in diseases in which a greater degree of information is available from GWASs than is currently the case for asthma, risk assessment with genetics alone does not appear to have sufficient discriminatory power to have clinical utility. The identification of further risk factors (and their interactions57) that explain a larger proportion of the heritability of the disease and the development of better methods for incorporating genetic factors into risk models are likely to substantially increase the value of genotypic risk factors and might provide a means for predicting progression to severe disease and targeting of preventative treatment in the future.58 The final clinical utility of any genetic test will depend on its sensitivity, positive and negative predictive values, and whether there are any possible interventions, along with their costs and potential benefits to the patient.50, 59

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Genetic prediction of the natural history of asthma 

It is apparent from heritability studies that there is genetic influence in many aspects of asthma (Table II),58 from susceptibility to atopy60, 61, 62 and regulation of total and specific IgE levels63, 64 to blood eosinophil levels,64 susceptibility to asthma per se,65, 66 degree of BHR,63, 64 severity of asthma symptoms,67, 68 and even risk of mortality from asthma.69 Genetic factors also play a role in determining asthma remission, with family history of both atopy and asthma being associated with lower rates of remission.15

Table II. Stages of asthma progression influenced by interpatient genetic variation
Adapted from Holloway et al.58
Stages of asthma progressionExamples of genes associated with stage of progression
Susceptibility to atopyIL13, FCER1A
Atopic marchFilaggrin, COL29A1
Susceptibility to asthma through altered lung developmentADAM33, HHIP, MMP12
Susceptibility to asthmaADAM33, PHF11, DPP10, HLA-G, GPRA (GPCR 154), IRAK3, ORMDL3
Susceptibility to asthma through altered response to environmentCD14, TLR4, GSTM1 GSTT1, GSTP1
Progression to severe diseaseADRB2, TNFA
Fixed airway obstruction and accelerated decrease in lung functionADAM33, GSTM1 and GSTT1, estrogen receptor, possibly TNS1, GSTCD, AGER, HTR4, THSD4, GPR126, ADAM19, FAM13A, PTCH1, PID1 (all associated with lung function)
Altered response to preventative treatmentADRB2, CRHR1, ALOX5, ALOX5AP

Susceptibility to atopy 

The majority of genetic studies in the field have concentrated on identification of genes increasing susceptibility to asthma and other allergic disease manifestations, such as atopic dermatitis. Nonetheless, recent GWASs of atopy-related phenotypes, such as total serum IgE levels and blood eosinophil numbers, have identified a number of loci that appear to conform to our knowledge of TH2-mediated immunity. For example, a GWAS analysis of 1,530 patients identified functional variants in the gene encoding the α chain of the high-affinity receptor for IgE (FCER1A) on chromosome 1q23 as being associated with serum IgE levels and allergic sensitization, as well as confirming previous candidate gene studies that implicated variants in both the signal transducer and activator of transcription 6 (STAT6) and the IL4-IL13 locus, including the regulatory locus control elements extending into the adjacent RAD50 gene in regulating total IgE levels and atopy.70 Equally, the study of genes associated with blood eosinophil counts in an Icelandic population identified sequence variants in proinflammatory cytokine genes, such as Interleukin 1 Receptor Like 1 (IL1RL1) and the TH2-promoting IL33, alongside genes that encode molecules regulating hematopoietic progenitors cell differentiation and proliferation, such as the oncogene MYB.71 As might be expected, loci identified in both these studies are also associated with disease phenotypes involving TH2-mediated immunity or a role for eosinophils. For example, in the Icelandic population several of the loci associated with blood eosinophil counts were also associated with asthma and myocardial infarction.71 Variation within the Interleukin 4 (IL4) - Interleukin 13 (IL13) locus has long been recognized as being associated with a wide range of atopy and atopic disease phenotypes72, 73, 74, 75, 76, 77, 78 and more recently as an asthma susceptibility locus in a GWAS of severe asthma.79

This overlap between genetic variation identified as predisposing to atopy and that underlying asthma is not surprising given a current understanding of the role played by IgE and subsequently TH2-mediated immune responses in the pathogenesis of asthma and studies of heritability that have suggested that genes that predispose to atopy overlap with those that predispose to asthma.80 However, as will be discussed below, the remarkable observation of recent GWASs of allergic disease pathogenesis is that the majority of asthma susceptibility loci identified appear to regulate tissue-specific aspects of disease, such as response to inflammation and lung development, rather than TH2-mediated inflammation.

Susceptibility to early manifestations of allergic disease 

The recent identification of loss-of-function mutations in the filaggrin gene that leads to reduced barrier function is not only a risk factor for atopic dermatitis but also for rhinitis and asthma,81, 82 possibly through an age-related progressive allergen sensitization of the skin, nose, and lung.83, 84 This might also hold true for the recent identification of Collagen, type XXIX, alpha-1 (COL29A1), another epidermal barrier function gene recently identified as a susceptibility gene for atopic dermatitis.85

Genes and the early-life origins of asthma 

Lower rates of fetal growth are associated with impaired lung development in children.86 These early alterations in lung function might result from the interaction of environmental factors with the developing lung, which, in genetically susceptible subjects, leads to structural changes in the airways. One example of this is the a disintegrin and metalloproteinase domain 33 gene (ADAM33). ADAM33 was identified as an asthma susceptibility gene on chromosome 20q13 by using a genome-wide positional cloning approach in 2002.87 As in adult airways, multiple ADAM-33 protein isoforms exist in the human embryonic lung when assessed at 8 to 12 weeks of development,88 and polymorphism in ADAM33 is associated with early-life measures of lung function (specific airway resistance at age 3 years)89 and increased rates of decrease in lung function in adulthood,90, 91 suggesting that variability in this gene is acting in utero or in early life to determine lung development and subsequently the risk of airways disease. As elegantly summarized by Weiss,57 recent genome-wide analysis of lung function92, 93 and analysis of candidate genes, such as Matrix metalloproteinase 12 (MMP12), in populations of both children and adults94 suggests that COPD and asthma might have in part a common genetic origin in genes that are involved in the regulation of lung development.

Genes and susceptibility to asthma 

Identification of genetic factors that predispose to asthma susceptibility per se has been the main focus of the majority of studies of the genetics of asthma over the previous 2 decades. It is clear that there are multiple genetic factors that act synergistically to determine overall risk of disease in patients. The era of positional cloning has resulted in the identification of several novel asthma genes, such as ADAM33, Dipeptidyl peptidase X (DPP10), PHD Finger Protein 11 (PHF11), Human Leukocyte Antigen G (HLAG), Neuropeptide - S Receptor 1 (NSPR1), Urokinase Plasminogen Activated Receptor (PLAUR), Protocadherin 1 (PCDH1), and Interleukin 1 Receptor-Associated Kinase 3 (IRAK3).87, 95, 96, 97, 98, 99, 100, 101 The identification of a locus, such as that containing the genes ORMDL3/GSDMB,102 DENN/MADD Domain-Containing 1B (DENND1B),103 and the confirmation of the functional candidate Phosphodiesterase 4D (PDE4D),104 illustrate how GWAS approaches are rapidly increasing our knowledge of the major genetic factors underlying disease susceptibility. It is clear from these studies and those in patients with other allergic diseases, such as atopic dermatitis, that genetic variation in genes regulating atopic immune responses are not the only or even the major factor in determining susceptibility to disease, highlighting the importance of local tissue response factors and epithelial susceptibility factors in the pathogenesis of these conditions.2, 105, 106

Asthma is dependent on both genetic and environmental triggers for disease initiation and progression.107 Inhaled and ingested environmental factors have been hypothesized to contribute to the development of asthma, including allergens, diet, respiratory tract viruses, air pollutants, environmental tobacco smoke, endotoxin, and occupational exposures.108 However, epidemiologic studies have identified some unexpected risk factors for the development of asthma, such as exposure to chemicals in the home,109 paracetamol use,110 and vitamin E and D intake.111 Studies of the interaction between genetic variability and environmental exposures has not only the potential to lead to more accurate prediction of disease risk, it also has the potential to exclude confounding as an explanation of epidemiologic observations.112, 113 For example, supporting evidence for a direct effect of prenatal acetaminophen exposure during pregnancy on subsequent risk of childhood asthma and wheezing has recently been provided by the observation that the effects of the exposure are modified by maternal (thus excluding confounding of postnatal exposure) Glutathione-S-transferase μ1 (GSTM1) and Glutathione-S-transferase θ1 (GSTT1) functional polymorphisms affecting oxidant responses (a plausible biological response to acetaminophen).114 Thus future identification of the factors that influence variability to environmental exposure would help to identify at-risk groups who would benefit the most from preventive strategies. This identification of at-risk groups, the degree of their sensitivity to exposure, and their frequency in the population will aid in the cost-benefit analysis of safe exposure levels in the public health setting.115

Genes that could predict development of severe asthma 

The concept of genes interacting to alter the effects of mutations in susceptibility genes is not unknown. A number of genetic disorders caused by mutations in single genes are known to exhibit interfamilial and intrafamilial variability.116 A proportion of interfamilial variability can be explained by differences in environmental factors and differences in effects of different mutations in the same gene. Intrafamilial variability, especially in siblings, cannot be so readily accredited to these types of mechanisms. There is increasing evidence that many genetic disorders are influenced by modifier genes that are distinct from the disease susceptibility locus. The identification of such modifier genes in patients with asthma is difficult because of the complex interactions between susceptibility, environment, and treatment. However, despite these difficulties, a number of studies have identified polymorphisms of genes, such as the β2-adrenergic receptor,117, 118 Tumor necrosis factor α(TNFA),119 and RANTES/CCL5,120 that are associated with measures of asthma severity, such as exacerbation frequency or treatment indices. The advent of GWASs in populations of patients with severe asthma79 might aid in the subclassification of these patients into subphenotypes that might reflect differing pathogenicity and response to treatment, allowing better targeting of treatments.121

Severe asthma and progression to COPD 

In the 1960s, Orie et al122 proposed that all airway diseases, including asthma, emphysema, and chronic bronchitis, should be considered a single disease with common genetic origins (the Dutch hypothesis). Although there is still considerable debate as to the validity of this hypothesis,123, 124 with treatment only one of several issues limiting interpretation of long-term studies, it is interesting to note that a number of studies have identified genetic factors that are associated with both susceptibility to asthma and COPD. Some of these factors might alter susceptibility and severity through amplification of different inflammatory responses in asthma and COPD, such as polymorphisms in the promoter of the gene encoding TNF-α.125, 126 Other genetic commonalities between these 2 conditions might result from a common inherited susceptibility to environmental exposure that manifests itself differently as a consequence of differences in the route and timing of exposure127 and, as discussed above, polymorphisms of genes that regulate lung development in utero and in early infancy that increases susceptibility to both conditions.

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Ethics of predictive genotype for asthma 

Even if genetic prediction of disease progression becomes a reality in asthma, there are considerable ethical issues that will need to be considered before this can enter clinical practice.128, 129 Patients with chronic diseases often have little understanding of the benefits and risks of genetic testing, and there is evidence that favorable attitudes toward the use of genetic testing have decreased.130 Furthermore, for a childhood-onset disease, consideration must be made between balancing a parent's right to decide what is in a child's best interests versus the rights of a child to wait for test results until they can make an autonomous decision.131 The majority of guidelines suggest that testing in children should be delayed for adult-onset conditions, especially when no interventions are available should the predictive test result be positive. In the case of a childhood-onset disease, such as asthma, this case for delaying testing is less clear. Considerable thought will need to be given to issues, such as adequacy of consent, data confidentiality, and how to report results to patients, as well as population issues, such as the possibility of stigmatizing patients and cultural and social attitudes to genetic testing.

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Conclusions 

In conclusion, although the study of the genetic basis of asthma has greatly increased our understanding of the pathogenesis of this common condition, even with the advent of genome-wide association analysis, the ability of genetics alone to predict disease onset is poor, and the combined effect of multiple, individual susceptibility factors can be effectively modeled by considering family history in disease prediction models. However, it is likely that in the future genetic tests involving combinations of multiple polymorphisms might increase the specificity and sensitivity of prediction based on clinical factors alone.132 More importantly, the use of genetic testing might enable the categorization of asthmatic patients who, although phenotypically similar, have different underlying disease pathology and who will respond better to different treatment strategies. This approach is likely to yield important benefits with the increasing realization of clinical heterogeneity in asthma.121, 133 Application of hierarchic clustering approaches to the classification of asthma phenotype together with genetic markers has the potential to radically alter our view of this disease.

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 Series editors: Joshua A. Boyce, MD, Fred Finkelman, MD, William T. Shearer, MD, PhD, and Donata Vercelli, MD

 Terms in boldface and italics are defined in the glossary on page 201.

PII: S0091-6749(10)00904-8

doi:10.1016/j.jaci.2010.06.006

The Journal of Allergy and Clinical Immunology
Volume 126, Issue 2 , Pages 200-209, August 2010

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