Asthma: advancing gene–environment studies (2007)

The lung is one of the three major organs (lung, gut, skin) continuously exposed to the external environment. It has been recognized for over 350 years that asthma results from both inherited (familial factors) and environmental exposures (by Sennartus in 1650, cited in [1]; also [2, 3]). The development of lung disease depends on the interaction between inherited susceptibility (genomics) and response to exposures (environment). Ideally, gene–environment studies of chronic lung disease should address current and future environmental challenges to the susceptible lung, providing a framework for understanding interactions at the molecular, lung tissue and patient levels [4]. Asthma is likely to result from the effects of environmental stimuli in genetically susceptible individuals [5]. Determining the relative contributions of genes and the environment has been the subject of intense investigation, in family, twins, case–control and cohort studies. The positive predictive value of a family history of asthma ranges from 11% to 37% for risk of childhood asthma [6]. Estimates of the heritability of asthma, the proportion of phenotypic variance of an asthma trait that is due to genetic effects, varies from ∼0.6 to 1.0 in different studies, and suggests an overall genetic contribution of around 50–60% [7]. Studying twins is a classical approach to quantifying the genetic contribution of a trait. The twin approach is based on the fact that monozygotic twins are genetically identical and dizygotic twins share half of their genes. To compare the concordance rate in monozygotic and dizygotic twins, one can obtain insight into the genetic contribution of a trait. A trait with a high genetic contribution gives a higher concordance rate (approaching 100%) in monozygotic twins than in dizygotic twins, and the concordance rate for monozygotic twins reared apart will be about the same as in monozygotic twins reared together. More than 10 twin studies of asthma have now shown the importance of the heritability of asthma and other related atopy phenotypes. In this issue of Clinical and Experimental Allergy, Hemminki et al. [8] add to the body of evidence supporting gene–environment interaction in asthma, in their analysis of a large family-based study in Sweden. Using a national database, they calculated the risk of asthma in siblings, including twins, by linking to a database of hospitalizations. Their study is remarkable for its use of large-scale population data: 3.5 million families, 67 370 hospitalized cases and 5638 affected siblings. They observed a gradient of familial risk for asthma hospitalizations, with standardized incidence ratios of 3.70 for singleton siblings, 5.17 for different sex (dizygotic) twins and the highest risk of 17.10 for monozygotic twins. Although these data apply to hospitalizations for asthma, this data registry lends a certain robustness, in that a hospital medical team has diagnosed asthma in the subjects and the asthma is phenotypically severe enough to require hospitalization. Overall, the authors interpreted their data as showing evidence for (i) gene–environment interaction in asthma, because of the differences in asthma risks between singleton siblings and different-sex twins, and (ii) added genetic effects seen in monozygotic twins, as shown by the differences in risks between different-sex twins and monozygotic twins. They concluded that analysis of gene-environmental interactions should be included in future epidemiological studies of asthma. How can this goal be achieved now? For the ‘genomics’ side of the equation, a number of ‘high-end’ approaches are now available, which extend the classical approaches used in studying the genetics of complex chronic lung diseases such as asthma. These include whole-genome-association studies, gene expression microarrays and proteomics. Also of importance will be epigenomic studies, such as investigations of promoter hypermethylation and histone modification that regulate gene expression. The advances of the Human Genome Project and the International HapMap Project [9] have given rise to the possibility of genome-wide association studies in complex genetic diseases. In these studies of cases and controls, array-based technology is used to genotype single-nucleotide polymorphisms (SNPs) or copy number variations (over 1 million genetic markers in the latest chips) across the genome. Recently, results from large disease genetics consortia have provided exciting gene discovery results for genetic susceptibility to chronic diseases. For example, the Wellcome Trust Case Control Consortium studied ∼2000 cases from each of seven chronic diseases and from a shared set of ∼3000 controls, and discovered genes associated with bipolar disorder, coronary artery disease, Crohn's disease, rheumatoid arthritis, and type I and type II diabetes mellitus, with P-values <5 × 10−7 [10]. This approach has also been recently applied to the study of asthma [11]. Thus these high-density SNP arrays scale up genetic association studies to the whole-genome level. Microarray technology also enables gene expression profiling of multiple pathways and genes simultaneously. For example, we have applied mRNA gene expression profiling to lung tissue from lung cancer and have shown a distinct gene expression profile for recurrent lung squamous cell carcinomas, which may be clinically useful as a prognostic tool [12, 13]. In asthma, gene expression microarray studies have been performed on mRNA from bronchial biopsies [14, 15], revealing novel and known genes differentially expressed between asthmatic and control tissues. Regarding environmental exposures, gene microarray studies have described the airway epithelial transcriptome and differences with exposure to cigarette smoke [16]. Proteomics is the systematic isolation and identification of all the proteins within cells or tissues. Identification is usually performed by mass spectrometry. One human study has examined differences in proteomic profile in peripheral T lymphocytes from asthmatics and controls [17]. These examples demonstrate that the ‘omics’ approach can be applied successfully to the study of chronic lung disease, and the next few years will likely see the publication of landmark studies in this field. While the study of environmental exposure is equally important as genomics in understanding the pathogenesis of asthma, accurate measurement of exposure is a relatively difficult task. Various approaches to measurement have been employed, including self-reported exposure (e.g. to farm animals or environmental tobacco smoke), correlation with epidemiological data (e.g. concurrent measurement of house dust endotoxin) and controlled exposure in the laboratory (e.g. to air pollutants [18]). Many studies have observed positive associations of specific genetic polymorphisms with differential response to environmental factors in asthma and other respiratory phenotypes [5, 19]. More sophisticated measurements of the effects of environmental exposure are required, to bring the environmental side of gene–environment interaction ‘up to speed’ with advances in molecular profiling. In the near future, technology may enable the development of environmental biosensors to record exposure to environmental stressors in real time [20], somewhat like the exposure badges that radiation workers wear but measuring a larger scale of exposures (paradoxically, using nanotechnology). The challenge will then be to incorporate environmental exposure data into studies of asthma that use the molecular profiling techniques described above. This will undoubtedly require intensive bioinformatics and computational approaches, using systems biology to detect signals and interacting networks. The complexity of the task in assessment of an individual's lifetime exposure is illustrated by observations that environmental exposure before birth, in addition to that in childhood, is also an important determinant of asthma susceptibility [21, 22]. The complexity of the task is illustrated by the problem of how we integrate the two dimensions of genetic and environmental measurements in cross-sectional studies with the third dimension of the matrix, time, including differences in exposure over time and differences in gene–environmental exposure over time. Key questions now in asthma pathogenesis are: which genes (which SNPs), which environmental factors, and how do they interact in asthma? Studies such as the national Swedish database study of asthma by Hemminki et al. [8] have provided a strong body of evidence for gene–environmental interaction in asthma. The translational research challenge now is to integrate asthma studies of genomics, epigenomics, transcriptomics and proteomics, as well as functional studies of mechanisms of response to environmental agents, to provide leads that can be applied to improve health outcomes. Robust and convincing studies of gene–environmental interaction would facilitate risk prognostication, improve preventive strategies and help to develop personalized interventions for people with asthma.