Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry

Abstract

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Keywords: -omics; Atopic march; admixture mapping; allergic rhinitis; asthma; atopic dermatitis; food allergy; gene-environment interaction; phenotyping; racial ancestry.

Figure 1. Schematic diagram illustrating the age-dependent progression “atopic march” of atopic disorders

Food allergy and atopic dermatitis peak in the first years of life and decline after that time. Asthma and allergic rhinitis increase over time as sensitization develops further. Food sensitization can be used as an early indicator for identifying children at risk for subsequent allergic disease who may benefit from early intervention.

Figure 2. Genes associated in the literature with each atopic disorder

Based on mining of 15.73 million PubMed abstracts ((01/01/90 to the present) by Literature Lab™ from Acumenta Biotech, genes enriched in FA, AD, AR, and asthma are shown. The rankings of relative strengths of the gene/disease associations in the literature are as follows. The genes with the strongest relationship to each atopic disorder revolve around the center as measured by LPF (log of the product of frequency) starting at the 12 o’clock position in the inner ring descending in a clockwise direction, as indicated by arrows, and outward. Thus IGHG4, FLG, RMASE3, and IL5 have the most co-occurrence and genes MIR604, HRH1, GBP5, and ALOX5AP have the least co-occurrence with FA, AD, AR, and asthma, respectively. The LPF measures relative intensity or strength of co-occurrences for pairs of items in a large corpus, in this case genes and diseases. The LPFs are computed by Literature Lab™ for co-occurrences of all human genes in the NCBI Gene Database and each disease identified in the NCBI MeSH Diseases ontology (about 4,200 terms) in 15.73 million PubMed abstracts. The LPF is calculated as: LPF= Lg(x/G * x/T), where G is number of genes, T is the number of terms and x is co-occurrence between G and T. The greater the absolute magnitude of the LPF the weaker the association ; the closer the LPF value to zero, the stronger the association.

Figure 2. Genes associated in the literature with each atopic disorder

Based on mining of 15.73 million PubMed abstracts ((01/01/90 to the present) by Literature Lab™ from Acumenta Biotech, genes enriched in FA, AD, AR, and asthma are shown. The rankings of relative strengths of the gene/disease associations in the literature are as follows. The genes with the strongest relationship to each atopic disorder revolve around the center as measured by LPF (log of the product of frequency) starting at the 12 o’clock position in the inner ring descending in a clockwise direction, as indicated by arrows, and outward. Thus IGHG4, FLG, RMASE3, and IL5 have the most co-occurrence and genes MIR604, HRH1, GBP5, and ALOX5AP have the least co-occurrence with FA, AD, AR, and asthma, respectively. The LPF measures relative intensity or strength of co-occurrences for pairs of items in a large corpus, in this case genes and diseases. The LPFs are computed by Literature Lab™ for co-occurrences of all human genes in the NCBI Gene Database and each disease identified in the NCBI MeSH Diseases ontology (about 4,200 terms) in 15.73 million PubMed abstracts. The LPF is calculated as: LPF= Lg(x/G * x/T), where G is number of genes, T is the number of terms and x is co-occurrence between G and T. The greater the absolute magnitude of the LPF the weaker the association ; the closer the LPF value to zero, the stronger the association.

Figure 2. Genes associated in the literature with each atopic disorder

Based on mining of 15.73 million PubMed abstracts ((01/01/90 to the present) by Literature Lab™ from Acumenta Biotech, genes enriched in FA, AD, AR, and asthma are shown. The rankings of relative strengths of the gene/disease associations in the literature are as follows. The genes with the strongest relationship to each atopic disorder revolve around the center as measured by LPF (log of the product of frequency) starting at the 12 o’clock position in the inner ring descending in a clockwise direction, as indicated by arrows, and outward. Thus IGHG4, FLG, RMASE3, and IL5 have the most co-occurrence and genes MIR604, HRH1, GBP5, and ALOX5AP have the least co-occurrence with FA, AD, AR, and asthma, respectively. The LPF measures relative intensity or strength of co-occurrences for pairs of items in a large corpus, in this case genes and diseases. The LPFs are computed by Literature Lab™ for co-occurrences of all human genes in the NCBI Gene Database and each disease identified in the NCBI MeSH Diseases ontology (about 4,200 terms) in 15.73 million PubMed abstracts. The LPF is calculated as: LPF= Lg(x/G * x/T), where G is number of genes, T is the number of terms and x is co-occurrence between G and T. The greater the absolute magnitude of the LPF the weaker the association ; the closer the LPF value to zero, the stronger the association.

Figure 2. Genes associated in the literature with each atopic disorder

Based on mining of 15.73 million PubMed abstracts ((01/01/90 to the present) by Literature Lab™ from Acumenta Biotech, genes enriched in FA, AD, AR, and asthma are shown. The rankings of relative strengths of the gene/disease associations in the literature are as follows. The genes with the strongest relationship to each atopic disorder revolve around the center as measured by LPF (log of the product of frequency) starting at the 12 o’clock position in the inner ring descending in a clockwise direction, as indicated by arrows, and outward. Thus IGHG4, FLG, RMASE3, and IL5 have the most co-occurrence and genes MIR604, HRH1, GBP5, and ALOX5AP have the least co-occurrence with FA, AD, AR, and asthma, respectively. The LPF measures relative intensity or strength of co-occurrences for pairs of items in a large corpus, in this case genes and diseases. The LPFs are computed by Literature Lab™ for co-occurrences of all human genes in the NCBI Gene Database and each disease identified in the NCBI MeSH Diseases ontology (about 4,200 terms) in 15.73 million PubMed abstracts. The LPF is calculated as: LPF= Lg(x/G * x/T), where G is number of genes, T is the number of terms and x is co-occurrence between G and T. The greater the absolute magnitude of the LPF the weaker the association ; the closer the LPF value to zero, the stronger the association.

Figure 3. Venn diagram of unique and shared genes among atopic disorders (FA, AD, AR and asthma)

There are more genes shared between AR and asthma than between any other pair of atopic disorders. To understand the relationship among atopic disorders in terms of their genetic etiology, we investigated genetic overlap using ranked gene lists from each disorder. Among the top ranked genes listed in Figure 2, there were 43 gene overlaps between AR and asthma, 29 between AD and asthma, 28 between FA and asthma, 27 between AR and AD, 22 between FA and AD, 22 between FA and AR, and 16 among all disorders. AR and asthma commonly coexist and are regarded as “unified airways disease.” Thus, there appeared to be more genetic commonalities between AR and asthma than between AR and FA or between AR and AD. However, it is to be noted that our overlap measure uses gene lists in each disorder based on studies in the literature so far, and these diseases are not equality studied (for example, asthma has 110,428 PubMed abstract compared with 6,348 for AR). As we generate more data for all diseases, we will be able to determine disease-specific or overlapping genes with more confidence.

Figure 4. IPA network for 16 genes shared among FA, AD, AR and asthma

Genes with red nodes are focus genes in our analysis, the others are generated through the network analysis from the Ingenuity Pathways Knowledge Base (http://www.ingenuity.com). Edges are displayed with labels that describe the nature of the relationship between the nodes. The lines between genes represent known interactions, with solid lines representing direct interactions and dashed lines representing indirect interactions. Nodes are displayed using various shapes that represent the functional class of the gene product. Examination of the networks would move us toward a holistic understanding of atopic disorders.

Figure 5. The genetic and environmental interplay of atopic disorders

The relationship between genetic and environmental factors determine the overall outcome of atopic disorders. Skin barrier dysfunctions are part of the spectrum of allergic disorders with immunoglobulin E (IgE)-mediated sensitization and T helper type 2 (Th2) immune dysregulation. The interactions between epidermal barrier dysfunction and dysregulation of innate and adaptive immunity along with environmental risk factors contribute to the pathogenesis of atopic disorders.

Figure 6. Deconstructing atopic disorders using genetic and molecular information

Complex interplay between functional gene clusters and interpretation based on a model that integrates each atopic disorder in a central theme are presented. A mechanism by which epidermal barrier dysfunction may lead to inflammation and allergic sensitization in atopic disorders is shown. In parallel, genetic and molecular analysis of atopic disorders have evidenced the key role played in the disease mechanisms by epithelial skin cells, especially epidermal keratinocytes, with an abnormal pattern of production of cytokines and chemokines that could trigger sustained, chronic inflammation. Current data favor the paradigm shift in which the downstream systemic effect of allergen penetration through the impaired skin barrier causes immune cells to mount an exaggerated inflammatory response at any allergen-exposed epithelial surface.^, A decreased skin barrier response allows for increased susceptibility of the skin to allergens. In addition, decreased skin barrier leads to an increase in skin pH, altered keratinocyte adhesion properties, and both increased serine protease activity and inflammation. Among multiple reported genes relevant to the cellular location of gene products, only the well-established ones are shown for clarity.

Figure 7. Atopic disorder NHGRI GWAS catalog variants grouped by racial ancestry

Atopic disorder–related variants identified through genome-wide association studies (GWAS) [NHGRI catalogue of published GWAS was searched using the following terms: food allergy, atopic dermatitis, asthma, and allergic rhinitis]. PhenoGram was used to plot and visualize NHGRI GWAS catalog association results for potentially pleiotropic single-nucleotide polymorphisms (SNPs) in atopic disorders. An Ideogram of all 22 chromosomes is shown, along with the X and Y chromosomes. Horizontal lines on the chromosomes correspond to the base-pair location of each SNP, while lines which project from the chromosomes connect the SNP to colored circles that represent the phenotype(s) associated with the SNP. Overlapping and allergy-specific susceptibility loci among ancestries from GWAS might help to understand the putative pathogenetic relationship between allergy-related phenotypes and racial ancestry. However, loci from GWAS should be interpreted with caution, due to potential issues such as limited sample size in minority populations. Longitudinal and deep phenotyping (measurements at different molecular levels), of individuals along with information on genetic and disease specific environmental risk factors are relevant to study atopic disorders across racial ancestry.

Figure 8. Atopic disorder variant discovery and follow-up strategies

Methods ranging from genome-wide linkage, candidate gene, genome-wide association and admixture studies are presented. These gene mapping approaches yielded promising association results in the field of allergic diseases. However, association does not necessarily imply biological functionality, and follow-up studies are needed to translate initial findings into the biological insights that ultimately will advance prognostics, diagnostics and therapeutics.