Functional Genomics of Allergic Diseases

  • Donata Vercelli
Part of the Allergy Frontiers book series (ALLERGY, volume 1)

Allergic inflammation and its most common phenotypes (asthma, allergy and atopic dermatitis) are one of the most eloquent examples of human complex diseases, disorders caused by a constellation of genetic hits that are individually mild but lead to major phenotypic effects when they act on multiple steps along a mechanistic pathway. The literature is rich in association and linkage studies pointing to candidate genes that might act as critical determinants of allergy/asthma susceptibility. However, the abundance of single nucleotide polymorphisms (SNPs) in the human genome, and the complex patterns of linkage disequilibrium (LD) found at most genetic loci, prevent the tools of genetic epidemiology from deciphering the contribution of individual polymorphisms to increased disease risk. As a result, the mechanisms underlying the associations between patterns of genetic variation and disease phenotypes are in most cases unclear. Functional genomics studies provide a powerful tool to understand how genetic factors affect the pathogenesis of, and the susceptibility to, complex diseases such as allergic inflammation.

Functional genomics is still in its infancy. Indeed, as yet there is no universally accepted approach to defining the impact of genetic variants on gene expression and/or function. Interestingly, the more we experiment, the more we realize how subtle, even devious, the effects of genetic variants can be, and how lightly we must tread on the uncharted ground of functional genomics. Here we shall briefly review some of the results our group recently obtained studying the functional genomics of interleukin (IL)13, a major candidate gene for allergic inflammation [1, 2], and we shall discuss how our findings have contributed to advancing the field of functional genomics.


Atopic Dermatitis Allergic Disease Functional Genomic Allergy Clin Immunol Allergic Inflammation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2009

Authors and Affiliations

  • Donata Vercelli
    • 1
  1. 1.Arizona Respiratory Center and Department of Cell Biology, College of Medicine, and The Bio5 InstituteUniversity of ArizonaTucson

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