Yonsei Med J.  2019 Jan;60(1):1-9. 10.3349/ymj.2019.60.1.1.

Clinical Implications of Single Nucleotide Polymorphisms in Diagnosis of Asthma and its Subtypes

• Park JS
• Son JH
• Park CS
• Chang HS

• KMID: 2428272
• DOI: http://doi.org/10.3349/ymj.2019.60.1.1

Abstract

For the past three decades, a large number of genetic studies have been performed to examine genetic variants associated with asthma and its subtypes in hopes of gaining better understanding of the mechanisms underlying disease pathology and to identify genetic biomarkers predictive of disease outcomes. Various methods have been used to achieve these objectives, including linkage analysis, candidate gene polymorphism analysis, and genome-wide association studies (GWAS); however, the degree to which genetic variants contribute to asthma pathogenesis has proven to be much less significant than originally expected. Subsequent application of GWAS to well-defined phenotypes, such as occupational asthma and non-steroidal anti-inflammatory drugexacerbated respiratory diseases, has overcome some of these limitations, although with only partial success. Recently, a combinatorial analysis of single nucleotide polymorphisms (SNPs) identified by GWAS has been used to develop sets of genetic markers able to more accurately stratify asthma subtypes. In this review, we discuss the implications of the identified SNPs in diagnosis of asthma and its subtypes and the progress being made in combinatorial analysis of genetic variants.

Keyword

Asthma; aspirin; non-steroidal anti-inflammatory agents; biomarkers; single nucleotide polymorphism; genetic techniques

MeSH Terms

Anti-Inflammatory Agents, Non-Steroidal
Aspirin
Asthma*
Asthma, Occupational
Biomarkers
Diagnosis*
Genetic Association Studies
Genetic Markers
Genetic Techniques
Genome-Wide Association Study
Hope
Pathology
Phenotype
Polymorphism, Single Nucleotide*
Anti-Inflammatory Agents, Non-Steroidal
Aspirin
Biomarkers
Genetic Markers

Figure

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  Fig. 1 Area under receiver operator characteristic (ROC) curve values predictive of a hypothetical condition carry modest (1.5), sizeable (10), and large (50) odds ratios (ORs), showing false-positive fractions at 80% sensitivity (dotted line; fractions are >75, >25, and <10%, respectively). The graph demonstrates that very large ORs are needed to provide acceptably low false-positive fractions. Reprinted from Jakobsdottir, et al. PLoS Genet 2009;5:e1000337, with permission of Jakobsdottir, et al.30

• 

  Fig. 2 ROC curves used to devise the best combinatorial model using the 10 most statistically significant SNPs. Values were taken from the first genomewide association study, which used 100K Bead Chips, to examine 80 NERD and 100 ATA subjects. RR was calculated for each individual subject using a multiple logistic regression analysis examining all 1023 combinations (210−1) of the 10 SNPs described in reference 34. A model consisting of eight SNPs shows the highest area under the ROC curve of 0.9 with an accuracy of 82.01%. The sensitivity and specificity are 78% and 88%, respectively, with an odds ratio of 20.74 (p=3.24E^−19). The eight SNPs are listed in Table 2. Reprinted from Shin, et al. DNA Cell Biol 2012;31:1604–9, with permission of Mary Ann Liebert, Inc.34 ROC, receiver operator characteristic; SNP, single nucleotide polymorphism; NERD, nonsteroidal antiinflammatory drug-exacerbated respiratory diseases; ATA, aspirin-tolerant asthma; RR, relative risk.

• 

  Fig. 3 Linkage disequilibrium (LD) between the six exonic SNPs in the HLA genes. The number in the box indicates LDr2. Reprinted from Shin, et al. PLoS One 2014;9:e111887, with permission of Shin, et al.36

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