Abstract
The first genome wide association study (GWAS) for childhood asthma identified a novel major susceptibility locus on chromosome 17q21 harboring the ORMDL3 gene, but the role of previous asthma candidate genes was not specifically analyzed in this GWAS. We systematically identified 89 SNPs in 14 candidate genes previously associated with asthma in >3 independent study populations. We re-genotyped 39 SNPs in these genes not covered by GWAS performed in 703 asthmatics and 658 reference children. Genotyping data were compared to imputation data derived from Illumina HumanHap300 chip genotyping. Results were combined to analyze 566 SNPs covering all 14 candidate gene loci. Genotyped polymorphisms in ADAM33, GSTP1 and VDR showed effects with p-values <0.0035 (corrected for multiple testing). Combining genotyping and imputation, polymorphisms in DPP10, EDN1, IL12B, IL13, IL4, IL4R and TNF showed associations at a significance level between p = 0.05 and p = 0.0035. These data indicate that (a) GWAS coverage is insufficient for many asthma candidate genes, (b) imputation based on these data is reliable but incomplete, and (c) SNPs in three previously identified asthma candidate genes replicate in our GWAS population with significance after correction for multiple testing in 14 genes.
Highlights
Genome wide association studies (GWAS) have recently identified genetic susceptibility for many complex diseases by genotyping hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome in very large sets of patients and controls [1]
For the current study we searched for the terms ‘‘asthma’’ and ‘‘association’’ and ‘‘SNP’’ or ‘‘polymorphism’’ or ‘‘genetic’’, retrieving 1,341 abstracts
Different forms of asthma like childhood, adult or atopic asthma were not discriminated and reports were not weighted for study size
Summary
Genome wide association studies (GWAS) have recently identified genetic susceptibility for many complex diseases by genotyping hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome in very large sets of patients and controls [1]. Imputation was recently introduced to predict allelic status of SNPs not covered by direct genotyping in GWAS, extrapolating information from neighbouring SNPs for which genotyping data and linkage disequilibrium (LD) from HapMap was available. The value of these approaches is not yet determined. We used our GWAS data set on asthma [2] to study these open questions for candidate genes in asthma
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