Abstract
Asthma is a complex trait for which different strategies have been used to identify its environmental and genetic predisposing factors. Here, we describe a novel methodological approach to select candidate genes for asthma genetic association studies. In this regard, the Genes to Diseases (G2D) computational tool has been used in combination with a genome-wide scan performed in a sub-sample of the Saguenay−Lac-St-Jean (SLSJ) asthmatic familial collection (n = 609) to identify candidate genes located in two suggestive loci shown to be linked with asthma (6q26) and atopy (10q26.3), and presenting differential parent-of-origin effects. This approach combined gene selection based on the G2D data mining analysis of the bibliographic and protein public databases, or according to the genes already known to be associated with the same or a similar phenotype. Ten genes (LPA, NOX3, SNX9, VIL2, VIP, ADAM8, DOCK1, FANK1, GPR123 and PTPRE) were selected for a subsequent association study performed in a large SLSJ sample (n = 1167) of individuals tested for asthma and atopy related phenotypes. Single nucleotide polymorphisms (n = 91) within the candidate genes were genotyped and analysed using a family-based association test. The results suggest a protective association to allergic asthma for PTPRE rs7081735 in the SLSJ sample (p = 0.000463; corrected p = 0.0478). This association has not been replicated in the Childhood Asthma Management Program (CAMP) cohort. Sequencing of the regions around rs7081735 revealed additional polymorphisms, but additional genotyping did not yield new associations. These results demonstrate that the G2D tool can be useful in the selection of candidate genes located in chromosomal regions linked to a complex trait.
Highlights
Asthma involves genetic and environmental factors in its development, chronicity and severity [1,2]
Replication Study Considering that the phosphatase receptor type E gene (PTPRE) rs7081735 association to allergic asthma is the strongest in the SLSJ sample, we evaluated it in an independent familial cohort, the Childhood Asthma Management Program (CAMP) [34]
This study proposes a novel approach for the selection of candidate genes for asthma association studies using the computational G2D tool to find genetic determinants for this disease
Summary
Asthma involves genetic and environmental factors in its development, chronicity and severity [1,2]. We describe a novel methodological approach that combines classical genetic approaches with a computational data-mining tool In this regard, a genome-wide scan for asthma and atopy in families originating from the Saguenay–Lac-St-Jean (SLSJ) founder population (Northeastern Quebec, Canada) [11,12,13,14,15,16] has been combined with the Genes to Diseases (G2D) new computational tool in the prioritization of asthma candidate genes [17,18]. G2D performs the selection of the candidates in the chromosomal regions genetically linked to a disease by highlighting genes whose functions are related to the phenotype of the disease according to a data mining analysis of the bibliographic and protein public databases, or according to the genes already known to be associated with the same or a similar phenotype
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