Search

SummaryBackground Genetic studies have identified numerous genes reproducibly associated with asthma, yet these studies have focussed almost entirely on single nucleotide polymorphisms (SNPs), and virtually ignored another highly prevalent form of genetic variation: Copy Number Variants (CNVs). Objective To survey the prevalence of CNVs in genes previously associated with asthma, and to assess whether CNVs represent the functional asthma-susceptibility variants at these loci. Methods We genotyped 383 asthmatic trios participating in the Childhood Asthma Management Program (CAMP) using a competitive genomic hybridization (CGH) array designed to interrogate 20 092 CNVs. To ensure comprehensive assessment of all potential asthma candidate genes, we purposely used liberal asthma gene inclusion criteria, resulting in consideration of 270 candidate genes previously implicated in asthma. We performed statistical testing using FBAT-CNV. Results Copy number variation in asthma candidate genes was prevalent, with 21% of tested genes residing near or within one of 69 CNVs. In six instances, the complete candidate gene sequence resides within the CNV boundaries. On average, asthmatic probands carried six asthma-candidate CNVs (range 1–29). However, the vast majority of identified CNVs were of rare frequency (

Genome-wide association studies of human gene expression promise to identify functional regulatory genetic variation that contributes to phenotypic diversity. However, it is unclear how useful this approach will be for the identification of disease-susceptibility variants. We generated gene expression profiles for 22 184 mRNA transcripts using RNA derived from peripheral blood CD4+ lymphocytes, and genome-wide genotype data for 516 512 autosomal markers in 200 subjects. We screened for cis-acting variants by testing variants mapping within 50 kb of expressed transcripts for association with transcript abundance using generalized linear models. Significant associations were identified for 1585 genes at a false discovery rate of 0.05 (corresponding to P-values ranging from 1 × 10(-91) to 7 × 10(-4)). Importantly, we identified evidence of regulatory variation for 119 previously mapped disease genes, including 24 examples where the variant with the strongest evidence of disease-association demonstrates strong association with specific transcript abundance. The prevalence of cis-acting variants among disease-associated genes was 63% higher than the genome-wide rate in our data set (P = 6.41 × 10(-6)), and although many of the implicated loci were associated with immune-related diseases (including asthma, connective tissue disorders and inflammatory bowel disease), associations with genes implicated in non-immune-related diseases including lipid profiles, anthropomorphic measurements, cancer and neurologic disease were also observed. Genetic variants that confer inter-individual differences in gene expression represent an important subset of variants that contribute to disease susceptibility. Population-based integrative genetic approaches can help identify such variation and enhance our understanding of the genetic basis of complex traits.

It is increasingly recognized that asthma is a heterogeneous disease. Therefore, it is possible that analysis of gene expression in the airway will reveal clinically meaningful transcriptional endotypes of asthma (TEA clusters).We measured whole transcriptome gene expression profiles in the sputum and whole blood of 100 individuals with asthma and 12 control subjects using the Affymetrix HuGene ST 1.0 gene arrays. Unsupervised clustering was conducted using pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG). This identified three TEA clusters that were correlated with clinical, physiologic, and inflammatory characteristics of the disease. TEA cluster 1 is a cluster of patients with asthma with a significantly higher rate of intubation (P = 0.05), a lower prebronchodilator FEV1 (P = 0.006), a higher bronchodilator response (P = 0.03), and higher exhaled nitric oxide levels (P = 0.04) than the other two TEA clusters. TEA cluster 2 has a higher rate of hospitalization for asthma (P = 0.04) and is heterogeneous. TEA cluster 3 is the largest cluster and has normal lung function, low exhaled nitric oxide levels, and lower inhaled steroid requirements. TEA cluster 1 had the highest sputum Th2 gene signature (IL-4, -5, and -13) compared with the other clusters. A classifier was developed that predicts TEA cluster assignment using 53 predictive genes in the circulation. The classifier was applied to gene expression data of children from the Asthma Biorepository for Integrative Genomic Exploration (Asthma BRIDGE) consortium cohort and confirmed that TEA clusters 1 and 2 are associated with history of intubation (P = 5.58 × 10(-06)) and hospitalization (P = 0.01), respectively.Analysis of the sputum transcriptome reveals three TEA clusters with different clinical and physiologic characteristics of disease in children and adults with asthma. This suggests that there are common transcriptomic signatures in the blood in children and adults with asthma that are associated with features of severe asthma.

Background The relationships between total serum IgE levels and gene expression patterns in peripheral blood CD4+ T cells (in all subjects and within each sex specifically) are not known. Methods Peripheral blood CD4+ T cells from 223 participants from the Childhood Asthma Management Program (CAMP) with simultaneous measurement of IgE. Total RNA was isolated, and expression profiles were generated with Illumina HumanRef8 v2 BeadChip arrays. Modeling of the relationship between genome-wide gene transcript levels and IgE levels was performed in all subjects, and stratified by sex. Results Among all subjects, significant evidence for association between gene transcript abundance and IgE was identified for a single gene, the interleukin 17 receptor B (IL17RB), explaining 12% of the variance (r2) in IgE measurement (p value = 7 × 10-7, 9 × 10-3 after adjustment for multiple testing). Sex stratified analyses revealed that the correlation between IL17RB and IgE was restricted to males only (r2 = 0.19, p value = 8 × 10-8; test for sex-interaction p < 0.05). Significant correlation between gene transcript abundance and IgE level was not found in females. Additionally we demonstrated substantial sex-specific differences in IgE when considering multi-gene models, and in canonical pathway analyses of IgE level. Conclusions Our results indicate that IL17RB may be the only gene expressed in CD4+ T cells whose transcript measurement is correlated with the variation in IgE level in asthmatics. These results provide further evidence sex may play a role in the genomic regulation of IgE.

Background Personalized health-care promises tailored health-care solutions to individual patients based on their genetic background and/or environmental exposure history. To date, disease prediction has been based on a few environmental factors and/or single nucleotide polymorphisms (SNPs), while complex diseases are usually affected by many genetic and environmental factors with each factor contributing a small portion to the outcome. We hypothesized that the use of random forests classifiers to select SNPs would result in an improved predictive model of asthma exacerbations. We tested this hypothesis in a population of childhood asthmatics. Methods In this study, using emergency room visits or hospitalizations as the definition of a severe asthma exacerbation, we first identified a list of top Genome Wide Association Study (GWAS) SNPs ranked by Random Forests (RF) importance score for the CAMP (Childhood Asthma Management Program) population of 127 exacerbation cases and 290 non-exacerbation controls. We predict severe asthma exacerbations using the top 10 to 320 SNPs together with age, sex, pre-bronchodilator FEV1 percentage predicted, and treatment group. Results Testing in an independent set of the CAMP population shows that severe asthma exacerbations can be predicted with an Area Under the Curve (AUC) = 0.66 with 160-320 SNPs in comparison to an AUC score of 0.57 with 10 SNPs. Using the clinical traits alone yielded AUC score of 0.54, suggesting the phenotype is affected by genetic as well as environmental factors. Conclusions Our study shows that a random forests algorithm can effectively extract and use the information contained in a small number of samples. Random forests, and other machine learning tools, can be used with GWAS studies to integrate large numbers of predictors simultaneously.

Asthma is the leading serious pediatric chronic illness in the United States, affecting 7.1 million children. The prevalence of asthma in children under 4 years of age has increased dramatically in the last 2 decades. Existing evidence suggests that this increase in prevalence derives from early environmental exposures acting on a pre-existing asthma-susceptible genotype. We studied the origins of asthma susceptibility in developing lung in rat strains that model the distinct phenotypes of airway hyperresponsiveness (Fisher rats) and atopy (brown Norway [BN] rats). Postnatal BN rat lungs showed increased epithelial proliferation and tracheal goblet cell hyperplasia. Fisher pups showed increased lung resistance at age 2 weeks, with elevated neutrophils throughout the postnatal period. Diverse transcriptomic signatures characterized the distinct respiratory phenotypes of developing lung in both rat models. Linear regression across age and strain identified developmental variation in expression of 1,376 genes, and confirmed both strain and temporal regulation of lung gene expression. Biological processes that were heavily represented included growth and development (including the T Box 1 transcription factor [Tbx5], the epidermal growth factor receptor [Egfr], the transforming growth factor beta-1-induced transcript 1 [Tgfbr1i1]), extracellular matrix and cell adhesion (including collagen and integrin genes), and immune function (including lymphocyte antigen 6 (Ly6) subunits, IL-17b, Toll-interacting protein, and Ficolin B). Genes validated by quantitative RT-PCR and protein analysis included collagen III alpha 1 Col3a1, Ly6b, glucocorticoid receptor and Importin-13 (specific to the BN rat lung), and Serpina1 and Ficolin B (specific to the Fisher lung). Innate differences in patterns of gene expression in developing lung that contribute to individual variation in respiratory phenotype are likely to contribute to the pathogenesis of asthma.

We propose a new approach for the analysis of copy number variants (CNVs)for genome-wide association studies in family-based designs. Our new overall association test combines the between-family component and the within-family component of the family-based data so that the new test statistic is fully efficient and, at the same time, maintains robustness against population-admixture and stratification, like classical family-based association tests that are based only on the within-family component. Although all data are incorporated into the test statistic, an adjustment for genetic confounding is not needed, even for the between-family component. The new test statistic is valid for testing either quantitative or dichotomous phenotypes. If external CNV data are available, the approach can also be applied to completely ascertained samples. Similar to the approach by Ionita-Laza et al. ([2008]. Genet Epidemiol 32:273-284), the proposed test statistic does not require a CNV-calling algorithm and is based directly on the CNV probe intensities. We show, via simulation studies, that our methodology increases the power of the FBAT statistic to levels comparable to those of population-based designs. The advantages of the approach in practice are demonstrated by an application to a genome-wide association study for body mass index.

Background Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes. Results Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics. Conclusion Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.

Background In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. Results To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. Conclusions In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.