Rationale: Variability in pulmonary disease severity is found in patients with cystic fibrosis (CF) who have identical mutations in the CF transmembrane conductance regulator (CFTR) gene. We hypothesized that one factor accounting for heterogeneity in pulmonary disease severity is variation in the family of genes affecting the biology of interleukin-1 (IL-1), which impacts acquisition and maintenance of Pseudomonas aeruginosa infection in animal models of chronic infection. Methods: We genotyped 58 single nucleotide polymorphisms (SNPs) in the IL-1 gene cluster in 808 CF subjects from the University of North Carolina and Case Western Reserve University (UNC/CWRU) joint cohort. All were homozygous for ΔF508, and categories of “severe” (cases) or “mild” (control subjects) lung disease were defined by the lowest or highest quartile of forced expired volume (FEV1) for age in the CF population. After adjustment for age and gender, genotypic data were tested for association with lung disease severity. Odds ratios (ORs) comparing severe versus mild CF were also calculated for each genotype (with the homozygote major allele as the reference group) for all 58 SNPs. From these analyses, nine SNPs with a moderate effect size, OR ≤ 0.5or > 1.5, were selected for further testing. To replicate the case-control study results, we genotyped the same nine SNPs in a second population of CF parent-offspring trios (recruited from Children’s Hospital Boston), in which the offspring had similar pulmonary phenotypes. For the trio analysis, both family-based and population-based associations were performed. Results: SNPs rs1143634 and rs1143639 in the IL1B gene demonstrated a consistent association with lung disease severity categories (P < 0.10) and longitudinal analysis of lung disease severity (P < 0.10) in CF in both the case-control and family-based studies. In females, there was a consistent association (false discovery rate adjusted joint P-value < 0.06 for both SNPs) in both the analysis of lung disease severity in the UNC/CWRU cohort and the family-based analysis of affection status. Conclusion: Our findings suggest that IL1β is a clinically relevant modulator of CF lung disease.
gene modifiers; cystic fibrosis; CFTR; IL-1 gene family
Asthma is a complex disease characterized by striking ethnic disparities not explained entirely by environmental, social, cultural, or economic factors. Of the limited genetic studies performed on populations of African descent, notable differences in susceptibility allele frequencies have been observed.
To test the hypothesis that some genes may contribute to the profound disparities in asthma.
We performed a genome-wide association study in two independent populations of African ancestry (935 African American asthma cases and controls from the Baltimore-Washington, D.C. area, and 929 African Caribbean asthmatics and their family members from Barbados) to identify single-nucleotide polymorphisms (SNPs) associated with asthma.
Meta-analysis combining these two African-ancestry populations yielded three SNPs with a combined P-value <10-5 in genes of potential biological relevance to asthma and allergic disease: rs10515807, mapping to alpha-1B-adrenergic receptor (ADRA1B) gene on chromosome 5q33 (3.57×10-6); rs6052761, mapping to prion-related protein (PRNP) on chromosome 20pter-p12 (2.27×10-6); and rs1435879, mapping to dipeptidyl peptidase 10 (DPP10) on chromosome 2q12.3-q14.2. The generalizability of these findings was tested in family and case-control panels of UK and German origin, respectively, but none of the associations observed in the African groups were replicated in these European studies.
Evidence for association was also examined in four additional case-control studies of African Americans; however, none of the SNPs implicated in the discovery population were replicated. This study illustrates the complexity of identifying true associations for a complex and heterogeneous disease such as asthma in admixed populations, especially populations of African descent.
Asthma; GWAS; ADRA1B; PRNP; DPP10; African ancestry; ethnicity; polymorphism; genetic association
Rationale: Animal models demonstrate that aberrant gene expression in utero can result in abnormal pulmonary phenotypes.
Objectives: We sought to identify genes that are differentially expressed during in utero airway development and test the hypothesis that variants in these genes influence lung function in patients with asthma.
Methods: Stage 1 (Gene Expression): Differential gene expression analysis across the pseudoglandular (n = 27) and canalicular (n = 9) stages of human lung development was performed using regularized t tests with multiple comparison adjustments. Stage 2 (Genetic Association): Genetic association analyses of lung function (FEV1, FVC, and FEV1/FVC) for variants in five differentially expressed genes were conducted in 403 parent-child trios from the Childhood Asthma Management Program (CAMP). Associations were replicated in 583 parent-child trios from the Genetics of Asthma in Costa Rica study.
Measurements and Main Results: Of the 1,776 differentially expressed genes between the pseudoglandular (gestational age: 7–16 wk) and the canalicular (gestational age: 17–26 wk) stages, we selected 5 genes in the Wnt pathway for association testing. Thirteen single nucleotide polymorphisms in three genes demonstrated association with lung function in CAMP (P < 0.05), and associations for two of these genes were replicated in the Costa Ricans: Wnt1-inducible signaling pathway protein 1 with FEV1 (combined P = 0.0005) and FVC (combined P = 0.0004), and Wnt inhibitory factor 1 with FVC (combined P = 0.003) and FEV1/FVC (combined P = 0.003).
Conclusions: Wnt signaling genes are associated with impaired lung function in two childhood asthma cohorts. Furthermore, gene expression profiling of human fetal lung development can be used to identify genes implicated in the pathogenesis of lung function impairment in individuals with asthma.
asthma; lung development; lung function; genetic variation; gene expression
Rationale: Current understanding of the molecular regulation of lung development is limited and derives mostly from animal studies.
Objectives: To define global patterns of gene expression during human lung development.
Methods: Genome-wide expression profiling was used to measure the developing lung transcriptome in RNA samples derived from 38 normal human lung tissues at 53 to 154 days post conception. Principal component analysis was used to characterize global expression variation and to identify genes and bioontologic attributes contributing to these variations. Individual gene expression patterns were verified by quantitative reverse transcriptase–polymerase chain reaction analysis.
Measurements and Main Results: Gene expression analysis identified attributes not previously associated with lung development, such as chemokine-immunologic processes. Lung characteristics attributes (e.g., surfactant function) were observed at an earlier-than-anticipated age. We defined a 3,223 gene developing lung characteristic subtranscriptome capable of describing a majority of the process. In gene expression space, the samples formed a time-contiguous trajectory with transition points correlating with histological stages and suggesting the existence of novel molecular substages. Induction of surfactant gene expression characterized a pseudoglandular “molecular phase” transition. Individual gene expression patterns were independently validated. We predicted the age of independent human lung transcriptome profiles with a median absolute error of 5 days, supporting the validity of the data and modeling approach.
Conclusions: This study extends our knowledge of key gene expression patterns and bioontologic attributes underlying early human lung developmental processes. The data also suggest the existence of molecular phases of lung development.
microarrays; surfactant; principal component analysis
We introduce a stepwise approach for family-based designs for selecting a set of markers in a gene that are independently associated with the disease. The approach is based on testing the effect of a set of markers conditional on another set of markers. Several likelihood-based approaches have been proposed for special cases, but no model-free based tests have been proposed. We propose two types of tests in a family-based framework that are applicable to arbitrary family structures and completely robust to population stratification. We propose methods for ascertained dichotomous traits and unascertained quantitative traits. We first propose a completely model-free extension of the FBAT main genetic effect test. Then, for power issues, we introduce two model-based tests, one for dichotomous traits and one for continuous traits. Lastly, we utilize these tests to analyze a continuous lung function phenotype as a proxy for asthma in the Childhood Asthma Management Program. The methods are implemented in the free R package fbati.
Binary trait; Candidate gene analysis; Family-based association tests; FBAT-C; Linkage disequilibrium (LD); Model-based test; Model-free test; Nuclear families; Quantitative trait
The effects of vitamin D on bone metabolism and calcium homeostasis have long been recognized. Emerging evidence has implicated vitamin D as a critical regulator of immunity, playing a role in both the innate and cell-mediated immune systems. Vitamin D deficiency has been found to be associated with several immune-mediated diseases, susceptibility to infection and cancer. Recently, there has been increasing interest in the possible link between vitamin D and asthma. Further elucidation of the role of vitamin D in lung development and immune system function may hold profound implications for the prevention and treatment of asthma.
asthma; autoimmune disease; immune system regulation; T-regulatory cell; vitamin D; vitamin D deficiency
Genetic variants influencing lung function in children and adults may ultimately lead to the development of chronic obstructive pulmonary disease (COPD), particularly in high-risk groups.
We tested for an association between single-nucleotide polymorphisms (SNPs) in the gene encoding matrix metalloproteinase 12 (MMP12) and a measure of lung function (prebronchodilator forced expiratory volume in 1 second [FEV1]) in more than 8300 subjects in seven cohorts that included children and adults. Within the Normative Aging Study (NAS), a cohort of initially healthy adult men, we tested for an association between SNPs that were associated with FEV1 and the time to the onset of COPD. We then examined the relationship between MMP12 SNPs and COPD in two cohorts of adults with COPD or at risk for COPD.
The minor allele (G) of a functional variant in the promoter region of MMP12 (rs2276109 [−82A→G]) was positively associated with FEV1 in a combined analysis of children with asthma and adult former and current smokers in all cohorts (P=2×10−6). This allele was also associated with a reduced risk of the onset of COPD in the NAS cohort (hazard ratio, 0.65; 95% confidence interval [CI], 0.46 to 0.92; P = 0.02) and with a reduced risk of COPD in a cohort of smokers (odds ratio, 0.63; 95% CI, 0.45 to 0.88; P = 0.005) and among participants in a family-based study of early-onset COPD (P = 0.006).
The minor allele of a SNP in MMP12 (rs2276109) is associated with a positive effect on lung function in children with asthma and in adults who smoke. This allele is also associated with a reduced risk of COPD in adult smokers.
Bronchodilator response tests measure the effect of β2-agonists, the most commonly used short-acting reliever drugs for asthma. We sought to relate candidate gene SNP data with bronchodilator response and measure the predictive accuracy of a model constructed with genetic variants.
Materials & methods
Bayesian networks, multivariate models that are able to account for simultaneous associations and interactions among variables, were used to create a predictive model of bronchodilator response using candidate gene SNP data from 308 Childhood Asthma Management Program Caucasian subjects.
The model found that 15 SNPs in 15 genes predict bronchodilator response with fair accuracy, as established by a fivefold cross-validation area under the receiver-operating characteristic curve of 0.75 (standard error: 0.03).
Bayesian networks are an attractive approach to analyze large-scale pharmacogenetic SNP data because of their ability to automatically learn complex models that can be used for the prediction and discovery of novel biological hypotheses.
asthma; Bayesian networks; β2-agonists; bronchodilator response; prediction
Rationale: Association studies have implicated many genes in asthma pathogenesis, with replicated associations between single-nucleotide polymorphisms (SNPs) and asthma reported for more than 30 genes. Genome-wide genotyping enables simultaneous evaluation of most of this variation, and facilitates more comprehensive analysis of other common genetic variation around these candidate genes for association with asthma.
Objectives: To use available genome-wide genotypic data to assess the reproducibility of previously reported associations with asthma and to evaluate the contribution of additional common genetic variation surrounding these loci to asthma susceptibility.
Methods: Illumina Human Hap 550Kv3 BeadChip (Illumina, San Diego, CA) SNP arrays were genotyped in 422 nuclear families participating in the Childhood Asthma Management Program. Genes with at least one SNP demonstrating prior association with asthma in two or more populations were tested for evidence of association with asthma, using family-based association testing.
Measurements and Main Results: We identified 39 candidate genes from the literature, using prespecified criteria. Of the 160 SNPs previously genotyped in these 39 genes, 10 SNPs in 6 genes were significantly associated with asthma (including the first independent replication for asthma-associated integrin β3 [ITGB3]). Evaluation of 619 additional common variants included in the Illumina 550K array revealed additional evidence of asthma association for 15 genes, although none were significant after adjustment for multiple comparisons.
Conclusions: We replicated asthma associations for a minority of candidate genes. Pooling genome-wide association study results from multiple studies will increase the power to appreciate marginal effects of genes and further clarify which candidates are true “asthma genes.”
asthma; replication; single-nucleotide polymorphism; integrin β3; association
To evaluate phenotypic and genetic variables associated with a poor long-term response to inhaled corticosteroid therapy for asthma, based independently on lung function changes or asthma exacerbations.
Materials & methods
We tested 17 phenotypic variables and polymorphisms in FCER2 and CRHR1 in 311 children (aged 5–12 years) randomized to a 4-year course of inhaled corticosteroid during the Childhood Asthma Management Program (CAMP).
Predictors of recurrent asthma exacerbations are distinct from predictors of poor lung function response. A history of prior asthma exacerbations, younger age and a higher IgE level (p < 0.05) are associated with recurrent exacerbations. By contrast, lower bronchodilator response to albuterol and the minor alleles of RS242941 in CRHR1 and T2206C in FCER2 (p < 0.05) are associated with poor lung function response. Poor lung function response does not increase the risk of exacerbations and vice versa (p = 0.72).
Genetic and phenotypic predictors of a poor long-term response to inhaled corticosteroids differ markedly depending on definition of outcome (based on exacerbations vs lung function). These findings are important in comparing outcomes of clinical trials and in designing future pharmacogenetic studies.
asthma; corticosteroid; exacerbation; lung function; pharmacogenetics
Rationale: Maternal vitamin D intake during pregnancy has been inversely associated with asthma symptoms in early childhood. However, no study has examined the relationship between measured vitamin D levels and markers of asthma severity in childhood.
Objectives: To determine the relationship between measured vitamin D levels and both markers of asthma severity and allergy in childhood.
Methods: We examined the relation between 25-hydroxyvitamin D levels (the major circulating form of vitamin D) and markers of allergy and asthma severity in a cross-sectional study of 616 Costa Rican children between the ages of 6 and 14 years. Linear, logistic, and negative binomial regressions were used for the univariate and multivariate analyses.
Measurements and Main Results: Of the 616 children with asthma, 175 (28%) had insufficient levels of vitamin D (<30 ng/ml). In multivariate linear regression models, vitamin D levels were significantly and inversely associated with total IgE and eosinophil count. In multivariate logistic regression models, a log10 unit increase in vitamin D levels was associated with reduced odds of any hospitalization in the previous year (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.004–0.71; P = 0.03), any use of antiinflammatory medications in the previous year (OR, 0.18; 95% CI, 0.05–0.67; P = 0.01), and increased airway responsiveness (a ≤8.58-μmol provocative dose of methacholine producing a 20% fall in baseline FEV1 [OR, 0.15; 95% CI, 0.024–0.97; P = 0.05]).
Conclusions: Our results suggest that vitamin D insufficiency is relatively frequent in an equatorial population of children with asthma. In these children, lower vitamin D levels are associated with increased markers of allergy and asthma severity.
Several family-based approaches for testing genetic association with traits obtained from longitudinal or repeated measurement studies have been previously proposed. These approaches utilize the multivariate data more efficiently by using estimated optimal weights to combine univariate tests. We show that these FBAT approaches are still robust against hidden population stratification, but their power can be heavily affected since the estimated weights might provide poor approximation of the true theoretical optimal weights with the presence of population stratification. We introduce a permutation-based approach FBAT-MinP and an equal combination approach FBAT-EW, both of which do not involve the use of estimated weights. Through simulation studies, FBAT-MinP and FBAT-EW are shown to be powerful even in the presence of population stratification, when other approaches may substantially lose their power. An application of these approaches to the Childhood Asthma Management Program (CAMP) study data for testing an association between body mass index and a previously reported candidate SNP is given as an example.
Purpose of review
Patient response to the asthma drug classes, bronchodilators, inhaled corticosteroids and leukotriene modifiers, are characterized by a large degree of heterogeneity, which is attributable in part to genetic variation. Herein, we review and update the pharmacogenetics and pharmaogenomics of common asthma drugs.
Early studies suggest that bronchodilator reversibility and asthma worsening in patients on continuous short-acting and long-acting β-agonists are related to the Gly16Arg genotype for the ADRB2. More recent studies including genome-wide association studies implicate variants in other genes contribute to bronchodilator response heterogeneity and fail to replicate asthma worsening associated with continuous β-agonist use. Genetic determinants of the safety of long-acting β-agonist require further study. Variants in CRHR1, TBX21, and FCER2 contribute to variability in response for lung function, airways responsiveness, and exacerbations in patients taking inhaled corticosteroids. Variants in ALOX5, LTA4H, LTC4S, ABCC1, CYSLTR2, and SLCO2B1 contribute to variability in response to leukotriene modifiers.
Identification of novel variants that contribute to response heterogeneity supports future studies of single nucleotide polymorphism discovery and include gene expression and genome-wide association studies. Statistical models that predict the genomics of response to asthma drugs will complement single nucleotide polymorphism discovery in moving toward personalized medicine.
asthma; genes; personalized medicine; polymorphisms; response heterogeneity
For genome-wide association studies in family-based designs, we propose a new, universally applicable approach. The new test statistic exploits all available information about the association, while, by virtue of its design, it maintains the same robustness against population admixture as traditional family-based approaches that are based exclusively on the within-family information. The approach is suitable for the analysis of almost any trait type, e.g. binary, continuous, time-to-onset, multivariate, etc., and combinations of those. We use simulation studies to verify all theoretically derived properties of the approach, estimate its power, and compare it with other standard approaches. We illustrate the practical implications of the new analysis method by an application to a lung-function phenotype, forced expiratory volume in one second (FEV1) in 4 genome-wide association studies.
In genome-wide association studies, the multiple testing problem and confounding due to population stratification have been intractable issues. Family-based designs have considered only the transmission of genotypes from founder to nonfounder to prevent sensitivity to the population stratification, which leads to the loss of information. Here we propose a novel analysis approach that combines mutually independent FBAT and screening statistics in a robust way. The proposed method is more powerful than any other, while it preserves the complete robustness of family-based association tests, which only achieves much smaller power level. Furthermore, the proposed method is virtually as powerful as population-based approaches/designs, even in the absence of population stratification. By nature of the proposed method, it is always robust as long as FBAT is valid, and the proposed method achieves the optimal efficiency if our linear model for screening test reasonably explains the observed data in terms of covariance structure and population admixture. We illustrate the practical relevance of the approach by an application in 4 genome-wide association studies.
Identify clinical factors that modulate the risk of progression to COPD among asthma patients using data extracted from electronic medical records.
Demographic information and comorbidities from adult asthma patients who were observed for at least 5 years with initial observation dates between 1988 and 1998, were extracted from electronic medical records of the Partners Healthcare System using tools of the National Center for Biomedical Computing “Informatics for Integrating Biology to the Bedside” (i2b2).
A predictive model of COPD was constructed from a set of 9,349 patients (843 cases, 8,506 controls) using Bayesian networks. The model's predictive accuracy was tested using it to predict COPD in a future independent set of asthma patients (992 patients; 46 cases, 946 controls), who had initial observation dates between 1999 and 2002.
A Bayesian network model composed of age, sex, race, smoking history, and 8 comorbidity variables is able to predict COPD in the independent set of patients with an accuracy of 83.3%, computed as the area under the Receiver Operating Characteristic curve (AUROC).
Our results demonstrate that data extracted from electronic medical records can be used to create predictive models. With improvements in data extraction and inclusion of more variables, such models may prove to be clinically useful.
Genetic variants at the vitamin D receptor (VDR) locus are associated with asthma and atopy. We hypothesized that polymorphisms in other genes of the vitamin D pathway are associated with asthma or atopy.
Eleven candidate genes were chosen for this study, five of which code for proteins in the vitamin D metabolism pathway (CYP27A1, CYP27B1, CYP2R1, CYP24A1, GC) and six that are known to be transcriptionally regulated by vitamin D (IL10, IL1RL1, CD28, CD86, IL8, SKIIP). For each gene, we selected a maximally informative set of common SNPs (tagSNPs) using the European-derived (CEU) HapMap dataset. A total of 87 SNPs were genotyped in a French-Canadian family sample ascertained through asthmatic probands (388 nuclear families, 1064 individuals) and evaluated using the Family Based Association Test (FBAT) program. We then sought to replicate the positive findings in four independent samples: two from Western Canada, one from Australia and one from the USA (CAMP).
A number of SNPs in the IL10, CYP24A1, CYP2R1, IL1RL1 and CD86 genes were modestly associated with asthma and atopy (p < 0.05). Two-gene models testing for both main effects and the interaction were then performed using conditional logistic regression. Two-gene models implicating functional variants in the IL10 and VDR genes as well as in the IL10 and IL1RL1 genes were associated with asthma (p < 0.0002). In the replicate samples, SNPs in the IL10 and CYP24A1 genes were again modestly associated with asthma and atopy (p < 0.05). However, the SNPs or the orientation of the risk alleles were different between populations. A two-gene model involving IL10 and VDR was replicated in CAMP, but not in the other populations.
A number of genes involved in the vitamin D pathway demonstrate modest levels of association with asthma and atopy. Multilocus models testing genes in the same pathway are potentially more effective to evaluate the risk of asthma, but the effects are not uniform across populations.
Many candidate genes have been studied for asthma, but replication has varied. Novel candidate genes have been identified for various complex diseases using genome-wide association studies (GWASs). We conducted a GWAS in 492 Mexican children with asthma, predominantly atopic by skin prick test, and their parents using the Illumina HumanHap 550 K BeadChip to identify novel genetic variation for childhood asthma. The 520,767 autosomal single nucleotide polymorphisms (SNPs) passing quality control were tested for association with childhood asthma using log-linear regression with a log-additive risk model. Eleven of the most significantly associated GWAS SNPs were tested for replication in an independent study of 177 Mexican case–parent trios with childhood-onset asthma and atopy using log-linear analysis. The chromosome 9q21.31 SNP rs2378383 (p = 7.10×10−6 in the GWAS), located upstream of transducin-like enhancer of split 4 (TLE4), gave a p-value of 0.03 and the same direction and magnitude of association in the replication study (combined p = 6.79×10−7). Ancestry analysis on chromosome 9q supported an inverse association between the rs2378383 minor allele (G) and childhood asthma. This work identifies chromosome 9q21.31 as a novel susceptibility locus for childhood asthma in Mexicans. Further, analysis of genome-wide expression data in 51 human tissues from the Novartis Research Foundation showed that median GWAS significance levels for SNPs in genes expressed in the lung differed most significantly from genes not expressed in the lung when compared to 50 other tissues, supporting the biological plausibility of our overall GWAS findings and the multigenic etiology of childhood asthma.
Asthma is a leading chronic childhood disease with a presumed strong genetic component, but no genes have been definitely shown to influence asthma development. Few genetic studies of asthma have included Hispanic populations. Here, we conducted a genome-wide association study of asthma in 492 Mexican children with asthma, predominantly atopic by skin prick test, and their parents to identify novel genetic variation for childhood asthma. We implicated several polymorphisms in or near TLE4 on chromosome 9q21.31 (a novel candidate region for childhood asthma) and replicated one polymorphism in an independent study of childhood-onset asthmatics with atopy and their parents of Mexican ethnicity. Hispanics have differing proportions of Native American, European, and African ancestries, and we found less Native American ancestry than expected at chromosome 9q21.31. This suggests that chromosome 9q21.31 may underlie ethnic differences in childhood asthma and that future replication would be most effective in populations with Native American ancestry. Analysis of publicly available genome-wide expression data revealed that association signals in genes expressed in the lung differed most significantly from genes not expressed in the lung when compared to 50 other tissues, supporting the biological plausibility of the overall GWAS findings and the multigenic etiology of asthma.
Glucocorticoid function is dependent on efficient translocation of the glucocorticoid receptor (GR) from the cytoplasm to the nucleus of cells. Importin-13 (IPO13) is a nuclear transport receptor that mediates nuclear entry of GR. In airway epithelial cells, inhibition of IPO13 expression prevents nuclear entry of GR and abrogates anti-inflammatory effects of glucocorticoids. Impaired nuclear entry of GR has been documented in steroid-non-responsive asthmatics. We hypothesize that common IPO13 genetic variation influences the anti-inflammatory effects of inhaled corticosteroids for the treatment of asthma, as measured by change in methacholine airway hyperresponsiveness (AHR-PC20).
10 polymorphisms were evaluated in 654 children with mild-to-moderate asthma participating in the Childhood Asthma Management Program (CAMP), a clinical trial of inhaled anti-inflammatory medications (budesonide and nedocromil). Population-based association tests with repeated measures of PC20 were performed using mixed models and confirmed using family-based tests of association.
Among participants randomized to placebo or nedocromil, IPO13 polymorphisms were associated with improved PC20 (i.e. less AHR), with subjects harboring minor alleles demonstrating an average 1.51–2.17 fold increase in mean PC20 at 8-months post-randomization that persisted over four years of observation (p = 0.01–0.005). This improvement was similar to that among children treated with long-term inhaled corticosteroids. There was no additional improvement in PC20 by IPO13 variants among children treated with inhaled corticosteroids.
IPO13 variation is associated with improved AHR in asthmatic children. The degree of this improvement is similar to that observed with long-term inhaled corticosteroid treatment, suggesting that IPO13 variation may improve nuclear bioavailability of endogenous glucocorticoids.
Genetic association studies of complex traits often rely on standardised quantitative phenotypes, such as percentage of predicted forced expiratory volume and body mass index to measure an underlying trait of interest (eg lung function, obesity). These phenotypes are appealing because they provide an easy mechanism for comparing subjects, although such standardisations may not be the best way to control for confounders and other covariates. We recommend adjusting raw or standardised phenotypes within the study population via regression. We illustrate through simulation that optimal power in both population- and family-based association tests is attained by using the residuals from within-study adjustment as the complex trait phenotype. An application of family-based association analysis of forced expiratory volume in one second, and obesity in the Childhood Asthma Management Program data, illustrates that power is maintained or increased when adjusted phenotype residuals are used instead of typical standardised quantitative phenotypes.
body mass index; confounding factors; covariate adjustment; forced expiratory volume; heritable quantitative traits
With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age.
We performed the comparison via sequence-based probe matching between the two platforms. "Significance grouping" was defined as a measure of comparability. Using both expression correlation and significance grouping as measures of comparability, we demonstrated that despite overall cross-platform differences at the single gene level, increased correlation between the two platforms was found in genes with higher expression level, higher probe overlap, and lower p-value. We also demonstrated that biological function as determined via KEGG pathways or GO categories is more consistent across platforms than single gene analysis.
We conclude that while the comparability of the platforms at the single gene level may be increased by increasing sample size, they are highly comparable ontologically even for subtle differences in a relatively small sample size. Biologically relevant inference should therefore be reproducible across laboratories using different platforms.
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.
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.
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.
The ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) is a measure used to diagnose airflow obstruction and is highly heritable. We performed a genome-wide association study in 7,691 Framingham Heart Study participants to identify single-nucleotide polymorphisms (SNPs) associated with the FEV1/FVC ratio, analyzed as a percent of the predicted value. Identified SNPs were examined in an independent set of 835 Family Heart Study participants enriched for airflow obstruction. Four SNPs in tight linkage disequilibrium on chromosome 4q31 were associated with the percent predicted FEV1/FVC ratio with p-values of genome-wide significance in the Framingham sample (best p-value = 3.6e-09). One of the four chromosome 4q31 SNPs (rs13147758; p-value 2.3e-08 in Framingham) was genotyped in the Family Heart Study and produced evidence of association with the same phenotype, percent predicted FEV1/FVC (p-value = 2.0e-04). The effect estimates for association in the Framingham and Family Heart studies were in the same direction, with the minor allele (G) associated with higher FEV1/FVC ratio levels. Results from the Family Heart Study demonstrated that the association extended to FEV1 and dichotomous airflow obstruction phenotypes, particularly among smokers. The SNP rs13147758 was associated with the percent predicted FEV1/FVC ratio in independent samples from the Framingham and Family Heart Studies producing a combined p-value of 8.3e-11, and this region of chromosome 4 around 145.68 megabases was associated with COPD in three additional populations reported in the accompanying manuscript. The associated SNPs do not lie within a gene transcript but are near the hedgehog-interacting protein (HHIP) gene and several expressed sequence tags cloned from fetal lung. Though it is unclear what gene or regulatory effect explains the association, the region warrants further investigation.
Cigarette smoking is the primary risk factor for impaired lung function, yet only 20% of smokers develop chronic obstructive pulmonary disease (COPD). This observation, along with family studies of lung function and COPD, suggests that genetic factors influence susceptibility to cigarette smoke. We examined the relationship between common genetic variants and measures of lung function in a sample of 7,691 participants from the Framingham Heart Study and confirmed our observations in 835 participants from the Family Heart Study selected to include cases of airflow obstruction. We identified a variant on chromosome 4 that was strongly associated with FEV1/FVC in the Framingham Study and confirmed the association in the Family Heart Study. The accompanying manuscript identified the same region to be associated with COPD. Several interesting genes are present in the region that we identified, including a gene (HHIP) interacting with a biological pathway involved in lung development, but it is not yet clear which gene in the region explains the association. Our results identified a region of chromosome 4 that warrants further study to understand the genetic effects influencing lung function.