Description of three data sets: Collaborative Study on the Genetics of Asthma (CSGA), the German Affected-Sib-Pair Study, and the Hutterites of South Dakota

Intermediary quantitative traits--an alternative in the identification of disease genes in asthma?

Intermediary quantitative traits are a possible alternative for the identification of disease genes. This may be particularly relevant when diagnostic criteria are not very well defined as described for asthma. We analyzed serum samples from 944 individuals of 218 asthma families for 17 cytokines (eotaxin, GM-CSF, IFNγ, IL1B, IL1RA, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12(p40), IL-13, IL-17, IL-23, IL-33, TSLP and TNF-α) and determined the heritability. Linked chromosomal regions were identified by a genome-wide analysis using 334 autosomal microsatellite marker and association tested by further 550 SNP marker at genes implicated earlier with immune response. Heritability varied with TNF-α and IL-8 levels having the highest and TSLP having the lowest heritability. Linkage was significantly increased only for IL-12(p40) at D17S949. There were multiple significant single-nucleotide polymorphisms (SNP) associations (P<0.05) as found in the transmission disequilibrium test, whereas only a few replicated in parents or children only. These include SNPs in IL1RN that were associated with IL-33 and TSLP levels, and a SNP in NR3C2 that was associated with eotaxin, IL-13 and IFN-γ levels. Circulating level of serum cytokines exhibits genetic associations with asthma traits that are otherwise not detected using clinical diagnosis or when the clinical details are ambiguous.

Genetics of asthma susceptibility and severity

This article summarizes major findings in genome-wide studies of asthma susceptibility and severity. Two large meta-analyses identified four chromosomal regions which were consistently associated with development of asthma. Genes that are associated with asthma subphenotypes such as lung function, biomarker levels, and asthma therapeutic responses can provide insight into mechanisms of asthma severity and disease progression. Future genetic studies will incorporate sequencing in comprehensively phenotyped asthmatics to lead to the development of personalized therapy.

The IL6R variation Asp(358)Ala is a potential modifier of lung function in subjects with asthma

BACKGROUND

The IL6R single nucleotide polymorphism (SNP) rs4129267 has recently been identified as an asthma susceptibility locus in subjects of European ancestry but has not been characterized with respect to asthma severity. The SNP rs4129267 is in linkage disequilibrium (r(2) = 1) with the IL6R coding SNP rs2228145 (Asp(358)Ala). This IL6R coding change increases IL-6 receptor (IL-6R) shedding and promotes IL-6 transsignaling.

OBJECTIVES

We sought to evaluate the IL6R SNP rs2228145 with respect to asthma severity phenotypes.

METHODS

The IL6R SNP rs2228145 was evaluated in subjects of European ancestry with asthma from the Severe Asthma Research Program (SARP). Lung function associations were replicated in the Collaborative Study on the Genetics of Asthma (CSGA) cohort. Serum soluble IL-6R levels were measured in subjects from SARP. Immunohistochemistry was used to qualitatively evaluate IL-6R protein expression in bronchoalveolar lavage cells and endobronchial biopsies.

RESULTS

The minor C allele of IL6R SNP rs2228145 was associated with a lower percent predicted FEV(1) in the SARP cohort (P= .005), the CSGA cohort (P= .008), and in a combined cohort analysis (P= .003). Additional associations with percent predicted forced vital capacity (FVC), FEV(1)/FVC ratio, and PC(20) were observed. The rs2228145 C allele (Ala(358)) was more frequent in severe asthma phenotypic clusters. Elevated serum soluble IL-6R levels were associated with lower percent predicted FEV(1) (P= .02) and lower percent predicted FVC (P= .008) (n= 146). IL-6R protein expression was observed in bronchoalveolar lavage macrophages, airway epithelium, vascular endothelium, and airway smooth muscle.

CONCLUSIONS

The IL6R coding SNP rs2228145 (Asp(358)Ala) is a potential modifier of lung function in subjects with asthma and might identify subjects at risk for more severe asthma. IL-6 transsignaling might have a pathogenic role in the lung.

Genetic variants of TSLP and asthma in an admixed urban population

BACKGROUND

Thymic stromal lymphopoietin (TSLP), an IL7-like cytokine produced by bronchial epithelial cells is upregulated in asthma and induces dendritic cell maturation supporting a Th2 response. Environmental pollutants, including tobacco smoke and diesel exhaust particles upregulate TSLP suggesting that TSLP may be an interface between environmental pollution and immune responses in asthma. Since asthma is prevalent in urban communities, variants in the TSLP gene may be important in asthma susceptibility in these populations.

OBJECTIVES

To determine whether genetic variants in TSLP are associated with asthma in an urban admixed population. METHODOLOGY AND MAIN RESULTS: Ten tag-SNPs in the TSLP gene were analyzed for association with asthma using 387 clinically diagnosed asthmatic cases and 212 healthy controls from an urban admixed population. One SNP (rs1898671) showed nominally significant association with asthma (odds ratio (OR) = 1.50; 95% confidence interval (95% CI): 1.09-2.05, p = 0.01) after adjusting for age, BMI, income, education and population stratification. Association results were consistent using two different approaches to adjust for population stratification. When stratified by smoking status, the same SNP showed a significantly increased risk associated with asthma in ex-smokers (OR = 2.00, 95% CI: 1.04-3.83, p = 0.04) but not significant in never-smokers (OR = 1.34; 95% CI: 0.93-1.94, p = 0.11). Haplotype-specific score test indicated that an elevated risk for asthma was associated with a specific haplotype of TSLP involving SNP rs1898671 (OR = 1.58, 95% CI: 1.10-2.27, p = 0.01). Association of this SNP with asthma was confirmed in an independent large population-based cohort consortium study (OR = 1.15, 95% CI: 1.07-1.23, p = 0.0003) and the results stratified by smoking status were also validated (ex-smokers: OR = 1.21, 95% CI: 1.08-1.34, p = 0.003; never-smokers: OR = 1.06, 95% CI: 0.94-1.17, p = 0.33).

CONCLUSIONS

Genetic variants in TSLP may contribute to asthma susceptibility in admixed urban populations with a gene and environment interaction.

Combined linkage and association mapping of quantitative trait loci by multiple markers

Using multiple diallelic markers, variance component models are proposed for high-resolution combined linkage and association mapping of quantitative trait loci (QTL) based on nuclear families. The objective is to build a model that may fully use marker information for fine association mapping of QTL in the presence of prior linkage. The measures of linkage disequilibrium and the genetic effects are incorporated in the mean coefficients and are decomposed into orthogonal additive and dominance effects. The linkage information is modeled in variance-covariance matrices. Hence, the proposed methods model both association and linkage in a unified model. On the basis of marker information, a multipoint interval mapping method is provided to estimate the proportion of allele sharing identical by descent (IBD) and the probability of sharing two alleles IBD at a putative QTL for a sib-pair. To test the association between the trait locus and the markers, both likelihood-ratio tests and F-tests can be constructed on the basis of the proposed models. In addition, analytical formulas of noncentrality parameter approximations of the F-test statistics are provided. Type I error rates of the proposed test statistics are calculated to show their robustness. After comparing with the association between-family and association within-family (AbAw) approach by Abecasis and Fulker et al., it is found that the method proposed in this article is more powerful and advantageous based on simulation study and power calculation. By power and sample size comparison, it is shown that models that use more markers may have higher power than models that use fewer markers. The multiple-marker analysis can be more advantageous and has higher power in fine mapping QTL. As an application, the Genetic Analysis Workshop 12 German asthma data are analyzed using the proposed methods.

Meta-Analysis of Linkage Studies for Complex Diseases: An Overview of Methods and a Simulation Study

Linkage genome scans for complex diseases have low power with the usual sample sizes, and hence meta-analysis of several scans for the same disease might be a promising approach. Appropriate data are now becoming accessible. Here we give an overview of the available statistical methods and current applications. In a simulation study, we compare the power of different methods to combine multipoint linkage scores, namely Fisher's p-value combination, the truncated product method, the Genome Search Meta-Analysis (GSMA) method and our weighting methods. In particular, we investigate the effects of heterogeneity introduced by different genetic marker sets and sample sizes between genome scans. The weighting methods explicitly take those differences into account and have more power in the simulated scenarios than the other methods.

Reducing sample sizes in genome scans: Group sequential study designs with futility stops

Group sequential study designs can greatly facilitate analyses of genetic linkage in complex traits. We recently proposed designs allowing stopping investigations early if the result is significant (König et al. [2001] Am. J. Hum. Genet. 69:590-600), thereby decreasing average sample sizes under the alternative hypothesis. However, average sample sizes were slightly increased under the null hypothesis. We now present designs where the analysis of markers is additionally stopped in case of futility, i.e., if the probability for significant results is sufficiently low. These sequential designs are applied to linkage analyses of single loci. We calculated sample sizes, time points, and critical boundaries for all analyses for 2- and 3-stage designs at an overall significance level of 0.0001. To confirm the validity of asymptotic approximations, Monte Carlo simulations were performed. The utility is demonstrated analyzing genome scan data provided for the Genetic Analysis Workshop 12. Application of the novel sequential designs yields tremendous decreases in average sample sizes, regardless of the size of the underlying genetic effect at investigated loci. Depending on the applied design, almost half of the sample size is spared on average. These enormous savings are expected to have a special impact on costs and time of large-scale studies such as genome scans.

Finding starting points for Markov chain Monte Carlo analysis of genetic data from large and complex pedigrees

Genetic data from founder populations are advantageous for studies of complex traits that are often plagued by the problem of genetic heterogeneity. However, the desire to analyze large and complex pedigrees that often arise from such populations, coupled with the need to handle many linked and highly polymorphic loci simultaneously, poses challenges to current standard approaches. A viable alternative to solving such problems is via Markov chain Monte Carlo (MCMC) procedures, where a Markov chain, defined on the state space of a latent variable (e.g., genotypic configuration or inheritance vector), is constructed. However, finding starting points for the Markov chains is a difficult problem when the pedigree is not single-locus peelable; methods proposed in the literature have not yielded completely satisfactory solutions. We propose a generalization of the heated Gibbs sampler with relaxed penetrances (HGRP) of Lin et al., ([1993] IMA J. Math. Appl. Med. Biol. 10:1-17) to search for starting points. HGRP guarantees that a starting point will be found if there is no error in the data, but the chain usually needs to be run for a long time if the pedigree is extremely large and complex. By introducing a forcing step, the current algorithm substantially reduces the state space, and hence effectively speeds up the process of finding a starting point. Our algorithm also has a built-in preprocessing procedure for Mendelian error detection. The algorithm has been applied to both simulated and real data on two large and complex Hutterite pedigrees under many settings, and good results are obtained. The algorithm has been implemented in a user-friendly package called START.