An autosome-wide search for loci underlying wheezing age of onset in German asthmatic children identifies a new region of interest on 6q24-q25

Integrating genomic and clinical medicine: searching for susceptibility genes in complex lung diseases

The integration of molecular, genomic, and clinical medicine in the post-genome era provides the promise of novel information on genetic variation and pathophysiologic cascades. The current challenge is to translate these discoveries rapidly into viable biomarkers that identify susceptible populations and into the development of precisely targeted therapies. In this article, we describe the application of comparative genomics, microarray platforms, genetic epidemiology, statistical genetics, and bioinformatic approaches within examples of complex pulmonary pathobiology. Our search for candidate genes, which are gene variations that drive susceptibility to and severity of enigmatic acute and chronic lung disorders, provides a logical framework to understand better the evolution of genomic medicine. The dissection of the genetic basis of complex diseases and the development of highly individualized therapies remain lofty but achievable goals.

Evidence for a pleiotropic QTL on chromosome 5q13 influencing both time to asthma onset and asthma score in French EGEA families

Although many genome screens have been conducted for asthma as a binary trait, there is limited information regarding the genetic factors underlying variation of asthma expression. Phenotypes related to variable disease expression include time to asthma onset and variation in clinical expression as measured by an asthma score built from EGEA data. A recent genome scan conducted for this score led to detection of a new region (18p11) not revealed by analysis of dichotomous asthma. Our goal was to characterize chromosomal regions harboring genes underlying time to asthma onset and to search for pleiotropic QTL influencing both time to asthma onset and the asthma score. We conducted a genome-wide linkage screen for time to asthma onset, modeled by martingale residuals from Cox survival model, in EGEA families with at least two asthmatic sibs. This was followed by a bivariate linkage scan of these residuals and asthma score. Univariate linkage analysis was performed using the Maximum Likelihood Binomial method that we extended to bivariate analysis. This screen revealed two regions potentially linked to time to asthma onset, 1p31 (LOD = 1.70, P = 0.003) and 5q13 (LOD = 1.87, P = 0.002). Bivariate linkage analysis led to a substantial improvement of the linkage signal on 5q13 (P = 0.00007), providing evidence for a pleiotropic QTL influencing both variation of time to asthma onset and of clinical expression. Use of quantitative phenotypes of variable disease expression and suitable statistical methodology can improve the power to detect new regions harboring genes which may play an important role in onset and course of disease.

CysLT1 receptor upregulation by TGF-beta and IL-13 is associated with bronchial smooth muscle cell proliferation in response to LTD4

BACKGROUND

Airway remodeling is a feature of chronic asthma. It involves a number of structural changes, including bronchial smooth muscle cell (BSMC) hyperplasia and hypertrophy. Cysteinyl leukotrienes (cysLTs) have been suggested to play a role in airway remodeling in addition to their numerous other physiopathologic effects.

OBJECTIVES

This work was aimed at characterizing the potential modulation of CysLT1 receptor expression by cytokines and the eventual functional relevance of this modulation.

METHODS

Expression of CysLT1 receptor was measured by flow cytometry and immunofluorescence microscopy. Transcripts were measured by RT-PCR and BSMC proliferation by crystal violet staining.

RESULTS

When human BSMC were exposed to transforming growth factor (TGF)-beta, IL-13, or IFN-gamma, their expression of CysLT1 receptor was significantly augmented in a time- and concentration-dependent manner. Interestingly, IL-4 had no significant effect on CysLT1 receptor expression in BSMC. Moreover, IL-13 and IFN-gamma but not TGF-beta were able to increase CysLT1 mRNA levels. Finally, when BSMC were pretreated with TGF-beta or IL-13 but not IFN-gamma, their responsiveness to LTD(4) was markedly enhanced in terms of BSMC proliferation. Whereas TGF-beta, IL-13, or LTD(4) alone had little effect on BSMC proliferation, preexposure of the cells to TGF-beta or IL-13 for 24 hours resulted in a significant increase in proliferation in response to LTD(4). The enhanced proliferation was totally prevented by pretreating the cytokine-primed BSMC with the selective CysLT1 receptor antagonist Montelukast.

CONCLUSIONS

Taken together, our findings indicate a synergy between certain cytokines and cysLTs, mediated by the augmented expression of the CysLT1 receptor and subsequent LTD(4)-triggered BSMC proliferation. These findings support a role for cysLTs in the airway remodeling observed in asthmatic patients and may provide a rationale for preventive and therapeutic intervention.

Removing phenotypic distribution assumptions from tests of linkage disequilibrium for quantitative traits

The transmission disequilibrium test (TDT) has become a family-based method of reference to search for linkage disequilibrium (LD). Although it was first developed for dichotomous traits, numerous approaches have extended the TDT to quantitative phenotypes that either rely on regression or variance component techniques. Both of these approaches are based on some phenotypic distribution assumptions, the violation of which can lead to inflation of type I error rates, and derive information from phenotypic variability, so that their power is very low under some selection schemes (e.g., one-tailed selection). We propose a new family-based test of association for quantitative traits, denoted maximum-likelihood-binomial (MLB)-QTDT, which addresses the two previous issues by incorporating a latent binary variable that captures the LD information between the marker allele and the quantitative phenotype. The method can be understood as a classical TDT for binary traits that would include pure affected and pure unaffected children, and the probability for a child to be affected or unaffected depends on his/her quantitative phenotypic value. Simulation studies under the null hypothesis show that the MLB-QTDT provides very consistent type I errors even in small and/or selected samples. Under the alternative hypothesis, the MLB-QTDT has good power to analyze one-tailed selected samples, and performs as well as a classical approach in other designs. The MLB-QTDT is a flexible distribution-free method to test for LD with quantitative phenotypes in nuclear families, and can easily incorporate previous extensions developed in the context of family-based association studies with binary traits.

Incorporation of covariates in multipoint model-free linkage analysis of binary traits: how important are unaffecteds?

When the mode of inheritance is unknown, genetic linkage analysis of binary trait is commonly performed using affected-sib-pair approaches. When there is evidence that some covariates influence the phenotype, incorporation of this information is expected to increase the power of the analysis since it allows (1) a better specification of the phenotype and (2) to take into account unaffected subjects. Here, we show how to account for covariates in the sibship-oriented Maximum-Likelihood-Binomial (MLB) linkage method by means of Pearson's logistic regression residuals which are computed using phenotypic and covariate information on both affected and unaffected subjects. These residuals are subsequently analysed as a quantitative phenotype with the corresponding extension of the MLB approach which can be used without any assumption on the distribution of these residuals. Then, a large simulation study is performed to study the relative power of incorporating or not unaffected sibs. To this aim, two different strategies in the multipoint analysis of family data are compared: (1) using residuals of the whole sibships (ie both covariate and genotypic information on unaffecteds is needed), and (2) using affecteds only (no information on unaffecteds is needed), under different generating models according to genetic and covariate effects. The results show that there is a clear increment in the power to detect the susceptibility locus when making use of the information carried by unaffecteds, in particular for dominant mode of inheritance and when values of the covariates influencing the disease are shared by all the members of the family.