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Human Molecular Genetics Advance Access originally published online on October 27, 2004
Human Molecular Genetics 2004 13(24):3103-3113; doi:10.1093/hmg/ddh340
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Human Molecular Genetics, Vol. 13, No. 24 © Oxford University Press 2004; all rights reserved

Clustering patterns of LOD scores for asthma-related phenotypes revealed by a genome-wide screen in 295 French EGEA families

Emmanuelle Bouzigon1, Marie-Hélène Dizier2, Christine Krähenbühl1, Arnaud Lemainque3, Isabella Annesi-Maesano4, Christine Betard3, Jean Bousquet5, Denis Charpin6, Frédéric Gormand7, Michel Guilloud-Bataille2, Jocelyne Just8, Nicole Le Moual4, Jean Maccario4, Régis Matran9, Françoise Neukirch10, Marie-Pierre Oryszczyn4, Evelyne Paty11, Isabelle Pin12, Myriam Rosenberg-Bourgin1, Daniel Vervloet13, Francine Kauffmann4, Mark Lathrop3 and Florence Demenais1,*

1INSERM EMI0006, Evry, France, 2INSERM U535, Villejuif, France, 3Centre National de Génotypage, Evry, France, 4INSERM U472-IFR69, Villejuif, France, 5Clinique des Maladies Respiratoires, INSERM U454, Hôpital Arnaud de Villeneuve, Montpellier, France, 6Service de Pneumologie-Allergologie, Hôpital Nord, Marseille, France, 7Service de Pneumologie, Centre Hospitalier Lyon-Sud, Pierre Benite, France, 8Centre de Diagnostic et Traitement de l'Asthme, Hôpital Trousseau, Paris, France, 9Laboratoire d'Exploration Fonctionnelle, Hôpital Calmette, Lille, France, 10INSERM U408, Paris, France, 11Service de Pneumologie Infantile, Hôpital Necker, Paris, France, 12Service Pneumologie, CHU Michallon, Grenoble, France and 13Service de Pneumo-Allergologie, Hôpital Ste Marguerite, Marseille, France

Received July 28, 2004; Accepted October 19, 2004


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 EGEA COOPERATIVE GROUP
 REFERENCES
 
A genome-wide scan for asthma phenotypes was conducted in the whole sample of 295 EGEA families selected through at least one asthmatic subject. In addition to asthma, seven phenotypes involved in the main asthma physiopathological pathways were considered: SPT (positive skin prick test response to at least one of 11 allergens), SPTQ score being the number of positive skin test responses to 11 allergens, Phadiatop® (positive specific IgE response to a mixture of allergens), total IgE levels, eosinophils, bronchial responsiveness (BR) to methacholine challenge and %predicted FEV1. Four regions showed evidence for linkage (P≤0.001): 6q14 for %FEV1, 12p13 for IgE, 17q22–q24 for SPT and 21q21 for both SPTQ and %FEV1. Nine other regions indicated smaller linkage signals (0.001<P≤0.005). While most of these regions have been reported by previous asthma and lung function screens, 6q14 appears to be a new region potentially linked to %FEV1. To determine which of these various asthma phenotypes are more likely to share common genetic determinants, a principal component analysis was applied to the genome-wide LOD scores. This analysis revealed clustering of LODs for asthma, SPT and Phadiatop® on one axis and clustering of LODs for %FEV1, BR and SPTQ on the other, while LODs for IgE and eosinophils appeared to be independent from all other LODs. These results provide new insights into the potential sharing of genetic determinants by asthma-related phenotypes.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 EGEA COOPERATIVE GROUP
 REFERENCES
 
Asthma is a complex disorder characterized by airway inflammation, reversible airflow obstruction and bronchial hyper-responsiveness to various environmental stimuli. Asthma is associated with intermediate physiological phenotypes which include phenotypes related to atopy and inflammation (IgE levels, skin tests, specific IgE to common aero-allergens and eosinophilia) and phenotypes related to lung function [forced expiratory volume in 1 s (FEV1) and bronchial responsiveness (BR)]. Asthma and its associated phenotypes are characterized by heterogeneity. Although the genetic component of asthma and asthma-related phenotypes is known to be substantial, the extent to which the underlying genetic mechanisms are common or specific to these traits and to asthma is still unclear. Analyses of the inter-relationships of asthma-related phenotypes in Australian and French EGEA families have suggested that IgE levels and phenotypes related to lung function are likely to be genetically distinct while a few genetic determinants may be shared by IgE and specific responses to allergens on the one hand and IgE and eosinophils on the other (1,2). However, these studies were only based on phenotypic data without incorporating the information of genetic markers.

Considerable efforts have been made over the past 10 years to map the chromosomal location of genes that may be involved in the development of asthma. Candidate-gene and genome-wide linkage studies pointed to at least 20 regions linked to asthma and asthma-related phenotypes (3). Recently, these genome screens have led successfully to the identification of four genes by positional cloning: ADAM33 (20p13) associated with asthma and bronchial hyperresponsiveness (BHR) (4), PHF11 (13q14) associated with IgE levels and severe asthma (5), DPP10 (2q14) associated with asthma (6) and GPRA (7p) associated with high serum IgE or asthma (7). An interesting feature of the published genome screens is that a given chromosomal region is linked to different intermediate phenotypes across studies, suggesting that these phenotypes may share genetic determinants in these regions. To our knowledge, the formal characterization of pleiotropic effects of genes underlying asthma-related phenotypes has received little attention. Recent studies have shown that multivariate analysis of correlated quantitative phenotypes can enhance the power of detecting quantitative-trait loci with pleiotropic effects (8,9). Thus, identifying the phenotypes which are more likely to be under some common genetic control can provide guidance for further joint linkage and association analyses of these phenotypes with genetic markers. Ultimately, this can provide better insight into the inter-relationships of the genetic pathways underlying these complex phenotypes and asthma.

The Epidemiological study on the Genetic and Environmental factors of Asthma (EGEA) is a multicenter collaborative study, whose purpose is to identify genes and gene-environment interactions contributing to the development of asthma and asthma-associated phenotypes. An initial genome-wide screen performed in a subsample of 107 EGEA nuclear families with at least two asthmatic sibs and genotyped with 254 markers has led to the detection of seven regions linked to asthma and four asthma-related phenotypes (10). To better characterize important regions of linkage that may harbour candidate genes of interest to be further investigated, this genome screen was extended to the whole sample of 295 nuclear families with at least one asthmatic subject and genotyped with a panel of 396 microsatellites. In addition to asthma, we considered seven intermediate phenotypes involved in the main asthma physiopathological pathways, i.e. atopy, inflammation and lung function. These phenotypes included the following: SPT (positive skin prick test response to at least one of 11 allergens), a quantitative score measuring the degree of allergen polysensitization (SPTQ being the number of positive skin test responses to 11 allergens), Phadiatop® (positive specific IgE response to a mixture of allergens), IgE levels, eosinophils, bronchial responsiveness to methacholine challenge (BR) and %predicted FEV1. Note that two of these phenotypes have been scarcely (%FEV1) or never (SPTQ) considered by previous scans of asthma. Besides identifying the regions linked to each of these eight phenotypes, we conducted principal component (PC) analysis of the genome-wide LOD scores for these phenotypes to determine which phenotypes are more likely to share common genetic determinants.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 EGEA COOPERATIVE GROUP
 REFERENCES
 
Sample characteristics
The phenotypic characteristics of the 726 genotyped siblings in the 295 families are shown in Table 1. Among these siblings, 53% were males and their mean age was 16.1±7.7 (SD) years. The proportion of asthmatic siblings was 53.2% with a mean age at first attack of asthma of 6.4±5.7 (SD). Table 2 gives the distribution of sibships according to the number of genotyped sibs with a known phenotype.


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  		Table 1. Phenotypic characteristics of 726 genotyped siblings in 295 families
 

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  		Table 2. Distribution of families according to the number of genotyped sibs available for linkage analyses of asthma and asthma-related phenotypes
 
Genome-wide screen
The genome-wide linkage-test results for asthma and the seven asthma-related phenotypes are shown in Figure 1. For continuous phenotypes, only the LOD scores based on the variance component (VC) analyses are presented, provided the LOD curves obtained using the Haseman–Elston regression approach led to similar patterns. Four regions showed suggestive evidence of linkage to four asthma-related phenotypes (pointwise P-value≤0.001, obtained either from the theoretical distribution of the LOD score statistic for binary or polychotomous phenotypes or from the empirical LOD score distributions for continuous phenotypes). A critical threshold of 0.001 was chosen to select the regions of interest to reduce the rate of false positive results due to multiple testing. These regions were as follows: 6q14 for %FEV1 (LOD score=2.94 at 89.8 cM; P=0.001), 12p13 for total IgE (LOD score=2.07 at 12.6 cM; P=0.0005), 17q24 for SPT (LOD score=1.95 at 93.3 cM; P=0.001), 21q21 for %FEV1 (LOD score=2.81 at 19.4 cM; P=0.001) and SPTQ (LOD score=2.12 at 19.4 cM; P=0.0009). As shown in Table 3, nine other regions indicated smaller linkage signals (theoretical or empirical P-value comprised between 0.005 and 0.001) and included 1p34 for asthma, 2q22 and 7p15 for %FEV1, 3q21 for BR, 5p15 and 13q33–q34 for SPT, 8p22 for IgE, 9q21 for SPTQ and Phadiatop® and 22q12 for EOS.



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  		Figure 1. Multipoint results of the genome-wide linkage scan for asthma and seven asthma-related phenotypes conducted in 295 French EGEA families. For each phenotype, genetic distance is plotted on the x-axis and LOD score on the y-axis.

 

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  		Table 3. Markers showing multipoint LOD scores with P-values ≤0.005 in 295 EGEA families ascertained through at least one asthmatic subject (markers with P≤0.001 are shown in bold)
 
Principal component analysis
PC analysis was applied to the genome-wide LOD scores previously obtained for asthma and the seven asthma-related phenotypes to reveal clustering patterns of these LOD scores and, possibly, to indicate which phenotypes are more likely to share genetic determinants. The first four PCs identified in this analysis contributed to 70% of the overall variance among the genome-wide LOD score values for the eight phenotypes. The correlations among each of these four PCs and each of the phenotype-specific LOD scores are shown in Table 4. The first component was mainly correlated with LODs for asthma, SPT and Phadiatop® (correlations ranging between 0.68 and 0.85) whereas the correlations with other LODs were also positive but at most equal to 0.30. The highest correlations with the second component were obtained for the LODs of %FEV1, BR and SPTQ (correlations ranging between 0.60 and 0.68) while all other correlations were relatively small (≤0.19 in absolute value). The third component was mainly positively correlated with the LODs for EOS (correlation of 0.77) and to a much smaller extent with LODs for BR (correlation=0.39) and total IgE (correlation=0.27) while the correlations with all other LODs were negative and equal at most to –0.35 and –0.34 for SPTQ and %FEV1, respectively. Finally, the fourth component was almost only correlated with the LODs for total IgE (correlation of 0.93). Altogether, these results indicate possible sharing of genetic determinants by asthma, SPT and Phadiatop® on the one hand and %FEV1, SPTQ and BR on the other while the respective determinants of EOS and total IgE seem to be mainly independent from all others. Since families were ascertained through asthmatics, PC analysis was repeated after exclusion of the asthma LOD scores. As seen in Table 4, similar patterns as those previously described were obtained.


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  		Table 4. Outcomes of PC analysis of genome-wide LOD scores for the eight asthma-related phenotypes expressed as the correlation coefficients between each PC and each phenotype-specific-LOD score
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 EGEA COOPERATIVE GROUP
 REFERENCES
 
The present genome scan, which was conducted in a sample of 1317 individuals from 295 families, was applied to a large number of intermediate phenotypes covering the main physiopathological mechanisms involved in asthma. This study, taking advantage of extensively documented phenotypes in the EGEA study, is the first to assess the extent to which asthma-related traits may share common genetic determinants from genome-wide linkage results. We also believe ours is the first report of a genome-wide linkage analysis of a quantitative measure of allergen polysensitization (SPTQ).

Various methods of linkage analysis have been used according to the phenotype considered: the MLB method for binary phenotypes (asthma, SPT, Phadiatop®), an extension of this method for ordered polychotomous phenotypes (SPTQ, BR) and both the Haseman–Elston (H–E) regression method and VC approach for continuous traits (total IgE, EOS, %FEV1). Since the latter two methods, especially the VC method, are known to be influenced by departure from normality assumptions of the trait distribution, empirical P-values associated with the observed VC and H–E test statistics were computed by simulations. In agreement with previous simulation studies (11,12), the empirical P-values associated with the VC LOD scores were similar to the nominal P-values for IgE and EOS, which exhibited non-significant kurtosis (kurtosis coefficient, {kappa}, being equal to 0.21 for IgE and 0.07 for EOS) while they were four to ten times higher than the nominal P-values for %FEV1 that showed significant kurtosis ({kappa}=1.02, P<10–6). As previously reported (12–15), the H–E method was more robust to normality assumption than the VC approach (empirical P-values being always close to the nominal ones) but had lower power (H–E-associated P-values being always equal to or higher than those associated with the VC statistic). Interestingly, the peak values of the VC and H–E test statistics were always reached at the same marker position. Moreover, use of the robust multivariate t-distribution for %FEV1 (16) instead of the multivariate normal distribution led to VC LOD score peaks in the same regions with nominal P-values close to the empirical P-values associated with LOD scores computed under normality assumptions.

This screen led to the detection of four regions potentially linked to four asthma-associated phenotypes (P≤0.001): 6q14 for %FEV1, 12p13 for total IgE, 17q22–q24 for SPT and 21q21 for both SPTQ and %FEV1. Two of these four regions led also to smaller linkage signals (0.01≤P≤0.05) for other phenotypes: 17q22–q24 for asthma and Phadiatop® and 21q21 for SPT. Nine other regions were detected at the 0.5% level. It should be noted that the highest peaks were mainly observed for quantitative phenotypes using the full sibship information when compared with binary phenotypes for which linkage analysis being restricted to affected siblings was applied to smaller sample sizes. The sample size was also reduced for BR since the methacholine challenge test was not performed in subjects with baseline FEV1 <80% predicted.

As in most published scans for asthma except four (4,17–19), none of the regions reached the stringent genome-wide level of significance (P≤2x10–5) proposed by Lander and Kruglyack (20). However, accumulating evidence for linkage across studies may be more important than unreplicated high linkage peaks to identify regions of interest, as discussed in Demenais et al. (21). We thus compared our results to the published scans conducted to date in 11 different populations (without counting the EGEA study). We considered all previously reported linkage peaks with P≤0.01, located at <20 cM from one of the 13 peaks presented here (Table 3). Table 5 shows the number of times a given region was found to follow these criteria for each asthma phenotype studied. Regardless of the phenotype, all our 13 regions have been previously reported by at least one screen: 5p15, 9q21 and 17q22–24 by four screens; 1p34, 2q22, 3q21, 7p15, 8p22, 21q21 and 22q12 by three screens; 12p13 and 13q33–q34 by two screens; and 6q14 by one screen. As seen in Table 5, linkages to a given region were often found to a variety of asthma phenotypes. Since %FEV1 had been only considered by one published asthma scan (17), the present linkage results were also compared with those reported by scans conducted for lung function phenotypes in families with chronic obstructive pulmonary diseases (22–24) and from the general population (25). Only 21q21 was reported to be linked to FEV1 and FVC (forced vital capacity) in the Framingham Heart Study (25). The 6q14 region appears therefore to be a new region potentially linked to %FEV1 but requiring confirmation.


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  		Table 5. Number of genome-wide screens showing a linkage signal (P≤0.01) for asthma-related phenotypes in the 13 regions detected by the present scan
 
With respect to our previous genome screen carried out in a subset of 107 EGEA families with at least two asthmatic siblings (10), two of the seven regions previously reported, 1p31 for asthma and 17q12–22 for SPT, were found by the presented study. Smaller linkage signals (0.005≤P≤0.02) were obtained in the present scan for 12q24 (EOS as before but also asthma, BR and SPT), 13q31 (EOS) and 19q13 (%FEV1 instead of BR) while no linkage of IgE to 11p13 or 11q13 was observed. The differences in these results may be partly explained by the marker set used and the different mode of selection of the two data sets. Further analysis of the EGEA subset selected through two asthmatic sibs with the present set of microsatellites will permit more direct comparisons with our previous screen (10).

The PC analysis applied to the genome-wide LOD scores for eight asthma phenotypes showed clustering of LODs for asthma, SPT and Phadiatop® on one axis and clustering of LODs for %FEV1, BR and SPTQ on the other, while LODs for total IgE and eosinophils appeared to be independent from all other LODs. Similar patterns were observed when excluding LODs for asthma from this PC analysis. The correlations between LODs for SPT and Phadiatop® are in agreement with the known association between these phenotypes (26,27). Their associations with LODs for asthma can be partly explained by the high proportion of asthmatic siblings being atopics in our sample (84.5% of asthmatics having positive SPT and 84.5% having a positive Phadiatop® test). The independence between LODs for the specific responses to allergens and those for total IgE levels is more surprising provided these phenotypes are classically considered to represent different components of atopy and are often found associated in unrelated subjects (28,29). However, the individual associations between IgE and skin tests were found to vary according to the type of allergens (28,29). Previous familial analyses of the inter-relationships between asthma phenotypes indicated only a small overlap of familial determinants between total IgE and SPTQ in French families (2), while an Australian study reported significant evidence for 70% overlap of genetic variance between total IgE and a RAST index for house dust mite and Timothy grass (1). However, these analyses did not include genetic markers and thus could not formally distinguish between genetic and shared environmental determinants. The independence between LODs for eosinophils and those for specific responses to allergens or IgE agrees with the lack of evidence for sharing familial determinants between EOS and SPTQ in the French EGEA families, and between IgE and EOS and Australian families (2,30). The independence between LODs for lung function phenotypes and those for either eosinophils or atopy-related phenotypes (except SPTQ) fits well the absence of familial inter-relationships between BR or FEV1 and the other phenotypes, as previously reported (1,2,30). However, while the associations between the LODs for %FEV1 and BR could be expected given the known physiopathological mechanisms underlying asthma, associations of these LODs with those for SPTQ is more puzzling. Interestingly, the LOD score peaks for %FEV1 and SPTQ on chromosome 21q21 were observed at the same position (D21S1914) and removing all LODs for this chromosome from PC analysis did not change the PC clustering patterns. In the EGEA study, a previous analysis restricted to asthmatic children did not show evidence for individual phenotypic associations between SPTQ and FEV1 (31) but this is not contradictory with the present findings based on linkage results from all family members. Common genes determining both SPTQ and FEV1, would be consistent with the so-called Dutch hypothesis, according to which asthma and chronic obstructive pulmonary disease (characterized by low FEV1) share common determinants. One aspect of that hypothesis is that atopy may be a factor of FEV1 decline, a still controversial issue, but quantitative scores for atopy have rarely been considered in such analyses (32). The possible sharing of common genetic determinants by %FEV1, BR and SPTQ may also fit the mechanisms recently proposed for asthma pathogenesis (33). According to this new view, airway remodelling linked to bronchial hyper-responsiveness and decrease in lung function may result from early life events involving interactions between the epithelial mesenchymal trophic unit and environmental triggers in susceptible individuals. Allergic predisposition could interact with airway susceptibility to promote persistent inflammation and asthma. This is supported by recent experiments in epithelial cultures showing functional relationships between exposure to allergens and Th2 cytokines (IL-4, IL-13) in the bronchial epithelium linked to proinflammatory and remodelling responses (34) and by studies in mice where continuous exposure to house dust mite have been found to elicit chronic airway inflammation and structural remodelling (35). Genetic determinants common to lung function phenotypes and polysensitization, as evidenced by the present study, might play a key role in controlling the inter-relationships between these pathways. However, correlations between LOD scores for %FEV1 and SPTQ cannot predict whether the potential pleiotropic genes will have a synergistic or conversely an antagonist effect on these phenotypes.

In conclusion, this genome scan has strengthened the evidence for few linkage signals previously reported and has led to detect new ones. This screen has provided new insights into the potential sharing of genetic determinants by asthma-related phenotypes. Further fine mapping studies and multivariate linkage and association analyses of the phenotypes displaying correlated LOD scores will allow to confirm the present findings and will help in disentangling the complex relationships among the different pathways underlying asthma.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
EGEA COOPERATIVE GROUP
 REFERENCES
 
Family data
Subjects were enrolled between 1991 and 1995 in the EGEA study, a French epidemiological survey on the environmental and genetic factors of asthma, bronchial hyperresponsiveness and atopy (36,37). Written informed consent was obtained from all family members participating to the study under a protocol approved by the Institutional Review Board.

Briefly, the main EGEA sample included a total of 348 nuclear families selected through one asthmatic proband, aged 7–65 years (213 adult and 135 pediatric probands) and followed in chest clinics of five French cities. An additional sample of 40 families ascertained through two asthmatic sibs was also recruited in order to increase the number of families for linkage analyses of asthma. The inclusion criteria for asthma have been described in detail elsewhere (36). In our analyses, asthma was defined as positive response to at least one of the two questions (Have you ever had attacks of breathlessness at rest with wheezing? Have you ever had an asthma attack?) associated with the presence of bronchial hyperresponsiveness (defined as a fall in baseline FEV1, the forced expiratory volume at 1 s, ≥20% at ≤4 mg ml methacholine or ≥15% increase of baseline FEV1 after bronchodilator use), hospitalization for asthma in life or asthma therapy.

Clinical evaluation
Subjects answered a detailed questionnaire on upper and lower airway symptoms, allergic symptoms, medical history and environmental factors (36). Environmental factors included mainly active and passive smoking, exposures to indoor and outdoor allergens and occupational exposures.

The following biological and physiological tests were performed on each participant. Total serum IgE levels were measured by radioimmunoassay (Phadebas PRIST technique; Pharmacia diagnostics, AB, France) in a central laboratory (Pasteur Institute, Lyon, France). A Phadiatop® (Pharmacia diagnostics, AB, France) test, an in vitro test detecting specific IgE production in response to a mixture of common inhalant allergens and expressed as a positive or negative response with respect to a reference sample, was carried out. Skin-prick tests were performed for 11 allergens (including moulds, indoors and outdoors allergens). A positive response was defined as a wheal size minus the negative control being ≥3 mm. The SPT phenotype was defined as a positive response to at least one allergen. A quantitative score (SPTQ) was constructed as being the number of positive test results and thus measuring the degree of polysensitization. This score was shown to have valid biometric properties (38). Total eosinophil count (EOS) was performed using standard procedures. Spirometric measures were carried out for all subjects >7 years of age according to the European Respiratory Health Survey protocol (39). The best of three pre-bronchodilator FEV1 measures was used to calculate a percentage of predicted FEV1 values (%FEV1) based on age, height and gender (40,41). In addition, a methacholine bronchial challenge test was performed in subjects with baseline FEV1>80% predicted and who did not decrease their FEV1 by more than 10% postdiluent. The test was stopped before the maximum cumulative dose if FEV1 decreased by at least 20% when compared with the postdiluent value and/or if the maximal dose was reached (4 mg in most subjects). The individual BR to methacholine bronchial challenge test was measured by the slope of the dose–response curve, as proposed by O'Connor et al. (42).

Phenotypes analyzed
Eight phenotypes were considered by the present study: three binary traits (asthma, SPT, Phadiatop®), three quantitative traits (total IgE levels, EOS, %FEV1) and two traits analyzed as categorical phenotypes (BR and SPTQ).

A log10 transformation was used to reduce skewness of total serum IgE levels and EOS. Since %FEV1 had no significant skewness, this phenotype was analyzed in its raw form. Prior to linkage analysis, these three phenotypes were adjusted for relevant covariates including age, gender and smoking habits using multiple regression. These regression models, including main effects and interaction terms, were built separately in three groups of family members (parents, paediatric offspring <16 years of age, adult offspring ≥16 years of age), as explained in detail by Bouzigon et al. (2). The log10 BR value was also adjusted using the same procedure. However, since the distribution of residual log10 BR values displayed highly significant skewness and kurtosis, they were divided into ten classes according to their deciles distribution to be analyzed as a polychotomous trait.

SPTQ included originally 12 classes (from 0 to 11) but classes 4 to 11 were combined into the last category because of small sample size. Since SPTQ was found to be significantly associated with gender, each of the five classes was subdivided into two gender specific categories (males belonging to higher risk classes than females), making a total of 10 classes.

Genotypes
From the whole EGEA sample, 307 families with at least two sibs with DNA available were genotyped. A total of 1355 subjects were initially genotyped with a panel of 396 microsatellites (378 autosomal markers). The Linkage Marker Set MD 10 (Applied Biosystems, Foster City, CA, USA) formed the core marker set for the genome-wide screen. These microsatellite markers, labelled with fluorescent dyes (FAMTM, HEXTM, NEDTM), are distributed at an average marker density of 10 cM (roughly every 10 million bases in the genome) and have an average heterozygosity of 75%. The Centre National de Génotypage (CNG) has developed a protocol allowing the co-amplification of up to six of these markers in a single reaction to be robust using dual 384-well GeneAmp® PCR 9700 cyclers (Applied Biosytems) and an automated procedure for PCR and purification set-up. Automatic genotyping was performed based on a series of Genetic Profiler software (version 1.1). Before statistical analysis, rigorous genotype quality assurance was performed to ensure accurate binning of alleles. Consistency of the data with mendelian inheritance and lack of double recombination between loci was evaluated using Pedcheck (43) and other purpose-written software. After excluding families with genotyping problems or families showing non-mendelian transmission, the analysed sample included 295 families (1317 subjects) with at least two sibs with genotypes available. Among these families, 97.8% of parents were genotyped.

Allele frequencies for the 378 autosomal markers were estimated from our family data by Vitesse 2.0 program (44). Marker order and intermarker distances were obtained from the published Marshfield maps (http://research.marshfieldclinic.org/genetics) and were fine-tuned by Vitesse in our 295 families.

Linkage analysis
Linkage analyses of binary (asthma, SPT, Phadiatop®) and polychotomous phenotypes (BR, SPTQ) were performed by a model-free approach, the maximum likelihood binomial (MLB) method which can be applied to the whole sibships (45,46). Briefly, the principle of this method for binary phenotypes is based on the binomial distribution of the number of affected sibs receiving a given parental allele from a heterozygous parent. The probability {alpha} for an affected sib to receive from his/her parent the marker allele transmitted with the disease allele is equal to 0.5 under the null hypothesis of no linkage and {alpha} is <0.5 under the hypothesis of linkage. Test for linkage is performed using a likelihood ratio test statistic, {Lambda}=2 ln[L({alpha})/L({alpha}=0.5)] with the statistic {Lambda} being distributed asymptotically as a mixture distribution of 0.5 {chi}20df and 0.5 {chi}21df. Note that the statistic {Lambda} divided by 2 ln 10 is a LOD score. This method has been extended to categorical traits by introducing an individual latent binary variable (Y={0;1}) which captures the linkage information between the observed categorical phenotype (Z) and the marker (M). This method requires to assign the probability of the latent variable (i.e. being affected or not) according to each observed category of the phenotype. The likelihood of the observations is then written using binomial distributions of parental marker alleles among offspring according to the value of the unobserved binary variable. Multipoint linkage analysis of binary and polychotomous phenotypes were conducted with MLBGH (45), a modified version of Genehunter.

Two methods were used for linkage analysis of continuous phenotypes (IgE, EOS, %FEV1): the original H–E regression method (47) using Genehunter (48) and the VC method (49) using QTDT and MERLIN (50,51). In the H–E method, the test statistic is a one-sided t-test which compares the estimation of the regression slope of the squares of sib-pair trait differences on the number of marker alleles shared identical by descent (IBD) to the expected value of zero under the null hypothesis of no linkage. The VC method separates the total variation of a trait into genetic and environmental components and evaluates linkage by comparing a model incorporating both a genetic additive variance at a putative quantitative trait locus (QTL) and a polygenic component with a purely polygenic model (QTL variance being set to zero) by a likelihood ratio test. Minus twice the natural logarithm of this likelihood ratio is assumed to follow a one-sided chi-square (this chi-square divided by 2 ln 10 being a LOD score). Since linkage analyses of continuous phenotypes, especially those based on the VC method, are known to be sensitive to departure from normality assumptions of the trait distributions, empirical P-values associated with the observed VC and H–E test statistics were computed by simulations. A total of 10 000–100 000 simulations were conducted for each chromosome harbouring linkage signals (theoretical P-value ≤5x10–3) for continuous phenotypes using the Simulate2 program (52). Random genotypes were generated in the offspring, under the null hypothesis of no linkage, by keeping the original nuclear family structure, observed phenotypes, known genotypes in the parents and marker density. Empirical P-values were estimated by the proportion of simulated data sets showing VC or H–E test-statistic values exceeding those observed in the actual data at a given marker position.

Principal component analysis
To investigate the clustering of linkage results for the eight phenotypes investigated, a PC analysis was applied to the genome-wide LOD score values for these traits. The initial correlation matrix submitted to PC analysis included 378 observations (marker locations) and eight variables (standardized phenotype-specific LOD scores). We estimated the correlation between each variable and each PC (axis) by the product of each PC loading multiplied by the square root of the eigenvalue. If phenotype-specific LOD scores are highly correlated with the same PC, this indicates that genetic determinants may be shared by these phenotypes. Alternatively, if phenotype-specific LOD scores are highly correlated to different PCs, the genetic determinants of these phenotypes are likely to be mostly independent. All computations were done with STATA 7.0.


   EGEA COOPERATIVE GROUP
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 EGEA COOPERATIVE GROUP
REFERENCES
 
Respiratory epidemiology
I. Annesi-Maesano, F. Kauffmann (coordinator), M.P. Oryszczyn (INSERM U472, Villejuif); M. Korobaeff, F. Neukirch (INSERM U408, Paris).

Genetics
F. Demenais (INSERM EMI 00-06, Evry); M.H. Dizier (INSERM U535, Villejuif); J. Feingold (INSERM U393, Paris); M. Lathrop (CNG, Evry).

Clinical centers
Grenoble: I. Pin, C. Pison; Lyon: D. Ecochard (deceased), F. Gormand, Y. Pacheco; Marseille: D. Charpin, D. Vervloet; Montpellier: J. Bousquet; Paris Cochin: A. Lockhart, R.Matran (now in Lille); Paris Necker: E. Paty, P. Scheinmann; Paris Trousseau: A. Grimfeld.

Data management
J. Hochez (INSERM ex-U155, Paris), N. Le Moual (INSERM U472).


   ACKNOWLEDGEMENTS
 
We are grateful to INSERM, Université d'Evry and Genopole for their support. We also thank Conseil Régional d'Ile-de-France for contributing to our computer facilities (HHJ01G), Fondation pour la Recherche Médicale and AGIR à dom.

Kits for IgE and Phadiatop® determinations were kindly provided by Pharmacia. This work was partly supported by INSERM-Ministry of Research ‘Cohortes et Collections’ grant (4CH06G).


   FOOTNOTES
 
* To whom correspondence should be addressed at: INSERM EMI0006, Tour Evry 2, 523, Place des Terrasses de l'Agora, 91034 Evry Cedex France. Tel: +33 160873820; Fax: +33 160873848; Email: demenais{at}evry.inserm.fr


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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
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