Human Molecular Genetics, 2001, Vol. 10, No. 8 891-899
© 2001 Oxford University Press
Meta-analysis for linkage to asthma and atopy in the chromosome 5q31–33 candidate region
1Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Harvard University, Boston, MA 02115, USA, 2Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA, 3Genetic Epidemiology Unit, TVW Telethon Institute for Child Health Research, Perth, Australia, 4Johns Hopkins University, Baltimore, MD, USA, 5Genetic Epidemiology Unit, Department of Epidemiology and Public Health, University of Leicester, Leicester, UK, 6Department of Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China, 7Asthma Genetics Group, Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, UK, 8Terri Beaty: Johns Hopkins University, Baltimore, MD, USA; Eugene Bleecker: University of Maryland, Baltimore, MD, USA; Malcolm Blumenthal: University of Minnesota, Minneapolis, MN, USA; Carole Ober: University of Chicago, Chicago, IL, USA; Stephen Rich: Wake Forest University School of Medicine, Winston-Salem, NC, USA, 9Childrens’ Hospital, University of Freiburg, Germany, 10Asthma Genetics Group, Human Genetics Research Division, Duthie Building, Southampton General Hospital, UK, 11Department of Medical Genetics, University of Helsinki, Finland and 12German Asthma Genetics Group: Institute of Epidemiology, GSF National Research Center for Environment and Health, Neuherberg, Germany
Received 7 December 2000; Revised and Accepted 5 February 2001.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONSUBJECTS AND METHODSREFERENCES |
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Asthma is a common, complex human disease. Gene discovery in asthma has been complicated by substantial etiological heterogeneity, the possibility of genes of small effect and the concomitant requirement for large sample sizes. Linkage to asthma phenotypes has been investigated most intensively in the 5q chromosomal region, although results have been inconsistent across studies and all studies have had modest sample sizes. One potential solution to these issues is to combine data from multiple studies in a retrospective meta-analysis by pooling either summary statistics or raw data. The International Consortium on Asthma Genetics combined data from 11 data sets (n = 6277 subjects) to investigate evidence for linkage of 35 markers spanning the cytokine cluster on chromosome 5q31–33 to ‘asthma’ dichotomy and total serum immunoglobulin E (IgE) levels. Chromosome 5q markers typed in different centers were integrated into a consensus map to facilitate effective data pooling. Multipoint linkage analyses using a new Haseman–Elston method were performed with all data sets pooled together, and also separately with the resulting linkage statistics pooled by meta-analytic methods. Our results did not provide any evidence significant at the 5% level that loci conferring susceptibility to asthma or atopy are present in the 5q31–33 region; however, there was some weak evidence (empirical P = 0.077) of linkage to asthma affection. This study suggests that loci in 5q31–33 have at most a modest effect on susceptibility to asthma or total serum IgE levels, may not be detectable or present in all human populations and are difficult to detect even using combined linkage evidence from 2400–2600 full sibling pairs.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONSUBJECTS AND METHODSREFERENCES |
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Asthma is the most common chronic childhood disease in developed nations (1), affecting more than 155 million individuals. The cost of treating this disease in the US alone approaches six billion dollars per annum (2). Asthma is closely associated with the familial syndrome of atopy, which is accompanied by increased levels of total serum immunoglobulin E (IgE) (3). Total serum IgE levels are the best characterized intermediate phenotype associated with symptoms of asthma (3–5).
Mapping human susceptibility loci for asthma is made difficult by a high population frequency, incomplete penetrance, phenocopies, genetic heterogeneity, possible epistasis and pleiotropy (6,7); replication of any positive results may be difficult and often the significance of different findings among studies is controversial. Although significant progress has been made in defining the genetic basis of asthma and atopy over the last decade (6,7), even large studies are likely to have had low power to map genes of modest effect by linkage. This is particularly true for the phenotype ‘asthma’ affection (8). One potential solution to these issues is to combine data from multiple studies (9–11).
The Consortium on Asthma Genetics (COAG) was established in 1999 as an international effort to define the candidate regions and genes for asthma and atopy with greater precision and reliability than can be achieved by smaller individual studies. Given that one of the most investigated regions of the human genome for linkage to asthma phenotypes has been the 5q region, an initial retrospective study focusing on combination of evidence for the cytokine region on chromosome 5q was established (COAG-5). Participation by research groups was on the basis of availability of linkage data in the 5q31–33 region; there was no intentional bias for or against positive studies. This region is regarded as a biologically sensible candidate location for atopy and asthma susceptibility loci (12), as it contains a large number of important candidate genes (13) including the cytokine genes interleukin 4 (IL-4), interleukin 5 (IL-5), interleukin 9 (IL-9), interleukin 13 (IL-13) and their receptors. Other candidate genes in this region include granulocyte macrophage colony stimulating factor (GM-CSF), fibroblast growth factor acidic (FGFA) and the ß2-adrenergic receptor gene. Linkage of asthma-associated phenotypes has been reported to this region by several locus-specific studies (13–15) and whole genome screens (16–19). However, despite these positive studies, other studies have not shown linkage (20–23). All studies had modest sample sizes and some level of positive publication bias is possible.
In the current study, therefore, the evidence for linkage to asthma and atopy phenotypes in the 5q31–33 region has been investigated by combining linkage data from 11 diverse studies comprising a total of 1037 families and 6278 subjects.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONSUBJECTS AND METHODSREFERENCES |
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Linkage analysis
The multipoint pedigree likelihood-based method used to check for possible genotyping errors (24) suggested that the maximal potential error rate in any one data set was <1% of all genotypes. Therefore, our error-checking procedure resulted in the exclusion of very few families at a given marker from any of the data sets.
Analysis of the among-study variance of the estimates of the pooled regression coefficients (
The results of the linkage analyses for asthma and ln(total IgE) levels adjusted for age and sex are shown for each separate study population in Figure 1. The Oxfordshire and Southampton populations showed some evidence (0.01 < empirical P < 0.05) of linkage to asthma in the proximal part of this chromosomal region (Fig. 1). The Collaborative Study on the Genetics of Asthma African-American and the Chinese samples showed similar evidence of linkage to markers in the distal part of the region (Fig. 1). The remaining study populations showed little evidence of significant linkage to asthma across the 5q31–33 region. The Barbados and Finnish populations both showed some evidence of linkage (0.01 < empirical P < 0.05) to ln(total IgE) levels in the proximal part of the region. The remaining study populations showed no evidence of significant linkages to ln(total IgE) levels across the 5q31–33 region.
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The results of the pooled linkage analyses of the dichotomous ‘asthma’ phenotype and the ln(total IgE) are shown in Figure 2. For the dichotomous asthma phenotype, 2573 full sibling pairs were informative. For ln(total IgE), 2416 full sibling pairs were informative. No evidence for linkage (significant at the 0.05 level) with either of the phenotypes was found when the data sets were combined and analyzed together, or when linkage evidence was pooled either by weighting by the number of pairs contributed by each data set [method W1] or by the inverse variance of the estimated regression parameter [method W2] (Fig. 2). All empirical P values were
0.077 for both phenotypes. Evidence of linkage to the dichotomous asthma phenotype using weights W1 was maximal over an ~6 cM region between D5S421 and D5S404, and peaked at interpolated position 144.75 cM from pter (empirical P = 0.077, t = 1.428, 2194 full sibling pairs). The regression coefficient at this position (the variance in the trait due to a trait locus, if any, in this position) explained 2.16% of the variance of asthma dichotomy after adjustment for age and sex. Additional analyses of asthma affection using an all-affected-relative-pairs linkage test (26) in the pooled data set gave consistent results: evidence of linkage was maximal at D5S2501 (LOD = 1.58, using 1133 affected relative pairs). The evidence of linkage to ln(total IgE) was maximal using the pooled raw data at marker D5S1505 (empirical P = 0.126; t = 1.145; 2416 full sibling pairs).
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Identical multipoint analyses using simpler Haseman–Elston models (27) were consistent with the linkage results presented in Figures 1 and 2 (data not shown), although they had less statistical power (28,29).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONSUBJECTS AND METHODSREFERENCES |
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This study was designed to investigate the evidence for linkage to asthma phenotypes in the 5q31–33 region in a retrospective analysis of combined data from 11 separate studies. Our results did not reach conventional significance levels (P < 0.05) for any phenotype using any method, although our meta-analysis suggested that there was some weak evidence of linkage (empirical P = 0.077) to asthma in the proximal part (D5S2501–D5S622) of the 5q31–33 region. Our results were consistent with the conclusions that loci in 5q31–33 have at most a modest effect on susceptibility to asthma or total serum IgE levels, may not be detectable or present in all human populations and are difficult to detect even using combined linkage evidence from 2400 to 2600 full sibling pairs.
Previous studies, both positive and negative, had modest sample sizes and individual studies may have involved low power or small effects or both. The evidence for linkage between asthma and/or atopy and 5q markers has been variable amongst published studies, and most of the positive linkage studies showed only modest evidence of linkage (7). Whereas some studies show significant evidence of linkage of asthma and/or total IgE levels to 5q31–33 (Fig. 1), not all previous studies of chromosome 5q31–33 may have been large enough to detect modest effects or to exclude the region from further consideration. The results from our analyses regarding individual studies suggested no obvious selective bias for or against positive studies; around half of the studies yielded some evidence of linkage (Fig. 1).
A primary concern when combining data from diverse populations is the potential inter-population heterogeneity in study design, phenotype definition, genetic structure, environmental exposures and markers typed. The current study implements a number of strategies for dealing with these issues: the ln(total IgE) phenotype was standardized within each study/ethnic group; the same ‘consensus’ genetic map derived from well-validated public databases was used in each study population and in the overall pooled data analyses, for uniform multipoint linkage and interpolation at untyped locations; a new and more powerful Haseman–Elston linkage test was used; P values were generated empirically; and several data pooling and meta-analytic strategies were applied. Extrapolation of allele sharing idendical-by-discent (i.b.d.) in some places could have led to less precise estimates of linkage. However, the meta-analysis weighting method, W2, appropriately accounted for the differences in precision of estimates at different points across the 5q31–33 region among studies. If one or more common genes linked to asthma susceptibility or total IgE levels were present in this region in the combined study populations, this combination of analyses should probably have found some evidence of these loci.
An important advantage of our study was that the COAG retrospective collaboration allowed us to pool raw data across data sets, which, in combination with multipoint procedures, is an obvious and probably optimal method for combining linkage evidence from multiple studies (30–32). Weighting our meta-analysis by either the inverse of the variance of the regression coefficient (9) or by the number of pairs gave consistent results, suggesting that our analysis was robust to the implicit assumptions of the meta-analyses.
The most optimistic interpretation of our analyses is that they suggest the possible presence of susceptibility loci for asthma having modest effects (explaining <5% of total variance) in the proximal area of the 5q31–33 region. The genes for IL-4 and IL-13 are in this area. Less evidence of linkage to total IgE levels was found. If one or more loci that have an important effect on asthma susceptibility or total IgE levels in multiple study populations do exist in the 5q31–33 chromosomal region, there are several reasons why our pooled analyses may not have found them. The effect size of any susceptibility gene(s) may have been below the limit of the size detectable even in a combined sample of 2400–2600 sibling pairs. This in turn could imply gene(s) of quite minor effect and/or low frequency. Alternatively, genetic heterogeneity involving multiple susceptibility loci in the 5q31–33 region could have negatively affected the power of our study to detect linkage. Other possible reasons include differences between studies in the definition of asthma and/or marker density (Tables 2 and 3), the presence of epistatic interactions, gene–environment interactions and/or substantial inter-population genetic and environmental heterogeneity. Finally, it should be noted that several studies that have previously reported positive linkage of asthma or atopy to this region were not available for this meta-analysis (14,17,33).
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Our results regarding total IgE levels were consistent with a contemporaneous meta-analysis of the same data that used different statistical methods to combine LOD scores calculated at fixed locations within each data set using data set-specific genetic maps (34). However, in contrast to our study, their meta-analysis of asthmatic sibling pairs found significant evidence of linkage to asthma in the proximal area of the cytokine cluster, around reference locus D5S1505 (LOD = 2.61, P = 0.0025) (34). The difference in linkage results for asthma may be due to the use of differing statistical techniques, to the use of differing definitions for asthma and atopy, including those derived from a principal components analysis, and to the omission of two large negative studies (the AuB and GerF samples) from the primary analysis of affected sibling pairs by Lonjou et al. (34). Other potentially important differences between these studies include the use of empirical versus asymptotic P values, the use of genetic maps generated within each study population versus the use of a consensus map and differing approaches for dealing with potential genotyping errors.
Several well-designed SNP association studies have provided evidence that polymorphisms in the IL-13 and IL-4 genes in the cytokine cluster of 5q31–33 regulate serum IgE levels (35–38). However, the largest of these studies (37) found that polymorphism in the IL-13 gene, while strongly associated with total IgE levels, explained only 1.6% of the variation in this trait. Together with our results, these data are consistent with the argument that association analysis is likely to be far more powerful than linkage for detecting susceptibility loci (which may be of small effect) for common, complex diseases (39,40).
Conclusions
This study has provided a new perspective on the potential significance of the 5q31–33 region in asthma genetics for the phenotypes studied. Meta-analysis is an emerging methodology in complex disease genetics; the combination of evidence from multiple studies can be expected to be critical to the successful localization of genes of modest effect for any common, complex disease such as asthma. This study also illustrates the benefits of cooperation between groups studying a complex human disease, that data from diverse studies and populations can be effectively pooled and that new and important information can result from the analysis of large, pooled data sets. It is hoped that further research and methodological development, together with growing international participation in COAG, will lead to major advances in our understanding of the genetic basis of asthma.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONSUBJECTS AND METHODSREFERENCES |
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Study subjects and phenotypic data
A total of 1037 pedigrees, consisting of 1561 nuclear families, 6278 individuals and 2583 sibling pairs were available for analysis. Data came from Australian (20,41), Barbadian (42), Chinese (43), Finnish (21), German (22,44), British (45,46) and US Caucasian and African-American (16) populations (Table 1). Data from these 11 studies (Tables 1 and 2) were analyzed for linkage. Two data sets (41,46) contained a subset of families that were ascertained for asthma or atopy as well as a subset unselected with regard to asthma or atopy. Thus, the data comprised three random population samples and 10 that were selected for asthma or atopy (Tables 1 and 2). A total of 35 markers from the chromosome 5q region were analyzed, although differing numbers of markers were typed in each individual study (Table 3).
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The primary response variables, which were available in all 11 studies, were the dichotomous trait ‘asthma affection’, defined in varying ways in the 11 studies (Table 2), and total serum IgE levels, measured by standard ELISA techniques (Pharmacia AB).
Total serum IgE levels were positively skewed and were loge transformed and converted to Z-scores prior to analysis, i.e. they were standardized to a mean of 0 and a standard deviation of 1 within each data set and ethnic group. Sex, age and age x sex interaction were allowed for as covariates in the statistical analyses.
All study populations were collected under appropriate institutional ethics approvals, as detailed in the original papers (referenced in Table 1).
Consensus map
A consensus map of chromosome 5q31–33 that incorporated all typed markers in each of the 11 data sets was constructed to pool data across samples (47) (http://cedar.genetics.soton.ac.uk/public.html/). This map provided a framework of markers upon which typed markers on chromosome 5q were integrated; marker order was validated by consulting physical maps (http://www-shgc.stanford.edu/).
Linkage analysis
The GENIBD program (http://darwin.cwru.edu/pub/sage.html) was used to estimate multipoint allele sharing i.b.d. at 58 locations spanning the cytokine cluster on chromosome 5q31–33 from D5S5201 to D5S5422 using a variety of techniques (48–51). These locations consisted of the 35 markers typed across the 11 data sets and 23 points spaced at 
2 cM intervals between markers. Since each data set contained a different subset of typed markers (Table 3), allele sharing i.b.d. was interpolated or extrapolated when necessary using all available marker data.
Linkage analysis was performed on the phenotypes of interest and the estimated allele sharing i.b.d. using the SIBPAL2 (version 4.0 beta 6) program (http://darwin.cwru.edu/pub/sage.html), which implements an enhanced multipoint version of the Haseman–Elston (27) linkage method. The new test uses an improved weighted generalized least squares procedure to obtain a uniformly more powerful procedure for detecting linkage (28,29,52,53). As a consistency check, both simpler Haseman–Elston linkage analyses based only on the squared trait difference (27) and a full conditional linkage test using all-affected-relative-pairs (26) were also performed.
A Monte Carlo permutation test was used to obtain empirical estimates of significance. After permuting i.b.d sharing estimates among sibling pairs within each data set, each replicate was analyzed using an identical model to that used in the original analysis.
Error checking. To detect likely genotyping errors, we calculated the multipoint pedigree likelihood for all available genotypes (54). Single genotypes that implied a large number of recombination events (and induced an unexpectedly large change in the likelihood, greater than a factor of 100) were flagged as errors and the entire family containing that individual removed from the analysis for that marker.
Combination of evidence for linkage
Two methods for combining linkage evidence across data sets (meta-analysis), pooling raw data and pooling data set-specific linkage statistics, were investigated. Pooling raw data assumes that there is some degree of homogeneity among populations, although additional analyses that minimized the effect of this assumption (e.g. by explicitly allowing polygenic and residual phenotypic variability to differ from study to study; 55) confirmed our primary conclusions (data not shown). The second method is robust to heterogeneity among data sets, and allows all estimated parameter values to differ among data sets.
In order to perform a meta-analysis of the linkage results within each study population, two different weighted least squares estimates
Formally:
and
where ni is the number of informative full sibling pairs from the ith data set,
2 is the among-study variance of 
whereas with weights W2 it is
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ACKNOWLEDGEMENTS |
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We are grateful to Professor Newton Morton for his generous contribution of data to this paper. COAG was founded under a grant from the National Asthma Campaign, UK and has received ongoing support from the Medical Research Council, UK. Some of the methodological work used in the analysis was supported by Program Grant 00\3209 from the National Health and Medical Research Council of Australia. Some of the results in this paper were obtained by using the program package S.A.G.E., which is supported by a US Public Health Resource Grant (1 P41 RR03655) from the National Center for Research Resources. During the work, Dr Lyle Palmer held Fulbright and Churchill Fellowships, and was a National Health and Medical Research Council of Australia Postdoctoral Research Fellow. Many people in 14 research groups contributed to this collaboration. They and the grants that supported their work are cited in the references given in Table 1.
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FOOTNOTES |
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+ To whom correspondence should be addressed. Tel: +1 617 525 0872; Fax: +1 617 525 0958; Email: reljp@channing.harvard.edu.
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