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See detailGenome-wide environmental interaction analysis using multidimensional data reduction principles to identify asthma pharmacogenetic loci in relation to corticosteroid therapy
Van Lishout, François ULg; Bessonov, Kyrylo ULg; Duan, Quingling et al

Poster (2013, October 25)

Genome-wide gene-environment (GxE) and gene-gene (GxG) interaction studies share a lot of challenges via the common genetic component they involve. GWEI studies may therefore benefit from the abundance of ... [more ▼]

Genome-wide gene-environment (GxE) and gene-gene (GxG) interaction studies share a lot of challenges via the common genetic component they involve. GWEI studies may therefore benefit from the abundance of methodologies that are available in the context of genome-wide epistasis detection methods. One of these is Model-Based Multifactor Dimensionality Reduction (MB-MDR), which does not make any assumption about the genetic inheritance model. MB-MDR involves reducing a high-dimensional GxE space to GxE factor levels that either exhibit high or low or no evidence for their association to disease outcome. In contrast to logistic regression and random forests, MB-MDR can be used to detect GxE interactions in the absence of any main effects or when sample sizes are too small to be able to model all main and GxE interaction effects. In this ongoing study, we demonstrate the opportunities and challenges of MB-MDR for genome-wide GxE interaction analysis and analyzed the difference in prebronchodilator FEV1 following 8 weeks of inhaled corticosteroid therapy, for 565 pediatric Caucasian CAMP (ages 5-12) from the SHARE project. [less ▲]

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See detailAn efficient algorithm to perform multiple testing in epistasis screening
Van Lishout, François ULg; Mahachie John, Jestinah ULg; Gusareva, Elena ULg et al

in BMC Bioinformatics (2013), 14

Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved ... [more ▼]

Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. Gene-gene interaction studies will require a memory proportional to the squared number of SNPs. A genome-wide epistasis search would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MBMDR-3.0.3. We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohn's disease. Results: In the case of a binary (affected/unaffected) trait, the parallel workflow of MBMDR-3.0.3 analyzes all gene-gene interactions with a dataset of 100,000 SNPs typed on 1000 individuals within 4 days and 9 hours, using 999 permutations of the trait to assess statistical significance, on a cluster composed of 10 blades, containing each four Quad-Core AMD Opteron Processor 2352 2.1 GHz. In the case of a continuous trait, a similar run takes 9 days. Our program found 14 SNP-SNP interactions with a multiple-testing corrected p-value of less than 0.05 on real-life Crohn's disease data. Conclusions: Our software is the first implementation of the MB-MDR methodology able to solve large-scale SNP-SNP interactions problems within a few days, without using much memory, while adequately controlling the type I error rates. A new implementation to reach genome-wide epistasis screening is under construction. In the context of Crohn's disease, MBMDR-3.0.3 could identify epistasis involving regions that are well known in the field and could be explained from a biological point of view. This demonstrates the power of our software to find relevant phenotype-genotype higher-order associations. [less ▲]

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See detailApplication of mixed polygenic model to control for cryptic/genuine relatedness and population stratification.
Gusareva, Elena ULg; Mahachie John, Jestinah ULg; Isaacs, Aaron et al

Poster (2012, March 12)

In genome-wide association studies (GWAs), population stratification may cause inflated type I errors and overly-optimistic test results, when not properly corrected for. During the past decade, several ... [more ▼]

In genome-wide association studies (GWAs), population stratification may cause inflated type I errors and overly-optimistic test results, when not properly corrected for. During the past decade, several methods have been proposed for association testing in the presence of population stratification. Among these, principal components-based approaches are the most popular. Principal component analysis (PCA) allows data transformation to a new coordinate system such that the projection of the data along the first new coordinate (called the PC1) has the largest variance; the second PC has the second largest variance, and so on. In practice, two components are usually enough to adjust or to control for population stratification. They can easily be included in parametric association models as covariates. Despite the success of this strategy, there are still some caveats which need further attention. Among these are that principal component-based methods generally do not account for cryptic relatedness (kinship) between supposedly unrelated individuals, are not straightforwardly adapted to accommodate family-based designs or mixtures of families and unrelated individuals, and do not always take proper account of the trait under investigation. In this work, we present an easy-to-use alternative that addresses the aforementioned issues. For quantitative traits, we propose to first use the mixed polygenic model (possibly taking into account important non-genetic confounders as covariates), second to derive “polygenic” residuals from this model – hereby removing genomic kinship relationships, and third to consider these residuals as new traits in a classical genome-wide QTL analysis for “unrelated individuals”. The polygenic component of the aforementioned mixed polygenic model describes the contribution from multiple independently segregating genes, all having a small additive effect on the trait under investigation. Via an extensive simulation study, with various settings of population stratification and admixture, we show that this approach not only removes most of the “relatedness” between individuals (cryptic relatedness or known relatedness), but also removes most of the remaining substructures caused by population stratification or admixture. As a proof of concept, we demonstrate the efficiency of this robust method to control for population stratification on real-life genome-scale data from the SNP Health Association Resource (SHARe) Asthma Resource project (SHARP) (dbGaP accession number phs000166.v2.p1). We also provide leads to extend this method to dichotomous traits. [less ▲]

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See detailLower-Order Effects Adjustment in Quantitative Traits Model-Based Multifactor Dimensionality Reduction
Mahachie John, Jestinah ULg; Cattaert, Tom ULg; Van Lishout, François ULg et al

in PLoS ONE (2012)

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to ... [more ▼]

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2- way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on ‘‘top’’ findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using ‘‘on-the-fly’’ lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis. [less ▲]

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See detailAn Efficient Algorithm to Perform Multiple Testing in Epistasis Screening
Van Lishout, François ULg; Cattaert, Tom ULg; Mahachie John, Jestinah ULg et al

Conference (2011, December 13)

Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown exponentially over the last few years. It has been marked by promising methodological developments ... [more ▼]

Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown exponentially over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. In main-effects detection, this is not a problem since the memory required is thus proportional to the number of SNPs. In contrast, gene-gene interaction studies will require a memory proportional to the squared amount of SNPs. A genome wide epistasis would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. Methods: In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MB-MDR-2.6.2 and compared to MB-MDR's first implementation as an R-package (Calle et al., Bioinformatics 2010). We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohn's disease. Results: The sequential version of MBMDR-2.6.2 is approximately 5,500 times faster than its R counterparts. The parallel version (tested on a cluster composed of 14 blades, containing each 4 quad-cores Intel Xeon CPU E5520@2.27 GHz) is approximately 900,000 times faster than the latter, for results of the same quality on the simulated data. It analyses all gene-gene interactions of a dataset of 100,000 SNPs typed on 1000 individuals within 4 days. Our program found 14 SNP-SNP interactions with a p-value less than 0.05 on the real-life Crohn’s disease data. Conclusions: Our software is able to solve large-scale SNP-SNP interactions problems within a few days, without using much memory. A new implementation to reach genome wide epistasis screening is under construction. In the context of Crohn's disease, MBMDR-2.6.2 found signal in regions well known in the field and our results could be explained from a biological point of view. This demonstrates the power of our software to find relevant phenotype-genotype associations. [less ▲]

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See detailComparison Of Different Methods For Detecting Gene-Gene Interactions In Case-Control Data
Cattaert, Tom ULg; Rial Garcia, J. A.; Gusareva, Elena ULg et al

Poster (2011, September 19)

It is generally believed that epistasis makes an important contribution to the genetic architecture of complex disease, and numerous statistical and bioinformatics methods have been developed to detect it ... [more ▼]

It is generally believed that epistasis makes an important contribution to the genetic architecture of complex disease, and numerous statistical and bioinformatics methods have been developed to detect it. We compare several state-of-the-art epistasis detection methods in terms of empirical power, type-I error control, and CPU time. The methods compared include Model-Based Multifactor Dimensionality Reduction (MB-MDR) [1, 2], BOolean Operation-based Screening and Testing (BOOST) [3], EPIBLASTER [4], Random Jungle (RJ) [5], Logistic Regression and PLINK. Our comparative study is based on an extensive simulation study using different two-locus models, exhibiting both main effects and epistasis [3]. In these simulations, 100 SNPs are generated, no LD between them. All genotypes are assumed to be in Hardy-Weinberg equilibrium. Furthermore, 2 disease-associated SNPs are selected, with MAFs set to 0.1, 0.2 and 0.4. The MAFs of the non-disease associated SNPs are uniformly distributed on [0.05, 0.5]. In order to achieve high accuracy in empirical power estimation, all simulation settings involve 1000 replicates. All methods are applied to WTCCC Crohn's Disease data. [1] Calle, M.L. et al. (2008), Tech. Rep. No. 24, Dep. of Systems Biology, Univ. de Vic [2] Cattaert, T. et al. (2011), Ann. Hum. Gen. 75, 78-89 [3] Wan, X. et al. (2010), Am. J. Hum. Gen. 87, 325-340 [4] Kam-Thong, T. et al. (2011), Eur. J. Hum. Gen. 19, 465-471 [5] Schwartz, D.F. et al. (2010), Bioinf. 26, 1752-1758 [less ▲]

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See detailA robustness study to investigate the performance of parametric and non-parametric tests used in Model-Based Multifactor Dimensionality Reduction Epistasis Detection.
Mahachie John, Jestinah ULg; Gusareva, Elena ULg; Van Lishout, François ULg et al

Poster (2011, September 19)

Model-Based Multifactor Dimensionality Reduction (MB-MDR) is data mining technique to identify gene-gene interactions among 1000nds of SNPs in a fast way, without making assumptions about the mode of ... [more ▼]

Model-Based Multifactor Dimensionality Reduction (MB-MDR) is data mining technique to identify gene-gene interactions among 1000nds of SNPs in a fast way, without making assumptions about the mode of genetic interactions. By construction, one of the implementations of MB-MDR involves testing one multi-locus genotype cell versus the remaining cells, hereby creating two imbalanced groups for trait distribution comparison. To date, for continuous traits, we have adopted a standard F-test to compare these groups. When normality assumption or homoscedasticity no longer hold, highly inflated results are to be expected. The power and type I error control of MB-MDR under these assumptions has been thoroughly investigated in Mahachie John et al [1]. The aim of this study is to assess, through simulations, the effects of ANOVA model violations on the performance of Model-Based Multifactor Dimensionality Reduction (MB-MDR). We quantify their effect on MB-MDR using default options, but at the same time introduce alternative options with increased performance. The better handling of imbalanced data using robust approaches [2] within a MB-MDR context is exemplified on real data for asthma-related phenotypes. 1. EJHG (2011), Early view 2. David Freedman, Statistical Models: Theory and Practice, Cambridge University Press (2000), ISBN 978-0521671057 [less ▲]

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See detailGenome-wide epistasis screening for Crohns’ disease
Gusareva, Elena ULg; Van Steen, Kristel ULg

Poster (2011, September 19)

Genome-wide association (GWA) studies of Crohn's disease have identified numerous genes. However, a substantial portion of the heritability of this disease remains unexplained. Some gene variants, not ... [more ▼]

Genome-wide association (GWA) studies of Crohn's disease have identified numerous genes. However, a substantial portion of the heritability of this disease remains unexplained. Some gene variants, not detectable via main effects GWA study, may manifest themselves only in interaction with other variants. To search for interacting genes involved in the regulation of Crohn's disease, we performed GWA epistasis screening in a large human cohort (1851 cases/2938 controls) belonging to the Wellcome Trust Case Control Consortium (WTCCC). All subjects were genotyped with the GeneChip 500K Mapping Array Set (Affymetrix chip). SNPs that passed our quality control (359,479 SNPs) were processed in Biofilter (a software package that looks for candidate epistatic genes contributing to disease risk) giving rise to 14,185 SNPs. Subsequent MB-MDR epistasis screening discovered four pairs of interacting SNPs on chromosome 4q35.1 and eight pairs on chromosome 11q23.2. The identified pairs of SNPs were confirmed with synergy-based measures. Notably, despite their mapping to the same genomic regions, the interacting SNPs were not in LD (r^2 < 0.5). Our findings support the idea of close chromosomal localization of two pairs of interacting genes that are involved in development of Crohn's disease. [less ▲]

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See detailA genome-wide linkage study of individuals with high scores on NEO personality traits
Amin, Najaf; Schuur, M.; Gusareva, Elena ULg et al

in Molecular Psychiatry (2011)

The NEO-Five-Factor Inventory divides human personality traits into five dimensions: neuroticism, extraversion, openness, conscientiousness and agreeableness. In this study, we sought to identify regions ... [more ▼]

The NEO-Five-Factor Inventory divides human personality traits into five dimensions: neuroticism, extraversion, openness, conscientiousness and agreeableness. In this study, we sought to identify regions harboring genes with large effects on the five NEO personality traits by performing genome-wide linkage analysis of individuals scoring in the extremes of these traits ( > 90th percentile). Affected-only linkage analysis was performed using an Illumina 6K linkage array in a family-based study, the Erasmus Rucphen Family study. We subsequently determined whether distinct, segregating haplotypes found with linkage analysis were associated with the trait of interest in the population. Finally, a dense single-nucleotide polymorphism genotyping array (Illumina 318K) was used to search for copy number variations (CNVs) in the associated regions. In the families with extreme phenotype scores, we found significant evidence of linkage for conscientiousness to 20p13 (rs1434789, log of odds (LOD) = 5.86) and suggestive evidence of linkage (LOD > 2.8) for neuroticism to 19q, 21q and 22q, extraversion to 1p, 1q, 9p and12q, openness to 12q and 19q, and agreeableness to 2p, 6q, 17q and 21q. Further analysis determined haplotypes in 21q22 for neuroticism (P-values = 0.009, 0.007), in 17q24 for agreeableness (marginal P-value = 0.018) and in 20p13 for conscientiousness (marginal P-values = 0.058, 0.038) segregating in families with large contributions to the LOD scores. No evidence for CNVs in any of the associated regions was found. Our findings imply that there may be genes with relatively large effects involved in personality traits, which may be identified with next-generation sequencing techniques. [less ▲]

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See detailGenome-wide epistasis screening for asthma associated traits
Gusareva, Elena ULg; Huyghe, Jeroen; Van Steen, Kristel ULg

Poster (2011, August 01)

Genome-wide association (GWA) studies of asthma and associated traits have identified numerous genes. A substantial portion of the heritability of these traits remains unexplained. Some variants, not ... [more ▼]

Genome-wide association (GWA) studies of asthma and associated traits have identified numerous genes. A substantial portion of the heritability of these traits remains unexplained. Some variants, not detectable via main effects GWA study may manifest themselves only in interaction with other variants. To search for interacting genes involved in regulation of asthma associated traits (total IgE, eosinophils, FEV1, FVC, FEV1/FVC) we performed GWA epistasis screening in two family groups of asthma patients:CAMP (Childhood Asthma Management Program:814 cases and 467 trios) and CARE (Childhood Asthma Research and Education:796 cases and 338 trios) [dbGaP accession number phs000166.v1.p1.c1]. Individuals were genotyped with the Aymetrix 6.0 array. After quality control 574922 and 575010 SNPs in CAMP and CARE respectively, were tested with FBAT. No main effects genome-wide significant associations were found. We prioritized candidate pairs of SNPs for MB-MDR epistasis screening using Biofilter leading to 7632 SNPs for CAMP and 7603 SNPs for CARE. The most significant pair-wise interaction was identified between SNPs from loci 7p21.1 and 12q23.3 influencing eosinophil level in asthmatics. [less ▲]

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See detailAtopy controlling loci in Czech and Russian populations
Gusareva, Elena ULg; Badalová, Jana; Havelková, Helena et al

Poster (2010, April)

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See detailChromosome 12q24.3 controls sensitization to cat allergen in patients with asthma from Siberia, Russia
Gusareva, Elena ULg; Bragina, Elena; Buinova, Svetlana et al

in Immunol Letters (2009), 125(1), 1-6

In Russian population of Siberia asthma is usually concomitant with high sensitization to indoor allergens (cat, dog and house dust mites), overproduction of total immunoglobulin E (IgE) and airway ... [more ▼]

In Russian population of Siberia asthma is usually concomitant with high sensitization to indoor allergens (cat, dog and house dust mites), overproduction of total immunoglobulin E (IgE) and airway hyperreactivity. Definition of genes that predispose to development of various sub-components of the asthma phenotype is important for understanding of etiology of this disease. To map genes predisposing to asthma,we tested 21 microsatellite markers from candidate chromosomal regions in 136 Russian nuclear families with asthma from Siberia.We performed non-parametric analysis for linkage with asthma, total IgE, specific IgE to cat, dog, and dust mites, and spirometric indices (FEV1 (%) – percentage of predicted forced expiratory volume in 1 s, FVC (%) – percentage of predicted forced vital capacity, and FEV1/FVC (%) – Tiffenau index). The most significant linkagewas to the candidate region on chromosome 12. Locus controlling cat-specific IgE, which is the most abundant in asthma patients fromSiberian population, mapped within the interval between 136 and 140 cM on chromosome 12q24.3, with the suggestive linkage at the marker D12S1611 (LOD= 2.23, P = 0.0007). Total IgE was also linked to this region (D12S1611 – LOD= 1.12, P = 0.012). FEV1 (%) exceeded LOD> 1 threshold for significance with the same locus 12q24.3, but with the peak at a more proximal region at 111.87cM (D12S338 – LOD= 1.21, P = 0.009). Some evidence of linkage (LOD> 1.0) was also detected for asthma at 6p21.31 (D6S291) and total IgE at 13q14.2 (D13S165). These data indicate that the locus 12q24.3 is the most promising candidate for identification of asthma genes in Russian population of Siberia. [less ▲]

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See detailMouse to human comparative genetics reveals a novel immunoglobulin E-controlling locus on Hsa8q12
Gusareva, Elena ULg; Havelková, Helena; Blažková, Hana et al

in Immunogenetics (2009), 61(1), 15-25

Atopy is a predisposition to hyperproduction of immunoglobulin E (IgE) against common environmental allergens. It is often associated with development of allergic diseases such as asthma, rhinitis, and ... [more ▼]

Atopy is a predisposition to hyperproduction of immunoglobulin E (IgE) against common environmental allergens. It is often associated with development of allergic diseases such as asthma, rhinitis, and dermatitis. Production of IgE is influenced by genetic and environmental factors. In spite of progress in the study of heredity of atopy, the genetic mechanisms of IgE regulation have not yet been completely elucidated. The analysis of complex traits can benefit considerably from integration of human and mouse genetics. Previously, we mapped a mouse IgE-controlling locus Lmr9 on chromosome 4 to a segment of <9 Mb. In this study, we tested levels of total IgE and 25 specific IgEs against inhalant and food allergens in 67 Czech atopic families. In the position homologous to Lmr9 on chromosome 8q12 marked by D8S285, we demonstrated a novel human IgE-controlling locus exhibiting suggestive linkage to composite inhalant allergic sensitization (limit of detection, LOD=2.11, P=0.0009) and to nine specific IgEs, with maximum LOD (LOD=2.42, P=0.0004) to plantain. We also tested 16 markers at previously reported chromosomal regions of atopy. Linkage to plant allergens exceeding the LOD>2.0 was detected at 5q33 (D5S1507, LOD=2.11, P=0.0009) and 13q14 (D13S165, LOD=2.74, P=0.0002). The significant association with plant allergens (quantitative and discrete traits) was found at 7p14 (D7S2250, corrected P=0.026) and 12q13 (D12S1298, corrected P=0.043). Thus, the finding of linkage on chromosome 8q12 shows precision and predictive power of mouse models in the investigation of complex traits in humans. Our results also confirm the role of loci at 5q33, 7p14, 12q14, and 13q13 in control of IgE. [less ▲]

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See detailRelationship between total and specific IgE in patients with asthma from Siberia
Gusareva, Elena ULg; Ogorodova, Lyudmila; Chernyak, Boris et al

in Journal of Allergy and Clinical Immunology (The) (2008), 121(3), 781

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See detailDifferent environmental influences on etiology of atopic diseases in European populations as a basis for study of geneenvironment interactions.
Gusareva, Elena ULg; Belozorov, Aleksey; Havelková, Helena et al

in Torres, S. L.; Marin, M. S. (Eds.) Genetic Predisposition to Disease (2008)

Atopy is a predisposition to hyperproduction of immunoglobulin E (IgE) against common environmental allergens. Sensitization to various airborne and food allergens contributes to different types of atopic ... [more ▼]

Atopy is a predisposition to hyperproduction of immunoglobulin E (IgE) against common environmental allergens. Sensitization to various airborne and food allergens contributes to different types of atopic diseases, including asthma, eczema, and allergic rhinitis. The development of these diseases is influenced by both genetic and environmental factors. Several loci and genes that control IgE level have been described in different chromosomal regions. Some of them have been detected in several populations, others only in one or a few populations. These differences might be caused by variations of genetic composition between populations, different lifestyles and/or by environmental variations in major allergens triggering development of atopic diseases. Thus, the environmental conditions may likely determine, which from the potential atopy-controlling genes will operate in a certain population. As the first step in study of such gene-environment interactions we analyzed the specificity and intensity of sensitization to 40 different allergens in atopic patients from the Czech Republic and Ukraine, representing two genetically not very distant populations, which live in different environmental conditions. The atopic patients from both countries displayed a higher reactivity to inhalant than to food allergens. We found highly significant differences in sensitization to airborne allergens between patients from the two countries. The most pronounced allergens for the atopic patients from Ukraine were allergens from dust mites Dermatophagoides pteronyssinus (38.5%), Dermatophagoides farinae (48.1%) and cat (44.2%). In the atopic patients from the Czech Republic the level of sensitization to these allergens was similar, but the level of sensitization to outdoor allergens, grasses and trees was dramatically higher. More than 68% of the patients from the Czech Republic in comparison with less than 25% of the patients from Ukraine have been sensitized to cocksfoot, sweet vernal grass, timothy grass and cultivated rye (Bonferroni-corrected P values ranged from 0.0007 to 0.000000003). More than 50% and 60% of the patients from the Czech Republic but only 2% and 19.2% of the patients from Ukraine reacted to alder (corrected P < 0.00009) and birch (corrected P < 0.002), respectively. The higher sensitization to plant allergens of the patients from the Czech Republic was present in those with asthma and rhinitis, but not with dermatitis. The higher sensitization levels to outdoor allergens in the Czech Republic suggest an influence of westernization on development of allergic reactivity. Genetic analysis of atopic patients from these two countries will establish which geneloci control development of atopy under different environmental conditions. [less ▲]

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See detailCat is a major allergen in patients with asthma from west Siberia, Russia
Gusareva, Elena ULg; Bragina, Elena; Deeva, Evgenia et al

in Allergy (2006), 61(4), 509-510

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See detailIgE controlling loci in Czech atopic patients.
Gusareva, Elena ULg; Havelková, Helena; Blažková, Hanna et al

Poster (2006, September 07)

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See detailThe major allergens in Russian atopic asthmatic patients.
Gusareva, Elena ULg; Bragina, Elena; Deeva, Evgenia et al

Poster (2005, July)

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