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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 detailReplication of large-scale epistasis studies: an example on ankylosing spondylitis
Bessonov, Kyrylo ULg; Van Steen, Kristel ULg

Poster (2013, September 17)

Ankylosing spondylitis (AS) is a common form of inflammatory arthritis occurring in approximately 5 out of 1,000 adults of European descent. Recently, the Australo-Anglo-American Spondyloarthritis ... [more ▼]

Ankylosing spondylitis (AS) is a common form of inflammatory arthritis occurring in approximately 5 out of 1,000 adults of European descent. Recently, the Australo-Anglo-American Spondyloarthritis Consortium and the WTCCC2 showed that polymorphisms of ERAP1 only affect AS risk in HLA-B27-positive individuals, hereby establishing an interaction between ERAP1 and HLA in the TASC, WTCCC2 and replication datasets [2,5]. We were able to confirm this interaction although using other SNPs. In this study, we use the aforementioned data from WTTCC2 on AS to address unresolved issues when performing large-scale SNP-SNP interaction studies, so as to better guarantee “stable” and “truly replicable” results. These issues are 1) the choice of variable selection method (e.g., of known loci mapping to genes part of know pathways), 2) the choice of SNPs representing a genomic region (e.g., SNPs with modest versus negligible LD between them), 3) the choice of analysis method (e.g., regression-based versus data-reduction (non-parametric) based), 4) different adjustment schemes for lower-order effects (using additive/co-dominant genetic models). We show that even modest changes in 1)-4) may give rise to quite varying epistasis findings for AS, and motivate some “optimal” choices via extensive simulation studies. In this work we rely on a minimal GWAI protocol for genome-wide epistasis detection using SNPs, as developed in our lab [6][9], using the advanced non-parametric Model-Based Multifactor Dimensionality Reduction (MB-MDR) method [1] and an adapted [*] BOolean Operation-based Screening and Testing (BOOST) algorithm [4]. [*] A BOOST [4] like implementation based on the original BOOST algorithm which accounts for missing genotypes [less ▲]

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See detailTherapeutic Strategy and Patient Outcome during the First 2 Years of Pediatric Crohn’s Disease
Veereman, G; Mahachie John, Jestinah ULg; De Greef, E et al

in Journal of Pediatric Gastroenterology and Nutrition (2013, May)

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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 detailTherapeutic Strategy and Patient Outcome during the First 2 Years of Pediatric Crohn’s Disease
Veereman, G; Mahachie John, Jestinah ULg; De Greef, E et al

in Acta Gastroenterologica Belgica (2013, February)

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See detailProfile of Pediatric Crohn’s Disease in Belgium
De Greef, E; Mahachie John, Jestinah ULg; Hoffman, I et al

in Journal of Crohn's and Colitis (2013)

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See detailSurvival analysis: finding relevant epistatic SNP pairs using Model- Based Multifactor Dimensionality Reduction
Van Lishout, François ULg; Vens, Céline; Urrea, Victor et al

Conference (2012, December 03)

Analyzing the combined effects of genes (and/or environmental factors) on the development of complex diseases is quite challenging, both from the statistical and computational perspective, even using a ... [more ▼]

Analyzing the combined effects of genes (and/or environmental factors) on the development of complex diseases is quite challenging, both from the statistical and computational perspective, even using a relatively small number of genetic and non-genetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR). Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new dimensionality reduction technique, is able to unify the best of both nonparametric and parametric worlds, and has proven its utility in a variety of theoretical and practical settings. Until now, MB-MDR software has only accommodated traits that are measured on a binary or interval scale. Time-to-event data could therefore not be analyzed with the MB-MDR methodology. MB-MDR-3.0.0 overcomes this shortcoming of earlier versions. We show the added value of MB-MDR for censored traits by comparing the implemented strategies with more classical methods such as those based on a parametric regression paradigm. The simulation results are supplemented with an application to real-life data. [less ▲]

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See detailPrognostic Value of Serologic and Histologic Markers on Clinical Relapse in Ulcerative Colitis Patients With Mucosal Healing
Bessissow, Talat; Lemmens, Bart; Ferrante, Marc et al

in American Journal of Gastroenterology (2012, November), 11(107), 1684-92

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See detailRestoration Of B Cells Correlates With Clinical Response To Anti-Tnf Therapy
Li, Z; Vermeire, S; Bullens, D et al

Poster (2012, October)

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See detailFactors determining therapeutic strategy at diagnosis and evolution of disease severity in a cohort of Belgian pediatric Crohn's disease patients (BELCRO)
De Greef, E; Mahachie John, Jestinah; Hoffman, I et al

in Gastroenterology (2012, May)

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See detailSequential T2 Relaxometry as a Non-Invasive Assessment of Transmural Inflammation in a Murine Model of Chronically Relapsing Colitis
Breynaert, C; Dresselaers, T; Cremer, J et al

Poster (2012, May)

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See detailRepeated cycles of DSS inducing a chronically relapsing inflammation: a novel model to study fibrosis using in vivo MRI T2 relaxometry
Breynaert, C; Dresselaers, T; Cremer, J et al

Poster (2012, May)

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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 detailGenetic and functional evidence for a role of CYLD in Crohn’s Disease: results from a European consortium
Cleynen, I; Vazeille, E; Artieda, M et al

in Journal of Crohn’s and Colitis [=JCC] (2012, February)

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See detailMultiple functional variants at the 3p21 locus contribute to ulcerative colitis: Results from a European consortium
Cleynen, I; Artieda, M; Verspaget, H et al

in Journal of Crohn’s and Colitis [=JCC] (2012, February)

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See detailRepeated cycles of DSS inducing a chronically relapsing inflammation: A novel model to study fibrosis using in vivo MRI T2 relaxometry
Breynaert, C; Dresselaers, T; Cremer, J et al

Poster (2012, February)

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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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