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See detailUsing IPCAPS to identify fine-scale population structure
Chaichoompu, Kridsadakorn ULg; Fentaw Abegaz, Yazew ULg; Tongsima, Sissades et al

Poster (2017, September 09)

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See detailDetermining fine population structure using iterative pruning
Chaichoompu, Kridsadakorn ULg; Yazew, Fentaw Abegaz; Tongsima, Sissades et al

Poster (2017, July 10)

SNP-based information is used in several existing clustering methods to detect shared genetic ancestry or to identify population substructure (Price et al. 2006, Raj et al. 2016). Here, we present an ... [more ▼]

SNP-based information is used in several existing clustering methods to detect shared genetic ancestry or to identify population substructure (Price et al. 2006, Raj et al. 2016). Here, we present an unsupervised clustering algorithm called the iterative pruning method to capture population structure (IPCAPS). Our method supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis (ipPCA) algorithm (Intarapanich et al. 2009). The IPCAPS involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and Clustering EM estimation as in Lebret et al. (2015). In each iteration, rough clusters and outliers are also identified using our own method called RubikClust. The fixation index (FST) is known to measure a distance between populations and FST = 0.001 may be said to be genetically distinct among the European populations (Tian et al. 2008, Huckins et al. 2014). To observe fine-level population structure using FST, we examined simulated scenarios of one population, 500-8,000 individuals, 5,000-10,000 independent SNPs in HWE (Balding and Nichols 1995), with 100 replicates for each scenario. The simulated SNPs were encoded as additive coding and there was no missing genotype generated. We introduced negative control by subjecting individuals to be separated into two groups using kmeans. We observed that FST values of divided groups were lower than 0.0008, which can be defined as the minimum FST to detect fine-level population structure. To evaluate the performance of our method, we tested different simulated data sets of 2-3 populations, 250 individuals per population, 10,000 independent SNPs in HWE, and FST=[0.0008,0.005], with 100 replicates for each data set. For real-life data sets, we applied the IPCAPS to Thai (Wangkumhang et al. 2013) and HapMap populations. Our method showed that a population classification accuracy was superior to the ipPCA in simulated scenarios of extremely subtle structure (FST=[0.0009,0.005]). In case of the Thai population, results to detect fine-level structure were obtained as well as in case of the HapMap populations. We are convinced that the IPCAPS has a potential to detect fine-level structure and it will be important in molecular reclassification studies of patients once underlying population structure has been removed. [less ▲]

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See detailDetermining fine population structure using iterative pruning
Chaichoompu, Kridsadakorn ULg; Yazew, Fentaw Abegaz; Tongsima, Sissades et al

Poster (2017, April 25)

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See detailUsing unsupervised clustering method and SNP-based information to identify fine-level population structure
Chaichoompu, Kridsadakorn ULg; Yazew, Fentaw Abegaz; Tongsima, Sissades et al

Poster (2017, February 01)

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See detailIntegration of Gene Expression and Methylation to unravel Biological Networks in Glioblastoma Patients
Bessonov, Kyrylo ULg; Gadaleta, Francesco; Van Steen, Kristel ULg

in Genetic Epidemiology (2017)

The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information ... [more ▼]

The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylome data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genome or –Methylome (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified respectively 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Whereas the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response and several cancer types. Importantly, we observed significant over-representation of cancer related pathways including glioma, especially in the XORnet network, suggesting a non-ignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors. [less ▲]

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See detailPractical aspects of gene regulatory inference via conditional inference forests from expression data
Bessonov, Kyrylo ULg; Van Steen, Kristel ULg

in Genetic Epidemiology (2016)

Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs ... [more ▼]

Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIFcond) may lead to improved performance compared to Breiman's implementation of Random Forests (RF). Among all newly introduced CIF-based methods and five network scenarios obtained from the DREAM4 challenge, CIFcond performed best. Networks derived from well-tuned CIFs, obtained by simply averaging P-values over tree ensembles (CIFmean) are particularly attractive, because they combine adequate performance with computational efficiency. Moreover, thresholds for variable selection are based on significance levels for P-values and, hence, do not need to be tuned. From a practical point of view, our extensive simulations show the potential advantages of CIFmean-based methods. Although more work is needed to improve on speed, especially when fully exploiting the advantages of CITs in the context of heterogeneous and correlated data, we have shown that CIF methodology can be flexibly inserted in a framework to infer biological interactions. Notably, we confirmed biologically relevant interaction between IL2RA and FOXP1, linked to the IL-2 signaling pathway and to type 1 diabetes. [less ▲]

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See detailCapturing fine-level structure using unsupervised clustering method with multiple data types
Chaichoompu, Kridsadakorn ULg; Tongsima, Sissades; Shaw, Philip James et al

Poster (2016, June 17)

Several methods exist to detect shared genetic ancestry or to identify population substructure using SNP-based or haplotype-based information (Price et al. 2006, Lawson et al. 2012). Here, we propose an ... [more ▼]

Several methods exist to detect shared genetic ancestry or to identify population substructure using SNP-based or haplotype-based information (Price et al. 2006, Lawson et al. 2012). Here, we propose an unsupervised clustering method built on the ipPCA algorithm (Intarapanich et al. 2009). Our method supports both ordinal and categorical data, and it can be applied to panels of single locus and/or multiple loci data, or gene-based integrative summaries (Fouladi et al. 2015). Our method involves an iterative process using binary and ternary splits based on multivariate Gaussian mixture modeling of PCs and Clustering EM estimation as in (Lebret et al. 2015). To evaluate its performance, we examined different simulated scenarios of 2-4 populations, 500-8,000 individuals, 5,000-20,000 independent SNPs in HWE, and FST=[0.0007,0.006] (Balding and Nichols 1995), with 100 replicates for each scenario. SNPs were treated as categorical or continuous including ancestry-corrected SNPs. Haplotype-based runs used HapMap 3 data: CHB, CHD, and JPT. In simulated scenarios of extremely subtle structure (FST=[0.0009,0.006]), a population classification accuracy of 92% or greater was obtained, which was superior to ipPCA. Also in case of the HapMap populations, promising results to detect fine structure were obtained. We are convinced that our method has a potential to detect fine-level structure and it will be important in molecular reclassification studies of patients once underlying population structure has been removed. [less ▲]

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See detailA novel unsupervised clustering approach with multiple data types to reveal fine-level structure
Chaichoompu, Kridsadakorn ULg; Tongsima, Sissades; Shaw, Philip James et al

Poster (2016, May 21)

Introduction: Several methods exist to identify population substructure that is due to shared genetic ancestry or regional proximity. These may be SNP-based or haplotype-based (Price et al. 2006, Lawson ... [more ▼]

Introduction: Several methods exist to identify population substructure that is due to shared genetic ancestry or regional proximity. These may be SNP-based or haplotype-based (Price et al. 2006, Lawson et al. 2012). Here, we present a flexible unsupervised clustering approach that is built on the ipPCA machinery (Intarapanich et al. 2009). Methods: Our method supports both numeric and categorical data, and can be applied to panels of SNPs and/or haplotypes, or gene-based integrative summaries (Fouladi et al. 2015). Unlike ipPCA, our method involves an iterative process using binary and ternary splits based on multivariate Gaussian mixture modeling of PCs and Clustering EM (CEM) estimation as in (Lebret et al. 2015). To assess performance, we considered different simulated scenarios of FST=[0.0005,0.006], 5,000-20,000 independent SNPs in HWE, 500-8,000 individuals, and 2-4 populations (Balding and Nichols 1995), with 100 replicates for each scenario. SNPs were treated as categorical or continuous (including ancestry-corrected SNPs). Haplotype-based runs used HapMap 3 data: CHB-JPT (FST=0.007) and CEU-TSI (FST=0.004). Result and Conclusion: In simulated scenarios of extremely subtle structure (FST=[0.0009,0.002]), a population classification accuracy of 92.56% or greater was obtained, which was superior to ipPCA. Promising results to detect fine structure were also obtained in case of the HapMap populations. We believe that the ability of our approach to detect subtle structure, including outlier individuals, will be important in molecular reclassification studies of patients from whom underlying population patterns have been removed. Grants: KC and KVS acknowledge FNRS, AS acknowledges ANR, ST acknowledges NSTDA, and PJS acknowledges TRF. [less ▲]

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See detailIterative pruning method of unsupervised clustering for categorical data
Chaichoompu, Kridsadakorn ULg; Tongsima, Sissades; Shaw, Philip James et al

Poster (2016, April 03)

Single Nucleotide Polymorphisms (SNPs) are commonly used to identify population structures. Iterative pruning Principal Component Analysis (ipPCA) utilizes SNP profiles to assign individuals to ... [more ▼]

Single Nucleotide Polymorphisms (SNPs) are commonly used to identify population structures. Iterative pruning Principal Component Analysis (ipPCA) utilizes SNP profiles to assign individuals to subpopulations without making assumptions about ancestry. The strategy can be extrapolated to patient samples to identify molecular classes of patients. It is challenging to investigate the utility of substructure detection using profiles based on pre-defined genomic regions-of-interest rather than profiles based on SNPs. Using principles outlined in Fouladi, 2015, we can construct gene-based categorical variables representing different summary gene profiles in a region. These gene-based new constructs no longer have an equal number of unordered category levels. Here, we present C-PCA, an extension of ipPCA to target perform iterative pruning for categorical variables using optimal scaling. It allows performing non-linear principal component analyses to handle possibly non-linearly related variables with different measurement levels. To show the power of C-PCA compared to ipPCA, we simulated 500 individuals and assigned them to two populations of equal size. We considered genetic population distances using Fixation Index from 0.001 to 0.006. For each dataset, we simulated 10,000 independent random SNPs for 100 replicates using the Balding–Nichols model. These were used numerically in ipPCA and as categorical in C-PCA analysis. In conclusion, like ipPCA, we expect C-PCA to perform well in the presence of fine substructures. This paves the way to apply C-PCA to DNA-seq data and input categorical variable derived from genomic regions-of-interest to which common and rare variants are mapped. We foresee additional advantages of C-PCA in this context since region-based categorical variables are likely to be non-linearly associated at the background of underlying gene-gene interaction networks. C-PCA is implemented in R. [less ▲]

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See detailA roadmap to multifactor dimensionality reduction methods.
Gola, Damian; Mahachie John, Jestinah M.; Van Steen, Kristel ULg et al

in Briefings in Bioinformatics (2016), 17(2), 293-308

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been ... [more ▼]

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity. [less ▲]

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See detailIntegration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer
Pineda San Juan, Silvia ULg; Real, Francisco X; Kogevinas, Manolis et al

in PLoS Genetics (2015)

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See detailgammaMAXT: a fast multiple-testing correction algorithm
Van Lishout, François ULg; Gadaleta, Francesco; Moore, Jason H. et al

in BioData Mining (2015), 8(36),

Background: The purpose of the maxT algorithm is to provide a significance test algorithm that controls the family-wise error rate (FWER) during simultaneous hypothesis testing. However, the requirements ... [more ▼]

Background: The purpose of the maxT algorithm is to provide a significance test algorithm that controls the family-wise error rate (FWER) during simultaneous hypothesis testing. However, the requirements in terms of computing time and memory of this procedure are proportional to the number of investigated hypotheses. The memory issue has been solved in 2013 by Van Lishout’s implementation of MaxT, which makes the memory usage independent from the size of the dataset. This algorithm is implemented in MBMDR-3.0.3, a software that is able to identify genetic interactions, for a variety of SNP-SNP based epistasis models effectively. On the other hand, that implementation turned out to be less suitable for genome-wide interaction analysis studies, due to the prohibitive computational burden. Results: In this work we introduce gammaMAXT, a novel implementation of the maxT algorithm for multiple testing correction. The algorithm was implemented in software MBMDR-4.2.2, as part of the MB-MDR framework to screen for SNP-SNP, SNP-environment or SNP-SNP-environment interactions at a genome-wide level. We show that, in the absence of interaction effects, test-statistics produced by the MB-MDR methodology follow a mixture distribution with a point mass at zero and a shifted gamma distribution for the top 10 % of the strictly positive values. We show that the gammaMAXT algorithm has a power comparable to MaxT and maintains FWER, but requires less computational resources and time. We analyze a dataset composed of 106 SNPs and 1000 individuals within one day on a 256-core computer cluster. The same analysis would take about 104 times longer with MBMDR-3.0.3. Conclusions: These results are promising for future GWAIs.However, the proposed gammaMAXT algorithm offers a general significance assessment and multiple testing approach, applicable to any context that requires performing hundreds of thousands of tests. It offers new perspectives for fast and efficient permutation-based significance assessment in large-scale (integrated) omics studies. [less ▲]

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See detailEpistasis associated to inflammatory bowel disease (IBD) in humans
Gusareva, Elena ULg; Wei, Zhi; Traherne, J.A. et al

Poster (2015, October 05)

Gene-gene interactions underlie biochemical pathways and have been well demonstrated in model organisms. Very few examples exist on replicated epistasis in humans. Here, we performed genome-wide scans to ... [more ▼]

Gene-gene interactions underlie biochemical pathways and have been well demonstrated in model organisms. Very few examples exist on replicated epistasis in humans. Here, we performed genome-wide scans to detect epistasis associated to Crohn’s disease (CD) and ulcerative colitis (UC). We used extensive data of the IIBDGC consisting of 18277 and 14224 CD and UC patients, respectively, and ~34050 healthy controls from 15 European countries typed on the Immunochip. At first, we removed rare variants at MAF<0.05 and filtered common variants at linkage disequilibrium (LD) of r2>0.75. To limit our results to independent effects, SNPs on chromosome 6 (which contains the HLA locus), were furthermore pruned to ensure an LD of r2<0.35. We adjusted the binary traits, CD and UC, for population stratification by regressing out the first 5 principal components in R-3.0.1. The study cohorts were randomly stratified into two subgroups (referred as discovery and replication). We then performed screenings for epistatic interactions with new adjusted trait values in the two subgroups using multidimensional reduction tool MB-MDR with permutation-based (step-down MaxT) multiple testing correction and significance assessment at 0.05. We identified 14 and 6 SNP-pairs associated to CD and UC, respectively, which were concordant between the discovery and replication groups. All SNP-pairs involved concomitant variants located on the same chromosomes (for CD at 1p31.3, 5p13.1, 16q12.1 and for UC at 1p31.3, 6p21.3). A more detailed investigation of these findings, as well as the implementation of different analysis protocols, will further increase our understanding of possible epistatic mechanisms underpinning IBD. [less ▲]

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See detailDetecting patient subgroups using reduced set of disease-related markers with iterative pruning Principal Component Analysis (ipPCA)
Chaichoompu, Kridsadakorn ULg; Cleynen, Isabelle; Fouladi, Ramouna ULg et al

Poster (2015, October 03)

Genetic markers such as Single Nucleotide Polymorphisms (SNPs) can be used to find subgroups of populations or patients with carefully selected clustering algorithms. The iterative pruning principal ... [more ▼]

Genetic markers such as Single Nucleotide Polymorphisms (SNPs) can be used to find subgroups of populations or patients with carefully selected clustering algorithms. The iterative pruning principal component analysis (ipPCA) has been shown to be a powerful tool to identify fine substructures within general populations based on SNP profiles. Usually, SNPs contributing to such profiles have passed rigorous quality control procedures, similar to the ones used for GWAs. Alternatively, attention is restricted to a smaller subset such as PCA-correlated SNPs. Here, we applied ipPCA on real-life data consisting of the 163 known inflammatory-bowel disease (IBD) associated loci in 13,400 healthy individuals and 29,500 IBD (16,902 Crohn’s disease (CD), and 12,598 ulcerative colitis (UC)) patients from the IIBDGC. Prior to clustering by ipPCA, in each group separately, we regressed out the first five Principal Components (PCs) that were computed from a filtered panel of genome-wide SNPs, to account for general population strata. Next, we applied ipPCA on the healthy group, to learn about the presence of a population-specific partitioning in controls. Then we performed three subphenotype analyses: CD only, UC only and the combined group of CD and UC patients (IBD). For each patient subgroup analysis and for the ipPCA analysis on controls, we highlighted and compared the key SNP drivers. CD patients could be molecularly reclassified in two groups, and similar for UC patients. The combined patient group could be subdivided in four groups. Finally, we compared demographic and clinical features among the different groups and looked for meaningful characterizations of adjusted patient clusters by performing pathway analysis on driver genes. [less ▲]

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See detailBiological validation of statistical epistasis signals.
Gusareva, Elena ULg; van der Lee, Sven; Katsumata, Yuriko et al

Speech/Talk (2015)

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See detailA cautionary note on the impact of protocol changes for Genome-Wide Association SNP x SNP Interaction studies: an example on ankylosing spondylitis
Bessonov, Kyrylo ULg; Gusareva, Elena ULg; Van Steen, Kristel ULg

in Human Genetics (2015)

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control ... [more ▼]

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control strategy, SNP filtering, linkage disequilibrium (LD) pruning, analytic tool to model or to test for genetic interactions. Each of these can have an impact on the final epistasis findings and may affect their reproducibility in follow-up analyses. Choosing an analytic tool is not straightforward, as different such tools exist and current understanding about their performance is based on often very particular simulation settings. In the present study, we wish to create awareness for the impact of (minor) changes in a GWAI analysis protocol can have on final epistasis findings. In particular, we investigate the influence of marker selection and marker prioritization strategies, LD pruning and the choice of epistasis detection analytics on study results, giving rise to 8 GWAI protocols. Discussions are made in the context of the ankylosing spondylitis (AS) data obtained via the Wellcome Trust Case Control Consortium (WTCCC2). As expected, the largest impact on AS epistasis findings is caused by the choice of marker selection criterion, followed by marker coding and LD pruning. In MB-MDR, co-dominant coding of main effects is more robust to the effects of LD pruning than additive coding. We were able to reproduce previously reported epistasis involvement of HLA-B and ERAP1 in AS pathology. In addition, our results suggest involvement of MAGI3 and PARK2, responsible for cell adhesion and cellular trafficking. Gene Ontology (GO) biological function enrichment analysis across the 8 considered GWAI protocols also suggested that AS could be associated to the Central Nervous System (CNS) malfunctions, specifically, in nerve impulse propagation and in neurotransmitters metabolic processes. [less ▲]

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See detailHigh-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis.
Goyette, Philippe; Boucher, Gabrielle; Mallon, Dermot et al

in Nature Genetics (2015), 47(2), 172-9

Genome-wide association studies of the related chronic inflammatory bowel diseases (IBD) known as Crohn's disease and ulcerative colitis have shown strong evidence of association to the major ... [more ▼]

Genome-wide association studies of the related chronic inflammatory bowel diseases (IBD) known as Crohn's disease and ulcerative colitis have shown strong evidence of association to the major histocompatibility complex (MHC). This region encodes a large number of immunological candidates, including the antigen-presenting classical human leukocyte antigen (HLA) molecules. Studies in IBD have indicated that multiple independent associations exist at HLA and non-HLA genes, but they have lacked the statistical power to define the architecture of association and causal alleles. To address this, we performed high-density SNP typing of the MHC in >32,000 individuals with IBD, implicating multiple HLA alleles, with a primary role for HLA-DRB1*01:03 in both Crohn's disease and ulcerative colitis. Noteworthy differences were observed between these diseases, including a predominant role for class II HLA variants and heterozygous advantage observed in ulcerative colitis, suggesting an important role of the adaptive immune response in the colonic environment in the pathogenesis of IBD. [less ▲]

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See detailPerspectives on Data Integration in Human Complex Disease Analysis
Van Steen, Kristel ULg; Malats, Nuria

in Big Data Analytics in Bioinformatics and Healthcare (2015)

The identification of causal or predictive variants/genes/mechanisms for disease-associated traits is characterized by “complex” networks of molecular phenotypes. Present technology and computer power ... [more ▼]

The identification of causal or predictive variants/genes/mechanisms for disease-associated traits is characterized by “complex” networks of molecular phenotypes. Present technology and computer power allow building and processing large collections of these data types. However, the super-rapid data generation is counterweighted by a slow-pace for data integration methods development. Most currently available integrative analytic tools pertain to pairing omics data and focus on between-data source relationships, making strong assumptions about within-data source architectures. A limited number of initiatives exist aiming to find the most optimal ways to analyze multiple, possibly related, omics databases, and fully acknowledge the specific characteristics of each data type. A thorough understanding of the underlying assumptions of integrative methods is needed to draw sound conclusions afterwards. In this chapter, the authors discuss how the field of “integromics” has evolved and give pointers towards essential research developments in this context. [less ▲]

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See detailRestoration of Foxp3+ Regulatory T-cell Subsets and Foxp3- Type 1 Regulatory-like T Cells in Inflammatory Bowel Diseases During Anti-tumor Necrosis Factor Therapy.
Li, Zhe; Vermeire, Severine; Bullens, Dominique et al

in Inflammatory Bowel Diseases (2015), 21(10), 2418-28

BACKGROUND: A defect in regulatory T cells (Tregs) may be involved in the pathogenesis of inflammatory bowel diseases (IBD). Several subsets of human Foxp3+ Tregs (activated and resting Tregs) have now ... [more ▼]

BACKGROUND: A defect in regulatory T cells (Tregs) may be involved in the pathogenesis of inflammatory bowel diseases (IBD). Several subsets of human Foxp3+ Tregs (activated and resting Tregs) have now been identified, as well as an IL-10 and IFN-gamma double producing Foxp3 type 1 regulatory-like T cell (Tr1L). We have quantified these Tregs in patients with active IBD and during therapy with infliximab (IFX). METHODS: Blood samples were obtained from healthy controls (n = 54) and patients with active IBD, either before (n = 62) or during IFX therapy (n = 75). Tregs were identified by immunofluorescent staining and flow cytometry analysis. Resting and activated Foxp3+ Tregs can be differentiated from Foxp3+ effector T cells (Foxp3+ Teff) by the expression of CD45RA. Tr1L are identified as CD4+CD45RA-CD25-CD127-Foxp3- T cells. RESULTS: A numerical deficiency of circulating resting Tregs, activated Treg cells, and Tr1L was documented in patients with active IBD. Baseline levels of these Treg subsets predicted clinical responses to IFX. We documented an upregulation of all 3 subsets during IFX therapy. Moreover, after therapy, significant differences in Treg subsets were seen between responders and nonresponders to IFX. Restoration of Tregs correlated with the clinical and biological response to IFX therapy. Trough serum levels of IFX positively correlated with the proportion of activated Treg cells and Tr1L during therapy. CONCLUSIONS: IFX therapy, when successful, results in upmodulation of the different types of Treg cells in the blood of patients with IBD. This effect might be relevant for understanding the mechanism of action of anti-TNF agents. [less ▲]

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