References of "Genetic Epidemiology"
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See detailExome copy number variation detection: Use of a pool of unrelated healthy tissue as reference sample
Wenric, Stéphane ULg; Sticca, Tiberio ULg; CABERG, Jean-Hubert ULg et al

in Genetic Epidemiology (2017)

An increasing number of bioinformatic tools designed to detect CNVs (copy number variants) in tumor samples based on paired exome data where a matched healthy tissue constitutes the reference have been ... [more ▼]

An increasing number of bioinformatic tools designed to detect CNVs (copy number variants) in tumor samples based on paired exome data where a matched healthy tissue constitutes the reference have been published in the recent years. The idea of using a pool of unrelated healthy DNA as reference has previously been formulated but not thoroughly validated. As of today, the gold standard for CNV calling is still aCGH but there is an increasing interest in detecting CNVs by exome sequencing. We propose to design a metric allowing the comparison of two CNV profiles, independently of the technique used and assessed the validity of using a pool of unrelated healthy DNA instead of a matched healthy tissue as reference in exome-based CNV detection. We compared the CNV profiles obtained with three different approaches (aCGH, exome sequencing with a matched healthy tissue as reference, exome sequencing with a pool of eight unrelated healthy tissue as reference) on three multiple myeloma samples. We show that the usual analyses performed to compare CNV profiles (deletion/amplification ratios and CNV size distribution) lack in precision when confronted with low LRR values, as they only consider the binary status of each CNV. We show that the metric-based distance constitutes a more accurate comparison of two CNV profiles. Based on these analyses, we conclude that a reliable picture of CNV alterations in multiple myeloma samples can be obtained from whole-exome sequencing in the absence of a matched healthy sample. [less ▲]

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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 detailClustering of Crohn’s disease patients: Identification of sub-phenotypes and population stratification
Maus, Bärbel ULg; Génin, Emmanuelle; Mahachie John, Jestinah ULg et al

in Genetic Epidemiology (2012), 36(7), 729

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See detailA Family-Based Association Test to Detect Gene-Gene Interactions in the Presence of Linkage
De Lobel, L.; De Meyer, H.; Thijs, L. et al

in Genetic Epidemiology (2009), 33(8), 77168

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See detailA Family-based Association Test for Quantitative Traits to Detect Gene-Gene Interactions
De Lobel, L.; De Meyer, H.; Thijs, L. et al

in Genetic Epidemiology (2008), 32(7), 686-686

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See detailGenomic screening in family-based association testing
Van Steen, Kristel ULg; McQueen, M.; Herbert, A. et al

in Genetic Epidemiology (2004), 27

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See detailUsing word frequencies for testing equivalence between two DNA sequences
Jansen, I.; Van Steen, Kristel ULg; Molenberghs, G. et al

in Genetic Epidemiology (2002), 23

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See detailMerits of the multivariate Dale model in genetic association studies
Van Steen, Kristel ULg; Molenberghs, G.; Tahri, N.

in Genetic Epidemiology (2002), 23

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