References of "Van Steen, Kristel"
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See detailIPCAPS: an R package for iterative pruning to capture population structure
Chaichoompu, Kridsadakorn ULiege; Abegaz Yazew, Fentaw; Tongsima, Sissades et al

in Source Code for Biology and Medicine (in press)

Resolving population genetic structure is challenging, especially when dealing with closely related populations. Although Principal Component Analysis (PCA)-based methods and genomic var- iation with ... [more ▼]

Resolving population genetic structure is challenging, especially when dealing with closely related populations. Although Principal Component Analysis (PCA)-based methods and genomic var- iation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic an- cestry, improvements can be made targeting fine-level population structure. This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-level population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipP- CA) framework to systematically assign individuals to genetically similar subgroups. Our tool is able to detect and eliminate outliers in each iteration to avoid misclassification. It can be extended to de- tect subtle subgrouping in patients as well. In addition, IPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated. [less ▲]

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See detailModelling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology
Cottone, F; Deliu, N; Collins, GS et al

in Quality of Life Research (2019)

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See detailHow to increase our belief in discovered genetic interactions via large-scale association studies?
Van Steen, Kristel ULiege; Moore, Jason

in Human Genetics (2019)

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See detailTowards an accurate cancer diagnosis modelization: Comparison of Random Forest strategies
Debit, Ahmed ULiege; Poulet, Christophe ULiege; JOSSE, Claire ULiege et al

Poster (2018, October 05)

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a ... [more ▼]

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a high-level of precision. Random Forests have been already used in cancer diagnosis, prognosis, and screening. Numerous Random Forests methods have been derived from the original random forest algorithm from Breiman et al. in 2001. Nevertheless, the precision of their generated models remains unknown when facing biological data. The precision of such models can be therefore too variable to produce models with the same accuracy of classification, making them useless in daily clinics. Here, we perform an empirical comparison of Random Forest based strategies, looking for their precision in model accuracy and overall computational time. An assessment of 15 methods is carried out for the classification of paired normal - tumor patients, from 3 TCGA RNA-Seq datasets: BRCA (Breast Invasive Carcinoma), LUSC (Lung Squamous Cell Carcinoma), and THCA (Thyroid Carcinoma). Results demonstrate noteworthy differences in the precisions of the model accuracy and the overall time processing, between the strategies for one dataset, as well as between datasets for one strategy. Therefore, we highly recommend to test each random forest strategy prior to modelization. This will certainly improve the precision in model accuracy while revealing the method of choice for the candidate data. [less ▲]

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See detailPrincipals about principal components in statistical genetics
Abegaz, F; Chaichoompu, K; Genin, E et al

in Briefings in Bioinformatics (2018)

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See detailStatistical methods for genome-wide association studies.
Wang, Maggie Haitian T.; Cordell, Heather Jane; Van Steen, Kristel ULiege

in Seminars in Cancer Biology (2018)

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See detailMale-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease.
Gusareva, Elena S.; Twizere, Jean-Claude ULiege; Sleegers, Kristel et al

in Neurobiology of Aging (2018), 72

Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening ... [more ▼]

Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening for epistasis contributing to Alzheimer's disease (AD) susceptibility. We identified a statistical epistasis signal between the single nucleotide polymorphisms rs3733980 and rs7175766 that was associated with AD in males (genome-wide significant pBonferroni-corrected=0.0165). This signal pointed toward the genes WW and C2 domain containing 1, aka KIBRA; 5q34 and TLN2 (talin 2; 15q22.2). Gene-based meta-analysis in 3 independent consortium data sets confirmed the identified interaction: the most significant (pmeta-Bonferroni-corrected=9.02*10(-3)) was for the single nucleotide polymorphism pair rs1477307 and rs4077746. In functional studies, WW and C2 domain containing 1, aka KIBRA and TLN2 coexpressed in the temporal cortex brain tissue of AD subjects (beta=0.17, 95% CI 0.04 to 0.30, p=0.01); modulated Tau toxicity in Drosophila eye experiments; colocalized in brain tissue cells, N2a neuroblastoma, and HeLa cell lines; and coimmunoprecipitated both in brain tissue and HEK293 cells. Our finding points toward new AD-related pathways and provides clues toward novel medical targets for the cure of AD. [less ▲]

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See detailAnti-infliximab antibody concentrations can guide treatment intensification in patients with Crohn's disease who lose clinical response
Dreesen, E.; Van Stappen, T.; Ballet, V. et al

in Alimentary Pharmacology and Therapeutics (2018), 47(3), 346-355

Background: The presence of antibodies towards infliximab (ATI) is associated with lower infliximab (IFX) trough concentrations and loss of response. IFX treatment intensification is effective for ... [more ▼]

Background: The presence of antibodies towards infliximab (ATI) is associated with lower infliximab (IFX) trough concentrations and loss of response. IFX treatment intensification is effective for restoring response in most, but not all patients with Crohn's disease (CD). Aim: To compare outcome, pharmacokinetics and immunogenicity of treatment intensification strategies in patients with CD who lost clinical response to IFX. Methods: A retrospective cohort study was conducted, including 103 patients with CD who lost clinical response during IFX maintenance therapy and therefore received a double dose IFX (10 mg/kg) and/or a next infusion after a shortened interval. IFX and ATI concentrations were measured in consecutive trough samples, just before (T0) and after (T+1) treatment intensification. Results: Clinical response (physicians' global assessment) and biological response and remission (CRP) were restored in 63%, 42% and 24% of patients (evaluated at T+1). Treatment intensification increased IFX trough concentrations from 1.2 μg/mL [0.3-3.6] at T0 to 3.6 μg/mL [0.5-10.2] at T+1 (P <.0001). Using a drug tolerant assay, ATI were detected in the T0 sample of 47% of patients. ATI negatively impacted the achieved IFX trough concentration (Spearman r −0.57, P <.0001) and the probability of clinical response (P = 0.034) at T+1. When ATI were quantifiable but <282 ng/mL eq. at T0, combined interval shortening and dose doubling was more effective for restoring therapeutic IFX trough concentrations (≥3 μg/mL at T+1) than dose doubling alone, which in turn was more effective than interval shortening alone (P <.001). Conclusion: Antibodies towards infliximab can guide clinical decision-making on treatment intensification. © 2017 John Wiley & Sons Ltd [less ▲]

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

Poster (2017, September 09)

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See detailDetermining fine population structure using iterative pruning
Chaichoompu, Kridsadakorn ULiege; 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 detailNormalization and correction for batch effects via RUV for RNA-seq data: practical implications for Breast Cancer Research
Debit, Ahmed ULiege; Wenric, Stéphane; JOSSE, Claire ULiege et al

Poster (2017, May)

The whole transcriptome contains information about nonsense, missense, silent, in-frame and frameshift mutations, as observed at whole-exome level, as well as splicing and (allelic) gene-expression ... [more ▼]

The whole transcriptome contains information about nonsense, missense, silent, in-frame and frameshift mutations, as observed at whole-exome level, as well as splicing and (allelic) gene-expression changes which are missed by DNA analysis. One important step in the analysis of gene expression data arising from RNA-seq is the detection of differential expression (DE) levels. Several methods are available and the choice is sometimes controversial. For a reliable DE analysis that reduces False Positive DE genes, and accurate estimation of gene expression levels, a good and suitable normalization approach (including correction for confounders) is mandatory. Several normalization methods have been proposed to correct for both within-sample and between-sample biases. RUV (Removing Unwanted Variation) is one of them and has the advantage to correct for batch effects including potentially unknown unwanted variation in gene expression. In this study, we present a comparison on real-life Illumina paired-end sequencing data for Estrogen-Receptor-Positive (ER+) Breast Cancer tissues versus matched controls between RUV (RUVg using in silico negative control genes) and more commonly used methods for RNA-seq data normalization, such as DESeq2, edgeR, and UQ. The set of in silico empirical negative control genes for RUVg was defined as the set of least significant DE genes obtained after a first DE analysis performed prior to RUVg correction. Box plots of relative log expression (RLE) among the samples and PCA plots show that RUVg performs well and leads to a stabilization of read count across samples with a clear clustering of biological replicates. [less ▲]

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

Poster (2017, February 01)

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See detailPit pattern analysis with high-definition chromoendoscopy and narrow-band imaging for optical diagnosis of dysplasia in patients with ulcerative colitis
Bisschops, R.; Bessissow, T.; Dekker, E. et al

in Gastrointestinal Endoscopy (2017), 86(6), 1100-11061

Background and Aims Patients with longstanding ulcerative colitis (UC) are at increased risk of developing colorectal neoplasia. Chromoendoscopy (CE) increases detection of lesions, and Kudo pit pattern ... [more ▼]

Background and Aims Patients with longstanding ulcerative colitis (UC) are at increased risk of developing colorectal neoplasia. Chromoendoscopy (CE) increases detection of lesions, and Kudo pit pattern classification I and II have been suggested to be predictive of benign polyps in UC. Little is known on the use of this classification in nonmagnified high-definition (HD) (virtual) CE and narrow-band Imaging (NBI) or on the interobserver agreement. The aim of this pilot study was to assess the diagnostic accuracy and the interobserver agreement of the Kudo pit pattern classification in UC patients undergoing surveillance with methylene blue CE or NBI in a multicenter study. Methods Fifty images of lesions identified in 27 UC patients (13 neoplastic) either with classical CE (methylene blue.1%; n = 24) or NBI (n = 26) were selected by an independent investigator. Images were selected from a randomized controlled trial to compare CE and NBI. All nonmagnified images were obtained with a processor and mounted in a PowerPoint file in a standardized way (same size; black background). Ten endoscopists with extensive experience in NBI/CE were asked to assess the lesions for the predominant Kudo pit pattern (I, II, IIIL, IIIS, IV, and V) to indicate if they believed the lesion was neoplastic and how confident they were about the diagnosis. Histology was used as the criterion standard. Results Median sensitivity, specificity, negative predictive value, and positive predictive value for diagnosing neoplasia based on the presence of pit pattern other than I or II was 77%, 68%, 88%, and 46%, respectively. Diagnostic accuracy was significantly higher when a diagnosis was made with a high level of confidence (77% vs 21%, P <.001). The overall interobserver agreement for any pit pattern was only fair (κ =.282), with CE being significantly better than NBI (.322 vs.224, P <.001). From a clinical viewpoint the difference between neoplastic and non-neoplastic lesions is important. The agreement for differentiation between non-neoplastic patterns (I, II) and neoplastic patterns (IIIL, IIIS, IV, or V) was moderate (κ =.587) and even significantly better for NBI in comparison with CE (κ =.653 vs.495, P <.001). Conclusions Differentiation between non-neoplastic and neoplastic pit patterns in UC lesions shows a moderate to substantial agreement among expert endoscopists. The agreement for differentiating neoplastic from non-neoplastic lesions is significantly better for NBI in comparison with HD CE. The assessment of pit pattern I or II with nonmagnified HD CE or NBI has a high negative predictive value to rule out neoplasia. (Clinical trial registration number: NCT01882205.) © 2017 American Society for Gastrointestinal Endoscopy [less ▲]

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See detailThe association between etanercept serum concentration and psoriasis severity is highly age-dependent
Detrez, I; Van Steen, Kristel ULiege; Segaert, S et al

in Clinical Science (2017), 131(11), 1179-1189

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See detailIntegrative eQTL analysis of tumor and host omics data in individuals with bladder cancer
Pineda, S.; Van Steen, Kristel ULiege; Malats, N.

in Genetic Epidemiology (2017), 41(6), 567-573

Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating ... [more ▼]

Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood. Previously, we demonstrated that penalized regression methods in combination with a permutation-based MaxT method (Global-LASSO) is a promising tool to fix some of the challenges that high-throughput omics data analysis imposes. Here, we assessed whether Global-LASSO can also be applied when tumor and blood omics data are integrated. We further compared our strategy with two 2SR-approaches, one using multiple linear regression (2SR-MLR) and other using LASSO (2SR-LASSO). We applied the three models to integrate genomic, epigenomic, and transcriptomic data from tumor tissue with blood germline genotypes from 181 individuals with bladder cancer included in the TCGA Consortium. Global-LASSO provided a larger list of eQTLs than the 2SR methods, identified a previously reported eQTLs in prostate stem cell antigen (PSCA), and provided further clues on the complexity of APBEC3B loci, with a minimal false-positive rate not achieved by 2SR-MLR. It also represents an important contribution for omics integrative analysis because it is easy to apply and adaptable to any type of data. © 2017 WILEY PERIODICALS, INC. [less ▲]

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See detailSpecific members of the predominant gut microbiota predict pouchitis following colectomy and IPAA in UC
Machiels, K.; Sabino, J.; Vandermosten, L. et al

in Gut (2017), 66(1), 79-88

OBJECTIVE: Pouchitis is the most common complication after colectomy with ileal pouch-anal anastomosis (IPAA) for UC and the risk is the highest within the 1st year after surgery. The pathogenesis is not ... [more ▼]

OBJECTIVE: Pouchitis is the most common complication after colectomy with ileal pouch-anal anastomosis (IPAA) for UC and the risk is the highest within the 1st year after surgery. The pathogenesis is not completely understood but clinical response to antibiotics suggests a role for gut microbiota. We hypothesised that the risk for pouchitis can be predicted based on the faecal microbial composition before colectomy.DESIGN: Faecal samples from 21 patients with UC undergoing IPAA were prospectively collected before colectomy and at predefined clinical visits at 1 month, 3 months, 6 months and 12 months after IPAA. The predominant microbiota was analysed using community profiling with denaturing gradient gel electrophoresis followed by quantitative real-time PCR validation.RESULTS: Cluster analysis before colectomy distinguished patients with pouchitis from those with normal pouch during the 1st year of follow-up. In patients developing pouchitis, an increase of Ruminococcus gnavus (p<0.001), Bacteroides vulgatus (p=0.043), Clostridium perfringens (p=0.011) and a reduction of two Lachnospiraceae genera (Blautia (p=0.04), Roseburia (p=0.008)) was observed. A score combining these five bacterial risk factors was calculated and presence of at least two risk factors showed a sensitivity and specificity of 100% and 63.6%, respectively.CONCLUSIONS: Presence of R. gnavus, B. vulgatus and C. perfringens and absence of Blautia and Roseburia in faecal samples of patients with UC before surgery is associated with a higher risk of pouchitis after IPAA. Our findings suggest new predictive and therapeutic strategies in patients undergoing colectomy with IPAA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/. [less ▲]

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See detailA systematic SNP selection approach to identify mechanisms underlying disease aetiology: Linking height to post-menopausal breast and colorectal cancer risk
Elands, R. J. J.; Simons, C. C. J. M.; Riemenschneider, M. et al

in Scientific Reports (2017), 7

Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for ... [more ▼]

Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10-5 and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10-7) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC. © The Author(s) 2017. [less ▲]

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