Andreas Ziegler

University of KwaZulu-Natal, Port Natal, KwaZulu-Natal, South Africa

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Publications (549)2966.26 Total impact

  • Senologie - Zeitschrift für Mammadiagnostik und -therapie 05/2015; 12(02). DOI:10.1055/s-0035-1550460
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    ABSTRACT: Cutaneous lupus erythematosus (CLE) is a chronic autoimmune disease of the skin with typical clinical manifestations. Here, we genotyped 906,600 single nucleotide polymorphisms (SNPs) in 183 CLE cases and 1288 controls of Central European ancestry. Replication was performed for 13 SNPs in 219 case subjects and 262 controls from Finland. Association was particularly pronounced at 4 loci, all with genome-wide significance (P ≤ 5 x 10(-8) ): rs2187668 (PGWAS = 1.4 x 10(-12) ); rs9267531 (PGWAS = 4.7 x 10(-10) ); rs4410767 (PGWAS = 1.0 x 10(-9) ); rs3094084 (PGWAS = 1.1 x 10(-9) ). All mentioned SNPs are located within the major histocompatibility complex (MHC) region of chromosome 6 and near genes of known immune functions or associations with other autoimmune diseases such as HLA-DQ alpha chain 1 (HLADQA1), MICA, MICB, MSH5, TRIM39, and RPP21. E.g., TRIM39-RPP21 read through transcript is known mediator of the interferon response, a central pathway involved in the pathogenesis of CLE and systemic lupus erythematosus (SLE). Taken together, this genome-wide analysis of disease-association of CLE identified candidate genes and genomic regions that may contribute to pathogenic mechanisms in CLE via dysregulated antigen presentation (HLADQA1), apoptosis regulation, RNA processing and interferon response (MICA, MICB, MSH5, TRIM39, RPP21). This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Experimental Dermatology 04/2015; DOI:10.1111/exd.12708 · 4.12 Impact Factor
  • Marvin N. Wright, Andreas Ziegler
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    ABSTRACT: Caries infiltration is a novel treatment option for proximal caries lesions. The idea is to build a diffusion barrier inside the lesion to slow down or stop the caries progression. If a lesion still reaches a critical size, restorative treatment is required. Clinical trials investigating caries infiltration thus produce multiple censored ordinal data. Standard statistical models do not take into account this censoring, and we therefore propose the Multiple Ordered Tobit (MOT) model. The model is implemented in R and compared with standard approaches. Simulation studies demonstrate that for all sample sizes and scenarios the MOT model has the largest statistical power among all methods compared, and it is robust against heteroscedasticity to some extent. Finally, a comparison with dichotomous and ordinal scaled models shows that the use of metric data for the lesion size reduces the required sample size considerably. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
    Biometrical Journal 03/2015; 57(3). DOI:10.1002/bimj.201400118 · 1.24 Impact Factor
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    ABSTRACT: Background: We report the development of a cutaneous melanoma risk algorithm based upon 7 factors; hair colour, skin type, family history, freckling, nevus count, number of large nevi and history of sunburn, intended to form the basis of a self-assessment webtool for the general public. Methods: Predicted odds of melanoma were estimated by analysing a pooled dataset from 16 case-control studies using logistic random coefficients models. Risk categories were defined based on the distribution of the predicted odds in the controls from these studies. Imputation was used to estimate missing data in the pooled datasets. The 30th, 60th and 90th centiles were used to distribute individuals into four risk groups for their age, sex and geographic location. Cross-validation was used to test the robustness of the thresholds for each group by leaving out each study one by one. Performance of the model was assessed in an independent UK case-control study dataset. Results: Cross-validation confirmed the robustness of the threshold estimates. Cases and controls were well discriminated in the independent dataset (area under the curve 0.75, 95% CI 0.73-0.78). 29% of cases were in the highest risk group compared with 7% of controls, and 43% of controls were in the lowest risk group compared with 13% of cases. Conclusions: We have identified a composite score representing an estimate of relative risk and successfully validated this score in an independent dataset. Impact: This score may be a useful tool to inform members of the public about their melanoma risk. Copyright © 2015, American Association for Cancer Research.
    Cancer Epidemiology Biomarkers & Prevention 02/2015; 24(5). DOI:10.1158/1055-9965.EPI-14-1062 · 4.32 Impact Factor
  • Oscar Ngesa, Andreas Ziegler
    Biometrical Journal 02/2015; 57(3). DOI:10.1002/bimj.201500015 · 1.24 Impact Factor
  • Andreas Ziegler, Henry Mwambi
    Biometrical Journal 02/2015; 57(3). DOI:10.1002/bimj.201500014 · 1.24 Impact Factor
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    ABSTRACT: To the editor,Heo et al. (2014) recently reported male-specific associations between hypertension and 6 SNPs [rs2093395 (TREML2), rs17249754 (ATP2B1), rs12229654 (MYL2), rs3782889 (MYL2), rs11066280 (C12orf51), rs2072134 (OAS3)] in two Korean cohorts with a total of 3,551 cases and 4,725 controls who were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Five of the six reported SNPs were claimed to show a male-specific association with hypertension. We question the validity of the findings.The main results of Heo et al. displayed in Table 2 of the article are weakened by two methodological aspects. First, the KARE data consist of two cohorts, one from rural Ansung and one from urban Ansan. In the non-genetic analysis, the variable indicating region is significantly associated with hypertension (p = 2.1 × 10−22) with an effect size of OR = 1.86 (1.64, 2.11) (main article, Table 1). However, this variable is not included in this analysis (‘‘… data were adjusted for age, se ...
    Human Genetics 01/2015; 134(3). DOI:10.1007/s00439-014-1523-4 · 4.52 Impact Factor
  • Andreas Ziegler, Henry Mwambi, Inke R König
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    ABSTRACT: The term Mendelian randomization is popular in the current literature. The first aim of this work is to describe the idea of Mendelian randomization studies and the assumptions required for drawing valid conclusions. The second aim is to contrast Mendelian randomization and path modeling when different 'omics' levels are considered jointly. We define Mendelian randomization as introduced by Katan in 1986, and review its crucial assumptions. We introduce path models as the relevant additional component to the current use of Mendelian randomization studies in 'omics'. Real data examples for the association between lipid levels and coronary artery disease illustrate the use of path models. Numerous assumptions underlie Mendelian randomization, and they are difficult to be fulfilled in applications. Path models are suitable for investigating causality, and they should not be mixed up with the term Mendelian randomization. In many applications, path modeling would be the appropriate analysis in addition to a simple Mendelian randomization analysis. Mendelian randomization and path models use different concepts for causal inference. Path modeling but not simple Mendelian randomization analysis is well suited to study causality with different levels of 'omics' data. © 2015 S. Karger AG, Basel.
    Human Heredity 01/2015; 79(3-4):194-204. DOI:10.1159/000381338 · 1.64 Impact Factor
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    ABSTRACT: We assessed whether type 1 diabetes (T1D) can be diagnosed earlier using a new approach based on prediction and natural history in autoantibody-positive individuals. Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Natural History Study (TNNHS) participants were studied. A metabolic index, the T1D Diagnostic Index60 (Index60), was developed from 2-h oral glucose tolerance tests (OGTTs) using the log fasting C-peptide, 60-min C-peptide, and 60-min glucose. OGTTs with Index60 ≥2.00 and 2-h glucose <200 mg/dL (Ind60+Only) were compared with Index60 <2.00 and 2-h glucose ≥200 mg/dL (2hglu+Only) OGTTs as criteria for T1D. Individuals were assessed for C-peptide loss from the first Ind60+Only OGTT to diagnosis. Areas under receiver operating characteristic curves were significantly higher for Index60 than for the 2-h glucose (P < 0.001 for both DPT-1 and the TNNHS). As a diagnostic criterion, sensitivity was higher for Ind60+Only than for 2hglu+Only (0.44 vs. 0.15 in DPT-1; 0.26 vs. 0.17 in the TNNHS) OGTTs. Specificity was somewhat higher for 2hglu+Only OGTTs in DPT-1 (0.97 vs. 0.91) but equivalent in the TNNHS (0.98 for both). Positive and negative predictive values were higher for Ind60+Only OGTTs in both studies. Postchallenge C-peptide levels declined significantly at each OGTT time point from the first Ind60+Only OGTT to the time of standard diagnosis (range -22 to -34% in DPT-1 and -14 to -27% in the TNNHS). C-peptide and glucose patterns differed markedly between Ind60+Only and 2hglu+Only OGTTs. An approach based on prediction and natural history appears to have utility for diagnosing T1D. © 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
    Diabetes Care 12/2014; 38(2). DOI:10.2337/dc14-1813 · 8.57 Impact Factor
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    ABSTRACT: This article is part of a For-Discussion-Section of Methods of Information in Medicine about the papers "The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling" [1] and "Extending Statistical Boosting - An Overview of Recent Methodological Developments" [2], written by Andreas Mayr and co-authors. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Mayr et al. papers. In subsequent issues the discussion can continue through letters to the editor.
    Methods of Information in Medicine 11/2014; 53(6):436-445. DOI:10.3414/13100122 · 1.08 Impact Factor
  • Owino Ngesa, Andreas Ziegler
    Biometrical Journal 11/2014; 57(3). DOI:10.1002/bimj.201400228 · 1.24 Impact Factor
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    ABSTRACT: -Dimethylarginines (DMA) interfere with nitric oxide (NO) formation by inhibiting NO synthase (asymmetric dimethylarginine, ADMA) and L-arginine uptake into the cell (ADMA and symmetric dimethylarginine, SDMA). In prospective clinical studies ADMA has been characterized as a cardiovascular risk marker whereas SDMA is a novel marker for renal function and associated with all-cause mortality after ischemic stroke. The aim of the current study was to characterise the environmental and genetic contributions to inter-individual variability of these biomarkers.
    Circulation Cardiovascular Genetics 09/2014; 7(6). DOI:10.1161/CIRCGENETICS.113.000264 · 6.73 Impact Factor
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    ABSTRACT: Biomarkers are considered as tools to enhance cardiovascular risk estimation. However, the value of biomarkers on risk estimation beyond European risk scores, their comparative impact among different European regions and their role towards personalised medicine remains uncertain. Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) is an European collaborative research project with the primary objective to assess the value of established and emerging biomarkers for cardiovascular risk prediction. BiomarCaRE integrates clinical and epidemiological biomarker research and commercial enterprises throughout Europe to combine innovation in biomarker discovery for cardiovascular disease prediction with consecutive validation of biomarker effectiveness in large, well-defined primary and secondary prevention cohorts including over 300,000 participants from 13 European countries. Results from this study will contribute to improved cardiovascular risk prediction across different European populations. The present publication describes the rationale and design of the BiomarCaRE project. Electronic supplementary material The online version of this article (doi:10.1007/s10654-014-9952-x) contains supplementary material, which is available to authorized users.
    European Journal of Epidemiology 09/2014; 29(10). DOI:10.1007/s10654-014-9952-x · 5.15 Impact Factor
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    ABSTRACT: The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.
    Frontiers in Genetics 09/2014; 5:323. DOI:10.3389/fgene.2014.00323
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    ABSTRACT: High-throughput sequencing data can be used to predict phenotypes from genotypes, and this corresponds to establishing a prognostic model. In extended pedigrees the relatedness of subjects provides additional information so that genetic values, fixed or random genetic components, and heritability can be estimated. At the Genetic Analysis Workshop 18, the working group on genetic prediction dealt with both establishing a prognostic model and, in one contribution, comparing standard logistic regression with robust logistic regression in a sample of unrelated affected or unaffected individuals. Results of both logistic regression approaches were similar. All other contributions to this group used extended family data, in general using the quantitative trait blood pressure. The individual contributions varied in several important aspects, such as the estimation of the kinship matrix and the estimation method. Contributors chose various approaches for model validation, including different versions of cross-validation or within-family validation. Within-family validation included model building in the upper generations and validation in later generations. The choice of the statistical model and the computational algorithm had substantial effects on computation time. If decorrelation approaches were applied, the computational burden was substantially reduced. Some software packages estimated negative eigenvalues, although eigenvalues of correlation matrices should be non-negative. Most statistical models and software packages have been developed for experimental crosses and planned breeding programs. With their specialized pedigree structures, they are not sufficiently flexible to accommodate the variability of human pedigrees in general, and improved implementations are required.
    Genetic Epidemiology 09/2014; 38 Suppl 1:S57-62. DOI:10.1002/gepi.21826 · 2.95 Impact Factor
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    ABSTRACT: The aim of this study was to determine the impact of functional single nucleotide polymorphism (SNP) pathways involved in the ROS pathway, DNA repair, or TGFB1 signaling on acute or late normal toxicity as well as individual radiosensitivity.
    Strahlentherapie und Onkologie 08/2014; 191(1). DOI:10.1007/s00066-014-0741-y · 2.73 Impact Factor
  • Andreas Ziegler, Inke R König
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    ABSTRACT: We review the scope of the scientific discipline genetic epidemiology by considering the steps of genetic epidemiologic research. Starting from the classical definition of genetic epidemiology as provided by Morton and Chung [1978, ISBN-13: 9780125080507], we propose a slightly modernized definition of the term genetic epidemiology.
    Genetic Epidemiology 07/2014; 38(5):379-80. DOI:10.1002/gepi.21816 · 2.95 Impact Factor
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    ABSTRACT: Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi: 10.1002/bimj.201300077).
    Biometrical Journal 07/2014; 56(4). DOI:10.1002/bimj.201300068 · 1.24 Impact Factor
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    ABSTRACT: Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.
    BMC proceedings 06/2014; 8(1):S1. DOI:10.1186/1753-6561-8-S1-S1
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    ABSTRACT: Sequencing technologies have enabled the investigation of whole genomes of many individuals in parallel. Studies have shown that the joint consideration of multiple rare variants may explain a relevant proportion of the genetic basis for disease so that grouping of rare variants, termed collapsing, can enrich the association signal. Following this assumption, we investigate the type I error and the power of two proposed collapsing methods (combined multivariate and collapsing method and the functional principal component analysis [FPCA]-based statistic) using the case-control data provided for the Genetic Analysis Workshop 18 with knowledge of the true model. Variants with a minor allele frequency (MAF) of 0.05 or less were collapsed per gene for combined multivariate and collapsing. Neither of the methods detected any of the truly associated genes reliably. Although combined multivariate and collapsing identified one gene with a power of 0.66, it had an unacceptably high false-positive rate of 75%. In contrast, FPCA covered the type I error level well but at the cost of low power. A strict filtering of variants by small MAF might lead to a better performance of the collapsing methods. Furthermore, the inclusion of information on functionality of the variants could be helpful.
    BMC proceedings 06/2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S8. DOI:10.1186/1753-6561-8-S1-S8

Publication Stats

21k Citations
2,966.26 Total Impact Points

Institutions

  • 2014–2015
    • University of KwaZulu-Natal
      • School of Mathematics, Statistics and Computer Science
      Port Natal, KwaZulu-Natal, South Africa
  • 2005–2015
    • Universitätsklinikum Schleswig - Holstein
      • Institut für Medizinische Biometrie und Statistik (Lübeck)
      Kiel, Schleswig-Holstein, Germany
  • 2002–2015
    • Universität zu Lübeck
      • • Institut für Medizinische Biometrie und Statistik
      • • Department of Surgery
      • • Institut für Medizinische Informatik
      Lübeck Hansestadt, Schleswig-Holstein, Germany
  • 2005–2014
    • University Medical Center Schleswig-Holstein
      Kiel, Schleswig-Holstein, Germany
  • 2004–2013
    • Charité Universitätsmedizin Berlin
      • Institute of Immunogenetics
      Berlín, Berlin, Germany
  • 1991–2013
    • Freie Universität Berlin
      Berlín, Berlin, Germany
  • 2012
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
  • 2009
    • Technische Universität München
      München, Bavaria, Germany
    • Central Institute of Mental Health
      Mannheim, Baden-Württemberg, Germany
    • Boston University
      Boston, Massachusetts, United States
  • 2008
    • Bernhard Nocht Institute for Tropical Medicine
      • Department of Molecular Medicine
      Hamburg, Hamburg, Germany
  • 2003–2008
    • Technische Universität Dresden
      • Department of Surgery Research
      Dresden, Saxony, Germany
    • National Cancer Institute (USA)
      Maryland, United States
    • Ruhr-Universität Bochum
      Bochum, North Rhine-Westphalia, Germany
    • Universitätsklinikum Knappschaftskrankenhaus Bochum
      Bochum, North Rhine-Westphalia, Germany
    • The University of Sheffield
      Sheffield, England, United Kingdom
  • 1986–2008
    • University of Tuebingen
      Tübingen, Baden-Württemberg, Germany
  • 2007
    • University of Leicester
      • Department of Cardiovascular Sciences
      Leiscester, England, United Kingdom
    • Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG)
      Köln, North Rhine-Westphalia, Germany
    • University of Bonn
      Bonn, North Rhine-Westphalia, Germany
  • 1997–2007
    • Philipps University of Marburg
      • • Institut für Medizinische Biometrie und Epidemiologie
      • • Klinik für Strahlendiagnostik (Marburg)
      • • Klinik für Kinder- und Jugendpsychiatrie und -psychotherapie (Marburg)
      Marburg, Hesse, Germany
  • 1996–2007
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 2006
    • Universität Regensburg
      Ratisbon, Bavaria, Germany
    • Max Planck Institute for Infection Biology
      • Department of Immunology
      Berlin, Land Berlin, Germany
  • 2001–2004
    • Carl Gustav Carus-Institut
      Pforzheim, Baden-Württemberg, Germany
  • 1993
    • University Hospital München
      München, Bavaria, Germany