Andreas Ziegler

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

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Publications (559)3013.65 Total impact

  • Marvin N. Wright · Andreas Ziegler
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    ABSTRACT: We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and compare runtime and memory usage with other implementations. The new software proves to scale best with the number of features, samples, trees, and features tried for splitting. Finally, we show that ranger is the fastest and most memory efficient implementation of random forests to analyze data on the scale of a genome-wide association study.
  • 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 07/2015; 79(3-4):194-204. DOI:10.1159/000381338 · 1.64 Impact Factor
  • Henry Mwambi · Andreas Ziegler
    Biometrical Journal 06/2015; DOI:10.1002/bimj.201500059 · 1.24 Impact Factor
  • 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
  • Andreas Ziegler · Henry Mwambi
    Biometrical Journal 02/2015; 57(3). DOI:10.1002/bimj.201500014 · 1.24 Impact Factor
  • Oscar Ngesa · Andreas Ziegler
    Biometrical Journal 02/2015; 57(3). DOI:10.1002/bimj.201500015 · 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
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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: Providing less invasive surfactant administration (LISA) to spontaneously breathing preterm infants has been reported to reduce mechanical ventilation and bronchopulmonary dysplasia (BPD) in randomised controlled trials. This large cohort study compared these outcome measures between LISA treated infants and controls. Infants receiving LISA, who were born before 32 gestational weeks and enrolled in the German Neonatal Network, were matched to control infants by gestational age, umbilical cord pH, Apgar-score at five minutes, small for gestational age status, antenatal treatment with steroids, gender and highest supplemental oxygen during the first 12 hours of life. Outcome data were compared with chi-square and Mann-Whitney U tests and adjusted for multiple comparisons. Between 2009 and 2012, 1,103 infants were treated with LISA at 37 centres. LISA infants had lower rates of mechanical ventilation (41% versus 62%, p<0.001), postnatal dexamethasone treatment (2.5% versus 7%, p<0.001), BPD (12% versus 18%, p=0.001) and BPD or death (14% versus 21%, p<0.001) than the controls. Surfactant treatment of spontaneously breathing infants was associated with lower rates of mechanical ventilation and BPD. Additional large scale randomised controlled trials are needed to assess the possible long-term benefits of LISA. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Acta paediatrica (Oslo, Norway: 1992). Supplement 12/2014; 104(3). DOI:10.1111/apa.12883
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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
  • Andreas Ziegler · Nora Bohossian · Vincent P Diego · Chen Yao
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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(S1):S57-62. DOI:10.1002/gepi.21826 · 2.95 Impact Factor
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    ABSTRACT: Background and purpose 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. Materials and methods Patients receiving breast-conserving surgery and radiotherapy were examined either for erythema (n = 83), fibrosis (n = 123), or individual radiosensitivity (n = 123). The 17 SNPs analyzed are involved in the ROS pathway (GSTP1, SOD2, NQO1, NOS3, XDH), DNA repair (XRCC1, XRCC3, XRCC6, ERCC2, LIG4, ATM) or TGFB signaling (SKIL, EP300, APC, AXIN1, TGFB1). Associations with biological and clinical endpoints were studied for single SNPs but especially for combinations of SNPs assuming that a SNP is either beneficial or deleterious and needs to be weighted. Results With one exception, no significant association was seen between a single SNP and the three endpoints studied. No significant associations were also observed when applying a multi-SNP model assuming that each SNP was deleterious. In contrast, significant associations were obtained when SNPs were suggested to be either beneficial or deleterious. These associations increased, when each SNP was weighted individually. Detailed analysis revealed that both erythema and individual radiosensitivity especially depend on SNPs affecting DNA repair and TGFB1 signaling, while SNPs in ROS pathway were of minor importance. Conclusion Functional pathways of SNPs may be used to form a risk score allowing to predict acute and late radiation-induced toxicity but also to unravel the underlying biological mechanisms.
    Strahlentherapie und Onkologie 08/2014; 191(1). DOI:10.1007/s00066-014-0741-y · 2.73 Impact Factor
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    ABSTRACT: Background The mitochondrial m.1555A>G mutation is associated with a high rate of permanent hearing loss, if aminoglycosides are given. Preterm infants have an increased risk of permanent hearing loss and are frequently treated with aminoglycoside antibiotics. Methods We genotyped preterm infants with a birth weight below 1500 grams who were prospectively enrolled in a large cohort study for the m.1555A>G mutation. Treatment with aminoglycoside antibiotics in combination with mitochondrial m.1555A>G mutation was tested as a predictor for failed hearing screening at discharge in a multivariate logistic regression analysis. Results 7056 infants were genotyped and analysed. Low birth weight was the most significant predictor of failed hearing screening (p = 7.3 × 10-10). 12 infants (0.2%) had the m.1555A>G-mutation. In a multivariable logistic regression analysis, the combination of aminoglycoside treatment with m.1555A>G-carrier status was associated with failed hearing screening (p = 0.0058). However, only 3 out of 10 preterm m.1555A>G-carriers who were exposed to aminoglycosides failed hearing screening. The m.1555A>G-mutation was detected in all mothers of m.1555A>G-positive children, but in none of 2993 maternal DNA-samples of m.1555A>G-negative infants. Conclusion Antenatal screening for the m.1555A>G mutation by maternal genotyping of pregnant women with preterm labour might be a reasonable approach to identify infants who are at increased risk for permanent hearing loss. Additional studies are needed to estimate the relevance of cofactors like aminoglycoside plasma levels and birth weight and the amount of preterm m.1555A>G-carriers with permanent hearing loss.
    BMC Pediatrics 08/2014; 14(1):210. DOI:10.1186/1471-2431-14-210 · 1.92 Impact Factor

Publication Stats

21k Citations
3,013.65 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
      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
    • Boston University
      Boston, Massachusetts, United States
    • Central Institute of Mental Health
      Mannheim, Baden-Württemberg, Germany
  • 2003–2008
    • Technische Universität Dresden
      • Department of Surgery Research
      Dresden, Saxony, Germany
    • The University of Sheffield
      Sheffield, England, United Kingdom
    • National Cancer Institute (USA)
      Maryland, United States
  • 1986–2008
    • University of Tuebingen
      Tübingen, Baden-Württemberg, Germany
  • 2007
    • Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG)
      Köln, North Rhine-Westphalia, Germany
    • University of Bonn
      Bonn, North Rhine-Westphalia, Germany
  • 1996–2007
    • 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
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 2006
    • University of Bonn - Medical Center
      Bonn, North Rhine-Westphalia, Germany
  • 2001–2004
    • Carl Gustav Carus-Institut
      Pforzheim, Baden-Württemberg, Germany
  • 1993
    • University Hospital München
      München, Bavaria, Germany