Kneib, Thomas

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Wildlife Biology"],["dc.bibliographiccitation.volume","2019"],["dc.contributor.author","Signer, Johannes"],["dc.contributor.author","Filla, Marc"],["dc.contributor.author","Schoneberg, Sebastian"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Bufka, Ludek"],["dc.contributor.author","Belotti, Elisa"],["dc.contributor.author","Heurich, Marco"],["dc.date.accessioned","2019-07-09T11:50:45Z"],["dc.date.available","2019-07-09T11:50:45Z"],["dc.date.issued","2019"],["dc.description.abstract","Eurasian lynx Lynx lynx L. are recolonizing parts of their former range in Europe. Not only are lynx strictly protected as a species, but also their habitat and in particular their resting sites are protected. As the known characteristics of lynx resting sites are restricted to vegetation structure, it is difficult to take resting sites into account in planning processes. Here, we show the importance of rock formations for potential resting sites selection and analyzed the frequencies at which GPS-collared lynx returned to potential resting sites in the Bohemian Forest Ecosystem at the border between the Czech Republic and Germany. Lynx showed a strong selection for proximity of rocks for resting site selection, and the distance of potential resting sites to rocks was an important predictor for determining whether lynx return to the resting site or not. Furthermore, the frequency of returns to the resting site was positively influenced by the distance to roads and geomorphology. Our findings highlight the importance of rock formations as resting sites for lynx, which can help with the implementation of concrete protection measures."],["dc.identifier.doi","10.2981/wlb.00489"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15992"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59822"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","0909-6396"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","519"],["dc.title","Rocks rock: the importance of rock formations as resting sites of the Eurasian lynx Lynx lynx"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"],["local.message.claim","2021-10-06T08:18:57.112+0000|||rp114797|||submit_approve|||dc_contributor_author|||None"]]

[["dc.bibliographiccitation.firstpage","6742763"],["dc.bibliographiccitation.journal","Computational and mathematical methods in medicine"],["dc.bibliographiccitation.lastpage","17"],["dc.bibliographiccitation.volume","2017"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Burger, Patricia"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Wichmann, Heinz-Erich"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Hofner, Benjamin"],["dc.date.accessioned","2018-03-13T14:55:43Z"],["dc.date.available","2018-03-13T14:55:43Z"],["dc.date.issued","2017"],["dc.description.abstract","The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility."],["dc.identifier.doi","10.1155/2017/6742763"],["dc.identifier.pmid","28785300"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14774"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13019"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1748-6718"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","527"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","545"],["dc.bibliographiccitation.volume","22"],["dc.contributor.affiliation","Stadlmann, Stanislaus; 1Chairs of Statistics and Econometrics, Georg-August University Göttingen, Germany"],["dc.contributor.affiliation","Kneib, Thomas; 1Chairs of Statistics and Econometrics, Georg-August University Göttingen, Germany"],["dc.contributor.author","Stadlmann, Stanislaus"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2021-07-05T14:57:48Z"],["dc.date.available","2021-07-05T14:57:48Z"],["dc.date.issued","2021"],["dc.date.updated","2022-11-11T13:12:53Z"],["dc.description.abstract","A newly emerging field in statistics is distributional regression, where not only the mean but each parameter of a parametric response distribution can be modelled using a set of predictors. As an extension of generalized additive models, distributional regression utilizes the known link functions (log, logit, etc.), model terms (fixed, random, spatial, smooth, etc.) and available types of distributions but allows us to go well beyond the exponential family and to model potentially all distributional parameters. Due to this increase in model flexibility, the interpretation of covariate effects on the shape of the conditional response distribution, its moments and other features derived from this distribution is more challenging than with traditional mean-based methods. In particular, such quantities of interest often do not directly equate the modelled parameters but are rather a (potentially complex) combination of them. To ease the post-estimation model analysis, we propose a framework and subsequently feature an implementation in R for the visualization of Bayesian and frequentist distributional regression models fitted using the bamlss, gamlss and betareg R packages."],["dc.description.abstract","A newly emerging field in statistics is distributional regression, where not only the mean but each parameter of a parametric response distribution can be modelled using a set of predictors. As an extension of generalized additive models, distributional regression utilizes the known link functions (log, logit, etc.), model terms (fixed, random, spatial, smooth, etc.) and available types of distributions but allows us to go well beyond the exponential family and to model potentially all distributional parameters. Due to this increase in model flexibility, the interpretation of covariate effects on the shape of the conditional response distribution, its moments and other features derived from this distribution is more challenging than with traditional mean-based methods. In particular, such quantities of interest often do not directly equate the modelled parameters but are rather a (potentially complex) combination of them. To ease the post-estimation model analysis, we propose a framework and subsequently feature an implementation in R for the visualization of Bayesian and frequentist distributional regression models fitted using the bamlss, gamlss and betareg R packages."],["dc.identifier.doi","10.1177/1471082X211007308"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87739"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-441"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Interactively visualizing distributional regression models with distreg.vis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","123"],["dc.bibliographiccitation.journal","Procedia Environmental Sciences"],["dc.bibliographiccitation.lastpage","126"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Ríos-Pena, Laura"],["dc.contributor.author","Cadarso-Suárez, Carmen"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Pérez, Manuel"],["dc.date.accessioned","2017-09-07T11:47:13Z"],["dc.date.available","2017-09-07T11:47:13Z"],["dc.date.issued","2015"],["dc.description.abstract","Studies on causes and dynamics of wildfires make an important contribution to environmental. In the north of Spain, Galicia is one of the areas in which wildfires are the main cause of forest destruction. The main aim of this work is to model geographical and environmental effects on the risk of wildfires in Galicia using flexible regression techniques based on Structured Additive Regression (STAR) models. This methodology represents a new contribution to the classical logistic Generalized Linear Models (GLM) and Generalized Additive Models (GAM), commonly used in this environmental context. Their advantage lies on the flexibility of including spatial and temporal covariates, jointly with the other continuous covariates information. Moreover, these models generate maps of both structured and the unstructured effects, and they plotted separately. Working at spatial scales with a voxel resolution level of 1Km x 1Km per day, with the possibility of mapping the predictions in a color range, the binary STAR model represents an important tool for planning and management for the prevention of wildfires. Also, this statistical tool can accelerate the progress of fire behavior models that can be very useful for developing plans of prevention and firefighting."],["dc.identifier.doi","10.1016/j.proenv.2015.07.121"],["dc.identifier.gro","3149299"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13649"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5960"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.orgunit","Wirtschaftswissenschaftliche Fakultät"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0/"],["dc.title","Applying Binary Structured Additive Regression (STAR) for Predicting Wildfire in Galicia, Spain"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","422"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Urologia Internationalis"],["dc.bibliographiccitation.lastpage","428"],["dc.bibliographiccitation.volume","95"],["dc.contributor.author","Winter, Alexander"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Rohde, Martin"],["dc.contributor.author","Henke, Rolf-Peter"],["dc.contributor.author","Wawroschek, Friedhelm"],["dc.date.accessioned","2017-09-07T11:47:15Z"],["dc.date.available","2017-09-07T11:47:15Z"],["dc.date.issued","2015"],["dc.description.abstract","Introduction: Existing nomograms predicting lymph node involvement (LNI) in prostate cancer (PCa) are based on conventional lymphadenectomy. The aim of the study was to develop the first nomogram for predicting LNI in PCa patients undergoing sentinel guided pelvic lymph node dissection (sPLND). Materials and Methods: Analysis was performed on 1,296 patients with PCa who underwent radioisotope guided sPLND and retropubic radical prostatectomy (2005-2010). Median prostate specific antigen (PSA): 7.4 ng/ml (IQR 5.3-11.5 ng/ml). Clinical T-categories: T1: 54.8%, T2: 42.4%, T3: 2.8%. Biopsy Gleason sums: ≤6: 55.1%, 7: 39.5%, ≥8: 5.4%. Multivariate logistic regression models tested the association between all of the above predictors and LNI. Regression-based coefficients were used to develop a nomogram for predicting LNI. Accuracy was quantified using the area under the curve (AUC). Results: The median number of LNs removed was 10 (IQR 7-13). Overall, 17.8% of patients (n = 231) had LNI. The nomogram had a high predictive accuracy (AUC of 82%). All the variables were statistically significant multivariate predictors of LNI (p = 0.001). Univariate predictive accuracy for PSA, Gleason sum and clinical stage was 69, 75 and 69%, respectively. Conclusions: The sentinel nomogram can predict LNI at a sPLND very accurately and, for the first time, aid clinicians and patients in making important decisions on the indication of a sPLND. The high rate of LN+ patients underscores the sensitivity of sPLND."],["dc.identifier.doi","10.1159/000431182"],["dc.identifier.gro","3149305"],["dc.identifier.pmid","26159232"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5967"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","S. Karger AG"],["dc.relation.eissn","1423-0399"],["dc.relation.issn","0042-1138"],["dc.rights","https://www.karger.com/Services/SiteLicenses"],["dc.title","First Nomogram Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients Undergoing Radioisotope Guided Sentinel Lymph Node Dissection"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","259"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","270"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Glennie, Richard"],["dc.contributor.author","Michelot, Théo"],["dc.date.accessioned","2017-09-07T11:47:51Z"],["dc.date.available","2017-09-07T11:47:51Z"],["dc.date.issued","2017"],["dc.description.abstract","We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation. We further illustrate the approach using two real data applications, modelling (i) how sales data depend on advertising spending and (ii) how energy price in Spain depends on the Euro/Dollar exchange rate."],["dc.identifier.doi","10.1007/s11222-015-9620-3"],["dc.identifier.gro","3149380"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13666"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6050"],["dc.language.iso","en"],["dc.notes","Open Access"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0960-3174"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Markov-switching generalized additive models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","990"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Scandinavian Journal of Statistics"],["dc.bibliographiccitation.lastpage","1010"],["dc.bibliographiccitation.volume","47"],["dc.contributor.author","Säfken, Benjamin"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T08:41:23Z"],["dc.date.available","2020-04-06T08:41:23Z"],["dc.date.issued","2019"],["dc.description.abstract","Abstract The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross‐validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial‐, the t‐, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia."],["dc.description.sponsorship","German Research Association (DFG) Research Training Group Scaling Problems in Statistics"],["dc.identifier.doi","10.1111/sjos.12437"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17168"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63822"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","0303-6898"],["dc.relation.issn","1467-9469"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Conditional covariance penalties for mixed models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","International Journal of Health Geographics"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Buck, Christoph"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Tkaczick, Tobias"],["dc.contributor.author","Konstabel, Kenn"],["dc.contributor.author","Pigeot, Iris"],["dc.date.accessioned","2017-09-07T11:47:19Z"],["dc.date.available","2017-09-07T11:47:19Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1186/s12942-015-0027-3"],["dc.identifier.gro","3149329"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13210"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5993"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer Nature"],["dc.relation.issn","1476-072X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1471082X1982994"],["dc.bibliographiccitation.firstpage","386"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","409"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Otto-Sobotka, Fabian"],["dc.contributor.author","Spiegel, Elmar"],["dc.date.accessioned","2020-04-06T09:04:47Z"],["dc.date.available","2020-04-06T09:04:47Z"],["dc.date.issued","2020-08"],["dc.description.abstract","Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014."],["dc.identifier.doi","10.1177/1471082X19829945"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63825"],["dc.language.iso","en"],["dc.notes.intern","DeepGreen Import"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.relation.issn","1477-0342"],["dc.title","Spatio-temporal expectile regression models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","64"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Human Heredity"],["dc.bibliographiccitation.lastpage","75"],["dc.bibliographiccitation.volume","76"],["dc.contributor.author","Freytag, Saskia"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Schlather, Martin"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2017-09-07T11:47:18Z"],["dc.date.available","2017-09-07T11:47:18Z"],["dc.date.issued","2014"],["dc.description.abstract","Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms."],["dc.identifier.doi","10.1159/000357567"],["dc.identifier.gro","3149315"],["dc.identifier.pmid","24434848"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10822"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5978"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","S. Karger AG"],["dc.relation.eissn","1423-0062"],["dc.relation.issn","0001-5652"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]