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Article ; Online: Implications of the Co-Dominance Model for Hardy-Weinberg Testing in Genetic Association Studies.

Wellek, Stefan / Mueller-Nurasyid, Martina / Strauch, Konstantin

2024

Abstract: Introduction: The standard way of using tests for compatibility of genetic markers with the Hardy-Weinberg equilibrium (HWE) assumptionvas a means of quality control in genetic association studies (GAS) is to vcarry out this step of preliminary data ... ...

Abstract	Introduction: The standard way of using tests for compatibility of genetic markers with the Hardy-Weinberg equilibrium (HWE) assumptionvas a means of quality control in genetic association studies (GAS) is to vcarry out this step of preliminary data analysis with the sample of non-diseased vindividuals only. We show that this strategy has no rational basis whenever the genotype--phenotype relation for avmarker under consideration satisfies the assumption of co-dominance. Methods/results: The justification of this statement is the fact rigorously shown here that under co-dominance, the genotype distribution of a diallelic marker is in HWE among the controls if and only if the same holds true for the cases. Conclusion: The major practical consequence of that theoretical result is that under the co-dominance model, testing for HWE should be done both for cases and controls aiming to establish the combined (intersection) hypothesis of compatibility of both underlying genotype distributions with the HWE assumption. A particularly useful procedure serving this purpose is obtained through applying the confidence-interval inclusion rule derived by Wellek, Goddard and Ziegler (Biom J. 2010; 52:253-270) to both samples separately and combining these two tests by means of the intersection-union principle.
Language	English
Publishing date	2024-03-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2424-7
ISSN	1423-0062 ; 0001-5652
ISSN (online)	1423-0062
ISSN	0001-5652
DOI	10.1159/000537832
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Book ; Thesis: Kopplungsanalyse bei genetisch komplexen Erkrankungen mit genomischem Imprinting und Zwei-Genort-Krankheitsmodellen

Strauch, Konstantin

(Medizinische Informatik, Biometrie und Epidemiologie ; 87 ; Medizin & Wissen)

2002

Author's details	Konstantin Strauch
Series title	Medizinische Informatik, Biometrie und Epidemiologie ; 87 Medizin & Wissen
Collection
Keywords	Erbkrankheit ; Genetisches Imprinting ; Faktorenkopplung
Subject	Genkopplung ; Genetische Kopplung ; Genomische Prägung ; Genomisches Imprinting ; Genetische Krankheit ; Heredopathie ; Genetisch bedingte Krankheit ; Genetisches Syndrom ; Erbkrankheiten
Language	German
Size	120 S. : graph. Darst.
Edition	1. Aufl.
Publisher	Urban und Vogel
Publishing place	München
Publishing country	Germany
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Bonn, Univ., Diss., 2000
HBZ-ID	HT013251933
ISBN	3-86094-174-7 ; 978-3-86094-174-4
Database	Catalogue ZB MED Medicine, Health

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Article ; Online: Interpretability of bi-level variable selection methods.

Buch, Gregor / Schulz, Andreas / Schmidtmann, Irene / Strauch, Konstantin / Wild, Philipp S

Biometrical journal. Biometrische Zeitschrift

2024 Volume 66, Issue 2, Page(s) e2300063

Abstract: Variable selection is usually performed to increase interpretability, as sparser models are easier to understand than full models. However, a focus on sparsity is not always suitable, for example, when features are related due to contextual similarities ... ...

Abstract	Variable selection is usually performed to increase interpretability, as sparser models are easier to understand than full models. However, a focus on sparsity is not always suitable, for example, when features are related due to contextual similarities or high correlations. Here, it may be more appropriate to identify groups and their predictive members, a task that can be accomplished with bi-level selection procedures. To investigate whether such techniques lead to increased interpretability, group exponential LASSO (GEL), sparse group LASSO (SGL), composite minimax concave penalty (cMCP), and least absolute shrinkage, and selection operator (LASSO) as reference methods were used to select predictors in time-to-event, regression, and classification tasks in bootstrap samples from a cohort of 1001 patients. Different groupings based on prior knowledge, correlation structure, and random assignment were compared in terms of selection relevance, group consistency, and collinearity tolerance. The results show that bi-level selection methods are superior to LASSO in all criteria. The cMCP demonstrated superiority in selection relevance, while SGL was convincing in group consistency. An all-round capacity was achieved by GEL: the approach jointly selected correlated and content-related predictors while maintaining high selection relevance. This method seems recommendable when variables are grouped, and interpretation is of primary interest.
Language	English
Publishing date	2024-03-22
Publishing country	Germany
Document type	Journal Article
ZDB-ID	131640-0
ISSN	1521-4036 ; 0323-3847 ; 0006-3452
ISSN (online)	1521-4036
ISSN	0323-3847 ; 0006-3452
DOI	10.1002/bimj.202300063
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Article ; Online: Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer.

Brugger, Markus / Lutz, Manuel / Müller-Nurasyid, Martina / Lichtner, Peter / Slater, Emily P / Matthäi, Elvira / Bartsch, Detlef K / Strauch, Konstantin

Human heredity

2024 Volume 89, Issue 1, Page(s) 8–31

Abstract: Introduction: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, ... ...

Abstract	Introduction: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. Methods: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). Results: Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. Conclusion: Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.
MeSH term(s)	Humans ; Pancreatic Neoplasms/genetics ; Linkage Disequilibrium ; Haplotypes/genetics ; Genetic Linkage ; Software ; Pedigree ; Models, Genetic ; Female ; Male ; Genetic Predisposition to Disease ; Computer Simulation ; Gene Frequency/genetics ; Polymorphism, Single Nucleotide/genetics ; Chromosome Mapping/methods ; Carcinoma
Language	English
Publishing date	2024-01-10
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2424-7
ISSN	1423-0062 ; 0001-5652
ISSN (online)	1423-0062
ISSN	0001-5652
DOI	10.1159/000535840
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Article ; Online: GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data.

Marini, Federico / Ludt, Annekathrin / Linke, Jan / Strauch, Konstantin

BMC bioinformatics

2021 Volume 22, Issue 1, Page(s) 610

Abstract: Background: The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats-normalized expression values, ... ...

Abstract	Background: The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats-normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. Results: We developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. Conclusion: GeneTonic is distributed as an R package in the Bioconductor project ( https://bioconductor.org/packages/GeneTonic/ ) under the MIT license. Offering both bird's-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.
MeSH term(s)	Base Sequence ; RNA ; Reproducibility of Results ; Sequence Analysis, RNA ; Software
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2021-12-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-021-04461-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic.

Ludt, Annekathrin / Ustjanzew, Arsenij / Binder, Harald / Strauch, Konstantin / Marini, Federico

Current protocols

2022 Volume 2, Issue 4, Page(s) e411

Abstract: The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can ... ...

Abstract	The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.
MeSH term(s)	RNA/genetics ; RNA-Seq ; Reproducibility of Results ; Sequence Analysis, RNA/methods ; Workflow
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2022-04-25
Publishing country	United States
Document type	Journal Article
ISSN	2691-1299
ISSN (online)	2691-1299
DOI	10.1002/cpz1.411
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Conference proceedings: Testing methods to analyze small sample size GWAS with different settings

Poplawski, Alicia / Strauch, Konstantin

2021 , Page(s) Abstr. 446

Event/congress	65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS); Berlin; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2020
Keywords	Medizin, Gesundheit ; GWAS ; SNP ; analysis ; small sample size
Publishing date	2021-02-26
Publisher	German Medical Science GMS Publishing House; Düsseldorf
Document type	Conference proceedings
DOI	10.3205/20gmds380
Database	German Medical Science

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Article ; Online: A systematic review and evaluation of statistical methods for group variable selection.

Buch, Gregor / Schulz, Andreas / Schmidtmann, Irene / Strauch, Konstantin / Wild, Philipp S

Statistics in medicine

2022 Volume 42, Issue 3, Page(s) 331–352

Abstract: This review condenses the knowledge on variable selection methods implemented in R and appropriate for datasets with grouped features. The focus is on regularized regressions identified through a systematic review of the literature, following the ... ...

Abstract	This review condenses the knowledge on variable selection methods implemented in R and appropriate for datasets with grouped features. The focus is on regularized regressions identified through a systematic review of the literature, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 14 methods are discussed, most of which use penalty terms to perform group variable selection. Depending on how the methods account for the group structure, they can be classified into knowledge and data-driven approaches. The first encompass group-level and bi-level selection methods, while two-step approaches and collinearity-tolerant methods constitute the second category. The identified methods are briefly explained and their performance compared in a simulation study. This comparison demonstrated that group-level selection methods, such as the group minimax concave penalty, are superior to other methods in selecting relevant variable groups but are inferior in identifying important individual variables in scenarios where not all variables in the groups are predictive. This can be better achieved by bi-level selection methods such as group bridge. Two-step and collinearity-tolerant approaches such as elastic net and ordered homogeneity pursuit least absolute shrinkage and selection operator are inferior to knowledge-driven methods but provide results without requiring prior knowledge. Possible applications in proteomics are considered, leading to suggestions on which method to use depending on existing prior knowledge and research question.
MeSH term(s)	Humans ; Computer Simulation
Language	English
Publishing date	2022-12-22
Publishing country	England
Document type	Systematic Review ; Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.9620
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Article ; Online: Multi-household social gatherings contribute to the second SARS-CoV-2 wave in Rhineland-Palatinate, Germany, August to November 2020.

Schepers, Markus / Zanger, Philipp / Jahn, Klaus / König, Jochem / Strauch, Konstantin / Gianicolo, Emilio

The Journal of infection

2022 Volume 84, Issue 4, Page(s) 551–557

Abstract: Background: Although the private household setting is considered a major driver of viral spread, only little is known about the contextual details of SARS-CoV-2 household transmission, thus hampering political decision-making.: Materials and methods: ...

Abstract	Background: Although the private household setting is considered a major driver of viral spread, only little is known about the contextual details of SARS-CoV-2 household transmission, thus hampering political decision-making. Materials and methods: We analyzed individual case and cluster data from statutory notifications from August to November 2020 in Rhineland-Palatinate - the period preceding the second SARS-CoV-2 wave. We also conducted an into-depth survey on contextual details of household transmission in a representative sample of 149 private household clusters that had occurred during this period. Results: During the study period, 18,695 PCR-confirmed SARS-CoV-2 cases were notified, 3,642 of which occurred in 911 clusters (private households (67.3%), the workplace (7.8%), elderly homes (1.8%), others (23.2%). Demographically, clustered cases were representative of all notified cases. Two-thirds (77/113, 68%) of sample response clusters involved more than one private household. These caused on average more close contact persons (mean 13.5, ±SD 15.8) and secondary cases (3.9, ±SD 0.4) than clusters involving one household only (5.1 ± 13.8 and 2.9 ± 0.2). About one in six multi-household clusters in the private setting (13/77) followed a social gathering (e.g. birthday party). Breaches of one or more of the three major barrier concepts (mask, ventilation, and distance) were identified in most (10/13) of these social gatherings. SARS-CoV-2 clusters following social gatherings were overrepresented during the second half of the study period. Conclusion: In times of increasing infectious pressure in a given population, multi-household social gatherings appear to be an important target for reducing SARS-CoV-2 transmission.
MeSH term(s)	Aged ; COVID-19/epidemiology ; Family Characteristics ; Germany/epidemiology ; Humans ; Nucleic Acid Amplification Techniques ; SARS-CoV-2
Language	English
Publishing date	2022-01-23
Publishing country	England
Document type	Journal Article
ZDB-ID	424417-5
ISSN	1532-2742 ; 0163-4453
ISSN (online)	1532-2742
ISSN	0163-4453
DOI	10.1016/j.jinf.2022.01.028
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Article ; Online: Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning.

Ha, Chung Shing Rex / Müller-Nurasyid, Martina / Petrera, Agnese / Hauck, Stefanie M / Marini, Federico / Bartsch, Detlef K / Slater, Emily P / Strauch, Konstantin

PloS one

2023 Volume 18, Issue 1, Page(s) e0280399

Abstract: Background: The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early ... ...

Abstract	Background: The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results' interpretability. Methods: We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios. Results: In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC. Conclusion: Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection.
MeSH term(s)	Humans ; Proteomics ; Pancreatic Neoplasms/diagnosis ; Pancreatic Neoplasms/genetics ; Pancreatic Neoplasms/pathology ; Carcinoma, Pancreatic Ductal/diagnosis ; Carcinoma, Pancreatic Ductal/pathology ; Biomarkers ; Biomarkers, Tumor/genetics ; Pancreatic Neoplasms
Chemical Substances	Biomarkers ; Biomarkers, Tumor
Language	English
Publishing date	2023-01-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0280399
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