Current Developments of Clinical Sequencing and the Clinical Utility of Polygenic Risk Scores in Inflammatory Diseases

In this mini-review, we highlight selected research by the Deutsche Forschungsgemeinschaft (DFG) Cluster of Excellence "Precision Medicine in Chronic Inflammation" focusing on clinical sequencing and the clinical utility of polygenic risk scores as well as its implication on precision medicine in the field of the inflammatory diseases inflammatory bowel disease, atopic dermatitis and coronary artery disease. Additionally, we highlight current developments and discuss challenges to be faced in the future. Exemplary, we point to residual challenges in detecting disease-relevant variants resulting from difficulties in the interpretation of candidate variants and their potential interactions. While polygenic risk scores represent promising tools for the stratification of patient groups, currently, polygenic risk scores are not accurate enough for clinical setting. Precision medicine, incorporating additional data from genomics, transcriptomics and proteomics experiments, may enable the identification of distinct disease pathogeneses. In the future, data-intensive biomedical innovation will hopefully lead to improved patient stratification for personalized medicine

Machine learning and data mining in complex genomic data a review on the lessons learned in Genetic Analysis Workshop Nineteen

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets

Polymorphisms in the Mitochondrial Genome Are Associated With Bullous Pemphigoid in Germans

Bullous pemphigoid (BP) is the most prevalent autoimmune skin blistering disease and is characterized by the generation of autoantibodies against the hemidesmosomal proteins BP180 (type XVII collagen) and BP230. Most intriguingly, BP is distinct from other autoimmune diseases because it predominantly affects elderly individuals above the age of 75 years, raising the question why autoantibodies and the clinical lesions of BP emerges mostly in this later stage of life, even in individuals harboring known putative BP-associated germline gene variants. The mitochondrial genome (mtDNA) is a potential candidate to provide additional insights into the BP etiology; however, the mtDNA has not been extensively explored to date. Therefore, we sequenced the whole mtDNA of German BP patients (n = 180) and age- and sex-matched healthy controls (n = 188) using next generation sequencing (NGS) technology, followed by the replication study using Sanger sequencing of an additional independent BP (n = 89) and control cohort (n = 104). While the BP and control groups showed comparable mitochondrial haplogroup distributions, the haplogroup T exhibited a tendency of higher frequency in BP patients suffering from neurodegenerative diseases (ND) compared to BP patients without ND (50%; 3 in 6 BP with haplogroup T). A total of four single nucleotide polymorphisms (SNPs) in the mtDNA, namely, m.16263T>C, m.16051A>G, and m.16162A>G in the D-loop region of the mtDNA, and m.11914G>A in the mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 4 gene (MT-ND4), were found to be significantly associated with BP based on the meta-analysis of our NGS data and the Sanger sequencing data (p = 0.0017, p = 0.0129, p = 0.0076, and p = 0.0132, respectively, Peto's test). More specifically, the three SNPs in the D-loop region were negatively, and the SNP in the MT-ND4 gene was positively associated with BP. Our study is the first to interrogate the whole mtDNA in BP patients and controls and to implicate multiple novel mtDNA variants in disease susceptibility. Studies using larger cohorts and more diverse populations are warranted to explore the functional consequences of the mtDNA variants identified in this study on immune and skin cells to understand their contributions to BP pathology

Polymorphisms in the Mitochondrial Genome Are Associated With Bullous Pemphigoid in Germans

Bullous pemphigoid (BP) is the most prevalent autoimmune skin blistering disease and is characterized by the generation of autoantibodies against the hemidesmosomal proteins BP180 (type XVII collagen) and BP230. Most intriguingly, BP is distinct from other autoimmune diseases because it predominantly affects elderly individuals above the age of 75 years, raising the question why autoantibodies and the clinical lesions of BP emerges mostly in this later stage of life, even in individuals harboring known putative BP-associated germline gene variants. The mitochondrial genome (mtDNA) is a potential candidate to provide additional insights into the BP etiology; however, the mtDNA has not been extensively explored to date. Therefore, we sequenced the whole mtDNA of German BP patients (n = 180) and age- and sex-matched healthy controls (n = 188) using next generation sequencing (NGS) technology, followed by the replication study using Sanger sequencing of an additional independent BP (n = 89) and control cohort (n = 104). While the BP and control groups showed comparable mitochondrial haplogroup distributions, the haplogroup T exhibited a tendency of higher frequency in BP patients suffering from neurodegenerative diseases (ND) compared to BP patients without ND (50%; 3 in 6 BP with haplogroup T). A total of four single nucleotide polymorphisms (SNPs) in the mtDNA, namely, m.16263T>C, m.16051A>G, and m.16162A>G in the D-loop region of the mtDNA, and m.11914G>A in the mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 4 gene (MT-ND4), were found to be significantly associated with BP based on the meta-analysis of our NGS data and the Sanger sequencing data (p = 0.0017, p = 0.0129, p = 0.0076, and p = 0.0132, respectively, Peto's test). More specifically, the three SNPs in the D-loop region were negatively, and the SNP in the MT-ND4 gene was positively associated with BP. Our study is the first to interrogate the whole mtDNA in BP patients and controls and to implicate multiple novel mtDNA variants in disease susceptibility. Studies using larger cohorts and more diverse populations are warranted to explore the functional consequences of the mtDNA variants identified in this study on immune and skin cells to understand their contributions to BP pathology

A roadmap to multifactor dimensionality reduction methods

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.status: publishe

Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status

Coronary artery disease (CAD) is the leading global cause of mortality and has substantial heritability with a polygenic architecture. Recent approaches of risk prediction were based on polygenic risk scores (PRS) not taking possible nonlinear effects into account and restricted in that they focused on genetic loci associated with CAD, only. We benchmarked PRS, (penalized) logistic regression, naive Bayes (NB), random forests (RF), support vector machines (SVM), and gradient boosting (GB) on a data set of 7,736 CAD cases and 6,774 controls from Germany to identify the algorithms for most accurate classification of CAD status. The final models were tested on an independent data set from Germany (527 CAD cases and 473 controls). We found PRS to be the best algorithm, yielding an area under the receiver operating curve (AUC) of 0.92 (95% CI [0.90, 0.95], 50,633 loci) in the German test data. NB and SVM (AUC similar to 0.81) performed better than RF and GB (AUC similar to 0.75). We conclude that using PRS to predict CAD is superior to machine learning methods

Table_1_Retrospective analysis of the long-term therapeutic effectiveness and safety profile of rituximab in the treatment of mucous membrane pemphigoid in a German university center between 2008 and 2019.docx

BackgroundThe B-cell-depleting anti-CD20 antibody rituximab (RTX) is often used as an adjuvant drug for the treatment of refractory cases of mucous membrane pemphigoid (MMP).ObjectiveThis study aims to determine the therapeutic effectiveness and the safety profile of RTX in MMP.MethodsThe medical records of all cases of MMP treated with RTX between 2008 and 2019 in our university medical center located in northern Germany, which specialized in autoimmune blistering skin diseases, were retrieved and systemically analyzed for treatment responses and potential adverse events over a median period of 27 months.ResultsWe identified 18 MMP patients who received at least one cycle of RTX to treat MMP. RTX was always used as an adjuvant treatment, and its application did not change concomitant treatments. Under treatment with RTX, 67% of the patients achieved an improvement in their disease activity within 6 months. This was also reflected in a statistically significant reduction in the Mucous Membrane Pemphigoid Disease Index (MMPDAI) activity score. The frequency of infections under RTX treatment increased only slightly.ConclusionsThe use of RTX is associated with an attenuation of MMP in a large proportion of MMP patients in our study. At the same time, its application was not found to further increase the susceptibility of the most strongly immunocompromised population of MMP patients to opportunistic infections. Collectively, our results suggest that the potential benefits of RTX outweigh its risks in patients with refractory MMP.</p