Biology subjects

Lange, C.

Publications and source records attributed to Lange, C..

At least 19 recordsLinked to original sources

bioRxiv2026

petVAE: A Data-Driven Model for Identifying Amyloid PET Subgroups Across the Alzheimer's Disease Continuum

Amyloid-{beta} (A{beta}) PET imaging is a core biomarker and is sufficient for the biological diagnosis of Alzheimers disease (AD). Here, we aimed to identify biologically meaningful subgroups across the continuum of A{beta} accumulation using a data-driven deep learning approach, without imposing predefined thresholds for A{beta} negativity or positivity. We analyzed 3,110 A{beta} PET scans from the Alzheimers Disease Neuroimaging Initiative and Anti-Amyloid Treatment in Asymptomatic Alzheimers Disease studies to develop petVAE, a two-dimensional variational autoencoder. The model accurately reconstructed scans without prior labeling, selection by scanner or region of interest. Latent representations of scans extracted from petVAE were used to visualize and cluster the AD continuum. Clustering yielded four groups: two predominantly A{beta} negative (A{beta}-, A{beta}-+) and two predominantly A{beta} positive (A{beta}+, A{beta}++). All clusters differed in standardized uptake value ratio (p < 1.64x10-) and cerebrospinal fluid (CSF) A{beta} (p < 0.02), demonstrating petVAEs ability to assign scans along the A{beta} continuum. Extreme clusters (A{beta}-, A{beta}++) resembled conventional A{beta} negative and positive groups and differed in cognition, APOE {varepsilon}4 prevalence, A{beta} and tau CSF biomarkers (p < 3x10-). Intermediate clusters (A{beta}-+, A{beta}+) showed higher odds of carrying at least one APOE {varepsilon}4 allele versus A{beta}- (p < 0.03). Participants in A{beta}+ or A{beta}++ clusters exhibited faster progression to AD (A{beta}+ hazard ratio = 2.42, A{beta}++ HR = 9.43; p < 1.17x10-). Thus, petVAE was capable of reconstructing PET scans while extracting latent features that capture the AD continuum and define biologically meaningful subgroups, enabling data-driven characterization of preclinical disease stages.

bioinformatics↗

bioRxiv2025

Analyzing associations and higher-order effects in multi-omics data with double machine learning

MotivationIntegrative omics analyses enhance our understanding of disease mechanisms and biomarkers by investigating relationships among traits, omics measurements, genetic variants, and epidemiological factors. Statistically, these analyses are challenging and require robust, flexible methodologies due to high dimensionality, non-standard data distributions, and potentially complex, non-linear confounding effects. ResultsTo facilitate the integration and analysis of multi-omics data, we introduce the Robust Omics MethodologY (ROMY) framework and its corresponding R implementation, the romy package. ROMY enables users to (a) perform robust association testing between two target variables while incorporating flexible covariate adjustments, (b) examine effects on measurement variances and covariances (e.g., co-expression, co-abundance), and (c) conduct rigorous interaction-effect testing. ROMY builds on recent advances in theoretical statistics and double machine learning to ensure robustness and statistical validity. We illustrate the performance of our framework through simulation studies. Availability and implementationromy is an R package available under the GNU GPLv3 license on GitHub: https://github.com/julianhecker/romy.

bioinformatics↗

bioRxiv2025

Prior Information Shapes Perceptual Evidence Accumulation Dynamics Differentially in Psychosis

Humans rely on prior information to navigate sensory uncertainty: Such priors could shape the decision process before sensory evidence is gathered (origin model), or could amplify sensory evidence dynamically (gain model). Dysfunctionalities in the utilisation of priors may underlie hallucinatory percepts and delusional ideation in psychosis, yet their impact on decision-making across sensory modalities has remained unclear. Using a perceptual target-detection task across auditory and visual domains in laboratory and online samples, we applied hierarchical drift diffusion modelling to examine how prior probabilities shape evidence accumulation in clinical and non-clinical populations. We show that in healthy individuals, prior information enhances sensory gain and decision flexibility, as represented by drift criterion and rate, consistent with the gain model. In contrast, individuals with psychosis exhibit diminished sensory gain, relying instead on pre-evidence biases, consistent with the origin model. Notably, greater positive symptom severity predicted a reduction in traditional criterion decision bias. These results suggest that sensory gain deficits may serve as a computational marker for psychosis progression, linking dysfunctional prior use to perceptual aberrancies. By demonstrating how prior information modulates evidence accumulation across sensory modalities, our study advances the understanding of psychotic perception and decision-making, offering insights for computational psychiatry and fine-tuned clinical diagnostics.

neuroscience↗

bioRxiv2025

Modeling the role of urokinase plasminogen activator, uPA, and circulating Cancer-Associated Fibroblasts (cCAFS) in breast cancer cell extravasation

Circulating Cancer-Associated Fibroblasts (cCAFs) have been discovered in circulating tumor cell clusters from all stages of disease progression in breast cancer patients. We have shown that CAFs promote lung metastases in the mouse tail vein model when they are clustered with triple negative breast cancer (TNBC) MDA-MB231 cells. Following on this observation, we saw that MDA-MB231-luciferase labeled cells persist at higher levels when present in CAF23/MDA-MB231 co-clusters compared to MDA-MB231 mono-clusters within the first 3 days after tail vein injection. This prompted us to investigate whether CAFs aid cancer cell extravasation from capillary venules into the lung parenchyma, which would impart better survival and faster seeding of metastases. Ex vivo lung extravasation assays showed that within the first 8-24 hrs after tail vein injection, more cells from CAF23/MDA-MB231 co-clusters extravasated than cells from MDA-MB231 mono-clusters. Using in vitro endothelial binding assays, we determined that CAF/TNBC co-clusters bind to HUVEC endothelial cells better than TNBC mono-clusters. Single Cell RNA-seq identified several genes in the fibrinolysis pathway whose expression increases in TNBC cells when they are clustered with CAFs. One of these genes is PLAU, which encodes the urokinase-type plasminogen activator, uPA. siRNA knockdown of PLAU decreased in vitro TNBC-endothelial cell interactions and ex vivo extravasation of MDA-MB231 mono-clusters, revealing a role for uPA/PLAU in breast cancer cell extravasation. Our data helps to define the role of CAFs in breast cancer extravasation and highlights the importance of our previous work showing that CAFs promote tumor cell dissemination and metastasis.

cancer biology↗

bioRxiv2025

Quantification of individual dataset contributions to prediction accuracy in cooperative learning

We consider cooperative learning, a recently proposed technique to leverage the predictive power of several datasets (also called data views) to improve the prediction accuracy of an outcome of interest. Cooperative learning uses a Lasso-type penalty to fit several datasets to a given outcome, while a so-called agreement penalty enforces that the predictions made by the individual datasets agree. We are interested in the question of whether the predictive power of each individual dataset can be quantified using cooperative learning. In this work, we demonstrate that certain trace plots, analogously to the ones for the classic Lasso, allow one to quantify the predictive power of each individual dataset. Importantly, this allows one to detect datasets which do not carry any predictive power on the outcome. In an experimental study, we quantify the predictive power of three real datasets in the context of the Childhood Asthma Management Program (CAMP), with the three datasets containing information on epidemiological variables, metabolites, and clinical data, respectively.

bioinformatics↗

bioRxiv2025

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease

BackgroundA significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimers disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals. MethodsWe analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on {beta}-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution. ResultsTranscriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience. ConclusionsWe identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

neuroscience↗

bioRxiv2024

Serratia sp. dominates the lung microbiome of patients with tuberculosis and non-tuberculous mycobacterial lung diseases

BackgroundPathogenic mycobacteria, such as the Mycobacterium tuberculosis complex (Mtbc), and non-tuberculous mycobacteria (NTMs) can cause severe chronic pulmonary infections. However, not all infected patients develop active disease. Yet, it is unclear whether certain key taxa in the lung microbiome play a role in the pathogenesis of tuberculosis (TB) and NTM lung disease (LD). Material and methodsWe employed 16S rRNA amplicon sequencing (V3-V4) to characterize the baseline microbiome in bronchoalveolar lavage fluid from a patient cohort diagnosed with TB (n=23), NTM-LD (n=19), or non-infectious disease (n=4) prior to the initiation of therapy. The analysis included the depletion of human cells, removal of extracellular DNA, implementation of a decontamination strategy, and exploratory whole-metagenome sequencing (WMS) of selected specimens. ResultsThe genera Serratia and unclassified Yersiniaceae dominated the lung microbiome of all patients with a mean relative abundance of >15% and >70%, respectively. However, at the sub-genus level, as determined by amplicon sequence variants (ASVs), TB-patients exhibited increased community diversity, and TB specific ASV_7 (unclassified Yersiniaceae), and ASV_21 (Serratia) signatures. Exploratory analysis by WMS and ASV similarity analysis suggested the presence of Serratia liquefaciens, Serratia grimesii, Serratia myotis and/or Serratia quinivorans in both TB and NTM-LD patients. Overall, presence/absence of certain Serratia ASVs was significantly associated with disease state. ConclusionThe lung microbiome of TB patients harbors a distinct, and heterogenous microbiome structure with specific occurrences of certain Serratia traits. Serratia sp. plays a pivotal role in our understanding of microbial interactions in the lung microbiome of patients infected with Mtbc.

microbiology↗

bioRxiv2024

Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model

The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time dependent hazard and survival (defined as disease free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of the absolute survival as a function of time. Second, to compute the time dependent risk of an individual, we use published methodology to fit a Coxs proportional hazard model to data from a genetic SNP study of time to Alzheimers disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, ten leading principal components, and selected genomic loci. We apply the lasso for Coxs proportional hazards to a dataset of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Coxs proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire dataset of AD patients, thus enabling it to handle datasets with 60, 000 to 100, 000 subjects in less than one hour.

bioinformatics↗

bioRxiv2023

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (A{beta}) aggregation in the brain is a key factor for early identification and diagnosis of Alzheimers disease (AD). We aimed to develop a deep learning model to predict A{beta} cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 A{beta} PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical A{beta}. We also compared the brain regions relevance for the models CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted A{beta} CSF values correlated strongly with measured A{beta} CSF values (r=0.81; p<0.001) and showed correlations with SUVR and episodic memory measures in all participants except in those with AD. For both clinical and biological classifications, cerebral white matter significantly contributed to CSF prediction (q<0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, brain stem, subcortical areas, cortical lobes, limbic lobe, and basal forebrain made more significant contributions (q<0.01). Considering cortical gray matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted A{beta} CSF concentration from A{beta} PET scans, offering potential clinical utility for A{beta} level determination and early AD detection.

neuroscience↗

bioRxiv2022

Fast computation of principal components of genomic similarity matrices

The computation of a similarity measure for genomic data, for instance using the (genomic) covariance matrix, the Jaccard matrix, or the genomic relationship matrix (GRM), is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases in, for instance, linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (the genomic covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for an exact, faster SVD computation. An exception is the Jaccard matrix, which does not have a structure applicable for the fast SVD computation. An exact algorithm is proposed to compute the principal components of the genomic covariance, weighted Jaccard, and genomic relationship matrices. The algorithm is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the distance (in L2 norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation. We illustrate all computations on both simulated data and data of the 1000 Genome Project, showing that the approximation error is very low in practice.

bioinformatics↗

bioRxiv2022

Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest

As of June 2022, the GISAID database contains more than one million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.

bioinformatics↗

bioRxiv2021

Unsupervised genome-wide cluster analysis: nucleotide sequences of the omicron variant of SARS-CoV-2 are similar to sequences from early 2020

The GISAID database contains more than 1,000,000 SARS-CoV-2 genomes, including sequences of the recently discovered SARS-CoV-2 omicron variant and of prior SARS-CoV-2 strains that have been collected from patients around the world since the beginning of the pandemic. We applied unsupervised cluster analysis to the SARS-CoV-2 genomes, assessing their similarity at a genome-wide level based on the Jaccard index and principal component analysis. Our analysis results show that the omicron variant sequences are most similar to sequences that have been submitted early in the pandemic around January 2020. Furthermore, the omicron variants in GISAID are spread across the entire range of the first principal component, suggesting that the strain has been in circulation for some time. This observation supports a long-term infection hypothesis as the omicron strain origin.

bioinformatics↗

bioRxiv2021

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since statistical power is often limited, the specification of environmental effects is nontrivial, and such misspecifications can lead to false positive findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy increases power to detect interactions, identifying contributing key genes and pathways is difficult based on these global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate multiple genetic variants and/or multiple environmental factors. Using sample splitting, a screening step enables the selection and combination of potential interactions into scores with improved interpretability, based on the users unrestricted choices for statistical/machine learning approaches. In the testing step, the application of robust test statistics minimizes the susceptibility of the results to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified genome-wide significant interactions with subcomponents of genetic risk scores. While the contributing single variant interactions are moderate, our analysis results indicate interesting interaction patterns that result in strong aggregated signals that provide further insights into gene-environment interaction mechanisms.

bioinformatics↗

bioRxiv2021

The Human Eye Transcriptome Atlas: A Searchable Comparative Transcriptome Database for Healthy and Diseased Human Eye Tissue

The applications of deep sequencing technologies in life science research and clinical diagnostics have increased rapidly over the last decade. Although fast algorithms for data processing exist, intuitive, portable solutions for data analysis are still rare. For this purpose, we developed a web-based transcriptome database, which provides a platform-independent, intuitive solution to easily explore and compare ocular gene expression of 100 diseased and healthy human tissue samples from 15 different tissue types collected at the Eye Center of the University of Freiburg. To ensure comparability of expression between different tissues, reads were normalized across all 100 samples. Differentially expressed genes were calculated between each tissue type to determine tissue-specific genes. Unsupervised analysis of all 100 samples revealed an accurate clustering according to different tissue types. Cluster analysis based on known cell type-specific marker genes allowed differentiation of respective tissues. Several tissue-specific marker genes were identified. These genes were involved in tissue- or disease-specific processes, such as myelination for the optic nerve, visual perception for retina, keratinocyte differentiation for conjunctival carcinoma, as well as endothelial cell migration for choroidal neovascularization membranes. The results are accessible at the Human Eye Transcriptome Atlas website at https://www.eye-transcriptome.com. In summary, this searchable transcriptome database enables easy exploration of ocular gene expression in healthy and diseased human ocular tissues without bioinformatics expertise. Thus, it provides rapid access to detailed insights into the molecular mechanisms of various ocular tissues and diseases, as well as the rapid retrieval of potential new diagnostic and therapeutic targets.

bioinformatics↗

bioRxiv2021

The influence of unmeasured confounding on the MR Steiger approach

The Mendelian Randomization (MR) Steiger approach is used to determine the direction of a possible causal effect between two phenotypes [1]. For two phenotypes, denoted phenotype 1 and 2, the MR Steiger approach is composed of two parts: (1) MR is performed for a set of single nucleotide polymorphisms (SNPs) that serve as instrumental variables for phenotype 1 and (2) the difference of two correlations, the correlation between the SNPs and phenotype 1 and the correlation between the SNPs and phenotype 2, is calculated. These two parts are then used to determine the direction of a possible causal effect between the two phenotypes. The original MR Steiger paper [1] shows that unmeasured confounding of the two phenotypes affects the validity of the MR Steiger approach, but does not elucidate as to how this occurs. In particular, it was argued that if the magnitude of the observational variance explained between the two phenotypes is above 0.2, the MR Steiger method may return the incorrect causal direction due to unmeasured confounding. This may initially seem surprising since unmeasured confounding does not induce spurious associations between the SNP and phenotype 2, as we demonstrate using directed acyclic graphs. In this note, we show that this is because unmeasured confounding may rescale the magnitude of a non-zero association, and thereby distort the comparison of the correlation between the SNP and phenotype 2 and the correlation between the SNP and phenotype 1. We will end with a number of cautionary remarks on the MR Steiger method, which are partly motivated by this and mentioned in the original MR Steiger paper [1].

genetics↗

bioRxiv2021

Engineered Bacteriophage T4 Nanoparticle as a Potential Targeted Activator of HIV-1 Latency in CD4+ Human T cells

A major barrier for HIV-1 eradication is the latent virus reservoir containing stably integrated and silent proviruses in CD4+ T-cells. Targeted reactivation and removal of this latent reservoir is a potential strategy for HIV-1 cure but remains a major challenge. Here, we investigated whether CD4-targeted bacteriophage T4 capsid nanoparticles that mimic HIV envelope can reactivate HIV-1 latency. The nanoparticles were arrayed with CD4-binding CD4-DARPin, or HIV-1 gp140 envelope trimer. When exposed to J-Lat T-cell model of HIV-1 latency or primary T-lymphocytes from human PBMCs, these nanoparticles activated CD4+ T-cells without causing global T-cell activation, which led to activation of HIV-1 proviral transcription, viral protein production and release. Intriguingly, the observed T-cell activation and HIV-1 latency reversal do not involve the classic PKC or NFAT pathways and did not lead to cytokine storm. These studies indicate that engineered non-infectious bacteriophages can be exploited for HIV-1 cure and targeted T-cell therapies.

immunology↗

bioRxiv2021

Comparative transcriptome analysis of human and murine choroidal neovascularization identifies fibroblast growth factor inducible-14 as phylogenetically conserved mediator of neovascular age-related macular degeneration

BackgroundVisual outcome of patients with neovascular age-related macular degeneration has significantly improved during the last years following the introduction of anti-vascular endothelial growth factor (VEGF) therapy. However, about one third of patients show persistent exudation and decreasing visual acuity despite recurrent anti-VEGF treatment, which implies a role of other, still unknown proangiogenic mediators. MethodsThe present study applied transcriptional profiling of human and mouse (C57BL/6J wildtype) choroidal neovascularization (CNV) membranes each with reference to healthy control tissue to identify yet unrecognized mediators of CNV formation. Key factors were further investigated by immunohistochemistry as well as by intravitreal inhibition experiments and multiplex protein assays in the laser-induced CNV mouse model. ResultsTranscriptional profiles of CNV membranes were characterized by enhanced activation of blood vessel development, cytoskeletal organization, and cytokine production, with angiogenesis and wound healing processes predominating in humans and activation of immune processes in mice. Besides several species-specific factors, 95 phylogenetically conserved CNV-associated genes were detected, among which fibroblast growth factor inducible-14 (FN14), a member of the tumor necrosis factor (TNF) receptor family, was identified as a key player of CNV formation. Blocking the pathway by intravitreal injection of a FN14 decoy receptor modulated the cytokine profile - most notably IL-6 - and led to a significant reduction of CNV size in vivo. ConclusionsThis study characterizes the transcriptome of human and mouse CNV membranes in an unprejudiced manner and identifies FN14 as a phylogenetically conserved mediator of CNV formation and a promising new therapeutic target for neovascular AMD. FundingThis study was funded by the Helmut-Ecker-Stiftung and the Volker-Homann-Stiftung.

genomics↗

bioRxiv2021

A smoothed version of the Lassosum penalty for fitting integrated risk models

Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the "Lassosum" penalty used to fit polygenic risk scores and integrated risk models. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimers disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm, allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.

bioinformatics↗