Biology subjects

Maj, C.

Publications and source records attributed to Maj, C..

6 recordsLinked to original sources

bioRxiv2026

AlphaVaR: an R framework for the statistical interpretation of AlphaGenome variant-effect predictions

SummaryAlphaGenome (Google DeepMind) scores a DNA variant across thousands of functional tracks at single-base resolution, reporting both the magnitude of each predicted effect and its rarity against a genome-wide background. That volume is itself the obstacle to biological interpretation. Here we present AlphaVaR, an R package that gives AlphaGenomes output a typed structure together with the statistical methods and visualizations needed to interpret it. The output schema is identical for every variant, so the same tests apply throughout it. AlphaVaR provides localization tests with multiple-testing correction and effect sizes, a specificity index measuring how far an effect concentrates on a few elements of a chosen variable, and a transparent prioritization that ranks candidates across interpretable criteria and maps each to a target gene. Results feed a plot library, reproducible reports and a code-free Shiny application. Applied to rs1427407, the lead common variant for fetal-haemoglobin level, AlphaVaR recovers the established biology of the BCL11A erythroid enhancer. Availability and implementationhttps://github.com/KarimMarhaba/AlphaVaR, released under the MIT licence, R [≥] 4.2, with documentation at https://karimmarhaba.github.io/AlphaVaR/. The released version is archived at Zenodo (doi:10.5281/zenodo.21939265); the AlphaGenome scores analysed here are archived as a separate dataset (doi:10.5281/zenodo.21920988), and the scripts that regenerate every figure and reported number are in the repository (Supplementary Section S5). Contactpouria.dasmeh@uni-marburg.de Supplementary informationSupplementary data are available online.

genomics↗

bioRxiv2026

(Epi-)Genomic Data in the German TwinLife Study: TwinSNPs and TECS Cohort Profiles

The German Twin Family Panel TwinLife is a nationwide longitudinal study of twins and their family members. Primarily focusing on the development of social inequalities over the life course, TwinLife has been collecting data since October 2014 starting with 4,096 twin families (Ntotal = 16,951 individuals). As Germanys largest twin study to date, TwinLife has been surveying four birth cohorts of monozygotic and dizygotic same-sex twin pairs (initially [~]5, 11, 17, and 23 years old) and their families for 11 years. Survey data have been collected through five biennial face-to-face interviews with four computer-assisted telephone interviews in the years between. In addition, saliva samples were collected before the COVID-19 pandemic (2018-2020), during the pandemic (2021), and after (2022-2024). In this Cohort Profile, we describe the curation and initial analyses of molecular genetic and epigenetic data from the two TwinLife satellite projects TwinSNPs and TECS. Together, these projects currently comprise 12,108 processed DNA samples from 6,450 participants, extracted from the first two saliva collections before and during the COVID-19 pandemic. We compared the subsamples with the overall TwinLife sample and provide an overview of derived polygenic scores (PGS), epigenetic clocks and other methylation profile scores (MPS). We found that PGS predicted sample attrition in TwinLife, with small but significant associations between higher PGS for educational attainment and continued participation. Epigenetic clocks derived from saliva were highly correlated with chronological age (r = .71 to r = .94) and were generally more stable over time than other MPS. PGS for epigenetic clocks were associated with the respective clock only during but not before the start of the pandemic. We discuss opportunities of combining prospectively assessed molecular (epi)genetic data in within-family designs such as TwinLife and its implications and avenues for future research.

genetics↗

bioRxiv2025

Gene Interaction Network Architecture of Human Polygenic Traits Reveals Domain-Specific Connectivity and Evolutionary Pressures

Human polygenic phenotypes arise from complex interactions among genes within regulatory networks. To gain insights into the structural and evolutionary characteristics of these networks, we analyzed gene interaction networks across 4756 human polygenic phenotypes, contrasting the network properties of genes associated with polygenic phenotypes against those of non-associated genes. Our results indicate that genes associated with polygenic phenotypes exhibit a significantly higher connectivity in their corresponding gene-interaction networks, with connectivity varying markedly across different trait domains. Notably, genes within highly connected networks are enriched for immune-related biological processes, whereas genes in less connected networks are more involved in neurogenesis. Furthermore, we found that genes embedded in highly connected networks are, on average, under weaker selective constraints than those in lowly connected networks. Overall, our findings provide a systematic analysis of gene interaction networks underlying human polygenic traits and reveal how selective constraints vary with network connectivity. These insights offer a framework for prioritizing genes based on their connectivity within trait-associated networks. To support this effort, we developed the online portal http://netpolygen.com enabling researchers to generate and explore hypotheses by identifying genes that are both highly associated and highly connected across thousands of polygenic phenotypes.

evolutionary biology↗

bioRxiv2024

Single cell analysis of Barrett's esophagus and carcinoma reveals cell types conferring risk via genetic predisposition

Inherited genetic variants contribute to Barretts esophagus (BE) and esophageal adenocarcinoma (EAC) but it is unknown which cell types are involved in this process. We performed single cell RNA-sequencing of BE, EAC and paired normal tissues and integrated data of a genome-wide association study to determine cell type-specific genetic risk and cellular processes that contribute to BE and EAC. The analysis revealed that EAC development is driven to a greater extent by local cellular processes than BE development. One cell type of BE origin (BE-EAC) and cellular processes that control the differentiation of columnar cells are of particular relevance for EAC development. Further, specific subtypes of fibroblasts and endothelial cells contribute to BE and EAC development, while dendritic cells and CD4+ memory T cells contribute exclusively to BE development. The diagnostic use of markers characterizing the identified cell types and cellular processes should be explored in future for EAC prediction.

cancer biology↗

bioRxiv2022

Boosting polygenic risk scores

Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in the field of clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise L2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types.

bioinformatics↗

bioRxiv2022

Statistical learning for sparser fine-mapped polygenic models: the prediction of LDL-cholesterol

Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models that tend to be more robust regarding deviations from the target population.

genetics↗