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PubMed2026

Comparative phylogenomics and transcriptional regulatory networks of AQPs, HSPs, and LEA proteins in salt-stressed Portulaca oleracea.

Soil salinization severely threatens global food security, necessitating systematic investigations of halophytes like Portulaca oleracea to decode the molecular mechanisms of environmental resilience. Utilizing an integrated framework of deep learning-based genome annotation (58,817 predicted genes; 96.5% BUSCO completeness), multi-tissue RNA-Seq, phylogenomics, and gene regulatory network (GRN) inference, the synergistic orchestration of 78 aquaporins (AQPs), 525 heat shock proteins (HSPs), and 119 late embryogenesis abundant (LEA) proteins was elucidated. The active transcriptome, encompassing 39,065 expressed loci, revealed a systemic growth-defense trade-off. Tissues displayed distinct adaptive mechanisms: leaves modulated intracellular water balance via specialized AQPs, whereas adult roots maintained proteostasis through robust HSP20/HSP70 induction. Phylogenomic clustering across 154 species demonstrated that salinity tolerance constitutes an evolutionary mosaic, identifying 81 halophyte-exclusive orthogroups and 1129 species-specific clusters. Comparative topology across six independent GRNs (4.2M-5.3 M edges) unmasked a highly modular transcriptional reprogramming strategy governed by a core apparatus of 22 stress-exclusive regulators, with functional enrichment heavily prioritizing protein dimerization and chromatin remodeling. Theoretically, the distinct convergence of Trihelix transcription factors with guard cell differentiation pathways offers a candidate transcriptomic framework to explain the plant's characteristic C4-CAM photosynthetic plasticity under severe osmotic pressure. Practically, these evolutionary blueprints and specific master switches transcend single-gene transgenic limitations. Utilizing these root-sustained and stress-inducible targets under localized promoters provides a naturally optimized, network-level precision engineering roadmap to transfer robust, compartmentalized halotolerance to sensitive glycophytic crops.

Gene Regulatory Networks↗
PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗
PubMed2026

Venomous Lepidoptera: defensive toxin systems, venom composition, and clinical significance.

Venomous Lepidoptera constitute an underrecognized yet medically significant group of toxin-producing arthropods that employ contact-mediated defensive envenomation through specialized integumentary structures such as setae, spines, and scoli. Unlike actively stinging arthropods, these insects deliver venom passively upon contact, eliciting a diverse spectrum of clinical manifestations collectively termed lepidopterism. Clinical outcomes range from localized pain and dermatitis to severe systemic effects, including hemorrhagic syndromes, complement activation, and chronic inflammatory disorders. Recent advances in proteomic and transcriptomic technologies have transformed our understanding of lepidopteran venoms, revealing unexpectedly complex toxin repertoires comprising serine proteases, phospholipases, pore-forming proteins, disulfide-rich peptides, neuroactive RF-amide peptides, and immune-modulating components. These findings have provided new insights into the molecular basis of toxicity, host-pathogen interactions, and the evolutionary diversification of venom systems within Lepidoptera. This review synthesizes current knowledge on the morphology of venom-delivery structures, venom composition, mechanisms of action, and associated clinical manifestations, while highlighting medically important taxa, particularly species of the genus Lonomia. The successful development of antivenom against Lonomia envenomation underscores the translational relevance of lepidopteran toxin research and its potential for therapeutic innovation. By integrating molecular, clinical, and evolutionary perspectives, this review repositions venomous Lepidoptera as a legitimate and important component of arthropod toxinology. Furthermore, it identifies critical methodological limitations and key knowledge gaps, providing a framework for future investigations aimed at advancing our understanding of toxin biology, immunopathology, and the development of novel biomedical applications.

Animals↗
PubMed2026

Norgestrel drives mitochondrial collapse and plasma membrane impairment in Pacific oyster (Crassostrea gigas) sperm by triggering premature acrosome reaction.

The toxic mechanisms of norgestrel (NGT), an emerging marine pollutant, on the sperm from externally fertilized invertebrates remain elusive. This study employed an integrated physiological and multi-omics framework to elucidate how NGT (10 and 1000 ng/L) disrupts acrosome reaction (AR) signaling machinery, thereby impairing the functional integrity of Pacific oyster (Crassostrea gigas, also known as Magallana gigas) sperm. Exposure to NGT triggered a significant, dose-dependent premature AR, characterized by elevated acrosin activity and a loss of acrosomal integrity. Multi-omics integration supports a model in which this premature exocytosis is linked to signaling disturbances, including disruption of calcium signaling and reduced transcript abundance of calmodulin (CaM) and the primary recognition protein zonadhesin (Zan). This signaling interference induced an premature AR, subsequently driving a cascade of bioenergetic and structural failures. At the mitochondrial level, NGT induced abnormal mitochondrial permeability transition pore (mPTP) opening and elevated the transcript levels of antioxidant defense genes (e.g., peroxiredoxin-5, PRDX5). These alterations indicate the occurrence of mitochondrial collapse. Concurrently, scanning electron microscopy verified localized plasma membrane wrinkling and pore formation in sperm. In addition, NGT exposure decreased the transcript abundance of cytoskeleton-related genes, including solute carrier family 26 member 6 (SLC26A6), actin (ACT), and tubulin polymerization promoting protein family member 3 (TPPP3). These molecular changes further disrupted membrane phospholipid homeostasis, as represented by altered glycerophospholipid metabolism. At the same time, cumulative cellular stress was associated with decreased transcript abundance of cytoprotective factors (e.g., baculoviral IAP repeat-containing proteins, birc2) and changes in apoptosis-related genes consistent with activation of a caspase-8-mediated apoptotic programme. In conclusion, NGT, as a representative synthetic progestin, exerts reproductive toxicity by interfering with signaling mediators to induce premature AR, which subsequently exhausts metabolic energy and triggers plasma membrane impairment. These findings provide a critical mechanistic basis for the aquatic ecological risk assessment of synthetic progestins.

Animals↗
PubMed2026

Nephropathies Associated with Sickle Cell Trait and How to Study Them.

Sickle cell trait (SCT), which carries a single point mutation in the hemoglobin-β (HBB) gene, has long been considered a benign condition. However, epidemiological evidence challenges this assumption, revealing that individuals with SCT face an elevated risk of renal dysfunction. However, this field of study remains ill-defined as it has focused on sickle cell disease (SCD), where renal complications are severe. As SCT is more prevalent than SCD, consequences of nephropathies in this group translate into a substantial and largely unaddressed public health burden. Clinical data, primarily observational, implicate age and sex in the development of SCT-associated nephropathies. These manifestations span glomerular hyperfiltration, tubular damage, hematuria, renal papillary necrosis, renal medullary carcinoma, and progression to chronic kidney disease, all complications that cluster disproportionately in older male individuals. Despite this, the mechanistic basis of SCT nephropathy, the thresholds at which renal injury becomes clinically significant, and the optimal strategies for early identification and prevention remain inadequately defined. In vitro studies have primarily focused on SCD blood cell biology, with SCT receiving comparatively little attention. Humanized murine models (i.e., Berkeley and Townes) have recapitulated some SCT-associated renal phenotypes but need to be more fully characterized. This review aims to provide an overview of the biology of sickle cell trait nephropathies, the gaps in our knowledge, and the model systems we can use to fill those gaps.

Journal Article↗
PubMed2026

Overcoming Immunological Barriers in MSC-Derived Insulin-Producing Cells through CRISPR-Based Hypoimmunogenic Engineering and Translational Perspectives for Type 1 Diabetes.

Mesenchymal stromal cell (MSC)-derived insulin-producing cells (IPCs) represent an emerging strategy for β-cell replacement in type 1 diabetes mellitus (T1DM) owing to their differentiation potential, intrinsic immunomodulatory properties, and lower tumorigenic risk compared with pluripotent stem cell-derived platforms. However, accumulating evidence indicates that differentiation-associated immunogenicity, context-dependent immune recognition, and recurrent autoimmune responses may substantially limit long-term graft survival and therapeutic durability following transplantation. This review critically examines the immunological barriers associated with MSC-derived IPCs, including altered MHC expression, susceptibility to alloimmune and autoimmune-mediated rejection, and potential reactivation of autoreactive immune memory. We discuss the application of CRISPR-based hypoimmunogenic engineering strategies targeting antigen presentation pathways, NK-cell activation, and immune checkpoint modulation to generate more immune-evasive MSC-derived IPCs while preserving β-cell functionality. By integrating insights from T1DM immunopathogenesis, MSC biology, genome editing, and translational immunology, we propose a framework linking immune engineering with controlled differentiation, functional maturation, and long-term safety evaluation. In parallel, we comparatively position MSC-derived IPCs alongside clinically advancing iPSC-derived β-cell platforms to highlight their distinct translational niche, including potential advantages related to safety, immunomodulatory capacity, manufacturing accessibility, and scalability, while acknowledging the superior functional maturity and clinical progression currently demonstrated by iPSC-derived systems. Finally, we discuss key translational challenges, including genomic stability, immune-evasion durability, GMP-compliant manufacturing, and the need for rigorous functional and immunological benchmarking prior to clinical application of hypoimmunogenic MSC-derived IPC therapies in T1DM.

Humans↗
PubMed2026

Measurable Residual Disease and the Unresolved Biology of Leukemic Stem Cells.

Measurable residual disease (MRD) testing has transformed the management of hematologic cancers by enabling detection of residual malignant cells after therapy. Current approaches rely on qPCR and next-generation sequencing to monitor leukemia-associated somatic mutations, while multiparameter flow cytometry identifies aberrant leukemic immunophenotypes. Although these methods provide valuable prognostic and therapeutic information, MRD negativity remains an imperfect surrogate for cure. Most MRD platforms evaluate CD45+, rapidly dividing leukemic populations and fail to detect quiescent cells that may survive cytotoxic therapies which efficiently target proliferating hematopoietic cells. Relapse frequently occurs despite deep molecular remission, suggesting persistence of rare leukemic stem cells (LSCs) that are intrinsically resistant to chemotherapy and targeted therapies. The paradox of relapse despite molecular remission could be explained by the presence of very small embryonic-like stem cells (VSELs) which are pluripotent, quiescent stem cells sitting at the top of cellular hierarchy in multiple adult tissues including bone marrow. A pluripotent VSEL divides through asymmetrical cell division to give rise to two cells of different sizes and fates, smaller cell is to self-renew while the bigger is lineage-restricted and tissue-committed progenitor which undergoes extensive epigenetic changes, divides rapidly and undergoes clonal expansion before further differentiation. Dysfunctions of VSELs initiate both solid and hematologic cancers. Based on this view, somatic mutations monitored during MRD assessment possibly represent downstream consequences of clonal expansion rather than the initiating drivers of disease persistence. Thus, exclusive monitoring of somatic mutations and CD45 + leukemic populations possibly overlook rare, small-sized, CD45- VSELs that contribute to therapeutic resistance and relapse.

Humans↗
PubMed2026

Immune dysregulation in depression and psychosis: summary of current evidence and future perspectives.

Despite compelling epidemiological, genetic and cellular evidence linking immune dysregulation to depression and schizophrenia (and other psychotic disorders), causality remains contested and no immune biomarker has yet demonstrated robust clinical utility. Emerging methodological approaches - from target trial emulation on observational data to functional genomics - offer a potential path towards precision immunopsychiatry and stratified immunomodulatory treatment.

Neuroimmunology↗
PubMed2026

PaNDA: Efficient Optimization of Phylogenetic Diversity in Networks.

Phylogenetic diversity (PD) plays an important role in biodiversity, conservation, and evolutionary studies by measuring the diversity of a set of taxa based on their phylogenetic relationships. In phylogenetic trees, a subset of k taxa with maximum PD can be found by a simple and efficient greedy algorithm. However, this algorithmic tractability is lost when considering phylogenetic networks, which incorporate reticulate evolutionary events such as hybridization and horizontal gene transfer. To address this challenge, we introduce PaNDA (Phylogenetic Network Diversity Algorithms), the first software package and interactive graphical user-interface for exploring, visualizing, and maximizing diversity in phylogenetic networks. PaNDA includes a novel algorithm to find a subset of k taxa with maximum diversity, running in polynomial time for networks of bounded scanwidth, a measure of tree-likeness of a network that grows slower than the well-known level measure. This algorithm considers the variant of PD on networks in which the branch lengths of all paths from the root to the selected taxa contribute towards their diversity. We demonstrate the scalability of this algorithm on simulated networks, successfully analyzing level-15 networks with up to 200 taxa in seconds. We also provide a proof-of-concept analysis using a phylogenetic network on Xiphophorus species, illustrating how the tool can support diversity studies based on real genomic data. The software is easily installable and freely available at https://github.com/nholtgrefe/panda. Additionally, we extend the definition of PD to semi-directed phylogenetic networks, which are mixed graphs increasingly used in phylogenetic analysis to model uncertainty of the root location. We prove that finding a subset of k taxa with maximum diversity remains NP-hard on semi-directed networks, but do present a polynomial-time algorithm for networks with bounded level.

network↗
PubMed2026

Retinoid dynamics in immune cells during age-related diseases.

Retinoids comprise vitamin A and its structurally related natural and synthetic derivatives. Retinoid dynamics involves multiple retinoid forms, carrier proteins, and enzymes that orchestrate the absorption, transport, storage and biotransformation of dietary vitamin A. Beyond their canonical metabolic functions, metabolites and proteins involved in retinoid metabolism also play distinct roles in signal transduction and transcriptome reprogramming, broadening the mechanisms that influence immune cell fate decisions. Age‑related changes in retinoid bioavailability and signaling intensity alter immune cell polarization and function, thereby contributing to the pathogenesis of chronic inflammation in neurodegenerative diseases, cardiovascular diseases, osteoarthritis, and other age-related diseases. In this review, we focus on age-related alterations in the retinoid metabolic pathway and their impact on inflammation and the progression of age-related diseases. This review highlights the pivotal role of retinoid metabolism in anti-ageing interventions and considers future directions and challenges in this field.

Humans↗
PubMed2026

Emerging Principles in Spatial Functional Genomics.

Spatial transcriptomic and proteomic atlases have enabled mapping of gene programs within intact tissues, but these measurements remain largely descriptive and do not define the mechanisms controlling tissue biology. Pooled CRISPR screening provides scalable causal interrogation of gene function but remains largely confined to dissociated systems that lack spatial context. In vivo spatial functional genomics (SFG) bridges these approaches by integrating genetic perturbations with in situ transcriptomic and proteomic readouts to measure gene function within intact tissue ecosystems. By preserving spatial organization, SFG enables interpretation of perturbations through effects on cell-cell interactions, diffusible signals, multicellular niches, and tissue architecture. Here, we outline key design axes of SFG: perturbation strategy, barcoding strategy, and phenotypic readout. We discuss computational challenges, including spatial autocorrelation, neighborhood dependence, and context-aware null modeling, and highlight how SFG reveals non-cell-autonomous, architecture-dependent mechanisms of gene function, advancing toward predictive models of tissue organization and gene function.

Genomics↗
PubMed2026

Gene-environment interaction between perinatal oxytocin exposure and Pten mutation shapes epigenetic reprogramming of oxytocin signaling and behavior in mice.

Synthetic oxytocin (Pitocin) is the most commonly used pharmacologic agent for induction and augmentation of labor. Beyond its uterotonic effects, oxytocin plays a critical role in neurodevelopment and social behavior. Dysregulated oxytocin signaling has been implicated in autism spectrum disorder (ASD), raising concern that perinatal exposure to exogenous oxytocin may have lasting neurodevelopmental consequences. This study aimed to determine whether offspring harboring a genetic predisposition for ASD are differentially impacted by perinatal oxytocin exposures, with a focus on long-term oxytocin signaling and autism-like behavior. Pregnant mice carrying offspring with heterozygous mutations in phosphatase and tensin homolog deleted on chromosome ten (Pten), a well-established monogenic risk factor for ASD, received continuous oxytocin versus phosphate-buffered saline (PBS) control via micro-osmotic pumps during late gestation. Wild-type (WT) offspring exposed to each treatment served as a secondary control. Adult offspring were assessed for oxytocin receptor (Oxtr) methylation in the frontal cortex and hippocampus, oxytocin expression in the hypothalamus, serum oxytocin levels, and were subject to a battery of social and anxiety-related behavior tests. Perinatal oxytocin exposure produced genotype-dependent effects in offspring. Epigenetic analyses revealed bidirectional remodeling of Oxtr methylation in the frontal cortex and hippocampus, with increased exon 1 methylation in WT mice and decreased methylation in Pten-mutant mice, resulting in significant genotype-treatment interactions. Hypothalamic oxytocin expression increased following treatment regardless of genotype, though baseline levels were higher in Pten-mutant mice. Neither oxytocin treatment nor genotype impacted long-term serum oxytocin levels. Behavioral outcomes were modest but context-specific: repetitive behaviors and cognition performance were unchanged, but oxytocin-treated Pten-mutant mice exhibited increased anxiety-like behavior alongside improved social memory. In contrast, oxytocin-treated WT mice showed reduced social novelty preference. Exploratory analyses suggested potential sex-dependent trends. Our findings support a model in which genetic susceptibility shapes the epigenetic encoding of early-life hormonal signals, thereby recalibrating oxytocin system function and downstream behavioral outcomes. Together, these data highlight the context-dependent effects of perinatal oxytocin exposure and argue against uniformly beneficial or detrimental effects, emphasizing the importance of gene-environment interactions in neurodevelopmental trajectories.

Animals↗
PubMed2026

Gastric carcinoma classification in the WHO 6th edition (2026): Updated framework and emerging entities.

The sixth edition of the WHO Classification of Digestive System Tumours (2026) represents an important step in the continuing evolution of gastric carcinoma classification. While preserving morphology as the foundation of diagnosis, it incorporates advances in molecular pathology, genotype-phenotype correlations, tumour evolution, and predictive biomarker assessment. This review summarizes the development of the WHO classification from the third edition (2000) to the sixth edition (2026) and highlights its relationship with other major classification systems, including those of Laurén, Nakamura, and the Japanese Gastric Carcinoma Association (JGCA). Major histological categories remain largely unchanged; however, several important conceptual and diagnostic refinements have been introduced. These include recognition of crawling-type adenocarcinoma as a distinctive variant of tubular adenocarcinoma, subclassification of poorly cohesive carcinoma into signet-ring cell and non-signet-ring cell subtypes, introduction of the concept of pure signet-ring cell carcinoma, and increased emphasis on tumour evolution. The sixth edition also expands and refines the spectrum of uncommon gastric carcinoma subtypes, including gastric carcinoma with lymphoid stroma, AFP-producing carcinoma, micropapillary adenocarcinoma, gastric adenocarcinoma of fundic-gland type, and gastric sarcomatoid carcinoma. Crucially, molecular subgroups originally proposed by The Cancer Genome Atlas (TCGA) and actionable biomarkers-including HER2 (ERBB2), Claudin 18.2, mismatch repair deficiency/microsatellite instability (dMMR/MSI), and programmed death-ligand 1 (PD-L1)-have transitioned from research-based categories into essential tools for precision oncology. Rather than providing exhaustive diagnostic criteria, this review offers a conceptual framework and encourages consultation of the original WHO text for full details. These advances illustrate the transition of gastric carcinoma classification from a predominantly morphology-based system toward an integrated histomolecular framework that more closely links pathological diagnosis with tumour biology, prognostication, and therapeutic stratification.

Crawling-type adenocarcinoma↗
PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗
PubMed2026

Alternative genetic codes in bacteria and archaea identified with a fast k-mer-based algorithm.

The genetic code is conserved across all domains of life and is often described as universal. Nevertheless, many exceptions to the "universal" code have now been documented, most of these through manual or semiautomated inspection of highly conserved genes. Modern bioinformatics tools improved our ability to find alternative genetic codes but remain computationally expensive, preventing widespread use on thousands of new species identified by sequencing environmental samples. Here, I report a >100-fold accelerated method for inferring the genetic code directly from assembled genomes and apply it to thousands of previously uncharacterized assemblies from archaea and bacteria. I describe three candidate genetic code variations, one of which, an alternative genetic code used by a family of Asgard archaea, is a unique example of sense codon reassignments for this domain. Identifying genetic code variations is important for understanding evolution of the standard code and improving accuracy of protein databases and open reading frame identification.

Genetic Code↗
PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗
PubMed2026

Statistical test to compare the linkage model and the admixture model based on central limit results.

In the Admixture Model, the probability that an individual carries a certain allele at a specific marker depends on the allele frequencies in K ancestral populations and the proportion of the individual's genome originating from these populations. The markers are assumed to be independent. The Linkage Model is a Hidden Markov Model that extends the Admixture Model by incorporating linkage between neighboring loci. We prove consistency and asymptotic normality of maximum likelihood estimators for the ancestry of individuals in the Linkage Model, complementing earlier results by (Pfaff et al., 2004; Pfaffelhuber and Rohde, 2022; Heinzel, 2025) for the Admixture Model. These results are used to prove that a statistical test that allows for model selection between the Admixture Model and the Linkage Model is an asymptotic level-α-test. Finally, we demonstrate the practical relevance of our results by applying the test to real-world data from The 1000 Genomes Project Consortium (2015).

Genetic Linkage↗