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PubMed2025

The role of iPSC research for insight into inherited arrhythmia conditions.

Human induced pluripotent stem cells (iPSCs) have emerged as a transformative platform for modeling inherited cardiac arrhythmia syndromes and uncovering human-specific disease mechanisms. However, the promise of iPSC-derived cardiomyocytes lies beyond the recapitulation of arrhythmogenic phenotypes and channelopathies. In this review, we explore recent works which have enabled mechanistic interrogation and therapeutic insight for inherited arrhythmia syndromes, beyond the capabilities of traditional animal models. Such studies have leveraged iPSCs to elucidate the role of splice variants, transcriptional regulation, and mitochondrial stress in arrhythmogenesis. Further, iPSC systems have proven important for reclassifying variants of uncertain significance and in modeling idiopathic arrhythmias where genotype-phenotype links are elusive. Advances in directed differentiation now permit chamber-specific cardiac cell generation, allowing for atrial and ventricular disease modeling and revealing critical cell-cell interactions. iPSCs also serve as high-fidelity precursor platforms for drug testing, offering predictive insight into mutation-specific responses to pharmacologic and genetic therapies. Though limitations in maturation and scalability persist, ongoing efforts for integration with tissue engineering, multi-cellular models, and computational frameworks are evolving to improve model reliability. iPSC-based systems now occupy a critical role in arrhythmia research, bridging basic discovery with translational applications, thereby contributing to personalizing care and advancing therapeutics in inherited and idiopathic arrhythmic syndromes.

Humans↗
PubMed2026

OscillomeR infers ultradian oscillations and targets of the Hes family.

The Hes family, basic-helix-loop-helix transcription factors and downstream effectors of Notch signaling, regulate the fate choices of pancreatic progenitors, muscle stem cells, neuronal progenitors, and presomitic mesoderm cells. Bioluminescence imaging (BLI) has revealed ultradian oscillatory dynamics of Hes-family members Hes1, Hes5, and Hes7. However, identifying which of the Hes target genes also oscillate remains challenging due to the time-consuming and costly nature of tracking individual target genes using BLI. Here, we propose OscillomeR, a computational framework that reconstructs ultradian oscillations from RNA-sequencing data to identify oscillatory target genes at high throughput. OscillomeR predicts thousands of oscillatory genes in synchronized or unsynchronized cell types, identifying both known and novel Hes-family targets. It also captures the dynamic rewiring of gene-regulatory networks during cell differentiation. Overall, OscillomeR is an effective tool for elucidating the functions of oscillatory transcription factors at the genomic scale.

Hes family↗
PubMed2026

Integrating single-cell transcriptomics to construct an oncogene-driven prognostic model and elucidate metabolic-immune crosstalk in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, its progression and treatment heterogeneity are mainly influenced by driver gene and tumor micro-environment (TME) interactions. Nevertheless, the mechanisms of this process at the single-cell level remain unclear. This study integrated TCGA and multi-center single-cell transcriptome data to identify a 575 genes HCC-specific core set, developing a single-cell "oncogene scoring" system to quantify individual carcinogenic activity. This score is significantly elevated in malignant and proliferative T cells and is closely associated with metabolic reprogramming, aberrant cell‒cell communication, and immunosuppressive phenotypes. Based on these characteristics, we constructed a machine learning-based Random Survival Forest (RSF) prognostic model validated in multiple independent cohorts, which classifies patients into distinct risk subtypes. The high-risk group exhibits genomic instability, increased tumor stemness, and immune evasion, while the low-risk group was more sensitive to drugs such as sorafenib. This study highlights the potential pathways by which high oncogenic activity is associated with HCC progression, suggesting a profound link with single-cell metabolic‒immune crosstalk. The constructed RSF model offers a promising computational framework for risk stratification and provides hypothesis-generating insights that may inform future personalized treatment strategies for HCC patients.

Hepatocellular carcinoma↗
PubMed2026

Dynamic metabolic modelling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic flux balance analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modelling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modelling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Humans↗
PubMed2025

Computational modeling of human genetic variants in mice.

Mouse models represent a powerful platform to study genes and variants associated with human diseases. While genome editing technologies have increased the rate and precision of model development, predicting and installing specific types of mutations in mice that mimic the native human genetic context is complicated. Computational tools can identify and align orthologous wild-type genetic sequences from different species; however, predictive modeling and engineering of equivalent mouse variants that mirror the nucleotide and/or polypeptide change effects of human variants remains challenging. Here, we present H2M (human-to-mouse), a computational pipeline to analyze human genetic variation data to systematically model and predict the functional consequences of equivalent mouse variants. We show that H2M can integrate mouse-to-human and paralog-to-paralog variant mapping analyses with precision genome editing pipelines to devise strategies tailored to model specific variants in mice. We leveraged these analyses to establish a database containing > 3 million human-mouse equivalent mutation pairs, as well as in silico-designed base and prime editing libraries to engineer 4,944 recurrent variant pairs. Using H2M, we also found that predicted pathogenicity and immunogenicity scores were highly correlated between human-mouse variant pairs, suggesting that variants with similar sequence change effects may also exhibit broad interspecies functional conservation. Overall, H2M fills a gap in the field by establishing a robust and versatile computational framework to identify and model homologous variants across species while providing key experimental resources to augment functional genetics and precision medicine applications. The H2M database (including software package and documentation) can be accessed at https://human2mouse.com.

Journal Article↗
PubMed2025

Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk.

Fanconi Anemia (FA) is a heritable syndrome characterized by DNA damage repair deficits, frequent malformations and a significantly elevated risk of bone marrow failure, leukemia, and mucosal head and neck squamous cell carcinomas (HNSCC). Hematopoietic stem cell gene therapy can prevent marrow failure and lower leukemia risk, but mucosal gene therapy to lower HNSCC risk remains untested. Major knowledge gaps include an incomplete understanding of how rapidly gene-corrected cellular lineages could spread through the oral epithelium, and which delivery parameters are critical for ensuring efficient gene correction. To answer these questions, we extended an agent-based model of the oral epithelium to include the delivery of gene correction in situ to FA cells and the competitive dynamics between cellular lineages with and without gene correction. We found that only gene-corrected lineages with substantial proliferative advantages (probability of resisting displacement out of the basal layer ≥ 0.1) could spread on clinically relevant timelines, and that these lineages were initially at high risk of loss in the generations following correction. Delivering gene correction to many cells minimizes the risk of loss, while delivery to many distinct locations within a tissue maximizes the rate of spread. To determine the impact of mucosal gene therapy in preventing the clonal expansion of pre-cancerous mutations, we compared the expected burden of TP53 mutations in simulated tissue sections with and without gene correction. We found that when FA cells have elevated genome instability or a TP53-dependent proliferative advantage, gene correction can substantially reduce the accumulation of pro-tumorigenic mutations. This model illustrates the power of computational frameworks to identify critical determinants of therapeutic success to enable experimental optimization and support novel and effective gene therapy applications.

Journal Article↗
PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

Journal Article↗
PubMed2021

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Genomics↗
PubMed2026

Long-read sequencing reveals widespread novel splicing and neojunction-derived neoantigens in nasopharyngeal carcinoma.

The widespread transcriptomic diversity driven by alternative splicing (AS) contributes to all hallmarks of cancer and represents a critical source of neoantigens for personalized immunotherapy. However, unlike other major malignancies, the full repertoire of AS in nasopharyngeal carcinoma (NPC) remains underexplored. Here, we employ long-read sequencing (LR-seq) to generate a high-resolution, isoform-level transcriptomic atlas from a cohort of 14 NPC tumor samples and four immortalized nasopharyngeal epithelial cell lines. We identify a substantial number of full-length novel transcripts (22,687; ∼44.38%), which reveal diverse splicing patterns and previously unannotated splicing events. By integrating short-read RNA-seq data to quantify isoform expression, we discover a subset of novel transcripts that are differentially expressed between tumor samples and immortalized nasopharyngeal epithelial cell lines. Furthermore, LR-seq enables precise identification of chimeric readthrough fusion transcripts, such as CLDN15-FIS1 and FOXRED2-TXN2 Finally, we develop a computational framework, tumor-specific splicing neoantigen detection (TS-SNAD), to predict neoantigens originating from novel exon-exon junctions (neojunctions) in tumor-specific novel transcripts. Using this framework, we identify neojunction-derived neoantigens and experimentally validate the immunogenicity of selected HLA-B*40:01-restricted neoantigens. These neojunction-derived peptides constitute a new class of noncanonical neoantigens with significant potential for developing personalized cancer vaccines for NPC.

Humans↗
PubMed2026

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗
PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗
PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

Journal Article↗
PubMed2026

A Network Pharmacology and Molecular Docking Study of TongBi Formula for Osteoarthritis.

This study applied network pharmacology combined with molecular docking to predict the potential therapeutic targets and molecular mechanisms of TongBi Formula (TBF) in osteoarthritis (OA). Active components and corresponding targets of TBF were retrieved from the traditional Chinese medicine Systems Pharmacology Database and Analysis Platform, while OA-related targets were collected from Online Mendelian Inheritance in Man, GeneCards, DrugBank, and Therapeutic Target Database. A network visualization and analysis software was used to construct compound-target and protein-protein interaction (PPI) networks. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery platform. Molecular docking analysis was conducted using a molecular docking software to evaluate the predicted binding affinity between key active compounds and core target proteins. A total of 47 overlapping targets between TBF and OA were identified. PPI network analysis highlighted JUN, RELA, IL6, MAPK1, and IL10 as potential hub targets. Enrichment analysis suggested that TBF may regulate inflammation, lipid metabolism, and multiple intracellular signaling pathways associated with OA progression. Molecular docking results demonstrated favorable predicted binding affinities between core active compounds and key OA-related protein targets. These findings provide a computational framework for understanding the potential mechanisms of TBF against OA and support further experimental validation.

Molecular Docking Simulation↗
PubMed2026

Systematic common and rare variant association testing in 392,030 whole genomes in All of Us.

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide ("All by All") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.

Journal Article↗
PubMed2025

Privacy-preserving framework for genomic computations via multi-key homomorphic encryption.

MOTIVATION: The affordability of genome sequencing and the widespread availability of genomic data have opened up new medical possibilities. Nevertheless, they also raise significant concerns regarding privacy due to the sensitive information they encompass. These privacy implications act as barriers to medical research and data availability. Researchers have proposed privacy-preserving techniques to address this, with cryptography-based methods showing the most promise. However, existing cryptography-based designs lack (i) interoperability, (ii) scalability, (iii) a high degree of privacy (i.e. compromise one to have the other), or (iv) multiparty analyses support (as most existing schemes process genomic information of each party individually). Overcoming these limitations is essential to unlocking the full potential of genomic data while ensuring privacy and data utility. Further research and development are needed to advance privacy-preserving techniques in genomics, focusing on achieving interoperability and scalability, preserving data utility, and enabling secure multiparty computation. RESULTS: This study aims to overcome the limitations of current cryptography-based techniques by employing a multi-key homomorphic encryption scheme. By utilizing this scheme, we have developed a comprehensive protocol capable of conducting diverse genomic analyses. Our protocol facilitates interoperability among individual genome processing and enables multiparty tests, analyses of genomic databases, and operations involving multiple databases. Consequently, our approach represents an innovative advancement in secure genomic data processing, offering enhanced protection and privacy measures. AVAILABILITY AND IMPLEMENTATION: All associated code and documentation are available at https://github.com/farahpoor/smkhe.

Computer Security↗
PubMed2026

Molecular origins of pH gradients in charge-regulated biomolecular condensates.

Biomolecular condensates exhibit spontaneous electrochemical microenvironments characterized by asymmetric ion distributions and pH gradients that emerge from protein-sequence-dependent charge regulation. Despite their biological importance, mechanistic understanding of these microenvironments has been constrained by the absence of computationally tractable frameworks capable of treating proton exchange, counterion partitioning, and buffer equilibria on consistent thermodynamic footing. Here, we introduce the buffered Charge-Regulation Monte Carlo (b-CR-MC) framework, which couples grand-canonical exchange of ions and buffer species with explicit charge regulation of titratable residues. By extending the CR-MC ion-merging strategy to multicomponent reservoirs and employing the restricted primitive model, b-CR-MC achieves computational efficiency while maintaining thermodynamic rigor, achievingquantitative agreement with the more expensive generalized grand-reaction Monte Carlo approach. Applied to full-length FUS (net positive) and PGL-3 (net negative) under physiological conditions, the framework reveals sequence-dependent pH gradients: the dense phase of FUS exhibits an alkaline shift, while that of PGL-3 exhibits an acidic shift, in both cases driving the condensate interior toward the protein's isoelectric point. Slab-geometry simulations further resolve the Donnan potential and continuous ion profiles across the condensate interface, confirming the direction of these electrochemical shifts. Additionally, we identify spatially resolved buffer depletion within dense phases, establishing that dynamic charge regulation is a primary determinant rather than a secondary correction to condensate electrochemistry. By establishing a sequence-resolved, thermodynamically consistent computational platform, b-CR-MC enables quantitative prediction of how mutations and post-translational modifications reprogram condensate microenvironments across biological and pathophysiological contexts.

Hydrogen-Ion Concentration↗
PubMed2026

Multicellular ecosystems: Linking cellular diversity to tissue function and disease.

Tissue function emerges from coordinated interactions among diverse cell populations, whereas disruption of these interactions can lead to dysfunction. Recent advances in single-cell and spatial genomics have not only cataloged cellular diversity but also revealed how tissues are organized as dynamic multicellular ecosystems. Moving beyond descriptive cell atlases toward functional, system-level representations represents a major frontier in tissue biology. In this review, we outline conceptual and methodological frameworks for dissecting multicellular coordination, highlight recurrent multicellular ecosystems across physiological and pathological contexts, and explore translational opportunities such as patient stratification, therapeutic reprogramming, and regenerative strategies. Viewing tissues through an ecosystem lens provides a unifying framework that links cellular diversity to emergent tissue function and informs strategies for disease intervention.

Humans↗
PubMed2026

IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.

Genetic contributions to complex traits are often mediated through coordinated gene-gene interaction networks, yet most existing association frameworks focus on marginal single-gene effects and overlook higher-order dependency structures. Direct modeling of interactions remains challenging due to combinatorial complexity and statistical instability. We introduce Interaction-Bridged Association Study (IBAS), a general framework that incorporates pathway-level interaction patterns into genotype-phenotype association analysis without explicitly enumerating interactions. IBAS leverages transcriptomic reference data to construct low-dimensional representations of pathway activity, which guide SNP-weighting and gene-level association testing within a kernel-based framework. In perturbation-based simulations, IBAS demonstrates improved stability and reproducibility compared to conventional TWAS and gene-based methods, while maintaining well-calibrated Type I error under phenotype permutation. Application to the WTCCC datasets identifies both known and novel genes across multiple complex diseases, including candidates with modest marginal effects missed by standard approaches. These findings are supported by replication in an independent cohort, and analyses across multiple reference tissues revealing both shared and tissue-specific signals. Overall, IBAS provides a statistically robust and computationally tractable framework for incorporating interaction effects into association mapping, extending beyond the single-gene paradigm and enabling more comprehensive characterization of complex trait. IBAS is available on GitHub at: https://github.com/QingrunZhangLab/IBAS.

Polymorphism, Single Nucleotide↗