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PubMed2025

Inference and visualization of complex genotype-phenotype maps with gpmap-tools.

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

Gaussian process↗

PubMed2025

Repeated evolution of photoperiodic plasticity by different genetic architectures during recurrent colonizations in a butterfly.

In cases of recurrent colonizations of similar habitats from the same base population, it is commonly expected that repeated phenotypic adaptation is caused by parallel changes in genetic variation. However, it is becoming increasingly clear that similar phenotypic variation may also evolve by alternative genetic pathways. Here, we explore the repeated evolution of photoperiodic plasticity for diapause induction across Swedish populations of the speckled wood butterfly, Pararge aegeria. This species has colonized Scandinavia at least twice, and population genomic results show that one of the candidate regions associated with spatial variation in photoperiodism is situated on the Z-chromosome. Here, we assay hybrid crosses between several populations that differ in photoperiodic plasticity for sex-linked inheritance of the photoperiodic reaction norm. We find that while a cross between more distantly related populations from the two different colonization events shows strong sex-dependent inheritance of photoperiodic plasticity, a cross between two more closely related populations within the oldest colonization range shows no such effect. We conclude that the genotype-phenotype map for photoperiodic plasticity varies across these populations and that similar local phenotypic adaptation has evolved during recurrent colonization events by partly non-parallel genetic changes.

Butterflies↗

PubMed2026

Hologenomic interactions promote the higher-order evolvability of phenotypic complexity.

Current models for evolvability and complexity generally focus on mutational and regulatory processes in the host genome alone, limiting their ability to explain the origin, inheritance, and dynamics of many phenotypes. We describe a framework treating multigenome interactions in the holobiont as a central process that impacts the genotype-phenotype map, expanding the dimensionality of mechanisms producing heritable variation, generating novel traits, and exploring adaptive trajectories. These mechanisms can promote both complex phenotypic innovation and evolutionary systems drift. Many evolutionary pathways and novelties cannot be fully understood from host data alone but require consideration of hologenomic targets of selection. We outline hypotheses and methods to quantify and evaluate their impacts as a fundamental macroevolutionary process.

cellular innovation↗

PubMed2025

FuNTB: a functional network clustering tool for the analysis of genome-wide genetic variants in Mycobacterium tuberculosis.

MOTIVATION: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), still claims around 1.25 million lives each year. The growing threat of drug resistance-often driven by single‑nucleotide polymorphisms (SNPs) in Mtb genomes underscores the need for high‑quality genomic data and powerful bioinformatics tools. We present FuNTB, a python‑based pipeline that detects non‑synonymous SNPs in Mtb and builds functional network clusters to reveal genotype-phenotype relationships. RESULTS: FuNTB profiles non‑synonymous SNPs at the gene level across user‑defined phenotypes, pinpointing both shared and unique mutations. It ingests annotated Variant Call Format (VCF) files or MTBseq outputs and merges them with clinical metadata to produce network‑XML files compatible with Cytoscape and Gephi. When applied to the CRyPTIC Mtb collection, FuNTB rapidly recovered established resistance genes and surfaced novel candidates, validating its utility for mapping genotype-phenotype associations. AVAILABILITY AND IMPLEMENTATION: FuNTB is implemented in Python 3.8+ and is freely available under the MIT license at https://doi.org/10.5281/zenodo.15399917.

Mycobacterium tuberculosis↗

PubMed2025

Comparative Genomic Screening Identifies Developmental Constraint Loci Underscoring the Phenotypic Evolution of Syngnathids.

Seahorses and their relatives (syngnathids) exhibit remarkable diversity in morphology and function, characterized by their distinctive body shapes and specialized feeding mechanisms. Despite recent advances in uncovering the genetic basis of some traits, the genotype-phenotype map in syngnathids remains incomplete. In this study, we employed forward-genomic approaches and developed a method to enrich for human disease amino acid loci at a genomic scale. Our aim was to identify genetic loci associated with fin size reduction, tooth loss, and spinal curvature in syngnathids. Intriguingly, we identified a convergent amino acid change in the lat4a gene shared by syngnathids and some flying fishes, with in vitro analysis confirming its role in fin size evolution in both lineages. While genes critical for tooth development are conserved in syngnathids, the absence of key regulatory elements, such as pitx2, likely contributes to tooth loss. Additionally, we implicated col6a3 in spinal curvature development in seadragons. These findings reveal novel genetic signatures and developmental constraints underlying syngnathid diversity, demonstrating the utility of comparative genomics and targeted gene enrichment in exploring vertebrate evolution.

Animals↗

PubMed2026

Exploring genomic regions regulating the liver transcriptome and energy homeostasis in pigs.

In pigs, energy homeostasis has an impact on meat quality and health. In a Duroc pig population, 30 quantitative trait locus (QTL) regions associated with fatty acid (FA) composition in adipose tissue, plasma, liver and muscle were previously identified. Mapping of expression quantitative trait locus (eQTL) regions will provide a molecular hypothesis for genotype-phenotype interactions and may allow the identification of shared causal variants, key to increasing our understanding of the genetic regulation of FA composition and energy homeostasis. However, gene expression is impacted by environmental factors, while individual-level allelic imbalance (AI) can be more reliable and can be surveyed via allelic-specific expression (ASE) analysis. Furthermore, treatment of ASE as a quantitative trait allows the identification of allele-specific expression quantitative trait loci (aseQTLs), which are variants whose heterozygosity is linked to the AI of a nearby single-nucleotide polymorphism (SNP), pointing to regulatory elements. In this study, liver was selected as a key metabolic hub with an important role in the regulation of energy homeostasis, and 310 liver RNA sequencing samples were analysed using a combination of (1) eQTL mapping, (2) ASE analysis, and (3) aseQTL mapping methods. A total of 2 188 eQTL regions were identified, mostly cis-eQTL regions (73.17%). ASE analysis reported 1 964 ASE SNPs, associated with 633 genes. Finally, aseQTL mapping reported 64 172 aseQTL, associated with the AI of 31 genes. Colocalisation analysis combined with ASE analysis showed that the expression of FADS1 and FADS2 genes is associated with the polyunsaturated FA composition in several tissues, where microRNA regulation may be present. Finally, in the DGAT2 gene, annotated in a QTL region associated with multiple FAs in adipose tissue, ASE revealed allelic imbalance in the 3' untranslated region (UTR) of this gene. Allelic differential expression can be caused by a 13-bp insertion affecting messenger RNA stability, previously described, exemplifying how allelic imbalance is caused by a post-transcriptional regulatory mechanism undetectable by eQTL mapping. Furthermore, aseQTLs were associated with this gene, linked to a previously identified copy number variant not yet associated with DGAT2 expression. These results demonstrate how ASE analysis and aseQTL mapping can complement eQTL mapping, as they resolved a complex region affected by allelic heterogeneity, a main confounding effect of QTL mapping. In conclusion, the combination of eQTL mapping, ASE analysis and aseQTL mapping allowed the characterisation of the regulation of liver gene expression, improving our understanding of the genetic determinism of energy homeostasis.

Allele-specific expression↗

PubMed2025

A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Quantitative Trait Loci↗

PubMed2026

Epidermolysis Bullosa Classification and Current Approach to Diagnosis.

Epidermolysis bullosa (EB) is a heterogeneous group of rare genodermatoses marked by skin fragility and bullae formation induced by minor trauma. Pathologic variants in at least 21 genes are associated with EB, grouped into four major subtypes based predominantly on the plane of cleavage within the skin. EB simplex is characterized by epidermal bullae formation and is due to gene mutations that affect epidermal proteins, most commonly keratin filaments. Junctional EB is due to gene mutations affecting proteins in the basement membrane zone, causing a split within the lamina lucida of the dermal-epidermal junction. Dystrophic EB is characterized by subepidermal bullae formation and is due to mutations in the gene encoding type VII collagen, which makes up the anchoring fibrils in the papillary dermis. Kindler EB is the rarest subtype and may be associated with cleavage at various levels within the skin due to a mutation in the FERMT1 gene causing defects in kindlin-1, a protein associated with integrins and focal adhesions. Because EB is such a heterogeneous disease, an understanding of genotype-phenotype correlations is necessary to help guide management. Traditionally, the first step in diagnosis was inducing a blister that was biopsied for immunofluorescence mapping. Currently, the gold standard for diagnosis is a blood sample or buccal swab for extraction of genomic DNA via next-generation sequencing, which can identify the exact causative gene. A diagnosis of EB is life altering for patients and families alike. A firm understanding of EB classification and initial diagnostic workup can help dermatologists feel empowered to support and counsel families.

Humans↗

PubMed2026

Genetic architecture of endometriosis: risk factors, comorbidities and clinical implications.

BACKGROUND: In 1999, Dr Susan Treloar and colleagues conducted a landmark twin study in Australia and reported their estimate of 51% for the heritability of endometriosis. This important result led several groups to begin mapping genetic factors contributing to increased endometriosis risk. Despite early challenges, advances in genome-wide association studies (GWAS) have identified multiple genetic risk factors and some target genes implicated in follow-up studies on genetic regulation of transcription. Access to large publicly available genetic datasets and analysis with endometriosis GWAS results is also providing new opportunities to answer important questions about comorbid conditions associated with endometriosis and their implications for clinical practice. OBJECTIVE AND RATIONALE: The objective of the review is to summarize the last 25 years of genetic studies in endometriosis, outline contributions to our understanding of the disease, and suggest future directions to accelerate biological insights from genetic studies to improve clinical outcomes. SEARCH METHODS: A comprehensive review of scientific literature on the genetics of endometriosis was conducted through searches in PubMed and Google Scholar up to June 2026. Search terms included "endometriosis AND (genetics OR GWAS OR genetic risk factors)", For studies addressing the functional characterization of genetic risk loci, additional searches employed the terms "endometriosis AND (genotype-phenotype associations OR colocalization OR eQTL OR mQTL OR multi omics methods)". To identify studies examining shared genetic risk between endometriosis and comorbid conditions, the search strategy included "endometriosis AND (genetic correlation OR colocalization OR Mendelian randomisation)". Publications reporting discoveries related to genetic risk factors for endometriosis and studies interpreting their biological and clinical significance were critically evaluated, and 144 publications were discussed in the review. OUTCOMES: Discovery of genetic risk factors started slowly and has accelerated in recent years with developments in technology and international collaborations to combine data and increase statistical power. GWAS have mapped 80 genetic risk factors that implicate gene regulation of hormonal targets, development of the reproductive tract, regulation of cell proliferation, and regulation of epithelial cell differentiation. In common with most other complex diseases, effects of individual common genetic risk factors are small. However, several examples demonstrate that small effect sizes are not a good predictor for the impact of drugs developed against genetically validated targets. Genetic risk factors implicate five genes regulating gonadotrophin release and oestrogen action, the major target pathway of current drugs for treatment of endometriosis demonstrating proof-of-principal for biologically meaningful results. Genetic correlation and Mendelian Randomization studies highlight important causal relationships between endometriosis and comorbid conditions including a possible role for testosterone during development and shared genetic risk factors for gynaecological, gastrointestinal, pain, psychiatric, and inflammatory conditions. Understanding causal relationships between endometriosis and related conditions will aid clinical management and more personalized treatments. WIDER IMPLICATIONS: Genetic studies provide novel insights into endometriosis pathogenesis and associations with related comorbid conditions. Genetic factors modifying gene regulation and disease risk likely act in specific cell types, and access to datasets from genetically informed cell-based models, single-cell and spatial omics data are needed to accelerate progress. Future studies should address critical questions of heterogeneity and disease subtypes, expand the search for genetic risk factors to non-European populations, evaluate the role of rare and structural variants, and better integrate data from functional, genomics, genetics, and clinical studies to reduce diagnostic delay, develop novel treatment strategies, and translate discoveries into personalized management strategies for affected individuals. REGISTRATION NUMBER: N/A.

comorbid conditions↗

PubMed2026

Genotype-structure-phenotype correlations define divergent natural history in early-onset spastic paraplegia type 4.

Hereditary spastic paraplegia type 4 (SPG4), caused by variants in SPAST, is the most common form of HSP and exhibits a remarkable phenotypic heterogeneity ranging from late-onset pure presentations to severe, early-onset complex disease. Robust genotype-phenotype correlations and detailed natural history data are lacking, limiting clinical trial readiness. We analyzed 206 patients with genetically confirmed SPG4 enrolled across seven international centers, complemented by high-quality literature-derived cases. Deep phenotyping included standardized motor scales, spasticity ratings, developmental milestones, and patient-reported outcomes. We developed an extended essentiality-mapping framework to classify SPAST missense variants by integrating in silico pathogenicity predictions, evolutionary constraint, physicochemical residue connectivity, and variant enrichment within the human spastin hexamer structure. Plasma neurofilament light chain (pNfL) using was quantified using Simoa in 26 patients and 101 controls. We identified 136 distinct SPAST variants, including 10 novel variants. Variant class segregated strongly by inheritance, with de novo cases enriched for missense variants and inherited cases showing a variety of variant classes with enrichment for truncating variants. Longitudinal analysis revealed two latent trajectories: a rapidly progressive severe subgroup enriched for de novo missense variants, and a biphasic moderate subgroup enriched for inherited truncating variants. Patient stratification integrating spastin essentiality mapping (missense variants affecting essential, neutral, or context-dependent residues) with established genetic modifiers (biallelic pathogenic variants or modifier variants in trans) classified patients into predicted severe and moderate subgroups with divergent age at onset and clinical disease progression. The severe subgroup showed early developmental delays, rapid loss of ambulation, and declining quality of life, while the moderate subgroup displayed delayed but accelerating disease progression. pNfL levels were elevated in both subgroups, most pronounced in severe early disease. This study provides the most detailed natural history of SPG4 to date and introduces a biologically informed stratification framework that links variant class and location to divergent clinical trajectories. These data establish clinically meaningful benchmarks and offer a genotype-based framework to improve anticipatory care and optimize trial design for SPG4.

SPAST↗

PubMed2026

A single-cell lens into the co-evolution of genotypes and phenotypes in cancer.

Genetic heterogeneity and clonal outgrowths are observed even in otherwise healthy human tissues, shaping the genetic composition of cell populations in non-malignant disease and during physiological ageing. This clonal mosaicism likely provides the pre-cancerous seeds for malignant transformation. Once a tumour arises, clonal evolution poses a major challenge to achieving cure, as clonal diversification provides an expanded number of substrates upon which therapy can act as a selective pressure, leading to the selection of resistant clones that ultimately fuel disease recurrence. Understanding somatic clonal evolution requires not only mapping genetic diversity but also defining the resulting phenotypes that provide a fitness advantage to mutated clones. This Review discusses multimodal single-cell technologies that enable the measurement of genotypes and additional molecular features from the same cell. These technologies unveil mutant-specific phenotypic traits, often show cell-state specificity in genotype-phenotype effects and can define therapeutic vulnerabilities for precision elimination of disease-propagating mutant cells. Furthermore, the combination of phylogenetic reconstruction with phenotypic measurements allows for the temporal mapping of clonal evolution and phenotypic plasticity. These breakthroughs have created a unique opportunity to define, directly in primary human samples, the mechanisms underlying clonal expansion in both healthy and malignant tissues.

Journal Article↗

PubMed2026

Single-cell profiling of mitochondrial phenotyping-coupled mtDNA genotyping.

Simultaneously profiling mitochondrial DNA (mtDNA) heteroplasmy and phenotypic variability at the single-cell level remains a challenge due to the absence of integrated methods that map mitochondrial genotypes alongside their functional states. We introduce human single-cell mitochondrial phenotype-coupled mtDNA sequencing (scMPCDS), a platform that quantifies mtDNA mutations and heteroplasmy together with mitochondrial membrane potential and reactive oxygen species within individual cells. Unlike bulk sequencing or separate single-omics techniques, scMPCDS directly correlates mitochondrial genomic instability with functional outcomes. Using this approach, we demonstrate that DdCBE-mediated mtDNA editing induces cell-specific off-target mutations in the mitochondrial genome, which coincide with diverse phenotypic changes. Applying scMPCDS to HeLa cells and clear cell renal cell carcinoma tissues, we identify single-cell subpopulations exhibiting distinct mtDNA mutation burdens and altered bioenergetic profiles, implicating potential mitochondrial heterogeneity-driven tumor evolution. Overall, scMPCDS serves as a versatile tool to unravel mitochondrial genotype-phenotype relationships at the single-cell level in both normal and disease states, thereby advancing precise mitochondrial diagnostics and therapeutics.

Humans↗

PubMed2025

Mapping genetic and phenotypic diversity of Pseudomonas aeruginosa across clinical and environmental isolation sites.

Pseudomonas aeruginosa is a clinically significant opportunistic pathogen adept at thriving in both host-associated and environmental settings. To define the extent to which P. aeruginosa isolates specialize across niches and identify genotype-phenotype correlates, we performed whole genome sequencing and comprehensive phenotypic characterization of 125 P. aeruginosa isolates from diverse clinical and environmental sites, evaluating virulence-associated traits, including motility, cytotoxicity, biofilm formation, pyocyanin production, and antimicrobial susceptibility. We identify that genomic diversity does not correlate with isolation source or most virulence phenotypes. Instead, we find that the two major P. aeruginosa clades (Groups A and B) delineate phylogeny and cytotoxicity, with Group B strains showing significantly higher cytotoxicity than Group A. Sequence analysis revealed previously uncharacterized alleles of genes encoding type III secretion effector proteins. We observed high variability amongst strains and isolation sources in all four assayed virulence phenotypes. Antimicrobial resistance (AMR) is exclusively observed in clinical isolates, not environmental, reflecting antibiotic exposure-driven selection. Bacterial GWAS revealed a statistically significant association between cytotoxicity and exoU presence, and we identified a novel exoU allelic variant with decreased cytotoxicity, demonstrating that functional diversity within well-characterized virulence factors may still influence pathogenic outcomes. In summary, our analyses of 125 diverse isolates suggest that the ability of P. aeruginosa to thrive across diverse niches is driven by broadly conserved genetic repertoire rather than niche-specific accessory genes.

Journal Article↗

PubMed2026

Comprehensive in silico genomics analysis of global trends and host-specific emergence of aminoglycoside resistance in Staphylococcus aureus: a One-Health perspective.

BACKGROUND: Aminoglycosides remain clinically valuable against Staphylococcus aureus. Aminoglycoside resistance in S. aureus represents a critical One Health concern and is primarily driven by aminoglycoside-modifying enzymes (AMEs), which are frequently plasmid-encoded. Although regional studies have provided valuable insights, the global epidemiology of aminoglycoside resistance determinants remains poorly characterized because comprehensive data integrating human, animal, and environmental reservoirs are still lacking. This study addresses this gap by analyzing over 110,000 S. aureus genomes (2000-2025) to map the global resistome, quantify temporal and host-specific trends, and assess the association between genetic determinants and phenotypic resistance. METHODS: We performed a retrospective One Health meta-analysis of 110,309 S. aureus genomes collected between 2000 and 2025 from 128 countries. Genomes were quality-filtered and aminoglycoside resistance determinants were identified using NCBI AMRFinderPlus (v4.0.23). Multilocus sequence typing and host-source harmonization (Human, Animal, Environment, Unknown) enabled clonal and reservoir stratification. Temporal trends in gene prevalence and resistance burden were modeled with robust regression. Geographic and host-associated structuring of key genes was assessed via χ2 and enrichment tests. Machine-learning models (elastic-net, random forests, XGBoost) were benchmarked for minimum inhibitory concentration (MIC) prediction via nested cross-validation, with performance evaluated by mean absolute error, RMSE, and SHAP-based feature importance. All analyses were conducted in R and Python using publicly available, de-identified genomic data. RESULTS: Aminoglycoside resistance-associated genes were dominated by modifying enzyme determinants, with ant(6)-Ia, ant(9)-Ia, aph(3')-IIIa, sat4, aadD1, and aac(6')-Ie/aph(2'')-Ia occurring in 14-22% of isolates worldwide. Temporal analysis revealed significant declines in several major determinants, most notably ant(9)-Ia (-2.22 percentage points per year, p < 0.001), whereas apmA exhibited a non-significant decreasing trend in animal isolates. Host structuring was marked: human clinical isolates concentrated common determinants, while animal and environmental isolates harbored rare alleles (apmA, spw, str, spd). Geographic mapping confirmed near-universal distribution of common genes but focal restriction of rare ones. Publicly available phenotypic data indicated strong activity of amikacin, whereas gentamicin showed a distinct resistant subpopulation that closely corresponded with AME gene carriage. Genotype-phenotype analyses demonstrated strong concordance, with gene-rich complements predicting resistant MIC strata and absence of determinants predicting susceptibility. Analysis across different gene classes revealed frequent co-occurrence of aminoglycoside resistance genes with determinants from other classes, such as mecA, blaZ, and MLS_B, embedding them within multidrug-resistant (MDR) genomic contexts. CONCLUSION: Over 25 years, the prevalence of aminoglycoside resistance-associated genes in S. aureus has declined for several common determinants, while rare veterinary-linked alleles are emerging in animal isolates. Strong genotype-phenotype concordance supports genomic prediction for gentamicin and amikacin, where MIC data are available, although phenotypic confirmation remains essential. The frequent co-occurrence of aminoglycoside resistance genes with other antimicrobial resistance determinants indicates their integration within co-occurrence patterns of MDR genes, defined here as clusters of co-occurring resistance genes often carried on shared mobile genetic elements. These patterns highlight the need for integrated One Health surveillance combining clinical, veterinary, and environmental monitoring with plasmid-context resolution to anticipate emerging threats.

Aminoglycosides↗

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↗