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PubMed2026

Testing for Genetic Interactions in Complex Disease With Distance Correlation.

Understanding epistasis (genetic interaction) may shed some light on the genomic basis of common diseases, including disorders of maximum interest due to their high socioeconomic burden, like schizophrenia. Distance correlation is an association measure that characterizes general statistical independence between random variables, not only the linear one. Here, we propose distance correlation as a novel tool for the detection of epistasis from case-control data of single-nucleotide polymorphisms. On the methodological side, we highlight the derivation of the explicit asymptotic null distribution of the test statistic. We show that this is the only way to obtain enough computational speed for the method to be used in practice, in a scenario where the resampling techniques found in the literature are impractical. Our simulations show satisfactory calibration of significance, as well as comparable or better power than existing methodology. We conclude with the application of our technique to a schizophrenia genetics dataset, obtaining biologically sound insights.

Epistasis, Genetic↗

PubMed2026

An encyclopedia of human enhancer-gene regulatory interactions.

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the effect of human genetic variation on disease1-6. Here we create and evaluate a resource of more than 92 million enhancer-gene regulatory interactions across 1,458 biosamples covering 369 cell types and tissues, by integrating predictive models, chromatin states, three-dimensional contacts and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,356 element-gene pairs measured in CRISPR perturbation experiments, more than 30,000 fine-mapped expression quantitative trait loci and 569 fine-mapped genome-wide association study (GWAS) variants linked to a probable causal gene. Using this framework, we develop ENCODE-rE2G, a predictive model achieving state-of-the-art performance across several prediction tasks, demonstrating that iterative perturbations and supervised machine learning can build increasingly accurate predictive models of enhancer regulation. Using ENCODE-rE2G, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes and improving analyses linking noncoding variants to target genes and cell types for common complex diseases. By interpreting the model, we find that beyond enhancer activity and three-dimensional enhancer-promoter contacts, additional features that guide enhancer-promoter communication include promoter class and enhancer-enhancer synergy. These genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

Humans↗

PubMed2026

Evaluation of epistasis detection methods for quantitative phenotypes.

MOTIVATION: Epistasis, or genetic interaction, plays a crucial role in shaping complex traits and has been increasingly recognized for its widespread influence in genetic architectures. While epistasis detection has been extensively evaluated in case-control studies, its performance with quantitative phenotypes remains comparatively understudied. RESULTS: We identified and evaluated six epistasis detection methods applicable to quantitative trait analysis: EpiSNP, Matrix Epistasis, MIDESP, PLINK Epistasis, QMDR, and REMMA. Using the EpiGEN simulator, we generated synthetic datasets modeling four classes of pairwise SNP interactions-dominant, multiplicative, recessive, and XOR. We also assessed BOOST and MDR algorithms using discretized (case-control) versions of the same datasets. Performance varied notably by interaction type: REMMA achieved the highest overall detection rate (55%), particularly excelling with dominant interactions (100%). MDR excelled with multiplicative (57%) and XOR (69%) interactions. Meanwhile, EpiSNP attained the best performance for recessive interactions (67%). All methods except BOOST produced F1 scores below 0.05 for most interaction types. We further evaluated the methods using a real-world dataset. When applied to the Adolescent Brain Cognitive Development dataset to analyse the externalizing behavior phenotype, both PLINK Epistasis and PLINK BOOST identified SNPs within the DRD2 and DRD4 genes, consistent with previously reported genetic associations. Given the variability in tool performance across interaction types, no single method provides optimal detection across all scenarios. Leveraging multiple detection algorithms may therefore yield more comprehensive insights into epistatic effects in quantitative trait analyses. AVAILABILITY AND IMPLEMENTATION: All relevant code and simulated datasets can be found at github.com/staslist/Epistasis_Review repository.

Epistasis, Genetic↗

PubMed2025

Interstrain Recombinants of Human Cytomegalovirus Reveal Complex Genetic Correlates and Epistasis Influencing Glycoprotein Display, Virion Infectivity and Spread Characteristics.

Most of the nucleotide diversity in the human cytomegalovirus (HCMV) genome is due to approximately 17 genes with 2-14 alleles each. These allelic genes are interspersed among longer stretches of highly conserved sequences with signatures of extensive recombination that would shuffle the allelic genes into a vast number of allelic haplotypes. Bacterial artificial chromosome clones derived from 3 independent clinical isolates (TB40/e (TB), TR and Merlin (ME)) display dramatic differences in the abundance of entry-mediating glycoproteins gH/gL/gO and gH/gL/UL128-131, virion infectivity and efficiency of cell-free and cell-to-cell modes of spread. Of these, TB and ME are the most phenotypically different and share only 2 of the 17 allelic genes. A set of recombinant HCMV was generated by coinfecting cells with TB and ME and restriction fragment length polymorphism (RFLP) analyses demonstrated complex crossover patterns. Most recombinants were either "TB-like" with much more gH/gL/gO than gH/gL/UL128-131, or "ME-like" with much more gH/gL/UL128-131. This correlated with a TB or ME UL128 sequence, consistent with a G/T polymorphism affecting UL128 pre-mRNA splicing. One recombinant had a gH/gL/gO:gH/gL/UL128-131 ratio of 0.8, suggesting genetic determinants beyond UL128. Virion infectivity correlated with TB versus ME-like glycoprotein display, but intragroup variability indicated additional factors and variability in spread efficiency and the contribution of cell-free and cell-to-cell spread modes indicated an influence of characteristics beyond virion infectivity. Results suggest that the relationships among these three phenotypes are not strictly causal and that all three phenotypes are genetically complex and influenced by epistasis among polymorphic loci across the genome.

Journal Article↗

PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗

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↗

PubMed2026

The emergence of putative epistatic mutations and iSNVs in SARS-CoV-2 XBB.1.16 variants linked with alteration in immunogenic determinants.

The SARS-CoV-2 XBB variants have been proposed to evolve towards immune evasion against vaccination or natural infection, which may contribute to higher transmissibility. The XBB.1.16 independently emerged due to accumulation of two important substitutions, E180V and T478R in the spike protein. Its pseudoviral infectivity and evasion of humoral immunity were similar to XBB.1 and XBB.1.5. In March 2023, XBB.1.16 had outcompeted other dominant XBB variants in India, which indicate a potential growth advantage. Here, intra-host single nucleotide variations (iSNV) and mutations were screened in SARS-CoV-2 genomes in closely related individuals at two time points: at symptoms onset, and during recovery. The prominence of putative epistatic iSNVs (E180V, G184V, G252V, D253G, and P521S/T) in XBB.1.16 variants were detected during the recovery phase. E180V exhibits mutational constellations with the G252V and P521T in a subset of samples, and this pattern was also detected in contemporary SARS-CoV-2 genomes. Higher order protein structural predictions suggested that the putative epistatic interactions among E180V, G184V, and G252V, D253G may be associated with S protein folding and structural stability. This study involving genomics and computational analyses highlights the potential role of these putative epistatic interactions in immune evasion, which may have contributed to dominance of XBB variants.

Humans↗

PubMed2007

Renal albumin excretion: twin studies identify influences of heredity, environment, and adrenergic pathway polymorphism.

Albumin excretion marks early glomerular injury in hypertension. This study investigated heritability of albumin excretion in twin pairs and its genetic determination by adrenergic pathway polymorphism. Genetic associations used single nucleotide polymorphisms at adrenergic pathway loci spanning catecholamine biosynthesis, storage, catabolism, receptor action, and postreceptor signal transduction. We studied 134 single nucleotide polymorphisms at 46 loci for a total of >51,000 genotypes. Albumin excretion heritability was 45.2+/-7.4% (P=2x10(-7)), and the phenotype aggregated significantly with adrenergic, renal, metabolic, and hemodynamic traits. In the adrenergic system, excretions of both norepinephrine and epinephrine correlated with albumin. In the kidney, albumin excretion correlated with glomerular and tubular traits (Na(+) and K(+) excretion; fractional excretion of Na(+) and Li(+)). Albumin excretion shared genetic determination (genetic covariance) with epinephrine excretion, and environmental determination with glomerular filtration rate and electrolyte intake/excretion. Albumin excretion associated with polymorphisms at multiple points in the adrenergic pathway: catecholamine biosynthesis (tyrosine hydroxylase), catabolism (monoamine oxidase A), storage/release (chromogranin A), receptor target (dopamine D1 receptor), and postreceptor signal transduction (sorting nexin 13 and rho kinase). Epistasis (gene-by-gene interaction) occurred between alleles at rho kinase, tyrosine hydroxylase, chromogranin A, and sorting nexin 13. Dopamine D1 receptor polymorphism showed pleiotropic effects on both albumin and dopamine excretion. These studies establish new roles for heredity and environment in albumin excretion. Urinary excretions of albumin and catecholamines are highly heritable, and their parallel suggests adrenergic mediation of early glomerular permeability alterations. Albumin excretion is influenced by multiple adrenergic pathway genes and is, thus, polygenic. Such functional links between adrenergic activity and glomerular injury suggest novel approaches to its prediction, prevention, diagnosis, and treatment.

Adolescent↗

PubMed2024

QTL mapping for seed vigor-related traits under artificial aging in common wheat in two introgression line (IL) populations.

BACKGROUND: Seed vigor recognized as a quantitative trait is of particular importance for agricultural production. However, limited knowledge is available for understanding genetic basis of wheat seed vigor. METHODS: The aim of this study was to identify quantitative trait loci (QTL) responsible for 10 seed vigor-related traits representing multiple aspects of seed-vigor dynamics during artificial aging with 6 different treatment times (0, 24, 36, 48, 60, and 72 h) under controlled conditions (48 °C, 95% humidity, and dark). The mapping populations were two wheat introgression lines (IL-1 and IL-2) derived from recipient parent (Lumai 14) and donor parent (Shaanhan 8675 or Jing 411). RESULTS: A total of 26 additive QTLs and 72 pairs of epistatic QTLs were detected for wheat seed-vigor traits. Importantly, chromosomes 1B and 7B contained several co-located QTLs, and chromosome 2A had a QTL-rich region near the marker Xwmc667, indicating that these QTLs may affect wheat seed vigor with pleiotropic effects. Furthermore, several possible consistent QTLs (hot-spot regions) were examined by comparison analysis of QTLs detected in this study and reported previously. Finally, a set of candidate genes for wheat seed vigor were predicted to be involved in transcription regulation, carbohydrate and lipid metabolism. CONCLUSION: The present findings lay new insights into the mechanism underlying wheat seed vigor, providing valuable information for wheat genetic improvement especially marker-assisted breeding to increase seed vigor and consequently achieve high grain yield despite of further investigation required.

Triticum↗

PubMed2026

GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.

Genetic interactions (GIs) underpin the functional connectivity of genes and pathways, and are important for dissecting genotype-phenotype relationships and identifying therapeutic targets for diseases. However, the scale of the human genome restricts systematic experimental interrogation of GIs. Existing computational tools focus on predicting synthetic lethality (SL) and synthetic viability (SV), the two primary forms of GIs, yet their accuracy and biological interpretability are compromised by inadequate modeling of the molecular mechanisms behind positive and negative interactions, as well as the limitation of negative samples. To overcome these challenges, we developed Genetic Interaction Graph Convolutional Network (GiGCN), a signed network modeling framework for the joint identification of gene pairs with SL and SV. We built a high-confidence signed genetic network by integrating verified GIs, and non-interacting gene pairs, together with gene semantic similarity derived from biological processes. By leveraging disentangled subspace decomposition, this framework separately models distinct functional dimensions within gene networks, enabling robust representation of context-dependent regulatory relationships and accurate discrimination of SL and SV events. Benchmark experiments demonstrate that GiGCN outperforms state-of-the-art approaches (area under receiver operating-characteristic curve: 0.978, and area under precision-recall curve: 0.944). Further analyses reveal biologically meaningful insights, including known and novel SL interactions centered on the oncogene MYC Proto-Oncogene (MYC), as well as SV interactions linked to autophagy and mitophagy pathways. This study provides a robust and interpretable network-based strategy for systematically exploring GIs. The GiGCN framework not only improves the precision of SL and SV prediction, but also offers mechanistic insights into gene functional relationships, thereby supporting the discovery of actionable therapeutic targets for cancer and other human diseases.

Humans↗

PubMed2012

Role of genes linked to sporadic Alzheimer's disease risk in the production of β-amyloid peptides.

Alzheimer's disease (AD) is characterized by the presence of toxic protein aggregates or plaques composed of the amyloid β (Aβ) peptide. Various lengths of Aβ peptide are generated by proteolytic cleavages of the amyloid precursor protein (APP). Mutations in many familial AD-associated genes affect the production of the longer Aβ42 variant that preferentially accumulates in plaques. In the case of sporadic or late-onset AD, which accounts for greater than 95% of cases, several genes are implicated in increasing the risk, but whether they also cause the disease by altering amyloid levels is currently unknown. Through loss of function studies in a model cell line, here RNAi-mediated silencing of several late onset AD genes affected Aβ levels is shown. However, unlike the genes underlying familial AD, late onset AD-susceptibility genes do not specifically alter the Aβ42/40 ratios and suggest that these genes probably contribute to AD through distinct mechanisms.

Age of Onset↗

PubMed2026

Deconstructing empirical fitness seascapes across scales of granularity.

The fitness landscape metaphor remains resonant in evolutionary theory and has facilitated the birth of newer concepts, like the fitness seascape, that consider the role of environmental context in shaping the dynamics of evolution. Since its emergence, the seascape has appeared in numerous studies examining how different and fluctuating environments shape evolutionary outcomes. Despite growing interest, we lack comprehensive examinations of how environmental context shapes features of fitness seascapes. In this study, we address this gap by deconstructing empirical fitness seascapes across scales of granularity: loci, locus interactions (epistasis), alleles, trajectories, and entire seascapes. For each, we examine how environmental context influences qualitative and quantitative aspects of seascapes, and find that they change appreciably, with patterns specific to individual systems of study. We also quantify how much each scale varies across environments, and find that certain scales tend to be more sensitive to context than others. In summary, we reflect on the implications of the seascape metaphor for the incorporation of environmental effects into theoretical population genetics, for understanding how the environment shapes evolution in disease systems, and for contemporary bioengineering efforts.

Genetic Fitness↗

PubMed2026

Pervasive context-dependent effects in the genetic architecture of complex and quantitative traits revealed by a powerful multiparent mapping population in yeast.

The genetic dissection of complex traits remains a major challenge in basic and biomedical research, but is essential for understanding the molecular pathways that shape phenotypic variation and for developing predictive models of trait and disease susceptibility. Here, we leverage a novel multiparent mapping population of budding yeast, CYClones, comprising 9,344 haploid strains derived from eight genetically diverse founders (~270,000 SNVs, ~ 1 per 44 bp, capturing 56% of common variants and 32% of all variants with a minor allele frequency greater than 0.005 in the global population), to identify quantitative trait loci (QTL) and systematically investigate the genetic architecture of growth rates across ten environmental conditions. In total, we identified 349 QTL (ranging from 18 to 49 QTL per growth condition) that explained between 60% and 100% of narrow sense heritability across traits. The high power and resolution of CYClones revealed that growth traits exhibited distinct, condition-specific genetic architectures with extensive allelic heterogeneity, where a QTL was the result of multiple tightly linked causal variants. We also observed pleiotropy among QTL with complex, trait-dependent allele effects that are also consistent with allelic heterogeneity. Genetic complexity varied widely, with some traits showing nearly Mendelian architectures, while others were highly polygenic. Introgressed loci played a prominent role in the landscape of growth rate QTL, including a QTL localized to a 2.4 kb interval in the PCA1 cadmium transporter that explains 72% of variation in cadmium resistance and is largely driven by an introgression, and a non-additive interaction between the GAL3 regulator and introgressed GAL1/7/10 alleles, extending a previously described three-locus GAL-pathway incompatibility to a four-locus interaction. In both cadmium and galactose conditions, we show that allelic variation at a small number of loci stratifies the population into regulatory or physiological subgroups, each with distinct genetic architectures, a specific manifestation of epistasis we term allele-dependent stratification. Collectively, our results provide novel insights into the genetics of growth rates in budding yeast, the architectural features of genetic complexity, and demonstrate that CYClones is a powerful platform for revealing the molecular basis of complex trait variation.

Quantitative Trait Loci↗

PubMed2026

Dual functional genomics reveals a broad and convergent landscape of asciminib resistance in BCR::ABL1.

BACKGROUND: Drug resistance is a constantly evolving challenge. The allosteric inhibitor asciminib is a novel therapy for chronic myelogenous leukemia (CML) that targets the myristoyl pocket of the BCR::ABL1 kinase. While it can overcome resistance to active-site inhibitors like imatinib, new resistance mutations to asciminib are emerging. The complete landscape of these mutations, particularly those outside the kinase domain or those arising from epistatic interactions between mutations, are not well understood. METHODS: This study employed a dual functional genomics approach in CML cell line models. A high-throughput adenosine base editing (ABE) screen was used to identify broad hotspots of asciminib resistance across the entire BCR::ABL1 protein. Deep mutational scanning (DMS) was then used to create a high-resolution map of all possible amino acid changes within these hotspots. An "edit-on-edit" screen was performed to investigate epistasis by introducing a library of mutations into a cell line that was pre-edited to incorporate the common imatinib-resistance mutation, Y253H. Finally, a novel Förster resonance energy transfer (FRET) biosensor was developed to measure the conformational state of BCR::ABL1 in live cells and link it to drug sensitivity. RESULTS: The screens identified 279 asciminib resistance mutations and revealed resistance hotspots distributed across the SH3, SH2, and kinase domains, in contrast to imatinib resistance, which is largely confined to the kinase domain. The study uncovered a potent epistatic interaction between a mutation in the SH3 domain (V73A) and a mutation in the kinase domain P-loop (Y253H), which synergistically conferred high-level resistance. The FRET biosensor demonstrated that asciminib resistance mutations tend to destabilize the "closed" inactive conformation of the ABL1 kinase. CONCLUSIONS: The landscape of asciminib resistance is broader and more complex than previously appreciated, involving mutations across multiple domains that disrupt ABL1 autoinhibition. Epistasis between mutations acquired during sequential therapies can create unexpected and potent resistance. However, these diverse genetic resistance mechanisms converge on a single biophysical measurement of the openness of the active ABL1 conformation. This provides a unified framework for understanding asciminib resistance and underscores the need for routine clinical resistance monitoring to include the SH3 and SH2 domains in first line and later line therapy.

Fusion Proteins, bcr-abl↗

PubMed2025

Pleiotropic mutational effects on function and stability constrain the antigenic evolution of influenza hemagglutinin.

The evolution of human influenza virus hemagglutinin (HA) involves simultaneous selection to acquire antigenic mutations that escape population immunity while preserving protein function and stability. Epistasis shapes this evolution, as an antigenic mutation that is deleterious in one genetic background may become tolerated in another. However, the extent to which epistasis can alleviate pleiotropic conflicts between immune escape and protein function/stability is unclear. Here, we measure how all amino acid mutations in the HA of a recent human H3N2 influenza strain affect its cell entry function, acid stability, and neutralization by human serum antibodies. We find that epistasis has entrenched certain mutations so that reverting to the ancestral amino acid identity in earlier strains is no longer tolerated. Epistasis has also enabled the emergence of antigenic mutations that were detrimental to HA's cell entry function in earlier strains. However, epistasis appears insufficient to overcome the pleiotropic costs of antigenic mutations that impair HA's stability, explaining why some mutations that strongly escape human antibodies never fix in nature. Our results refine our understanding of the mutational constraints that shape recent H3N2 influenza evolution: epistasis can enable antigenic change, but pleiotropic effects can restrict its trajectory.

Journal Article↗

PubMed2025

Epistasis of ERAP1 With 4 Major Histocompatibility Complex Class I Alleles in Frontal Fibrosing Alopecia: A Genome-Wide Association Study Meta-Analysis.

IMPORTANCE: Frontal fibrosing alopecia (FFA) is an inflammatory and scarring form of hair loss of increasing prevalence that most commonly affects women. An improved understanding of the genetic basis of FFA will support the identification of pathogenic mechanisms and therapeutic targets. OBJECTIVE: To identify novel genomic loci at which common genetic variation affects FFA susceptibility and assess nonadditive effects on genetic risk between susceptibility loci. DESIGN, SETTING, AND PARTICIPANTS: Four genome-wide association studies were combined using an SE-weighted meta-analysis. Within the major histocompatibility complex (MHC) locus, stepwise conditional analysis was undertaken to determine independently associated classical MHC class I alleles. Statistical tests for epistatic interaction were performed between risk alleles at the MHC and endoplasmic reticulum aminopeptidase 1 (ERAP1) loci. MAIN OUTCOMES AND MEASURES: Genome-wide significant locus associated with FFA and nonadditive effects on genetic risk between susceptibility loci. RESULTS: Of 6668 included patients, there were 1585 European female individuals with FFA and 5083 controls. Genome-wide significant associations were identified at 4 genomic loci, including a novel susceptibility locus at 5q15, and the association signal could be fine-mapped to a single nucleotide substitution (rs10045403) in the 5' untranslated region of ERAP1 (rs10045403; odds ratio, 1.30; 95% CI, 1.19-1.43; P = 3.6 × 10-8). Within the MHC, FFA risk was statistically independently associated with HLA-A*11:01, HLA-A*33:01, HLA-B*07:02, and HLA-B*35:01. FFA risk was affected by genetic variation at the ERAP1 locus only in individuals who carried at least 1 of the MHC class I risk alleles. CONCLUSIONS AND RELEVANCE: In this genome-wide meta-analysis, a supra-additive effect of genetic variation was found that affected peptide trimming and antigen presentation on FFA susceptibility. Patients with FFA may benefit from emerging therapeutic approaches that modulate ERAP-mediated processes.

Female↗

PubMed2026

Maize Gametophytic factor loci Ga3 through Ga11 modify reproductive barriers.

Gametophytic factor (Ga) barriers are maize (Zea mays ssp. mays) reproductive barriers controlled by molecular incompatibilities between pollen and silks. Twelve distinct Ga loci have been identified in maize populations since the first genetic evidence of a Ga barrier was reported in 1901. Of the twelve, however, only three have been validated by modern molecular, functional and genomic studies: Ga1, Ga2, and Tcb1. The remaining "higher" Ga loci, spanning Ga3 to Ga11, were reported in the historical literature, but their associated phenotypes segregated in unexpected ways or disappeared over subsequent generations. Here we introduce and explore the hypothesis that the higher Ga loci represent modifiers of Ga1, Ga2, and Tcb1 barrier functions. By revisiting the historical literature, we found that barrier phenotypes fall into two phenotypic and functional categories. Phenotypically, the two categories represented healthy pollen with a silk-length effect and unhealthy pollen without a silk-length effect. These phenotypic categories were supported by genomic data; we identified candidate genes in each higher Ga locus by comparing historical linkage mapping data to the corresponding genomic sequence of maize reference line B73. We discovered candidate genes related to two broad pathways: pollen tube growth and RNA-directed DNA methylation. We conclude that the past century of evidence aligns with our hypothesis that maize loci Ga3 through Ga11 modify rather than directly control Ga barriers. This brief investigation provides a starting point for geneticists and evolutionary biologists to explore how strong reproductive barriers are shaped by epistatic interactions.

Epistasis↗

PubMed2026

The distribution of fitness effects of nonsynonymous mutations varies phylogenetically across animals.

The distribution of fitness effects (DFE) describes the selection coefficients of newly arising mutations and fundamentally influences population genetic processes. However, the extent and mechanisms of differences in the DFE for non-synonymous mutations have not been systematically investigated across species with divergent phylogenetic histories and ecologies. Here, we inferred the DFE in natural populations of 11 animal (sub)species, including humans, mice, fin whales, vaquitas, wolves, collared flycatchers, pied flycatchers, halictid bees, Drosophila, and mosquitoes. We found that mammals have a higher proportion of strongly deleterious mutations (defined as s≤-0.01; 22% to 47% in mammals; 0.0% to 5.4% in insects and birds) and a lower proportion of weakly deleterious mutations than insects and birds. Further, the DFE co-varies with phylogeny, such that the mean mutation effects are more similar in closely related species (Pagel's λ = 0.84, P = 0.01). Next, we investigated whether various summary statistics of the DFE were related to variation in life-history traits across these organisms. We found some support for genome size, body mass, and long-term effective population size being correlated with the DFE. Overall, our findings are consistent with predictions derived independently from the Fisher's Geometric Model (FGM), which defines organismal complexity as the number of phenotypes under selection. FGM predicts that mutations are more deleterious in complex organisms, while strongly deleterious mutations occur more frequently in smaller populations. Our study demonstrates strong phylogenetic signal in the evolution of a fundamental population genetics parameter, and proposes that, through mechanisms of epistasis, long-term population size and organismal complexity could be underlying variation in the DFE across animals.

Journal Article↗