Complex genetic architecture of the chicken Growth1 QTL region.

The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin's finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.

ANK3 rs10994336 and ZNF804A rs7597593 polymorphisms: genetic interaction for emotional and behavioral symptoms of alcohol withdrawal syndrome.

OBJECTIVE: Alcohol withdrawal syndrome (AWS) is a complex condition associated with alcohol use disorder (AUD), characterized by significant variations in symptom severity among patients. The psychological and emotional symptoms accompanying AWS significantly contribute to withdrawal distress and relapse risk. Despite the importance of neural adaptation processes in AWS, limited genetic investigations have been conducted. This study primarily focuses on exploring the single and interaction effects of single-nucleotide polymorphisms in the ANK3 and ZNF804A genes on anxiety and aggression severity manifested in AWS. By examining genetic associations with withdrawal-related psychopathology, we ultimately aim to advance understanding the genetic underpinnings that modulate AWS severity. METHODS: The study involved 449 male patients diagnosed with alcohol use disorder. The Self-Rating Anxiety Scale (SAS) and Buss-Perry Aggression Questionnaire (BPAQ) were used to assess emotional and behavioral symptoms related to AWS. Genomic DNA was extracted from peripheral blood, and genotyping was performed using PCR. RESULTS: Single-gene analysis revealed that naturally occurring allelic variants in ANK3 rs10994336 (CC homozygous vs. T allele carriers) were associated with mood and behavioral symptoms related to AWS. Furthermore, the interaction between ANK3 and ZNF804A was significantly associated with the severity of psychiatric symptoms related to AWS, as indicated by MANOVA. Two-way ANOVA further demonstrated a significant interaction effect between ANK3 rs10994336 and ZNF804A rs7597593 on anxiety, physical aggression, verbal aggression, anger, and hostility. Hierarchical regression analyses confirmed these findings. Additionally, simple effects analysis and multiple comparisons revealed that carriers of the ANK3 rs10994336 T allele experienced more severe AWS, while the ZNF804A rs7597593 T allele appeared to provide protection against the risk associated with the ANK3 rs10994336 mutation. CONCLUSION: This study highlights the gene-gene interaction between ANK3 and ZNF804A, which plays a crucial role in modulating emotional and behavioral symptoms related to AWS. The ANK3 rs10994336 T allele is identified as a risk allele, while the ZNF804A rs7597593 T allele offers protection against the risk associated with the ANK3 rs10994336 mutation. These findings provide initial support for gene-gene interactions as an explanation for psychiatric risk, offering valuable insights into the pathophysiological mechanisms involved in AWS.

SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions.

BACKGROUND: Detecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge. RESULTS: Here, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs. CONCLUSIONS: Here, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP-SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer. AVAILABILITY AND IMPLEMENTATION: https://github.com/scutdy/SSO/blob/master/SEEI.zip .

Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers.

Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.

Genome-wide association study and network analysis of in vitro transformation in Populus trichocarpa support key roles of diverse phytohormone pathways and cross talk.

Wide variation in amenability to transformation and regeneration (TR) among many plant species and genotypes presents a challenge to the use of genetic engineering in research and breeding. To help understand the causes of this variation, we performed association mapping and network analysis using a population of 1204 wild trees of Populus trichocarpa (black cottonwood). To enable precise and high-throughput phenotyping of callus and shoot TR, we developed a computer vision system that cross-referenced complementary red, green, and blue (RGB) and fluorescent-hyperspectral images. We performed association mapping using single-marker and combined variant methods, followed by statistical tests for epistasis and integration of published multi-omic datasets to identify likely regulatory hubs. We report 409 candidate genes implicated by associations within 5 kb of coding sequences, and epistasis tests implicated 81 of these candidate genes as regulators of one another. Gene ontology terms related to protein-protein interactions and transcriptional regulation are overrepresented, among others. In addition to auxin and cytokinin pathways long established as critical to TR, our results highlight the importance of stress and wounding pathways. Potential regulatory hubs of signaling within and across these pathways include GROWTH REGULATORY FACTOR 1 (GRF1), PHOSPHATIDYLINOSITOL 4-KINASE ß1 (PI-4Kß1), and OBF-BINDING PROTEIN 1 (OBP1).

Genetic interaction mapping reveals functional relationships between peptidoglycan endopeptidases and carboxypeptidases.

Peptidoglycan (PG) is the main component of the bacterial cell wall; it maintains cell shape while protecting the cell from internal osmotic pressure and external environmental challenges. PG synthesis is essential for bacterial growth and survival, and a series of PG modifications are required to allow expansion of the sacculus. Endopeptidases (EPs), for example, cleave the crosslinks between adjacent PG strands to allow the incorporation of newly synthesized PG. EPs are collectively essential for bacterial growth and must likely be carefully regulated to prevent sacculus degradation and cell death. However, EP regulation mechanisms are poorly understood. Here, we used TnSeq to uncover novel EP regulators in Vibrio cholerae. This screen revealed that the carboxypeptidase DacA1 (PBP5) alleviates EP toxicity. dacA1 is essential for viability on LB medium, and this essentiality was suppressed by EP overexpression, revealing that EP toxicity both mitigates, and is mitigated by, a defect in dacA1. A subsequent suppressor screen to restore viability of ΔdacA1 in LB medium identified hypomorphic mutants in the PG synthesis pathway, as well as mutations that promote EP activation. Our data thus reveal a more complex role of DacA1 in maintaining PG homeostasis than previously assumed.

Learning epistatic polygenic phenotypes with Boolean interactions.

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Ensemble-Based Mutational Profiling and Network Analysis of the SARS-CoV-2 Spike Omicron XBB Lineages for Interactions with the ACE2 Receptor and Antibodies: Cooperation of Binding Hotspots in Mediating Epistatic Couplings Underlies Binding Mechanism and Immune Escape.

In this study, we performed a computational study of binding mechanisms for the SARS-CoV-2 spike Omicron XBB lineages with the host cell receptor ACE2 and a panel of diverse class one antibodies. The central objective of this investigation was to examine the molecular factors underlying epistatic couplings among convergent evolution hotspots that enable optimal balancing of ACE2 binding and antibody evasion for Omicron variants BA.1, BA2, BA.3, BA.4/BA.5, BQ.1.1, XBB.1, XBB.1.5, and XBB.1.5 + L455F/F456L. By combining evolutionary analysis, molecular dynamics simulations, and ensemble-based mutational scanning of spike protein residues in complexes with ACE2, we identified structural stability and binding affinity hotspots that are consistent with the results of biochemical studies. In agreement with the results of deep mutational scanning experiments, our quantitative analysis correctly reproduced strong and variant-specific epistatic effects in the XBB.1.5 and BA.2 variants. It was shown that Y453W and F456L mutations can enhance ACE2 binding when coupled with Q493 in XBB.1.5, while these mutations become destabilized when coupled with the R493 position in the BA.2 variant. The results provided a molecular rationale of the epistatic mechanism in Omicron variants, showing a central role of the Q493/R493 hotspot in modulating epistatic couplings between convergent mutational sites L455F and F456L in XBB lineages. The results of mutational scanning and binding analysis of the Omicron XBB spike variants with ACE2 receptors and a panel of class one antibodies provide a quantitative rationale for the experimental evidence that epistatic interactions of the physically proximal binding hotspots Y501, R498, Q493, L455F, and F456L can determine strong ACE2 binding, while convergent mutational sites F456L and F486P are instrumental in mediating broad antibody resistance. The study supports a mechanism in which the impact on ACE2 binding affinity is mediated through a small group of universal binding hotspots, while the effect of immune evasion could be more variant-dependent and modulated by convergent mutational sites in the conformationally adaptable spike regions.

Poor statistical power in population-based association study of gene interaction.

BACKGROUND: Statistical epistasis, or "gene-gene interaction" in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven't found enough clues of statistical epistasis so far. METHODS: We developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene-gene interaction was calculated and then compared in different hypothesis scenarios. RESULTS: Our results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene-gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10-8, the same as that of many genome-wide association studies, there is little chance to identify the gene-gene interaction in all kind of circumstances. CONCLUSIONS: The lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

Genomic Prediction from Multi-Environment Trials of Wheat Breeding.

Genomic prediction relates a set of markers to variability in observed phenotypes of cultivars and allows for the prediction of phenotypes or breeding values of genotypes on unobserved individuals. Most genomic prediction approaches predict breeding values based solely on additive effects. However, the economic value of wheat lines is not only influenced by their additive component but also encompasses a non-additive part (e.g., additive × additive epistasis interaction). In this study, genomic prediction models were implemented in three target populations of environments (TPE) in South Asia. Four models that incorporate genotype × environment interaction (G × E) and genotype × genotype (GG) were tested: Factor Analytic (FA), FA with genomic relationship matrix (FA + G), FA with epistatic relationship matrix (FA + GG), and FA with both genomic and epistatic relationship matrices (FA + G + GG). Results show that the FA + G and FA + G + GG models displayed the best and a similar performance across all tests, leading us to infer that the FA + G model effectively captures certain epistatic effects. The wheat lines tested in sites in different TPE were predicted with different precisions depending on the cross-validation employed. In general, the best prediction accuracy was obtained when some lines were observed in some sites of particular TPEs and the worse genomic prediction was observed when wheat lines were never observed in any site of one TPE.

Identifying latent genetic interactions in genome-wide association studies using multiple traits.

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform.

Genetic interactions mediate the emergence of phenotype from genotype, but technologies for combinatorial genetic perturbation in mammalian cells are challenging to scale. Here, we identify background-independent paralog synthetic lethals from previous CRISPR genetic interaction screens, and find that the Cas12a platform provides superior sensitivity and assay replicability. We develop the in4mer Cas12a platform that uses arrays of four independent guide RNAs targeting the same or different genes. We construct a genome-scale library, Inzolia, that is ~30% smaller than a typical CRISPR/Cas9 library while also targeting ~4000 paralog pairs. Screens in cancer cells demonstrate discrimination of core and context-dependent essential genes similar to that of CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes. Importantly, the in4mer platform offers a fivefold reduction in library size compared to other genetic interaction methods, substantially reducing the cost and effort required for these assays.

scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.

BACKGROUND: Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and have outperformed statistic approaches in cell clustering. RESULTS: Using cross-correlation to capture gene-gene interactions, this study proposes the scCompressSA method to integrate topological patterns from scRNA-seq data, with support of self-attention (SA) based coefficient compression (CC) block. This SA-based CC block is able to extract and employ static gene-gene interactions from scRNA-seq data. This proposed scCompressSA method has enhanced clustering accuracy in multiple benchmark scRNA-seq datasets by integrating topological and temporal features. CONCLUSION: Static gene-gene interactions have been extracted as temporal features to boost clustering performance in single-cell clustering For the scCompressSA method, dual-channel SA based CC block is able to integrate topological features and has exhibited extraordinary detection accuracy compared with previous clustering approaches that only employ temporal patterns.

BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS.

Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.

Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction.

The problem of missing heritability requires the consideration of genetic interactions among different loci, called epistasis. Current GWAS statistical models require years to assess the entire combinatorial epistatic space for a single phenotype. We propose Next-Gen GWAS (NGG) that evaluates over 60 billion single nucleotide polymorphism combinatorial first-order interactions within hours. We apply NGG to Arabidopsis thaliana providing two-dimensional epistatic maps at gene resolution. We demonstrate on several phenotypes that a large proportion of the missing heritability can be retrieved, that it indeed lies in epistatic interactions, and that it can be used to improve phenotype prediction.

Type I interferon associated epistasis may contribute to early disease-onset and high disease activity in juvenile-onset lupus.

Pathologic type I interferon (T1IFN) expression is a key feature in systemic lupus erythematosus (SLE) that associates with disease activity. When compared to adult-onset disease, juvenile-onset (j)SLE is characterized by increased disease activity and damage, which likely relates to increased genetic burden. To identify T1IFN-associated gene polymorphisms (TLR7, IRAK1, miR-3142/miR-146a, IRF5, IRF7, IFIH1, IRF8, TYK2, STAT4), identify long-range linkage disequilibrium and gene:gene interrelations, 319 jSLE patients were genotyped using panel sequencing. Coupling phenotypic quantitative trait loci (QTL) analysis identified 10 jSLE QTL that associated with young age at onset (<12 years; IRAK1 [rs1059702], TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760, rs3747517], STAT4 [rs3021866], TYK2 [rs280501], IRF8 [rs1568391, rs6638]), global disease activity (SLEDAI-2 K >10; IFIH1 [rs1990760], STAT4 [rs3021866], IRF8 [rs903202, rs1568391, rs6638]), and mucocutaneous involvement (TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760]). This study suggests T1IFN-associated polymorphisms and gene:gene interrelations in jSLE. Genotyping of jSLE patients may allow for individualized treatment and care.

Dominance and multi-locus interaction.

Dominance is usually considered a constant value that describes the relative difference in fitness or phenotype between heterozygotes and the average of homozygotes at a focal polymorphic locus. However, the observed dominance can vary with the genetic background of the focal locus. Here, alleles at other loci modify the observed phenotype through position effects or dominance modifiers that are sometimes associated with pathogen resistance, lineage, sex, or mating type. Theoretical models have illustrated how variable dominance appears in the context of multi-locus interaction (epistasis). Here, we review empirical evidence for variable dominance and how the observed patterns may be captured by proposed epistatic models. We highlight how integrating epistasis and dominance is crucial for comprehensively understanding adaptation and speciation.

Modeling within and between Sub-Genomes Epistasis of Synthetic Hexaploid Wheat for Genome-Enabled Prediction of Diseases.

Common wheat (Triticum aestivum) is a hexaploid crop comprising three diploid sub-genomes labeled A, B, and D. The objective of this study is to investigate whether there is a discernible influence pattern from the D sub-genome with epistasis in genomic models for wheat diseases. Four genomic statistical models were employed; two models considered the linear genomic relationship of the lines. The first model (G) utilized all molecular markers, while the second model (ABD) utilized three matrices representing the A, B, and D sub-genomes. The remaining two models incorporated epistasis, one (GI) using all markers and the other (ABDI) considering markers in sub-genomes A, B, and D, including inter- and intra-sub-genome interactions. The data utilized pertained to three diseases: tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB), for synthetic hexaploid wheat (SHW) lines. The results (variance components) indicate that epistasis makes a substantial contribution to explaining genomic variation, accounting for approximately 50% in SNB and SB and only 29% for TS. In this contribution of epistasis, the influence of intra- and inter-sub-genome interactions of the D sub-genome is crucial, being close to 50% in TS and higher in SNB (60%) and SB (60%). This increase in explaining genomic variation is reflected in an enhancement of predictive ability from the G model (additive) to the ABDI model (additive and epistasis) by 9%, 5%, and 1% for SNB, SB, and TS, respectively. These results, in line with other studies, underscore the significance of the D sub-genome in disease traits and suggest a potential application to be explored in the future regarding the selection of parental crosses based on sub-genomes.

Parental histone transfer caught at the replication fork.

In eukaryotes, DNA compacts into chromatin through nucleosomes1,2. Replication of the eukaryotic genome must be coupled to the transmission of the epigenome encoded in the chromatin3,4. Here we report cryo-electron microscopy structures of yeast (Saccharomyces cerevisiae) replisomes associated with the FACT (facilitates chromatin transactions) complex (comprising Spt16 and Pob3) and an evicted histone hexamer. In these structures, FACT is positioned at the front end of the replisome by engaging with the parental DNA duplex to capture the histones through the middle domain and the acidic carboxyl-terminal domain of Spt16. The H2A-H2B dimer chaperoned by the carboxyl-terminal domain of Spt16 is stably tethered to the H3-H4 tetramer, while the vacant H2A-H2B site is occupied by the histone-binding domain of Mcm2. The Mcm2 histone-binding domain wraps around the DNA-binding surface of one H3-H4 dimer and extends across the tetramerization interface of the H3-H4 tetramer to the binding site of Spt16 middle domain before becoming disordered. This arrangement leaves the remaining DNA-binding surface of the other H3-H4 dimer exposed to additional interactions for further processing. The Mcm2 histone-binding domain and its downstream linker region are nested on top of Tof1, relocating the parental histones to the replisome front for transfer to the newly synthesized lagging-strand DNA. Our findings offer crucial structural insights into the mechanism of replication-coupled histone recycling for maintaining epigenetic inheritance.

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.