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Results for “Models, Genetic”

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At least 19 recordsLinked to original sources

PubMed2025

A single-locus quantitative genetic model incorporating DNA methylation.

We describe a single-locus quantitative genetic model that incorporates effects due to DNA methylation. Extending Fisher's decomposition of the genotypic value, we distinguish two quantities to predict an individual's phenotypic or genetic values: the "basic genetic value" and the "expressed genetic value". We show how these quantities relate to the concept of breeding value and derive their corresponding formulas, along with those for phenotypic variance and covariance between relatives. The resulting parameters are influenced by several factors, including the population distribution of DNA methylation levels, the functional relationship between methylation and phenotype, the magnitudes of genetic and methylation effects, and allele frequencies. We show that under the conditions modeled, the presence of DNA methylation does not bias estimated breeding values.

DNA Methylation↗

PubMed2026

Interfering with DNA repair pathways to enhance CRISPR-Cas9-mediated homology-directed repair in a chelicerate genetic model.

The two-spotted spider mite, Tetranychus urticae, is a major pest and an emerging genetic model. Recent CRISPR-Cas9 advances, especially the SYNCAS method for maternal delivery of Cas9 ribonucleoproteins, have enabled precise genome editing in this and other difficult-to-transform arthropods. Yet SYNCAS-mediated knockins vary in efficiency, possibly due to competition between DNA repair pathways, whose mechanisms in T. urticae and other chelicerates remain unknown. Here, we provide the first functional analysis of double-strand break repair in a chelicerate. Loss of DNA polymerase theta (Polθ) redirects repair almost entirely toward homology-directed repair, whereas absence of Ligase IV has no detectable impact. Using a reporter assay targeting phytoene desaturase, we demonstrate that Polθ-deficient strains enhance incorporation of repair templates, even when mutations are distant from the cut site. Also, insertion of larger fragments is improved. Finally, disrupting Polθ imposes only a modest fitness cost, highlighting its value for future genome engineering in this species.

Acari↗

PubMed2018

Using mouse transgenic and human stem cell technologies to model genetic mutations associated with schizophrenia and autism.

Solid progress has occurred over the last decade in our understanding of the molecular genetic basis of neurodevelopmental disorders, and of schizophrenia and autism in particular. Although the genetic architecture of both disorders is far more complex than previously imagined, many key loci have at last been identified. This has allowed in vivo and in vitro technologies to be refined to model specific high-penetrant genetic loci involved in both disorders. Using the DISC1/NDE1 and CYFIP1/EIF4E loci as exemplars, we explore the opportunities and challenges of using animal models and human-induced pluripotent stem cell technologies to further understand/treat and potentially reverse the worst consequences of these debilitating disorders.This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'.

Animals↗

PubMed2025

Rat somatic genome editing enables ER+ breast cancer modeling.

Genetically engineered mouse models have advanced cancer research but often fail to capture key features of certain human tumors. Rats, with distinct physiology and tumor biology, offer a powerful alternative, yet their use has been constrained by technical barriers to genome editing. Here, we report efficient somatic genome editing in rats, enabling both Indel and substitution mutations. We then apply this approach to model estrogen receptor (ER)-positive breast cancer, which accounts for ~70% of human cases but remains poorly represented in mice. The resulting rat tumors reproduce hallmarks of human ER+ breast cancer, including ductal histology, hormone responsiveness, and immune-microenvironmental features. By contrast, identical genetic alterations in mice failed to yield ER+ tumors, underscoring critical species differences in tumorigenesis. Together, this work establishes a versatile platform for rapid generation of clinically relevant rat tumor models, opening new avenues to dissect tumor biology, therapeutic response, and immune interactions in previously inaccessible cancer subtypes.

Journal Article↗

PubMed2026

Using deep learning models as a genetic architecture for the simulation of breeding schemes.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations that we aim to replicate, thereby highlighting a fundamental limitation of current classical quantitative genetic simulation models. Deep learning (DL) models have demonstrated promising results in capturing complex interactions essential for maintaining genetic variance; thus, we hypothesize that DL-based genetic simulation models may preserve more genetic variance than classical models, because the biological pathways underlying complex traits exhibit interactions that classical models ignore. The primary objective of this study was to introduce alternative DL-based genetic simulation models and compare them with classical genetic simulation models in terms of their retention of additive genetic variance under truncation selection in a simulated full-sib pig breeding scheme using real haplotypes as founders. After 20 generations of directional truncation selection, the classical models (A, ADAA, and ADAAADDD) retained between 55% and 64% of their initial additive genetic variance. In contrast, while the DL_simple model lost all its additive variance, the DL medium retained 92% to 98% of its additive variance, and the DL_complex model's initial additive variance increased by 296% to 314%. This paper introduces DL-based genetic simulation models and concludes that their ability to retain additive genetic variance depends on the models' architectural complexity. When sufficiently complex, DL-based models exhibit greater retention of additive genetic variance because they intrinsically capture epistatic interactions that are converted into additive variance, as selection progresses, thus, affirming the role of non-additive genetic effects in maintaining long-term genetic variation.

Deep Learning↗

PubMed2026

A Landscape of Drosophila melanogaster Disease Models: From Genetic Platforms to Cross-Disease Mechanisms and Translational Research.

Modeling human diseases using the fruit fly (Drosophila melanogaster) has established itself as a cornerstone of functional genomics and preclinical medicine. Despite its anatomical simplicity, the Drosophila genome shares remarkable functional conservation with human disease-related genes, enabling the study of complex physiological traits through accessible tissue models. Furthermore, beyond individual disease models, we propose a framework demonstrating how these diseases converge at common molecular centers, such as the breakdown of protein homeostasis, mitochondrial dysfunction, chronic inflammation, and organ-to-organ communication. Finally, we discuss strategies for integrating the Drosophila platform into drug development pipelines and establishing standards to enhance inter-laboratory reproducibility. Overall, this review highlights the enduring value of fruit flies as a model system, particularly when combined with AI-omics approaches to transform complex biological datasets into actionable therapeutic strategies.

Drosophila↗

PubMed2026

A systematic review and network meta-analysis of single nucleotide polymorphisms associated with oral submucous fibrosis risk.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic and insidious oral disease characterized by hyalinization of the subepithelial connective tissue and progressive fibrosis of the oral submucosa. It is a precancerous condition of oral squamous cell carcinoma. Studies have demonstrated that single nucleotide polymorphisms (SNPs) are closely associated with susceptibility to OSF. This study aims to comprehensively evaluate the association between SNPs and OSF risk and to rank the strength of the association between different genetic models and OSF susceptibility. METHODS: Literature related to OSF was comprehensively searched from PubMed, Web of Science, Embase, Cochrane Library, CNKI, and Wangfang databases up to July 2025. Full-text case-control studies with patients diagnosed with OSF were included. Quality assessment was performed to evaluate the risk of bias. RevMan 5.4, GeMTC 0.14.3, and STATA 17.0 were used for the pairwise and Bayesian network meta-analysis. RESULTS: A total of 24 studies with 2545 cases and 3772 controls, covering 13 SNPs in 11 genes, were included in our meta-analysis. We found that CYP1A1 rs4646903:T>C, CYP1A1 rs1048943:A>G, GSTT1 null genotype, GSTM1 null genotype, and XRCC3 rs861539:C>T were associated with an increased risk of OSF, while MMP2 rs243865:C>T and MMP3 rs3025058: 5A>6A were associated with a decreased risk of OSF. Further Bayesian network meta-analysis indicated the top 5 genetic models with the highest association with OSF risk in network group 1 were the dominant model, homozygous model, allelic model, and recessive model of CYP1A1 rs1048943:A>G (ranked 1-4), and the heterozygous/dominant model of CYP1A1 rs4646903:T>C (both ranked 5). While the allelic models of XRCC3 rs861539:C>T and MMP3 rs3025058: 5A>6A ranked first for predicting OSF in group 2 and group 3, respectively. CONCLUSION: Some specific SNPs are significantly related to the risk of OSF. Among them, the dominant model of CYP1A1 rs1048943:A>G may be the most strongly associated genetic model with OSF risk. Future large-sample, well-designed studies with detailed genotype data are needed to validate the roles of these SNPs in OSF risk.

Humans↗

PubMed2023

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Preprint↗

PubMed2026

Pharmacologic inhibition of SOX9-CDK4 by CYD-4-61 impairs gastric adenocarcinoma growth and amplifies anti-PD-1 response.

Gastric adenocarcinoma (GAC) remains a leading cause of cancer-related mortality, particularly in patients with peritoneal carcinomatosis, for whom effective therapies are limited. We investigated the therapeutic efficacy and molecular mechanism of CYD-4-61, a BAX activator, using human GAC cell lines, patient-derived xenograft models, genetically engineered mouse models, and a syngeneic mouse model. CYD-4-61 potently inhibited tumor cell proliferation, induced apoptosis, and suppressed cancer stem cell-like properties, with enhanced activity in radiation-resistant GAC cells. Mechanistically, CYD-4-61 activated the BAX-caspase pathway, leading to SOX9 protein reduction. Integrated bulk and single-cell transcriptomic analyses identified SOX9-dependent transcriptional programs as major targets of CYD-4-61. Functional rescue experiments together with chromatin immunoprecipitation and CUT&RUN analyses supported CDK4 as a SOX9-regulated gene and demonstrated suppression of the SOX9-CDK4 regulatory axis following CYD-4-61 treatment. In multiple preclinical models, CYD-4-61 significantly inhibited tumor growth and improved the therapeutic response to anti-programmed cell death protein 1 (PD-1) therapy while modulating the tumor immune microenvironment. Clinically, co-expression of SOX9 and CDK4 was associated with diffuse-type GAC and poor patient outcomes. These findings identify the BAX-SOX9-CDK4 axis as an important mechanism contributing to the antitumor activity of CYD-4-61 and provide a strong preclinical rationale for its further development as a therapeutic strategy for aggressive GAC.

Animals↗

PubMed2024

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

PURPOSE: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records with genetic data to understand which decisions may affect performance. METHODS: Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from 2 large independent health systems, and polygenic risk scores (PRS) were generated across all patients of European ancestry with genetic data in the corresponding biobanks. Crohn's disease was studied based on its substantial genetic component, established electronic health records-based definition, and sufficient prevalence for training and testing. We investigated the impact of choices regarding the PRS integration method, training sample, model complexity, and performance metrics. RESULTS: Overall, our results showed that including PRS resulted in higher performance, but this gain was only robust in situations with limited clinical information. We found consistent performance increases from more compute-intensive models, such as random forest, but the impact of other decisions varied by site. CONCLUSION: This work highlights the importance of considering methodological decision points in interpreting the impact of PRS on prediction performance in clinical models.

Humans↗

PubMed2021

DNA Damage Responses during the Cell Cycle: Insights from Model Organisms and Beyond.

Genome damage is a threat to all organisms. To respond to such damage, DNA damage responses (DDRs) lead to cell cycle arrest, DNA repair, and cell death. Many DDR components are highly conserved, whereas others have adapted to specific organismal needs. Immense progress in this field has been driven by model genetic organism research. This review has two main purposes. First, we provide a survey of model organism-based efforts to study DDRs. Second, we highlight how model organism study has contributed to understanding how specific DDRs are influenced by cell cycle stage. We also look forward, with a discussion of how future study can be expanded beyond typical model genetic organisms to further illuminate how the genome is protected.

Animals↗

PubMed2026

Association of ERBB4 and SHBG gene polymorphisms with polycystic ovarian syndrome in South Indian women: a case-control genetic analysis.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a multifactorial endocrinological disorder with a substantial genetic component. However, the role of genes involved in follicular development and androgen regulation remains incompletely understood, particularly in South Indian populations. This study aimed to evaluate how variations in the ERBB4 and SHBG genes affect PCOS risk. METHODOLOGY: A hospital-based case-control study was conducted among 400 South Indian women, comprising 200 women with PCOS and 200 age-matched healthy controls. Genomic DNA was extracted to study SNPs at ERBB4 (rs2178575 and rs1351592) and SHBG (rs1799941 and rs727428) using ARMS-PCR genotyping. The study compared genotype and allele frequencies between cases and controls while assessing their associations with allelic, homozygous, heterozygous, dominant, recessive, and over-dominant genetic models. Genotyping accuracy was confirmed by re-genotyping and Sanger sequencing of a subset of samples. RESULTS: The ERBB4 rs2178575 polymorphism demonstrated a significant association with PCOS, as the AA genotype and A allele combination increased risk across all three genetic models, including homozygous, recessive, and allelic models. The ERBB4 rs1351592 variant was associated with 3-fold higher risk of PCOS in heterozygous and GC carriers. The SHBG rs1799941 polymorphism showed a significant link to PCOS through its effects on heterozygous and allelic states, whereas rs727428 displayed no significant connection due to its monomorphic distribution. CONCLUSION: These findings suggest that polymorphisms in ERBB4 and SHBG may contribute to PCOS susceptibility in South Indian women in a locus- and model-specific manner, revealing the intricate genetic structure that defines this medical condition.

Humans↗

PubMed2026

Systems genetics approaches model the heritable architecture of polyendocrine metabolic ovarian syndrome.

Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is the most common endocrine disorder in women and is closely associated with complex diseases such as cardiovascular disease and type 2 diabetes. However, the mechanistic links between PMOS and its comorbidities remain poorly understood. Here, we present an integrative systems genetics platform that leverages genetic diversity in both mice and humans to dissect the drivers of PMOS and its associated complications. This framework uncovered conserved genetic and environmental factors underlying PMOS, identified susceptible cell types and organs, and elucidated mechanisms linking PMOS to subsequent pathologies. For instance, we showed that increased ovarian area contributes to both PMOS susceptibility and ovarian cancer progression, while specific ovary-heart signaling circuits modulate cardiac function with aging. We further identified ovarian SF3B1-mediated alternative splicing as a key mechanistic link between PMOS and metabolic traits. Pharmacologic inhibition of SF3B1 in mice reduced circulating testosterone, insulin, and glucose levels as well as fat mass expansion. Transcriptomics analysis of ovaries from mice and experiments using human cell lines localized these effects to exon skipping events in granulosa cells. Together, this study offers a mechanistic framework for modeling the diversity of PMOS pathologies and uncovers SF3B1-mediated splicing as a link between ovary function and systemic metabolism.

Female↗

PubMed2025

Association of MTHFD1 G1958A (rs2236225) gene polymorphism with the risk of congenital heart disease: a systematic review and meta-analysis.

BACKGROUND: We did this study to better clarify the correlations of methylenetetrahydrofolate dehydrogenase 1 (MTHFD1)-G1958A (rs2236225) gene polymorphism with the risk of congenital heart diseases (CHD) and its subgroups. METHODS: Relevant articles were searched in PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP database and Wanfang DATA until October 2023. We will use odds ratios (ORs) and 95% confidence intervals (CIs) to examine the potential associations of MTHFD1- G1958A gene polymorphism with CHD and its subgroups. RESULTS: We included a total of 9 eligible studies, encompassing 1917 children with CHD, 1863 healthy children, 1717 mothers of the children with CHD and 1666 mothers of healthy children. In our study, the meta-analysis of fetal group revealed no significant association between any of the five genetic models for the MTHFD1-G1958A polymorphism and the risk of CHD. Subgroup analysis showed that associations between the MTHFD1-G1958A polymorphism and Tetralogy of Fallot (TOF) risk in the homozygote model (AA vs. GG, OR = 2.82, 95%CI [1.16, 6.86], P = 0.02) and recessive model (AA vs. GG + GA, OR = 3.09, 95%CI [1.36, 7.03], P = 0.007). In addition, the MTHFD1-G1958A polymorphism was associated with the risk of CHD in racial subgroup, increasing the risk of CHD in Caucasians. In maternal analysis, 2 genetic models of MTHFD1-G1958A polymorphism increased the risk of CHD: the heterozygote model (GA vs. GG, OR = 1.22, 95%CI [1.04, 1.42], P = 0.01), and the dominance model (GA + AA vs. GG, OR = 1.17, 95%CI [1.01, 1.34], P = 0.03). CONCLUSIONS: The fetal MTHFD1-G1958A (rs2236225) gene polymorphism increase their risk of TOF. The maternal MTHFD1-G1958A polymorphism has a strong correlation with the risk of CHD, and there are racial differences in this correlation. Compared with GG genotype, the GA genotype increases the risk of CHD.

Humans↗

PubMed2025

Computational modeling of human genetic variants in mice.

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

Journal Article↗

PubMed2025

MyESL: A Software for Evolutionary Sparse Learning in Molecular Phylogenetics and Genomics.

Evolutionary sparse learning uses supervised machine learning to build evolutionary models where genomic sites loci are parameters. It uses the Least Absolute Shrinkage and Selection Operator with bi-level sparsity to connect a specific phylogenetic hypothesis with sequence variation across genomic loci. The MyESL software addresses the need for open-source tools to perform evolutionary sparse learning analyses, offering features to preprocess input phylogenomic alignments, post-process output models to generate molecular evolutionary metrics, and make Least Absolute Shrinkage and Selection Operator regression adaptable and efficient for phylogenetic trees and alignments. The core of MyESL, which constructs models with logistic regressions using bi-level sparsity, is written in C++. Its input data preprocessing and result post-processing tools are developed in Python. Compared to other tools, MyESL is more computationally efficient and provides evolution-friendly inputs and outputs. These features have already enabled the use of MyESL in two phylogenomic applications, one to identify outlier sequences and fragile clades in inferred phylogenies and another to build genetic models of convergent traits. In addition to the use in a Python environment, MyESL is available as a standalone executable compatible across multiple platforms, which can be directly integrated into scripts and third-party software. The source code, executable, and documentation for MyESL are openly accessible at https://github.com/kumarlabgit/MyESL.

Phylogeny↗

PubMed2026

Research on multi-trait genome association study method based on Shannon information entropy.

BACKGROUND: Genetic analysis of complex traits is crucial for elucidating disease mechanisms and biological inheritance processes. However, traditional Genome-wide Association Study (GWAS) for single trait often fail to capture the synergistic effects of genetic loci on multiple traits. METHODS: This study proposes a method for analyzing the association between multiple traits and gene regions based on Shannon information entropy. Innovatively, Shannon information entropy is introduced to integrate gene region information as genetic entropy, thereby constructing an Inverse Shannon Entropy-Multi-Trait Association Analysis of Gene Region genetic model (InvSE-MTAGR). Furthermore, a partial regression test is applied to the model to establish the Inverse Partial Shannon Entropy-Multi-Trait Association Analysis of Gene Region method (InvPSE-MTAGR). When performing multi-trait analysis with InvSE-MTAGR, the method achieved statistical significance by accumulating minor effects, thereby enhancing the ability to identify pleiotropic gene regions. RESULTS: The simulation results showed that the proposed multi-trait gene region association analysis method performed well in terms of both Type I error rate control and statistical power. Leveraging tomato and sorghum datasets for validation, the proposed multi-trait gene region association analysis method based on Shannon information entropy accurately pinpointed most of the gene regions harboring candidate genes. CONCLUSION: The study reveals the advantage of multi-trait method in integrating weak-effect pleiotropic signals and capturing the correlation among traits, which provides an efficient theoretical tool for dynamic analysis of complex multi-trait genetic networks and multi-target collaborative breeding of crops.

Genome-Wide Association Study↗

PubMed2025

Genetic mapping and predictive modeling of paralog synthetic lethality.

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of 36,648 paralogous pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality showed that the strength of synthetic lethal interactions was largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralog pairs. Collectively, this study tested 36,648 digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities.

Humans↗