Frontiers in Genetics

We applied the latest RIC-seq technology to study the RNA-RNA interactions in human liver HepG2 cells. Integrating ChIP-seq data with the RIC-seq data, we clas-sified vari ous transcription factors (TFs) into two distinct clusters based on the in-teraction enrichment scores of the uaRNA (RNA transcribed in promoter regions) and eRNA (RNA tran scribed in enhancer regions) potentially transcribed by the TF binding sites. We went on to compare the genomic features and clinical significance across different clusters, by further integrating Hi-C, ATAC-seq, and clinical data from liver cancer patients. Our results showed that Cluster 1 exhibited low spatial interaction (RIC-seq derived RNA-RNA interactions between regulatory RNA ele-ments) and high tissue specificity (high proportion of binding sites covered by shared peaks between two different cell lines for each histone modification marker), showed higher associations with the B compartments and nucleoli, contained less densely clustered TF binding sites, and is more correlated with clinical outcomes. In contrast, Cluster 2 has higher spatial interaction, is more likely to be found in the A compartments and nuclear speckles, contained more densely clustered TF binding sites, and is more prone to form loops. These findings suggested that these transcrip-tion factors might play critical roles in regulating gene expression and serve as po-tential biomarkers and therapeutic targets with significant implications for cancer prognosis and treatment. In summary, groups of TFs could occur as distinct sub-networks based on spatial proximity of non-coding RNAs transcribed by the TF binding sites. These sub-networks appear to be related to regulatory differences for the TF groups and may partially explain the chromosome compartments/domains.

An Ischemic stroke is one of the main causes of death and long-term disability around the world. There are very few treatment options available, especially for the cases where the short time period to give clot dissolving medicine has passed (Powers et al., 2019). While single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for understanding cell-to-cell differences within at issue (cellular heterogeneity) (Tang et al., 2009). However translating these high dimensional datasets into clinically useful biomarkers remains challenging (Rajko-mar et al., 2019). A major problem with current biomarker studies is their reliance on a single machine learning model, where results may reflect model bias rather than true biological signals (Lopez-Rincon et al., 2020). Methods: We developed a multi-model consensus framework integrating eight different machine learning methods from three groups: traditional models (logistic regression, random forest, XGBoost), a hybrid deep learning model (CNN-XGBoost), and transformer-inspired models (scGPT, scBERT, sc-Former, Geneformer). The proposed framework was applied to scRNA-seq data from a mouse middle cerebral artery occlusion (MCAO) model (GSE174574; n=6 mice, 54,599 cells). To prevent data leakage, we implemented rigorous subject-wise train/test splitting, ensuring all cells from individual animals remained either in the training sets or in the testsets. Results: All eight models achieved excellent performance on the independent test data, with AUC scores between 0.9778 and 0.9975. From six models that allowed gene level importance extraction, we identified 63 consensus genes (threshold: [≥] 4/6 models), with 17 genes selected by all 6 models. Pathway enrichment analysis showed significant enrichment of interferon signaling, with a novel dual-interferon pattern. Twelve of the consensus genes respond to interferon, and five of them (Ifitm6, Ifitm1, Gbp2, Igtp, Tgtp1) respond to both Type I (IFN-/{beta}) and Type II (IFN-{gamma}) interferons. Notably the consensus included Il1rn (interleukin-1 receptor antagonist), the target of Anakinra -- an FDA-approved drug that has completed Phase II stroke trials with promising safety profiles (Smith et al., 2018; Emsley et al., 2005). Conclusions: The Multi-model consensus framework, proposed in this paper, provides a robust and reliable approach to biomarker discovery that goes beyond the limitations of any single method and reduces individual algorithm bias. The convergence of classical, ensemble, and deep learning methods on interferon and IL-1 pathway genes supports their biological validity as stroke biomarkers. Our findings provide a prioritized gene list for further clinical validation and highlight interferon signaling as a possible treatment strategy warranting further investigation. Keywords: ischemic stroke; machine learning; single-cell RNA sequencing; biomarker discovery; consensus analysis; interferon signaling; neuroinflammation

Polygenic risk scores (PRS) offer considerable potential for precision medicine. How ever, their predictive performance often attenuates when applied to populations that differ from the genome-wide association study (GWAS) training population. There are many potential sources of this portability problem, and one relatively under-explored contributor is the presence of residual confounding in GWAS summary statistics. In particular, confounding specific to the training population may contribute to predictive performance that does not transfer to other populations, such that improved control of population stratification could potentially improve PRS portability. Here, we investigated whether varying levels of population stratification adjustment, through the inclusion of principal components and the use of mixed models, altered PRS portability in three broad ancestry groups in the UK Biobank. The PRS were built using European training data for coronary artery disease and type 2 diabetes and subsequently evaluated in South Asian, African, and Latin American participants. We found that increasing PC adjustment did not produce a consistent trend in portability across ancestry groups or phenotypes, despite modest reductions in the LDSC intercept. However, substantial ancestry- and phenotype-specific effects on transferability were observed. Mixed-model association provided no significant change in PRS discrimination or portability. These findings highlight the need for a better understanding of the nature of residual confounding in PRS and whether improving the causal validity of GWAS results can ultimately improve the transferability of predictive accuracy between populations.

BackgroundGenome-wide association studies (GWAS) often fail to identify higher-order epistatic interactions that contribute to complex inheritance patterns of traits and diseases. While machine learning (ML) can capture non-linear relationships, extracting interpretable insights from these models remains a challenge. We propose a novel tree-based feature engineering framework that uses Classification and Regression Trees (CART) to explicitly encode high-order interaction decision paths as dummy variables. We investigate three path-based encoding strategies: (i) all decision paths, (ii) leaf-node paths only, and (iii) internal-node paths only. This approach aims to transform complex decision boundaries into discrete features that capture nonlinear interactions that are not readily captured by traditional association models. ResultsThe framework was evaluated using genetic data for ANCA-associated vasculitis (AAV). To manage the high dimensionality of the engineered feature space, we applied a comprehensive suite of ML methods across three tasks: (1) Ensemble Learning (Random Forest, XGBoost, and Gradient Boosting Machine); (2) Decision Tree Analysis (CART); and (3) Regression and Classification Tasks (Regularized Linear Regression/LASSO, Support Vector Machine, and Logistic Regression). Stepwise feature selection and regularization were employed to isolate the most informative interaction patterns. Results indicate that incorporating CART-derived interaction paths--particularly those from high-impact regions of the tree--significantly improves classification accuracy and model interpretability compared to using the original feature space alone. ConclusionsThe proposed framework provides a robust, scalable methodology for identifying high-order genetic interactions. By bridging the gap between the predictive power of ensemble ML and the necessity for mechanistic insight, this approach offers a clearer mapping of the combinatorial genetic processes underlying complex diseases. While applied here to AAV, the method is highly adaptable for exploring the genetic architecture of diverse populations and complex traits.

Ancestry-related genomic variation may contribute to phenotypic diversity. However, its cellular consequences remain difficult to evaluate. To address this, we developed a human induced pluripotent stem cell (hiPSC)-based framework for investigating such phenotypes. We used the Japanese population as a model of Jomon-related and continental East Asian ancestry admixture. Jomon ancestry, estimated using the Jomon allele score (JAS), was associated with clinical measures, including renal-related traits and serum triglycerides in 34,152 Tohoku Medical Megabank participants. Peripheral blood mononuclear cell transcriptomics of donors from the upper and lower extremes of the JAS distribution (n = 10) suggested group-level differences in immune-related gene expression. To test whether the serum triglyceride association coincided with hepatic cellular phenotypes, we differentiated hiPSCs from these donors into hepatocyte-like cells. Neither intracellular triglycerides nor lipid-related gene expression differed between groups. These findings highlight the value of a genetically characterized hiPSC-based framework for investigating ancestry-associated phenotypes across cellular contexts.

Environmental and dietary factors can exert multigenerational effects on health and development. In this study, we investigated whether early-life metabolic challenge affects the germline genome and epigenome across three generations. Using a murine model of early life obesity via litter size reduction (overnutrition group, ON) and a control group (CT), we followed the paternal lineage focusing on germline genomic and methylation changes employing Genotyping-by-Sequencing (GBS) coupled with methyl-immunoprecipitation (GBS-MeDIP). We found that unrelated ON families clustered together based on identified Single-Nucleotide Polymorphism (SNP), suggesting that the treatment may have genomic impact. Copy number variations (CNVs) events were identified in ON individuals, being enriched in Long Interspersed Nuclear Elements (LINEs) and Long Terminal Repeats (LTRs). While Principal Component Analysis (PCA) of the methylome showed no clear treatment effect, pathway enrichment and regional analyses revealed methylation changes associated with transposable elements and developmental genes. Notably, the ON group exhibited a disruption in the methylation of Repetitive Elements (RE), which was significant in the same type of RE that were also enriched in the observed CNVs. The ON also showed reduced emergence of novel SNPs in offspring compared to the CT group. These findings suggest that multigenerational metabolic challenge can constrain genetic variability and induce genome instability, potentially mediated by transposable element activity rather than widespread changes in DNA methylation. This work highlights the importance of studying both genome and epigenome dynamics under realistic, multigenerational exposure scenarios and suggests that early metabolic challenges can have long-lasting impacts on genomic architecture and evolutionary potential.

The phenotype of a focal individual can be affected not only by the individuals own genes (direct genetic effects, DGE) but also by genes of other individuals it interacts with (indirect genetic effects, IGE). Better understanding IGE can help better understand the impact and mechanisms of social effects. Here, we investigated, in laboratory mice, the modulation of IGE by genetic background of the focal individual (social epistasis) and by sex. We focused on IGE arising from Epha4, a gene that has been associated with the individuals own stress-related and social behaviours (i.e. DGE) and the stress-coping behaviour and wound healing of the other mice in the cage (i.e. IGE). Among four different genetic backgrounds for focal mice, significant IGE from Epha4 were detected in CBA females only. A formal model selection analysis was consistent with the modulation of IGE by the similarity of the genetic backgrounds of the focal mouse and its Epha4 cage mate, as well as by sex. Observations of fighting in cages of males with dissimilar genetic backgrounds suggested aggression might underpin these modulatory effects, which prompted to us to expand the conceptual framework describing the mechanisms of social epistasis. Thus, our results strengthen the emerging view that IGE are not fixed properties of social genotypes but can depend on the genetic and sex context of interacting individuals, revealing an underexplored layer of their genetic architecture.

Non-coding variants altering mRNA splicing are increasingly recognized as important cause of Mendelian disorders. Deep intronic variants (DIV) that activate cryptic exons (CEs) cause ~20% inherited retinal diseases (IRD) cases, yet it remains challenging to predict intronic variant pathogenicity accurately, thus we used a high throughput splicing assay (HTSA) to measure the effects of rare deep intronic variants in recessive IRDs with one confirmed pathogenic variant. 640 very rare deep intronic variants were chosen in trans-position with known mutation from 76 patients. We tested them with HTSA, which consists of a split-GFP minigene, separated by an SMN1 gene intron into which the 270bp sequences flanking the DIVs were cloned. The plasmid library was transfected into 293HEK cells, and 48 hours later total RNA was extracted from the transfected cells. RNA transcripts produced by HTSA minigene were amplified by RT-PCR and sequenced. The intron sequences spliced between the two GFP exons were identified and quantified. 98 variants activated CE >100 times more in altered oligo compared to reference oligo and were classified as pathogenic in this experimental setting, validated in longer context, 26 variants were classified as VUS. Interestingly, only 6 from 90 variants (6,6%) were predicted to be pathogenic by in silico algorithms (Splice AI) which leaves room for significant improvement of prediction algorithms. The results of this experiment confirmed diagnosis for 50 of 78 patients (64%). So, 19,4% of deep intronic variants were shown to cause CE activation. This can lead to conclusion that significant portion of undiagnosed patients with recessive IRDs carry pathogenic intronic mutation which causes the disease.

The exponential increase in the number of genotyped animals, combined with the availability of high-density SNP chips has introduced computational challenges for routine genomic evaluations, particularly during the construction of the genomic relationship matrix. Although higher-density SNP panels can facilitate the identification of causal mutations, their use substantially increases computational requirements without a proportional gain in genomic prediction performance. To optimize computational efficiency while maintaining accuracy of genomic predictions, this study compared five SNP selection strategies (i.e., random sampling, random sampling with inclusion of informative SNPs, linkage disequilibrium (LD)-based pruning, a Shannon entropy-based machine learning approach, and [[EQUATION]]-based prioritization) to develop reduced-density panels for Nellore cattle. Using high-density (HD) genotype data comprising 437,650 SNPs from 304,782 animals (after quality control) as reference, three reduced-density panels (25K, 45K, and 65K SNPs) panels were tested across five traits (i.e., Age at first calving, Stayability, Weaning weight, Yearling weight, Muscling) with diverse genetic architectures. Genomic estimated breeding values (GEBVs) derived from these reduced panels were compared to those obtained from the HD reference panel using Pearsons correlations, under both genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) methods. In the GBLUP model, prediction accuracy generally improved with increased marker density. Random selection with and without the informative SNPs consistently yielded the highest accuracies, whereas the [[EQUATION]]-based approach showed the lowest agreement with the HD reference across all densities. In contrast, ssGBLUP demonstrated strong robustness to marker reduction, producing uniformly high correlations {approx}1.00) across all SNP densities and selection strategies. These findings indicate that optimized low-density SNP panels maintain prediction accuracy comparable to HD panels, offering a cost-effective tool for large-scale genomic evaluations.

Individuals carrying a G4C2 repeat expansion in C9orf72 can develop amyotrophic lateral sclerosis (ALS) and/or frontotemporal dementia (FTD), at various and unpredictable ages. The contribution of the expansion to this extreme clinical variability remained a matter of debate. In this work, we validated the use of optical genome mapping to efficiently size this expansion from blood and applied this method to a cohort of 54 repeat expansion carriers including patients with FTD (n = 23), ALS (n = 22) and presymptomatic individuals (n = 9). Despite the high variability in expansion profiles, distinct groups can be inferred. Although consensus repeat profiles were similar between FTD and ALS patients, relationships between repeat number, indexes of repeat instability, and the age at sampling differed between clinical conditions. An age at sampling-dependent increase of repeat instability in blood was observed in presymptomatic carriers and FTD patients (p = 0.01 and p = 0.04 respectively), in contrast to patients with ALS (p = 0.9). The proportion of shorter repeats ([500-1500 repeats]) increased with the age at sampling in ALS patients but decreased in presymptomatic and FTD cohorts, while the proportion of larger repeats (>2000 G4C2), in particular those exceeding 4000 repeats, increased along with the age at sampling in presymptomatic carriers and FTD cases (p <1.10-e4 and p = 0.006 respectively) conversely to patients with ALS (p = 0.98). In conclusion, this work strongly supports different dynamics of repeat instability of the C9orf72 pathogenic expansion in blood linked to the clinical evolution. These findings provide new insights into the relationship between expansion profiles and clinical variability.

Genomic signatures of selection can reveal loci underlying adaptation and disease resistance in livestock populations, but such analyses in water buffalo (Bubalus bubalis) have historically been constrained by the absence of a chromosome-level, species-native reference genome for SNP array data. We re-analyzed genotype data from 85 Nili-Ravi buffalo (Axiom Buffalo Genotyping 90K array, originally positioned using bovine (Bos taurus, UMD3.1) proxy coordinates, by performing a full coordinate liftover to the buffalo-native UOA_WB_1 assembly using an independently published SNP remapping resource. Following quality control (51,209 markers retained), haplotype phasing, and genome-wide integrated haplotype score (iHS) and Wrights Fst (case/control) selection scans, we evaluated 14 classical bovine-tuberculosis (bTB) candidate genes and identified six additional genes with putative immune function through an unbiased genome-wide screen. None of the 14 classical candidates (including SLC11A1, the Toll-like receptors, and IFNG) reached genome-wide significance in either scan. In contrast, six novel loci TNFSF18, IL2RB, TNFRSF19, IRF2, IL15, and CD28 showed significant iHS or Fst signals, four of which (TNFSF18, IL2RB, IL15, CD28) converge functionally on T-cell costimulation and cytokine receptor signaling (KEGG pathways map04660 and map04060, Bos taurus proxy annotation). Using extended haplotype homozygosity (EHH) decay, haplotype furcation structure, and per-marker haplotype counts as three independent lines of corroborating evidence, we classified these six genes into confidence tiers: TNFSF18 and IL2RB showed the strongest, most balanced support, while CD28 and IL15 signals were driven by very few haplotypes (3 and 5 of 30, respectively) and should be interpreted cautiously pending replication. These findings suggest that adaptive, cell-mediated immune signaling rather than the innate/macrophage-centred mechanisms emphasized by existing bTB candidate gene panels may be a more productive avenue for future selection studies in Nili-Ravi buffalo, while underscoring the value of buffalo-native coordinate systems for accurate genomic inference in this species.

The sheep research community benefits from a genome assembly that has been refined over the last 15 years. However, the rich diversity of sheep breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, twelve breeds were selected to represent (mostly French) sheep genetic diversity: Basco-Bearnaise, Black Belly, Blanche du Massif Central, Corse, Ile de France, Lacaune, Manech Tete Noire, Noire du Velay, Ouessant, Rambouillet, Vendeen, Soay. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. The reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Lacaune breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 11 standard sheep assemblies that pave the way for pangenome construction and structural variant functional studies.

Abstract Polycystic ovarian syndrome and Type 2 diabetes mellitus are complex multivariate diseases that share well-established clinical pathophysiology, yet the molecular basis of their overlap is unknown. Prior bioinformatics studies have identified common differentially expressed genes between these two conditions. However, they have not accounted for the directionality of the gene expression changes observed, potentially obscuring any biological distinctions they may have. This study applied a direction-aware approach to classify molecular signatures between PCOS and T2DM as concordant or discordant. Transcriptomic analysis of GEO datasets GSE138518 (ovarian granulosa tissue, PCOS) and GSE25724 (pancreatic islet tissue, T2DM) identified 225 and 1,302 DEGs, respectively. Venn diagram analysis showed that only three genes (SLC6A8, RGS4, and SORL1) were shared between PCOS and T2DM from the total of 1,527 genes, and all 3 genes were regulated in opposing directions. Disease gene retrieval from the Comparative Toxicogenomics Database, Online Mendelian Inheritance in Man database, and GeneCards showed 214 shared disease-associated genes, with 311 genes unique to PCOS and 496 genes unique to T2DM. Protein-protein interaction construction using STRING (version 12.0) identified 70 interacting nodes. CytoHubba analysis across 6 scoring methods identified 18 high-confidence hub genes, including INS, BCL2, MTOR, LEP, MFN2, and PIK3CD. Functional enrichment analysis identified biological processes including immune activation, apoptosis, and insulin signaling, which were confirmed as the main pathways in KEGG pathway analysis. These findings show that while PCOS and T2DM share limited DEGs, they share common pathogenic pathways.

Perkinsus marinus is a protist that causes the wasting disease dermo in the Eastern oyster, Crassostrea virginica. The disease causes high mortality of both wild and cultured oysters, impacting ecosystem health and aquaculture. Despite decades of research, the genetic mechanisms of dermo resistance remain poorly understood. Traditional selective breeding based on phenotypes has yielded improvements in dermo resistance, but progress has been slow, likely due to the trait's complex genetic architecture. Understanding the genetic mechanisms underlying dermo resistance may facilitate advanced breeding to accelerate improvement. This study conducted genome-wide association studies (GWAS) of dermo resistance, leveraging a multi-generational dataset comprising 2,423 oysters phenotyped for survival following a dermo challenge and genotyped using a high-density 66K SNP array. A two-stage GWAS with more inclusive quality control identified 48 dermo-resistance markers, including many associated with genes for innate immune response and energy metabolism. Most dermo-resistance markers showed rare minor alleles occurring at higher frequencies in oysters that died after challenge, suggesting that these rare alleles are deleterious and associated with dermo susceptibility. The strongest association was with a polymorphism in the mucin-5AC-like gene, explaining 8.1% of phenotypic variation and suggesting that mucus production plays a crucial role in dermo resistance. The inclusion of these markers has improved genomic prediction accuracy, and their identification provides new insights into genomic variation and the architecture of dermo resistance, informing future strategies for genetic improvement.

Centronuclear myopathy (CNM) is a rare form of inherited diseases often caused by single base mutations. Modelling CNM is challenging due to the diversity of CNM mutations and genetic background of individual patient. To address this, we used base editing to introduce CNM mutations into an induced pluripotent stem cell (iPSC) line from a healthy donor, generating a collection of iPSC lines (iPS-CNM) carrying distinct CNM mutations. We found that the efficiency of base editing depended critically on selecting base editor (BE) variants and the target sequences. Optimization using different BE variant and sgRNA pair was required for each target site. Moreover, whole genome sequencing (WGS) was performed to confirm on-targets and detect off-targets in order to select iPSC clones for the collection. Our findings highlight the feasibility of base editing for generating an iPSC collection from one parental iPSC line combined with thorough evaluation of rare off-targets using WGS.

In the recent growing interest of AI research toward biology, genetic association studies of AI- derived phenotypes from high-content modalities such as images emerges as a powerful means for biological discovery. However, such AI-phenotyping methods still lacks a good optimization target and an efficient evaluation framework. The number of discovered loci was the major criterion for evaluating the quality of AI-derived phenotypes. However, the time and computational resources required for running the GWAS and subsequent loci-clumping are substantial, limiting the rapid development and iteration of deep learning representation algorithms. Here we present a 1000x faster and lightweight framework, sum-h2, than traditional GWAS framework for evaluating genetic discovery through total heritability. We revisit tr(P^(-1) G), previously proposed in the context of evolutionary studies, as a measure of multi-phenotype heritability. We showed that sum-h2, the sum of heritability over phenotypic Principal Components (PCs), is equivalent to the linear transformation-invariant tr(P^(-1) G), through both theoretical proof and simulation studies. Moreover, sum-h2 can be estimated rapidly with minimal information loss over a relatedness-enriched sample, while preserving the relative ranking of endophenotypes by GWAS loci counts. Based on selected UKB data and sum-h2, we set up a Genetic Discovery Arena, enabling rapid and fair comparisons for the development and optimization of deep learning-derived phenotyping methods.

Standard polygenic risk scores (PRSs) are constructed based on additive genome-wide association study (GWAS) summary statistics. Nonlinear machine learning methods have been increasingly applied to construct PRSs directly from individual-level data, with the aim of improving predictive performance over standard PRSs through their ability to model non-additive genetic effects. However, their superiority across studies has been inconsistent, and the conditions under which they provide meaningful improvements remain unclear. We combined theoretical analysis, simulations and a real-world application to investigate when two widely used nonlinear machine learning methods, random forest and XGBoost, outperform standard PRSs. Theoretical analysis showed that standard PRSs can implicitly capture part of the genetic variance attributable to nonadditive genetic effects through their contributions to marginal SNP effects, thereby losing less information than commonly assumed. Although nonlinear models have a higher theoretical potential, their greater flexibility incurs a bias-variance trade-off that can limit predictive gains at finite sample sizes. Simulations showed that XGBoost outperformed the standard PRS only when the genetic architecture involves a sufficiently large proportion of interaction genetic variance concentrated across relatively few interaction effects and large training samples were available. Random forest consistently underperformed the standard PRS. In an application to ischemic heart disease prediction using UK Biobank data, XGBoost showed no meaningful improvement in predictive performance over the standard PRS, whereas random forest again performed worse. Together, these findings suggest that nonlinear machine learning do not uniformly outperform standard PRSs; rather, their relative performance depends jointly on genetic architecture and training sample size. Our study helps to reconcile the inconsistent results reported across previous studies and provides a framework for identifying settings in which more complex PRS models are likely to be beneficial.

Acute myocardial infarction (AMI) remains one of the leading causes of mortality worldwide, and the following post-effects, such as post-AMI inflammation and tissue repair, involve peripheral blood mononuclear cells playing a critical role. The influence of imputation methods in biological data is assessed with respect to high-resolution single-cell RNA sequencing (scRNAseq) data relevant to these cells. Still scRNAseq data often encounter a lot of dropout events, leading to sparse and noisy datasets, hampering downstream results. To assess the influence of the missingness in the data, we artificially impose different levels of dropout in available scRNAseq data by leveraging various imputation techniques. Specifically, we introduce artificial missingness at 10%, 20%, and 30% levels under a missing completely at random (MCAR) framework, repeated across 10 independent runs. We benchmarked six imputation strategies - MAGIC, IterativeImputer, KNNImputer, Mean Imputation, SoftImpute, and a Generative adversarial network (GAN) - based approaches using multiple evaluation metrics: marker gene preservation, clustering consistency (Adjusted Rand Index - ARI), gene-wise correlation with ground truth, and structural separation (silhouette scores). The results clearly underline that no single imputation method dominated across all metrics. Overall, Mean and KNN imputers showed limited recovery across all benchmarks. GAN excelled in global transcriptional recovery and SoftImpute in preserving biologically meaningful cell-type signals. Our results highlight the importance of selecting the imputation methods as part of the pre-processing step towards the downstream biological questions related to transcriptome recovery, detection of marker genes, or maintaining cell-type-specific resolution.

Background: Preeclampsia (PE) is a hypertensive pregnancy disorder with contributions from both maternal and fetal genetic factors, but how both genomes regulate placental transcription remains incompletely understood. Results: We analyzed placental 5'-end RNA sequencing data from 59 pregnancies in the FINNPEC cohort and performed separate maternal and fetal cis-eQTL analyses using gene-level expression and promoter-level expression defined as Transcript Far 5' Ends (TFE). Genotype-by-PE interaction models were used to identify genetic effects that differ according to PE status. No gene-level interaction eQTLs remained significant after multiple-testing correction, although suggestive signals included LILRB2 in the maternal and LPCAT3 in the fetal analyses. In contrast, we identified two significant syntenic promoter-level interaction eQTLs: rs12504642-TFE61820 at the FHDC1 locus in the maternal analysis and rs1557150-TFE9032 at the PPIAP31 locus in the fetal analysis. Conclusion: These findings suggest that PE-dependent genetic interaction effects may be detectable at specific transcript promoter regions even when they are not captured at annotated gene levels.

ABSTRACT Objective: Skeletal muscle growth rate differs substantially among diverse pig breeds. In the present study, RNA-seq was performed to identify differentially expressed genes (DEGs) across pig breeds with distinct growth rates. CCND2 showed obvious differential expression among pig breeds. Although CCND2 is essential for cell cycle control, its biological function in skeletal muscle differentiation has rarely been reported. This work aims to identify DEGs in pig breeds with different growth rates and explore CCND2 role in porcine skeletal muscle development. Methods: RNA-seq was performed to identify differentially expressed genes (DEGs) across pig breeds with distinct growth rates. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules potentially associated with skeletal muscle growth and development. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis was performed to validate the role of CCND2 in skeletal muscle differentiation. Results We identified 5,416, 4,846, and 2,777 DEGs in the Duroc pig (DU) vs. Jiangquan black pig (JQ), DU vs. Laiwu pigs (LW), and JQ vs. LW comparison groups, respectively. WGCNA identified two gene modules (lightgreen and darkmagenta) potentially associated with skeletal muscle growth and development. CCND2 exhibited differential expression in comparisons between DU vs JQ and DU vs LW, with higher expression observed in Duroc pigs. qRT-PCR analysis revealed that overexpression of CCND2 at day 6 (D6) of cell differentiation significantly increased the expression levels of CCND2 gene and markers of myogenic differentiation (p < 0.05). Conversely, knockdown of CCND2 significantly decreased the expression levels of CCND2 gene and myogenic differentiation markers (p < 0.05). Conclusions This study identified breed-specific DEGs and key gene modules in porcine skeletal muscle and verified that CCND2 promotes myogenic differentiation. These findings provide a genetic theoretical basis for further investigations into skeletal muscle growth and development. Keywords pig, skeletal muscle, transcriptome, WGCNA, CCND2