BMC Medical Genomics

Background: Several omics studies have been completed in recent years, with the goal of identifying biomarkers of complex multifactorial diseases, such as bronchopulmonary dysplasia (BPD). Objective: To evaluate the performance of 3 distinct omics platforms, using a machine learning pipeline with integration of sparse, reliable and adaptive biomarker identification (Stabl). Methods: Using a well-characterized birth cohort, cord blood metabolomics, proteomics and adductomics data were integrated with Least Absolute Shrinkage and Selection Operator (LASSO) regression and Stabl, to evaluate predictive performance for BPD. Results: Sparse multivariable modeling of 45,000 features measured in 217 infants (52 term, 165 extremely preterm <28 weeks; 82 with BPD and 35 with severe BPD/death) identified a perfect signature for preterm birth with both LASSO and Stabl (AUROC=1.0; p<0.001). Analysis of the preterm group yielded excellent predictive power for severe BPD (AUROC=0.83; p=0.005). Stabl identified a set of 12 biomarkers (2 adducts, 3 proteins and 7 metabolites) with good performance for predicting grade III BPD (AUROC=0.76; P=0.03). Biomarkers across the 3 omics platforms revealed dysregulated pathways of innate/adaptive immune responses, metabolic programming and oxidative stress. Conclusions: The sparse machine learning pipeline is a complementary approach for identifying novel pathways and biomarkers of multifactorial BPD and its endotypes.

Purpose. To determine which proposed keratoconus (KC) mechanisms carry its genetic susceptibility and how it relates to corneal structure, and which describe its corneal tissue. Methods. Six mechanism modules and two attribution sets (transforming growth factor-{beta} [TGF-{beta}] and canonical type-2 atopy) were tested for enrichment in three independent European KC genome-wide association studies (GWAS) and in GWAS of central corneal thickness (CCT) and corneal resistance factor (CRF), with colocalization at matrix loci. Tissue expression was pooled across nine arms from eight studies with control-source meta-regression; activator protein-1 (AP-1)/immediate-early regulatory activity was scored in 107 donors, spatial expression in 21, and serum responses in human and rabbit stromal cells. Results. Extracellular matrix (ECM) was the only module enriched in all three KC sources (P [&le;] 5.46 x 10-) and for CCT, CRF, and thickness-independent CRF (P [&le;] 2.07 x 10-). COL5A1 shared causal variants with CCT in all three sources (posterior probability 0.985-0.991), and FNDC3B and COL1A1 with CRF or its thickness-independent component (0.961-0.986). Pooled, KC corneas had lower general and corneal-local inflammatory and stress/immediate-early expression (log2 fold-change -0.97, -0.98, -0.82; P [&le;] 0.049). In whole-cornea and stromal tissue, ECM expression reversed between postmortem-donor and pathological controls (log2 fold-change +0.36, -2.34), whereas inflammatory and TGF-{beta} programs were lower against both. AP-1/immediate-early regulatory activity was lower in four donor cohorts (Hedges' g -1.50) and stress/immediate-early expression in two spatial cohorts; serum raised immediate-early readouts in human and rabbit cells. Conclusions. KC combines matrix-mechanical genetic susceptibility with a low-inflammatory, low-immediate-early corneal state whose matrix expression direction depends on the control source. Because this susceptibility is genetically shared with corneal thickness and resistance, interventions on the inherited disease are judged by corneal structure and mechanics.

Manual clinical DNA variant classification is the bottleneck of every clinical and research rare disease workflow. The process typically requires a curator to assemble evidence from numerous databases, weigh 28 criteria, reconcile competing evidence, and produce a defensible case for the final classification. Additionally, the framework used to assess variants is not static and successive addenda have revised individual criteria. The most complete and current evidence aggregators available are commercial platforms, which can limit researcher access. We present HeartVar, an open-source web tool that automates the evidence-gathering and interpretation steps of variant classification associated with cardiovascular disease. Given a gene, a variant, and clinical context, HeartVar queries 20 public databases in parallel and assigns ACMG/AMP criteria through a hybrid rule-based/large language model (LLM) approach. Criteria that can be resolved from structured data are computed programmatically, and only those requiring interpretation of unstructured evidence are passed to the LLM. HeartVar returns a classification, point score, per-criterion breakdown, clinical-narrative summary, and database annotations. Benchmarking of 106 expert-curated ClinGen variants showed HeartVar outperformed other curation tools, assigning the correct ACMG tier in 72% of cases. HeartVar demonstrates that an LLM constrained by a domain-specific prompt and grounded in structured evidence can produce variant interpretations of first-pass quality for a cardiovascular disease cohort; however, it is not intended to replace manual assessment by a qualified variant curator. The tool is freely available to use and hosted at www.heartvar.victorchang.edu.au.

In this work, we investigate the homogeneity of performance in binary classification between the Rotterdam phenotypes and phenotype prediction. For this purpose, a multi-task neural network architecture is designed to improve the prediction accuracy of phenotypes while maintaining the accuracy of binary classification. Using a clinical PCOS dataset which contains unlabeled samples of patients that cannot be attributed to any of the known phenotypes, three models are evaluated: optimized binary Random Forest (RF), six-class weighted class RF, and multi-task neural network (with a common hidden layer and two heads for binary and six-class classifications) optimized by a genetic algorithm. Techniques used for evaluation include ten-fold out-of-fold predictions, paired McNemar tests, bootstrap tests, ablation study, and clustering (Adjusted Rand Index, ARI). Binary RF model reached 90.2% accuracy; however, recall of different phenotypes was significantly unbalanced - from 75.0% of phenotype B to 97.8% of phenotype A. This difference of 22.8 % points was observed (23.3 points) even after excluding phenotype-specific features. While direct phenotype prediction improved recall for common phenotypes, it decreased recall for phenotype B to 43.8% and unclassified cases to 31.5%. Our multi-task model preserved 90.2% binary accuracy and significantly recovered phenotype B recall to 87.5% and unclassified cases to 79.6% (p<0.05). The cluster analysis confirmed the weak correlation between the results and labels (ARI=0.045). The ablation analysis demonstrates that feature selection and class weighting drive this improvement, isolating the specific components responsible for performance gains. We establish that aggregate binary accuracy does not reflect the heterogeneous detection of PCOS phenotypes. Direct phenotype prediction alone redistributes the classification errors, but the multi-task architecture successfully improves the detection of rare phenotypes without compromising binary accuracy. Phenotype-disaggregated evaluations, supported by significance testing and ablation, are essential for clinical classifier research.

Genetic feature engineering, used in methods such as transcriptome-wide association study, supports gene-trait association testing by aggregating single variants into gene-level features predictive of expression. To evaluate how different model architectures, LD filtering thresholds, and variant prioritization methods affect expression prediction quality, we trained over 3 million models and evaluated their performance in independent cohorts. Using the best performing models to impute expression and immunotherapy response as an example trait, we found a significant association with the reactive oxygen species pathway (p=0.032). Our model training workflow will support genetic feature engineering towards improved complex trait modeling.

Polygenic risk scores (PRS) have emerged as promising tools for stratifying inherited disease risk, yet their translation into clinical practice is constrained by a critical and frequently unmet requirement: demonstration that scores derived in one cohort retain discriminatory value when applied independently in a different population. For suspected familial hypercholesterolemia (FH), a substantial proportion of patients that meet clinical criteria still test negative for a monogenic cause. These patients are presumed to carry polygenic LDL-C burden, yet PRS validation is still scarce. Here, we evaluated a published hypercholesterolemia PRS (PGS000936) spanning 5,386 genome-wide loci. We tested the score in 117 monogenic-negative hypercholesterolemia cases and 496 controls from the German general population genotyped on the Illumina Global Screening Array v3.0, with imputation to approximately 11 million variants. The score was applied without retraining, using externally derived {beta}-coefficients. The PRS clearly distinguished cases from controls (p < 2x10-16), with a mean PRS of 0.97 in cases versus 0.52 in controls. Decile analysis revealed a seven-fold increase in odds of hypercholesterolemia in the top 10% of the distribution (OR 7.55; 95% CI 4.36-13.07; p < 0.0001). In 94 cases for which clinical data was available, higher PRS was associated with significantly higher LDL-C levels before treatment. Importantly, the PRS distribution in the German control cohort was shifted relative to that of the control cohort in the initial study, suggesting that population-matched calibration is required for accurate odds ratio estimation and proper interpretation of PRS values. These findings show that the published hypercholesterolemia PRS (PGS000936) can effectively stratify risk and identify individuals with a high genetic burden in a real-world German clinical setting, supporting its clinical utility when appropriately calibrated for the local population.

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.

Rare diseases are individually uncommon but collectively prevalent. Their primary clinical challenge lies not in treatment but in diagnosis. In the early stages of clinical management, it is frequently unclear whether the observed phenotypes are associated with a rare disease. Leveraging machine learning methods to mine latent associations between these phenotypes and rare diseases for early diagnosis offers a viable strategy to alleviate this diagnostic dilemma. In the present study, we demonstrated that machine learning can effectively discriminate rare disease cases from their controls using observed and excluded phenotypes as features. Among them, the Random Forest model achieved the best classification performance with a certain degree of generalizability. Based on further analysis of the feature selection results, we conclude that the two factors, specificity and occurrence count, are important for phenotype selection in rare disease discrimination, and comparable importance should be attached to both observed and excluded phenotypes, during feature construction.

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.

Purpose A significant proportion of cases with rare inherited retinal disease (IRD) remain genetically unresolved following short-read whole genome sequencing (WGS). This study aimed to increase the diagnostic yield of two cohorts in a research setting. One cohort was previously screened by short-read WGS (n=120 cases), while the second cohort (n=28 cases) was not included in any previous short-read WGS studies. In contrast to the larger cohort, these cases only underwent either exome sequencing (ES) or single-molecule molecular inversion probe (smMIPs) sequencing as pre-screening in earlier studies. For all cases, we performed a stepwise, case by case reanalysis in which short-read WGS was generated (n=28 cases) and (re)analyzed short-read WGS data using an optimized approach. For a subset of cases (n=20) that remained unresolved, this was followed by long read WGS. Together, these steps aimed to improve the diagnostic yield in these cohorts. Methods Short-read WGS data of 148 IRD-cases were (re)analyzed using updated allele frequency databases, new variant caller and variant predictor tools, as well as updated and extended gene-panels to identify causal single nucleotide variants (SNVs) and structural variants (SVs). For 20 genetically unresolved cases with sufficient high-quality DNA available, long-read WGS was performed. Results The (re)analysis of short-read WGS data in the 120 previously WGS-screened unresolved cases resulted in a diagnostic yield of 15% (18/120). For the 28 individuals without prior WGS, performing WGS analysis within this study provided an additional yield of 32% (9/28). Furthermore, long-read WGS contributed two genetic diagnoses among the 20 long-read WGS-sequenced probands. Amongst other reasons, new genetic diagnoses were attributed to pathogenic variants in genes newly associated with IRDs and in genes that were not included in the gene panels applied in previous WGS studies. The Mobile Element Locator Tool facilitated the identification of two pathogenic Alu insertions in short-read WGS. Long-read WGS identified two additional pathogenic variants. Conclusion These results support periodic reanalysis of existing short-read WGS data as a key component of IRD genetic diagnostics, with selective long-read WGS representing a valuable additional approach for unresolved cases. Together, these complementary strategies offer a practical framework for improving molecular diagnosis and narrowing the remaining diagnostic gap in IRDs.

Polygenic risk scores (PRS) have emerged as a powerful tool for quantifying genetic susceptibility to complex traits and diseases. However, their calculation and interpretation require standardized data curation, robust statistical methods, and clear reporting strategies. In this work, we present an integrated pipeline designed to address these challenges. The pipeline begins with the construction of a curated genotype/phenotype database derived from public repositories, ensuring that only phenotypes with appropriate metadata, statistical distributions, and ethical suitability are retained. The final dataset comprises 2,346 phenotypes covering 38,256,468 unique SNPs. These phenotypes serve as the final analytical units for PRS calculation, risk stratification, and individual-level interpretation. The generated reports integrate sample-level results, phenotype categorization, risk classification, study references, and variant tables, providing a structured and interpretable output for end users. Together, the curated database and reporting framework establish a comprehensive toolbox for PRS analysis, enhancing reproducibility, transparency, and usability in both research and clinical contexts.

Targeted precision therapies are increasingly used in the treatment of individuals with vascular anomalies (VAs). This increases the need for rapid, accurate and inexpensive genetic diagnosis. Droplet digital polymerase chain reaction (ddPCR) is an alternative to next-generation sequencing (NGS), permitting rapid, highly sensitive interrogation of recurrent pathogenic mosaic variants. We examined the feasibility of ddPCR as a primary diagnostic tool in a large cohort of individuals with VAs. Lesional tissue was collected for ddPCR of up to 46 recurrent pathogenic variants across 16 genes associated with VAs. Specimens were assessed on a subset of assays for each individual based on clinical phenotype. Most individuals who had negative ddPCR results went on to high-depth gene panel or deep exome NGS, or Sanger sequencing. Here we report the phenotypic and molecular findings for 78 newly recruited and tested individuals in addition to the 60 individuals already reported from our cohort. The overall diagnostic yield for our cohort when combined with individuals previously reported was 104/138 (75%). Of 138 individuals tested, recurrent pathogenic variants were detected in 71 (51%) on ddPCR. Variants were most frequently identified in PIK3CA (n=28), TEK (n=18), GNAQ (n=12), or MAP2K1 (n=7). In a further 33 individuals, pathogenic variants were identified on NGS or Sanger sequencing. Our findings indicate that ddPCR is an efficient method achieving a high diagnostic yield in our cohort when used prior to sequencing.

Background: Uveal melanoma (UM) and cutaneous melanoma (CM) both originate from the same cell line. This proposes the possibility of a shared mechanism between entities, requiring explicit investigation. Methods: Data from GWAS Catalog and DisGeNET were used to identify shared variation-disease associations (VDAs) between UM and CM. The results were validated using the Ensembl database. In the next step, the STRING database was used to identify the protein-protein interaction. Results: Subsequently, 109 unique VDAs were identified for UM and 880 for CM. However, only 2 VDAs were found to be shared among UM and CM in different ethnic groups. These shared VDAs were rs12203592 of the IRF4 gene, rs12913832 of the HECT and RLD domain-containing E3 ubiquitin protein ligase 2 (HERC2) gene. Notably, PPI network assessment through STRING showcased that OCA2 and IRF4 directly interacted with HERC2. Conclusion: While HERC2 acts as a poor prognostic factor in uveal melanoma, IRF4 status is a key prognostic indicator in both UM and CM. Identifying IRF4 allele contributions enables a better understanding of melanoma pathogenesis and fosters the development of disease-specific approaches.

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.

BackgroundAsthma exacerbations are caused by dysregulated cellular interactions between airway and immune cell populations. Circulating microRNAs (miRNAs) are potential biomarkers for asthma exacerbations; however, their target airway cells remain poorly defined. ObjectiveTo identify the cell types that are regulated by the circulating microRNAs linked to asthma exacerbations and the extent to which the cells are regulated by miRNAs. MethodsWe integrated a curated panel of exacerbation-associated circulating miRNAs with single-cell RNA sequencing (scRNA-seq) profiles from induced sputum of 16 asthma patients and 8 healthy controls. Experimentally validated miRNA-target interactions were combined with cell-type-specific differential expression. Elastic Net regression and SHAP analysis quantified gene-level regulatory contributions, yielding a composite Regulation Strength metric. Findings were validated against four independent GEO datasets. ResultsImmune cells, including monocytes, dendritic cells, and macrophages, demonstrated the strongest statistically significant miRNA regulatory signals, in contrast to airway epithelial cells.hsa-miR-222-3p showed opposing regulatory effects in mature versus alveolar macrophages, indicating differentiation-state-dependent activity, while B_Plasma cells showed no detectable regulatory effect from any miRNA tested. Independent GEO validation confirmed higher expression of protective miRNAs (hsa-miR-126-3p, hsa-miR-146b-5p) in healthy individuals, consistent with prior CAMP cohort associations. ConclusionCirculating miRNAs show cell-type-specific regulatory activity, strongest in monocytes, dendritic cells, and macrophages. hsa-miR-222-3p showed opposing regulatory directions between macrophage subtypes, while B_Plasma cells showed no effect, validated across independent GEO cohorts.

Complex rearrangements are one of the rarest types of structural variants (SVs) and can be divided into two categories: complex chromosomal rearrangements (CCRs) and complex genomic rearrangements (CGRs). CCRs include structural rearrangements that present at least three breakpoints and show exchange of genetic material between more than two chromosomes and CGRs are rearrangements that present more than one junction and/or more than one SV in cis. They are usually formed by one of the chromoanagenesis mechanisms, where a massive disruptive cellular event leads to multiple structural rearrangements. Classical cytogenomic techniques have been commonly applied for their characterization, but methodologies that involve longer DNA molecules, namely optical genome mapping (OGM) and long-read genome sequencing (lrGS), present a considerably higher SV detection resolution, revealing more details about the rearrangements, including precise breakpoint location. Here, we describe six patients with complex rearrangements investigated through a combination of different techniques: karyotyping, chromosomal microarray, and OGM were performed to characterize the rearrangements. Subsequently, lrGS was used to further resolve the alterations, refine their breakpoints' location, and sequence their junction points. Three patients presented CCRs involving three, four, and six chromosomes, while three exhibited CGRs involving one different chromosome each, providing a variety of complex SVs to show the importance of each technique and their combination in rearrangement resolution. In total, the complex rearrangements presented 127 breakpoints, 66 junction points and involved 14 of the 24 chromosomes. Higher-resolution techniques revealed additional complexity in all cases. Despite the advances provided by OGM and lrGS, conventional karyotyping remained indispensable for complete rearrangement resolution. In two patients, the findings supported a novel mechanism combining features of the different chromoanagenesis processes. Furthermore, evidence of inherited alterations was identified, and the comprehensive characterization of the rearrangements enabled more accurate genotype-phenotype correlations. Our findings indicate that an integrated approach combining karyotyping, OGM, and lrGS can completely resolve SVs, including complex rearrangements.

Motivation: Copy-number variants (CNVs) contribute to human disease and population trait variation. CNV detection from large whole-genome sequencing cohorts remains computationally demanding, as most methods require BAM or CRAM files. Genomic VCF (gVCF) files are smaller, routinely generated by standard variant-calling workflows, and contain the read depth and allelic information needed for CNV detection. However, gVCF files are not directly compatible with established CNV callers that rely on Log R Ratio (LRR) and B Allele Frequency (BAF) signals. Results: We present gVCF2CNV, a Nextflow pipeline that converts gVCF files into Log R Ratio and B Allele Frequency signals compatible with established CNV callers. Applied to 12,509 individuals from the SPARK cohort, gVCF2CNV generated signals at an average of 2.7 million SNV positions per individual and completed signal extraction in 4 hours using 192 CPUs. CNV calling with PennCNV and QuantiSNP identified candidate CNVs across a broad size range, with trio-based Mendelian precision reaching approximately 80% or higher for deletions of at least 30 kb and duplications of at least 5 kb. Application to 414,824 individuals from the All of Us cohort was completed in 96 hours, demonstrating feasibility at biobank scale. These results show that gVCF files can serve as a scalable input for CNV detection in large WGS cohorts. Availability and Implementation: gVCF2CNV is available at https://github.com/JacquemontLab/gVCF2CNV, implemented as a Nextflow pipeline with Perl and Python components, supported on Linux. Contact: mame.seynabou.diop@umontreal.ca

Genetic association analyses of exome sequencing data pose distinct methodological challenges in small cohorts, which are common in rare disease research. Data filtering prior to association testing requires particular care, especially when analyzing datasets generated under different study designs. Most of current genetic association tools need to be integrated into complex data processing pipelines, whose implementation requires technical expertise and is prone to error. To alleviate mentioned issues we built a comprehensive, high performance Nextflow workflow for genetic association testing, which includes all commonly performed data filtering and preparation steps, controlled by a rich set of modifiable parameters. We compared our software against existing tools incorporating quality control and aggregated association testing steps and found our solution to offer the most complete suite of data preparation, association testing and reporting modules, making small-cohort association analyses accessible to teams with limited technical expertise.

Background and aims. Inflammatory Bowel Disease (IBD) is a complex immune-mediated condition with a strong genetic contribution. The genetics of IBD are highly complex, with inflammation, autoimmunity, and mucosal immunity pathways involved in its pathogenicity. GenePy is a gene-level disease burden metric. The integration of clinical and genetic data and the application of machine learning methods can aid patient stratification for personalised care. Methods. Lists of consensus IBD pathway genes were curated. GenePy scores were computed from whole exome sequencing data. A K-Means algorithm was optimised using clustering quality and stability metrics on the GenePy scores of genes in each IBD pathway, and the results replicated in three independent IBD cohorts (SPARC IBD, MSCCR IBD, and Southampton IBD). Chi-square tests assessed the association between cluster assignment and phenotypic features, and were multiple test corrected via Benjamini-Hochberg FDR. Results. A strong association was replicated for NOD signalling genes driving IBD subtype, Crohns disease behaviour, and IBD surgery across all cohorts, as well as granuloma in Southampton IBD. This was mainly driven by NOD2. Similar genetic clustering patterns were observed across all cohorts, with additional phenotypic enrichment in clusters driven by NLRP1 (surgery in MSCCR IBD), and nominally significant enrichment for disease behaviour linked to BIRC2 (B2 in Southampton IBD and B2B3 in SPARC IBD). Conclusions. This analysis identified a common genetic architecture across NOD signalling genes across IBD cohorts, replicated a signal for NOD2 as a monogenic driver of disease with a strong phenotypic enrichment, and identified other putative genetic drivers of IBD, such as the NLRP1 gene.

Preeclampsia (PE) is a hypertensive pregnancy-specific disorder and a leading cause of maternal and fetal mortality. A common feature of PE placentas and maternal plasma is dyslipidemia, or abnormal lipid levels, which can increase oxidative stress and endothelial dysfunction. However, the precise transcriptional, post-transcriptional, and epigenetic mechanisms underlying these abnormalities remain poorly characterized. Identifying such changes may clarify disease mechanisms and identify lipid-related PE biomarkers. We conducted a large-scale meta-analysis integrating public placental datasets from NCBI GEO, comprising four DNA methylation (DNAm) datasets (n = 172), three RNA-sequencing datasets (n = 92), and an independent RNA microarray validation cohort (n =146). We evaluated differential DNAm (limma), gene expression (DESeq2), transcript-level shifts (Swish), and alternative splicing (rMATS) in PE versus control placentas, with all analyses stratified by fetal sex via an interaction term model. We also performed placental cell-type deconvolution to quantify PE-associated cell-type proportion changes. Our results demonstrated that lipid-related regulation changes in PE placentas occur primarily at the gene and transcript level, with DNAm showing no changes. We also identified significant isoform switching in PE that were undetected by differential gene expression analysis, and primarily driven by alternative transcription initiation and termination sites rather than alternative splicing. A subset of these isoform switches mapped to pathways dysregulated in PE and were predicted to cause functional protein changes. An interaction term model identified several sex-specific differentially expressed genes (DEGs) in PE, including a subset of male-specific downregulated genes involved in oxidative metabolism. However, many of the remaining sex-specific DEGs across both sexes were previously uncharacterized in the literature. These findings suggest that transcriptional and isoform-level regulation play a role in PE-associated dyslipidemia, with certain regulatory pathways displaying fetal sex-specific patterns. Highlights- Preeclampsia-associated dyslipidemia manifests at the gene and transcript level - Reciprocal isoform switches were missed by standard gene-level analyses - Alternative transcript initiation and termination drove isoform switching - Sex-interaction modeling identified sex-specific transcriptional shifts in PE