Our study assesses the Horizon model, a novel CNV classification tool developed in line with American College of Medical Genetics (ACMG) guidelines, to enhance the classification of pathogenicity in CNVs. Horizon utilizes a ranking-based algorithm, incorporating multiple proprietary databases and variant inheritance models as per ACMG standards. The model’s effectiveness was verified through Area Under the Curve (AUC) analyses on three datasets comprising 635 pathogenic inherited or de novo variants, as classified by clinical geneticists and several established tools. Horizon achieved an AUC of 0.96 (accuracy: 0.9611) in the discovery cohort, demonstrating high accuracy in CNV interpretation and proficiency in predicting pathogenicity. We observed an AUC of 0.96 (accuracy: 0.8776) in the de novo variant cohort and an overall AUC of 0.93 across all cohorts, surpassing tools like ClassifyCNV (AUC: 0.81),AnnotSV (AUC: 0.85), and ISV-CNV (AUC: 0.84). It showed particular effectiveness in interpreting duplication CNVs and the highest performance for CNVs sized 3–5 Mb. The Horizon model offers robust and accurate CNV interpretation, outperforming existing tools and aligning closely with clinical evaluations. Its comprehensive approach, integrating a range of genomic features and following ACMG guidelines, makes it a crucial tool in the genomic interpretation landscape, facilitating the rapid and accurate diagnosis of genetic disorders.

Neonatal diabetes mellitus (NDM) typically presents within the first 6 months of life and generally lacks islet autoantibodies. Genetic elucidation of NDM is a prime example of precision medicine in diabetes. However, no published genetic data exist for NDM in Thailand. We aimed to assess the genetic etiology of Thai NDM using whole exome sequencing (WES). We enrolled 14 Thai patients with NDM and measured GAD65, IA-2, and ZnT8 autoantibodies. We then performed WES and analyzed 43 NDM-related genes to identify causative variants. All subjects tested negative for the three islet autoantibodies. Eight harbored variants in well-established NDM genes (KCNJ11 [n = 5], ABCC8 [n = 1], INS [n = 2 in identical twins]). Two patients with KCNJ11 variants (rs80356616: p.Val59Met and rs8035661: p.Arg50Gln) achieved excellent glycemic control on sulfonylureas, illustrating precision therapy. The remaining six carried pathogenic variants in genes associated with monogenic diabetes, including LRBA, EIF2AK3, DOCK8, WFS1, GATA6, CISD2/SLC9B1, and COQ2. This is the first report on the genetic etiology of NDM in Thailand. WES is an effective approach to identifying variants in this rare diabetes subtype. Larger cohort studies are needed to determine the true prevalence of NDM in Thailand.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00439-025-02815-0.

Most existing genotype-by-environment interaction (G×E) methods assume a known causal direction as an assumption that often does not hold and can lead to biased estimates and spurious findings. To address this, we introduce the Genetic Causality Inference Model (GCIM), a novel approach designed to infer causal directions in G×E studies. GCIM integrates polygenic risk scores (PRS) for both the exposure and the outcome to strengthen causal inference and reduce spurious interaction signals. We evaluated GCIM using simulated data across varying genetic and residual correlation settings and compared its performance to existing PRS-by-environment (PRS×E) models under both null and alternative G×E scenarios. GCIM was also applied to real-world UK Biobank data in both causal directions. GCIM consistently outperformed existing methods by accurately identifying the absence of G×E variance and avoiding false positives, even in the presence of strong phenotypic heteroscedasticity due to residual heterogeneity. Other methods often generated spurious associations, especially under reverse causality. Applying GCIM to UK Biobank data, we investigated 11 circulating biomarkers (including liver enzymes, lipids, and inflammatory markers) and three anthropometric traits (BMI, body fat, and waist-to-hip ratio [WHR]). GCIM identified that bilirubin modulates genetic effects on BMI and WHR, while body fat modulates genetic effects on C-reactive protein, with associations remaining significant after multiple testing corrections. Overall, GCIM provides a more reliable framework for GxE analysis, particularly under challenging conditions such as residual heterogeneity and uncertain causal direction. However, further development is needed to improve its statistical power.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00439-025-02799-x.

Microtia-anotia is a common congenital anomaly. In most cases, the genetic etiology remains unknown. The proper development of outer ear is closely related to cranial neural crest cells. Abnormal DNA recombination perturbing the function of long-range enhancers can lead to genomic disorder. Previously, we identified 4p16.1 duplications in microtia patients and revealed the enhancer function of an evolutionarily conserved region (ECR). Here we recruited additional patients and attempted to identify the minimal overlapping region and regulatory elements. We identified five individuals (F6-F10 probands) with 4p16.1 duplication. The duplications in F3 and F5 were refined to 192.6kb and 96.1kb. Precise junction breakpoints in F4 and F6-F10 were detected. The minimal overlapping region (chr4: 8,689,510-8712,827, hg19) contained conserved sequences in addition to ECR. Dual-luciferase assays detected enhancer activity in the TFAP2C binding and 1794 sequence. We present five additional cases of concha-type microtia with 4p16.1 duplication. The minimal overlapping region contains regulatory elements that function as in-cis tissue-specific modules, regulating downstream gene expression during development of cranial neural crest cell.

Hepatocellular carcinoma (HCC) is a primary liver malignancy with a dismal prognosis. This study established and validated a prognostic model based on antigen-processing and presenting machinery (APM)-related genes through Mendelian randomization and publicly available datasets. Systematic analysis revealed CXCL5, SGPP2, and GLP1R as critical prognostic biomarkers, which were subsequently integrated into a risk model. The model demonstrated significant associations with pathways linked to bile acid, fatty acid, and amino acid metabolism, alongside variations in immune cell infiltration and genomic mutations, including TTN, TP53, and MUC16. Patient stratification into high- and low-risk groups indicated that low-risk individuals exhibited reduced immune infiltration, potentially correlating with enhanced immunotherapy sensitivity. These findings offer a robust gene signature for HCC prognosis and a framework for evaluating responses to immunotherapy.

Hereditary spherocytosis (HS) is a common form of haemolytic anaemia caused by defects or deficiencies in genes encoding erythrocyte membrane proteins, such as ANK1, SPTB, SLC4A1, EPB42, and SPTA1. Among these, ANK1 and SPTB mutations are the most frequent causes of HS worldwide. This study analysed 53 Indian HS patients, identifying 33 novel and 12 previously reported SPTB variants using targeted next-generation sequencing (t-NGS). The identified SPTB variants included frameshift (28%), missense (24%), nonsense (44%), and splicing (4%) types, with nonsense variants being the most common. These nonsense variants typically result in truncated proteins. The variants were widely distributed across the gene, with the highest density observed in the spectrin repeats and ankyrin-binding domain, while no variants were found in the tetramerization domain. All identified SPTB variants exhibited heterozygous inheritance, consistent with an autosomal dominant inheritance pattern of the gene causing HS. One patient, however, carried compound heterozygous variants, leading to severe anaemia, and five patients had de novo SPTB variants. This study expands the spectrum of SPTB variants, enhances the understanding of spectrin-related molecular defects, establishes genotype-phenotype correlations, and provides valuable insights for laboratories developing genetic tests for HS. The high number of identified variants highlights the importance of advanced technologies like NGS for accurate molecular diagnosis in HS disorder. This approach not only supports clinical diagnostics but also aids in family counseling for improved management of HS.