NPJ Genomic Medicine最新文献

Fabry disease (FD) is a rare X-linked lysosomal storage disorder caused by variants in the alpha-galactosidase A gene (GLA). Cardiac complications are a major cause of mortality, but the large number of variants complicate early identification of at-risk patients. In this study, we assessed the microcirculation using Retinal Vessel Analysis (RVA) in 63 FD patients age- and gender-matched to 60 healthy controls, analyzing associations between RVA parameters, cardiac involvement, and GLA variants. FD patients showed reduced venular flicker-induced dilation, narrower retinal arterioles, and a lower arteriolar-to-venular ratio. Impaired retinal microcirculation was associated with cardiac involvement, and patients with cardiac-associated GLA variants exhibited narrower retinal arterioles. Markers of inflammation and endothelial dysfunction (ED) were significantly higher in FD patients. Higher inflammatory levels correlated with altered retinal microcirculation in patients carrying cardiac-associated GLA variants. RVA detects microvascular ED in FD patients and may serve as a non-invasive biomarker for cardiovascular risk stratification. Registration: https://clinicaltrials.gov/study/NCT06758648; Unique identifier: NCT06758648.

Structural variants (SVs) that disrupt topologically associating domains can cause disease by rewiring enhancer-promoter interactions. Duplications involving GPR101 are known to cause X-linked acrogigantism (X-LAG) through ectopic GPR101 expression, but not all of these duplications are pathogenic. This presents a diagnostic challenge, especially in the prenatal setting. We evaluated POSTRE, a tool that predicts the regulatory impact of SVs, to distinguish pathogenic from benign GPR101 duplications. We analyzed seven non-pathogenic duplications and 27 known X-LAG-associated duplications. To enable predictions in an X-LAG-relevant tissue, enhancer maps built using H3K27ac ChIP-seq, ATAC-seq, and RNA-seq data derived from human anterior pituitary samples (NIH research protocol 97-CH-0076, Clinicaltrials.gov Identifier NCT00001595, submitted on 11 March 1999) were integrated into POSTRE. POSTRE correctly classified all 34 duplications as benign or pathogenic. In addition, one X-LAG case with mild clinical features (i.e. severe growth hormone hypersecretion without pituitary tumorigenesis) was found to include only 2/5 VGLL1 enhancers, whereas all typical X-LAG cases had ≥4 enhancers duplicated. This suggests that partial enhancer hijacking at VGLL1 could explain the different clinical features in this individual. These findings support the utility of POSTRE to support diagnostic pipelines when interpreting SVs affecting chromatin architecture in pituitary disease and highlight its potential to reduce uncertainty in genetic counseling without requiring chromatin conformation capture assays.

Recently, some clinics have begun using preimplantation genetic testing for monogenic disorders (PGT-M) for moderately penetrant breast cancer (BC) risk variants, while other clinics use polygenic risk scores (PRS) in the context of preimplantation embryo screening. Using both simulation and formal mathematical approaches, we evaluated: (1) in what circumstances embryo selection using PRS could lead to systematically erroneous results due to failure to consider monogenic carrier status; and (2) whether PGT-M for moderate penetrance variants could lead to erroneous results due to unassessed, yet elevated PRS. Variants in BRCA1, BRCA2, and PALB2 resulted in a risk distribution that was essentially disjoint from the non-carriers, regardless of PRS. By contrast, for moderately penetrant genes, standard PGT-M would fail to select the lowest risk embryo approximately 5% of the time due to elevated PRS. This complex interplay suggests that caution should be exercised when considering preimplantation genetic testing involving exclusively monogenic variants of moderate penetrance or polygenic scores.

Reproductive carrier screening has evolved beyond ethnic-specific testing to include diverse populations, yet gene selection varies considerably. In Singapore, genomic data analysis identified severe paediatric conditions amongst Chinese, Indian and Malay populations absent from existing screening panels. We developed a model leveraging data from 9051 participants to guide gene selection for carrier screening representative of Asian genetic diversity, focusing on severe paediatric-onset conditions prevalent in these populations. After evaluating severity, genotype-phenotype variability, clinical utility and technical feasibility, we identified 88 genes associated with recessive severe paediatric onset prevalent amongst Chinese, Indian and Malay populations, irrespective of carrier frequency. Including 24 additional genes from our registry resulted in a 105-gene panel, predicted to identify 0.44% at-risk couples, with 86 genes overlapping existing panels. Broadening criteria to include moderate severity conditions while limiting carrier frequencies to less than 1 in 200 reduced the panel to 59 genes, increasing predicted at-risk couples to 0.47%, due to higher carrier frequencies, yet introducing counselling complexities from greater clinical variability. Using local genomic data, we identified genetic conditions relevant to Asian populations for carrier screening. Expanding national genomic sequencing initiatives provides an opportunity to assess genetic condition prevalence across diverse ancestries, improving equity in carrier screening programmes.

We present a rare case of low-grade diffusely infiltrative tumor (LGDIT), SMARCB1-mutant, recurred as an atypical teratoid/rhabdoid tumor (AT/RT) seven years after complete resection. Comprehensive genetic and epigenetic analysis of both LGDIT, SMARCB1-mutant, and AT/RT samples revealed that SMARCB1 mutation and methylation patterns are stable during the latent period and not the direct target that determines the malignant phenotype of cancer. However, there was a switch of oncogenic signaling pathways from the MAPK pathway to the PI3K/AKT pathway with accumulation of somatic variants responsible for the inherent malignant phenotype. Considering the dormant oncogenic traits behind an innocent phenotype, it is recommended to confirm the diagnosis of LGDIT, SMARCB1-mutant, by conducting a methylation classifier analysis or an SMARCB1 expression study to ensure accurate prognosis prediction.

Singapore has advanced in precision medicine, which is largely based on genetic testing and sequencing, yet its safeguard against genetic discrimination (GD) is limited to a non-binding insurance moratorium, with no protections in employment. This study examined the prevalence of self-reported GD and factors influencing willingness to undergo genetic testing in Singapore. A cross-sectional survey assessed experiences of GD, awareness of protections and testing willingness. Twenty percent reported GD in insurance and 9% in employment. The majority identified existing safeguards incorrectly. Sixty-four percent expressed willingness to undergo medically indicated genetic testing. Willingness was positively associated with education, trust in healthcare and perceived fair treatment and negatively associated with age, parental status, deterministic thinking and cultural-religious beliefs. The results highlight that, though policymakers aim to mitigate GD in Singapore, enhanced legal protections and public education are needed to support equitable access to genetic testing.

Genome sequencing (GS) was applied to 3317 individuals from 1452 Korean families with suspected rare genetic disorders to assess diagnostic yield and clinical utility. Patients were categorized into 16 clinical subgroups with curated phenotypes, and variant interpretation was refined by post-analytic phenotype matching. A molecular diagnosis was achieved in 46.2% of families, influencing clinical management in 18.5% of diagnosed cases. Family-based GS had a higher yield than singleton testing (48.5% vs. 41.5%). Neuromuscular and neurodevelopmental disorders showed the highest yields. GS-specific variant types, including deep intronic, noncoding, complex structural variants, and tandem repeat expansions, accounted for 14.6% of diagnoses. Secondary findings were identified in 4.3% of individuals. Novel disease-associated genes such as RYBP, DNAJA3, CAMK2D, and small nuclear RNA genes were also reported. These results highlight the diagnostic power of GS and support its use as a first-tier test, especially in underrepresented populations.

Eyes shut homolog (EYS) is the most common autosomal recessive causative gene of inherited retinal dystrophy (IRD) in the Japanese population, yet genotype-phenotype correlation data remain limited. We analyzed 291 probands (141 males, 150 females) with IRD caused by EYS (EYS-RD) from eight Japanese facilities. Clinical variables included age at onset, initial symptoms, best-corrected visual acuity (BCVA), and its progression alongside genotype information. Mean onset was 25.8 ± 14.9 years, most often night blindness (67.0%), and rod-cone dystrophy was observed in 95.9%. Initial BCVA averaged 0.34 ± 0.56 logMAR, declining 0.03 ± 0.06 logMAR/year, with low vision and blindness estimated at 48.4 and 73.6 years, respectively. Three major East Asian-specific pathogenic variants (S1653fs, Y2935X, and G843E) accounted for 88.7% of all cases. S1653fs homozygotes showed the earliest onset (mean, 18.4 years). These findings support the potential of genetic testing for personalized medicine tailored to population characteristics.

Precision medicine aims to tailor healthcare by integrating individual genetic, epigenetic, transcriptomic, proteomic, and clinical data, collectively referred to as multi-omic data. However, the scale and complexity of such multi-omics datasets challenge classical computing approaches. Quantum computing, which leverages superposition and entanglement (quantum-level correlations between particles), offers a fundamentally new paradigm for accelerating molecular simulations, biomarker discovery, and high-dimensional data analysis. This review explores the convergence of quantum computing and it's potential to provide unmet needs in precision biomedicine research, with emphasis on applications in diagnostic modeling, multi-omic data integration and drug discovery. We highlight early proof-of-concept studies demonstrating the use of quantum machine learning for disease prediction, quantum algorithms for protein folding, and quantum generative models for novel drug design. Hybrid quantum-classical workflows are also already enabling gene network inference and prioritization of variants of uncertain significance, the latter of which is a major focus of multi-omic research worldwide. Emerging directions include digital twin simulations and real-time clinical decision support powered by quantum models. Looking ahead, the long-term vision for quantum computing in biomedicine involves in silico modeling of entire biological systems to simulate cellular responses to perturbations like drug treatments, enabling clinicians to test therapies in virtual patients before real-world application. Despite these advances, practical implementation remains limited by hardware constraints, qubit decoherence, algorithm scalability, and regulatory barriers. Nonetheless, as quantum hardware evolves and AI-aligned quantum algorithms mature, their integration holds transformative potential. Quantum computing may eventually shorten diagnostic timelines, improve therapeutic precision, and make biomedical innovation more globally accessible. We outline a roadmap for translating these technologies into next-generation precision medicine.