NPJ Genomic Medicine最新文献

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.

Polygenic embryo screening (PES) is used to screen embryos for their genetic likelihood of developing complex conditions and traits. We surveyed 152 U.S. reproductive endocrinology and infertility specialists (REIs) on their views of PES. While most respondents (97%) were at least slightly familiar with PES, general approval of PES was low (12%), with the majority expressing disapproval (46%) or uncertainty (42%). A majority (58%) believed risks outweigh benefits, while only 16% felt benefits outweigh risks. Most clinicians (85-77%) were very or extremely concerned about low accuracy, confusion over results, false expectations, and eugenics. Nonetheless, when asked to vote on whether PES should be allowed, 44% would vote to allow it, 45% would vote to disallow it, and 10% would abstain from voting. REIs showed more support for PES when used to screen for physical and psychiatric health conditions (59-55% approving) rather than behavioral or physical traits (7-6% approving).

UBR5 encodes an E3 ubiquitin-protein ligase which targets distinct N-terminal residues of proteins for degradation. Heterozygous loss-of-function variants were reported in patients with Autism Spectrum Disorder (ASD) and developmental delay, and recently in a cohort of individuals with neurodevelopmental disorders and variable other features. Here, we report three unrelated individuals with de novo loss-of-function variants in UBR5, presenting with ASD and intellectual disability. We review the literature for other de novo predicted loss-of-function variants in probands with ASD or developmental delay (in total n = 11 variants), providing further evidence that UBR5 haploinsufficiency is associated with ASD and atypical neurodevelopmental trajectories, including developmental delay and intellectual disability.

Leukodystrophies are a diverse group of genetic disorders affecting the central nervous system white matter. Since their initial identification over a century ago, significant advancements have been made in understanding their genetic and clinical profiles. Yet, disease modifying therapies are limited, despite significant clinical impact characterized by progressive neurological decline leading to severe disability and early mortality. This underscores the need for advanced disease models to facilitate the understanding of disease mechanisms and the development of early therapeutic interventions. Stem cells have emerged as a transformative tool in leukodystrophy research, enabling the generation of patient-specific cells otherwise inaccessible for study. We have conducted the first scoping review of stem cell-based disease modeling in leukodystrophies, highlighting recent developments, challenges, and future directions in leveraging these models to enhance our understanding and aid in the development of therapies for these debilitating disorders.

Epstein-Barr virus (EBV) is an oncogenic virus ubiquitous in human populations. CD8 T cells play a crucial role in establishing a strong anti-EBV immune response. Among the various inborn errors of immunity (IEI) showing a restricted vulnerability to EBV, TNFRSF9 (CD137, 4-1BB) deficiency was described in 2019 in patients with chronic EBV viremia and EBV-associated lymphoproliferative diseases. We here investigated a patient with a history of chronic EBV infection and CD137 deficiency who had previously undergone transplantation from her HLA-identical brother. We found that the brother was also a homozygous carrier of the same TNFRSF9 variant, explaining the patient's inability to control EBV after transplantation. Remarkably, during a period of spontaneous clinical improvement and EBV control, we detected two somatic variants in the patient, which resulted in the emergence of two independent revertant CD8 T cell clones that accounted for up to 20% of CD8 T cells in peripheral blood. Using single cell RNA sequencing we demonstrated that both revertant clones originated post-transplant from donor-derived cells. We report here the first described case of a somatic reversion phenomenon in TNFRSF9 deficiency, correlating with clinical improvement and paving the way for future gene therapy strategies for this IEI.

Traditional methods for pharmacogenomics (PGx), like those using disease-specific polygenic risk scores (PRS-Dis), often fail to capture the full heritability of drug response, leading to poor predictions. Direct PGx PRS approaches could improve this, but the scarcity of relevant PGx datasets limits the wide application. To overcome these challenges, we introduce PRS-PGx-TL, a novel transfer learning method. It models large-scale disease summary statistics data alongside individual-level PGx data, leveraging both sources to create more accurate prognostic and predictive polygenic risk scores. In PRS-PGx-TL, we further develop a two-dimensional penalized gradient descent algorithm that starts with weights from disease data and then optimizes them using cross-validation. In simulations and an application to IMPROVE-IT (ClinicalTrials.gov, NCT00202878, September 13, 2005) PGx GWAS data, PRS-PGx-TL significantly enhances prediction accuracy and patient stratification compared to traditional PRS-Dis methods. Our approach shows great promise for advancing precision medicine by using an individual's genetic information to guide treatment decisions more effectively.

Polygenic risk scores (PRSs) are promising tools for genetic risk stratification, but their performance across ancestries remains uncertain. We evaluated 64 published PRSs for eight cardiometabolic traits in 4879 Thai individuals using imputed SNP-array data. Cross-sectional and six-year longitudinal analyses were performed to assess predictive performances. PRSs for type 2 diabetes (T2D) and lipid traits showed the strongest utility, with the best-performing LDL-C and TC scores explaining up to 9.8% and 7.8% of trait variance, respectively. The T2D PRS achieved an area under the curve (AUC) of 0.70 and consistently stratified disease risk over time. In contrast, PRSs for glycemic traits and cardiovascular disease (CVD) had weaker predictive value; notably, the best-performing CVD PRS showed an inverse association with disease risk. Reduced SNP retention and ancestry-related linkage disequilibrium differences contributed to variability. These findings highlight both the potential and current limitations of PRSs in underrepresented Southeast Asian populations.