NPJ Genomic Medicine最新文献

Despite advanced diagnostic tools, early detection of rare genetic conditions like Noonan syndrome (NS) remains challenging. We evaluated a deep learning model's real-world performance in identifying potential NS cases using electronic health record (EHR) data, validated through genetic sequencing and clinical assessment. The model analyzed 92,428 patients, identifying 171 high-risk individuals (score > 0.8) who underwent comprehensive review. Among these, 86 had prior genetic diagnoses, including three NS cases diagnosed during the study period. Genetic sequencing of remaining patients identified two additional NS cases with pathogenic variants. The model achieved 2.92% precision and 99.82% specificity. While precision was lower than prior validation (33.3%), this reflected expected differences in disease prevalence rather than model degradation. NS-associated phenotypes were enriched among high-risk patients, and trajectory analysis showed potential for earlier identification, highlighting both promise and limitations of EHR-based computational screening tools.

Asthma poses a significant public health burden. Despite identifying more than a hundred genetic risk loci in genome-wide association studies (GWAS), the underlying functional mechanisms remain poorly understood. Studying omics, especially microRNAs (miRNAs), is a promising approach to facilitate our understanding of the biological pathways of asthma. Here, we performed miRNA expression quantitative trait loci (miRNA-QTL) analyses using whole-genome sequencing and serum-based miRNA expression data from two independent cohorts of children with asthma (Genetic Epidemiology of Asthma in Costa Rica Study (GACRS), (NCT00021840, 2005-06-23) (N = 980, Discovery) and the Childhood Asthma Management Program (CAMP) (NCT00000575, 2005-06-23) (N = 354, Replication)). Our robust discovery analysis identified 28 significant cis-miRNA-QTL associations, where 12 were not reported in three independent miRNA-QTL studies. Three of these 12 signals were replicated in CAMP. The QTLs colocalize with expression and splicing QTL in asthma-relevant tissues and cells, and overlap with asthma-related and blood cell trait GWAS hits.

Pharmacogenetics can enhance cardiovascular disease (CVD) treatment by tailoring drug therapy to genetic profiles and minimising trial-and-error approaches. Genetic variability influences responses to common CVD drugs, including antiplatelet drugs (clopidogrel and aspirin), anticoagulants (warfarin), statins, and antihypertensives (ACE inhibitors and β-blockers). Understanding genetic polymorphisms can improve efficacy and safety. Despite this progress, further research is needed to optimise pharmacogenomic applications and advance personalised medicine to improve CVD treatment outcomes.

Variants affecting pre-mRNA splicing mechanisms are responsible for multiple monogenic disorders. However, their prioritization and interpretation remain challenging. Herein, we designed a strategy for the identification of likely spliceogenic variants in unsolved inherited retinal dystrophy (IRD) cases. We benchmarked thirteen splicing predictors on a curated training dataset, which revealed that the combination of SpliceAI and MaxEnt tools exhibited the best performance for the analysis of most splicing variants. However, for branch point variants, the BranchPoint tool (Alamut®-Batch) was the optimal choice. The proposed combination of tools was assessed using a validation cohort comprising 116 genetically diagnosed individuals with rare diseases, and subsequently applied for the analysis of 211 unsolved IRD families. The pipeline identified 30 likely pathogenic variants, 17 of which were predicted to alter splicing mechanisms. These results demonstrate an increase in diagnostic yield of up to 6.2%, reinforcing the importance of reanalysis strategies focused on identifying spliceogenic variants.

Limited information is available for TP53 pathogenic variants (PVs) in early-onset breast cancer patients in China. We investigated the prevalence and clinical relevance of TP53 PVs among 1492 BRCA1/2-negative early-onset breast cancer patients. Peripheral blood samples were collected for TP53 genetic testing through next-generation sequencing. Finally, TP53 PVs were identified in 7 patients (0.47%). The variants p.R248P, p.I251F, and p.G266R were identified for the first time in germline mutations. TP53 carriers exhibited significantly younger diagnosis age (p = 0.003) and higher prevalence of HER2-positive disease (p = 0.020). All carriers were diagnosed before age 35. In HER2-positive patients ≤35 years, the prevalence of TP53 PVs was 2.3%, significantly higher than others after adjusting for a family history of breast cancer and/or ovarian cancer and a personal history of bilateral breast cancer (OR = 13.57, p = 0.002). These results support TP53 genetic testing prioritization for HER2-positive patients under 35 years to guide clinical management, while validation in diverse populations remains essential.

Despite advances in genome sequencing, many individuals with rare genetic disorders remain undiagnosed. Transcriptional profiling via RNA-seq can reveal functional impacts of DNA variants and improve diagnosis. We assessed blood-derived RNA-seq in the largely undiagnosed SickKids Genome Clinic cohort (n = 134), which has been subjected to multiple analyses benchmarking the utility of genome sequencing. Our RNA-centric analysis identifies gene expression outliers, aberrant splicing, and allele-specific expression. In one-third of diagnosed individuals (20/61), RNA-seq reinforced DNA-based findings. In 2/61 cases, RNA-seq revised diagnoses (EPG5 to LZTR1 in an individual with a Noonan syndrome-like disorder) and discovered an additional relevant gene (CEP120 in addition to SON in an individual with ZTTK syndrome). Additionally, ~7% (5/73) of undiagnosed cases had at least one plausible candidate gene identified. This study highlights both the benefits and limitations of whole-blood RNA profiling in refining genetic diagnoses and uncovering novel disease mechanisms.

The Genomic Medicine for Everyone (Geno4ME) study was established across the seven-state Providence Health system to enable genomics research and genome-guided care across patients' lifetimes. We included multi-lingual outreach to underrepresented groups, a novel electronic informed consent and education platform, and whole genome sequencing with clinical return of results and electronic health record integration for 78 hereditary disease genes and four pharmacogenes. Whole genome sequences were banked for research and variant reanalysis. The program provided genetic counseling, pharmacist support, and guideline-based clinical recommendations for patients and their providers. Over 30,800 potential participants were initially contacted, with 2716 consenting and 2017 having results returned (47.5% racial and ethnic minority individuals). Overall, 432 (21.4%) had test results with one or more management recommendations related to hereditary disease(s) and/or pharmacogenomics. We propose Geno4ME as a framework to integrate population health genomics into routine healthcare.

Saliva is an accessible biofluid with potential for non-invasive disease diagnostics. This study explores how genetic susceptibility to common diseases is reflected in DNA methylation (DNAm) and gene expression profiles in saliva. We constructed cis-mQTL (n = 345) and cis-eQTL (n = 277) datasets and examined correlations between DNAm and gene expression. Saliva QTLs were integrated with summary statistics from 36 genome-wide association studies (GWAS) using Summary-based Mendelian Randomization (SMR) to identify disease-associated molecular traits. We found 501 CpG sites and 24 genes as candidate biomarkers, as well as 56 eQTMs linked to conditions such as prostate cancer, squamous cell carcinoma, coronary artery disease, type 2 diabetes, and Parkinson's disease. This work introduces a publicly available resource and suggests that saliva-based molecular signatures may capture systemic disease risk, supporting future exploration as diagnostic markers.

Genome-wide association studies (GWAS) have established key role of immune dysfunction in Age-related Macular Degeneration (AMD), though the precise role of immune cells remains unclear. Here, we develop an explainable machine-learning pipeline (ML) using transcriptome data of 453 donor retinas, identifying 81 genes distinguishing AMD from controls (AUC-ROC of 0.80, CI 0.70-0.92). Most of these genes were enriched in their expression within retinal glial cells, particularly microglia and astrocytes. Their role in AMD was further strengthened by cellular deconvolution, which identified distinct differences in microglia and astrocytes between normal and AMD. We corroborated these findings using independent single-cell data, where several ML genes exhibited differential expression. Finally, the integration of AMD-GWAS data identified a regulatory variant, rs4133124 at PLCG2, as a novel AMD association. Collectively, our study provides molecular insights into the recurring theme of immune dysfunction in AMD and highlights the significance of glial cell differences in AMD progression.

Identifying critically ill newborns who will benefit from whole genome sequencing (WGS) is difficult and time-consuming due to complex eligibility criteria and evolving clinical features. The Mendelian Phenotype Search Engine (MPSE) automates the prioritization of neonatal intensive care unit (NICU) patients for WGS. Using clinical data from 2885 NICU patients, we evaluated the utility of different machine learning (ML) classifiers, clinical natural language processing (CNLP) tools, and types of Electronic Health Record (EHR) data to identify sick newborns with genetic diseases. Our results show that MPSE can identify children most likely to benefit from WGS within the first 48 h after NICU admission, a critical window for maximally impactful care. Moreover, MPSE provided stable, robust means to identify these children using many combinations of classifiers, CNLP tools, and input data types-meaning MPSE can be used by diverse health systems despite differences in EHR contents and IT support.