In this study, we integrated phenotype data, genotyping, and gut metagenomic data to investigate how genetic predisposition to T2D interacts with the gut microbiome, as well as attenuating lifestyle factors, across metabolic health traits in a cohort of 821 individuals of European ancestry. We evaluated increased BMI and waist circumference, hypertension, and hypercholesterolemia, which are established and interrelated risk factors for type 2 diabetes (T2D). Strongest microbiome differences overall were found in obese and hypertensive phenotypes. Among the most consistent findings was depletion of multiple Prevotella species in metabolically adverse phenotypes and individuals with elevated genetic risk. Microbiome differences in specific phenotypes were generally dependent on the genetic context. Analysis of the genetic risk and lifestyle confirmed that although high T2D PRS is associated with earlier manifestation of hypertension and excess weight, physical activity tends to weaken PRS–phenotype associations and attenuate the inherited susceptibility. Our findings suggest that genetic predisposition modifies microbiome–phenotype relationships in a trait- and context-dependent manner. Genetic risk may increase susceptibility to microbiome alterations, while environmental and lifestyle factors remain major contributors to microbiome variation and the phenotype. Our results underscore the importance of genetic stratification in the microbiome.
Tier-1 conditions, including breast, colorectal, and prostate cancers and coronary artery disease, have been characterized primarily in patients with hereditary phenotypes but are also common in the general population, partly due to polygenic contributions. We evaluated pathogenic variants (PVs) in nine Tier-1 genes, externally developed polygenic risk scores (PRS), and GenProb, an integrated model combining PVs and PRS, in the Genomic Health Initiative (GHI), a U.S. community-based healthcare biobank. Population attributable risk percentage (PAR%), C-index, and net reclassification index (NRI) were estimated using Cox regression models. Among 20,477 participants, the median age was 64 years, 65% were female, and 36% were of non-European ancestry. Tier-1 conditions were common, affecting 5381 participants (26%). Across conditions, PRS explained substantially higher PAR% (3.44–13.96%) than PVs (0.17–2.65%). A monogenic-only testing strategy limited to individuals with family history identified 166 PV carriers, accounting for only 1.5% of all cases. In contrast, GenProb identified 6563 high-risk individuals, including 1259 cases, accounting for 23.4% of all cases. GenProb outperformed PV-only testing for risk assessment of Tier-1 conditions and may improve identification of high-risk individuals in the general population.
The Israeli population harbors 53 known founder pathogenic variants in 12 genes found in different ethnicities that increase cancer risk. The current diagnostic yield of founder pathogenic variant testing in Israeli patients with cancer is 2%. In Israeli patients with cancer, somatic mutational profiling is routinely performed to detect mutations in oncogenes and tumor suppressor genes. To better understand the utility of repurposing somatic mutation testing to identify carriers of the known Israeli founder pathogenic variants, we examined to what extent Israeli founder pathogenic variants are found in tumors. By comparing United States (n = 674,849) and Israeli cohorts (n = 2769) of tumors tested for somatic mutations, we found that 1 vs. 6% of samples, respectively, harbor an Israeli founder mutation. We tested 69 patients with founder pathogenic variants in their tumor and found a 99% concordance between somatic and germline results. These findings suggest that detection of a founder pathogenic variant in the tumor of a patient from the corresponding founder ethnic group is highly predictive of carrier status. This may warrant cascade screening of at-risk relatives following confirmation of a germline variant in the case index, whenever possible.
Inborn errors of metabolism (IEMs) exhibit substantial biochemical and genetic heterogeneity and cause severe, life-threatening conditions in neonatal intensive care unit (NICU) newborns with high morbidity and mortality. Early diagnosis via high-throughput technology is crucial for optimizing neonatal IEM prognosis. To evaluate the feasibility of mass spectrometry and next-generation sequencing in IEM diagnosis, we retrospectively analyzed the diagnosis of IEMs from 1003 full-term newborns in NICU underwent metabolomic testing by tandem mass spectrometry (MS/MS) and genomic sequencing by targeted next-generation sequencing (tNGS) on identical dried blood spots (DBS). The abnormal detection rates for metabolomic and genomic testing were 1.5% (15 out of 1003) and 15.6% (156 out of 1003) according to their respective reporting principles. Integrative analysis of MS, genetic sequencing and clinical features confirmed IEMs in seven patients, with suspected cases requiring long-term follow-up. Metabolomics enables rapid detection of metabolic abnormalities to guide immediate clinical management, while tNGS improves diagnostic accuracy and supports pre-symptomatic intervention in the NICU. Collectively, our study establishes an efficient dual-test diagnostic workflow for critically ill neonates, providing practical evidence for combined metabolomic and genomic IEM testing in future screening of large neonatal populations.
Long-read RNA sequencing resolves full-length transcript structures, complex splicing, alternative boundaries, transcriptional overlaps, and multigenic isoforms without short-read reconstruction. Current approaches include PacBio HiFi and Kinnex cDNA sequencing, Oxford Nanopore cDNA sequencing, and direct RNA sequencing of native molecules. We critically review platform-specific trade-offs, analytical workflows, sequencing-depth requirements, and biomedical applications, and provide practical guidance for robust transcript discovery, quantification, rare-disease interpretation, viral transcriptomics, and single-cell or spatial isoform analysis.
