Sequencing in over 50,000 cases identifies coding and structural variation underlying atrial fibrillation risk
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Rapid brain tumor classification from sparse epigenomic data
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.
Whole-genome sequencing analysis identifies rare, large-effect noncoding variants and regulatory regions associated with circulating protein levels
The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants. We identified 604 independent rare noncoding single-variant associations with circulating protein levels. Unlike protein-coding variation, rare noncoding genetic variation was almost as likely to increase or decrease protein levels. Rare noncoding aggregate testing identified 357 conditionally independent associated regions. Of these, 74 (21%) were not detectable by single-variant testing alone. Our findings have important implications for the identification, and role, of rare noncoding genetic variation associated with common human phenotypes, including the importance of testing aggregates of noncoding variants.
Periodontitis impacts on thrombotic diseases: from clinical aspect to future therapeutic approaches
Periodontitis is a chronic inflammatory disease initiated by biofilm microorganisms and mediated by host immune imbalance. Uncontrolled periodontal infections are the leading cause of tooth loss in adults. Thrombotic diseases can lead to partial or complete obstruction of blood flow in the circulatory system, manifesting as organ or tissue ischemia and necrosis in patients with arterial thrombosis, and local edema, pain and circulatory instability in patients with venous thrombosis, which may lead to mortality or fatality in severe case. Recent studies found that periodontitis might enhance thrombosis through bacterial transmission or systemic inflammation by affecting platelet-immune cell interactions, as well as the coagulation, and periodontal therapy could have a prophylactic effect on patients with thrombotic diseases. In this review, we summarized clinical findings on the association between periodontitis and thrombotic diseases and discussed several novel prothrombotic periodontitis-related agents, and presented a perspective to emphasize the necessity of oral health management for people at high risk of thrombosis.
Evaluation of polygenic scores for hypertrophic cardiomyopathy in the general population and across clinical settings
Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality, with pathogenic variants found in about a third of cases. Large-scale genome-wide association studies (GWAS) demonstrate that common genetic variation contributes to HCM risk. Here we derive polygenic scores (PGS) from HCM GWAS and genetically correlated traits and test their performance in the UK Biobank, 100,000 Genomes Project, and clinical cohorts. We show that higher PGS significantly increases the risk of HCM in the general population, particularly among pathogenic variant carriers, where HCM penetrance differs 10-fold between those in the highest and lowest PGS quintiles. Among relatives of HCM probands, PGS stratifies risks of developing HCM and adverse outcomes. Finally, among HCM cases, PGS strongly predicts the risk of adverse outcomes and death. These findings support the broad utility of PGS across clinical settings, enabling tailored screening and surveillance and stratification of risk of adverse outcomes.
EV DNA from pancreatic cancer patient-derived cells harbors molecular, coding, non-coding signatures and mutational hotspots
DNA packaged into cancer cell-derived EV is not well appreciated. Here, we uncovered signatures of EV DNA secreted by pancreatic cancer cells. The cancer cells and non-cancer counterparts exhibit distinct low vs. high molecular weight (LMW vs. HMW) EV DNA fragments distribution, respectively. Genome sequencing and Single Nucleotide Variants analysis revealed that 95% of reads and 94% of SNVs map to noncoding regions of the genome. Given that ~1% of the human genome represents coding regions, the 5% mapping rate to coding regions suggests a non-random enrichment of certain coding regions and mutations. The LMW DNA fragments not only set cancer cells apart, but also harbor cancer specific enrichment of unique coding regions, the top nine being FAM135B, COL22A1, TSNARE1, KCNK9, ZFAT, JRK, MROH5, GSDMD, and MIR3667HG. Additionally, the cancer cells’ LMW DNA fragments exhibit dense centromeric mapping more strikingly on chromosomes 3, 7, 9, 10, 11, 13, 17, and 20. Mutational profiling turned up close to 200 mutations specific for the cancer cells. Altogether, our analyses suggest that centromeric regions might hold clues to EV DNA content from pancreatic cancer, the molecular, mutational signatures thereof, and rationalizes the need for a new approach to DNA biomarker research.
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