Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation ...of missense variants but have limited performance. Here, we describe MVP (Missense Variant Pathogenicity prediction), a new prediction method that uses deep residual network to leverage large training data sets and many correlated predictors. We train the model separately in genes that are intolerant of loss of function variants and the ones that are tolerant in order to take account of potentially different genetic effect size and mode of action. We compile cancer mutation hotspots and de novo variants from developmental disorders for benchmarking. Overall, MVP achieves better performance in prioritizing pathogenic missense variants than previous methods, especially in genes tolerant of loss of function variants. Finally, using MVP, we estimate that de novo coding variants contribute to 7.8% of isolated congenital heart disease, nearly doubling previous estimates.
A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). To assess the contribution of ...noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; P = 8.7 × 10
). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, P = 1 × 10
). We observed significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1-5.0, P = 5.4 × 10
). CHD DNVs altered transcription levels in 5 of 31 enhancers assayed. Finally, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1-1.2, P = 8.8 × 10
). Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs.
Congenital diaphragmatic hernia (CDH) is a severe birth defect that is often accompanied by other congenital anomalies. Previous exome sequencing studies for CDH have supported a role of de novo ...damaging variants but did not identify any recurrently mutated genes. To investigate further the genetics of CDH, we analyzed de novo coding variants in 362 proband-parent trios including 271 new trios reported in this study. We identified four unrelated individuals with damaging de novo variants in MYRF (P = 5.3x10(-8)), including one likely gene-disrupting (LGD) and three deleterious missense (D-mis) variants. Eight additional individuals with de novo LGD or missense variants were identified from our other genetic studies or from the literature. Common phenotypes of MYRF de novo variant carriers include CDH, congenital heart disease and genitourinary abnormalities, suggesting that it represents a novel syndrome. MYRF is a membrane associated transcriptional factor highly expressed in developing diaphragm and is depleted of LGD variants in the general population. All de novo missense variants aggregated in two functional protein domains. Analyzing the transcriptome of patient-derived diaphragm fibroblast cells suggest that disease associated variants abolish the transcription factor activity. Furthermore, we showed that the remaining genes with damaging variants in CDH significantly overlap with genes implicated in other developmental disorders. Gene expression patterns and patient phenotypes support pleiotropic effects of damaging variants in these genes on CDH and other developmental disorders. Finally, functional enrichment analysis implicates the disruption of regulation of gene expression, kinase activities, intra-cellular signaling, and cytoskeleton organization as pathogenic mechanisms in CDH.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Pulmonary arterial hypertension (PAH) is a rare disease characterized by pulmonary arteriole remodeling, elevated arterial pressure and resistance, and subsequent heart failure. Compared with ...adult-onset disease, pediatric-onset PAH is more heterogeneous and often associated with worse prognosis. Although
mutations underlie ≈70% of adult familial PAH (FPAH) cases, the genetic basis of PAH in children is less understood.
We performed genetic analysis of 155 pediatric- and 257 adult-onset PAH patients, including both FPAH and sporadic, idiopathic PAH (IPAH). After screening for 2 common PAH risk genes, mutation-negative FPAH and all IPAH cases were evaluated by exome sequencing.
We observed similar frequencies of rare, deleterious
mutations in pediatric- and adult-onset patients: ≈55% in FPAH and 10% in IPAH patients in both age groups. However, there was significant enrichment of
mutations in pediatric- compared with adult-onset patients (IPAH: 10/130 pediatric versus 0/178 adult-onset), and
carriers had younger mean age-of-onset compared with
carriers. Mutations in other known PAH risk genes were infrequent in both age groups. Notably, among pediatric IPAH patients without mutations in known risk genes, exome sequencing revealed a 2-fold enrichment of de novo likely gene-damaging and predicted deleterious missense variants.
Mutations in known PAH risk genes accounted for ≈70% to 80% of FPAH in both age groups, 21% of pediatric-onset IPAH, and 11% of adult-onset IPAH. Rare, predicted deleterious variants in
are enriched in pediatric patients and de novo variants in novel genes may explain ≈19% of pediatric-onset IPAH cases.
The contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism ...in blood and cardiovascular tissue has not been determined.
We developed a new computational method, EM-mosaic (Expectation-Maximization-based detection of mosaicism), to analyze mosaicism in exome sequences derived primarily from blood DNA of 2530 CHD proband-parent trios. To optimize this method, we measured mosaic detection power as a function of sequencing depth. In parallel, we analyzed our cohort using MosaicHunter, a Bayesian genotyping algorithm-based mosaic detection tool, and compared the two methods. The accuracy of these mosaic variant detection algorithms was assessed using an independent resequencing method. We then applied both methods to detect mosaicism in cardiac tissue-derived exome sequences of 66 participants for which matched blood and heart tissue was available.
EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The estimated true frequency of mosaic variants above 10% mosaicism was 0.14/person in blood and 0.21/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.
We estimate that ~ 1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants with relatively higher allele fraction. Although blood is a readily available DNA source, cardiac tissues analyzed contributed ~ 5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.
Recent studies of somatic and germline mutations have led to the identification of a number of factors that influence point mutation rates, including CpG methylation, expression levels, replication ...timing, and GC content. Intriguingly, some of the effects appear to differ between soma and germline: in particular, whereas mutation rates have been reported to decrease with expression levels in tumors, no clear effect has been detected in the germline. Distinct approaches were taken to analyze the data, however, so it is hard to know whether these apparent differences are real. To enable a cleaner comparison, we considered a statistical model in which the mutation rate of a coding region is predicted by GC content, expression levels, replication timing, and two histone repressive marks. We applied this model to both a set of germline mutations identified in exomes and to exonic somatic mutations in four types of tumors. Most determinants of mutations are shared: notably, we detected an effect of expression levels on both germline and somatic mutation rates. Moreover, in all tissues considered, higher expression levels are associated with greater strand asymmetry of mutations. However, mutation rates increase with expression levels in testis (and, more tentatively, in ovary), whereas they decrease with expression levels in somatic tissues. This contrast points to differences in damage or repair rates during transcription in soma and germline.
High-pressure is a mechanical method to regulate the structure and internal interaction of materials. Therefore, observation of properties' change can be realized in a relatively pure environment. ...Furthermore, high-pressure affects the delocalization of wavefunction among materials' atoms and thus their dynamics process. Dynamics results are essential data for understanding the physical and chemical characteristics, which is valuable for materials application and development. Ultrafast spectroscopy is a powerful tool to investigate dynamics process and becoming a necessary characterization method for materials investigation. The combination of high-pressure with ultrafast spectroscopy in the nanocosecond∼femtosecond scale enables us to investigate the influence of the enhanced interaction between particles on the physical and chemical properties of materials, such as energy transfer, charge transfer, Auger recombination, etc. Base on this point of view, this review summarizes recent progress in the ultrafast dynamics under high-pressure for various materials, in which new phenomena and new mechanisms are observed. In this review, we describe in detail the principles of
high pressure ultrafast dynamics probing technology and its field of application. On this basis, the progress of the study of dynamic processes under high-pressure in different material systems is summarized. An outlook on
high-pressure ultrafast dynamics research is also provided.
Congenital heart disease (CHD) patients have an increased prevalence of extracardiac congenital anomalies (CAs) and risk of neurodevelopmental disabilities (NDDs). Exome sequencing of 1213 CHD ...parent-offspring trios identified an excess of protein-damaging de novo mutations, especially in genes highly expressed in the developing heart and brain. These mutations accounted for 20% of patients with CHD, NDD, and CA but only 2% of patients with isolated CHD. Mutations altered genes involved in morphogenesis, chromatin modification, and transcriptional regulation, including multiple mutations in RBFOX2, a regulator of mRNA splicing. Genes mutated in other cohorts examined for NDD were enriched in CHD cases, particularly those with coexisting NDD. These findings reveal shared genetic contributions to CHD, NDD, and CA and provide opportunities for improved prognostic assessment and early therapeutic intervention in CHD patients.
Congenital heart disease (CHD) is the leading cause of mortality from birth defects. Here, exome sequencing of a single cohort of 2,871 CHD probands, including 2,645 parent-offspring trios, ...implicated rare inherited mutations in 1.8%, including a recessive founder mutation in GDF1 accounting for ∼5% of severe CHD in Ashkenazim, recessive genotypes in MYH6 accounting for ∼11% of Shone complex, and dominant FLT4 mutations accounting for 2.3% of Tetralogy of Fallot. De novo mutations (DNMs) accounted for 8% of cases, including ∼3% of isolated CHD patients and ∼28% with both neurodevelopmental and extra-cardiac congenital anomalies. Seven genes surpassed thresholds for genome-wide significance, and 12 genes not previously implicated in CHD had >70% probability of being disease related. DNMs in ∼440 genes were inferred to contribute to CHD. Striking overlap between genes with damaging DNMs in probands with CHD and autism was also found.
ABSTRACT
Cancer and developmental disorders (DDs) share dysregulated cellular processes such as proliferation and differentiation. There are well‐known genes implicated in both in cancer and DDs. In ...this study, we aim to quantify this genetic connection using publicly available data. We found that among DD patients, germline damaging de novo variants are more enriched in cancer driver genes than non‐drivers. We estimate that cancer driver genes comprise about a third of DD risk genes. Additionally, de novo likely‐gene‐disrupting variants are more enriched in tumor suppressors, and about 40% of implicated de novo damaging missense variants are located in cancer somatic mutation hotspots, indicating that many genes have a similar mode of action in cancer and DDs. Our results suggest that we can view tumors as natural laboratories for assessing the deleterious effects of mutations that are applicable to germline variants and identification of causal genes and variants in DDs.
We quantified the genetic connection between cancer and developmental disorders (DD) using large‐scale exome sequencing data, and found that at least a third of DD risk genes are cancer drivers, and these genes often had similar mode of action in cancer and DD. As a result, germline missense de novo mutations located in cancer mutation hotspots are more likely to be implicated with DD. Our findings suggest that cancer genomics data can be used to improve DD risk gene discovery.