Systematic analysis of paralogous regions in 41,755 exomes uncovers clinically relevant variation
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001007513 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The short lengths of short-read sequencing reads challenge the analysis of paralogous genomic regions in exome and genome sequencing data. Most genetic variants within these homologous regions therefore remain unidentified in standard analyses. Here, we present a method (Chameleolyser) that accurately identifies single nucleotide variants and small insertions/deletions (SNVs/Indels), copy number variants and ectopic gene conversion events in duplicated genomic regions using whole-exome sequencing data. Application to a cohort of 41,755 exome samples yields 20,432 rare homozygous deletions and 2,529,791 rare SNVs/Indels, of which we show that 338,084 are due to gene conversion events. None of the SNVs/Indels are detectable using regular analysis techniques. Validation by high-fidelity long-read sequencing in 20 samples confirms >88% of called variants. Focusing on variation in known disease genes leads to a direct molecular diagnosis in 25 previously undiagnosed patients. Our method can readily be applied to existing exome data.
| S-EPMC10611741 | biostudies-literature
Project description:As part of the study "Systematic analysis of paralogous regions in 41,755 exomes uncovers clinically relevant variation" we wanted to identiffy the SNVs that are located in STRC and which ones in STRCP1. For this we applied targeted long-read sequencing.
| EGAD00001011305 | EGA
Project description:Sequencing of the 16S rRNA gene (16S) is a reference method for bacterial identification. Its expanded use has led to increased recognition of novel bacterial species. In most clinical laboratories, novel species are infrequently encountered, and their pathogenic potential is often difficult to assess. We reviewed partial 16S sequences from >26,000 clinical isolates, analyzed during February 2006-June 2010, and identified 673 that have <99% sequence identity with valid reference sequences and are thus possibly novel species. Of these 673 isolates, 111 may represent novel genera (<95% identity). Isolates from 95 novel taxa were recovered from multiple patients, indicating possible clinical relevance. Most repeatedly encountered novel taxa belonged to the genera Nocardia (14 novel taxa, 42 isolates) and Actinomyces (12 novel taxa, 52 isolates). This systematic approach for recognition of novel species with potential diagnostic or therapeutic relevance provides a basis for epidemiologic surveys and improvement of sequence databases and may lead to identification of new clinical entities.
| S-EPMC3309591 | biostudies-literature
Project description:G-protein-coupled receptors (GPCRs) are the largest family of integral membrane receptors with key roles in regulating signaling pathways targeted by therapeutics, but are difficult to study using existing proteomics technologies due to their complex biochemical features. To obtain a global view of GPCR-mediated signaling and to identify novel components of their pathways, we used a modified membrane yeast two-hybrid (MYTH) approach and identified interacting partners for 48 selected full-length human ligand-unoccupied GPCRs in their native membrane environment. The resulting GPCR interactome connects 686 proteins by 987 unique interactions, including 299 membrane proteins involved in a diverse range of cellular functions. To demonstrate the biological relevance of the GPCR interactome, we validated novel interactions of the GPR37, serotonin 5-HT4d, and adenosine ADORA2A receptors. Our data represent the first large-scale interactome mapping for human GPCRs and provide a valuable resource for the analysis of signaling pathways involving this druggable family of integral membrane proteins.
| S-EPMC5371730 | biostudies-literature
Project description:BackgroundDigital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized.ObjectiveWe performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research.MethodsWe conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types.ResultsThe search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded.ConclusionsSpecific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust.
| S-EPMC8482196 | biostudies-literature
Project description:It is becoming increasingly necessary to develop computerized methods for identifying the few disease-causing variants from hundreds discovered in each individual patient. This problem is especially relevant for Copy Number Variants (CNVs), which can be cheaply interrogated via low-cost hybridization arrays commonly used in clinical practice. We present a method to predict the disease relevance of CNVs that combines functional context and clinical phenotype to discover clinically harmful CNVs (and likely causative genes) in patients with a variety of phenotypes. We compare several feature and gene weighing systems for classifying both genes and CNVs. We combined the best performing methodologies and parameters on over 2,500 Agilent CGH 180k Microarray CNVs derived from 140 patients. Our method achieved an F-score of 91.59%, with 87.08% precision and 97.00% recall. Our methods are freely available at https://github.com/compbio-UofT/cnv-prioritization. Our dataset is included with the supplementary information.
| S-EPMC4593641 | biostudies-literature
Project description:BackgroundCommon variants have been associated with prostate cancer risk. Unfortunately, few are reproducibly linked to aggressive disease, the phenotype of greatest clinical relevance. One possible explanation is that rare genetic variants underlie a significant proportion of the risk for aggressive disease.MethodTo identify such variants, we performed a two-stage approach using whole-exome sequencing followed by targeted sequencing of 800 genes in 652 aggressive prostate cancer patients and 752 disease-free controls in both African and European Americans. In each population, we tested rare variants for association using two gene-based aggregation tests. We established a study-wide significance threshold of 3.125 × 10-5 to correct for multiple testing.ResultsTET2 in African Americans was associated with aggressive disease, with 24.4% of cases harboring a rare deleterious variant compared with 9.6% of controls (FET P = 1.84 × 10-5, OR = 3.0; SKAT-O P = 2.74 × 10-5). We report 8 additional genes with suggestive evidence of association, including the DNA repair genes PARP2 and MSH6 Finally, we observed an excess of rare truncation variants in 5 genes, including the DNA repair genes MSH6, BRCA1, and BRCA2 This adds to the growing body of evidence that DNA repair pathway defects may influence susceptibility to aggressive prostate cancer.ConclusionsOur findings suggest that rare variants influence risk of clinically relevant prostate cancer and, if validated, could serve to identify men for screening, prophylaxis, and treatment.ImpactThis study provides evidence that rare variants in TET2 may help identify African American men at increased risk for clinically relevant prostate cancer. Cancer Epidemiol Biomarkers Prev; 25(11); 1456-63. ©2016 AACR.
| S-EPMC5093030 | biostudies-literature
Project description:The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles experienced a reversal of fortune in the human lineage. We explored nonadaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Many nonadaptive hypotheses were tested, but failed to explain the data, which suggests that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This suggestion is supported by the fact that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and hundreds of CAP alleles are protective in genotype-phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of nonneutral candidate variants in the human proteome. We expect this collection to be enriched in beneficial variation. The EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.
| S-EPMC6063297 | biostudies-literature
Project description:Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have identified thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes1-3. However, rare-variant genetic architecture is not well characterized, and the relationship between common-variant and rare-variant architecture is unclear4. Here we quantify the heritability explained by the gene-wise burden of rare coding variants across 22 common traits and diseases in 394,783 UK Biobank exomes5. Rare coding variants (allele frequency < 1 × 10-3) explain 1.3% (s.e. = 0.03%) of phenotypic variance on average-much less than common variants-and most burden heritability is explained by ultrarare loss-of-function variants (allele frequency < 1 × 10-5). Common and rare variants implicate the same cell types, with similar enrichments, and they have pleiotropic effects on the same pairs of traits, with similar genetic correlations. They partially colocalize at individual genes and loci, but not to the same extent: burden heritability is strongly concentrated in significant genes, while common-variant heritability is more polygenic, and burden heritability is also more strongly concentrated in constrained genes. Finally, we find that burden heritability for schizophrenia and bipolar disorder6,7 is approximately 2%. Our results indicate that rare coding variants will implicate a tractable number of large-effect genes, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification.
| S-EPMC10614218 | biostudies-literature
Project description:The extent of intratumoral mutational heterogeneity remains unclear in gliomas, the most common primary brain tumors, especially with respect to point mutation. To address this, we applied single molecule molecular inversion probes targeting 33 cancer genes to assay both point mutations and gene amplifications within spatially distinct regions of 14 glial tumors.We find evidence of regional mutational heterogeneity in multiple tumors, including mutations in TP53 and RB1 in an anaplastic oligodendroglioma and amplifications in PDGFRA and KIT in two glioblastomas (GBMs). Immunohistochemistry confirms heterogeneity of TP53 mutation and PDGFRA amplification. In all, 3 out of 14 glial tumors surveyed have evidence for heterogeneity for clinically relevant mutations.Our results underscore the need to sample multiple regions in GBM and other glial tumors when devising personalized treatments based on genomic information, and furthermore demonstrate the importance of measuring both point mutation and copy number alteration while investigating genetic heterogeneity within cancer samples.
| S-EPMC4272528 | biostudies-literature