392 research outputs found
KoVariome: Korean National Standard Reference Variome database of whole genomes with comprehensive SNV, indel, CNV, and SV analyses
High-coverage whole-genome sequencing data of a single ethnicity can provide a useful catalogue of population-specific genetic variations, and provides a critical resource that can be used to more accurately identify pathogenic genetic variants. We report a comprehensive analysis of the Korean population, and present the Korean National Standard Reference Variome (KoVariome). As a part of the Korean Personal Genome Project (KPGP), we constructed the KoVariome database using 5.5 terabases of whole genome sequence data from 50 healthy Korean individuals in order to characterize the benign ethnicity-relevant genetic variation present in the Korean population. In total, KoVariome includes 12.7M single-nucleotide variants (SNVs), 1.7M short insertions and deletions (indels), 4K structural variations (SVs), and 3.6K copy number variations (CNVs). Among them, 2.4M (19%) SNVs and 0.4M (24%) indels were identified as novel. We also discovered selective enrichment of 3.8M SNVs and 0.5M indels in Korean individuals, which were used to filter out 1,271 coding-SNVs not originally removed from the 1,000 Genomes Project when prioritizing disease-causing variants. KoVariome health records were used to identify novel disease-causing variants in the Korean population, demonstrating the value of high-quality ethnic variation databases for the accurate interpretation of individual genomes and the precise characterization of genetic variation
Copy number variation in bipolar disorder.
Large (>100 kb), rare (500 kb) CNVs in BD compared with SZ, most notably for deletions >1 Mb (P=9 × 10(-4))
Using population admixture to help complete maps of the human genome
Tens of millions of base pairs of euchromatic human genome sequence, including many protein-coding genes, have no known location in the human genome. We describe an approach for localizing the human genome's missing pieces by utilizing the patterns of genome sequence variation created by population admixture. We mapped the locations of 70 scaffolds spanning four million base pairs of the human genome's unplaced euchromatic sequence, including more than a dozen protein-coding genes, and identified eight large novel inter-chromosomal segmental duplications. We find that most of these sequences are hidden in the genome's heterochromatin, particularly its pericentromeric regions. Many cryptic, pericentromeric genes are expressed in RNA and have been maintained intact for millions of years while their expression patterns diverged from those of paralogous genes elsewhere in the genome. We describe how knowledge of the locations of these sequences can inform disease association and genome biology studies
The genomic landscape of cutaneous SCC reveals drivers and a novel azathioprine associated mutational signature
Cutaneous squamous cell carcinoma (cSCC) has a high tumour mutational burden (50 mutations per megabase DNA pair). Here, we combine whole-exome analyses from 40 primary cSCC tumours, comprising 20 well-differentiated and 20 moderately/poorly differentiated tumours, with accompanying clinical data from a longitudinal study of immunosuppressed and immunocompetent patients and integrate this analysis with independent gene expression studies. We identify commonly mutated genes, copy number changes and altered pathways and processes. Comparisons with tumour differentiation status suggest events which may drive disease progression. Mutational signature analysis reveals the presence of a novel signature (signature 32), whose incidence correlates with chronic exposure to the immunosuppressive drug azathioprine. Characterisation of a panel of 15 cSCC tumour-derived cell lines reveals that they accurately reflect the mutational signatures and genomic alterations of primary tumours and provide a valuable resource for the validation of tumour drivers and therapeutic targets
Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis (vol 42, pg 579, 2010)
Copy-number variation in BMPR2 is not associated with the pathogenesis of pulmonary arterial hypertension
<p>Abstract</p> <p>Background</p> <p>Copy-number variations (CNVs) are structural variations in the genome involving 1 kb to 3 mb of DNA. CNV has been reported within intron 1 of the <it>BMPR2 </it>gene. We propose that CNV could affect phenotype in familial and/or sporadic pulmonary arterial hypertension (PAH) by altering gene expression.</p> <p>Methods</p> <p>97 human DNA samples were obtained which included 24 patients with familial PAH, 18 obligate carriers (<it>BMPR2 </it>mutation positive), 20 sporadic PAH patients, and 35 controls. Two sets of primers were designed within the CNV, and two sets of control primers were designed outside the CNV. Quantitative PCR was performed to quantify genomic copies of CNV and control sequences.</p> <p>Results</p> <p>A CNV in <it>BMPR2 </it>was present in one African American negative control subject.</p> <p>Conclusion</p> <p>We conclude that the CNV in intron 1 in <it>BMPR2 </it>is unlikely to play a role in the pathogenesis of either familial or sporadic PAH.</p> <p>Trial Registration</p> <p>NIH NCT00091546.</p
The disruption of proteostasis in neurodegenerative diseases
Cells count on surveillance systems to monitor and protect the cellular proteome which, besides being highly heterogeneous, is constantly being challenged by intrinsic and environmental factors. In this context, the proteostasis network (PN) is essential to achieve a stable and functional proteome. Disruption of the PN is associated with aging and can lead to and/or potentiate the occurrence of many neurodegenerative diseases (ND). This not only emphasizes the importance of the PN in health span and aging but also how its modulation can be a potential target for intervention and treatment of human diseases.info:eu-repo/semantics/publishedVersio
Partitioning of copy-number genotypes in pedigrees
<p>Abstract</p> <p>Background</p> <p>Copy number variations (CNVs) and polymorphisms (CNPs) have only recently gained the genetic community's attention. Conservative estimates have shown that CNVs and CNPs might affect more than 10% of the genome and that they may be at least as important as single nucleotide polymorphisms in assessing human variability. Widely used tools for CNP analysis have been implemented in <it>Birdsuite </it>and <it>PLINK </it>for the purpose of conducting genetic association studies based on the unpartitioned total number of CNP copies provided by the intensities from Affymetrix's Genome-Wide Human SNP Array. Here, we are interested in partitioning copy number variations and polymorphisms in extended pedigrees for the purpose of linkage analysis on familial data.</p> <p>Results</p> <p>We have developed <it>CNGen</it>, a new software for the partitioning of copy number polymorphism using the integrated genotypes from <it>Birdsuite </it>with the Affymetrix platform. The algorithm applied to familial trios or extended pedigrees can produce partitioned copy number genotypes with distinct parental alleles. We have validated the algorithm using simulations on a complex pedigree structure using frequencies calculated from a real dataset of 300 genotyped samples from 42 pedigrees segregating a congenital heart defect phenotype.</p> <p>Conclusions</p> <p><it>CNGen </it>is the first published software for the partitioning of copy number genotypes in pedigrees, making possible the use CNPs and CNVs for linkage analysis. It was implemented with the <it>Python </it>interpreter version 2.5.2. It was successfully tested on current Linux, Windows and Mac OS workstations.</p
Reconstructing CNV genotypes using segregation analysis: combining pedigree information with CNV assay
<p>Abstract</p> <p>Background</p> <p>Repeated blocks of genome sequence have been shown to be associated with genetic diversity and disease risk in humans, and with phenotypic diversity in model organisms and domestic animals. Reliable tests are desirable to determine whether individuals are carriers of copy number variants associated with disease risk in humans and livestock, or associated with economically important traits in livestock. In some cases, copy number variants affect the phenotype through a dosage effect but in other cases, allele combinations have non-additive effects. In the latter cases, it has been difficult to develop tests because assays typically return an estimate of the sum of the copy number counts on the maternally and paternally inherited chromosome segments, and this sum does not uniquely determine the allele configuration. In this study, we show that there is an old solution to this new problem: segregation analysis, which has been used for many years to infer alleles in pedigreed populations.</p> <p>Methods</p> <p>Segregation analysis was used to estimate copy number alleles from assay data on simulated half-sib sheep populations. Copy number variation at the Agouti locus, known to be responsible for the recessive self-colour black phenotype, was used as a model for the simulation and an appropriate penetrance function was derived. The precision with which carriers and non-carriers of the undesirable single copy allele could be identified, was used to evaluate the method for various family sizes, assay strategies and assay accuracies.</p> <p>Results</p> <p>Using relationship data and segregation analysis, the probabilities of carrying the copy number alleles responsible for black or white fleece were estimated with much greater precision than by analyzing assay results for animals individually. The proportion of lambs correctly identified as non-carriers of the undesirable allele increased from 7% when the lambs were analysed alone to 80% when the lambs were analysed in half-sib families.</p> <p>Conclusions</p> <p>When a quantitative assay is used to estimate copy number alleles, segregation analysis of related individuals can greatly improve the precision of the estimates. Existing software for segregation analysis would require little if any change to accommodate the penetrance function for copy number assay data.</p
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