Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genomic Variation: Lessons Learned from Whole-Genome CNV Analysis.

ABSTRACT: One of the most fundamental goals of the study of human genetics was to determine the relationship between genomic variation and human disease. The effects of large-scale structural variation, such as aneuploidy and other cytogenetically visible imbalances, as well as sequence-level variation, have been studied for several decades. However, compared to these, the impact of submicroscopic copy number variants (CNV) has only recently been appreciated. Despite this, lessons learned from the study of CNVs have already proven significant and broadly applicable. From expanding the concept of normal human variation to providing concrete examples of the utility of genomics in clinical care and challenging notions of the genetic architecture of complex disease, CNVs have provided valuable insights into the genomics of human health and development.

SUBMITTER: Riggs ER

PROVIDER: S-EPMC4129219 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Genomic Variation: Lessons Learned from Whole-Genome CNV Analysis.

Riggs Erin Rooney ER Ledbetter David H DH Martin Christa Lese CL

Current genetic medicine reports 20140718

One of the most fundamental goals of the study of human genetics was to determine the relationship between genomic variation and human disease. The effects of large-scale structural variation, such as aneuploidy and other cytogenetically visible imbalances, as well as sequence-level variation, have been studied for several decades. However, compared to these, the impact of submicroscopic copy number variants (CNV) has only recently been appreciated. Despite this, lessons learned from the study o ...[more]

PMID: 25152847

Similar Datasets

Lessons Learned from CNV Analysis of Major Birth Defects.

Project description:The treatment of major birth defects are key concerns for child health. Hitherto, for the majority of birth defects, the underlying cause remains unknown, likely to be heterogeneous. The implicated mortality and/or reduced fecundity in major birth defects suggest a significant fraction of mutational de novo events among the affected individuals. With the advent of systematic array-based molecular karyotyping, larger cohorts of affected individuals have been screened over the past decade. This review discusses the identification of disease-causing copy-number variations (CNVs) among individuals with different congenital malformations. It highlights the differences in findings depending on the respective congenital malformation. It looks at the differences in findings of CNV analysis in non-isolated complex congenital malformations, associated with central nervous system malformations or intellectual disabilities, compared to isolated single organ-system malformations. We propose that the more complex an organ system is, and the more genes involved during embryonic development, the more likely it is that mutational de novo events, comprising CNVs, will confer to the expression of birth defects of this organ system.

| S-EPMC7663563 | biostudies-literature

Lessons learned from Genetic Analysis Workshop 17: transitioning from genome-wide association studies to whole-genome statistical genetic analysis.

Project description:Genetic Analysis Workshop 17 (GAW17) focused on the transition from genome-wide association study designs and methods to the study designs and statistical genetic methods that will be required for the analysis of next-generation sequence data including both common and rare sequence variants. In the 166 contributions to GAW17, a wide variety of statistical methods were applied to simulated traits in population- and family-based samples, and results from these analyses were compared to the known generating model. In general, many of the statistical genetic methods used in the population-based sample identified causal sequence variants (SVs) when the estimated locus-specific heritability, as measured in the population-based sample, was greater than about 0.08. However, SVs with locus-specific heritabilities less than 0.03 were rarely identified consistently. In the family-based samples, many of the methods detected SVs that were rarer than those detected in the population-based sample, but the estimated locus-specific heritabilities for these rare SVs, as measured in the family-based samples, were substantially higher (>0.2) than their corresponding heritabilities in the population-based samples. Substantial inflation of the type I error rate was observed across a wide variety of statistical methods. Although many of the contributions found little inflation in type I error for Q4, a trait with no causal SVs, type I error rates for Q1 and Q2 were well above their nominal levels with the inflation for Q1 being higher than that for Q2. It seems likely that this inflation in type I error is due to correlations among SVs.

| S-EPMC3277851 | biostudies-literature

Whole-genome sequencing for neonatal intensive care unit outbreak investigations: Insights and lessons learned.

Project description:Infectious diseases outbreaks are a cause of significant morbidity and mortality among hospitalized patients. Infants admitted to the neonatal intensive care unit (NICU) are particularly vulnerable to infectious complications during hospitalization. Thus, rapid recognition of and response to outbreaks in the NICU is essential. At Rush University Medical Center, whole-genome sequencing (WGS) has been utilized since early 2016 as an adjunctive method for outbreak investigations. The use of WGS and potential lessons learned are illustrated for 3 different NICU outbreak investigations involving methicillin-resistant Staphylococcus aureus (MRSA), group B Streptococcus (GBS), and Serratia marcescens. WGS has contributed to the understanding of the epidemiology of outbreaks in our NICU, and it has also provided further insight in settings of unusual diseases or when lower-resolution typing methods have been inadequate. WGS has emerged as the new gold standard for evaluating strain relatedness. As barriers to implementation are overcome, WGS has the potential to transform outbreak investigation in healthcare settings.

| S-EPMC9495531 | biostudies-literature

Implementation of a prehospital whole blood program: Lessons learned.

Project description:Early blood administration by Emergency Medical Services (EMS) to patients suffering from hemorrhagic shock improves outcomes. Prehospital blood programs represent an invaluable resuscitation capability that directly addresses hemorrhagic shock and mitigates subsequent multiple organ dysfunction syndrome. Prehospital blood programs must be thoughtfully planned, have multiple safeguards, ensure adequate training and credentialing processes, and be responsible stewards of blood resources. According to the 2022 best practices model by Yazer et al, the four key pillars of a successful prehospital program include the following: (1) the rationale for the use and a description of blood products that can be transfused in the prehospital setting, (2) storage of blood products outside the hospital blood bank and how to move them to the patient in the prehospital setting, (3) prehospital transfusion criteria and administration personnel, and (4) documentation of prehospital transfusion and handover to the hospital team. This concepts paper describes our operational experience using these four pillars to make Maryland's inaugural prehospital ground-based low-titer O-positive whole blood program successful. These lessons learned may inform other EMS systems as they establish prehospital blood programs to help improve outcomes and enhance mass casualty response.

| S-EPMC10958095 | biostudies-literature

Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis.

Project description:BackgroundCopy number variation (CNV) analysis is an integral component of the study of human genomes in both research and clinical settings. Array-based CNV analysis is the current first-tier approach in clinical cytogenetics. Decreasing costs in high-throughput sequencing and cloud computing have opened doors for the development of sequencing-based CNV analysis pipelines with fast turnaround times. We carry out a systematic and quantitative comparative analysis for several low-coverage whole-genome sequencing (WGS) strategies to detect CNV in the human genome.MethodsWe compared the CNV detection capabilities of WGS strategies (short insert, 3 kb insert mate pair and 5 kb insert mate pair) each at 1×, 3× and 5× coverages relative to each other and to 17 currently used high-density oligonucleotide arrays. For benchmarking, we used a set of gold standard (GS) CNVs generated for the 1000 Genomes Project CEU subject NA12878.ResultsOverall, low-coverage WGS strategies detect drastically more GS CNVs compared with arrays and are accompanied with smaller percentages of CNV calls without validation. Furthermore, we show that WGS (at ≥1× coverage) is able to detect all seven GS deletion CNVs >100 kb in NA12878, whereas only one is detected by most arrays. Lastly, we show that the much larger 15 Mbp Cri du chat deletion can be readily detected with short-insert paired-end WGS at even just 1× coverage.ConclusionsCNV analysis using low-coverage WGS is efficient and outperforms the array-based analysis that is currently used for clinical cytogenetics.

| S-EPMC8131063 | biostudies-literature

Copy Number Variation (CNV): A New Genomic Insight in Horses.

Project description:Copy number variations (CNVs) are a new-fangled source of genetic variation that can explain changes in the phenotypes in complex traits and diseases. In recent years, their study has increased in many livestock populations. However, the study and characterization of CNVs in equines is still very limited. Our study aimed to investigate the distribution pattern of CNVs, characterize CNV regions (CNVRs), and identify the biological pathways affected by CNVRs in the Pura Raza Española (PRE) breed. To achieve this, we analyzed high-density SNP genotyping data (670,804 markers) from a large cohort of 654 PRE horses. In total, we identified 19,902 CNV segments and 1007 CNV regions in the whole population. The length of the CNVs ranged from 1.024 kb to 4.55 Mb, while the percentage of the genome covered by CNVs was 4.4%. Interestingly, duplications were more abundant than deletions and mixed CNVRs. In addition, the distribution of CNVs across the chromosomes was not uniform, with ECA12 being the chromosome with the largest percentage of its genome covered (19.2%), while the highest numbers of CNVs were found in ECA20, ECA12, and ECA1. Our results showed that 71.4% of CNVRs contained genes involved in olfactory transduction, olfactory receptor activity, and immune response. Finally, 39.1% of the CNVs detected in our study were unique when compared with CNVRs identified in previous studies. To the best of our knowledge, this is the first attempt to reveal and characterize the CNV landscape in PRE horses, and it contributes to our knowledge of CNVs in equines, thus facilitating the understanding of genetic and phenotypic variations in the species. However, further research is still needed to confirm if the CNVs observed in the PRE are also linked to variations in the specific phenotypical differences in the breed.

| S-EPMC9179425 | biostudies-literature

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges.

Project description:The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

| S-EPMC5645203 | biostudies-literature

Genomic Medicine: Lessons Learned From Monogenic and Complex Bone Disorders.

Project description:Current genetic studies of monogenic and complex bone diseases have broadened our understanding of disease pathophysiology, highlighting the need for medical interventions and treatments tailored to the characteristics of patients. As genomic research progresses, novel insights into the molecular mechanisms are starting to provide support to clinical decision-making; now offering ample opportunities for disease screening, diagnosis, prognosis and treatment. Drug targets holding mechanisms with genetic support are more likely to be successful. Therefore, implementing genetic information to the drug development process and a molecular redefinition of skeletal disease can help overcoming current shortcomings in pharmaceutical research, including failed attempts and appalling costs. This review summarizes the achievements of genetic studies in the bone field and their application to clinical care, illustrating the imminent advent of the genomic medicine era.

| S-EPMC7581702 | biostudies-literature

Modeling radiation-induced lung injury: lessons learned from whole thorax irradiation.

Project description:Models of thoracic irradiation have been developed as clinicians and scientists have attempted to decipher the events that led up to the pulmonary toxicity seen in human subjects following radiation treatment. The most common model is that of whole thorax irradiation (WTI), applied in a single dose. Mice, particularly the C57BL/6J strain, has been frequently used in these investigations, and has greatly informed our current understanding of the initiation and progression of radiation-induced lung injury (RILI). In this review, we highlight the sequential progression and dynamic nature of RILI, focusing primarily on the vast array of information that has been gleaned from the murine model. Ample evidence indicates a wide array of biological responses that can be seen following irradiation, including DNA damage, oxidative stress, cellular senescence and inflammation, all triggered by the initial exposure to ionizing radiation (IR) and heterogeneously maintained throughout the temporal progression of injury, which manifests as acute pneumonitis and later fibrosis. It appears that the early responses of specific cell types may promote further injury, disrupting the microenvironment and preventing a return to homeostasis, although the exact mechanisms driving these responses remains somewhat unclear. Attempts to either prevent or treat RILI in preclinical models have shown some success by targeting these disparate radiobiological processes. As our understanding of the dynamic cellular responses to radiation improves through the use of such models, so does the likelihood of preventing or treating RILI.

| S-EPMC6483900 | biostudies-literature

Parametric modeling of whole-genome sequencing data for CNV identification.

Project description:Copy number variants (CNVs) constitute an important class of genetic variants in human genome and are shown to be associated with complex diseases. Whole-genome sequencing provides an unbiased way of identifying all the CNVs that an individual carries. In this paper, we consider parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of sparse segment identification for count data into a sparse segment identification problem for a sequence of Gaussian data. We apply the optimal sparse segment identification procedure to the transformed data in order to identify the CNV segments. This provides a computationally efficient approach for RD-based CNV identification. Simulation results show that this approach often results in a small number of false identifications of the CNVs and has similar or better performances in identifying the true CNVs when compared with other RD-based approaches. We demonstrate the methods using the trio data from the 1000 Genomes Project.

| S-EPMC4059462 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data