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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Critical Region, Critical Values and Significance Level01:16

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Using ClinVar as a Resource to Support Variant Interpretation.

Steven M Harrison1,2, Erin R Riggs3, Donna R Maglott4

  • 1Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts.

Current Protocols in Human Genetics
|April 3, 2016
PubMed
Summary
This summary is machine-generated.

ClinVar is a public archive of genomic variant and phenotype relationships. This guide explains how to use ClinVar to evaluate variant clinical significance and interpret evidence.

Keywords:
ClinVarclinical geneticsdata sharingdatabasesvariant interpretation

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Area of Science:

  • Genomics
  • Clinical Genetics
  • Bioinformatics

Background:

  • ClinVar is a public archive detailing genomic variant-phenotype relationships.
  • Evaluating the clinical significance of genetic variants is crucial for patient care.
  • Understanding variant data aids in diagnosing and managing genetic disorders.

Purpose of the Study:

  • To provide a protocol for identifying and interpreting genomic variants within the ClinVar database.
  • To guide users in leveraging ClinVar resources for variant curation and clinical interpretation.
  • To enhance the understanding of variant data and supporting evidence for clinical decision-making.

Main Methods:

  • Utilizing ClinVar's search functionalities, including Variation Viewer, to identify specific sequence and structural variants.
  • Analyzing the display of variant submissions within ClinVar to assess supporting data and evidence.
  • Cross-referencing multiple submissions for the same variant to identify concordant or conflicting interpretations.

Main Results:

  • Users can effectively identify and retrieve variant information from ClinVar.
  • The protocol facilitates a clear understanding of the evidence supporting variant interpretations.
  • ClinVar's aggregated data helps in resolving conflicting interpretations and confirming variant significance.

Conclusions:

  • This protocol empowers users to effectively navigate and utilize ClinVar for variant interpretation.
  • Incorporating ClinVar data enhances the accuracy and efficiency of clinical variant curation.
  • ClinVar serves as an invaluable resource for advancing genomic medicine and personalized healthcare.