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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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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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Point and Frameshift Mutations01:30

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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Conservative Site-specific Recombination and Phase Variation02:53

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Genomic variant sharing: a position statement.

Caroline F Wright1, James S Ware2, Anneke M Lucassen3

  • 1Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK.

Wellcome Open Research
|December 31, 2019
PubMed
Summary
This summary is machine-generated.

Sharing de-identified genetic variant data is crucial for genomic medicine. Standardizing the sharing of genetic variants improves diagnoses and patient care, while protecting privacy.

Keywords:
data ethicsdata sharingmedical genomicsvariant

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Area of Science:

  • Genomics
  • Bioinformatics
  • Medical Genetics

Background:

  • Sharing de-identified genetic variant data is vital for accurate genomic medicine and patient diagnosis.
  • Lack of data sharing can lead to diagnostic errors, impacting medical management and patient outcomes.
  • Existing databases facilitate data sharing, but best practices and transparency are needed for wider adoption and public trust.

Purpose of the Study:

  • To advocate for the standardization of sharing de-identified genetic variant data in genomic medicine.
  • To address the need for clear best practices in genetic data sharing to maximize benefits and minimize risks.
  • To differentiate between fundamental biological knowledge and personal information regarding consent for data sharing.

Main Methods:

  • Literature review on existing genetic data sharing practices and their benefits/harms.
  • Analysis of the necessity of genetic variant data for accurate diagnosis and medical management.
  • Formulation of recommendations for best practices in genetic data sharing, including consent models.

Main Results:

  • Widespread sharing of de-identified genetic variants with limited clinical information should be standard practice.
  • Information linking genetic variants to conditions is fundamental biological knowledge, not personal data, and should not require consent.
  • Controlled-access models may be appropriate for more extensive genomic data or case-level details.

Conclusions:

  • Standardizing the sharing of de-identified genetic variants is essential for advancing genomic medicine.
  • Clear guidelines are needed to ensure privacy, public trust, and consistency in genetic data sharing.
  • Balancing open data access with appropriate consent mechanisms is key for maximizing the benefits of genomic data.