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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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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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Histone variants are the histone proteins with structural and sequence variations. These variants may be regarded as “mutant” forms that replace their canonical histone counterparts in the nucleosomes. Specific post-translational modifications on the histone variants enable further chromatin complexity and regulate tissue-specific gene expression. The most common histone variants are from histone H2A, H2B, and linker histone H1 families. However, several variants of histone H3...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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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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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Structural Variation Interpretation in the Genome Sequencing Era: Lessons from Cytogenetics.

Lucilla Pizzo1,2, M Katharine Rudd1,2

  • 1Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, United States.

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Genome sequencing (GS) can identify structural variations (SVs) like chromosomal rearrangements. Recognizing specific genomic signatures is crucial for accurate interpretation of these complex genetic changes in clinical diagnostics.

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Area of Science:

  • Genomics
  • Cytogenetics
  • Clinical Diagnostics

Background:

  • Structural variations (SVs), including chromosomal rearrangements, are significant causes of genetic and neoplastic diseases.
  • Chromosomal microarray analysis (CMA) is a common method for detecting large variants.
  • The increasing use of genome sequencing (GS) in clinical settings necessitates accurate interpretation of SVs, especially in pediatric cases.

Purpose of the Study:

  • To describe genomic signatures of common cytogenetic abnormalities detectable by GS.
  • To aid laboratorians in interpreting GS data for structural variations.
  • To differentiate complex chromosomal abnormalities from simple deletions/duplications for improved phenotypic interpretation and recurrence risk assessment.

Main Methods:

  • Review of genomic signatures associated with various SVs, including translocations, inversions, and aneuploidies.
  • Emphasis on the necessity of visualizing sequence data for SV pattern recognition.
  • Discussion of challenges and limitations in SV detection using GS pipelines.

Main Results:

  • Detailed description of genomic patterns for specific complex abnormalities: translocations, inverted duplications, recombinant chromosomes, marker chromosomes, ring chromosomes, isodicentric/isochromosomes, and mosaic aneuploidy.
  • Highlighting the importance of distinguishing complex SVs from simple copy number changes.

Conclusions:

  • Identifying chromosomal rearrangements via GS requires specialized processing and multiple analysis tools, unlike single-nucleotide variant calling.
  • SV databases have platform- and resolution-dependent limitations.
  • Integrating molecular and cytogenetic expertise is essential for optimal patient care in clinical genomics.