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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Detecting rare variants.

Tao Feng1, Xiaofeng Zhu

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH, USA. tfeng@darwin.epbi.cwru.edu

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2012
PubMed
Summary
This summary is machine-generated.

Geneticists are exploring rare variants for common diseases, moving beyond common variant studies. New sequencing and statistical methods, like variant collapsing, enhance the power to detect these associations.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWA) often rely on the common disease common variants (CDCV) hypothesis.
  • Limitations of CDCV studies necessitate exploring alternative genetic models for complex diseases.

Purpose of the Study:

  • To investigate the role of rare variants in common diseases, aligning with the common disease/rare variants (CDRV) hypothesis.
  • To introduce and discuss statistical methodologies for analyzing rare variants in genetic studies.

Main Methods:

  • High-throughput sequencing technologies enable the study of rare genetic variants.
  • Statistical approaches involve collapsing or aggregating multiple rare variants within a defined genomic region.
  • These methods aim to increase statistical power for association testing.

Main Results:

  • The development of new statistical methods makes the study of rare variants feasible.
  • Collapsing rare variants improves the ability to detect associations between genetic variations and phenotypes.

Conclusions:

  • Rare variants are increasingly recognized as potential contributors to common diseases.
  • Advanced sequencing and statistical techniques are crucial for uncovering the genetic architecture of complex diseases.