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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data.

Yun Li1, Wei Chen2, Eric Yi Liu3

  • 1Department of Genetics, University of North Carolina, Chapel Hill, NC 27599-7264, USA. Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7264, USA. Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-7264, USA.

Statistics in Biosciences
|February 4, 2014
PubMed
Summary

Massively parallel sequencing (MPS) has revolutionized genomics, aiding in identifying rare disease variants and explaining complex trait heritability. This review covers statistical methods for SNP detection and genotype calling from MPS data, crucial for future genomic studies.

Keywords:
Genotype callingLinkage disequilibrium (LD)Massively parallel sequencingNext-generation sequencingSNP detection

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Area of Science:

  • Genomics and Bioinformatics
  • Statistical Genetics

Background:

  • Massively parallel sequencing (MPS) has transformed genomic studies since 2005.
  • MPS technologies have successfully identified causal variants for rare Mendelian disorders.
  • MPS is beginning to explain missing heritability in genome-wide association studies (GWAS) of complex traits.

Purpose of the Study:

  • To review statistical methods for SNP detection and genotype calling from MPS data.
  • To discuss emerging issues and future directions in MPS data analysis.

Main Methods:

  • Review of statistical methods published in the last five years.
  • Analysis of challenges and future trends in SNP detection and genotype calling.

Main Results:

  • Identification of key statistical approaches for variant calling in MPS data.
  • Discussion of current limitations and potential advancements in the field.

Conclusions:

  • SNP detection and genotype calling are essential steps in analyzing MPS data.
  • Continued development of statistical methods is crucial for maximizing the utility of MPS in diverse genomic applications.