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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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Genome-wide compatible SNP intervals and their properties.

Jeremy Wang1, Fernando Pardo-Manual de Villena2, Kyle J Moore1

  • 1Dept. of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.

The 2010 ACM International Conference on Bioinformatics and Computational Biology : ACM-BCB 2010 : Niagara Falls, New York, U.S.A., August 2-4, 2010. ACM International Conference on Bioinformatics and Computational Biology (1St : 2010 :
|November 21, 2017
PubMed
Summary

Researchers developed new methods to partition genomes into blocks with limited diversity. This helps understand genetic diversity, disease associations, and evolutionary history across various genomic datasets.

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Area of Science:

  • Genomics
  • Computational Biology
  • Population Genetics

Background:

  • Intraspecific genomes contain blocks of limited diversity.
  • Understanding these blocks is crucial for identifying disease-associated genes, ancestral origins, and historical genomic events like recombination and gene conversion.

Purpose of the Study:

  • To present methods for partitioning genomes into blocks with no apparent recombination.
  • To analyze the computational complexity of genome partitioning and establish a lower-bound for minimal interval coverage.
  • To identify common properties of minimal interval partitions and define interval sets that maximize overlap.

Main Methods:

  • Utilized the four-gamete test to define parsimonious sets of compatible genome intervals.
  • Developed algorithms for partitioning both inbred (mice) and outbred (heterozygous) genomic data.
  • Extended the standard four-gamete test for broader applicability.

Main Results:

  • Provided a thorough computational complexity analysis for genome partitioning.
  • Established an achievable lower-bound for the minimal number of intervals required to cover genomic data.
  • Demonstrated algorithms applicable to diverse genomic datasets, including haplotype and genotype data.

Conclusions:

  • The developed methods enable effective partitioning of genomes into blocks of limited diversity.
  • These methods provide insights into genomic structure, evolutionary history, and complex disease associations.
  • The algorithms are versatile and applicable to a wide range of genomic data types.