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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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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.
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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.
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Mutation, Gene Flow, and Genetic Drift01:09

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Effects of Single Nucleotide Polymorphism Marker Density on Haplotype Block Partition.

Sun Ah Kim1, Yun Joo Yoo2

  • 1Department of Mathematics Education, Seoul National University, Seoul 08826, Korea.

Genomics & Informatics
|February 4, 2017
PubMed
Summary

Haplotype block partitioning results vary significantly with marker density. Using fewer than 50% of single nucleotide polymorphism (SNP) markers can lead to substantially different haplotype block structures, necessitating careful interpretation.

Keywords:
1,000 Genomes Projecthaplotype blockhaplotypeslinkage disequilibrium

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Area of Science:

  • Population Genetics
  • Genomic Analysis
  • Bioinformatics

Background:

  • The human genome is characterized by linkage disequilibrium, often partitioned into haplotype blocks containing few haplotypes.
  • Haplotype block structure is influenced by population genetic factors like selection, mutation, recombination, and population structure.

Purpose of the Study:

  • To investigate how marker density affects haplotype block partitioning results.
  • To compare the performance of five popular haplotype block partitioning methods under varying marker densities.

Main Methods:

  • Utilized subsets of single nucleotide polymorphism (SNP) markers from chromosome 22 in the 1000 Genomes Project and HapMap phase 3 data.
  • Applied five haplotype block partitioning algorithms: Haploview (confidence interval, four gamete test, solid spine), MIG++ (PLINK 1.9), and S-MIG++.
  • Compared results across datasets with marker densities ranging from 100% down to 20% of the original SNP markers.

Main Results:

  • Decreasing marker density led to a reduction in the total number of haplotype blocks and an increase in their length across all tested algorithms.
  • Haplotype block locations derived from datasets with less than 50% of the total SNP markers showed significant divergence from those using the complete marker set.
  • Marker density critically influences the stability and accuracy of identified haplotype blocks.

Conclusions:

  • Haplotype block construction is sensitive to the density and selection of single nucleotide polymorphism (SNP) markers used.
  • Results from haplotype block analyses should be interpreted with caution, considering the marker density and specific study objectives.
  • The choice of markers significantly impacts the reliability of population genetic inferences based on haplotype blocks.