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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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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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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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Related Experiment Video

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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fastSTRUCTURE: variational inference of population structure in large SNP data sets.

Anil Raj1, Matthew Stephens2, Jonathan K Pritchard3

  • 1Department of Genetics, Stanford University, Stanford, California 94305 rajanil@stanford.edu.

Genetics
|April 5, 2014
PubMed
Summary

New fastSTRUCTURE algorithms offer efficient population genetics inference. These tools significantly speed up analysis of large genetic datasets while maintaining accuracy, aiding population structure estimation.

Keywords:
population structurevariational inference

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Area of Science:

  • Population genetics
  • Computational biology
  • Bioinformatics

Background:

  • Estimating population structure from genetic data is crucial for many population genetics applications.
  • Large modern datasets present significant computational challenges for existing inference tools like STRUCTURE.
  • Approximate inference methods are needed to handle the scale of contemporary genetic data.

Purpose of the Study:

  • To develop efficient algorithms for approximate inference of population structure using a variational Bayesian framework.
  • To create heuristic scores for determining the number of populations and a hierarchical prior for detecting weak structure.
  • To provide a faster and accurate alternative to existing population genetics software.

Main Methods:

  • Developed variational Bayesian algorithms for approximate inference, reframing posterior distribution computation as an optimization problem.
  • Proposed heuristic scores for model complexity selection (number of populations) and a hierarchical prior for weak structure detection.
  • Tested algorithms on simulated data and the CEPH-Human Genome Diversity Panel genotype data.

Main Results:

  • The variational algorithms (fastSTRUCTURE) are nearly 100 times faster than STRUCTURE.
  • Accuracies achieved by fastSTRUCTURE are comparable to those of ADMIXTURE.
  • Heuristic scores effectively identified the number of populations with minimal bias, even for weak population structure.

Conclusions:

  • The developed fastSTRUCTURE algorithm provides a computationally efficient solution for population structure inference in large genetic datasets.
  • The heuristic scores offer a reliable method for selecting the appropriate number of populations.
  • fastSTRUCTURE enables more accessible and scalable population genetics research.