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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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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Relative Frequency Distribution00:55

Relative Frequency Distribution

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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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Related Experiment Video

Updated: Mar 27, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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The aggregate site frequency spectrum for comparative population genomic inference.

Alexander T Xue1, Michael J Hickerson2

  • 1Department of Biology: Subprogram in Ecology, Evolutionary Biology, and Behavior, City College and Graduate Center of City University of New York, 160 Convent Avenue, Marshak Science Building, Room 526, New York, NY, 10031, USA.

Molecular Ecology
|January 16, 2016
PubMed
Summary

New methods in population genomics allow scientists to study how multiple species responded to climate change together. This research introduces a novel approach for analyzing species assemblages, revealing synchronous expansion after the Last Glacial Maximum.

Keywords:
allele/site frequency spectrumapproximate Bayesian computationco-expansioncomparative phylogeographydemographic inferencehierarchical modellingpopulation genomics

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Area of Science:

  • Comparative phylogeography and population genomics
  • Community ecology
  • Bioinformatics and computational biology

Background:

  • Understanding species' responses to past climate change is crucial for predicting future ecological dynamics.
  • Advances in sequencing technology enable high-resolution demographic inference across multiple species.
  • Existing methods often focus on single species, limiting community-level insights.

Purpose of the Study:

  • To introduce a novel statistical method, the aggregate site frequency spectrum (aSFS), for demographic inference in species assemblages.
  • To demonstrate the utility of aSFS in differentiating complex multispecies demographic histories.
  • To estimate the synchronicity of demographic events, such as population expansions, across co-distributed taxa.

Main Methods:

  • Development and application of the aggregate site frequency spectrum (aSFS) using single nucleotide polymorphism (SNP) data.
  • Coupling aSFS with a hierarchical approximate Bayesian computation (hABC) framework for demographic modeling.
  • Empirical analysis of five populations of the threespine stickleback (Gasterosteus aculeatus) to test the method.

Main Results:

  • The aSFS method successfully differentiates various multispecies demographic histories under diverse sampling scenarios.
  • The joint aSFS/hABC analysis of stickleback data strongly supports synchronous population expansion after the Last Glacial Maximum (posterior probability = 0.99).
  • The method allows effective population sizes and expansion magnitudes to vary independently across species.

Conclusions:

  • The aggregate site frequency spectrum (aSFS) provides a powerful new tool for assemblage-level demographic inference in comparative population genomics.
  • The developed framework enables the estimation of temporal synchronicity in demographic events across multiple species.
  • This approach has broad applications for testing ecological and evolutionary models involving species assemblages and communities, especially with increasing availability of large SNP datasets.