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Related Concept Videos

What is Population Genetics?01:25

What is Population Genetics?

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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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Distributions to Estimate Population Parameter01:26

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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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Estimating Population Mean with Unknown Standard Deviation01:22

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Hardy-Weinberg Principle01:49

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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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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Genetic Drift03:33

Genetic Drift

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Kernel approximate Bayesian computation in population genetic inferences.

Shigeki Nakagome, Kenji Fukumizu, Shuhei Mano

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    |October 24, 2013
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    Summary
    This summary is machine-generated.

    Kernel-based Approximate Bayesian computation (ABC) improves Bayesian inference by effectively using many summary statistics, overcoming limitations of traditional methods in complex population genetics problems.

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    Area of Science:

    • Computational Statistics
    • Bayesian Inference
    • Population Genetics

    Background:

    • Approximate Bayesian computation (ABC) is a likelihood-free method for Bayesian inference.
    • Traditional ABC methods suffer from approximation errors due to insufficient statistics and non-zero tolerance.
    • Selecting informative summary statistics is challenging and can increase variance.

    Purpose of the Study:

    • To evaluate the utility of a kernel-based ABC method for complex problems requiring numerous summary statistics.
    • To assess the performance of kernel ABC in population genetic inference.
    • To demonstrate the ability of kernel ABC to handle a large number of summary statistics effectively.

    Main Methods:

    • Application of a kernel-based Approximate Bayesian computation (ABC) method.
    • Utilizing kernel Bayes' rule for Bayesian inference with positive definite kernels.
    • Testing the method on population genetic inference problems.

    Main Results:

    • Kernel ABC successfully incorporated a large number of summary statistics.
    • The kernel ABC method maintained high inference performance.
    • This approach overcomes limitations of conventional ABC methods in handling numerous statistics.

    Conclusions:

    • Kernel-based ABC is a powerful tool for complex Bayesian inference problems.
    • It effectively addresses the challenge of using many summary statistics in population genetics.
    • This method offers improved accuracy and performance over traditional ABC techniques.