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Related Concept Videos

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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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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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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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Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Estimation of population genetic parameters using an EM algorithm and sequence data from experimental evolution

Yasuhiro Kojima1, Hirotaka Matsumoto2, Hisanori Kiryu1

  • 1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 1277-8561, Japan.

Bioinformatics (Oxford, England)
|June 21, 2019
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Summary
This summary is machine-generated.

A new method, EMWER, efficiently estimates population genetic parameters from evolve and resequence (E&R) data. This tool improves selection strength estimation and reveals dominant beneficial alleles in experimental evolution.

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Genomics

Background:

  • Evolve and resequence (E&R) experiments track genome-wide allele frequency changes in real-time.
  • Estimating population genetic parameters, like selection on SNPs, is crucial for understanding evolution.
  • Current methods struggle with numerous SNPs and unreliable estimates in E&R data.

Purpose of the Study:

  • Develop an efficient methodology for estimating Wright-Fisher (WF) model parameters from E&R data.
  • Address the challenges of high SNP density and estimate unreliability in E&R analyses.
  • Enable a deeper understanding of evolutionary processes shaping genomes.

Main Methods:

  • Developed Expectation-Maximization for Wright-Fisher Evolution (EMWER), a novel method.
  • Applied an expectation maximization algorithm to the WF model's diffusion approximation.
  • Utilized EMWER to infer effective population size, selection coefficients, and dominance parameters.

Main Results:

  • EMWER demonstrated superior efficiency for selection strength estimation in multi-core environments.
  • The method provided accurate confidence intervals for both selection and dominance parameters.
  • Analysis of Drosophila E&R data revealed common selection within the In(3R)P inversion, with many beneficial alleles being dominant.

Conclusions:

  • EMWER is an efficient and accurate tool for estimating WF parameters from E&R data.
  • The method facilitates the study of selection and dominance in experimental evolution.
  • Findings highlight the role of dominant alleles in adaptation within fluctuating environments.