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Related Concept Videos

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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...
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...
Population Growth00:57

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...
What is Population Genetics?01:25

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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.
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Estimating variable effective population sizes from multiple genomes: a sequentially markov conditional sampling

Sara Sheehan1, Kelley Harris, Yun S Song

  • 1Computer Science Division, University of California, Berkeley, California 94720, USA.

Genetics
|April 24, 2013
PubMed
Summary
This summary is machine-generated.

A new method, diCal, reconstructs human population size changes using multiple genomes. This improves accuracy in the recent past compared to previous methods like the pairwise sequentially Markovian coalescent (PSMC).

Keywords:
hidden Markov model (HMM)population sizerecombinationsequentially Markov coalescent

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Area of Science:

  • Human evolutionary genomics
  • Population genetics
  • Computational biology

Background:

  • Human population size has fluctuated throughout history, impacting genetic variation.
  • Accurate demographic history is crucial for understanding human colonization and natural selection.
  • Existing methods like pairwise sequentially Markovian coalescent (PSMC) have limitations in reconstructing recent population sizes due to small sample sizes.

Purpose of the Study:

  • To develop a novel coalescent-based method for inferring population size changes from multiple genomes.
  • To improve the accuracy of demographic history reconstruction, particularly in the recent past.
  • To provide a more detailed understanding of recent human population dynamics.

Main Methods:

  • Developed a new coalescent-based method, diCal, generalizing the sequentially Markov conditional sampling distribution framework.
  • Applied the method to analyze genomic data from multiple individuals of European and African ancestry.
  • Utilized computational approaches to efficiently infer population size changes from multiple genomes.

Main Results:

  • The diCal method accurately reconstructs past population histories.
  • Significant improvement in accuracy for recent population size changes compared to PSMC.
  • Detailed population size change history obtained for recent human evolution.

Conclusions:

  • The new method, diCal, offers enhanced accuracy for inferring recent human demographic history.
  • Analyzing multiple genomes provides valuable insights into recent population dynamics.
  • This work contributes to a clearer understanding of human colonization and evolutionary processes.