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Related Concept Videos

Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Related Experiment Video

Updated: May 22, 2026

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

XVth QTLMAS: simulated dataset.

Jean-Michel Elsen1, Simon Tesseydre, Olivier Filangi

  • 1INRA UR0631 SAGA, chemin de borde rouge, BP 52627, 31326 Castanet-Tolosan, France. Jean-Michel.Elsen@toulouse.inra.fr.

BMC Proceedings
|May 30, 2012
PubMed
Summary
This summary is machine-generated.

This study simulated quantitative trait loci (QTL) data in a pig-like family structure using an oligogenic model. The simulation generated realistic genetic parameters for potential use in genetic analysis workshops.

Related Experiment Videos

Last Updated: May 22, 2026

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Simulated quantitative trait loci (QTL) data for the QTLMAS2011 workshop.
  • Employed a pig-type family structure.
  • Utilized an oligogenic model with specific QTL effects.

Purpose of the Study:

  • To generate realistic genetic data for the QTLMAS2011 workshop.
  • To test QTL detection methods under specific genetic architectures.
  • To provide a simulated dataset reflecting real-world pig populations.

Main Methods:

  • Simulated 3000 individuals from 20 sires and 200 dams.
  • Generated 10,000 single nucleotide polymorphisms (SNPs) across 5 chromosomes.
  • Incorporated eight QTL with diverse genetic models (e.g., linked, epistatic, imprinted).

Main Results:

  • Achieved a heritability of 0.30 with added random noise.
  • Simulated marker density, linkage disequilibrium (LD), and minor allele frequency (MAF) comparable to real populations.
  • Created distinct experimental and selection populations within families.

Conclusions:

  • The simulated dataset provides a valuable resource for QTL analysis.
  • The methodology allows for the creation of complex genetic scenarios for research.
  • The simulated parameters are relevant for validating genetic prediction models.