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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Stratified Sampling Method01:16

Stratified Sampling Method

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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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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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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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Systematic Sampling Method01:17

Systematic Sampling Method

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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.
Systematic sampling is one of the simplest methods...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Sequential sampling in determining linkage between marker loci and quantitative trait loci.

U Motro1, M Soller

  • 1Department of Evolution, Systematics and Ecology, and Department of Statistics, The Hebrew University of Jerusalem, Israel.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|November 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a sequential sampling method that significantly reduces the average sample size needed for detecting linkage between marker loci and quantitative trait loci. This approach offers efficiency gains compared to traditional fixed sample size methods.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Classical fixed sample size techniques are commonly used in genetic studies.
  • Detecting linkage between marker loci and quantitative trait loci (QTL) is crucial for understanding genetic architecture.
  • Optimizing sample size is essential for efficient experimental design.

Purpose of the Study:

  • To evaluate a proposed sequential sampling procedure for detecting linkage between marker loci and QTL.
  • To compare the efficiency and error rates of sequential sampling with classical fixed sample size methods.
  • To explore variations of sequential sampling, including truncation and group observations.

Main Methods:

  • Simulation studies were employed to compare different sampling strategies.
  • The proposed sequential sampling procedure was evaluated against fixed sample size techniques.
  • Variations included sequential sampling with truncation and sequential sampling with group observations.

Main Results:

  • The sequential sampling procedure demonstrated a substantial decrease (up to 50%) in mean sample size for linkage detection.
  • Sequential sampling with truncation offered a modest further decrease in sample size but increased error probabilities.
  • Sequential sampling with group observations increased mean sample size but considerably decreased error probabilities compared to straightforward sequential sampling.

Conclusions:

  • Sequential sampling is a valuable tool for efficiently detecting linkage between marker loci and QTL.
  • The method is particularly applicable to experiments investigating genetic differences between lines or strains with distinct traits.
  • Adjustments to sequential sampling, such as group observations, can balance sample size reduction with error probability control.