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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Sampling Plans01:23

Sampling Plans

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...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
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...
Stratified Sampling Method01:16

Stratified 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. 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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Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...

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Updated: Jun 4, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Sampling GWAS subjects from risk populations.

Konrad Oexle1, Thomas Meitinger

  • 1Institute of Human Genetics, Klinikum rechts der Isar, TU München, Munich, Germany. oexle@humangenetik.med.tum.de

Genetic Epidemiology
|February 18, 2011
PubMed
Summary

Optimizing sample collection for genome-wide association studies (GWAS) is key for complex disorders. Sampling strategies influence power, with specific schemes potentially increasing or decreasing necessary sample sizes for genetic discovery.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Genome-wide association studies (GWAS) are essential for identifying genetic variants underlying complex diseases.
  • Determining adequate sample size (power) is critical for the success of GWAS, especially for disorders influenced by many small genetic effects.
  • External risk factors can confound genetic association studies by altering allele frequencies in case and control populations.

Purpose of the Study:

  • To investigate the impact of sampling cases and controls from populations exposed to external risk factors on the statistical power of GWAS.
  • To derive formulas for calculating the required sample size based on the strength of the risk factor and the sampling strategy.
  • To propose an optimized sampling strategy for GWAS to minimize the required sample size.

Main Methods:

  • Utilized an additive threshold model to analyze the relationship between risk factor exposure, sampling scheme, and statistical power.
  • Derived analytical approximations for sample size calculations, particularly for scenarios with small genetic effect sizes.
  • Evaluated the power implications of different sampling strategies: both cases and controls from risk population, only cases from risk population, and non-risk cases with risk controls.

Main Results:

  • Sampling both cases and controls from a population exposed to a risk factor leads to a loss of statistical power (increased sample size requirement).
  • The most significant loss of power occurs when only cases are sampled from the risk population.
  • Sampling non-risk cases and risk controls enhances power because it enriches for disease-favoring alleles in cases and protective alleles in controls.

Conclusions:

  • The choice of sampling strategy significantly impacts the power and efficiency of GWAS.
  • A strategy involving non-risk cases and risk controls can increase GWAS power, reducing the necessary sample size.
  • These findings provide a basis for optimizing sample collection in GWAS, particularly when gene-environment interactions are minimal.