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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...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...

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Related Experiment Video

Updated: May 10, 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

Designing a GWAS: power, sample size, and data structure.

Roderick D Ball1

  • 1Scion (New Zealand Forest Research Institute Limited), Rotorua, New Zealand.

Methods in Molecular Biology (Clifton, N.J.)
|June 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach for designing genome-wide association studies (GWAS) to reliably detect genetic loci linked to trait variation. The method enhances the detection of significant genomic associations by using Bayes factors to overcome low prior odds.

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Related Experiment Videos

Last Updated: May 10, 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

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Area of Science:

  • Genetics
  • Statistical genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) aim to identify loci associated with trait variation.
  • Detecting significant effects among hundreds of thousands of loci is statistically challenging.
  • Low prior odds for genomic associations complicate effect detection.

Purpose of the Study:

  • To present a novel Bayesian approach for designing GWAS.
  • To ensure robust detection of genomic loci associated with trait variation.
  • To provide methods for various data structures and prior information elicitation.

Main Methods:

  • Utilizes a Bayesian framework to design GWAS experiments.
  • Employs Bayes factors, chosen to overcome low prior odds for associations.
  • Includes methods for diverse data structures: population samples, case-control, and family-based designs (including plant clonal replication).

Main Results:

  • The proposed Bayesian approach facilitates robust detection of trait-associated genomic loci.
  • Methods are provided for quantifying prior odds using expert information.
  • Generic formulas allow conservative computations with limited prior information.

Conclusions:

  • The Bayesian design strategy enhances the reliability of GWAS findings.
  • The approach is adaptable to various study designs and data types.
  • Subjective Bayesian methods offer a framework for integrating prior knowledge into GWAS.