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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...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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.
On...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
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Updated: May 10, 2026

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

Bayesian methods applied to GWAS.

Rohan L Fernando1, Dorian Garrick

  • 1Department of Animal Science, Iowa State University, Ames, IA, USA.

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

Bayesian genome-wide association studies (GWAS) offer a powerful alternative for genetic analysis. By controlling false positives differently, Bayesian GWAS enhance the power to detect genetic associations.

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

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: May 10, 2026

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

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 Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Bayesian multiple-regression methods are established for genomic prediction and selection.
  • These models handle more markers than observations by integrating prior beliefs with data via Bayes' theorem.
  • Markov chain Monte Carlo (MCMC) sampling is often required for complex analyses.

Purpose of the Study:

  • To adapt Bayesian multiple-regression methods for genome-wide association studies (GWAS).
  • To propose a novel approach for controlling false positives in GWAS.
  • To improve the power of detecting genetic associations compared to traditional methods.

Main Methods:

  • Utilizing Bayesian multiple-regression frameworks for genomic data analysis.
  • Implementing Markov chain Monte Carlo (MCMC) sampling for posterior inference.
  • Controlling false positives by limiting the proportion of false positives among all positives, rather than the genome-wise error rate.

Main Results:

  • Demonstrated the application of Bayesian methods to GWAS.
  • Showcased a new strategy for false positive control in Bayesian GWAS.
  • The proposed method maintains high power for association detection irrespective of the number of markers tested.

Conclusions:

  • Bayesian GWAS provide a robust framework for genetic association studies.
  • Altering the false positive control strategy significantly enhances detection power.
  • This approach overcomes limitations of traditional GWAS regarding the trade-off between marker number and power.