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Genome-wide Association Studies-GWAS

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...

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Related Experiment Video

Updated: Apr 27, 2026

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Multiple comparison procedures for neuroimaging genomewide association studies.

Wen-Yu Hua¹, Thomas E Nichols², Debashis Ghosh³

¹Department of Statistics, Penn State University, State College, PA 16802, USA wxh182@psu.edu.

Biostatistics (Oxford, England)

|June 26, 2014

Summary

This summary is machine-generated.

This study introduces novel dimensionality reduction techniques for analyzing brain imaging and genetic data, enhancing the discovery of genetic variants linked to brain structure. The new methods show increased power in detecting these associations.

Keywords:

Distance covariance Genomewide association studies Local false discovery rate Multivariate analysis Neuroimaging analysis Positive false discovery rate

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Basics of Multivariate Analysis in Neuroimaging Data

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Published on: July 24, 2010

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Last Updated: Apr 27, 2026

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

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Published on: November 14, 2017

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Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Neuroimaging
Genetics
Biostatistics

Background:

Neuroimaging research increasingly examines links between genome-wide genetic variants and brain phenotypes.
Current methods often use massively univariate analyses to identify single nucleotide polymorphisms affecting brain structure.

Purpose of the Study:

To propose dimensionality reduction methods for brain structural MRI and genomic data.
To introduce and evaluate a new multiple testing adjustment method.
To compare the proposed method with existing false discovery rate (FDR) approaches.

Main Methods:

Application of dimensionality reduction techniques to structural MRI scans and genomic data.
Development and implementation of a novel multiple testing adjustment method.
Comparison of the new method with two established FDR adjustment methods via simulations.

Main Results:

Simulation results indicate improved statistical power for the proposed method.
Real-data analysis identified associations between genetic variants and brain volume differences.
The findings suggest potential for novel biological insights.

Conclusions:

The proposed dimensionality reduction and multiple testing adjustment approach enhances the detection of genetic influences on brain structure.
This method offers a promising avenue for discovering new biological insights in neuroimaging genetics.