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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...

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Related Experiment Video

Updated: Mar 6, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Groupwise envelope models for imaging genetic analysis.

Yeonhee Park¹, Zhihua Su², Hongtu Zhu¹

¹Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.

|March 22, 2017

Summary

This summary is machine-generated.

This study introduces a groupwise envelope model to find links between genetic variants and brain imaging. The model improves the efficiency of genetic association studies, particularly for complex data like that from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Keywords:

Dimension reduction Envelope model Grassmann manifold Reducing subspace

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Published on: December 1, 2017

Area of Science:

Statistical genetics
Neuroimaging analysis
Multivariate statistics

Background:

Identifying associations between genetic variants and brain imaging phenotypes is crucial for understanding neurological disorders.
Existing methods may lack efficiency when dealing with complex, multivariate data structures.

Purpose of the Study:

To develop a novel groupwise envelope model for multivariate linear regression.
To establish associations between multivariate responses and covariates in genetic and neuroimaging studies.

Main Methods:

The proposed groupwise envelope model allows for distinct regression coefficients and error structures across different groups.
Theoretical properties of the model were rigorously established.
The model was applied to an imaging genetic dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Main Results:

The groupwise envelope model significantly enhances the efficiency of statistical tests and parameter estimation.
Numerical experiments and real-world data analysis demonstrated the model's effectiveness in efficient estimation.
The model successfully identified associations in the ADNI dataset.

Conclusions:

The developed groupwise envelope model is effective for analyzing complex genetic and neuroimaging data.
This approach offers improved statistical power and estimation accuracy for association studies.
The model shows promise for advancing research in neurodegenerative diseases like Alzheimer's.