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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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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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Memory management in genome-wide association studies.

Xiang Chen¹, Meizhuo Zhang, Minghui Wang

¹Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520-8034, USA. xiang.chen@yale.edu.

BMC Proceedings

|December 19, 2009

Summary

Genome-wide association studies (GWAS) generate vast data, posing memory challenges. A new memory management tool was tested on Rheumatoid Arthritis Consortium data, proving simple, efficient, and effective for handling large-scale genetic analyses.

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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

Area of Science:

Genetics
Computational Biology
Bioinformatics

Background:

Genome-wide association studies (GWAS) are crucial for identifying genes linked to common diseases.
GWAS generate billions of genotypes, presenting significant computational and memory limitations for researchers.
Efficient data handling is essential for the successful execution of large-scale genetic studies.

Purpose of the Study:

To evaluate the performance of a novel memory management tool for genome-wide association studies.
To assess the computational efficiency (CPU and memory usage) of the tool using real-world GWAS data.
To demonstrate a practical solution for overcoming memory constraints in genetic research.

Main Methods:

Application of a recently developed memory management tool.
Analysis of two datasets from the North American Rheumatoid Arthritis Consortium.
Measurement of central processing unit (CPU) and memory utilization during the analyses.

Main Results:

The memory management tool demonstrated effective performance in handling large genotype datasets.
CPU and memory usage were measured, indicating the tool's efficiency.
The approach proved to be a viable solution for memory-intensive GWAS.

Conclusions:

The implemented memory management approach is simple, efficient, and effective for genome-wide association studies.
This method offers a practical solution to computational challenges in genetic research.
The tool facilitates the analysis of large genetic datasets, aiding disease gene identification.