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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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Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

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Cloud computing-based TagSNP selection algorithm for human genome data.

Che-Lun Hung¹, Wen-Pei Chen², Guan-Jie Hua³

¹Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan. clhung@pu.edu.tw.

International Journal of Molecular Sciences

|January 9, 2015

Summary

This summary is machine-generated.

This study introduces an efficient algorithm for identifying genomic haplotype blocks, crucial for understanding genetic variation and disease associations. The parallelized version significantly improves computational efficiency on large datasets.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single nucleotide polymorphisms (SNPs) are key to human genetic variation, impacting medical diagnostics, phylogeny, and drug design.
Haplotypes, regions of linked genetic variants, are essential for mapping disease genes using association-based methods.
Identifying haplotype blocks is critical for high-resolution genetic analysis and understanding disease associations.

Purpose of the Study:

To propose an efficient algorithm for identifying haplotype blocks within genomic data.
To compare the performance of the proposed algorithm against existing methods using real-world data.
To enhance the algorithm's efficiency through parallelization using the Hadoop MapReduce framework.

Main Methods:

Development of an efficient combinatorial algorithm for haplotype block identification.
Application of the algorithm to chromosomal haplotype data from the HapMap project.
Extension of the algorithm into a parallel version utilizing the Hadoop MapReduce framework for data processing.

Main Results:

The proposed algorithm identified longer haplotype blocks compared to an existing method on HapMap data.
The MapReduce-paralleled algorithm demonstrated significant improvements in computational efficiency.
Performance enhancement was directly proportional to the number of processors utilized in the parallel implementation.

Conclusions:

The developed algorithm provides an efficient means for identifying genomic haplotype blocks.
Parallelization using Hadoop MapReduce substantially boosts computational efficiency for large-scale genomic analyses.
This approach enhances the utility of haplotype block analysis in genetic research and applications.