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Genome-wide Association Studies-GWAS

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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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Updated: Jun 6, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Efficient mining of haplotype patterns for linkage disequilibrium mapping.

Li Lin¹, Limsoon Wong, Tze-Yun Leong

¹School of Computing, National University of Singapore, Singapore. hi.linli@gmail.com

Journal of Bioinformatics and Computational Biology

|December 15, 2010

Summary

This study introduces LinkageTracker, a novel method for linkage disequilibrium mapping to identify disease-causing genes. LinkageTracker offers consistent predictive accuracy, even with limited disease samples or noisy data, outperforming existing methods.

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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

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Published on: July 27, 2021

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Accurate identification of disease-causing genes is crucial for effective patient management and improved healthcare outcomes.
Linkage disequilibrium mapping analyzes haplotype frequencies to locate disease genes by comparing disease and normal chromosomes.

Purpose of the Study:

To present a new, robust algorithm for linkage disequilibrium mapping named LinkageTracker.
To demonstrate the algorithm's effectiveness, particularly under challenging conditions like low disease sample prevalence and data noise.

Main Methods:

Developed a novel algorithm, LinkageTracker, for linkage disequilibrium mapping.
Compared LinkageTracker's performance against established methods including HapMiner, Blade, GeneRecon, and Haplotype Pattern Mining (HPM).

Main Results:

LinkageTracker demonstrates consistent and high predictive accuracy across various conditions, including extreme scenarios.
The algorithm achieves this accuracy within reasonable processing times.
LinkageTracker does not require population ancestry or haplotype genealogy information, enhancing its applicability.

Conclusions:

LinkageTracker is a valuable tool for linkage disequilibrium mapping, offering superior predictive accuracy and efficiency.
Its ability to function without ancestry or genealogy data makes it versatile for diverse genetic datasets.