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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...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Comparing Copy Number Variations and SNPs

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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Related Experiment Video

Updated: Jun 13, 2026

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

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

Published on: July 27, 2021

A Markov blanket-based method for detecting causal SNPs in GWAS.

Bing Han¹, Meeyoung Park, Xue-wen Chen

¹Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, 66045, USA. hanbing@ittc.ku.edu

BMC Bioinformatics

|May 5, 2010

Summary

We developed DASSO-MB, a novel Markov Blanket method to detect epistatic interactions in genome-wide association studies (GWAS). This approach efficiently identifies disease-associated SNPs with fewer false positives, aiding complex disease research.

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

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Last Updated: Jun 13, 2026

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

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

Published on: July 27, 2021

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

Published on: January 9, 2020

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Epistatic interactions are crucial for understanding complex diseases.
Genome-wide association studies (GWAS) generate large datasets, posing computational challenges for detecting these interactions.
Existing methods often struggle with large-scale data and can produce false positives.

Purpose of the Study:

To develop a robust and efficient computational method for identifying epistatic interactions in GWAS.
To overcome the limitations of existing methods in handling large genomic datasets and minimizing false positives.

Main Methods:

Proposed DASSO-MB (Detection of ASSOciations using Markov Blanket), a novel Markov Blanket-based approach.
Utilized a heuristic search strategy to calculate variable associations, avoiding computationally intensive training.
Applied the algorithm to simulated and real case-control GWAS datasets.

Main Results:

DASSO-MB effectively detects epistatic interactions associated with diseases.
The method guarantees strong association with diseases and minimizes false positives.
DASSO-MB significantly outperforms commonly-used methods in identifying disease-associated SNPs.

Conclusions:

DASSO-MB identifies a minimal set of causal SNPs with fewer false positives than existing methods.
This approach is critical for cost-effective biological experiments and guiding pathogenesis research in the era of large GWAS datasets.