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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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Related Experiment Video

Updated: May 20, 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

Using maximal segmental score in genome-wide association studies.

Ying-Chao Lin¹, Ching-Lin Hsiao, Ai-Ru Hsieh

¹Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan.

Genetic Epidemiology

|July 19, 2012

Summary

A new Maximal Segmental Score (MSS) method improves the power of genome-wide association studies (GWAS) for identifying disease genes. This approach enhances genetic association detection in high-density genotyping data.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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Last Updated: May 20, 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

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genetics
Bioinformatics
Statistical genomics

Background:

Genome-wide association studies (GWAS) are crucial for identifying disease susceptibility genes.
Current GWAS methods face limitations in power and resolution, leaving heritability unexplained.
High-density genotyping and next-generation sequencing generate vast amounts of data, increasing the challenge of multiple comparisons.

Purpose of the Study:

To introduce a novel two-stage Maximal Segmental Score (MSS) procedure for enhanced genetic association analysis.
To improve the power and resolution of identifying disease-associated genomic regions in large-scale genetic studies.
To provide an efficient tool for exploring high-density association data.

Main Methods:

Developed a two-stage Maximal Segmental Score (MSS) procedure.
Utilized region-specific empirical P-values to pinpoint disease gene locations.
Employed Fisher's P-value combining method to create region-specific scores from locus-specific significance.

Main Results:

Simulations demonstrated that MSS significantly increased the power to detect genetic associations compared to conventional methods at a 5% type I error rate.
The MSS procedure was successfully applied to a Parkinson's disease dataset, replicating known findings.
The method proved effective in identifying genomic segments likely harboring disease genes.

Conclusions:

The Maximal Segmental Score (MSS) procedure is an efficient exploratory tool for high-density association data.
MSS enhances the power of genome-wide association studies in the era of next-generation sequencing.
Freely available R source codes facilitate the implementation of the MSS procedure.