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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.
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Single Nucleotide Polymorphisms-SNPs

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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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Updated: Jul 2, 2026

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

A joint association test for multiple SNPs in genetic case-control studies.

Tao Wang¹, Howard Jacob, Soumitra Ghosh

¹Division of Biostatistics, Department of Population Health, Medical College of Wisconsin, Milwaukee, Wisconsin 53226-0509, USA. taowang@mcw.edu

Genetic Epidemiology

|September 5, 2008

Summary

This study introduces a novel latent variable method for genetic association testing with multiple single nucleotide polymorphisms (SNPs). This approach simplifies complex data and enhances statistical power in case-control studies.

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

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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
Statistical Genetics
Bioinformatics

Background:

Haplotype-based association tests are valuable for dense genetic markers like single nucleotide polymorphisms (SNPs) in high linkage disequilibrium.
Testing associations with multiple SNPs presents challenges due to data complexity and multiple testing problems, especially when the causal variant is unknown.

Purpose of the Study:

To propose a latent variable approach for testing association between disease phenotypes and multiple tightly linked SNPs in case-control studies.
To address the challenges of data complexity and multiple testing inherent in haplotype-based association studies.

Main Methods:

Introduce a latent variable into the penetrance model to represent a putative disease susceptible locus (DSL).
Utilize a retrospective likelihood and an expectation-maximization (EM)-based algorithm to fit the model and estimate joint haplotype frequencies.
Employ a likelihood ratio statistic for a joint association test of the marker set.

Main Results:

The latent variable approach allows for flexible modeling of the DSL's role.
The method simultaneously estimates DSL and marker haplotype frequencies while adjusting for case-control sampling.
Simulation results suggest improved statistical power compared to classical methods in certain scenarios.

Conclusions:

The proposed latent variable method offers a powerful and flexible approach for haplotype-based association testing with dense genetic markers.
This method effectively reduces data complexity and mitigates multiple testing issues in genetic association studies.
The approach demonstrates potential for enhanced power in identifying disease-associated loci.