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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...

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Updated: May 25, 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

Multi-locus stepwise regression: a haplotype-based algorithm for finding genetic associations applied to atopic

Sven Knüppel¹, Jorge Esparza-Gordillo, Ingo Marenholz

¹Max Delbrück Center for Molecular Medicine Berlin-Buch, Berlin, Germany.

BMC Medical Genetics

|January 31, 2012

Summary

This study introduces a novel multi-locus stepwise regression method to identify disease-associated risk haplotypes beyond single nucleotide polymorphisms (SNPs). The approach successfully identified significant haplotype patterns, offering deeper insights into complex disease genetics.

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

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Last Updated: May 25, 2026

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

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

Published on: July 27, 2021

Area of Science:

Genetics
Genomics
Statistical Genetics

Background:

Genome-wide association studies (GWAS) identify numerous single nucleotide polymorphisms (SNPs) linked to diseases.
Analyzing closely spaced SNPs in candidate regions is crucial for a deeper understanding of disease associations.

Purpose of the Study:

To develop and apply a multi-locus stepwise regression combined with haplotype analysis.
To identify risk haplotypes for complex diseases beyond single SNP associations.

Main Methods:

A novel multi-locus stepwise regression approach evaluating SNP combinations.
Haplotype regression applied stepwise, with corrections for multiple testing and replication in an independent dataset.
Application to a 259-SNP region within the epidermal differentiation complex (EDC) on chromosome 1q21 using case-control and family datasets.

Main Results:

A statistically significant 4-SNP haplotype pattern was identified in both case-control and family datasets.
The primary identified haplotype pattern was mainly attributed to the FLG gene.
A significant FLG-independent haplotype was also detected, demonstrating an increased risk in both study sets.

Conclusions:

The developed multi-locus stepwise regression is an effective tool for disease association studies.
This method enhances the identification of allele combinations associated with complex diseases within chromosomal candidate regions.