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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.
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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

Two-stage two-locus models in genome-wide association.

David M Evans¹, Jonathan Marchini, Andrew P Morris

¹Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. davide@well.ox.ac.uk

|September 28, 2006

Summary

Detecting gene interactions (epistasis) in complex human diseases is challenging. An exhaustive search for pairwise genetic markers complements single-locus genome-wide association studies for identifying interacting loci.

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

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Published on: January 9, 2020

Area of Science:

Genetics and Genomics
Complex Trait Association Studies
Epistasis and Gene-Gene Interactions

Background:

Epistasis, or gene-gene interaction, is hypothesized to contribute to complex human diseases and traits.
The increasing availability of large-scale genome-wide association studies (GWAS) necessitates evaluating methods for detecting epistasis.
Understanding the impact of undetected epistasis on single-locus association power is crucial for human genetic research.

Purpose of the Study:

To quantify the feasibility of detecting interacting genetic loci in humans using realistic sample sizes.
To assess the adverse effects of undetected epistasis on the power of single-locus association tests.
To compare the performance of different analytical strategies for detecting epistasis in quantitative trait models.

Main Methods:

Simulated extensive two-locus quantitative trait models with varying degrees of epistasis.
Compared power to detect associations using: (1) single-locus models, (2) full two-locus models, and (3) two-stage strategies.
Two-stage strategies involved initial single-locus tests followed by analysis of selected loci using two-locus models.

Main Results:

Fitting full two-locus models outperformed single-locus tests in many scenarios, especially for detecting individual loci effects.
Two-stage strategies reduced computational load compared to exhaustive searches but were less powerful for interacting loci.
Two-stage approaches increased the risk of missing interacting loci with small marginal effects.

Conclusions:

Exhaustive genome-wide searches for pairwise marker combinations can effectively complement single-locus GWAS.
This approach aids in identifying interacting loci that explain moderate proportions of phenotypic variance.
Careful consideration of analytical strategies is needed to balance computational efficiency and power in epistasis detection.