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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...

Genomic Imprinting and Inheritance

Genomic Imprinting and Inheritance

Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...

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Updated: Jun 27, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Practical issues in imputation-based association mapping.

Yongtao Guan¹, Matthew Stephens

¹Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America. ytguan@uchicago.edu

|December 6, 2008

Summary

This summary is machine-generated.

Imputation-based association methods enhance genetic studies by improving power to detect associations, even with imperfect data. These methods are computationally efficient and practical for genome-wide analysis.

Related Experiment Videos

Last Updated: Jun 27, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Imputation-based association methods are crucial for analyzing untyped genetic variants and meta-analyzing studies with diverse genotyping platforms.
Practical application of these methods involves challenges such as imputation accuracy, reference panel selection, and computational efficiency.

Purpose of the Study:

To evaluate factors influencing imputation accuracy and its impact on association study power.
To compare Bayesian and frequentist approaches for testing imputed genotypes.
To develop computationally efficient methods for calculating Bayes factors and combining results across studies.

Main Methods:

Assessed imputation accuracy and its effect on statistical power.
Compared Bayesian and frequentist association testing frameworks for imputed genotypes.
Developed and validated a computationally efficient Bayesian method using posterior means for genotype imputation.
Implemented methods in the BIMBAM software package.

Main Results:

Imputation-based methods demonstrate robustness to imputation accuracy and can increase association detection power, even with poor imputation quality.
A standard likelihood ratio test is equivalent to a Bayesian approach with a specific prior assumption about imputation difficulty and effect size.
A Bayesian approach without this assumption offers improved power.
Replacing unknown genotypes with posterior means provides a computationally efficient approximation for Bayesian analysis.

Conclusions:

Imputation-based association testing is a powerful and practical tool for genetic research, offering improved power and facilitating multi-study analyses.
The BIMBAM software package provides efficient and scalable implementation of these methods for genome-wide association studies.