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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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Updated: Jun 29, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Missing data imputation and haplotype phase inference for genome-wide association studies.

Sharon R Browning¹

¹Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand. s.browning@auckland.ac.nz

|October 14, 2008

Summary

Imputing missing genetic data and using haplotype association tests boost the power of genome-wide association studies (GWAS). This review covers optimal methods for haplotype inference and imputation in large datasets for improved GWAS accuracy and speed.

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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

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Last Updated: Jun 29, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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
Bioinformatics
Statistical Genomics

Background:

Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
Missing genetic data and the choice of association testing methods can significantly impact the power and accuracy of GWAS.
Haplotype-based methods offer potential advantages over single-marker analyses in certain scenarios.

Purpose of the Study:

To review and compare methods for haplotype inference and missing data imputation.
To discuss the application and impact of these methods on genome-wide association studies (GWAS).
To identify algorithms offering the best accuracy and computational performance for large-scale genetic datasets.

Main Methods:

Review of existing literature on haplotype inference algorithms.
Evaluation of various missing data imputation techniques.
Comparative analysis of different statistical approaches for association testing in GWAS.
Focus on methods suitable for large-scale genomic datasets.

Main Results:

Haplotype inference and missing data imputation are key strategies to enhance GWAS power.
Specific algorithms demonstrate superior accuracy and computational efficiency for large datasets.
Understanding the differences between various imputation and haplotype inference methods is crucial for optimal application.

Conclusions:

Effective imputation and haplotype-based testing significantly improve the power of genome-wide association studies.
Selection of appropriate algorithms for haplotype inference and missing data imputation is critical for successful GWAS.
The reviewed methods offer a pathway to more accurate and computationally efficient genetic association analyses.