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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.
GWAS does not require the identification of the target gene involved in...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Exploring effective approaches for haplotype block phasing.

Ziad Al Bkhetan¹, Justin Zobel¹, Adam Kowalczyk^1,2,3,4

¹School of Computing & Information Systems, University of Melbourne, Parkville, 3010, Australia.

BMC Bioinformatics

|November 1, 2019

Summary

This summary is machine-generated.

Choosing the right phasing and haplotype block determination methods is crucial for accurate genetic analyses. A consensus estimator combining three tools demonstrated the highest accuracy in phasing, outperforming individual methods.

Keywords:

Haplotype analysis Haplotype blocks Haplotype estimation Phasing

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Accurate detection of disease-associated mutations relies on understanding genetic phase (allele sequence on homologous chromosomes).
Phased haplotype association analysis is a key method, but phasing accuracy at the haplotype block scale is underexplored.
Assessing the combined impact of phasing tools and block determination methods on error rates is vital for reliable haplotype analyses.

Purpose of the Study:

To systematically evaluate the accuracy of seven widely used phasing methods in conjunction with two common haplotype block determination approaches.
To investigate the impact of different algorithmic choices on phasing accuracy at the haplotype block level.
To develop and validate a more accurate haplotype estimation method.

Main Methods:

Systematic comparison of seven phasing tools and two block determination methods.
Evaluation focused on the rate of incorrectly phased haplotype blocks.
Development of a consensus-based haplotype estimator utilizing multiple tools.

Main Results:

A consensus estimator integrating three phasing tools achieved the highest accuracy across all tested scenarios.
Individual tools like EAGLE2, BEAGLE, and SHAPEIT2 showed variable performance depending on the specific data.
Linkage disequilibrium-based block determination yielded more accurate phasing than sliding window approaches.
Phasing accuracy was comparable for genomic regions (e.g., genes) and entire chromosomes.
Phasing error locations varied across repeated analyses of the same data.

Conclusions:

The interplay between phasing algorithms and block determination strategies significantly influences the accuracy of phased haplotype blocks.
This study offers evidence-based guidance for selecting appropriate methods in haplotype block analyses.
Findings address potential limitations in the replicability of previous haplotype-based studies.