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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: Jul 6, 2026

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

CLUMPHAP: a simple tool for performing haplotype-based association analysis.

Jo Knight¹, David Curtis, Pak C Sham

¹Social Genetic & Developmental Psychiatry MRC Centre, Institute of Psychiatry, Kings College London, De Crespigny Park, London, UK. j.knight@iop.kcl.ac.uk

Genetic Epidemiology

|April 9, 2008

Summary

This summary is machine-generated.

This study introduces CLUMPHAP, a novel method for genetic association analysis. CLUMPHAP clusters similar haplotypes to improve statistical power for identifying genetic variants influencing complex traits.

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Last Updated: Jul 6, 2026

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

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Published on: June 28, 2018

Area of Science:

Genetics
Statistical genetics
Bioinformatics

Background:

High-throughput genotyping technologies enable identification of genetic variants for complex traits.
Haplotype analysis may offer advantages over single nucleotide polymorphism (SNP) analysis for capturing causal variants.
Challenges in haplotype analysis include reduced statistical power due to numerous haplotype combinations.

Purpose of the Study:

To present a novel statistical method, CLUMPHAP, for genetic association analysis.
To address the reduced statistical power issue in haplotype-based association studies.
To evaluate the performance of CLUMPHAP compared to existing methods.

Main Methods:

CLUMPHAP implements hierarchical clustering of similar haplotypes.
It computes chi-squared statistics between haplotype clusters and disease status.
Statistical significance is determined using permutation testing.

Main Results:

CLUMPHAP demonstrates greater statistical power than the omnibus haplotype test.
Its power is comparable to multiple regression locus-coding approaches.
Simulation studies validated the method's effectiveness in identifying susceptibility loci.

Conclusions:

CLUMPHAP offers a powerful approach for genetic association studies.
The method enhances the ability to detect genetic variants influencing complex traits.
CLUMPHAP provides a valuable tool for genetic research and disease association studies.