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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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Related Experiment Video

Updated: May 3, 2026

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

Published on: January 27, 2016

Epigenome-wide association studies without the need for cell-type composition.

James Zou¹, Christoph Lippert², David Heckerman²

¹1] eScience Research Group, Microsoft Research, Los Angeles, California, USA. [2] The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [3] School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.

|January 28, 2014

Summary

This summary is machine-generated.

Epigenome-wide association studies (EWAS) can be confounded by cell type differences. FaST-LMM-EWASher automatically corrects for this without needing cell type information, improving biological insights.

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TChIP-Seq: Cell-Type-Specific Epigenome Profiling

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TChIP-Seq: Cell-Type-Specific Epigenome Profiling

TChIP-Seq: Cell-Type-Specific Epigenome Profiling

Published on: January 23, 2019

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Cell-type composition varies between sample groups in epigenome-wide association studies (EWAS).
This variation can lead to spurious associations that mask true biological signals.
Existing methods to correct for cell-type composition require explicit cell-type data, which is often unavailable.

Purpose of the Study:

To develop a novel method for automatically correcting cell-type composition in EWAS.
To address the limitation of needing explicit cell-type information for EWAS analysis.

Main Methods:

Introduction of FaST-LMM-EWASher, a statistical method for EWAS.
The method automatically accounts for cell-type composition without prior knowledge.
Validation by comparison with existing state-of-the-art approaches.

Main Results:

FaST-LMM-EWASher effectively corrects for cell-type composition bias in EWAS.
The method provides accurate associations without requiring explicit cell-type data.
Performance is comparable to methods that do require cell-type information.

Conclusions:

FaST-LMM-EWASher offers a robust solution for analyzing EWAS data with varying cell-type compositions.
The method enhances the ability to identify true biological associations in epigenetics research.
Software for FaST-LMM-EWASher is publicly available.