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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Jun 23, 2026

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

Multiple interval mapping for gene expression QTL analysis.

Wei Zou¹, Zhao-Bang Zeng

¹Department of Statistics, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA.

|May 12, 2009

Summary

This summary is machine-generated.

We developed a statistical method to identify expression quantitative trait loci (eQTL) linking gene expression and genetic variations. Our approach, implemented in the MIM-eQTL R package, offers increased power for eQTL detection.

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Area of Science:

Genetics
Bioinformatics
Statistical Genomics

Background:

Understanding the relationship between genetic variation and gene expression is crucial for deciphering complex traits.
Identifying expression quantitative trait loci (eQTL) is a key step in this process.

Purpose of the Study:

To develop and validate a novel statistical method for detecting genome-wide expression quantitative trait loci (eQTL).
To compare the power of the new method against existing procedures for eQTL analysis.

Main Methods:

The study employed multiple interval mapping (MIM) for model selection.
Statistical significance was assessed using the false discovery rate (FDR).
The developed computational procedure was implemented in an R package named MIM-eQTL.

Main Results:

The proposed method demonstrated higher statistical power for eQTL detection compared to a similar procedure by Storey et al.
Simulations confirmed the identified features contributing to differences in statistical power.
The MIM-eQTL package provides a tool for estimating FDR in eQTL studies.

Conclusions:

The developed MIM-based method offers a powerful approach for genome-wide eQTL analysis.
The MIM-eQTL R package facilitates the application of this method and FDR estimation.
This work contributes to a better understanding of the genetic architecture of gene expression variation.