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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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Genome-wide prediction of DNase I hypersensitivity using gene expression.

Weiqiang Zhou¹, Ben Sherwood^1,2, Zhicheng Ji¹

¹Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA.

Nature Communications

|October 21, 2017

Summary

Gene expression can predict genome-wide regulatory element activity. Our BIRD method uses transcriptome data to map the regulome, enabling new insights and applications in genomics.

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Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Regulatory elements control gene expression and are crucial for cellular function.
Measuring regulatory element activity, such as via DNase I hypersensitivity (DH), is essential for understanding gene regulation.
Current methods for mapping regulatory elements can be resource-intensive.

Purpose of the Study:

To assess the feasibility of predicting genome-wide regulatory element activities using transcriptome data.
To develop a computational method for predicting DH from gene expression.
To explore the utility of predicted DH in various genomic applications.

Main Methods:

Development of BIRD (Big Data Regression for predicting DH), a novel high-dimensional regression model.
Application of BIRD to the Encyclopedia of DNA Elements (ENCODE) dataset.
Validation of predicted DH for downstream analyses, including TFBS prediction and differential activity assessment.

Main Results:

Gene expression significantly predicts DH, indicating a strong transcriptome-regulome relationship.
Predictive information is distributed across the whole transcriptome, not just local gene neighborhoods.
BIRD-predicted DH successfully identified transcription factor-binding sites (TFBSs) and facilitated regulome mapping.

Conclusions:

Transcriptome data is a powerful predictor of genome-wide regulatory element activity.
BIRD offers a scalable and effective approach for regulome mapping using gene expression data.
This study establishes transcriptome-based prediction as a valuable tool for genomic research and regulome analysis.