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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Varanto: variant enrichment analysis and annotation.

Oskari Timonen¹, Mikko Särkkä², Tibor Fülöp²

¹Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.

Bioinformatics (Oxford, England)

|January 29, 2019

Summary

This summary is machine-generated.

Varanto is a new web tool that helps researchers analyze human genetic variations from genome-wide association studies (GWAS). It annotates, visualizes, and interprets single-nucleotide polymorphisms (SNPs) and their associations with diseases.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies (GWAS) identify genetic variations linked to traits or diseases.
Interpreting GWAS results requires identifying common themes like pathways and biological processes.
Effective tools are needed for comprehensive analysis of human genetic variations.

Purpose of the Study:

To introduce Varanto, a novel web tool for annotating, visualizing, and analyzing human genetic variations.
To provide a platform for exploring associations between genetic variations and biological traits or diseases.
To facilitate the interpretation of results from genome-wide association studies.

Main Methods:

Varanto utilizes diverse data sources for annotation and analysis.
The tool allows users to query input variations and retrieve gene-level annotations.
It performs annotation enrichment analysis and visualizes the findings.

Main Results:

Varanto enables the retrieval of associated variation and gene-level annotations.
The tool facilitates annotation enrichment analysis for sets of genetic variations.
Results can be visualized to aid in the interpretation of genetic association data.

Conclusions:

Varanto is a valuable web tool for researchers conducting genome-wide association studies.
It enhances the interpretation of genetic variations by providing annotation and visualization capabilities.
The tool supports the exploration of biological pathways and disease associations linked to genetic data.