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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.
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Genome Size and the Evolution of New Genes

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Related Experiment Video

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Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

NuGO contributions to GenePattern.

P J De Groot¹, C Reiff, C Mayer

¹Nutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University, PO Box 8129, 6700 EV, Wageningen, The Netherlands, Philip.deGroot@wur.nl.

Genes & Nutrition

|November 27, 2008

Summary

This summary is machine-generated.

NuGO developed custom GenePattern modules for analyzing Affymetrix data using the latest Bioconductor version. These tools enhance genomic analysis with up-to-date annotations and user-friendly features for nutrigenomics research.

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Area of Science:

Genomics
Bioinformatics
Nutrigenomics

Background:

NuGO, the European Nutrigenomics Organization, requires robust computational infrastructure for data analysis.
Existing genomic analysis tools needed customization for specific Affymetrix arrays and up-to-date scientific developments.

Purpose of the Study:

To integrate custom Affymetrix array analysis into the GenePattern platform.
To provide user-friendly and up-to-date genomic analysis tools for NuGO partners.

Main Methods:

Development of custom NuGO modules for GenePattern, including array quality analysis, expression file creation, differential gene expression analysis, and Gene Ontology (GO) enrichment.
Utilizing the latest Bioconductor version for up-to-date annotations and scientific advancements.
Support for both standard Affymetrix and custom MBNI CDF-files, generating .chip files for Gene Set Enrichment Analysis (GSEA).

Main Results:

Successful implementation of NuGO-specific modules within GenePattern.
Enabled comprehensive quality control, data normalization, differential gene expression identification, and GO enrichment analysis.
Facilitated flexible probe remapping using custom CDF-files and enabled GSEA.

Conclusions:

The NuGO GenePattern modules provide a powerful, up-to-date, and user-friendly solution for Affymetrix data analysis in nutrigenomics.
The modular approach ensures adaptability and access to the latest scientific developments.
Distribution via Ubuntu packages simplifies installation and accessibility for researchers.