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Related Experiment Video

Updated: May 10, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

R for genome-wide association studies.

Cedric Gondro¹, Laercio R Porto-Neto, Seung Hwan Lee

¹The Centre for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia.

Methods in Molecular Biology (Clifton, N.J.)

|June 13, 2013

Summary

R is a popular statistical language for genomic data analysis. This chapter explores methods to enhance R

Area of Science:

Bioinformatics
Computational Statistics
Genomics

Background:

R is the leading statistical programming language and a widely used environment for high-throughput genomic data analysis.
Analyzing large single nucleotide polymorphism (SNP) datasets presents performance challenges in R.

Purpose of the Study:

To discuss approaches for optimizing R performance.
To address the challenges of analyzing large SNP datasets in R.

Main Methods:

The chapter focuses on performance optimization techniques within the R environment.
Specific methods for handling large SNP datasets are explored.

Main Results:

The presented approaches aim to improve the efficiency of R for large-scale genomic data processing.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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Last Updated: May 10, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Enhanced performance facilitates more effective analysis of SNP data.

Conclusions:

Optimizing R is crucial for efficient high-throughput genomic data analysis.
Implementing these approaches can significantly improve the handling of large SNP datasets.