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Related Experiment Video

Updated: May 25, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Genotype calling for the Affymetrix platform.

Arne Schillert¹, Andreas Ziegler

¹Institut für Medizinische Biometrie und Statistik, Universitätsklinikum Schleswig-Holstein, Universität zu Lubeck, Lübeck, Germany. Arne.Schillert@imbs.uni-luebeck.de

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

|February 7, 2012

Summary

This summary is machine-generated.

Accurate genotype calling is essential for genome-wide association studies. This study details the Birdseed and CRLMM algorithms for Affymetrix SNP Array 6.0, providing practical guidance for high-quality genetic epidemiology data analysis.

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Infinium Assay for Large-scale SNP Genotyping Applications

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Published on: November 19, 2013

Area of Science:

Genetic Epidemiology
Bioinformatics
Statistical Genetics

Background:

High-throughput genotyping data analysis is standard in genetic epidemiology.
High-quality data is critical for the success of genome-wide association (GWA) studies.
Accurate genotype generation from signal intensities is the initial step in statistical analysis.

Purpose of the Study:

To describe and compare the Birdseed and CRLMM genotype calling algorithms for the Affymetrix Genome-Wide Human SNP Array 6.0.
To provide detailed usage instructions and practical guidance for implementing these algorithms.
To facilitate the accurate analysis of high-throughput genotyping data in GWA studies.

Main Methods:

Description of the statistical methodologies underlying the Birdseed and CRLMM algorithms.
Detailed explanation of the practical application and execution of both genotype calling algorithms.
Provision of software links and sample code for algorithm installation and use.

Main Results:

The study provides a comprehensive overview of two commonly used genotype calling algorithms.
Practical resources including software links and sample code are made available.
A method for processing the output files from these algorithms is suggested.

Conclusions:

The Birdseed and CRLMM algorithms are key tools for genotype calling in GWA studies.
This work offers practical support for researchers to achieve high-quality genotype data.
Effective implementation of these methods enhances the reliability of genetic epidemiology findings.