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Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Multiple testing in large-scale genetic studies.

Matthieu Bouaziz¹, Marine Jeanmougin, Mickaël Guedj

¹Department of Biostatistics, Pharnext, Paris, France.

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

|June 6, 2012

Summary

High-throughput genomic studies face false positives and negatives due to multiple testing. This chapter discusses controlling these errors using methods like Bonferroni, false discovery rate (FDR), and local-FDR for accurate genetic association findings.

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Area of Science:

Genomics
Statistical Genetics
Bioinformatics

Background:

High-throughput genomic studies utilize advanced molecular biology and sequencing technologies.
These studies aim to identify associations between genetic markers and phenotypes.
A significant challenge is the multiple-testing problem, leading to false positives and false negatives.

Purpose of the Study:

To discuss the multiple-testing problem in genomic research.
To provide theoretical and intuitive definitions of multiple-testing adjustment methods.
To offer practical guidance for researchers in selecting appropriate procedures.

Main Methods:

Review of existing multiple-testing adjustment strategies.
Explanation of Bonferroni correction for family-wise error rate (FWER).
Description of false discovery rate (FDR) and local-FDR approaches.

Main Results:

Multiple-testing adjustments aim to control error rates and improve reliability of genomic study outcomes.
Bonferroni controls the probability of at least one false positive.
FDR controls the proportion of false positives among significant results, while local-FDR assesses individual marker probabilities.

Conclusions:

Careful selection and application of multiple-testing procedures are crucial for valid genomic research.
Understanding the nuances of FWER, FDR, and local-FDR aids researchers in choosing the most suitable method.
This work provides a foundation for informed decision-making in genetic association studies.