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Genome-wide Association Studies-GWAS

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Related Experiment Video

Updated: May 15, 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

Cumulative meta-analysis for genetic association: when is a new study worthwhile?

Michael A Rotondi¹, Shelley B Bull

¹School of Kinesiology and Health Science, York University, and Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada. mrotondi@yorku.ca

|December 22, 2012

Summary

This study introduces a simulation algorithm to determine the sample size needed for genome-wide significance in genetic association studies. It helps researchers assess if a planned study will achieve significant results when combined with existing meta-analyses.

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Area of Science:

Genetics
Statistical genetics
Bioinformatics

Background:

Meta-analyses are crucial for increasing statistical power in genetic association studies.
Determining optimal sample size for newly planned studies is essential for efficient research design.

Purpose of the Study:

To develop a method for estimating the required sample size for achieving genome-wide significance in meta-analyses.
To evaluate the potential impact of a new study on existing meta-analyses.

Main Methods:

A simulation-based algorithm is proposed to estimate the power of updated meta-analyses.
The algorithm calculates the empirical estimate of power to detect genome-wide significance (p<5.0×10(-8)).
It also estimates the expected p-value when combining current and proposed studies.

Main Results:

The technique is demonstrated using a meta-analysis of case-control studies for Paget's disease.
A second example shows the impact of adding a new study to a large meta-analysis of single nucleotide polymorphism (SNP) associations with human height.

Conclusions:

The algorithm aids in designing studies for high-priority SNP associations that are nearly significant.
It assists investigators in deciding if an updated meta-analysis is likely to achieve genome-wide significance.