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Statistical power and significance testing in large-scale genetic studies
1Centre for Genomic Sciences, Jockey Club Building for Interdisciplinary Research; State Key Laboratory of Brain and Cognitive Sciences, and Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Nature Reviews. Genetics
|April 18, 2014
Summary
Significance testing provides objective statistical evidence for hypotheses in genetic studies. However, it requires stringent thresholds and adequate statistical power, especially for genome-wide and exome-wide analyses of rare variants.
Area of Science:
- Genetics
- Statistical genetics
- Bioinformatics
Background:
- Significance testing is a standard statistical method for hypothesis evaluation.
- Its application is widespread in genetic research, including genome-wide association studies (GWAS) and exome sequencing.
- Challenges arise from the need for stringent significance thresholds due to multiple testing and the requirement for sufficient statistical power.
Purpose of the Study:
- To review the fundamental principles of significance testing in genetic studies.
- To discuss the practical applications and limitations of significance testing.
- To cover power calculations and novel gene-based tests for rare variants.
Main Methods:
- Review of statistical principles underlying significance testing.
- Discussion of power calculation methodologies.
- Overview of gene-based testing approaches for rare variants.
Main Results:
- Significance testing offers an objective summary of statistical evidence.
- Adequate statistical power is crucial for the utility of significance testing, influenced by phenotype, genetic variant, and study design.
- Stringent thresholds are necessary for multiple testing in genome-wide and exome-wide studies.
Conclusions:
- Significance testing is a valuable tool in genetic studies when applied appropriately.
- Understanding power calculations is essential for designing effective genetic studies.
- Emerging methods like gene-based tests address challenges in analyzing rare variants.
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