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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
Strategy to control type I error increases power to identify genetic variation using the full biological trajectory
1Johns Hopkins Bloomberg School of Public Health, Mental Health Department, Baltimore, Maryland 21205, USA. kbenke@jhsph.edu
Genetic Epidemiology
|May 2, 2013
Summary
Jointly testing single nucleotide polymorphism (SNP) effects over time using linear-mixed effects models offers optimal type I error control. This approach enhances power and information gain in genome-wide association studies.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) traditionally identify genetic loci for continuous traits at a single time point.
- Longitudinal trait analysis requires evaluating single nucleotide polymorphism (SNP) effects at baseline and over time.
- Linear-mixed effects models (LMMs) are suitable for longitudinal data but raise concerns about controlling type I error when estimating multiple SNP coefficients.
Purpose of the Study:
- To investigate optimal methods for controlling type I error in GWAS of longitudinal traits using LMMs.
- To compare the performance of joint tests versus single-effect tests for SNP associations over time.
- To assess the power and information gain from joint testing in GWAS.
Main Methods:
- Calculated type I error and power for joint tests (e.g., two degree of freedom likelihood ratio test) and single degree of freedom tests.
- Compared test performance across varying alpha levels.
- Evaluated closed-form power calculations against simulated power for different data structures.
Main Results:
- Joint tests, particularly the two degree of freedom likelihood ratio test, provide optimal control of type I error for longitudinal GWAS.
- Joint testing can increase power and yield valuable information even when individual SNP effects are underpowered.
- Closed-form power calculations approximate simulated power for balanced data but can overestimate it for complex residual error structures.
Conclusions:
- A two degree of freedom test is a recommended and attractive strategy for hypothesis-free genome-wide studies utilizing linear-mixed effects models.
- This approach enhances the analysis of longitudinal traits in GWAS.
- Proper statistical testing is crucial for accurate interpretation of SNP effects in longitudinal genetic studies.
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