Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Rare variants play a crucial role in genetic disease association studies.
• Evaluating the performance of different statistical tests for rare variants is essential for accurate genetic analysis.
• The Genetic Analysis Workshop 17 (GAW17) provided simulated data for evaluating genetic association methods.

Purpose of the Study:

• To evaluate the performance of four association tests for rare variants: Combined Multivariate and Collapsing (CMC) method, two weighted-sum methods, and a variable threshold method.
• To assess the Family-Wise Error Rate (FWER) and average power of these methods using simulated GAW17 data.
• To adapt and evaluate a fast permutation procedure for computationally intensive methods.

Main Methods:

• Application of four rare variant association tests to simulated GAW17 unrelated individual data.
• Evaluation criteria included Family-Wise Error Rate (FWER) and average power.
• Adaptation of a fast permutation procedure for p-value estimation in three of the tested methods.

Main Results:

• The CMC method failed to control FWER when analyzing all nonsynonymous SNPs (rare and common).
• All four methods controlled FWER well when analyzing only rare variants (minor allele frequency < 0.05).
• Power was comparable but low across all methods for the GAW17 data; a fast permutation procedure yielded results close to the standard procedure with reduced computational intensity.

Conclusions:

• When focusing on rare variants, the evaluated methods demonstrate good FWER control.
• A fast permutation procedure offers a computationally efficient alternative for rare variant association testing without significantly compromising accuracy.
• Further research may be needed to improve the power of rare variant association tests.