ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Set-based tests are popular for rare and low-frequency variant analysis in sequencing studies.
• Existing methods can lose power with few causal variants and are sensitive to variant characteristics.
• There is a need for more powerful and robust statistical tests for genetic association studies.

Purpose of the Study:

• To introduce the aggregated Cauchy association test (ACAT), a novel p-value combination method.
• To develop ACAT-V for enhanced power when few causal variants exist within a set.
• To develop ACAT-O as an omnibus test combining multiple set-based tests for improved robustness and power.

Main Methods:

• ACAT is a general, computationally efficient p-value combination method.
• ACAT-V combines variant-level p-values for set-based association testing.
• ACAT-O combines variant-set-level p-values from multiple tests (Burden, SKAT, ACAT-V).

Main Results:

• Simulations show ACAT-V complements existing methods like SKAT and Burden test.
• ACAT-O demonstrates substantially more robust and higher power than alternative tests.
• Analysis of Atherosclerosis Risk in Communities (ARIC) whole-genome sequencing data supports findings.

Conclusions:

• ACAT provides a powerful framework for genetic association studies, particularly for rare variants.
• ACAT-V and ACAT-O offer improved power and robustness in detecting genetic associations.
• These methods enhance the analysis of complex genetic data from large-scale sequencing studies.