Detecting rare variant effects using extreme phenotype sampling in sequencing association studies.

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Complex Trait Association Studies

Background:

• Sequencing studies increasingly identify rare variants linked to complex traits.
• Guided sampling, particularly extreme phenotype sampling, can enhance statistical power.
• Traditional rare variant tests require dichotomizing continuous phenotypes, potentially reducing power.

Purpose of the Study:

• To propose a novel statistical method for analyzing rare variant effects in extreme phenotype samples.
• To enable the use of continuous phenotypes, avoiding information loss from dichotomization.
• To demonstrate the increased power of this method compared to traditional approaches.

Main Methods:

• Development of a novel statistical method based on the optimal Sequence Kernel Association Test (SKAT).
• Application of the method to analyze rare variant effects using continuous phenotypes.
• Validation through simulations across diverse scenarios and analysis of real-world triglyceride data from the Dallas Heart Study.

Main Results:

• Extreme phenotype sampling effectively enriches for causal rare variants.
• The proposed method significantly increases statistical power for detecting rare variant associations.
• The method demonstrates robust performance in simulations and practical application.

Conclusions:

• Extreme phenotype sampling is a powerful strategy for identifying rare variants associated with complex traits.
• The novel SKAT-based method effectively utilizes continuous phenotypes in extreme samples, enhancing power.
• This approach offers a valuable tool for genetic studies of complex diseases.