Rare variant association test in family-based sequencing studies

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Family-based studies offer unique advantages for detecting genetic variants.
• Existing methods for rare variant analysis in family studies, particularly for binary traits, are limited.
• There is a need for robust statistical tools to analyze rare variants in family-based sequencing data.

Purpose of the Study:

• To introduce valid and robust methods for rare variant analysis in family-based exome chip, whole-exome sequencing (WES), and whole-genome sequencing (WGS) data.
• To extend existing association tests for rare variants to accommodate related samples and binary phenotypes.
• To provide a flexible framework for combining results from diverse family-based studies.

Main Methods:

• Extension of burden and kernel-based gene set association tests from population data to related samples.
• Integration of kernel machine methods with generalized estimating equations for binary traits.
• Development of an efficient generalized kernel score test for mega-analysis across studies.

Main Results:

• The proposed methods effectively analyze rare variants in family-based sequencing data, with a focus on binary phenotypes.
• The generalized kernel score test provides a powerful framework for meta-analysis of family studies.
• The methods were illustrated using an autism exome sequencing study.

Conclusions:

• The developed methods address a critical gap in rare variant analysis for family-based studies.
• The generalized kernel score test offers a robust and flexible approach for genetic association studies.
• An R package 'gskat' is available for implementing these advanced statistical methods.