Local ancestry-informed rare variant burden testing improves gene discovery in admixed populations

Area of Science:

• Genetics
• Population Genetics
• Statistical Genetics

Background:

• Rare-variant association studies identify high-impact genetic factors missed by common variant studies.
• Standard burden tests lack power in admixed genomes due to unaddressed local ancestry variations.

Purpose of the Study:

• Introduce Tractor-Burden, an ancestry-aware method for rare-variant association testing in admixed populations.
• Improve the detection of gene-level genetic effects across diverse ancestral backgrounds.

Main Methods:

• Developed Tractor-Burden, a gene-based method partitioning rare variants by local ancestry.
• Estimated ancestry-specific genetic effects within a unified regression framework.
• Validated using simulations and applied to whole-genome sequencing data from the All of Us Research Program.

Main Results:

• Tractor-Burden demonstrated good calibration and increased power over standard tests in simulations with effect heterogeneity.
• Analysis of 47,152 admixed individuals identified known associations, including ancestry-enriched effects at LDLR.
• Discovered additional suggestive genes and pathways associated with type 2 diabetes.

Conclusions:

• Tractor-Burden effectively extends rare-variant association testing to admixed genomes.
• Provides a scalable framework for detecting and interpreting gene-level effects considering local ancestry.
• Enhances understanding of genetic architecture in diverse populations.