A statistical approach for rare-variant association testing in affected sibships

Area of Science:

• Genetics
• Statistical genetics
• Complex disease genetics

Background:

• Advancements in sequencing and exome-chip technologies necessitate novel statistical approaches for identifying rare genetic variants associated with complex diseases.
• Existing rare-variant association tests are primarily designed for case-control or cross-sectional studies, with fewer methods available for family-based association studies.
• Family studies, particularly those utilizing affected sibships from linkage studies, offer a powerful design to amplify association signals for rare variants due to cosegregation with disease status.

Purpose of the Study:

• To develop and present novel statistical methods for testing the association of rare genetic variants in affected sibships.
• To propose a strategy that leverages the principle that rare susceptibility variants should be more frequent in regions shared identically by descent (IBD) among affected sibling pairs compared to non-shared regions.
• To provide association tests applicable to affected sibships of any size, without requiring data from unaffected siblings or external controls.

Main Methods:

• Development of burden and variance-component tests tailored for affected sibships.
• Utilizing the concept of identical by descent (IBD) sharing patterns within sibling pairs to detect association signals.
• Ensuring robustness to population stratification and generating analytic p-values for scalability to genome-wide studies.

Main Results:

• The proposed methods effectively identify rare genetic variation associated with complex diseases within family structures.
• Demonstrated utility of the burden and variance-component tests using simulated data.
• Successful application of the methods to exome chip data from hypertension-ascertained sibships in the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

Conclusions:

• The developed statistical approaches provide a robust framework for rare variant association testing in affected sibships.
• These methods enhance the power to detect rare genetic variants influencing complex diseases by utilizing familial data structures.
• The analytic nature and robustness of the tests facilitate their application in large-scale, genome-wide genetic studies of rare variants.