A combined association test for rare variants using family and case-control data

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Rare variants are challenging to analyze using traditional statistical methods.
• Existing rare variant association tests (burden and SKAT) were primarily for case-control data.
• Integrating family and case-control data can improve statistical power for genetic association studies.

Purpose of the Study:

• To extend the Combined Association in the Presence of Linkage (CAPL) test for rare variant association analysis.
• To incorporate burden and Sequence Kernel Association Test (SKAT) algorithms into the CAPL framework.
• To evaluate the performance of the new CAPL tests using simulations and real-world data.

Main Methods:

• Extended the CAPL test to accommodate rare variant analysis.
• Applied burden and SKAT algorithms within the CAPL framework.
• Conducted simulations to assess type I error rates and power.
• Applied the developed tests to the Genetic Analysis Workshop 19 hypertension dataset.

Main Results:

• Simulations confirmed correct type I error rates for the new CAPL tests.
• Power studies indicated adequate ability to detect rare variants associated with disease.
• The analysis of the Genetic Analysis Workshop 19 data identified several candidate genes for hypertension.

Conclusions:

• The extended CAPL test effectively integrates family and case-control data for rare variant association analysis.
• The new methods provide a powerful approach for identifying genetic variants underlying complex diseases.
• Several candidate genes for hypertension were identified, warranting further investigation.