Comparing family-based rare variant association tests for dichotomous phenotypes

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Family-based samples reduce genetic heterogeneity, enhancing power for detecting rare disease susceptibility loci.
• Numerous methods exist for rare-variant association analysis in family-based studies.

Purpose of the Study:

• To compare the performance of existing rare-variant association analysis methods using simulated family data from Genetic Analysis Workshop 19 (GAW19).
• To identify the most statistically and computationally efficient method for rare-variant analysis in extended families.

Main Methods:

• Evaluated five methods: rare variant transmission disequilibrium test (RV-TDT), GEE-KM test, Pedigree Combined Multivariate and Collapsing (PedCMC) test, PedGene, and family-based rare variant association test (FARVAT).
• Utilized simulated family data from GAW19 for performance comparison.

Main Results:

• PedGene and FARVAT demonstrated the highest efficiency among the tested methods.
• FARVAT's optimal test statistic proved robust across different disease models.
• FARVAT, implemented in C++, exhibited superior computational speed compared to other methods.

Conclusions:

• FARVAT is recommended as a suitable method for rare-variant analysis in extended family samples.
• The choice of FARVAT is supported by its combined statistical and computational efficiency.