Functional variants are likely to be aggregated in family studies enriched with affected members, and this aggregation increases the statistical power for rare variant detection. Longitudinal family studies provide additional information for identifying genetic and environmental factors associated with disease over time. However, methods for the analysis of rare variants in longitudinal family data remain fairly limited. These methods should be capable of accounting for different sources of correlations and handling large amounts of sequencing data efficiently. To identify rare variants in longitudinal family studies or family members with multiple phenotypes, we extended the powerful pedigree-based burden and kernel association tests to genetic longitudinal studies. Generalized estimating equation (GEE) approaches were used to generalize the pedigree-based burden and kernel tests to multiple correlated phenotypes under the generalized linear model framework. Adjustments were made for the fixed effects of confounding factors. These tests accounted for the complex correlations between multiple correlated phenotypes and between individuals within the same family. Comprehensive simulation studies were conducted to compare the proposed tests with mixed-effects models and marginal models using GEEs under various configurations. When the proposed tests were applied to the Diabetes Heart Study, exome variants of POMGNT1 and JAK1 geneswere found to be associated with type 2 diabetes.
