title: Generalization of Rare Variant Association Tests for Longitudinal Family Studies.
abstract: Given the functional relevance of many rare variants, their identification is frequently critical for dissecting disease etiology. Functional variants are likely to be aggregated in family studies enriched with affected members, and this aggregation increases the statistical power to detect rare variants associated with a trait of interest. Longitudinal family studies provide additional information for identifying genetic and environmental factors associated with disease over time. However, methods to analyze 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 associated with a phenotype in longitudinal family studies, we extended pedigree-based burden (BT) and kernel (KS) association tests to genetic longitudinal studies. Generalized estimating equation (GEE) approaches were used to generalize the pedigree-based BT and KS to multiple correlated phenotypes under the generalized linear model framework, adjusting for fixed effects of confounding factors. These tests accounted for complex correlations between repeated measures of the same phenotype (serial correlations) and between individuals in the same family (familial correlations). We conducted comprehensive simulation studies to compare the proposed tests with mixed-effects models and marginal models, using GEEs under various configurations. When the proposed tests were applied to data from the Diabetes Heart Study, we found exome variants of POMGNT1 and JAK1 genes were associated with type 2 diabetes.
authors.affiliation: Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
authors.forename: Li-Chu
authors.initials: LC
authors.lastname: Chien
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Fang-Chi
authors.initials: FC
authors.lastname: Hsu
authors.affiliation: Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.|Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.|Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Donald W
authors.initials: DW
authors.lastname: Bowden
authors.affiliation: Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan.
authors.forename: Yen-Feng
authors.initials: YF
authors.lastname: Chiu
mesh_terms: D001741:Black or African American / Q000235:genetics*; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000235:genetics*; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000235:genetics*; D059472:Exome / Q000235:genetics; D005190:Family; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D053613:Janus Kinase 1 / Q000235:genetics*; D016014:Linear Models; D008137:Longitudinal Studies; D008957:Models, Genetic; D017351:N-Acetylglucosaminyltransferases / Q000235:genetics*; D010375:Pedigree; D010641:Phenotype; D044465:White People / Q000235:genetics*
chemical_list: D017351:N-Acetylglucosaminyltransferases; C443106:protein O-mannose beta-1,2-N-acetylglucosaminyltransferase; C507904:JAK1 protein, human; D053613:Janus Kinase 1
keywords: burden test (BT); generalized estimating equations; kernel statistic (KS); longitudinal family study; rare variant association test