Longitudinal data analysis for rare variants detection with penalized quadratic inference function.

ABSTRACT: Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compare

SUBMITTER: Cao H 

PROVIDER: S-EPMC5429681 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature