Faculty Advisor

Zheyang Wu

Abstract

Compared to microarray-based genotyping, next-generation whole genome-sequencing (WGS) studies have the strength to provide greater information for the identification of rare variants, which likely account for a significant portion of missing heritability of common human diseases. In WGS, family-based studies are important because they are likely enriched for rare disease variants that segregate with the disease in relatives. We propose a multilevel model to detect disease variants using family-based WGS data with longitudinal measures. This model incorporates the correlation structure from family pedigrees and that from repeated measures. The iterative generalized least squares (IGLS) algorithm was applied to estimation of parameters and test of associations. The model was applied to the data of Genetic Analysis Workshop 18 and compared with existing linear mixed effect (LME) models. The multilevel model shows higher power at practical p-value levels and a better type I error control than LME model. Both multilevel and LME models, which utilize the longitudinal repeated information, have higher power than the method that only utilize data collected at one time point.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Mathematical Sciences

Project Type

Thesis

Date Accepted

2013-04-24

Accessibility

Unrestricted

Subjects

Multi-level Model, Whole Genome Sequencing, Longitudinal Traits

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