A Multi-level Model for Analysing Whole Genome Sequencing Family Data with Longitudinal Traits

Chen, Taoye

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A Multi-level Model for Analysing Whole Genome Sequencing Family Data with Longitudinal Traits

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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.

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