A multi-level model for analyzing whole genome sequencing family data with longitudinal traits

BMC Proceedings

Proceedings

Taoye Chen¹  Patchara Santawisook¹  Zheyang Wu¹ 

[1] Department of Mathematical Sciences, Worcester Polytechnic Institute, 100 Institute Road, 01609-2280, Worcester, MA, USA;

关键词: Rare Variant;    Multilevel Model;    Whole Genome Sequencing;    Kinship Matrix;    Genetic Analysis Workshop;   

DOI  :  10.1186/1753-6561-8-S1-S86

来源: Springer

【 摘 要 】

Compared with 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 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 models. The multilevel model shows higher power at practical p-value levels and a better type I error control than linear mixed-effect model. Both multilevel and linear mixed-effect models, which use the longitudinal repeated information, have higher power than the methods that only use data collected at one time point.

【 授权许可】

Unknown   
© Chen et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.