Mixed-effects models for GAW18 longitudinal blood pressure data Public Deposited

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Creator
  • Chung, Wonil
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Zou, Fei
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
Abstract
  • Abstract In this paper, we propose two mixed-effects models for Genetic Analysis Workshop 18 (GAW18) longitudinal blood pressure data. The first method extends EMMA, an efficient mixed-model association-mapping algorithm. EMMA corrects for population structure and genetic relatedness using a kinship similarity matrix. We replace the kinship similarity matrix in EMMA with an estimated correlation matrix for modeling the dependence structure of repeated measurements. Our second approach is a Bayesian multiple association-mapping algorithm based on a mixed-effects model with a built-in variable selection feature. It models multiple single-nucleotide polymorphisms (SNPs) simultaneously and allows for SNP-SNP interactions and SNP-environment interactions. We applied these two methods to the longitudinal systolic blood pressure (SBP) and diastolic blood pressure (DBP) data from GAW18. The extended EMMA method identified a single SNP on Chr5:75506197 (p-value = 4.67 × 10−7) for SBP and three SNPs on Chr3:23715851 (p-value = 9.00 × 10−8), Chr 17:54834217 (p-value = 1.98 × 10−7), and Chr21:18744081 (p-value = 4.95 × 10−7) for DBP. The Bayesian method identified several additional SNPs on Chr1:17876090 (Bayes factor [BF] = 102), Chr3:197469358 (BF = 69), Chr15:87675666 (BF = 43), and Chr19:41642807 (BF = 33) for SBP. Furthermore, for SBP, we found a single SNP on Chr3:197469358 (BF = 69) that has a strong interaction with age. We further evaluated the performances of the proposed methods by simulations.
Date of publication
Identifier
  • doi:10.1186/1753-6561-8-S1-S87
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Wonil Chung et al.; licensee BioMed Central Ltd.
License
Journal title
  • BMC Proceedings
Journal volume
  • 8
Journal issue
  • Suppl 1
Page start
  • S87
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1753-6561
Bibliographic citation
  • BMC Proceedings. 2014 Jun 17;8(Suppl 1):S87
Access
  • Open Access
Publisher
  • BioMed Central Ltd
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