ArticleBMC proceedings2014
Mixed-effects models for GAW18 longitudinal blood pressure data.
Article in BMC proceedings, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies.Genomics & informatics · 2022Article
- Statistical models and computational tools for predicting complex traits and diseases.Genomics & informatics · 2021Article
- Longitudinal data methods for evaluating genome-by-epigenome interactions in families.BMC genetics · 2018Article
- Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization -GALLOP algorithm.Scientific reports · 2018Article
- Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects.Scientific reports · 2017Article
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2 authors.
Funding
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.
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