Gene analysis for longitudinal family data using random-effects models

Jeanine J Houwing-Duistermaat, Quinta Helmer, Bruna Balliu, Erik van den Akker, Roula Tsonaka, Hae-Won Uh

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)


We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × 10-12).

Original languageEnglish
Article numberS88
JournalBMC Proceedings
Issue numberSuppl 1 Genetic Analysis Workshop 18Vanessa Olmo
Publication statusPublished - 17 Jun 2014
Externally publishedYes


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