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Xu L, Craiu RV, Derkach A, et al. Using a Bayesian latent variable approach to detect pleiotropy in the Genetic Analysis Workshop 18 data. BMC Proc. 2014;8:S77doi: 10.1186/1753-6561-8-S1-S77.
Xu, L., Craiu, R. V., Derkach, A., Paterson, A. D., & Sun, L. (2014). Using a Bayesian latent variable approach to detect pleiotropy in the Genetic Analysis Workshop 18 data. BMC proceedings, 8S77. https://doi.org/10.1186/1753-6561-8-S1-S77
Xu, Lizhen, et al. "Using a Bayesian latent variable approach to detect pleiotropy in the Genetic Analysis Workshop 18 data." BMC proceedings vol. 8 (2014): S77. doi: https://doi.org/10.1186/1753-6561-8-S1-S77
Xu L, Craiu RV, Derkach A, Paterson AD, Sun L. Using a Bayesian latent variable approach to detect pleiotropy in the Genetic Analysis Workshop 18 data. BMC Proc. 2014 Jun 17;8:S77. doi: 10.1186/1753-6561-8-S1-S77. eCollection 2014. PMID: 25519405; PMCID: PMC4143687.
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BMC Proc. 2014 Jun 17;8:S77. doi: 10.1186/1753-6561-8-S1-S77. eCollection 2014.

Using a Bayesian latent variable approach to detect pleiotropy in the Genetic Analysis Workshop 18 data.

BMC proceedings

Lizhen Xu, Radu V Craiu, Andriy Derkach, Andrew D Paterson, Lei Sun

Affiliations

  1. Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5S 3G3, Canada.
  2. Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto M5G 1X8, Canada ; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Ontario M5S 3G3, Canada.
  3. Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Ontario M5S 3G3, Canada ; Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5S 3G3, Canada.

PMID: 25519405 PMCID: PMC4143687 DOI: 10.1186/1753-6561-8-S1-S77

Abstract

Pleiotropy, which occurs when a single genetic factor influences multiple phenotypes, is present in many genetic studies of complex human traits. Longitudinal family data, such as the Genetic Analysis Workshop 18 data, combine the features of longitudinal studies in individuals and cross-sectional studies in families, thus providing richer information about the genetic and environmental factors associated with the trait of interest. We recently proposed a Bayesian latent variable methodology for the study of pleiotropy, in the presence of longitudinal and family correlation. The purpose of this work is to evaluate the Bayesian latent variable method in a real data setting using the Genetic Analysis Workshop 18 blood pressure phenotypes and sequenced genotype data. To detect single-nucleotide polymorphisms with pleiotropic effect on both diastolic and systolic blood pressure, we focused on a set of 6 single-nucleotide polymorphisms from chromosome 3 that was reported in the literature to be significantly associated with either diastolic blood pressure or the binary hypertension trait. Our analysis suggests that both diastolic blood pressure and systolic blood pressure are associated with the latent hypertension severity variable, but the analysis did not find any of the 6 single-nucleotide polymorphisms to have statistically significant pleiotropic effect on both diastolic blood pressure and systolic blood pressure.

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