Display options
Cite
Gu CC, Yang WW, Kraja AT, et al. Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes. BMC Proc. 2009;3:S86doi: 10.1186/1753-6561-3-s7-s86.
Gu, C. C., Yang, W. W., Kraja, A. T., de Las Fuentes, L., & Dávila-Román, V. G. (2009). Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes. BMC proceedings, 3S86. https://doi.org/10.1186/1753-6561-3-s7-s86
Gu, C Charles, et al. "Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes." BMC proceedings vol. 3 (2009): S86. doi: https://doi.org/10.1186/1753-6561-3-s7-s86
Gu CC, Yang WW, Kraja AT, de Las Fuentes L, Dávila-Román VG. Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes. BMC Proc. 2009 Dec 15;3:S86. doi: 10.1186/1753-6561-3-s7-s86. PMID: 20018082; PMCID: PMC2795989.
Copy Download .nbib Format: NLM
Share it on

BMC Proc. 2009 Dec 15;3:S86. doi: 10.1186/1753-6561-3-s7-s86.

Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes.

BMC proceedings

C Charles Gu, Wei Will Yang, Aldi T Kraja, Lisa de Las Fuentes, Victor G Dávila-Román

Affiliations

  1. Division of Biostatistics, Washington University School of Medicine, 660 South Euclid Avenue, Box 8067, St, Louis, Missouri 63110, USA. [email protected].

PMID: 20018082 PMCID: PMC2795989 DOI: 10.1186/1753-6561-3-s7-s86

Abstract

Studies of complex diseases collect panels of disease-related traits, also known as secondary phenotypes or endophenotypes. They reflect intermediate responses to environment exposures, and as such, are likely to contain hidden information of gene-environment (G x E) interactions. The information can be extracted and used in genetic association studies via latent-components analysis. We present such a method that extracts G x E information in longitudinal data of endophenotypes, and apply the method to repeated measures of multiple phenotypes related to coronary heart disease in Genetic Analysis Workshop 16 Problem 2. The new method identified many genes, including SCNN1B (sodium channel nonvoltage-gated 1 beta) and PKP2 (plakophilin 2), with potential time-dependent G x E interactions; and several others including a novel cardiac-specific kinase gene (TNNI3K), with potential G x E interactions independent of time and marginal effects.

References

  1. Am J Med Genet. 2001 Jan 8;105(1):42-4 - PubMed
  2. Am J Physiol Heart Circ Physiol. 2008 Aug;295(2):H708-16 - PubMed
  3. J Am Coll Cardiol. 2007 Jan 30;49(4):403-14 - PubMed
  4. Genet Epidemiol. 2008 Sep;32(6):528-38 - PubMed
  5. Neural Netw. 2000 May-Jun;13(4-5):411-30 - PubMed
  6. Lancet. 2002 Dec 14;360(9349):1903-13 - PubMed
  7. J Hypertens. 2008 Jul;26(7):1360-6 - PubMed
  8. Genet Epidemiol. 2006 Feb;30(2):143-54 - PubMed
  9. JAMA. 2001 May 16;285(19):2486-97 - PubMed
  10. Am J Psychiatry. 2003 Apr;160(4):636-45 - PubMed

Publication Types

Grant support