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Justice AE, Howard AG, Fernández-Rhodes L, et al. Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes. BMC Proc. 2018;12:22doi: 10.1186/s12919-018-0118-9.
Justice, A. E., Howard, A. G., Fernández-Rhodes, L., Graff, M., Tao, R., & North, K. E. (2018). Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes. BMC proceedings, 1222. https://doi.org/10.1186/s12919-018-0118-9
Justice, Anne E, et al. "Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes." BMC proceedings vol. 12 (2018): 22. doi: https://doi.org/10.1186/s12919-018-0118-9
Justice AE, Howard AG, Fernández-Rhodes L, Graff M, Tao R, North KE. Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes. BMC Proc. 2018 Sep 17;12:22. doi: 10.1186/s12919-018-0118-9. eCollection 2018. PMID: 30275878; PMCID: PMC6157130.
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BMC Proc. 2018 Sep 17;12:22. doi: 10.1186/s12919-018-0118-9. eCollection 2018.

Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes.

BMC proceedings

Anne E Justice, Annie Green Howard, Lindsay Fernández-Rhodes, Misa Graff, Ran Tao, Kari E North

Affiliations

  1. 1Department of Epidemiology, University of North Carolina, Chapel Hill, NC USA.
  2. 2Biomedical and Translational Informatics, Geisinger Health, Danville, PA USA.
  3. 3Department of Biostatistics, University of North Carolina, Chapel Hill, NC USA.
  4. 4Carolina Population Center, University of North Carolina, Chapel Hill, NC USA.
  5. 5Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN USA.

PMID: 30275878 PMCID: PMC6157130 DOI: 10.1186/s12919-018-0118-9

Abstract

Even though there has been great success in identifying lipid-associated single-nucleotide polymorphisms (SNPs), the mechanisms through which the SNPs act on each trait are poorly understood. The emergence of large, complex biological data sets in well-characterized cohort studies offers an opportunity to investigate the genetic effects on trait variability as a way of informing the causal genes and biochemical pathways that are involved in lipoprotein metabolism. However, methods for simultaneously analyzing multiple omics, environmental exposures, and longitudinally measured, correlated phenotypes are lacking. The purpose of our study was to demonstrate the utility of the structural equation modeling (SEM) approach to inform our understanding of the pathways by which genetic variants lead to disease risk. With the SEM method, we examine multiple pathways directly and indirectly through previously identified triglyceride (TG)-associated SNPs, methylation, and high-density lipoprotein (HDL), including sex, age, and smoking behavior, while adding in biologically plausible direct and indirect pathways. We observed significant SNP effects (

Conflict of interest statement

Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affilia

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