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Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.

Publication ,  Journal Article
Wang, Z; Maity, A; Luo, Y; Neely, ML; Tzeng, J-Y
Published in: Genet Epidemiol
February 2015

Studying complex diseases in the post genome-wide association studies (GWAS) era has led to developing methods that consider factor-sets rather than individual genetic/environmental factors (i.e., Multi-G-Multi-E studies), and mining for potential gene-environment (G×E) interactions has proven to be an invaluable aid in both discovery and deciphering underlying biological mechanisms. Current approaches for examining effect profiles in Multi-G-Multi-E analyses are either underpowered due to large degrees of freedom, ill-suited for detecting G×E interactions due to imprecise modeling of the G and E effects, or lack of capacity for modeling interactions between two factor-sets (e.g., existing methods focus primarily on a single E factor). In this work, we illustrate the issues encountered in constructing kernels for investigating interactions between two factor-sets, and propose a simple yet intuitive solution to construct the G×E kernel that retains the ease-of-interpretation of classic regression. We also construct a series of kernel machine (KM) score tests to evaluate the complete effect profile (i.e., the G, E, and G×E effects individually or in combination). We show, via simulations and a data application, that the proposed KM methods outperform the classic and PC regressions across a range of scenarios, including varying effect size, effect structure, and interaction complexity. The largest power gain was observed when the underlying effect structure involved complex G×E interactions; however, the proposed methods have consistent, powerful performance when the effect profile is simple or complex, suggesting that the proposed method could be a useful tool for exploratory or confirmatory G×E analysis.

Duke Scholars

Published In

Genet Epidemiol

DOI

EISSN

1098-2272

Publication Date

February 2015

Volume

39

Issue

2

Start / End Page

122 / 133

Location

United States

Related Subject Headings

  • Software
  • Models, Genetic
  • Humans
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Gene-Environment Interaction
  • Epidemiology
  • Environment
  • Computer Simulation
  • 4202 Epidemiology
 

Citation

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ICMJE
MLA
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Wang, Z., Maity, A., Luo, Y., Neely, M. L., & Tzeng, J.-Y. (2015). Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors. Genet Epidemiol, 39(2), 122–133. https://doi.org/10.1002/gepi.21877
Wang, Zhi, Arnab Maity, Yiwen Luo, Megan L. Neely, and Jung-Ying Tzeng. “Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Genet Epidemiol 39, no. 2 (February 2015): 122–33. https://doi.org/10.1002/gepi.21877.
Wang Z, Maity A, Luo Y, Neely ML, Tzeng J-Y. Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors. Genet Epidemiol. 2015 Feb;39(2):122–33.
Wang, Zhi, et al. “Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Genet Epidemiol, vol. 39, no. 2, Feb. 2015, pp. 122–33. Pubmed, doi:10.1002/gepi.21877.
Wang Z, Maity A, Luo Y, Neely ML, Tzeng J-Y. Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors. Genet Epidemiol. 2015 Feb;39(2):122–133.
Journal cover image

Published In

Genet Epidemiol

DOI

EISSN

1098-2272

Publication Date

February 2015

Volume

39

Issue

2

Start / End Page

122 / 133

Location

United States

Related Subject Headings

  • Software
  • Models, Genetic
  • Humans
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Gene-Environment Interaction
  • Epidemiology
  • Environment
  • Computer Simulation
  • 4202 Epidemiology