Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions
Abstract
:1. Introduction
2. Biology of Endocrine-Disrupting Chemicals and Implications for Diverse Populations
2.1. History and Exposure
2.2. Molecular Biology of EDCs
2.3. Effects on Biological Pathways
2.4. Implications for Population Diversity
3. Evidence for Differential Susceptibility to Endocrine-Disrupting Chemicals
4. Characterizing GxE Effects Associated with Endocrine-Disrupting Chemicals
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Approach | Sub-Approaches | Use Cases | Citation |
---|---|---|---|
FAMILY-BASED | generalized estimating equations | Pedigree data is available and exposure mis-specification is a concern | Basson et al. (2016) [161], Sitlani et al. (2016) [162] |
heirarchical linear model | Pedigree data is available and type I error is a concern | ||
linear mixed effects model | Pedigree data is available and type I error is a concern | ||
CASE-CONTROL | Penalized method with least absolute deviation loss function | When large genome-wide data is available and hierarchical “main effects, interactions” structure is a concern | Wu et al. (2018) [165] |
Similarity-based regression | When large genome-wide data is available and rare-variants with binary phenotypes are being investigated | Zhao et al. (2015) [124] | |
linear mixed model | When large genome-wide data is available and multiple exposure are being investigated | BIOS Consortium (2016) [166] | |
Parametric bootstrap | Removes need for permutation tests when large genome-wide data is available | Gauderman et al. (2017) [159], Coombes et al. (2018) [167] | |
CASE-ONLY | Traditional | Increases precision when independence between exposure and genetics can be assumed | Piegorsch et al. (1994) [169] |
Multiple maximum-likelihood | Increases precision and relaxes independence assumption | Umbach and Weinberg (1997) [170], Chatterjee and Carrol (2005) [173], Mukherjee and Chatterjee (2008) [171] | |
Bayesian | |||
2-STEP | Likelihood ratio to traditional | Increases power and reduces multiple testing correction in situations where traditional case-control or case-case only approaches would be appropriate | Murcray et al. (2008) [171], Pare (2010) [174], Kooperburg and LeBlanc (2008) [175] |
Levene’s test to traditional | |||
Marginal effects to traditional | |||
Modified Pare et al. | Robust in situations with with multiple exposure and reduce type I error versus other 2-step approaches | Zhang et al. (2016) [160] | |
Combined Pare and Kooperburg | |||
GENE-SET ANALYSIS (GSA) | Traditional | Increases power versus more traditional approaches | Biernacka et al. (2012) [176] |
With similarity regression | GSA when there are multiple covariates and opposite effects that may cancel each other out are a concern | Tzeng et al. (2013) [180] | |
GESAT | Established method for user friendly GSA | Lin et al. (2013) [180] | |
META-ANALYSIS | NA | Situations where investigators want to combine data from multiple studies to identify possible gene-environment interactions | Shi et al. (2017) [168] |
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Wallis, D.J.; Truong, L.; La Du, J.; Tanguay, R.L.; Reif, D.M. Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions. Toxics 2021, 9, 77. https://doi.org/10.3390/toxics9040077
Wallis DJ, Truong L, La Du J, Tanguay RL, Reif DM. Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions. Toxics. 2021; 9(4):77. https://doi.org/10.3390/toxics9040077
Chicago/Turabian StyleWallis, Dylan J., Lisa Truong, Jane La Du, Robyn L. Tanguay, and David M. Reif. 2021. "Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions" Toxics 9, no. 4: 77. https://doi.org/10.3390/toxics9040077