Characterization of the Contribution of Shared Environmental and Genetic Factors to Metabolic Syndrome Methylation Heritability and Familial Correlations

Fernandez-Rhodes, Lindsay; Howard, Annie Green; Tao, Ran; Young, Kristin L.; Graff, Mariaelisa; Aiello, Allison E.; North, Kari E.; & Justice, Anne E. (2018). Characterization of the Contribution of Shared Environmental and Genetic Factors to Metabolic Syndrome Methylation Heritability and Familial Correlations. BMC Proceedings, 19(Suppl. 1), 69. PMCID: PMC6157030

Fernandez-Rhodes, Lindsay; Howard, Annie Green; Tao, Ran; Young, Kristin L.; Graff, Mariaelisa; Aiello, Allison E.; North, Kari E.; & Justice, Anne E. (2018). Characterization of the Contribution of Shared Environmental and Genetic Factors to Metabolic Syndrome Methylation Heritability and Familial Correlations. BMC Proceedings, 19(Suppl. 1), 69. PMCID: PMC6157030

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Background: Transgenerational epigenetic inheritance has been posited as a possible contributor to the observed heritability of metabolic syndrome (MetS). Yet the extent to which estimates of epigenetic inheritance for DNA methylation sites are inflated by environmental and genetic covariance within families is still unclear. We applied current methods to quantify the environmental and genetic contributors to the observed heritability and familial correlations of four previously associated MetS methylation sites at three genes (CPT1A, SOCS3 and ABCG1) using real data made available through the GAW20. Results: Our findings support the role of both shared environment and genetic variation in explaining the heritability of MetS and the four MetS cytosine-phosphate-guanine (CpG) sites, although the resulting heritability estimates were indistinguishable from one another. Familial correlations by type of relative pair generally followed our expectation based on relatedness, but in the case of sister and parent pairs we observed nonsignificant trends toward greater correlation than expected, as would be consistent with the role of shared environmental factors in the inflation of our estimated correlations. Conclusions: Our work provides an interesting and flexible statistical framework for testing models of epigenetic inheritance in the context of human family studies. Future work should endeavor to replicate our findings and advance these methods to more robustly describe epigenetic inheritance patterns in human populations.




JOUR



Fernandez-Rhodes, Lindsay
Howard, Annie Green
Tao, Ran
Young, Kristin L.
Graff, Mariaelisa
Aiello, Allison E.
North, Kari E.
Justice, Anne E.



2018


BMC Proceedings

19

Suppl. 1

69








PMC6157030


10744

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