Gene-environment interactions and preterm birth predictors: A Bayesian network approach

Abstract

Preterm birth (PTB) is the main condition related to perinatal morbimortality worldwide. The aim of this study was to identify gene-environment interactions associated with spontaneous PTB or its predictors. We carried out a retrospective case-control study including parental sociodemographic and obstetric data as well as newborn genetic variants of 69 preterm and 61 at term newborns born at a maternity hospital from Tucumán, Argentina, between 2005 and 2010. A data-driven Bayesian network including the main PTB predictors was created where we identified gene-environment interactions. We used logistic regressions to calculate the odds ratios and confidence intervals of the interactions. From the main PTB predictors (nine exposures and six genetic variants) we identified an interaction between low neighbourhood socioeconomic status and rs2074351 ( PON1 , genotype GG) variant that was associated with an increased risk of toxoplasmosis (odds ratio 12.51, confidence interval 95%: 1.71 - 91.36). The results of this exploratory study suggest that structural social disparities could influence the PTB risk by increasing the frequency of exposures that potentiate the risk associated with individual characteristics such as genetic traits. Future studies with larger sample sizes are necessary to confirm these findings.