Interaction of a Mixture of Lead, Mercury, Arsenic, Cadmium, Aluminum, and Manganese with GSTP1 in Relation to Autism Spectrum Disorder in Jamaican Children

• Activity Number: 518 - Statistical Methods for Complex Interactions and Genetic and Environmental Epidemiology
• Type: Contributed
• Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Mohammad Rahbar* and Maureen Samms-Vaughan and MinJae Lee and Jing Zhang and MacKinsey A. Bach and Jan Bressler and Manouchehr Hessabi and Megan L. Grove and Sydonnie Shakespeare-Pellington and Compton Beecher and Wayne McLaughlin and Katherine A. Loveland
• Companies: Center for Clinical & Translational Sciences-UTHealth and The University of the West Indies and University of Texas McGovern Medical School and School of Public Health-UTHealth and Center for Clinical & Translational Sciences-UTHealth and Division of Epidemiology,Human Genetics,and Environmental,School of Public Health-UTHealth and Center for Clinical & Translational Sciences-UTHealth and Human Genetics Center, School of Public Health-UTHealth and The University of the West Indies and The University of the West Indies and Caribbean Genetics (CARIGEN), The University of the West Indies and McGovern Medical School-UTHealth
• Keywords: Heavy Metals; GST Genes; Autism Spectrum Disorder (ASD); Gene environment interaction; Weighted quantile sum (WQS); Mixture analysis

Abstract

Using weighted quantile sum (WQS) regression and data from 266 case-control pairs of children from our Autism Spectrum Disorder (ASD) project in Jamaica, we investigated the possible role of six metals, lead (Pb), mercury (Hg), arsenic, cadmium, manganese (Mn), and aluminum, and their potential interactions with glutathione S-transferase (GST) family genes (GSTP1, GSTM1 and GSTT1) in relation to ASD. We utilized improved generalized WQS estimation algorithms developed by members of our research team (Lee, et. al.) that not only can identify interactions between an individual metal within a mixture and GST genes, but also account for correlated data due to matching of cases and controls. Findings from the negative effect WQS model of our mixture analyses revealed a marginally significant interaction between GSTP1 and three metals (Pb, Hg, and Mn) within the mixture in relation to ASD (p=0.07). This p-value is calculated by comparing the models with and without these interactions of three metals with GSTP1. These findings are compared with those of non-mixture conditional logistics regression models that investigate interactive effects of each metal separately with GST genes.