Synergic effect of GSTP1 and blood manganese concentrations in Autism Spectrum Disorder

Rahbar, Mohammad H.; Samms‐Vaughan, Maureen; Ma, Jianchao; Bressler, Jan; Dickerson, Aisha S.; Hessabi, Manouchehr; Loveland, Katherine A.; Grove, Megan L.; Shakespeare-Pellington, Sydonnie; Beecher, Compton; McLaughlin, Wayne; Boerwinkle, Eric

doi:10.1016/j.rasd.2015.08.001

Cited by 31 publications

(40 citation statements)

References 62 publications

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“…The contribution of Mn for the other GSTP1 genotypes (Ile/Val and Val/Val) was not strong (i.e., <0.2) for both positive and negative models. This finding is consistent with the results from the analysis where interactive effect between Mn and GSTP1 in relation to ASD was tested without or with adjustment of other four metals in the ERAJ study (Rahbar et al., , ), which showed that higher Mn concentrations were significantly associated with ASD for only the GSTP1 Ile/Ile genotype, but this significant association was not found among children with Ile/Val or Val/Val.…”

Section: Application To the Epidemiological Research On Autism Spectrsupporting

confidence: 90%

“…Since 2009, the research team at University of Texas Health Science Center at Houston (UTHealth), in collaboration with investigators at the University of the West Indies (UWI), have conducted two phases of an age‐ and sex‐matched case‐control study to investigate the role of the GST genes (e.g., GSTP1 ) and their potential interactions with five metals, lead (Pb), mercury (Hg), arsenic (As), cadmium (Cd), and manganese (Mn), in relation to ASD in Jamaica. Details on the recruitment and assessment procedures of this study have been reported previously (Rahbar et al., , , , , , , , ). So far, a significant gene‐environment interaction between GSTP1 and Mn was found when Mn was the only metal that was assessed (Rahbar et al., ).…”

Section: Application To the Epidemiological Research On Autism Spectrmentioning

confidence: 99%

“…One of the main objectives of this research is to establish WQS index for a mixture of all five metals in order to minimize any potential effects of multicollinearity due to possible correlation among metal concentrations in blood, and investigate whether the effect of metals on ASD differs by GST gene polymorphisms. Specifically, based on the previous findings (Rahbar et al., , ), we conducted the proposed method that indicates a potential interaction effect between Mn and GSTP1 in relation to ASD within a mixture, under the conditional logistic regression model framework using

- l o g l i k e l i h o o d

in Equation to account for the within‐pair correlations. Suppose there are two dummy variables for three GSTP1 categories, that is, G 2 (=1 if Ile/Val) and G 3 (=1 if Val/Val), interactive model can be written as

\begin{matrix} true \\ right g (μ) = α + β ({} w_{1} q_{P b} + \dots + w_{5} q_{M n} + w_{6} q_{M n} G_{2} + w_{7} q_{M n} G_{3}) + γ_{11} G_{2} + γ_{12} G_{3} + {bold-italicu}^{T} bold-italicϕ |_{b} . \end{matrix}

Other exposure variables (a matrix u ) included demographics and socioeconomic status (SES), parents' education, and potential nutritional exposures.…”

Section: Application To the Epidemiological Research On Autism Spectrmentioning

confidence: 99%

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A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

et al. 2019

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A weighted quantile sum (WQS) regression has been used to assess the associations between environmental exposures and health outcomes. However, the currently available WQS approach, which is based on additive effects, does not allow exploring for potential interactions of exposures with other covariates in relation to a health outcome. In addition, the current WQS cannot account for clustering, thus it may not be valid for analysis of clustered data. We propose a generalized WQS approach that can assess interactions by estimating stratum‐specific weights of exposures in a mixture, while accounting for potential clustering effect of matched pairs of cases and controls as well as censored exposure data due to being below the limits of detection. The performance of the proposed method in identifying interactions is evaluated through simulations based on various scenarios of correlation structures among the exposures and with an outcome. We also assess how well the proposed method performs in the presence of the varying levels of censoring in exposures. Our findings from the simulation study show that the proposed method outperforms the traditional WQS, as indicated by higher power of detecting interactions. We also find no strong evidence that the proposed method falsely identifies interactions when there are no true interactive effects. We demonstrate application of the proposed method to real data from the Epidemiological Research on Autism Spectrum Disorder (ASD) in Jamaica (ERAJ) by examining interactions between exposure to manganese and glutathione S‐transferase family gene, GSTP1 in relation to ASD.

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Section: Application To the Epidemiological Research On Autism Spectrsupporting

confidence: 90%

Section: Application To the Epidemiological Research On Autism Spectrmentioning

confidence: 99%

- l o g l i k e l i h o o d

\begin{matrix} true \\ right g (μ) = α + β ({} w_{1} q_{P b} + \dots + w_{5} q_{M n} + w_{6} q_{M n} G_{2} + w_{7} q_{M n} G_{3}) + γ_{11} G_{2} + γ_{12} G_{3} + {bold-italicu}^{T} bold-italicϕ |_{b} . \end{matrix}

Other exposure variables (a matrix u ) included demographics and socioeconomic status (SES), parents' education, and potential nutritional exposures.…”

Section: Application To the Epidemiological Research On Autism Spectrmentioning

confidence: 99%

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A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

et al. 2019

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“…Furthermore, we found a significant gene-environment interaction between GSTP1 and blood manganese concentrations (BMC) indicating that among children who had the Ile/Ile genotype for GSTP1 , those with BMC ≥ 12 µg/L had about four times higher odds of ASD than those with BMC < 12 µg/L ( p = 0.03) [44]. …”

Section: Introductionmentioning

confidence: 99%

Role of Metabolic Genes in Blood Aluminum Concentrations of Jamaican Children with and without Autism Spectrum Disorder

Rahbar

Samms‐Vaughan

Pitcher

et al. 2016

IJERPH

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Aluminum is a neurotoxic metal with known health effects in animals and humans. Glutathione-S-transferase (GST) genes and enzymes play a major role in detoxification of several heavy metals. Besides a direct relationship with oxidative stress; aluminum decreases GST enzyme activities. Using data from 116 Jamaican children; age 2–8 years; with Autism Spectrum Disorder (ASD) and 116 sex- and age-matched typically developing (TD) children; we investigated the association of polymorphisms in three GST genes (GSTP1; GSTM1; and GSTT1) with mean blood aluminum concentrations in children with and without ASD. Using log-transformed blood aluminum concentration as the dependent variable in a linear regression model; we assessed the additive and interactive effects of ASD status and polymorphisms in the three aforementioned GST genes in relation to blood aluminum concentrations. Although none of the additive effects were statistically significant (all p > 0.16); we observed a marginally significant interaction between GSTP1 Ile105Val (rs1695) and ASD status (p = 0.07); even after controlling for parental education level and consumption of avocado; root vegetables; and tuna (canned fish). Our findings indicate a significantly lower (p < 0.03) adjusted geometric mean blood aluminum concentration for TD children who had the Val/Val genotype (14.57 µg/L); compared with those with Ile/Ile or Ile/Val genotypes who had an adjusted geometric mean of 23.75 µg/L. However; this difference was not statistically significant among the ASD cases (p = 0.76). Our findings indicate that ASD status may be a potential effect modifier when assessing the association between GSTP1 rs1695 and blood aluminum concentrations among Jamaican children. These findings require replication in other populations.

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“…Saliva has also been used as a DNA source for epigenetic analysis in bipolar disorder and schizophrenia [51], for genetic analysis and investigation of the impact of nutritional intake and environmental toxins in ASD [52]. Most recently, Frank Middleton and colleagues used the salivary microRNA profiles for identification of children with ASD [53].…”

Section: Discussionmentioning

confidence: 99%

Selection of Suitable Reference Genes for Analysis of Salivary Transcriptome in Non-Syndromic Autistic Male Children

Panahi

Moghaddam

Ghasemi

et al. 2016

IJMS

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Childhood autism is a severe form of complex genetically heterogeneous and behaviorally defined set of neurodevelopmental diseases, collectively termed as autism spectrum disorders (ASD). Reverse transcriptase quantitative real-time PCR (RT-qPCR) is a highly sensitive technique for transcriptome analysis, and it has been frequently used in ASD gene expression studies. However, normalization to stably expressed reference gene(s) is necessary to validate any alteration reported at the mRNA level for target genes. The main goal of the present study was to find the most stable reference genes in the salivary transcriptome for RT-qPCR analysis in non-syndromic male childhood autism. Saliva samples were obtained from nine drug naïve non-syndromic male children with autism and also sex-, age-, and location-matched healthy controls using the RNA-stabilizer kit from DNA Genotek. A systematic two-phased measurement of whole saliva mRNA levels for eight common housekeeping genes (HKGs) was carried out by RT-qPCR, and the stability of expression for each candidate gene was analyzed using two specialized algorithms, geNorm and NormFinder, in parallel. Our analysis shows that while the frequently used HKG ACTB is not a suitable reference gene, the combination of GAPDH and YWHAZ could be recommended for normalization of RT-qPCR analysis of salivary transcriptome in non-syndromic autistic male children.

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Synergic effect of GSTP1 and blood manganese concentrations in Autism Spectrum Disorder

Cited by 31 publications

References 62 publications

A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

Role of Metabolic Genes in Blood Aluminum Concentrations of Jamaican Children with and without Autism Spectrum Disorder

Selection of Suitable Reference Genes for Analysis of Salivary Transcriptome in Non-Syndromic Autistic Male Children

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