Gene-environment interactions in human health.

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

Epigenetic changes in sperm are associated with paternal and child quantitative autistic traits in an autism-enriched cohort.

There is a need to consider paternal contributions to autism spectrum disorder (ASD) more strongly. Autism etiology is complex, and heritability is not explained by genetics alone. Understanding paternal gametic epigenetic contributions to autism could help fill this knowledge gap. In the present study, we explored whether paternal autistic traits, and the sperm epigenome, were associated with autistic traits in children at 36 months enrolled in the Early Autism Risk Longitudinal Investigation (EARLI) cohort. EARLI is a pregnancy cohort that recruited and enrolled pregnant women in the first half of pregnancy who already had a child with ASD. After maternal enrollment, EARLI fathers were approached and asked to provide a semen specimen. Participants were included in the present study if they had genotyping, sperm methylation data, and Social Responsiveness Scale (SRS) score data available. Using the CHARM array, we performed genome-scale methylation analyses on DNA from semen samples contributed by EARLI fathers. The SRS-a 65-item questionnaire measuring social communication deficits on a quantitative scale-was used to evaluate autistic traits in EARLI fathers (n = 45) and children (n = 31). We identified 94 significant child SRS-associated differentially methylated regions (DMRs), and 14 significant paternal SRS-associated DMRs (fwer p < 0.05). Many child SRS-associated DMRs were annotated to genes implicated in ASD and neurodevelopment. Six DMRs overlapped across the two outcomes (fwer p < 0.1), and, 16 DMRs overlapped with previous child autistic trait findings at 12 months of age (fwer p < 0.05). Child SRS-associated DMRs contained CpG sites independently found to be differentially methylated in postmortem brains of individuals with and without autism. These findings suggest paternal germline methylation is associated with autistic traits in 3-year-old offspring. These prospective results for autism-associated traits, in a cohort with a family history of ASD, highlight the potential importance of sperm epigenetic mechanisms in autism.

Associations between cord blood acetaminophen biomarkers and childhood asthma with and without allergic comorbidities.

BACKGROUND: Previous studies have linked prenatal acetaminophen use to increased asthma risk in children. However, none have explored this association while differentiating between asthma cases with and without other allergic conditions or by employing objective biomarkers to assess acetaminophen exposure. OBJECTIVE: To evaluate whether the detection of acetaminophen biomarkers in cord blood is associated with the subgroups of asthma both with and without allergic comorbidities in children. METHODS: Acetaminophen biomarkers, including unchanged acetaminophen and acetaminophen glucuronide, were measured in neonatal cord blood samples from the Boston Birth Cohort. Asthma subgroups were defined on the basis of physician diagnoses of asthma and other allergic conditions (atopic dermatitis and allergic rhinitis). Multinomial regressions were used to evaluate the associations between acetaminophen biomarkers and asthma subgroups, adjusting for multiple confounders, including potential indications for maternal acetaminophen use such as maternal fever. RESULTS: The study included 142 children with asthma and at least 1 other allergic condition, 55 children with asthma but no other allergic condition, and 613 children free of asthma. Detection of acetaminophen in cord blood, reflecting maternal exposure to acetaminophen shortly before delivery, was associated with 3.73 times the odds of developing asthma without allergic comorbidities (95% CI: 1.79-7.80, P = .0004). In contrast, the detection of acetaminophen in cord blood was not associated with an elevated risk of asthma with allergic comorbidities. Analysis of acetaminophen glucuronide yielded consistent results. CONCLUSION: In a prospective birth cohort, cord blood acetaminophen biomarkers were associated with an increased risk of childhood asthma without allergic comorbidities, but were not associated with childhood asthma with allergic comorbidities.

Genetic liability for gastrointestinal inflammation disorders and association with gastrointestinal symptoms in children with and without autism.

Children with autism spectrum disorder (ASD) have a greater prevalence of gastrointestinal (GI) symptoms than children without ASD. We tested whether polygenic scores for each of three GI disorders (ulcerative colitis, inflammatory bowel disease, and Crohn's disease) were related to GI symptoms in children with and without ASD. Using genotyping data (564 ASD cases and 715 controls) and external genome-wide association study summary statistics, we computed GI polygenic scores for ulcerative colitis (UC-PGS), inflammatory bowel disease (IDB-PGS), and Crohn's disease (CD-PGS). Multivariable logistic regression models, adjusted for genetic ancestry, were used to estimate associations between each GI-PGS and (1) ASD case-control status, and (2) specific GI symptoms in neurotypical children and separately in ASD children. In children without ASD, polygenic scores for ulcerative colitis were significantly associated with experiencing any GI symptom (adjusted odds ratio (aOR) = 1.36, 95% confidence interval (CI) = 1.03-1.81, p = 0.03) and diarrhea specifically (aOR = 5.35, 95% CI = 1.77-26.20, p = 0.01). Among children without ASD, IBD-PGS, and Crohn's PGS were significantly associated with diarrhea (aOR = 3.55, 95% CI = 1.25-12.34, p = 0.02) and loose stools alternating with constipation (aOR = 2.57, 95% CI = 1.13-6.55, p = 0.03), respectively. However, the three PGS were not associated with GI symptoms in the ASD case group. Furthermore, polygenic scores for ulcerative colitis significantly interacted with ASD status on presentation of any GI symptom within a European ancestry subset (aOR = 0.42, 95% CI = 0.19-0.88, p = 0.02). Genetic risk factors for some GI symptoms differ between children with and without ASD. Furthermore, our finding that increased genetic risks for GI inflammatory disorders are associated with GI symptoms in children without ASD informs future work on the early detection of GI disorders.

Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies.

Genetic association studies of child health outcomes often employ family-based study designs. One of the most popular family-based designs is the case-parent trio design that considers the smallest possible nuclear family consisting of two parents and their affected child. This trio design is particularly advantageous for studying relatively rare disorders because it is less prone to type 1 error inflation due to population stratification compared to population-based study designs (e.g., case-control studies). However, obtaining genetic data from both parents is difficult, from a practical perspective, and many large studies predominantly measure genetic variants in mother-child dyads. While some statistical methods for analyzing parent-child dyad data (most commonly involving mother-child pairs) exist, it is not clear if they provide the same advantage as trio methods in protecting against population stratification, or if a specific dyad design (e.g., case-mother dyads vs. case-mother/control-mother dyads) is more advantageous. In this article, we review existing statistical methods for analyzing genome-wide marker data on dyads and perform extensive simulation experiments to benchmark their type I errors and statistical power under different scenarios. We extend our evaluation to existing methods for analyzing a combination of case-parent trios and dyads together. We apply these methods on genotyped and imputed data from multiethnic mother-child pairs only, case-parent trios only or combinations of both dyads and trios from the Gene, Environment Association Studies consortium (GENEVA), where each family was ascertained through a child affected by nonsyndromic cleft lip with or without cleft palate. Results from the GENEVA study corroborate the findings from our simulation experiments. Finally, we provide recommendations for using statistical genetic association methods for dyads.

The intersection of genome, epigenome and social experience in autism spectrum disorder: Exploring modifiable pathways for intervention.

The number of children diagnosed with autism spectrum disorder (ASD) has increased substantially over the past two decades. Current research suggests that both genetic and environmental risk factors are involved in the etiology of ASD. The goal of this paper is to examine how one specific environmental factor, early social experience, may be correlated with DNA methylation (DNAm) changes in genes associated with ASD. We present an innovative model which proposes that polygenic risk and changes in DNAm due to social experience may both contribute to the symptoms of ASD. Previous research on genetic and environmental factors implicated in the etiology of ASD will be reviewed, with an emphasis on the oxytocin receptor gene, which may be epigenetically altered by early social experience, and which plays a crucial role in social and cognitive development. Identifying an environmental risk factor for ASD (e.g., social experience) that could be modified via early intervention and which results in epigenetic (DNAm) changes, could transform our understanding of this condition, facilitate earlier identification of ASD, and guide early intervention efforts.

Contrasting Association of Maternal Plasma Biomarkers of Smoking and 1-Carbon Micronutrients with Offspring DNA Methylation: Evidence of Aryl Hydrocarbon Receptor Repressor Gene-Smoking-Folate Interaction.

BACKGROUND: Maternal prenatal smoking is known to alter offspring DNA methylation (DNAm). However, there are no effective interventions to mitigate smoking-induced DNAm alteration. OBJECTIVES: This study investigated whether 1-carbon nutrients (folate, vitamins B6, and B12) can protect against prenatal smoking-induced offspring DNAm alterations in the aryl hydrocarbon receptor repressor (AHRR) (cg05575921), GFI1 (cg09935388), and CYP1A1 (cg05549655) genes. METHODS: This study included mother-newborn dyads from a racially diverse US birth cohort. The cord blood DNAm at the above 3 sites were derived from a previous study using the Illumina Infinium MethylationEPIC BeadChip. Maternal smoking was assessed by self-report and plasma biomarkers (hydroxycotinine and cotinine). Maternal plasma folate, and vitamins B6 and B12 concentrations were obtained shortly after delivery. Linear regressions, Bayesian kernel machine regression, and quantile g-computation were applied to test the study hypothesis by adjusting for covariables and multiple testing. RESULTS: The study included 834 mother-newborn dyads (16.7% of newborns exposed to maternal smoking). DNAm at cg05575921 (AHRR) and at cg09935388 (GFI1) was inversely associated with maternal smoking biomarkers in a dose-response fashion (all P < 7.01 × 10-13). In contrast, cg05549655 (CYP1A1) was positively associated with maternal smoking biomarkers (P < 2.4 × 10-6). Folate concentrations only affected DNAm levels at cg05575921 (AHRR, P = 0.014). Regression analyses showed that compared with offspring with low hydroxycotinine exposure (<0.494) and adequate maternal folate concentrations (quartiles 2-4), an offspring with high hydroxycotinine exposure (≥0.494) and low folate concentrations (quartile 1) had a significant reduction in DNAm at cg05575921 (M-value, ß ± SE = -0.801 ± 0.117, P = 1.44 × 10-11), whereas adequate folate concentrations could cut smoking-induced hypomethylation by almost half. Exposure mixture models further supported the protective role of adequate folate concentrations against smoking-induced aryl hydrocarbon receptor repressor (AHRR) hypomethylation. CONCLUSIONS: This study found that adequate maternal folate can attenuate maternal smoking-induced offspring AHRR cg05575921 hypomethylation, which has been previously linked to a range of pediatric and adult diseases.

Using Epigenetic Clocks to Characterize Biological Aging in Studies of Children and Childhood Exposures: a Systematic Review.

Biological age, measured via epigenetic clocks, offers a unique and useful tool for prevention scientists to explore the short- and long-term implications of age deviations for health, development, and behavior. The use of epigenetic clocks in pediatric research is rapidly increasing, and there is a need to review the landscape of this work to understand the utility of these clocks for prevention scientists. We summarize the current state of the literature on the use of specific epigenetic clocks in childhood. Using systematic review methods, we identified studies published through February 2023 that used one of three epigenetic clocks as a measure of biological aging. These epigenetic clocks could either be used as a predictor of health outcomes or as a health outcome of interest. The database search identified 982 records, 908 of which were included in a title and abstract review. After full-text screening, 68 studies were eligible for inclusion. While findings were somewhat mixed, a majority of included studies found significant associations between the epigenetic clock used and the health outcome of interest or between an exposure and the epigenetic clock used. From these results, we propose the use of epigenetic clocks as a tool to understand how exposures impact biologic aging pathways and development in early life, as well as to monitor the effectiveness of preventive interventions that aim to reduce exposure and associated adverse health outcomes.

Cardiometabolic Pregnancy Complications in Association With Autism-Related Traits as Measured by the Social Responsiveness Scale in ECHO.

Prior work has examined associations between cardiometabolic pregnancy complications and autism spectrum disorder (ASD) but not how these complications may relate to social communication traits more broadly. We addressed this question within the Environmental Influences on Child Health Outcomes program, with 6,778 participants from 40 cohorts conducted from 1998-2021 with information on ASD-related traits via the Social Responsiveness Scale. Four metabolic pregnancy complications were examined individually, and combined, in association with Social Responsiveness Scale scores, using crude and adjusted linear regression as well as quantile regression analyses. We also examined associations stratified by ASD diagnosis, and potential mediation by preterm birth and low birth weight, and modification by child sex and enriched risk of ASD. Increases in ASD-related traits were associated with obesity (ß = 4.64, 95% confidence interval: 3.27, 6.01) and gestational diabetes (ß = 5.21, 95% confidence interval: 2.41, 8.02), specifically, but not with hypertension or preeclampsia. Results among children without ASD were similar to main analyses, but weaker among ASD cases. There was not strong evidence for mediation or modification. Results suggest that common cardiometabolic pregnancy complications may influence child ASD-related traits, not only above a diagnostic threshold relevant to ASD but also across the population.

Early Life Exposure to Air Pollution and Autism Spectrum Disorder: Findings from a Multisite Case-Control Study.

BACKGROUND: Epidemiologic studies have reported associations between prenatal and early postnatal air pollution exposure and autism spectrum disorder (ASD); however, findings differ by pollutant and developmental window. OBJECTIVES: We examined associations between early life exposure to particulate matter ≤2.5 µm in diameter (PM2.5) and ozone in association with ASD across multiple US regions. METHODS: Our study participants included 674 children with confirmed ASD and 855 population controls from the Study to Explore Early Development, a multi-site case-control study of children born from 2003 to 2006 in the United States. We used a satellite-based model to assign air pollutant exposure averages during several critical periods of neurodevelopment: 3 months before pregnancy; each trimester of pregnancy; the entire pregnancy; and the first year of life. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for study site, maternal age, maternal education, maternal race/ethnicity, maternal smoking, and month and year of birth. RESULTS: The air pollution-ASD associations appeared to vary by exposure time period. Ozone exposure during the third trimester was associated with ASD, with an OR of 1.2 (95% CI: 1.1, 1.4) per 6.6 ppb increase in ozone. We additionally observed a positive association with PM2.5 exposure during the first year of life (OR = 1.3 [95% CI: 1.0, 1.6] per 1.6 µg/m increase in PM2.5). CONCLUSIONS: Our study corroborates previous findings of a positive association between early life air pollution exposure and ASD, and identifies a potential critical window of exposure during the late prenatal and early postnatal periods.

Correction: Pleiotropic Mechanisms Indicated for Sex Differences in Autism.

[This corrects the article DOI: 10.1371/journal.pgen.1006425.].

Invited Commentary: Is DNA Methylation an Actionable Mediator of Prenatal Exposure Effects on Child Health?

A substantial body of literature has shown robust associations between prenatal smoking exposure and DNA methylation levels. The pattern of DNA methylation can be used as a molecular signature of past prenatal smoking exposure and might also provide mechanistic insights into associations between prenatal smoking exposure and adverse health outcomes. In this issue of the Journal, Cardenas et al. (Am J Epidemiol. 2019;188(11):1878-1886) evaluated whether DNA methylation mediates the association between prenatal smoking and low birth weight in a tissue that is mechanistically relevant to birth weight-the placenta-using formal mediation analyses. They found that methylation levels, at 5 loci, mediated smoking exposure effects on birth weight but only among children whose mothers smoked during pregnancy. Given the use of formal mediation methods and measurement in a mechanistically relevant tissue, this work has the potential to inform novel directions for intervention. Replication of these findings in larger and more racially and ethnically diverse samples, repeated measures to better tease apart the timing of DNA methylation changes with respect to exposure and birth weight, and continued use of intervention-focused mediation methods are needed before the impact of these findings will be fully realized.

The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing.

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other "-omics" (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.

Pleiotropic Mechanisms Indicated for Sex Differences in Autism.

Sexual dimorphism in common disease is pervasive, including a dramatic male preponderance in autism spectrum disorders (ASDs). Potential genetic explanations include a liability threshold model requiring increased polymorphism risk in females, sex-limited X-chromosome contribution, gene-environment interaction driven by differences in hormonal milieu, risk influenced by genes sex-differentially expressed in early brain development, or contribution from general mechanisms of sexual dimorphism shared with secondary sex characteristics. Utilizing a large single nucleotide polymorphism (SNP) dataset, we identify distinct sex-specific genome-wide significant loci. We investigate genetic hypotheses and find no evidence for increased genetic risk load in females, but evidence for sex heterogeneity on the X chromosome, and contribution of sex-heterogeneous SNPs for anthropometric traits to ASD risk. Thus, our results support pleiotropy between secondary sex characteristic determination and ASDs, providing a biological basis for sex differences in ASDs and implicating non brain-limited mechanisms.

The Changing Epidemiology of Autism Spectrum Disorders.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts. Genetic and environmental factors contribute to ASD etiology, which remains incompletely understood. Research on ASD epidemiology has made significant advances in the past decade. Current prevalence is estimated to be at least 1.5% in developed countries, with recent increases primarily among those without comorbid intellectual disability. Genetic studies have identified a number of rare de novo mutations and gained footing in the areas of polygenic risk, epigenetics, and gene-by-environment interaction. Epidemiologic investigations focused on nongenetic factors have established advanced parental age and preterm birth as ASD risk factors, indicated that prenatal exposure to air pollution and short interpregnancy interval are potential risk factors, and suggested the need for further exploration of certain prenatal nutrients, metabolic conditions, and exposure to endocrine-disrupting chemicals. We discuss future challenges and goals for ASD epidemiology as well as public health implications.

Presence of an epigenetic signature of prenatal cigarette smoke exposure in childhood.

Prenatal exposure to tobacco smoke has lifelong health consequences. Epigenetic signatures such as differences in DNA methylation (DNAm) may be a biomarker of exposure and, further, might have functional significance for how in utero tobacco exposure may influence disease risk. Differences in infant DNAm associated with maternal smoking during pregnancy have been identified. Here we assessed whether these infant DNAm patterns are detectible in early childhood, whether they are specific to smoking, and whether childhood DNAm can classify prenatal smoke exposure status. Using the Infinium 450K array, we measured methylation at 26 CpG loci that were previously associated with prenatal smoking in infant cord blood from 572 children, aged 3-5, with differing prenatal exposure to cigarette smoke in the Study to Explore Early Development (SEED). Striking concordance was found between the pattern of prenatal smoking associated DNAm among preschool aged children in SEED and those observed at birth in other studies. These DNAm changes appear to be tobacco-specific. Support vector machine classification models and 10-fold cross-validation were applied to show classification accuracy for childhood DNAm at these 26 sites as a biomarker of prenatal smoking exposure. Classification models showed prenatal exposure to smoking can be assigned with 81% accuracy using childhood DNAm patterns at these 26 loci. These findings support the potential for blood-derived DNAm measurements to serve as biomarkers for prenatal exposure.

Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.

MOTIVATION: The recently released Infinium HumanMethylation450 array (the '450k' array) provides a high-throughput assay to quantify DNA methylation (DNAm) at ∼450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years. RESULTS: Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/release/bioc/html/minfi.html. CONTACT: khansen@jhsph.edu; rafa@jimmy.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis.

Using advanced gene targeting methods, generating mouse models of cancer that accurately reproduce the genetic alterations present in human tumors is now relatively straightforward. The challenge is to determine to what extent such models faithfully mimic human disease with respect to the underlying molecular mechanisms that accompany tumor progression. Here we describe a method for comparing mouse models of cancer with human tumors using gene-expression profiling. We applied this method to the analysis of a model of Kras2-mediated lung cancer and found a good relationship to human lung adenocarcinoma, thereby validating the model. Furthermore, we found that whereas a gene-expression signature of KRAS2 activation was not identifiable when analyzing human tumors with known KRAS2 mutation status alone, integrating mouse and human data uncovered a gene-expression signature of KRAS2 mutation in human lung cancer. We confirmed the importance of this signature by gene-expression analysis of short hairpin RNA-mediated inhibition of oncogenic Kras2. These experiments identified both a pattern of gene expression indicative of KRAS2 mutation and potential effectors of oncogenic KRAS2 activity in human cancer. This approach provides a strategy for using genomic analysis of animal models to probe human disease.

Exposure to air pollution is associated with DNA methylation changes in sperm.

Exposure to air pollutants has been associated with adverse health outcomes in adults and children who were prenatally exposed. In addition to reducing exposure to air pollutants, it is important to identify their biologic targets in order to mitigate the health consequences of exposure. One molecular change associated with prenatal exposure to air pollutants is DNA methylation (DNAm), which has been associated with changes in placenta and cord blood tissues at birth. However, little is known about how air pollution exposure impacts the sperm epigenome, which could provide important insights into the mechanism of transmission to offspring. In the present study, we explored whether exposure to particulate matter less than 2.5 microns in diameter, particulate matter less than 10 microns in diameter, nitrogen dioxide (NO2), or ozone (O3) was associated with DNAm in sperm contributed by participants in the Early Autism Risk Longitudinal Investigation prospective pregnancy cohort. Air pollution exposure measurements were calculated as the average exposure for each pollutant measured within 4 weeks prior to the date of sample collection. Using array-based genome-scale methylation analyses, we identified 80, 96, 35, and 67 differentially methylated regions (DMRs) significantly associated with particulate matter less than 2.5 microns in diameter, particulate matter less than 10 microns in diameter, NO2, and O3, respectively. While no DMRs were associated with exposure to all four pollutants, we found that genes overlapping exposure-related DMRs had a shared enrichment for gene ontology biological processes related to neurodevelopment. Together, these data provide compelling support for the hypothesis that paternal exposure to air pollution impacts DNAm in sperm, particularly in regions implicated in neurodevelopment.

Prenatal Metal Exposures and Child Social Responsiveness Scale Scores in 2 Prospective Studies.

Background: Prenatal exposure to metals is hypothesized to be associated with child autism. We aim to investigate the joint and individual effects of prenatal exposure to urine metals including lead (Pb), mercury (Hg), manganese (Mn), and selenium (Se) on child Social Responsiveness Scale (SRS) scores. Methods: We used data from 2 cohorts enriched for likelihood of autism spectrum disorder (ASD): Early Autism Risk Longitudinal Investigation (EARLI) and the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) studies. Metal concentrations were measured in urine collected during pregnancy. We used Bayesian Kernel Machine Regression and linear regression models to investigate both joint and independent associations of metals with SRS Z-scores in each cohort. We adjusted for maternal age at delivery, interpregnancy interval, maternal education, child race/ethnicity, child sex, and/or study site. Results: The final analytic sample consisted of 251 mother-child pairs. When Pb, Hg, Se, and Mn were at their 75th percentiles, there was a 0.03 increase (95% credible interval [CI]: -0.11, 0.17) in EARLI and 0.07 decrease (95% CI: -0.29, 0.15) in MARBLES in childhood SRS Z-scores, compared to when all 4 metals were at their 50th percentiles. In both cohorts, increasing concentrations of Pb were associated with increasing values of SRS Z-scores, fixing the other metals to their 50th percentiles. However, all the 95% credible intervals contained the null. Conclusions: There were no clear monotonic associations between the overall prenatal metal mixture in pregnancy and childhood SRS Z-scores at 36 months. There were also no clear associations between individual metals within this mixture and childhood SRS Z-scores at 36 months. The overall effects of the metal mixture and the individual effects of each metal within this mixture on offspring SRS Z-scores might be heterogeneous across child sex and cohort. Further studies with larger sample sizes are warranted.