McArdle HJ, Ashworth CJ. Micronutrients in fetal growth and development. Br Med Bull Engl. 1999;55:499–510.
CAS
Google Scholar
Gambling L, Kennedy C, McArdle HJ. Iron and copper in fetal development. Semin Cell Dev Biol Engl. 2011;22:637–44.
CAS
Google Scholar
Fanni D, Gerosa C, Nurchi VM, Manchia M, Saba L, Coghe F, et al. The role of magnesium in pregnancy and in fetal programming of adult diseases. Biol Trace Element Res. 2020. https://doi.org/10.1007/s12011-020-02513-0.
Article
Google Scholar
Wood RJ. Manganese and birth outcome. Nutr Rev. 2009;67:416–20.
PubMed
Google Scholar
Rayman MP. The importance of selenium to human health. Lancet England. 2000;356:233–41.
CAS
Google Scholar
Saper RB, Rash R. Zinc: an essential micronutrient. Am Fam Physician. 2009;79:768–72.
PubMed
PubMed Central
Google Scholar
Li Z, Liang C, Huang K, Yan S, Tao R, Sheng J, et al. Umbilical serum copper status and neonatal birth outcomes: a prospective cohort study. Biol Trace Elem Res US. 2018;183:200–8.
CAS
Google Scholar
Chung SE, Cheong H-K, Ha E-H, Kim B-N, Ha M, Kim Y, et al. Maternal blood manganese and early neurodevelopment: the Mothers and Children’s Environmental Health (MOCEH) Study. Environ Health Perspect. 2015;123:717–22.
CAS
PubMed
PubMed Central
Google Scholar
Skröder HM, Hamadani JD, Tofail F, Persson LÅ, Vahter ME, Kippler MJ. Selenium status in pregnancy influences children’s cognitive function at 1.5 years of age. Clin Nutr Eng. 2015;34:923–30.
Google Scholar
Varsi K, Bolann B, Torsvik I, Rosvold Eik TC, Høl PJ, Bjørke-Monsen A-L. Impact of maternal selenium status on infant outcome during the first 6 months of life. Nutrients. 2017;9:486.
PubMed Central
Google Scholar
Jaishankar M, Tseten T, Anbalagan N, Mathew BB, Beeregowda KN. Toxicity, mechanism and health effects of some heavy metals. Interdiscip Toxicol. 2014;7:60–72.
PubMed
PubMed Central
Google Scholar
Shen S, Li X-F, Cullen WR, Weinfeld M, Le XC. Arsenic binding to proteins. Chem Rev. 2013;113:7769–92.
CAS
PubMed
PubMed Central
Google Scholar
Milton AH, Hussain S, Akter S, Rahman M, Mouly TA, Mitchell K. A review of the effects of chronic arsenic exposure on adverse pregnancy outcomes. Int J Environ Res Public Health. 2017;14:556.
PubMed Central
Google Scholar
Shirai S, Suzuki Y, Yoshinaga J, Mizumoto Y. Maternal exposure to low-level heavy metals during pregnancy and birth size. J Environ Sci Health A Tox Hazard Subst Environ Eng Engl. 2010;45:1468–74.
CAS
Google Scholar
Zhu M, Fitzgerald EF, Gelberg KH, Lin S, Druschel CM. Maternal low-level lead exposure and fetal growth. Environ Health Perspect. 2010;118:1471–5.
CAS
PubMed
PubMed Central
Google Scholar
Bellinger DC. Very low lead exposures and children’s neurodevelopment. Curr Opin Pediatr United States. 2008;20:172–7.
Google Scholar
Bose-O’Reilly S, McCarty KM, Steckling N, Lettmeier B. Mercury exposure and children’s health. Curr Probl Pediatr Adolesc Health Care. 2010;40:186–215.
PubMed
PubMed Central
Google Scholar
Tolins M, Ruchirawat M, Landrigan P. The developmental neurotoxicity of arsenic: cognitive and behavioral consequences of early life exposure. Ann Glob Health. 2014;80:303–14.
PubMed
Google Scholar
Wang Y, Chen L, Gao Y, Zhang Y, Wang C, Zhou Y, et al. Effects of prenatal exposure to cadmium on neurodevelopment of infants in Shandong. China Environ Pollut England. 2016;211:67–73.
CAS
Google Scholar
Signes-Pastor AJ, Doherty BT, Romano ME, Gleason KM, Gui J, Baker E, et al. Prenatal exposure to metal mixture and sex-specific birth outcomes in the New Hampshire Birth Cohort Study. Environ Epidemiol. 2019;3:e068.
PubMed
PubMed Central
Google Scholar
Vahter M, Akesson A, Lidén C, Ceccatelli S, Berglund M. Gender differences in the disposition and toxicity of metals. Environ Res Netherlands. 2007;104:85–95.
CAS
Google Scholar
Rahman ML, Oken E, Hivert M-F, Rifas-Shiman S, Lin P-ID, Colicino E, et al. Early pregnancy exposure to metal mixture and birth outcomes—a prospective study in Project Viva. Environ Int. Netherlands; 2021;156:106714.
Heindel JJ, Vandenberg LN. Developmental origins of health and disease: a paradigm for understanding disease cause and prevention. Curr Opin Pediatr NIH Public Access. 2015;27:248–53.
CAS
Google Scholar
Wadhwa PD, Buss C, Entringer S, Swanson JM. Developmental origins of health and disease: brief history of the approach and current focus on epigenetic mechanisms. Semin Reprod Med. 2009;27:358–68.
CAS
PubMed
PubMed Central
Google Scholar
Martin EM, Fry RC. Environmental influences on the epigenome: exposure-associated DNA methylation in human populations. Annu Rev Public Health. 2018;39:309–33.
PubMed
Google Scholar
Green BB, Karagas MR, Punshon T, Jackson BP, Robbins DJ, Houseman EA, et al. Epigenome-wide assessment of DNA methylation in the placenta and arsenic exposure in the New Hampshire Birth Cohort Study. Environ Health Perspect. 2016;124:1253–60.
CAS
PubMed
PubMed Central
Google Scholar
Koestler DC, Avissar-Whiting M, Houseman EA, Karagas MR, Marsit CJ. Differential DNA methylation in umbilical cord blood of infants exposed to low levels of arsenic in utero. Environ Health Perspect. 2013;121:971–7.
PubMed
PubMed Central
Google Scholar
Broberg K, Ahmed S, Engström K, Hossain MB, Jurkovic Mlakar S, Bottai M, et al. Arsenic exposure in early pregnancy alters genome-wide DNA methylation in cord blood, particularly in boys. J Dev Orig Health Dis. 2014;5:288–98.
CAS
PubMed
PubMed Central
Google Scholar
Cardenas A, Houseman EA, Baccarelli AA, Quamruzzaman Q, Rahman M, Mostofa G, et al. In utero arsenic exposure and epigenome-wide associations in placenta, umbilical artery, and human umbilical vein endothelial cells. Epigenetics. 2015;10:1054–63.
PubMed
PubMed Central
Google Scholar
Rojas D, Rager JE, Smeester L, Bailey KA, Drobná Z, Rubio-Andrade M, et al. Prenatal arsenic exposure and the epigenome: identifying sites of 5-methylcytosine alterations that predict functional changes in gene expression in newborn cord blood and subsequent birth outcomes. Toxicol Sci. 2015;143:97–106.
CAS
PubMed
Google Scholar
Heiss JA, Téllez-Rojo MM, Estrada-Gutiérrez G, Schnaas L, Amarasiriwardena C, Baccarelli AA, et al. Prenatal lead exposure and cord blood DNA methylation in PROGRESS: an epigenome-wide association study. Environ Epigenet. 2020;6:dvaa014.
PubMed
PubMed Central
Google Scholar
Wu S, Hivert M-F, Cardenas A, Zhong J, Rifas-Shiman SL, Agha G, et al. Exposure to low levels of lead in utero and umbilical cord Blood DNA methylation in Project Viva: an epigenome-wide association study. Environ Health Perspect. 2017;125:087019.
PubMed
PubMed Central
Google Scholar
Cardenas A, Rifas-Shiman SL, Agha G, Hivert MF, Litonjua AA, DeMeo DL, et al. Persistent DNA methylation changes associated with prenatal mercury exposure and cognitive performance during childhood. Sci Rep. Nature Publishing Group. 2017;7:288.
Google Scholar
Bozack AK, Cardenas A, Quamruzzaman Q, Rahman M, Mostofa G, Christiani DC, et al. DNA methylation in cord blood as mediator of the association between prenatal arsenic exposure and gestational age. Epigenetics. 2018;13:923–40.
PubMed
PubMed Central
Google Scholar
Kaushal A, Zhang H, Karmaus WJJ, Everson TM, Marsit CJ, Karagas MR, et al. Genome-wide DNA methylation at birth in relation to in utero arsenic exposure and the associated health in later life. Environ Health. 2017;16:50.
PubMed
PubMed Central
Google Scholar
Kile ML, Houseman EA, Baccarelli AA, Quamruzzaman Q, Rahman M, Mostofa G, et al. Effect of prenatal arsenic exposure on DNA methylation and leukocyte subpopulations in cord blood. Epigenetics. 2014;9:774–82.
CAS
PubMed
PubMed Central
Google Scholar
Button M, Jenkin GRT, Harrington CF, Watts MJ. Human toenails as a biomarker of exposure to elevated environmental arsenic. J Environ Monit England. 2009;11:610–7.
CAS
Google Scholar
Kippler M, Engström K, Mlakar SJ, Bottai M, Ahmed S, Hossain MB, et al. Sex-specific effects of early life cadmium exposure on DNA methylation and implications for birth weight. Epigenetics. 2013;8:494–503.
CAS
PubMed
PubMed Central
Google Scholar
Aschner JL, Aschner M. Nutritional aspects of manganese homeostasis. Mol Aspects Med. 2005;26:353–62.
CAS
PubMed
PubMed Central
Google Scholar
Balachandran RC, Mukhopadhyay S, McBride D, Veevers J, Harrison FE, Aschner M, et al. Brain manganese and the balance between essential roles and neurotoxicity. J Biol Chem. 2020;295:6312–29.
CAS
PubMed
PubMed Central
Google Scholar
Lin C-C, Chen Y-C, Su F-C, Lin C-M, Liao H-F, Hwang Y-H, et al. In utero exposure to environmental lead and manganese and neurodevelopment at 2 years of age. Environ Res Netherl. 2013;123:52–7.
CAS
Google Scholar
Chiu Y-HM, Henn BC, Hsu H-HL, Pendo MP, Coull BA, Austin C, et al. Sex differences in sensitivity to prenatal and early childhood manganese exposure on neuromotor function in adolescents. Environ Res. 2017;159:458–65.
Bauer JA, Henn BC, Austin C, Zoni S, Fedrighi C, Cagna G, et al. Manganese in teeth and neurobehavior: sex-specific windows of susceptibility. Environ Int. 2017;108:299–308.
CAS
PubMed
PubMed Central
Google Scholar
Maccani JZJ, Koestler DC, Houseman EA, Armstrong DA, Marsit CJ, Kelsey KT. DNA methylation changes in the placenta are associated with fetal manganese exposure. Reprod Toxicol. 2015;57:43–9.
CAS
PubMed
PubMed Central
Google Scholar
Fogel BL, Wexler E, Wahnich A, Friedrich T, Vijayendran C, Gao F, et al. RBFOX1 regulates both splicing and transcriptional networks in human neuronal development. Hum Mol Genet. 2012;21:4171–86.
CAS
PubMed
PubMed Central
Google Scholar
Kuroyanagi H. Fox-1 family of RNA-binding proteins. Cell Mol Life Sci. 2009;66:3895–907.
CAS
PubMed
PubMed Central
Google Scholar
Gehman LT, Stoilov P, Maguire J, Damianov A, Lin C-H, Shiue L, et al. The splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain. Nat Genet. 2011;43:706–11.
CAS
PubMed
PubMed Central
Google Scholar
Kong L-L, Miao D, Tan L, Liu S-L, Li J-Q, Cao X-P, et al. Genome-wide association study identifies RBFOX1 locus influencing brain glucose metabolism. Ann Transl Med. 2018;6:436.
CAS
PubMed
PubMed Central
Google Scholar
Bill BR, Lowe JK, Dybuncio CT, Fogel BL. Orchestration of neurodevelopmental programs by RBFOX1: implications for autism spectrum disorder. Int Rev Neurobiol. 2013;113:251–67.
CAS
PubMed
PubMed Central
Google Scholar
Roseboom PH, Nanda SA, Fox AS, Oler JA, Shackman AJ, Shelton SE, et al. Neuropeptide Y receptor gene expression in the primate amygdala predicts anxious temperament and brain metabolism. Biol Psychiatry. 2014;76:850–7.
CAS
PubMed
Google Scholar
Ramanathan S, Woodroffe A, Flodman PL, Mays LZ, Hanouni M, Modahl CB, et al. A case of autism with an interstitial deletion on 4q leading to hemizygosity for genes encoding for glutamine and glycine neurotransmitter receptor sub-units (AMPA 2, GLRA3, GLRB) and neuropeptide receptors NPY1R, NPY5R. BMC Med Genet. 2004;5:10.
PubMed
PubMed Central
Google Scholar
Su L, Shen T, Huang G, Long J, Fan J, Ling W, et al. Genetic association of GWAS-supported MAD1L1 gene polymorphism rs12666575 with schizophrenia susceptibility in a Chinese population. Neurosci Lett Ireland. 2016;610:98–103.
CAS
Google Scholar
Levey DF, Gelernter J, Polimanti R, Zhou H, Cheng Z, Aslan M, et al. Reproducible genetic risk loci for anxiety: results from ∼200,000 participants in the Million Veteran Program. Am J Psychiatry. 2020;177:223–32.
PubMed
PubMed Central
Google Scholar
Schnaas L, Rothenberg SJ, Flores M-F, Martinez S, Hernandez C, Osorio E, et al. Reduced intellectual development in children with prenatal lead exposure. Environ Health Perspect. 2006;114:791–7.
CAS
PubMed
Google Scholar
Shah-Kulkarni S, Ha M, Kim B-M, Kim E, Hong Y-C, Park H, et al. Neurodevelopment in early childhood affected by prenatal lead exposure and iron intake. Medicine. 2016;95:e2508.
CAS
PubMed
PubMed Central
Google Scholar
Silver MK, Li X, Liu Y, Li M, Mai X, Kaciroti N, et al. Low-level prenatal lead exposure and infant sensory function. Environ Health. 2016;15:65.
PubMed
PubMed Central
Google Scholar
Fruh V, Rifas-Shiman SL, Amarasiriwardena C, Cardenas A, Bellinger DC, Wise LA, et al. Prenatal lead exposure and childhood executive function and behavioral difficulties in Project Viva. Neurotoxicology. 2019;75:105–15.
CAS
PubMed
PubMed Central
Google Scholar
Sanchez OF, Lee J, King Hing NY, Kim S-E, Freeman JL, Yuan C. Lead (Pb) exposure reduces global DNA methylation level by non-competitive inhibition and alteration of dnmt expression. Metallomics Engl. 2017;9:149–60.
CAS
Google Scholar
Schneider JS, Kidd SK, Anderson DW. Influence of developmental lead exposure on expression of DNA methyltransferases and methyl cytosine-binding proteins in hippocampus. Toxicol Lett. 2013;217:75–81.
CAS
PubMed
Google Scholar
Pilsner JR, Hu H, Ettinger A, Sánchez BN, Wright RO, Cantonwine D, et al. Influence of prenatal lead exposure on genomic methylation of cord blood DNA. Environ Health Perspect. 2009;117:1466–71.
CAS
PubMed
PubMed Central
Google Scholar
Galluzzi L, López-Soto A, Kumar S, Kroemer G. Caspases connect cell-death signaling to organismal homeostasis. Immunity US. 2016;44:221–31.
CAS
Google Scholar
Ivins KJ, Thornton PL, Rohn TT, Cotman CW. Neuronal apoptosis induced by beta-amyloid is mediated by caspase-8. Neurobiol Dis United States. 1999;6:440–9.
CAS
Google Scholar
Monnier PP, D’Onofrio PM, Magharious M, Hollander AC, Tassew N, Szydlowska K, et al. Involvement of caspase-6 and caspase-8 in neuronal apoptosis and the regenerative failure of injured retinal ganglion cells. J Neurosci. 2011;31:10494–505.
CAS
PubMed
PubMed Central
Google Scholar
He X, Wu J, Yuan L, Lin F, Yi J, Li J, et al. Lead induces apoptosis in mouse TM3 Leydig cells through the Fas/FasL death receptor pathway. Environ Toxicol Pharmacol Netherl. 2017;56:99–105.
CAS
Google Scholar
Xu L, Huo X, Liu Y, Zhang Y, Qin Q, Xu X. Hearing loss risk and DNA methylation signatures in preschool children following lead and cadmium exposure from an electronic waste recycling area. Chemosphere. 2020;246:125829.
CAS
PubMed
Google Scholar
Shiina T, Hosomichi K, Inoko H, Kulski JK. The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet. 2009;54:15–39.
CAS
PubMed
Google Scholar
Matsuo R, Asada A, Fujitani K, Inokuchi K. LIRF, a gene induced during hippocampal long-term potentiation as an immediate-early gene, encodes a novel RING finger protein. Biochem Biophys Res Commun United States. 2001;289:479–84.
CAS
Google Scholar
Radonjic M, Cappaert NLM, de Vries EFJ, de Esch CEF, Kuper FC, van Waarde A, et al. Delay and impairment in brain development and function in rat offspring after maternal exposure to methylmercury. Toxicol Sci. 2013;133:112–24.
CAS
PubMed
Google Scholar
Carmel M, Michaelovsky E, Weinberger R, Frisch A, Mekori-Domachevsky E, Gothelf D, et al. Differential methylation of imprinting genes and MHC locus in 22q11.2 deletion syndrome-related schizophrenia spectrum disorders. World J Biol Psychiatry. 2020;0:1–12.
Rutten BPF, Vermetten E, Vinkers CH, Ursini G, Daskalakis NP, Pishva E, et al. Longitudinal analyses of the DNA methylome in deployed military servicemen identify susceptibility loci for post-traumatic stress disorder. Mol Psychiatry. 2018;23:1145–56.
CAS
PubMed
Google Scholar
Wiegand A, Kreifelts B, Munk MHJ, Geiselhart N, Ramadori KE, MacIsaac JL, et al. DNA methylation differences associated with social anxiety disorder and early life adversity. Transl Psychiatry. 2021;11:104.
CAS
PubMed
PubMed Central
Google Scholar
Kesselmeier M, Pütter C, Volckmar A-L, Baurecht H, Grallert H, Illig T, et al. High-throughput DNA methylation analysis in anorexia nervosa confirms TNXB hypermethylation. World J Biol Psychiatry Engl. 2018;19:187–99.
Google Scholar
Cuellar Partida G, Laurin C, Ring SM, Gaunt TR, McRae AF, Visscher PM, et al. Genome-wide survey of parent-of-origin effects on DNA methylation identifies candidate imprinted loci in humans. Hum Mol Genet. 2018;27:2927–39.
PubMed
PubMed Central
Google Scholar
Mallik S, Odom GJ, Gao Z, Gomez L, Chen X, Wang L. An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform. 2018;20:2224–35.
PubMed Central
Google Scholar
Lent S, Cardenas A, Rifas-Shiman SL, Perron P, Bouchard L, Liu C-T, et al. Detecting differentially methylated regions with multiple distinct associations. Epigenomics. 2021;13:451–64.
CAS
PubMed
PubMed Central
Google Scholar
Oken E, Baccarelli AA, Gold DR, Kleinman KP, Litonjua AA, De Meo D, et al. Cohort profile: project viva. Int J Epidemiol. 2015;44:37–48.
PubMed
Google Scholar
Rifas-Shiman SL, Rich-Edwards JW, Kleinman KP, Oken E, Gillman MW. Dietary quality during pregnancy varies by maternal characteristics in Project Viva: a US cohort. J Am Diet Assoc. 2009;109:1004–11.
PubMed
PubMed Central
Google Scholar
Aryee M, Jaffe A, Corrada-Bravo H, Ladd-Acosta C, Feinberg A, Hansen K, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA Methylation microarrays. Bioinformatics. 2014;30:1363–9.
CAS
PubMed
PubMed Central
Google Scholar
Chen Y, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the illumina infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–9.
CAS
PubMed
PubMed Central
Google Scholar
Triche TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013;41:e90.
CAS
PubMed
PubMed Central
Google Scholar
Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
PubMed
PubMed Central
Google Scholar
Bakulski KM, Feinberg JI, Andrews SV, Yang J, Brown S, McKenney SL, et al. DNA methylation of cord blood cell types: applications for mixed cell birth studies. Epigenetics. 2016;11:354–62.
PubMed
PubMed Central
Google Scholar
Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlén SE, Greco D, et al. Differential DNA methylation in purified human blood cells: Implications for cell lineage and studies on disease susceptibility. PLoS ONE. 2012;7:e41361.
CAS
PubMed
PubMed Central
Google Scholar
Ritchie M, Phipson B, Wu D, Hu Y, Law C, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
PubMed
PubMed Central
Google Scholar
Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinf BioMed Central; 2010;11:587.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;57:289–300.
Google Scholar
Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated p-values. Bioinformatics. 2012;28:2986–8.
CAS
PubMed
PubMed Central
Google Scholar
Xu Z, Niu L, Li L, Taylor JA. ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res. 2016;44:e20.
PubMed
Google Scholar
Maksimovic J, Oshlack A, Phipson B. Gene set enrichment analysis for genome-wide DNA methylation data. Genome Biol. 2021;22:173.
CAS
PubMed
PubMed Central
Google Scholar
Phipson B, Maksimovic J, Oshlack A. missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics. 2015;32:btv560.
Google Scholar
Geeleher P, Hartnett L, Egan LJ, Golden A, Raja Ali RA, Seoighe C. Gene-set analysis is severely biased when applied to genome-wide methylation data. Bioinformatics. 2013;29:1851–7.
CAS
PubMed
Google Scholar
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011;6:e21800.
CAS
PubMed
PubMed Central
Google Scholar
R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2015. Available from: https://www.r-project.org/