116 results on '"Ladd-Acosta C"'
Search Results
2. Common DNA methylation alterations in multiple brain regions in autism
- Author
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Ladd-Acosta, C, Hansen, K D, Briem, E, Fallin, M D, Kaufmann, W E, and Feinberg, A P
- Published
- 2014
- Full Text
- View/download PDF
3. Gene-by-Smoking Interaction Study on Lung Function Using a Polygenic Risk Score for COPD
- Author
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Moll, M., primary, Kim, W., additional, Hobbs, B.D., additional, Shrine, N., additional, Tobin, M.D., additional, Dudbridge, F., additional, Wain, L.V., additional, Ladd-Acosta, C., additional, Chatterjee, N., additional, Silverman, E.K., additional, Cho, M.H., additional, and Beaty, T.H., additional
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- 2021
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4. Cadmium, Smoking, and Human Blood DNA Methylation Profiles in Adults from the Strong Heart Study
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Domingo-Relloso A, Riffo-Campos A, Haack K, Rentero-Garrido P, Ladd-Acosta C, Fallin D, Tang W, Herreros-Martinez M, Gonzalez J, Bozack A, Cole S, Navas-Acien A, and Tellez-Plaza M
- Abstract
BACKGROUND: The epigenetic effects of individual environmental toxicants in tobacco remain largely unexplored. Cadmium (Cd) has been associated with smoking-related health effects, and its concentration in tobacco smoke is higher in comparison with other metals.; OBJECTIVES: We studied the association of Cd and smoking exposures with human blood DNA methylation (DNAm) profiles. We also evaluated the implication of findings to relevant methylation pathways and the potential contribution of Cd exposure from smoking to explain the association between smoking and site-specific DNAm.; METHODS: We conducted an epigenome-wide association study of urine Cd and self-reported smoking (current and former vs. never, and cumulative smoking dose) with blood DNAm in 790,026 CpGs (methylation sites) measured with the Illumina Infinium Human MethylationEPIC (Illumina Inc.) platform in 2,325 adults 45-74 years of age who participated in the Strong Heart Study in 1989-1991. In a mediation analysis, we estimated the amount of change in DNAm associated with smoking that can be independently attributed to increases in urine Cd concentrations from smoking. We also conducted enrichment analyses and in silico protein-protein interaction networks to explore the biological relevance of the findings.; RESULTS: At a false discovery rate (FDR)-corrected level of 0.05, we found 6 differentially methylated positions (DMPs) for Cd; 288 and 17, respectively, for current and former smoking status; and 77 for cigarette pack-years. Enrichment analyses of these DMPs displayed enrichment of 58 and 6 Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene sets, respectively, including biological pathways for cancer and cardiovascular disease. In in silico protein-to-protein networks, we observed key proteins in DNAm pathways directly and indirectly connected to Cd- and smoking-DMPs. Among DMPs that were significant for both Cd and current smoking (annotated to PRSS23, AHRR, F2RL3, RARA, and 2q37.1), we found statistically significant contributions of Cd to smoking-related DNAm.; CONCLUSIONS: Beyond replicating well-known smoking epigenetic signatures, we found novel DMPs related to smoking. Moreover, increases in smoking-related Cd exposure were associated with differential DNAm. Our integrative analysis supports a biological link for Cd and smoking-associated health effects, including the possibility that Cd is partly responsible for smoking toxicity through epigenetic changes. https://doi.org/10.1289/EHP6345.
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- 2020
5. Prenatal Particulate Air Pollution and DNA Methylation in Newborns: An Epigenome-Wide Meta-Analysis
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Gruzieva, O. (Olena), Xu, C.-J. (Cheng-Jian), Yousefi, P. (Paul), Relton, C.L. (Caroline), Merid, S.K. (Simon Kebede), Breton, C. (Carrie), Gao, L. (Lu), Volk, H.E. (Heather E.), Feinberg, J.I. (Jason I.), Ladd-Acosta, C. (Christine), Bakulski, K. (Kelly), Auffray, C. (C.), Lemonnier, N. (Nathanaël), Plusquin, M. (Michelle), Ghantous, A. (Akram), Herceg, Z. (Zdenko), Nawrot, T.S. (Tim S.), Pizzi, C. (Costanza), Richiardi, L. (Lorenzo), Rusconi, F. (Franca), Vineis, P. (Paolo), Kogevinas, M. (Manolis), Felix, J.F. (Janine), Duijts, L. (Liesbeth), Dekker, H.T. (Herman) den, Jaddoe, V.W.V. (Vincent), Ruiz, J.L. (José L), Bustamante, M. (Mariona), Anto, J.M. (Josep), Sunyer, J. (Jordi), Vrijheid, M. (Martine), Gutzkow, K.B. (Kristine B.), Grazuleviciene, R. (Regina), Hernandez-Ferrer, C. (Carles), Annesi-Maesano, I. (Isabella), Lepeule, J. (Johanna), Bousquet, J. (Jean), Bergström, A. (Anna), Kull, C.A. (Christian), Söderhäll, C. (Cilla), Kere, J. (Juha), Gehring, U. (Ulrike), Brunekreef, B. (Bert), Just, A.C. (Allan C.), Wright, R.J. (Rosalind J.), Peng, C. (Cheng), Gold, D.R. (Diane), Kloog, I. (Itai), Demeo, D.L. (Dawn), Pershagen, G. (Göran), Koppelman, G.H. (Gerard), London, S.J. (Stephanie J.), Baccarelli, A.A. (Andrea), Melén, E. (Erik), Gruzieva, O. (Olena), Xu, C.-J. (Cheng-Jian), Yousefi, P. (Paul), Relton, C.L. (Caroline), Merid, S.K. (Simon Kebede), Breton, C. (Carrie), Gao, L. (Lu), Volk, H.E. (Heather E.), Feinberg, J.I. (Jason I.), Ladd-Acosta, C. (Christine), Bakulski, K. (Kelly), Auffray, C. (C.), Lemonnier, N. (Nathanaël), Plusquin, M. (Michelle), Ghantous, A. (Akram), Herceg, Z. (Zdenko), Nawrot, T.S. (Tim S.), Pizzi, C. (Costanza), Richiardi, L. (Lorenzo), Rusconi, F. (Franca), Vineis, P. (Paolo), Kogevinas, M. (Manolis), Felix, J.F. (Janine), Duijts, L. (Liesbeth), Dekker, H.T. (Herman) den, Jaddoe, V.W.V. (Vincent), Ruiz, J.L. (José L), Bustamante, M. (Mariona), Anto, J.M. (Josep), Sunyer, J. (Jordi), Vrijheid, M. (Martine), Gutzkow, K.B. (Kristine B.), Grazuleviciene, R. (Regina), Hernandez-Ferrer, C. (Carles), Annesi-Maesano, I. (Isabella), Lepeule, J. (Johanna), Bousquet, J. (Jean), Bergström, A. (Anna), Kull, C.A. (Christian), Söderhäll, C. (Cilla), Kere, J. (Juha), Gehring, U. (Ulrike), Brunekreef, B. (Bert), Just, A.C. (Allan C.), Wright, R.J. (Rosalind J.), Peng, C. (Cheng), Gold, D.R. (Diane), Kloog, I. (Itai), Demeo, D.L. (Dawn), Pershagen, G. (Göran), Koppelman, G.H. (Gerard), London, S.J. (Stephanie J.), Baccarelli, A.A. (Andrea), and Melén, E. (Erik)
- Abstract
BACKGROUND: Prenatal exposure to air pollution has been associated with childhood respiratory disease and other adverse outcomes. Epigenetics is a suggested link between exposures and health outcomes. OBJECTIVES: We aimed to investigate associations between prenatal exposure to particulate matter (PM) with diameter [Formula: see text] ([Formula: see text]) or [Formula: see text] ([Formula: see text]) and DNA methylation in newborns and children. METHODS: We meta-analyzed associations between exposure to [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]) at maternal home addresses during pregnancy and newborn DNA methylation assessed by Illumina Infinium HumanMethylation450K BeadChip in nine European and American studies, with replication in 688 independent newborns and look-up analyses in 2,118 older children. We used two approaches, one focusing on single cytosine-phosphate-guanine (CpG) sites and another on differentially methylated regions (DMRs). We also related PM exposures to blood mRNA expression. RESULTS: Six CpGs were significantly associa
- Published
- 2019
- Full Text
- View/download PDF
6. Prenatal particulate air pollution and DNA methylation in newborns: An epigenome-wide meta-analysis
- Author
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Gruzieva, O., Xu, Cheng-Jian, Yousefi, P., Relton, C., Merid, S. K., Breton, C. V., Gao, L., Volk, H. E., Feinberg, J. I., Ladd-Acosta, C., Bakulski, K., Auffray, C., Lemonnier, N., Plusquin, M., Ghantous, A., Herceg, Z., Nawrot, T. S., Pizzi, C., Richiardi, L., Rusconi, Franca, Vineis, P., Kogevinas, M., Felix, Janine F, Duijts, L., Den Dekker, H. T., Jaddoe, V. W. V., Ruiz, José L., Bustamante, M., Antó, Josep M., Sunyer, J., Vrijheid, M., Gutzkow, K.B., Grazuleviciene, R., Hernandez-Ferrer, C., Annesi-Maesano, I., Lepeule, J., Bousquet, J., Bergström, A., Kull, I., Söderhäll, C., Kere, J., Gehring, U., Brunekreef, B., Just, A.C., Wright, R.J., Peng, C., Gold, D.R., Kloog, I., Demeo, D.L., Gruzieva, O., Xu, Cheng-Jian, Yousefi, P., Relton, C., Merid, S. K., Breton, C. V., Gao, L., Volk, H. E., Feinberg, J. I., Ladd-Acosta, C., Bakulski, K., Auffray, C., Lemonnier, N., Plusquin, M., Ghantous, A., Herceg, Z., Nawrot, T. S., Pizzi, C., Richiardi, L., Rusconi, Franca, Vineis, P., Kogevinas, M., Felix, Janine F, Duijts, L., Den Dekker, H. T., Jaddoe, V. W. V., Ruiz, José L., Bustamante, M., Antó, Josep M., Sunyer, J., Vrijheid, M., Gutzkow, K.B., Grazuleviciene, R., Hernandez-Ferrer, C., Annesi-Maesano, I., Lepeule, J., Bousquet, J., Bergström, A., Kull, I., Söderhäll, C., Kere, J., Gehring, U., Brunekreef, B., Just, A.C., Wright, R.J., Peng, C., Gold, D.R., Kloog, I., and Demeo, D.L.
- Abstract
BACKGROUND: Prenatal exposure to air pollution has been associated with childhood respiratory disease and other adverse outcomes. Epigenetics is a suggested link between exposures and health outcomes. OBJECTIVES: We aimed to investigate associations between prenatal exposure to particulate matter (PM) with diameter <10 (PM)or<2:5 lm (PM) and DNA methylation in newborns and children. METHODS: We meta-analyzed associations between exposure to PM (n = 1,949) and PM (n = 1,551) at maternal home addresses during pregnancy and newborn DNA methylation assessed by Illumina Infinium HumanMethylation450K BeadChip in nine European and American studies, with replication in 688 independent newborns and look-up analyses in 2,118 older children. We used two approaches, one focusing on single cytosine-phosphate-guanine (CpG) sites and another on differentially methylated regions (DMRs). We also related PM exposures to blood mRNA expression. RESULTS: Six CpGs were significantly associated [false discovery rate (FDR) <0:05] with prenatal PM and 14 with PM exposure. Two of the PM-related CpGs mapped to FAM13A (cg00905156) and NOTCH4 (cg06849931) previously associated with lung function and asthma. Although these associations did not replicate in the smaller newborn sample, both CpGs were significant (p <0:05) in 7-to 9-y-olds. For cg06849931, however, the direction of the association was inconsistent. Concurrent PM exposure was associated with a significantly higher NOTCH4 expression at age 16 y. We also identified several DMRs associated with either prenatal PM and or PM exposure, of which two PM-related DMRs, including H19 and MARCH11, replicated in newborns. CONCLUSIONS: Several differentially methylated CpGs and DMRs associated with prenatal PM exposure were identified in newborns, with annotation to genes previously implicated in lung-related outcomes. https://doi.org/10.1289/EHP4522.
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- 2019
7. Cohort Profile: Pregnancy And Childhood Epigenetics (PACE) Consortium
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Felix, JF, Joubert, BR, Baccarelli, AA, Sharp, GC, Almqvist, C, Annesi-Maesano, I, Arshad, H, Baïz, N, Bakermans-Kranenburg, MJ, Bakulski, KM, Binder, EB, Bouchard, L, Breton, CV, Brunekreef, B, Brunst, KJ, Burchard, EG, Bustamante, M, Chatzi, L, Munthe-Kaas, M, Corpeleijn, E, Czamara, D, Dabelea, D, Smith, G, De Boever, P, Duijts, L, Dwyer, T, Eng, C, Eskenazi, B, Everson, TM, Falahi, F, Fallin, MD, Farchi, S, Fernandez, MF, Gao, L, Gaunt, TR, Ghantous, A, Gillman, MW, Gonseth, S, Grote, V, Gruzieva, O, Håberg, SE, Herceg, Z, Hivert, M-F, Holland, N, Holloway, JW, Hoyo, C, Hu, D, Huang, R-C, Huen, K, Järvelin, M-R, Jima, DD, Just, AC, Karagas, MR, Karlsson, R, Karmaus, W, Kechris, KJ, Kere, J, Kogevinas, M, Koletzko, B, Koppelman, GH, Küpers, LK, Ladd-Acosta, C, Lahti, J, Lambrechts, N, Langie, SAS, Lie, RT, Liu, AH, Magnus, MC, Magnus, P, Maguire, RL, Marsit, CJ, McArdle, W, Melén, E, Melton, P, Murphy, SK, Nawrot, TS, Nisticò, L, Nohr, EA, Nordlund, B, Nystad, W, Oh, SS, Oken, E, Page, CM, Perron, P, Pershagen, G, Pizzi, C, Plusquin, M, Raikkonen, K, Reese, SE, Reischl, E, Richiardi, L, Ring, S, Roy, RP, Rzehak, P, Schoeters, G, Schwartz, DA, Sebert, S, Snieder, H, Sørensen, TIA, Starling, AP, Sunyer, J, Taylor, JA, Tiemeier, H, Ullemar, V, Vafeiadi, M, Van Ijzendoorn, MH, Vonk, JM, Vriens, A, Vrijheid, M, Wang, P, Wiemels, JL, Wilcox, AJ, Wright, RJ, Xu, C-J, Xu, Z, Yang, IV, Yousefi, P, Zhang, H, Zhang, W, Zhao, S, Agha, G, Relton, CL, Jaddoe, VWV, London, SJ, Epidemiology, Erasmus MC other, Pediatrics, Child and Adolescent Psychiatry / Psychology, Psychiatry, Research Methods and Techniques, dIRAS RA-2, One Health Chemisch, Reproductive Origins of Adult Health and Disease (ROAHD), Lifestyle Medicine (LM), Groningen Research Institute for Asthma and COPD (GRIAC), Life Course Epidemiology (LCE), Department of Psychology and Logopedics, Helsinki Collegium for Advanced Studies, Medicum, University of Helsinki, and Developmental Psychology Research Group
- Subjects
DNA Methylation/physiology ,Epidemiology ,Maternal Health ,education ,Embaràs ,DISEASE ,Environmental Pollution/analysis ,Epigenesis, Genetic ,Cohort Studies ,Prenatal Exposure Delayed Effects/epidemiology ,Folic Acid ,Pregnancy ,Journal Article ,Humans ,MATERNAL SMOKING ,CORD BLOOD ,GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries) ,Cohort Profiles ,METAANALYSIS ,PRENATAL EXPOSURE ,Maternal Exposure/adverse effects ,EPIGENOME-WIDE ASSOCIATION ,0104 Statistics ,Child Health ,Infant, Newborn ,DNA METHYLATION DATA ,DNA Methylation ,Epigenètica ,BIRTH-WEIGHT ,3142 Public health care science, environmental and occupational health ,Folic Acid/blood ,1117 Public Health And Health Services ,Maternal Exposure ,Prenatal Exposure Delayed Effects ,MENDELIAN RANDOMIZATION ,Epigenetics ,Female ,Human medicine ,Environmental Pollution - Abstract
UK Medical Research Council; Wellcome Trust [102215/2/13/2, WT088806, 084762MA]; UK Biotechnology and Biological Sciences Research Council [BB/I025751/1, BB/I025263/1]; UK Medical Research Council Integrative Epidemiology Unit; University of Bristol [MC_UU_12013_1, MC_UU_12013_2, MC_UU_12013_5, MC_UU_12013_8]; United States National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK10324]; Swedish Research Council; Swedish Heart-Lung Foundation; Freemason Child House Foundation in Stockholm; MeDALL (Mechanisms of the Development of ALLergy), within the European Union [261357]; Stockholm County Council (ALF); Swedish Foundation for Strategic Research (SSF) [RBc08-0027]; Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet; Swedish Research Council Formas; Swedish Environment Protection Agency; Center for Integrative Research on Childhood Leukemia and the Environment [P01ES018172]; NIH [P50ES018172, R01ES09137, 5P30CA082103, P01 ES009605, R01 ES021369, R01ES023067, K01ES017801, R01ES022216, P30ES007048, R01ES014447, P01ES009581, R826708-01, RD831861-01, P50ES026086, R01DK068001, R01 DK100340, R01 DK076648, R01ES022934, R01HL111108, R01NR013945, R37 HD034568, UL1 TR001082, P30 DK56350]; EPA [RD83451101, RD83615901, RD 82670901, RD 83451301, 83615801-0]; UCSF Comprehensive Cancer Center Support grant [P30 CA82103]; Swiss Science National Foundation [P2LAP3_158674]; Sutter-Stottner Foundation; Commission of the European Community, specific RTD Programme 'Quality of Life and Management of Living Resources' within the 5th Framework Programme [QLRT-2001-00389, QLK1-CT-2002-30582]; 6th Framework Programme [007036]; European Union's Seventh Framework Programme (FP7), project EarlyNutrition [289346]; European Research Council Advanced grant ERC-AdG [322605 META-GROWTH]; Autism Speaks grant [260377]; Funds for Research in Respiratory Health; French Ministry of Research: IFR program; INSERM Nutrition Research Program; French Ministry of Health: Perinatality Program; French National Institute for Population Health Surveillance (INVS); Paris-Sud University; French National Institute for Health Education (INPES); Nestle; Mutuelle Generale de l'Education Nationale (MGEN); French-speaking association for the study of diabetes and metabolism (Alfediam) [2012/51290-6]; EU; European Research Council [ERC-2012-StG.310898, 268479-BREATHE]; Flemish Scientific Research Council (FWO) [N1516112 / G.0.873.11N.10]; European Community's Seventh Framework Programme FP7 project EXPOsOMICS [308610]; People Program (Marie Curie Actions) of the European Union's Seventh Framework Program FP7 under REA grant [628858]; Bijzonder Onderzoeksfonds (BOF) Hasselt University; Ministry of the Flemish Community (Department of Economics, Science and Innovation); Ministry of the Flemish Community (Department of Environment, Nature and Energy); CEFIC LRI award by the Research Foundation-Flanders (FWO); CEFIC LRI award by the Research Foundation-Flanders (FWO) [12L5216N]; Flemish Institute for Technological Research (VITO) [12L5216N]; Bill AMP; Melinda Gates Foundation Grand Challenges Exploration grant [OPP119403]; Sandler Family Foundation; American Asthma Foundation; National Institutes of Health; National Heart, Lung and Blood Institute [HL117004]; National Institute of Environmental Health Sciences [ES24844]; National Institute on Minority Health and Health Disparities [MD006902, MD009523]; National Institute of General Medical Sciences [GM007546]; Tobacco-Related Disease Research Program [24RT-0025]; Hutchison Whampoa Ltd, Hong Kong; University of Groningen; Well Baby Clinic Foundation Icare; Noordlease; Youth Health Care Drenthe; Biobanking and Biomolecular Research Infrastructure Netherlands [CP2011-19]; Erasmus Medical Center, Rotterdam; Erasmus University Rotterdam; Netherlands Organization for Health Research and Development; Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO); Netherlands Consortium for Healthy Aging (NCHA) [050-060-810]; Genetic Laboratory of the Department of Internal Medicine, Erasmus MC; European Union's Horizon research and innovation programme [733206, 633595]; National Institute of Child and Human Development [R01HD068437]; Netherlands Organization for Health Research and Development [VIDI 016.136.361]; Consolidator grant from the European Research Council [ERC-2014-CoG-648916]; Netherlands' Organization for Scientific Research (NWO VICI); European Research Council ERC; Netherlands' Organization for Scientific Research (NWO Spinoza Award); Gravitation program of the Dutch Ministry of Education, Culture, and Science; Netherlands Organization for Scientific Research (NWO) [024.001.003]; Lung Foundation Netherlands [3.2.12.089]; Fonds de Recherche du Quebec en Sante (FRQ-S) [20697]; Canadian Institute of Health Reseach (CIHR) [MOP 115071]; Diabete Quebec grant; Canadian Diabetes Association operating grant [OG-3-08-2622]; American Diabetes Association Pathways Accelerator Early Investigator Award [1-15-ACE-26]; MRC Integrative Epidemiology Unit - Medical Research Council [MC_UU_12013/1-9]; National Institute of Environmental Health Sciences, National Institutes of Health [K99ES025817]; Instituto de Salud Carlos III [Red INMA G03/176, CB06/02/0041]; Spanish Ministry of Health [FIS-PI04/1436, FIS-PI08/1151]; Spanish Ministry of Health (FEDER funds) [FIS-PI11/00610, FIS-FEDER-PI06/0867, FIS-FEDER-PI03-1615]; Generalitat de Catalunya [CIRIT 1999SGR 00241]; Fundacio La Marato de TV3 [090430]; EU Commission [261357-MeDALL]; National Institute of Allergy and Infectious Diseases [N01-AI90052]; National Institutes of Health USA [R01 HL082925, R01 HL132321]; Asthma UK [364]; NIAID/NIH [R01AI091905, R01AI121226]; National Institute of Health [R01AI121226, R01 AI091905, R01HL132321]; NIH/NIEHS [N01-ES75558]; NIH/NINDS [1 UO1 NS 047537-01, 2 UO1 NS 047537-06A1]; Intramural Research Program of the NIH, National Institute of Environmental Health Sciences [Z01-ES-49019, Z01 ES044005, ES049033, ES049032]; Norwegian Research Council/BIOBANK [221097]; Oslo University Hospital; Unger-Vetlesens foundation; Norwegian American Womens Club; INCA/Plan Cancer-EVA-INSERM, France; International Childhood Cancer Cohort Consortium (I4C); INCA/Plan Cancer-EVA-INSERM (France); IARC Postdoctoral Fellowship; EC FP7 Marie Curie Actions-People-Co-funding of regional, national and international programmes (COFUND); NIEHS [R21ES014947, R01ES016772]; NIDDK [R01DK085173]; National Institute of Environmental Health Science [P30 ES025128]; University of Oulu grant [65354]; Oulu University Hospital [2/97, 8/97]; Ministry of Health and Social Affairs [23/251/97, 160/97, 190/97]; National Institute for Health and Welfare, Helsinki [54121]; Regional Institute of Occupational Health, Oulu, Finland [50621, 54231]; EU [QLG1-CT-2000-01643, E51560]; NorFA grant [731, 20056, 30167]; Academy of Finland; NIH-NIEHS [P01 ES022832]; US EPA [RD83544201]; NIH-NIGMS [P20GM104416]; NCI [R25CA134286]; Netherlands Organization for Scientific Research and Development; Netherlands Asthma Fund; Netherlands Ministry of Spatial Planning, Housing, and the Environment; Netherlands Ministry of Health, Welfare, and Sport; MeDALL; European Union under the Health Cooperation Work Program of the 7th Framework program [261357]; Italian National Centre for Disease Prevention and Control (CCM grant); Italian Ministry of Health (art 12); Italian Ministry of Health (12bis Dl.gs.vo) [502/92]; EraNet; EVO; University of Helsinki Research Funds; Signe and Ane Gyllenberg foundation; Emil Aaltonen Foundation; Finnish Medical Foundation; Jane and Aatos Erkko Foundation; Novo Nordisk Foundation; Paivikki and Sakari Sohlberg Foundation; Sigrid Juselius Foundation; University of Helsinki; University of Western Australia (UWA); Curtin University; Raine Medical Research Foundation; UWA Faculty of Medicine, Dentistry and Health Sciences; Telethon Kids Institute; Women's and Infant's Research Foundation (KEMH); Edith Cowan University; National Health and Medical Research Council [1059711]; National Health and Medical Research Council (NHMRC) fellowship [1053384]; Australian National Health and Medical Research Council; United States National Institute of Health; Greek Ministry of Health (programme of prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece); Greek Ministry of Health ('Rhea Plus': Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health); European Union (EU) [EU FP6-2003-Food-3-NewGeneris, EU FP7 ENV.2007.1.2.2.2, 211250 ESCAPE, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7 ENV.2008.1.2.1.6, 226285 ENRIECO]; National Institutes of Health [NIH-NIMH R01MH094609, NIH-NIEHS R01ES022223, NIH-NIEHS R01ES025145]; Centers for Disease Control and Prevention [U10DD000180, U10DD000181, U10DD000182, U10DD000183, U10DD000184, U10DD000498]; Autism Speaks [7659]; Swedish Research Council through the Swedish Initiative for research on Microdata in the Social And Medical Sciences (SIMSAM) [340-2013-5867]; Stockholm County Council (ALF projects); Strategic Research Program in Epidemiology at Karolinska Institutet; Swedish Asthma and Allergy Association's Research Foundation; Stiftelsen Frimurare Barnahuset Stockholm; Norwegian Ministry of Health and Care Services; Ministry of the Flemish Community (Flemish Agency for Care and Health); University of Bristol; Ministry of Education and Research; European Union (EU) (EU FP7-HEALTH-single stage CHICOS); European Union (EU) (EU-FP7-HEALTH) [308333 HELIX]; European Union (EU) (EU FP6. STREP HiWATE); UK Medical Research Council; Wellcome Trust [102215/2/13/2, WT088806, 084762MA]; UK Biotechnology and Biological Sciences Research Council [BB/I025751/1, BB/I025263/1]; UK Medical Research Council Integrative Epidemiology Unit; University of Bristol [MC_UU_12013_1, MC_UU_12013_2, MC_UU_12013_5, MC_UU_12013_8]; United States National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK10324]; Swedish Research Council; Swedish Heart-Lung Foundation; Freemason Child House Foundation in Stockholm; MeDALL (Mechanisms of the Development of ALLergy), within the European Union [261357]; Stockholm County Council (ALF); Swedish Foundation for Strategic Research (SSF) [RBc08-0027]; Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet; Swedish Research Council Formas; Swedish Environment Protection Agency; Center for Integrative Research on Childhood Leukemia and the Environment [P01ES018172]; NIH [P50ES018172, R01ES09137, 5P30CA082103, P01 ES009605, R01 ES021369, R01ES023067, K01ES017801, R01ES022216, P30ES007048, R01ES014447, P01ES009581, R826708-01, RD831861-01, P50ES026086, R01DK068001, R01 DK100340, R01 DK076648, R01ES022934, R01HL111108, R01NR013945, R37 HD034568, UL1 TR001082, P30 DK56350]; EPA [RD83451101, RD83615901, RD 82670901, RD 83451301, 83615801-0]; UCSF Comprehensive Cancer Center Support grant [P30 CA82103]; Swiss Science National Foundation [P2LAP3_158674]; Sutter-Stottner Foundation; Commission of the European Community, specific RTD Programme 'Quality of Life and Management of Living Resources' within the 5th Framework Programme [QLRT-2001-00389, QLK1-CT-2002-30582]; 6th Framework Programme [007036]; European Union's Seventh Framework Programme (FP7), project EarlyNutrition [289346]; European Research Council Advanced grant ERC-AdG [322605 META-GROWTH]; Autism Speaks grant [260377]; Funds for Research in Respiratory Health; French Ministry of Research: IFR program; INSERM Nutrition Research Program; French Ministry of Health: Perinatality Program; French National Institute for Population Health Surveillance (INVS); Paris-Sud University; French National Institute for Health Education (INPES); Nestle; Mutuelle Generale de l'Education Nationale (MGEN); French-speaking association for the study of diabetes and metabolism (Alfediam) [2012/51290-6]; EU; European Research Council [ERC-2012-StG.310898, 268479-BREATHE]; Flemish Scientific Research Council (FWO) [N1516112 / G.0.873.11N.10]; European Community's Seventh Framework Programme FP7 project EXPOsOMICS [308610]; People Program (Marie Curie Actions) of the European Union's Seventh Framework Program FP7 under REA grant [628858]; Bijzonder Onderzoeksfonds (BOF) Hasselt University; Ministry of the Flemish Community (Department of Economics, Science and Innovation); Ministry of the Flemish Community (Department of Environment, Nature and Energy); CEFIC LRI award by the Research Foundation-Flanders (FWO); CEFIC LRI award by the Research Foundation-Flanders (FWO) [12L5216N]; Flemish Institute for Technological Research (VITO) [12L5216N]; Bill AMP; Melinda Gates Foundation Grand Challenges Exploration grant [OPP119403]; Sandler Family Foundation; American Asthma Foundation; National Institutes of Health; National Heart, Lung and Blood Institute [HL117004]; National Institute of Environmental Health Sciences [ES24844]; National Institute on Minority Health and Health Disparities [MD006902, MD009523]; National Institute of General Medical Sciences [GM007546]; Tobacco-Related Disease Research Program [24RT-0025]; Hutchison Whampoa Ltd, Hong Kong; University of Groningen; Well Baby Clinic Foundation Icare; Noordlease; Youth Health Care Drenthe; Biobanking and Biomolecular Research Infrastructure Netherlands [CP2011-19]; Erasmus Medical Center, Rotterdam; Erasmus University Rotterdam; Netherlands Organization for Health Research and Development; Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO); Netherlands Consortium for Healthy Aging (NCHA) [050-060-810]; Genetic Laboratory of the Department of Internal Medicine, Erasmus MC; European Union's Horizon research and innovation programme [733206, 633595]; National Institute of Child and Human Development [R01HD068437]; Netherlands Organization for Health Research and Development [VIDI 016.136.361]; Consolidator grant from the European Research Council [ERC-2014-CoG-648916]; Netherlands' Organization for Scientific Research (NWO VICI); European Research Council ERC; Netherlands' Organization for Scientific Research (NWO Spinoza Award); Gravitation program of the Dutch Ministry of Education, Culture, and Science; Netherlands Organization for Scientific Research (NWO) [024.001.003]; Lung Foundation Netherlands [3.2.12.089]; Fonds de Recherche du Quebec en Sante (FRQ-S) [20697]; Canadian Institute of Health Reseach (CIHR) [MOP 115071]; Diabete Quebec grant; Canadian Diabetes Association operating grant [OG-3-08-2622]; American Diabetes Association Pathways Accelerator Early Investigator Award [1-15-ACE-26]; MRC Integrative Epidemiology Unit - Medical Research Council [MC_UU_12013/1-9]; National Institute of Environmental Health Sciences, National Institutes of Health [K99ES025817]; Instituto de Salud Carlos III [Red INMA G03/176, CB06/02/0041]; Spanish Ministry of Health [FIS-PI04/1436, FIS-PI08/1151]; Spanish Ministry of Health (FEDER funds) [FIS-PI11/00610, FIS-FEDER-PI06/0867, FIS-FEDER-PI03-1615]; Generalitat de Catalunya [CIRIT 1999SGR 00241]; Fundacio La Marato de TV3 [090430]; EU Commission [261357-MeDALL]; National Institute of Allergy and Infectious Diseases [N01-AI90052]; National Institutes of Health USA [R01 HL082925, R01 HL132321]; Asthma UK [364]; NIAID/NIH [R01AI091905, R01AI121226]; National Institute of Health [R01AI121226, R01 AI091905, R01HL132321]; NIH/NIEHS [N01-ES75558]; NIH/NINDS [1 UO1 NS 047537-01, 2 UO1 NS 047537-06A1]; Intramural Research Program of the NIH, National Institute of Environmental Health Sciences [Z01-ES-49019, Z01 ES044005, ES049033, ES049032]; Norwegian Research Council/BIOBANK [221097]; Oslo University Hospital; Unger-Vetlesens foundation; Norwegian American Womens Club; INCA/Plan Cancer-EVA-INSERM, France; International Childhood Cancer Cohort Consortium (I4C); INCA/Plan Cancer-EVA-INSERM (France); IARC Postdoctoral Fellowship; EC FP7 Marie Curie Actions-People-Co-funding of regional, national and international programmes (COFUND); NIEHS [R21ES014947, R01ES016772]; NIDDK [R01DK085173]; National Institute of Environmental Health Science [P30 ES025128]; University of Oulu grant [65354]; Oulu University Hospital [2/97, 8/97]; Ministry of Health and Social Affairs [23/251/97, 160/97, 190/97]; National Institute for Health and Welfare, Helsinki [54121]; Regional Institute of Occupational Health, Oulu, Finland [50621, 54231]; EU [QLG1-CT-2000-01643, E51560]; NorFA grant [731, 20056, 30167]; Academy of Finland; NIH-NIEHS [P01 ES022832]; US EPA [RD83544201]; NIH-NIGMS [P20GM104416]; NCI [R25CA134286]; Netherlands Organization for Scientific Research and Development; Netherlands Asthma Fund; Netherlands Ministry of Spatial Planning, Housing, and the Environment; Netherlands Ministry of Health, Welfare, and Sport; MeDALL; European Union under the Health Cooperation Work Program of the 7th Framework program [261357]; Italian National Centre for Disease Prevention and Control (CCM grant); Italian Ministry of Health (art 12); Italian Ministry of Health (12bis Dl.gs.vo) [502/92]; EraNet; EVO; University of Helsinki Research Funds; Signe and Ane Gyllenberg foundation; Emil Aaltonen Foundation; Finnish Medical Foundation; Jane and Aatos Erkko Foundation; Novo Nordisk Foundation; Paivikki and Sakari Sohlberg Foundation; Sigrid Juselius Foundation; University of Helsinki; University of Western Australia (UWA); Curtin University; Raine Medical Research Foundation; UWA Faculty of Medicine, Dentistry and Health Sciences; Telethon Kids Institute; Women's and Infant's Research Foundation (KEMH); Edith Cowan University; National Health and Medical Research Council [1059711]; National Health and Medical Research Council (NHMRC) fellowship [1053384]; Australian National Health and Medical Research Council; United States National Institute of Health; Greek Ministry of Health (programme of prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece); Greek Ministry of Health ('Rhea Plus': Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health); European Union (EU) [EU FP6-2003-Food-3-NewGeneris, EU FP7 ENV.2007.1.2.2.2, 211250 ESCAPE, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7 ENV.2008.1.2.1.6, 226285 ENRIECO]; National Institutes of Health [NIH-NIMH R01MH094609, NIH-NIEHS R01ES022223, NIH-NIEHS R01ES025145]; Centers for Disease Control and Prevention [U10DD000180, U10DD000181, U10DD000182, U10DD000183, U10DD000184, U10DD000498]; Autism Speaks [7659]; Swedish Research Council through the Swedish Initiative for research on Microdata in the Social And Medical Sciences (SIMSAM) [340-2013-5867]; Stockholm County Council (ALF projects); Strategic Research Program in Epidemiology at Karolinska Institutet; Swedish Asthma and Allergy Association's Research Foundation; Stiftelsen Frimurare Barnahuset Stockholm; Norwegian Ministry of Health and Care Services; Ministry of the Flemish Community (Flemish Agency for Care and Health); University of Bristol; Ministry of Education and Research; European Union (EU) (EU FP7-HEALTH-single stage CHICOS); European Union (EU) (EU-FP7-HEALTH) [308333 HELIX]; European Union (EU) (EU FP6. STREP HiWATE); [R01ES017646]; [R01ES01900]; [R01ES16443]; [USA / NIHH 2000 G DF682]; [50945]; [R01 HL095606]; [R01 HL1143396]
- Published
- 2018
8. In silico epigenetics of metal exposure and subclinical atherosclerosis in middle aged men: pilot results from the Aragon Workers Health Study
- Author
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Riffo-Campos A, Fuentes-Trillo A, Tang W, Soriano Z, De Marco G, Rentero-Garrido P, Adam-Felici V, Lendinez-Tortajada V, Francesconi K, Goessler W, Ladd-Acosta C, Leon-Latre M, Casasnovas J, Chaves F, Navas-Acien A, Guallar E, and Tellez-Plaza M
- Published
- 2018
9. Cohort profile: Pregnancy and childhood epigenetics (PACE) consortium
- Author
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Felix, J.F. (Janine F.), Joubert, B.R. (Bonnie), Baccarelli, A.A. (Andrea), Sharp, G.C. (Gemma C.), Almqvist, C. (Catarina), Annesi-Maesano, I. (Isabella), Arshad, H. (Hasan), Baïz, N. (Nour), Bakermans-Kranenburg, M.J. (Marian), Bakulski, K.M. (Kelly M.), Binder, E.B. (Elisabeth), Bouchard, L. (Luigi), Breton, C. (Carrie), Brunekreef, B. (Bert), Brunst, K.J. (Kelly J.), Burchard, E.G. (Esteban), Bustamante, M. (Mariona), Chatzi, L. (Leda), Munthe-Kaas, M.C. (Monica Cheng), Corpeleijn, W.E. (Willemijn), Czamara, D. (Darina), Dabelea, D. (Dana), Smith, G.D. (George Davey), Boever, P. (Patrick) de, Duijts, L. (Liesbeth), Dwyer, T. (Terence), Eng, C. (Celeste), Eskenazi, B. (B.), Everson, T.M. (Todd M.), Falahi, F. (Fahimeh), Fallin, M.D. (M. Daniele), Farchi, S. (Sara), Fernandez, M.F. (Mariana), Gao, L. (Lu), Gaunt, T.R. (Tom), Ghantous, A. (Akram), Gillman, M.W. (Matthew W.), Gonseth, S. (Semira), Grote, V. (Veit), Gruzieva, O. (Olena), Håberg, S.E. (Siri E), Herceg, Z. (Zdenko), Hivert, M.-F. (Marie-France), Holland, N. (Nina), Holloway, J.W. (John W.), Hoyo, C. (Cathrine), Hu, D. (Donglei), Huang, R.-C. (Rae-Chi), Huen, K. (Karen), Järvelin, M.-R. (Marjo-Riitta), Jima, D.D. (Dereje D.), Just, A.C. (Allan C.), Karagas, M.R. (Margaret), Karlsson, R. (Robert), Karmaus, W. (Wilfried), Kechris, K.J. (Katerina J.), Kere, J. (Juha), Kogevinas, M. (Manolis), Koletzko, B. (Berthold), Koppelman, G.H. (Gerard), Küpers, A.M. (Marlijn), Ladd-Acosta, C. (Christine), Lahti, J., Lambrechts, N. (Nathalie), Langie, S.A.S. (Sabine A.S.), Lie, R.T. (Rolv T.), Liu, A.H. (Andrew H.), Magnus, M.C. (Maria C.), Magnus, P. (Per), Maguire, R.L. (Rachel L.), Marsit, C.J. (Carmen J.), McArdle, W.L. (Wendy), Melen, E. (Erik), Melton, P. (Phillip), Murphy, S.K. (Susan K.), Nawrot, T.S. (Tim S.), Nisticò, L. (Lorenza), Nohr, C. (Christian), Nordlund, B. (Björn), Nystad, W. (Wenche), Oh, S.S. (Sam S.), Oken, E. (Emily), Page, C.M. (Christian M.), Perron, P. (Patrice), Pershagen, G. (Göran), Pizzi, C. (Costanza), Plusquin, M. (Michelle), Räikkönen, K. (Katri), Reese, S.E. (Sarah E.), Reischl, G. (Gunilla), Richiardi, L. (Lorenzo), Ring, S.M. (Susan), Roy, R.P. (Ritu P.), Rzehak, P. (Peter), Schoeters, G. (Greet), Schwartz, D.A. (David A.), Sebert, S. (Sylvain), Snieder, H. (Harold), Sørensen, T.I.A. (Thorkild), Starling, A.P. (Anne P.), Sunyer, J. (Jordi), Taylor, J.A. (Jack A), Tiemeier, H.W. (Henning), Ullemar, V. (Vilhelmina), Vafeiadi, M. (Marina), IJzendoorn, M.H. (Rien) van, Vonk, J.M. (Judith), Vriens, A. (Annette), Vrijheid, M. (Martine), Wang, P. (Pei), Wiemels, J. (Joseph), Wilcox, A.J. (Allen), Wright, R.J. (Rosalind J.), Xu, C.-J. (Cheng-Jian), Xu, Z. (Zongli), Yang, I.V. (Ivana V.), Yousefi, P. (Paul), Zhang, H. (Hongmei), Zhang, W. (Weiming), Zhao, S. (Shanshan), Agha, G. (Golareh), Relton, C.L. (Caroline), Jaddoe, V.W.V. (Vincent), London, S.J. (Stephanie J.), Felix, J.F. (Janine F.), Joubert, B.R. (Bonnie), Baccarelli, A.A. (Andrea), Sharp, G.C. (Gemma C.), Almqvist, C. (Catarina), Annesi-Maesano, I. (Isabella), Arshad, H. (Hasan), Baïz, N. (Nour), Bakermans-Kranenburg, M.J. (Marian), Bakulski, K.M. (Kelly M.), Binder, E.B. (Elisabeth), Bouchard, L. (Luigi), Breton, C. (Carrie), Brunekreef, B. (Bert), Brunst, K.J. (Kelly J.), Burchard, E.G. (Esteban), Bustamante, M. (Mariona), Chatzi, L. (Leda), Munthe-Kaas, M.C. (Monica Cheng), Corpeleijn, W.E. (Willemijn), Czamara, D. (Darina), Dabelea, D. (Dana), Smith, G.D. (George Davey), Boever, P. (Patrick) de, Duijts, L. (Liesbeth), Dwyer, T. (Terence), Eng, C. (Celeste), Eskenazi, B. (B.), Everson, T.M. (Todd M.), Falahi, F. (Fahimeh), Fallin, M.D. (M. Daniele), Farchi, S. (Sara), Fernandez, M.F. (Mariana), Gao, L. (Lu), Gaunt, T.R. (Tom), Ghantous, A. (Akram), Gillman, M.W. (Matthew W.), Gonseth, S. (Semira), Grote, V. (Veit), Gruzieva, O. (Olena), Håberg, S.E. (Siri E), Herceg, Z. (Zdenko), Hivert, M.-F. (Marie-France), Holland, N. (Nina), Holloway, J.W. (John W.), Hoyo, C. (Cathrine), Hu, D. (Donglei), Huang, R.-C. (Rae-Chi), Huen, K. (Karen), Järvelin, M.-R. (Marjo-Riitta), Jima, D.D. (Dereje D.), Just, A.C. (Allan C.), Karagas, M.R. (Margaret), Karlsson, R. (Robert), Karmaus, W. (Wilfried), Kechris, K.J. (Katerina J.), Kere, J. (Juha), Kogevinas, M. (Manolis), Koletzko, B. (Berthold), Koppelman, G.H. (Gerard), Küpers, A.M. (Marlijn), Ladd-Acosta, C. (Christine), Lahti, J., Lambrechts, N. (Nathalie), Langie, S.A.S. (Sabine A.S.), Lie, R.T. (Rolv T.), Liu, A.H. (Andrew H.), Magnus, M.C. (Maria C.), Magnus, P. (Per), Maguire, R.L. (Rachel L.), Marsit, C.J. (Carmen J.), McArdle, W.L. (Wendy), Melen, E. (Erik), Melton, P. (Phillip), Murphy, S.K. (Susan K.), Nawrot, T.S. (Tim S.), Nisticò, L. (Lorenza), Nohr, C. (Christian), Nordlund, B. (Björn), Nystad, W. (Wenche), Oh, S.S. (Sam S.), Oken, E. (Emily), Page, C.M. (Christian M.), Perron, P. (Patrice), Pershagen, G. (Göran), Pizzi, C. (Costanza), Plusquin, M. (Michelle), Räikkönen, K. (Katri), Reese, S.E. (Sarah E.), Reischl, G. (Gunilla), Richiardi, L. (Lorenzo), Ring, S.M. (Susan), Roy, R.P. (Ritu P.), Rzehak, P. (Peter), Schoeters, G. (Greet), Schwartz, D.A. (David A.), Sebert, S. (Sylvain), Snieder, H. (Harold), Sørensen, T.I.A. (Thorkild), Starling, A.P. (Anne P.), Sunyer, J. (Jordi), Taylor, J.A. (Jack A), Tiemeier, H.W. (Henning), Ullemar, V. (Vilhelmina), Vafeiadi, M. (Marina), IJzendoorn, M.H. (Rien) van, Vonk, J.M. (Judith), Vriens, A. (Annette), Vrijheid, M. (Martine), Wang, P. (Pei), Wiemels, J. (Joseph), Wilcox, A.J. (Allen), Wright, R.J. (Rosalind J.), Xu, C.-J. (Cheng-Jian), Xu, Z. (Zongli), Yang, I.V. (Ivana V.), Yousefi, P. (Paul), Zhang, H. (Hongmei), Zhang, W. (Weiming), Zhao, S. (Shanshan), Agha, G. (Golareh), Relton, C.L. (Caroline), Jaddoe, V.W.V. (Vincent), and London, S.J. (Stephanie J.)
- Published
- 2018
- Full Text
- View/download PDF
10. Cohort profile:pregnancy and childhood epigenetics (PACE) consortium
- Author
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Felix, J. F. (Janine F.), Joubert, B. R. (Bonnie R.), Baccarelli, A. A. (Andrea A.), Sharp, G. C. (Gemma C.), Almqvist, C. (Catarina), Annesi-Maesano, I. (Isabella), Arshad, H. (Hasan), Baiz, N. (Nour), Bakermans-Kranenburg, M. J. (Marian J.), Bakulski, K. M. (Kelly M.), Binder, E. B. (Elisabeth B.), Bouchard, L. (Luigi), Breton, C. V. (Carrie V.), Brunekreef, B. (Bert), Brunst, K. J. (Kelly J.), Burchard, E. G. (Esteban G.), Bustamante, M. (Mariona), Chatzi, L. (Leda), Munthe-Kaas, M. C. (Monica Cheng), Corpeleijn, E. (Eva), Czamara, D. (Darina), Dabelea, D. (Dana), Smith, G. D. (George Davey), De Boever, P. (Patrick), Duijts, L. (Liesbeth), Dwyer, T. (Terence), Eng, C. (Celeste), Eskenazi, B. (Brenda), Everson, T. M. (Todd M.), Falahi, F. (Fahimeh), Fallin, M. D. (M. Daniele), Farchi, S. (Sara), Fernandez, M. F. (Mariana F.), Gao, L. (Lu), Gaunt, T. R. (Tom R.), Ghantous, A. (Akram), Gillman, M. W. (Matthew W.), Gonseth, S. (Semira), Grote, V. (Veit), Gruzieva, O. (Olena), Haberg, S. E. (Siri E.), Herceg, Z. (Zdenko), Hivert, M.-F. (Marie-France), Holland, N. (Nina), Holloway, J. W. (John W.), Hoyo, C. (Cathrine), Hu, D. (Donglei), Huang, R.-C. (Rae-Chi), Huen, K. (Karen), Järvelin, M.-R. (Marjo-Riitta), Jima, D. D. (Dereje D.), Just, A. C. (Allan C.), Karagas, M. R. (Margaret R.), Karlsson, R. (Robert), Karmaus, W. (Wilfried), Kechris, K. J. (Katerina J.), Kere, J. (Juha), Kogevinas, M. (Manolis), Koletzko, B. (Berthold), Koppelman, G. H. (Gerard H.), Kupers, L. K. (Leanne K.), Ladd-Acosta, C. (Christine), Lahti, J. (Jari), Lambrechts, N. (Nathalie), Langie, S. A. (Sabine A. S.), Lie, R. T. (Rolv T.), Liu, A. H. (Andrew H.), Magnus, M. C. (Maria C.), Magnus, P. (Per), Maguire, R. L. (Rachel L.), Marsit, C. J. (Carmen J.), McArdle, W. (Wendy), Melen, E. (Erik), Melton, P. (Phillip), Murphy, S. K. (Susan K.), Nawrot, T. S. (Tim S.), Nistico, L. (Lorenza), Nohr, E. A. (Ellen A.), Nordlund, B. (Bjorn), Nystad, W. (Wenche), Oh, S. S. (Sam S.), Oken, E. (Emily), Page, C. M. (Christian M.), Perron, P. (Patrice), Pershagen, G. (Goran), Pizzi, C. (Costanza), Plusquin, M. (Michelle), Raikkonen, K. (Katri), Reese, S. E. (Sarah E.), Reischl, E. (Eva), Richiardi, L. (Lorenzo), Ring, S. (Susan), Roy, R. P. (Ritu P.), Rzehak, P. (Peter), Schoeters, G. (Greet), Schwartz, D. A. (David A.), Sebert, S. (Sylvain), Snieder, H. (Harold), Sorensen, T. I. (Thorkild I. A.), Starling, A. P. (Anne P.), Sunyer, J. (Jordi), ATaylor, J. (Jack), Tiemeier, H. (Henning), Ullemar, V. (Vilhelmina), Vafeiadi, M. (Marina), Van Ijzendoorn, M. H. (Marinus H.), Vonk, J. M. (Judith M.), Vriens, A. (Annette), Vrijheid, M. (Martine), Wang, P. (Pei), Wiemels, J. L. (Joseph L.), Wilcox, A. J. (Allen J.), Wright, R. J. (Rosalind J.), Xu, C.-J. (Cheng-Jian), Xu, Z. (Zongli), Yang, I. V. (Ivana V.), Yousefi, P. (Paul), Zhang, H. (Hongmei), Zhang, W. (Weiming), Zhao, S. (Shanshan), Agha, G. (Golareh), Relton, C. L. (Caroline L.), Jaddoe, V. W. (Vincent W. V.), London, S. J. (Stephanie J.), Felix, J. F. (Janine F.), Joubert, B. R. (Bonnie R.), Baccarelli, A. A. (Andrea A.), Sharp, G. C. (Gemma C.), Almqvist, C. (Catarina), Annesi-Maesano, I. (Isabella), Arshad, H. (Hasan), Baiz, N. (Nour), Bakermans-Kranenburg, M. J. (Marian J.), Bakulski, K. M. (Kelly M.), Binder, E. B. (Elisabeth B.), Bouchard, L. (Luigi), Breton, C. V. (Carrie V.), Brunekreef, B. (Bert), Brunst, K. J. (Kelly J.), Burchard, E. G. (Esteban G.), Bustamante, M. (Mariona), Chatzi, L. (Leda), Munthe-Kaas, M. C. (Monica Cheng), Corpeleijn, E. (Eva), Czamara, D. (Darina), Dabelea, D. (Dana), Smith, G. D. (George Davey), De Boever, P. (Patrick), Duijts, L. (Liesbeth), Dwyer, T. (Terence), Eng, C. (Celeste), Eskenazi, B. (Brenda), Everson, T. M. (Todd M.), Falahi, F. (Fahimeh), Fallin, M. D. (M. Daniele), Farchi, S. (Sara), Fernandez, M. F. (Mariana F.), Gao, L. (Lu), Gaunt, T. R. (Tom R.), Ghantous, A. (Akram), Gillman, M. W. (Matthew W.), Gonseth, S. (Semira), Grote, V. (Veit), Gruzieva, O. (Olena), Haberg, S. E. (Siri E.), Herceg, Z. (Zdenko), Hivert, M.-F. (Marie-France), Holland, N. (Nina), Holloway, J. W. (John W.), Hoyo, C. (Cathrine), Hu, D. (Donglei), Huang, R.-C. (Rae-Chi), Huen, K. (Karen), Järvelin, M.-R. (Marjo-Riitta), Jima, D. D. (Dereje D.), Just, A. C. (Allan C.), Karagas, M. R. (Margaret R.), Karlsson, R. (Robert), Karmaus, W. (Wilfried), Kechris, K. J. (Katerina J.), Kere, J. (Juha), Kogevinas, M. (Manolis), Koletzko, B. (Berthold), Koppelman, G. H. (Gerard H.), Kupers, L. K. (Leanne K.), Ladd-Acosta, C. (Christine), Lahti, J. (Jari), Lambrechts, N. (Nathalie), Langie, S. A. (Sabine A. S.), Lie, R. T. (Rolv T.), Liu, A. H. (Andrew H.), Magnus, M. C. (Maria C.), Magnus, P. (Per), Maguire, R. L. (Rachel L.), Marsit, C. J. (Carmen J.), McArdle, W. (Wendy), Melen, E. (Erik), Melton, P. (Phillip), Murphy, S. K. (Susan K.), Nawrot, T. S. (Tim S.), Nistico, L. (Lorenza), Nohr, E. A. (Ellen A.), Nordlund, B. (Bjorn), Nystad, W. (Wenche), Oh, S. S. (Sam S.), Oken, E. (Emily), Page, C. M. (Christian M.), Perron, P. (Patrice), Pershagen, G. (Goran), Pizzi, C. (Costanza), Plusquin, M. (Michelle), Raikkonen, K. (Katri), Reese, S. E. (Sarah E.), Reischl, E. (Eva), Richiardi, L. (Lorenzo), Ring, S. (Susan), Roy, R. P. (Ritu P.), Rzehak, P. (Peter), Schoeters, G. (Greet), Schwartz, D. A. (David A.), Sebert, S. (Sylvain), Snieder, H. (Harold), Sorensen, T. I. (Thorkild I. A.), Starling, A. P. (Anne P.), Sunyer, J. (Jordi), ATaylor, J. (Jack), Tiemeier, H. (Henning), Ullemar, V. (Vilhelmina), Vafeiadi, M. (Marina), Van Ijzendoorn, M. H. (Marinus H.), Vonk, J. M. (Judith M.), Vriens, A. (Annette), Vrijheid, M. (Martine), Wang, P. (Pei), Wiemels, J. L. (Joseph L.), Wilcox, A. J. (Allen J.), Wright, R. J. (Rosalind J.), Xu, C.-J. (Cheng-Jian), Xu, Z. (Zongli), Yang, I. V. (Ivana V.), Yousefi, P. (Paul), Zhang, H. (Hongmei), Zhang, W. (Weiming), Zhao, S. (Shanshan), Agha, G. (Golareh), Relton, C. L. (Caroline L.), Jaddoe, V. W. (Vincent W. V.), and London, S. J. (Stephanie J.)
- Published
- 2018
11. Cohort Profile: Pregnancy And Childhood Epigenetics (PACE) Consortium
- Author
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Felix, Janine, Joubert, BR, Baccarelli, AA, Sharp, GC, Almqvist, C, Annesi-Maesano, I, Arshad, H, Baiz, N, Bakermans-Kranenburg, MJ, Bakulski, KM, Binder, EB, Bouchard, L, Breton, CV, Brunekreef, B, Brunst, KJ, Burchard, EG, Bustamante, M, Chatzi, L, Munthe-Kaas, MC, Corpeleijn, E, Czamara, D, Dabelea, D, Smith, GD, De Boever, P, Duijts, Liesbeth, Dwyer, T, Eng, C, Eskenazi, B, Everson, TM, Falahi, F, Fallin, MD, Farchi, S, Fernandez, MF, Gao, L, Gaunt, TR, Ghantous, A, Gillman, MW, Gonseth, S, Grote, V, Gruzieva, O, Haberg, SE, Herceg, Z, Hivert, MF, Holland, N, Holloway, JW, Hoyo, C, Hu, DL, Huang, RC, Huen, K, Jarvelin, MR, Jima, DD, Just, AC, Karagas, MR, Karlsson, R, Karmaus, W, Kechris, KJ, Kere, J, Kogevinas, M, Koletzko, B, Koppelman, GH, Kupers, LK, Ladd-Acosta, C, Lahti, J, Lambrechts, N, Langie, SAS, Lie, RT, Liu, AH, Magnus, MC, Magnus, P, Maguire, RL, Marsit, CJ, McArdle, W, Melen, E, Melton, P, Murphy, SK, Nawrot, TS, Nistico, L, Nohr, EA, Nordlund, B, Nystad, W, Oh, SS, Oken, E, Page, CM, Perron, P, Pershagen, G, Pizzi, C, Plusquin, M, Raikkonen, K, Reese, SE, Reischl, E, Richiardi, L, Ring, S, Roy, RP, Rzehak, P, Schoeters, G, Schwartz, DA, Sebert, S, Snieder, H, Sorensen, TIA, Starling, AP, Sunyer, J, Ataylor, J, Tiemeier, Henning, Ullemar, V, Vafeiadi, M, van IJzendoorn, Marinus, Vonk, JM, Vriens, A, Vrijheid, M, Wang, P, Wiemels, JL, Wilcox, AJ, Wright, RJ, Xu, CJ, Xu, ZL, Yang, IV, Yousefi, P, Zhang, HM, Zhang, WM, Zhao, SS, Agha, G, Relton, CL, Jaddoe, Vincent, London, SJ, Felix, Janine, Joubert, BR, Baccarelli, AA, Sharp, GC, Almqvist, C, Annesi-Maesano, I, Arshad, H, Baiz, N, Bakermans-Kranenburg, MJ, Bakulski, KM, Binder, EB, Bouchard, L, Breton, CV, Brunekreef, B, Brunst, KJ, Burchard, EG, Bustamante, M, Chatzi, L, Munthe-Kaas, MC, Corpeleijn, E, Czamara, D, Dabelea, D, Smith, GD, De Boever, P, Duijts, Liesbeth, Dwyer, T, Eng, C, Eskenazi, B, Everson, TM, Falahi, F, Fallin, MD, Farchi, S, Fernandez, MF, Gao, L, Gaunt, TR, Ghantous, A, Gillman, MW, Gonseth, S, Grote, V, Gruzieva, O, Haberg, SE, Herceg, Z, Hivert, MF, Holland, N, Holloway, JW, Hoyo, C, Hu, DL, Huang, RC, Huen, K, Jarvelin, MR, Jima, DD, Just, AC, Karagas, MR, Karlsson, R, Karmaus, W, Kechris, KJ, Kere, J, Kogevinas, M, Koletzko, B, Koppelman, GH, Kupers, LK, Ladd-Acosta, C, Lahti, J, Lambrechts, N, Langie, SAS, Lie, RT, Liu, AH, Magnus, MC, Magnus, P, Maguire, RL, Marsit, CJ, McArdle, W, Melen, E, Melton, P, Murphy, SK, Nawrot, TS, Nistico, L, Nohr, EA, Nordlund, B, Nystad, W, Oh, SS, Oken, E, Page, CM, Perron, P, Pershagen, G, Pizzi, C, Plusquin, M, Raikkonen, K, Reese, SE, Reischl, E, Richiardi, L, Ring, S, Roy, RP, Rzehak, P, Schoeters, G, Schwartz, DA, Sebert, S, Snieder, H, Sorensen, TIA, Starling, AP, Sunyer, J, Ataylor, J, Tiemeier, Henning, Ullemar, V, Vafeiadi, M, van IJzendoorn, Marinus, Vonk, JM, Vriens, A, Vrijheid, M, Wang, P, Wiemels, JL, Wilcox, AJ, Wright, RJ, Xu, CJ, Xu, ZL, Yang, IV, Yousefi, P, Zhang, HM, Zhang, WM, Zhao, SS, Agha, G, Relton, CL, Jaddoe, Vincent, and London, SJ
- Published
- 2018
12. Integrating RNA Expression Identifies Candidate Gene for Orofacial Clefts
- Author
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Ladd-Acosta, C., primary and Beaty, T.H., additional
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- 2017
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13. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis
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Joubert, B.R. (Bonnie), Felix, J.F. (Janine), Yousefi, P. (Paul), Bakulski, K.M. (Kelly M.), Just, A.C. (Allan C.), Breton, C. (Carrie), Reese, S.E. (Sarah E.), Markunas, C.A. (Christina A.), Richmond, R.C. (Rebecca C.), Xu, C.-J. (Cheng-Jian), Küpers, L.K. (Leanne), Oh, S.S. (Sam S.), Hoyo, C. (Cathrine), Gruzieva, O. (Olena), Söderhäll, C. (Cilla), Salas, L.A. (Lucas A.), Baïz, N. (Nour), Zhang, H. (Hongmei), Lepeule, J. (Johanna), Ruiz, C. (Carlos), Ligthart, S. (Symen), Wang, T. (Tianyuan), Taylor, J.A. (Jack A.), Duijts, L. (Liesbeth), Sharp, G.C. (Gemma C.), Jankipersadsing, S.A. (Soesma A.), Nilsen, R.M. (Roy M.), Vaez, A. (Ahmad), Fallin, M.D. (M. Daniele), Hu, D. (Donglei), Litonjua, A.A. (Augusto), Fuemmeler, B.F. (Bernard F.), Huen, K. (Karen), Kere, J. (Juha), Kull, C.A. (Christian), Munthe-Kaas, M.C. (Monica Cheng), Gehring, U. (Ulrike), Bustamante, M. (Mariona), Saurel-Coubizolles, M.J. (Marie José), Quraishi, B.M. (Bilal M.), Ren, J. (Jie), Tost, J. (Jörg), Gonzalez, J.R. (Juan R.), Peters, M.J. (Marjolein), Håberg, S.E. (Siri E), Xu, Z. (Zongli), Meurs, J.B.J. (Joyce) van, Gaunt, T.R. (Tom), Kerkhof, M. (Marjan), Corpeleijn, W.E. (Willemijn), Feinberg, A.P. (Andrew P.), Eng, C. (Celeste), Baccarelli, A.A. (Andrea), Benjamin Neelon, S.E. (Sara E.), Bradman, A. (Asa), Merid, S.K. (Simon Kebede), Bergström, A. (Anna), Herceg, Z. (Zdenko), Hernandez-Vargas, H. (Hector), Brunekreef, B. (Bert), Pinart, M. (Mariona), Heude, B. (Barbara), Ewart, S. (Susan), Yao, J. (Jin), Lemonnier, N. (Nathanaël), Franco, O.H. (Oscar), Wu, M.C. (Michael), Hofman, A. (Albert), McArdle, W.L. (Wendy), Vlies, P. (P.) van der, Falahi, F. (Fahimeh), Gillman, M.W. (Matthew W.), Barcellos, L.F. (Lisa), Kumar, A. (Ashish), Wickman, M. (Magnus), Guerra, S. (S.), Charles, M.-A. (Marie-Aline), Holloway, J. (John), Auffray, C. (C.), Tiemeier, H.W. (Henning), Smith, A.V. (Davey), Postma, D.S. (Dirkje), Hivert, M.-F. (Marie-France), Eskenazi, B. (Brenda), Vrijheid, M. (Martine), Arshad, H. (Hasan), Anto, J.M. (Josep), Dehghan, A. (Abbas), Karmaus, W. (Wilfried), Annesi-Maesano, I. (Isabella), Sunyer, J. (Jordi), Ghantous, A. (Akram), Pershagen, G. (Göran), Holland, N. (Nina), Murphy, S.K. (Susan K.), Demeo, D.L. (Dawn), Burchard, E.G. (Esteban), Ladd-Acosta, C. (Christine), Snieder, H. (Harold), Nystad, W. (Wenche), Koppelman, G.H. (Gerard), Relton, C.L. (Caroline), Jaddoe, V.W.V. (Vincent), Wilcox, A.J. (Allen), Melén, E. (Erik), London, S.J. (Stephanie J.), Joubert, B.R. (Bonnie), Felix, J.F. (Janine), Yousefi, P. (Paul), Bakulski, K.M. (Kelly M.), Just, A.C. (Allan C.), Breton, C. (Carrie), Reese, S.E. (Sarah E.), Markunas, C.A. (Christina A.), Richmond, R.C. (Rebecca C.), Xu, C.-J. (Cheng-Jian), Küpers, L.K. (Leanne), Oh, S.S. (Sam S.), Hoyo, C. (Cathrine), Gruzieva, O. (Olena), Söderhäll, C. (Cilla), Salas, L.A. (Lucas A.), Baïz, N. (Nour), Zhang, H. (Hongmei), Lepeule, J. (Johanna), Ruiz, C. (Carlos), Ligthart, S. (Symen), Wang, T. (Tianyuan), Taylor, J.A. (Jack A.), Duijts, L. (Liesbeth), Sharp, G.C. (Gemma C.), Jankipersadsing, S.A. (Soesma A.), Nilsen, R.M. (Roy M.), Vaez, A. (Ahmad), Fallin, M.D. (M. Daniele), Hu, D. (Donglei), Litonjua, A.A. (Augusto), Fuemmeler, B.F. (Bernard F.), Huen, K. (Karen), Kere, J. (Juha), Kull, C.A. (Christian), Munthe-Kaas, M.C. (Monica Cheng), Gehring, U. (Ulrike), Bustamante, M. (Mariona), Saurel-Coubizolles, M.J. (Marie José), Quraishi, B.M. (Bilal M.), Ren, J. (Jie), Tost, J. (Jörg), Gonzalez, J.R. (Juan R.), Peters, M.J. (Marjolein), Håberg, S.E. (Siri E), Xu, Z. (Zongli), Meurs, J.B.J. (Joyce) van, Gaunt, T.R. (Tom), Kerkhof, M. (Marjan), Corpeleijn, W.E. (Willemijn), Feinberg, A.P. (Andrew P.), Eng, C. (Celeste), Baccarelli, A.A. (Andrea), Benjamin Neelon, S.E. (Sara E.), Bradman, A. (Asa), Merid, S.K. (Simon Kebede), Bergström, A. (Anna), Herceg, Z. (Zdenko), Hernandez-Vargas, H. (Hector), Brunekreef, B. (Bert), Pinart, M. (Mariona), Heude, B. (Barbara), Ewart, S. (Susan), Yao, J. (Jin), Lemonnier, N. (Nathanaël), Franco, O.H. (Oscar), Wu, M.C. (Michael), Hofman, A. (Albert), McArdle, W.L. (Wendy), Vlies, P. (P.) van der, Falahi, F. (Fahimeh), Gillman, M.W. (Matthew W.), Barcellos, L.F. (Lisa), Kumar, A. (Ashish), Wickman, M. (Magnus), Guerra, S. (S.), Charles, M.-A. (Marie-Aline), Holloway, J. (John), Auffray, C. (C.), Tiemeier, H.W. (Henning), Smith, A.V. (Davey), Postma, D.S. (Dirkje), Hivert, M.-F. (Marie-France), Eskenazi, B. (Brenda), Vrijheid, M. (Martine), Arshad, H. (Hasan), Anto, J.M. (Josep), Dehghan, A. (Abbas), Karmaus, W. (Wilfried), Annesi-Maesano, I. (Isabella), Sunyer, J. (Jordi), Ghantous, A. (Akram), Pershagen, G. (Göran), Holland, N. (Nina), Murphy, S.K. (Susan K.), Demeo, D.L. (Dawn), Burchard, E.G. (Esteban), Ladd-Acosta, C. (Christine), Snieder, H. (Harold), Nystad, W. (Wenche), Koppelman, G.H. (Gerard), Relton, C.L. (Caroline), Jaddoe, V.W.V. (Vincent), Wilcox, A.J. (Allen), Melén, E. (Erik), and London, S.J. (Stephanie J.)
- Abstract
Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathwa
- Published
- 2016
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- View/download PDF
14. Common DNA methylation alterations in multiple brain regions in autism
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Ladd-Acosta, C, primary, Hansen, K D, additional, Briem, E, additional, Fallin, M D, additional, Kaufmann, W E, additional, and Feinberg, A P, additional
- Published
- 2013
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- View/download PDF
15. Accurate genome-scale percentage DNA methylation estimates from microarray data
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Aryee, M. J., primary, Wu, Z., additional, Ladd-Acosta, C., additional, Herb, B., additional, Feinberg, A. P., additional, Yegnasubramanian, S., additional, and Irizarry, R. A., additional
- Published
- 2010
- Full Text
- View/download PDF
16. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer.
- Author
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Zhu CQ, Ding K, Strumpf D, Weir BA, Meyerson M, Pennell N, Thomas RK, Naoki K, Ladd-Acosta C, Liu N, Pintilie M, Der S, Seymour L, Jurisica I, Shepherd FA, Tsao MS, Zhu, Chang-Qi, Ding, Keyue, Strumpf, Dan, and Weir, Barbara A
- Published
- 2010
- Full Text
- View/download PDF
17. Integrating RNA Expression Identifies Candidate Gene for Orofacial Clefts.
- Author
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Ladd-Acosta, C. and Beaty, T. H.
- Subjects
CLEFT palate ,RNA ,GENE expression ,CLEFT lip ,MESENCHYMAL stem cells ,SINGLE nucleotide polymorphisms ,QUANTITATIVE research ,DISEASE risk factors ,GENES - Abstract
The article discusses the genes that act as risk factors for orofacial clefts (OFCs), including cleft lip and cleft palate, through using a study on RNA expression. The role that mesenchymal stem cells play in regulating OFCs is discussed. An overview of single-nucleotide polymorphisms' (SNPs') control of gene expression, including in regard to the use of expression quantitative trait loci (eQTLs) mapping, is provided.
- Published
- 2018
- Full Text
- View/download PDF
18. Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays
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Dobbin, K. K., Beer, D. G., Meyerson, M., Yeatman, T. J., Gerald, W. L., Jacobson, J. W., Conley, B., Buetow, K. H., Heiskanen, M., Simon, R. M., Minna, J. D., Girard, L., Misek, D. E., Taylor, J. M. G., Hanash, S., Naoki, K., Hayes, D. N., Ladd-Acosta, C., Enkemann, S. A., Viale, A., and Thomas Giordano
- Subjects
Gene Expression Regulation, Neoplastic ,Cancer Research ,Oncology ,Gene Expression Profiling ,Neoplasms ,Computational Biology ,Feasibility Studies ,Humans ,Nucleic Acid Hybridization ,Adenocarcinoma ,Laboratories ,Oligonucleotide Array Sequence Analysis - Abstract
A key step in bringing gene expression data into clinical practice is the conduct of large studies to confirm preliminary models. The performance of such confirmatory studies and the transition to clinical practice requires that microarray data from different laboratories are comparable and reproducible. We designed a study to assess the comparability of data from four laboratories that will conduct a larger microarray profiling confirmation project in lung adenocarcinomas. To test the feasibility of combining data across laboratories, frozen tumor tissues, cell line pellets, and purified RNA samples were analyzed at each of the four laboratories. Samples of each type and several subsamples from each tumor and each cell line were blinded before being distributed. The laboratories followed a common protocol for all steps of tissue processing, RNA extraction, and microarray analysis using Affymetrix Human Genome U133A arrays. High within-laboratory and between-laboratory correlations were observed on the purified RNA samples, the cell lines, and the frozen tumor tissues. Intraclass correlation within laboratories was only slightly stronger than between laboratories, and the intraclass correlation tended to be weakest for genes expressed at low levels and showing small variation. Finally, hierarchical cluster analysis revealed that the repeated samples clustered together regardless of the laboratory in which the experiments were done. The findings indicate that under properly controlled conditions it is feasible to perform complete tumor microarray analysis, from tissue processing to hybridization and scanning, at multiple independent laboratories for a single study.
19. Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders
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Laura M. Thornton, Paul Lichtenstein, Verneri Anttila, Diego Albani, Josep Antoni Ramos-Quiroga, Roger A.H. Adan, Monika Schlögelhofer, Stephen Sanders, Enrique Castelao, Klaus Berger, Nina Dalkner, Urs Heilbronner, Engilbert Sigurdsson, Pablo Mir, Fuquan Zhang, James T.R. Walters, Patrick F. Sullivan, Fragiskos Gonidakis, F. Kyle Satterstrom, Sara Marsal, Per Hoffmann, Amy Perry, Valentina Ciullo, Beate Herpertz-Dahlmann, Catharina Lavebratt, Kieran C. Murphy, Tammy Hedderly, Hyun Ju Hong, Evald Saemundsen, Sascha B. Fischer, Hailiang Huang, Andrew D. Grotzinger, Nienke Vulink, Murray B. Stein, Mark A. Frye, Laura J. Scott, David Curtis, Todd Lencz, Janiece E. DeSocio, Richard A. Belliveau, Eduard Vieta, Andrea Dietrich, Wade H. Berrettini, Kenneth S. Kendler, Marquis P. Vawter, Paul S. Nestadt, Michael E. Talkowski, Manuel Mattheisen, Ingrid Agartz, Elisa Docampo, Bernhard T. Baune, Stefan Ehrlich, Jolanta Lissowska, Felecia Cerrato, Terje Nærland, Robin M. Murray, Jennifer Reichert, Annette M. Hartmann, Hannelore Ehrenreich, Howard J. Edenberg, Katherine A. Halmi, Qingqin S. Li, Peristera Paschou, Marie Bækvad-Hansen, Esther Walton, Alessio Maria Monteleone, Ted Reichborn-Kjennerud, Frank Bellivier, Jungeun Song, D. Blake Woodside, Young Shin Kim, Jochen Seitz, Jacques Pantel, Palmiero Monteleone, Erika L. Nurmi, Rodney J. Scott, Kang Sim, Ekaterina A. Khramtsova, Udo Dannlowski, Rolf Adolfsson, Danielle Posthuma, Melissa J. Green, Laura Ibanez-Gomez, Jakob Grove, Elvira Bramon, Gregory L. Hanna, Cynthia M. Bulik, Yiran Guo, Stephan Ripke, Mary M. Robertson, Harald N. Aschauer, Adebayo Anjorin, Joanna Martin, Bertram Müller-Myhsok, Deborah Kaminská, Jose Guzman-Parra, Benedetta Nacmias, Erik G. Jönsson, Jonathan R. I. Coleman, Douglas F. Levinson, Hamdi Mbarek, Gun Peggy Knudsen, Karin Egberts, Mette Nyegaard, Patrik K. E. Magnusson, Mark Adams, Douglas Blackwood, Elisabeth B. Binder, Marcus Ising, Anna R. Docherty, Jim van Os, Nese Direk, Lina Martinsson, Maria Arranz, Christel M. Middeldorp, Stefan Kloiber, Sintia Iole Belangero, Eske M. Derks, Ingrid Melle, Erlend Bøen, Jan Haavik, Federica Piras, Unna N. Danner, Anil K. Malhotra, Gerome Breen, Stephen V. Faraone, Amanda B Zheutlin, Timothy Poterba, Stephan Ruhrmann, Inge Joa, Ulrik Fredrik Malt, Sarah E. Bergen, Federica Tozzi, Lauren A. Weiss, Hana Papezova, Dominic Holland, Elliot S. Gershon, Jaakko Kaprio, Merete Nordentoft, Scott D. Gordon, Christopher Pittenger, Keun-Ah Cheon, Jennifer Jordan, Philip Gorwood, Myrna M. Weissman, Preben Bo Mortensen, Melissa A. Munn-Chernoff, Isobel Heyman, Eun-Young Shin, Christie L. Burton, Katherine Gordon-Smith, Sietske G. Helder, Peter Nagy, Till F. M. Andlauer, Yunpeng Wang, Young Key Kim, Kate Langley, Søren Dalsgaard, Richard Delorme, Torbjørn Elvsåshagen, Bennett L. Leventhal, Giovanni Gambaro, Christos Androutsos, Jennifer Tübing, Marion Roberts, Annelie Nordin Adolfsson, Hakon Hakonarson, Dorothy E. Grice, Vaughan J. Carr, Konstantinos Tziouvas, Stephanie Zerwas, Cathy L. Barr, Michael Conlon O'Donovan, Per Qvist, Beate St Pourcain, Samuel Kuperman, Leila Karhunen, Jack Samuels, Markus M. Nöthen, Martien J H Kas, Alfonso Tortorella, Mikael Landén, Jennifer Crosbie, Marco A. Grados, Joanna M. Biernacka, Paul D. Arnold, Irene A. Malaty, Jurjen J. Luykx, Nicholas Bass, Naomi R. Wray, Catharina A. Hartman, Christina M. Hultman, Michael S. Okun, Brandon Wormley, Michael Bauer, Daniel J. Smith, Ian Jones, Kathryn Roeder, Brien P. Riley, Caroline M. Nievergelt, Katrin Gade, Sarah Kittel-Schneider, Roy H. Perlis, James R. Mitchell, Ziarih Hawi, James Lee, Liz Forty, William E. Bunney, Thomas Damm Als, Catherine Schaefer, Digby Quested, Matteo Cassina, Anna C. Koller, Patrick Turley, Agnes A. Steixner, Anu Raevuori, Assen Jablensky, Peter Holmans, Dong-Ho Song, S. Evelyn Stewart, Jan K. Buitelaar, Fernando S. Goes, Alexander Münchau, Ayman H. Fanous, Nicolas Ramoz, James B. Potash, Monica Gratacos Mayora, Tobias Banaschewski, Céline S. Reinbold, Renata Rizzo, Arianna Di Florio, Lenka Foretova, Gianfranco Spalletta, Aarno Palotie, Eleftheria Zeggini, Lawrence W. Brown, Julie K. O'Toole, Lynn E. DeLisi, Ulrich Schall, Mary Roberson, Barbara J. Coffey, Bryan J. Mowry, Murray J. Cairns, Dan J. Stein, Glyn Lewis, Marta Ribasés, C. Robert Cloninger, Bettina Konte, John B. Vincent, Duncan S. Palmer, Radhika Kandaswamy, Christine Ladd-Acosta, Lars Alfredsson, Frank Visscher, Ulrike Schmidt, Aiden Corvin, Susan L. Santangelo, Brenda W.J.H. Penninx, David J. Porteous, Tetsuya Ando, Arne E. Vaaler, Bru Cormand, Laura Carlberg, Claire Churchhouse, Manfred Stuhrmann, Niamh Mullins, Christine Søholm Hansen, Cathy L. Budman, Hartmut Imgart, Dan E. Arking, James J. McGough, Michael Gill, Christel Depienne, Roland Burghardt, Antonio Julià, Anders M. Dale, Sven Sandin, Katharina Domschke, Maria Grigoroiu-Serbanescu, Susana Jiménez-Murcia, Marianne Giørtz Pedersen, Zsanett Tarnok, Gisli Baldursson, Michele T. Pato, David M. Hougaard, Thorgeir E. Thorgeirsson, Katharina Bey, Kerstin J. Plessen, Margaret A. Richter, Ole A. Andreassen, Claudine Laurent-Levinson, Leonid Padyukov, Jacques Mallet, Daniela Degortes, John R. Kelsoe, Robert D. Levitan, Andreas Reif, Chaim Huyser, Derek W. Morris, Sina Wanderer, William Byerley, Edna Grünblatt, E.J.C. de Geus, Hyejung Won, Josephine Elia, Rudolf Uher, Jay A. Tischfield, Andreas Karwautz, Gustavo Turecki, Pieter J. Hoekstra, Dorret I. Boomsma, Jacob Rosenthal, Daniele Cusi, Michael C. Neale, Sara Mostafavi, Gwyneth Zai, F. Anthony O'Neill, Gary Donohoe, Karola Rehnström, Harry Brandt, Helena Gaspar, Francis J. McMahon, H-Erich Wichmann, Andrew W. Bergen, Giovanni Coppola, Lea K. 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R., Mcgough, J. J., Medland, S. E., Moran, J. L., Mors, O., Mortensen, P. B., Oades, R. D., Palmer, D. S., Pedersen, C. B., Pedersen, M. G., Peters, T., Poterba, T., Poulsen, J. B., Ramos-Quiroga, J. A., Reif, A., Ribases, M., Rothenberger, A., Rovira, P., Sanchez-Mora, C., Satterstrom, F. K., Schachar, R., Artigas, M. S., Steinberg, S., Stefansson, H., Turley, P., Walters, G. B., Werge, T., Zayats, T., Arking, D. E., Bettella, F., Buxbaum, J. D., Christensen, J. H., Collins, R. L., Coon, H., De Rubeis, S., Delorme, R., Grice, D. E., Hansen, T. F., Holmans, P. A., Hope, S., Hultman, C. M., Klei, L., Ladd-Acosta, C., Magnusson, P., Naerland, T., Nyegaard, M., Pinto, D., Qvist, P., Rehnstrom, K., Reichenberg, A., Reichert, J., Roeder, K., Rouleau, G. A., Saemundsen, E., Sanders, S. J., Sandin, S., St Pourcain, B., Stefansson, K., Sutcliffe, J. S., Talkowski, M. E., Weiss, L. A., Willsey, A. J., Agartz, I., Akil, H., Albani, D., Alda, M., Als, T. D., Anjorin, A., Backlund, L., Bass, N., Bauer, M., Baune, B. T., Bellivier, F., Bergen, S. E., Berrettini, W. H., Biernacka, J. M., Blackwood, D. H. R., Boen, E., Budde, M., Bunney, W., Burmeister, M., Byerley, W., Byrne, E. M., Cichon, S., Clarke, T. -K., Coleman, J. R. I., Craddock, N., Curtis, D., Czerski, P. M., Dale, A. M., Dalkner, N., Dannlowski, U., Degenhardt, F., Di Florio, A., Elvsashagen, T., Etain, B., Fischer, S. B., Forstner, A. J., Forty, L., Frank, J., Frye, M., Fullerton, J. M., Gade, K., Gaspar, H. A., Gershon, E. S., Gill, M., Goes, F. S., Gordon, S. D., Gordon-Smith, K., Green, M. J., Greenwood, T. A., Grigoroiu-Serbanescu, M., Guzman-Parra, J., Hauser, J., Hautzinger, M., Heilbronner, U., Herms, S., Hoffmann, P., Holland, D., Jamain, S., Jones, I., Jones, L. A., Kandaswamy, R., Kelsoe, J. R., Kennedy, J. L., Joachim, O. K., Kittel-Schneider, S., Kogevinas, M., Koller, A. C., Lavebratt, C., Lewis, C. M., Li, Q. S., Lissowska, J., Loohuis, L. M. O., Lucae, S., Maaser, A., Malt, U. F., Martin, N. G., Martinsson, L., Mcelroy, S. L., Mcmahon, F. J., Mcquillin, A., Melle, I., Metspalu, A., Millischer, V., Mitchell, P. B., Montgomery, G. W., Morken, G., Morris, D. W., Muller-Myhsok, B., Mullins, N., Myers, R. M., Nievergelt, C. M., Nordentoft, M., Adolfsson, A. N., Nothen, M. M., Ophoff, R. A., Owen, M. J., Paciga, S. A., Pato, C. N., Pato, M. T., Perlis, R. H., Perry, A., Potash, J. B., Reinbold, C. S., Rietschel, M., Rivera, M., Roberson, M., Schalling, M., Schofield, P. R., Schulze, T. G., Scott, L. J., Serretti, A., Sigurdsson, E., Smeland, O. B., Stordal, E., Streit, F., Strohmaier, J., Thorgeirsson, T. E., Treutlein, J., Turecki, G., Vaaler, A. E., Vieta, E., Vincent, J. B., Wang, Y., Witt, S. H., Zandi, P., Adan, R. A. H., Alfredsson, L., Ando, T., Aschauer, H., Baker, J. H., Bencko, V., Bergen, A. W., Birgegard, A., Perica, V. B., Brandt, H., Burghardt, R., Carlberg, L., Cassina, M., Clementi, M., Courtet, P., Crawford, S., Crow, S., Crowley, J. J., Danner, U. N., Davis, O. S. P., Degortes, D., Desocio, J. E., Dick, D. M., Dina, C., Docampo, E., Egberts, K., Ehrlich, S., Espeseth, T., Fernandez-Aranda, F., Fichter, M. M., Foretova, L., Forzan, M., Gambaro, G., Giegling, I., Gonidakis, F., Gorwood, P., Mayora, M. G., Guo, Y., Halmi, K. A., Hatzikotoulas, K., Hebebrand, J., Helder, S. G., Herpertz-Dahlmann, B., Herzog, W., Hinney, A., Imgart, H., Jimenez-Murcia, S., Johnson, C., Jordan, J., Julia, A., Kaminska, D., Karhunen, L., Karwautz, A., Kas, M. J. H., Kaye, W. H., Kennedy, M. A., Kim, Y. -R., Klareskog, L., Klump, K. L., Knudsen, G. P. S., Landen, M., Le Hellard, S., Levitan, R. D., Li, D., Lichtenstein, P., Maj, M., Marsal, S., Mcdevitt, S., Mitchell, J., Monteleone, P., Monteleone, A. M., Munn-Chernoff, M. A., Nacmias, B., Navratilova, M., O'Toole, J. K., Padyukov, L., Pantel, J., Papezova, H., Rabionet, R., Raevuori, A., Ramoz, N., Reichborn-Kjennerud, T., Ricca, V., Roberts, M., Rujescu, D., Rybakowski, F., Scherag, A., Schmidt, U., Seitz, J., Slachtova, L., Slof-Op't Landt, M. C. T., Slopien, A., Sorbi, S., Southam, L., Strober, M., Tortorella, A., Tozzi, F., Treasure, J., Tziouvas, K., van Elburg, A. A., Wade, T. D., Wagner, G., Walton, E., Watson, H. J., Wichmann, H. -E., Woodside, D. B., Zeggini, E., Zerwas, S., Zipfel, S., Adams, M. J., Andlauer, T. F. M., Berger, K., Binder, E. B., Boomsma, D. I., Castelao, E., Colodro-Conde, L., Direk, N., Docherty, A. R., Domenici, E., Domschke, K., Dunn, E. C., Foo, J. C., D, e. Geus E. J. C., Grabe, H. J., Hamilton, S. P., Horn, C., Hottenga, J. -J., Howard, D., Ising, M., Kloiber, S., Levinson, D. F., Lewis, G., Magnusson, P. K. E., Mbarek, H., Middeldorp, C. M., Mostafavi, S., Nyholt, D. R., Penninx, B. W., Peterson, R. E., Pistis, G., Porteous, D. J., Preisig, M., Quiroz, J. A., Schaefer, C., Schulte, E. C., Shi, J., Smith, D. J., Thomson, P. A., Tiemeier, H., Uher, R., van der Auwera, S., Weissman, M. M., Alexander, M., Begemann, M., Bramon, E., Buccola, N. G., Cairns, M. J., Campion, D., Carr, V. J., Cloninger, C. R., Cohen, D., Collier, D. A., Corvin, A., Delisi, L. E., Donohoe, G., Dudbridge, F., Duan, J., Freedman, R., Gejman, P. V., Golimbet, V., Godard, S., Ehrenreich, H., Hartmann, A. M., Henskens, F. A., Ikeda, M., Iwata, N., Jablensky, A. V., Joa, I., Jonsson, E. G., Kelly, B. J., Knight, J., Konte, B., Laurent-Levinson, C., Lee, J., Lencz, T., Lerer, B., Loughland, C. M., Malhotra, A. K., Mallet, J., Mcdonald, C., Mitjans, M., Mowry, B. J., Murphy, K. C., Murray, R. M., O'Neill, F. A., Oh, S. -Y., Palotie, A., Pantelis, C., Pulver, A. E., Petryshen, T. L., Quested, D. J., Riley, B., Sanders, A. R., Schall, U., Schwab, S. G., Scott, R. J., Sham, P. C., Silverman, J. M., Sim, K., Steixner, A. A., Tooney, P. A., van Os, J., Vawter, M. P., Walsh, D., Weiser, M., Wildenauer, D. B., Williams, N. M., Wormley, B. K., Zhang, F., Androutsos, C., Arnold, P. D., Barr, C. L., Barta, C., Bey, K., Bienvenu, O. J., Black, D. W., Brown, L. W., Budman, C., Cath, D., Cheon, K. -A., Ciullo, V., Coffey, B. J., Cusi, D., Davis, L. K., Denys, D., Depienne, C., Dietrich, A., Eapen, V., Falkai, P., Fernandez, T. V., Garcia-Delgar, B., Geller, D. A., Gilbert, D. L., Grados, M. A., Greenberg, E., Grunblatt, E., Hagstrom, J., Hanna, G. L., Hartmann, A., Hedderly, T., Heiman, G. A., Heyman, I., Hong, H. J., Huang, A., Huyser, C., Ibanez-Gomez, L., Khramtsova, E. A., Kim, Y. K., Kim, Y. -S., King, R. A., Koh, Y. -J., Konstantinidis, A., Kook, S., Kuperman, S., Leventhal, B. L., Lochner, C., Ludolph, A. G., Madruga-Garrido, M., Malaty, I., Maras, A., Mccracken, J. T., Meijer, I. A., Mir, P., Morer, A., Muller-Vahl, K. R., Munchau, A., Murphy, T. L., Naarden, A., Nagy, P., Nestadt, G., Nestadt, P. S., Nicolini, H., Nurmi, E. L., Okun, M. S., Paschou, P., Piras, F., Pittenger, C., Plessen, K. J., Richter, M. A., Rizzo, R., Robertson, M., Roessner, V., Ruhrmann, S., Samuels, J. F., Sandor, P., Schlogelhofer, M., Shin, E. -Y., Singer, H., Song, D. -H., Song, J., Spalletta, G., Stein, D. J., Stewart, S. E., Storch, E. A., Stranger, B., Stuhrmann, M., Tarnok, Z., Tischfield, J. A., Tubing, J., Visscher, F., Vulink, N., Wagner, M., Walitza, S., Wanderer, S., Woods, M., Worbe, Y., Zai, G., Zinner, S. H., Sullivan, P. F., Franke, B., Daly, M. J., Bulik, C. M., Mcintosh, A. M., O'Donovan, M. C., Zheutlin, A., Andreassen, O. A., Borglum, A. D., Breen, G., Edenberg, H. J., Fanous, A. H., Faraone, S. V., Gelernter, J., Mathews, C. A., Mattheisen, M., Mitchell, K. S., Neale, M. C., Nurnberger, J. I., Ripke, S., Santangelo, S. L., Scharf, J. M., Stein, M. B., Thornton, L. M., Walters, J. T. R., Wray, N. R., Geschwind, D. H., Neale, B. M., Kendler, K. S., and Smoller, J. W.
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Netherlands Twin Register (NTR) ,cross-disorder genetics ,Medizin ,Genome-wide association study ,Tourette syndrome ,functional genomics ,gene expression ,genetic architecture ,genetic correlation ,GWAS ,neurodevelopment ,pleiotropy ,psychiatric disorders ,Psychiatric genetics ,0302 clinical medicine ,Pleiotropy ,functional genomic ,WIDE ASSOCIATION ,cross-disorder genetic ,0303 health sciences ,Mental Disorders ,Genetic Pleiotropy ,HUMAN BRAIN ,INSIGHTS ,Autism spectrum disorder ,Schizophrenia ,DISEASES ,GENETIC CORRELATIONS ,medicine.medical_specialty ,Neurogenesis ,Quantitative Trait Loci ,BF ,Biology ,GENOTYPE IMPUTATION ,Psychiatric geneticscross-disorder geneticspsychiatric disorderspleiotropyneurodevelopmentGWASgenetic correlationgene expressiongenetic architecturefunctional genomics ,Article ,General Biochemistry, Genetics and Molecular Biology ,psychiatric disorder ,03 medical and health sciences ,SDG 3 - Good Health and Well-being ,medicine ,Humans ,Genetic Predisposition to Disease ,Bipolar disorder ,TRANSCRIPTOME ,Psychiatry ,030304 developmental biology ,Gwas ,Psychiatric Genetics ,Cross-disorder Genetics ,Functional Genomics ,Gene Expression ,Genetic Architecture ,Genetic Correlation ,Neurodevelopment ,Psychiatric Disorders ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,IDENTIFICATION ,MUTATIONS ,medicine.disease ,Genetic architecture ,DEMETHYLASE ,RC0321 ,1182 Biochemistry, cell and molecular biology ,030217 neurology & neurosurgery ,Genome-Wide Association Study - Abstract
Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.
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- 2019
20. Genome, Environment, Microbiome and Metabolome in Autism (GEMMA) Study Design: Biomarkers Identification for Precision Treatment and Primary Prevention of Autism Spectrum Disorders by an Integrated Multi-Omics Systems Biology Approach
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Troisi, Jacopo, Autio, Reija, Beopoulos, Thanos, Bravaccio, Carmela, Carraturo, Federica, Corrivetti, Giulio, Cunningham, Stephen, Devane, Samantha, Fallin, Daniele, Fetissov, Serguei, Gea, Manuel, Giorgi, Antonio, Iris, François, Joshi, Lokesh, Kadzielski, Sarah, Kraneveld, Aletta, Kumar, Himanshu, Ladd-Acosta, Christine, Leader, Geraldine, Mannion, Arlene, Maximin, Elise, Mezzelani, Alessandra, Milanesi, Luciano, Naudon, Laurent, Marzal, Lucia N Peralta, Pardo, Paula Perez, Prince, Naika Z, Rabot, Sylvie, Roeselers, Guus, Roos, Christophe, Roussin, Lea, Scala, Giovanni, Tuccinardi, Francesco Paolo, Fasano, Alessio, Afd Pharmacology, Pharmacology, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS), GEMMA project was funded by the European Commission by means of the Horizon 2020 program (call H2020-SC1-BHC-03-2018) with the project ID 825033., European Project: 7825033(1979), Afd Pharmacology, Pharmacology, Università degli Studi di Salerno (UNISA), University of Tampere [Finland], Bio-Modeling System [Paris], University of Naples Federico II, Promete srl, Azienda Sanitaria Locale [Salerno], National University of Ireland [Galway] (NUI Galway), Massachusetts General Hospital [Boston], Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), Medinok S.p.A., Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University [Utrecht], Danone Nutricia Research [Utrecht], Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Consiglio Nazionale delle Ricerche [Roma] (CNR), Euformatics, European Biomedical Research Institute of Salerno (EBRIS), European Commission Joint Research Centre 825033, Tampere University, Health Sciences, Troisi, J., Autio, R., Beopoulos, T., Bravaccio, C., Carraturo, F., Corrivetti, G., Cunningham, S., Devane, S., Fallin, D., Fetissov, S., Gea, M., Giorgi, A., Iris, F., Joshi, L., Kadzielski, S., Kraneveld, A., Kumar, H., Ladd-Acosta, C., Leader, G., Mannion, A., Maximin, E., Mezzelani, A., Milanesi, L., Naudon, L., Peralta Marzal, L. N., Pardo, P. P., Prince, N. Z., Rabot, S., Roeselers, G., Roos, C., Roussin, L., Scala, G., Tuccinardi, F. P., and Fasano, A.
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Proband ,Systems biology ,[SDV]Life Sciences [q-bio] ,microbiome ,autism ,Metabolomic ,Bioinformatics ,behavioral disciplines and activities ,Article ,lcsh:RC321-571 ,03 medical and health sciences ,precise medicine ,0302 clinical medicine ,study design ,mental disorders ,biomarker discovery ,medicine ,Metabolome ,Microbiome ,Biomarker discovery ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,030304 developmental biology ,0303 health sciences ,business.industry ,General Neuroscience ,medicine.disease ,metabolomics ,3. Good health ,Autism spectrum disorder ,[SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health ,Autism ,Observational study ,3111 Biomedicine ,business ,030217 neurology & neurosurgery - Abstract
Autism Spectrum Disorder (ASD) affects approximately 1 child in 54, with a 35-fold increase since 1960. Selected studies suggest that part of the recent increase in prevalence is likely attributable to an improved awareness and recognition, and changes in clinical practice or service availability. However, this is not sufficient to explain this epidemiological phenomenon. Research points to a possible link between ASD and intestinal microbiota because many children with ASD display gastro-intestinal problems. Current large-scale datasets of ASD are limited in their ability to provide mechanistic insight into ASD because they are predominantly cross-sectional studies that do not allow evaluation of perspective associations between early life microbiota composition/function and later ASD diagnoses. Here we describe GEMMA (Genome, Environment, Microbiome and Metabolome in Autism), a prospective study supported by the European Commission, that follows at-risk infants from birth to identify potential biomarker predictors of ASD development followed by validation on large multi-omics datasets. The project includes clinical (observational and interventional trials) and pre-clinical studies in humanized murine models (fecal transfer from ASD probands) and in vitro colon models. This will support the progress of a microbiome-wide association study (of human participants) to identify prognostic microbiome signatures and metabolic pathways underlying mechanisms for ASD progression and severity and potential treatment response. This work is conducted with support from the Advisory Board Members (California Institute of Technology, Winclove Probiotics, University of California Davis, Center for Autism and the Developing Brain, Ohio State University, the Autism Speaks Autism Treatment Network, Arizona State University, University College Cork), from the Consortium Partners: Fondazione EBRIS, in charge of project management and coordination and providing the gut permeability and immunological evaluation of the enrolled subjects; Nutricia Research Bv in charge of nutritional formulation development for interventional trial; Medinok Spa and Consiglio Nazionale Delle Ricerche (CNR) in charge of data analysis and multi-omics platform development; Bio Modeling Systems in charge of the mechanistic pathway hypothesis development; Euformatics in charge of analysis and the interpretation of the genomic variants of the patient material, and for the comparison of the variants from the different patient cohorts, Theoreo Srl and Imperial College Of Science in charge of metabolomics analysis and interpretation; National University of Ireland Galway, Azienda Sanitaria, Locale (ASL) Salerno and Massachusettse General Hospital for Children, in charge of enrollments for observation and interventional trials; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement in charge of analyzing the genomic and transcriptomic profiles of the host microbiota; Institut National de la Santé et de la Recherche Médicale, in charge of proteomics analysis; Utrecht University, in charge of pre-clinical studies; Tampereen Yliopisto, in charge of experimental design; Johns Hopkins University in charge of epigenomic evaluation and interpretation. Financial contributions were made by European Union. peer-reviewed
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- 2020
21. 3-generation family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions associated with autism: An open-source catalog of findings.
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Schendel D, Ejlskov L, Overgaard M, Jinwala Z, Kim V, Parner E, Kalkbrenner AE, Ladd Acosta C, Fallin MD, Xie S, Mortensen PB, and Lee BK
- Abstract
The relatively few conditions and family member types (e.g., sibling, parent) considered in investigations of family health history in autism spectrum disorder (ASD, or autism) limits understanding of the role of family history in autism etiology. For more comprehensive understanding and hypothesis-generation, we produced an open-source catalog of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are cataloged in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and nongenetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity., (© 2024 The Author(s). Autism Research published by International Society for Autism Research and Wiley Periodicals LLC.)
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- 2024
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22. Commonly used genomic arrays may lose information due to imperfect coverage of discovered variants for autism spectrum disorder.
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Yao M, Daniels J, Grosvenor L, Morrill V, Feinberg JI, Bakulski KM, Piven J, Hazlett HC, Shen MD, Newschaffer C, Lyall K, Schmidt RJ, Hertz-Picciotto I, Croen LA, Fallin MD, Ladd-Acosta C, Volk H, and Benke K
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- Humans, Multifactorial Inheritance, Genetic Predisposition to Disease, Male, Female, Genotype, Polymorphism, Single Nucleotide, Autism Spectrum Disorder genetics, Genome-Wide Association Study
- Abstract
Background: Common genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS., Methods: We studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants., Results: Prior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric., Limitations: The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries., Conclusions: We show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits., (© 2024. The Author(s).)
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- 2024
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23. Method for Testing Etiologic Heterogeneity Among Noncompeting Diagnoses, Applied to Impact of Perinatal Exposures on Autism and Attention Deficit Hyperactivity Disorder.
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Kalkbrenner AE, Zheng C, Yu J, Jenson TE, Kuhlwein T, Ladd-Acosta C, Grove J, and Schendel D
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- Humans, Female, Pregnancy, Risk Factors, Male, Smoking epidemiology, Child, Child, Preschool, Urban Population statistics & numerical data, Adult, Attention Deficit Disorder with Hyperactivity epidemiology, Autistic Disorder epidemiology, Prenatal Exposure Delayed Effects epidemiology
- Abstract
Background: Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic subcategorization because these disorders are heterogeneous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for noncompeting events in an open cohort of variable-length follow-up. Thus, we developed a new method., Methods: We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a codiagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism + ADHD. To calculate the risk of a single diagnosis (e.g., autism alone), we subtracted the risk for autism + ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors., Results: Urban residence was most strongly linked with autism + ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups., Conclusion: Our method allowed the calculation of appropriate P values to test the strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up., Competing Interests: Disclosure: C.L.-A. reports receiving consulting fees from the University of Iowa for providing expertise on epigenetics, outside the scope of this work. The other authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)
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- 2024
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24. Reproducibility between preschool and school-age Social Responsiveness Scale forms in the Environmental influences on Child Health Outcomes program.
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Patti MA, Croen LA, Dickerson AS, Joseph RM, Ames JL, Ladd-Acosta C, Ozonoff S, Schmidt RJ, Volk HE, Hipwell AE, Magee KE, Karagas M, McEvoy C, Landa R, Elliott MR, Mitchell DK, D'Sa V, Deoni S, Pievsky M, Wu PC, Barry F, Stanford JB, Bilder DA, Trasande L, Bush NR, and Lyall K
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- Humans, Child, Preschool, Child, Female, Male, Reproducibility of Results, Adolescent, Autism Spectrum Disorder, Child Health, Autistic Disorder, Social Behavior
- Abstract
Evidence suggests core autism trait consistency in older children, but development of these traits is variable in early childhood. The Social Responsiveness Scale (SRS) measures autism-related traits and broader autism phenotype, with two age-dependent forms in childhood (preschool, 2.5-4.5 years; school age, 4-18 years). Score consistency has been observed within forms, though reliability across forms has not been evaluated. Using data from the Environmental Influences on Child Health Outcomes (ECHO) program (n = 853), preschool, and school-age SRS scores were collected via maternal report when children were an average of 3.0 and 5.8 years, respectively. We compared reproducibility of SRS total scores (T-scores) and agreement above a clinically meaningful cutoff (T-scores ≥ 60) and examined predictors of discordance in cutoff scores across forms. Participant scores across forms were similar (mean difference: 3.3 points; standard deviation: 7), though preschool scores were on average lower than school-age scores. Most children (88%) were classified below the cutoff on both forms, and overall concordance was high (92%). However, discordance was higher in cohorts following younger siblings of autistic children (16%). Proportions of children with an autism diagnoses were also higher among those with discordant scores (27%) than among those with concordant scores (4%). Our findings indicate SRS scores are broadly reproducible across preschool and school-age forms, particularly for capturing broader, nonclinical traits, but also suggest that greater variability of autism-related traits in preschool-age children may reduce reliability with later school-age scores for those in the clinical range., (© 2024 International Society for Autism Research and Wiley Periodicals LLC.)
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- 2024
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25. Associations between cord blood acetaminophen biomarkers and childhood asthma with and without allergic comorbidities.
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Li Y, Hong X, Chandran A, Keet CA, Clish CB, Liang L, Jacobson LP, Wang X, and Ladd-Acosta C
- Subjects
- Humans, Female, Male, Pregnancy, Child, Infant, Newborn, Prenatal Exposure Delayed Effects epidemiology, Prenatal Exposure Delayed Effects blood, Child, Preschool, Maternal Exposure adverse effects, Analgesics, Non-Narcotic adverse effects, Adult, Dermatitis, Atopic blood, Dermatitis, Atopic epidemiology, Rhinitis, Allergic epidemiology, Rhinitis, Allergic blood, Acetaminophen adverse effects, Acetaminophen analogs & derivatives, Fetal Blood chemistry, Asthma blood, Asthma epidemiology, Biomarkers blood, Comorbidity
- Abstract
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., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
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- 2024
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26. Gene-environment interactions in human health.
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Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, and Wojcik GL
- Abstract
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., (© 2024. Springer Nature Limited.)
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- 2024
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27. Exposure to air pollution is associated with DNA methylation changes in sperm.
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Schrott R, Feinberg JI, Newschaffer CJ, Hertz-Picciotto I, Croen LA, Fallin MD, Volk HE, Ladd-Acosta C, and Feinberg AP
- Abstract
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 (NO
2 ), 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., Competing Interests: Dr Ladd-Acosta reports receiving consulting fees from the University of Iowa for providing expertise on autism spectrum disorder epigenetics outside of this work. No other authors have anything to declare., (© The Author(s) 2024. Published by Oxford University Press.)- Published
- 2024
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28. Prenatal Metal Exposures and Child Social Responsiveness Scale Scores in 2 Prospective Studies.
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Yu EX, Dou JF, Volk HE, Bakulski KM, Benke K, Hertz-Picciotto I, Schmidt RJ, Newschaffer CJ, Feinberg JI, Daniels J, Fallin MD, Ladd-Acosta C, and Hamra GB
- Abstract
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., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RJS has received funding to support the MARBLES Study from the Simons Foundation. RJS consults for the Beasley Law Firm and the Linus Technology, Inc. RJS has received travel support to present at the 35th Annual Meeting of the Organization of Teratology Information Specialists (OTIS). RJS has received compensation to serve on NIH Reviews., (© The Author(s) 2024.)
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- 2024
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29. Genetic liability for gastrointestinal inflammation disorders and association with gastrointestinal symptoms in children with and without autism.
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Morrill V, Benke K, Brinton J, Soke GN, Schieve LA, Fields V, Farzadegan H, Holingue C, Newschaffer CJ, Reynolds AM, Fallin MD, and Ladd-Acosta C
- Subjects
- Child, Humans, Genome-Wide Association Study, Diarrhea complications, Diarrhea genetics, Diarrhea diagnosis, Inflammation complications, Crohn Disease complications, Crohn Disease genetics, Autism Spectrum Disorder genetics, Autism Spectrum Disorder diagnosis, Colitis, Ulcerative complications, Colitis, Ulcerative genetics, Autistic Disorder, Gastrointestinal Diseases complications, Gastrointestinal Diseases genetics, Gastrointestinal Diseases diagnosis, Inflammatory Bowel Diseases complications, Inflammatory Bowel Diseases genetics
- Abstract
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., (© 2023 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics published by Wiley Periodicals LLC.)
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- 2024
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30. Epigenetic changes in sperm are associated with paternal and child quantitative autistic traits in an autism-enriched cohort.
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Feinberg JI, Schrott R, Ladd-Acosta C, Newschaffer CJ, Hertz-Picciotto I, Croen LA, Daniele Fallin M, Feinberg AP, and Volk HE
- Subjects
- Humans, Male, Female, Child, Preschool, Cohort Studies, Adult, Longitudinal Studies, Child, Pregnancy, Epigenesis, Genetic genetics, Spermatozoa metabolism, DNA Methylation genetics, Autism Spectrum Disorder genetics, Autistic Disorder genetics, Fathers
- Abstract
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., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)
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- 2024
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31. Sex-based differences in placental DNA methylation profiles related to gestational age: an NIH ECHO meta-analysis.
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Bulka CM, Everson TM, Burt AA, Marsit CJ, Karagas MR, Boyle KE, Niemiec S, Kechris K, Davidson EJ, Yang IV, Feinberg JI, Volk HE, Ladd-Acosta C, Breton CV, O'Shea TM, and Fry RC
- Subjects
- Child, Pregnancy, Humans, Male, Female, Infant, Gestational Age, Epigenesis, Genetic, Sex Characteristics, Placenta metabolism, DNA Methylation
- Abstract
The placenta undergoes many changes throughout gestation to support the evolving needs of the foetus. There is also a growing appreciation that male and female foetuses develop differently in utero , with unique epigenetic changes in placental tissue. Here, we report meta-analysed sex-specific associations between gestational age and placental DNA methylation from four cohorts in the National Institutes of Health (NIH) Environmental influences on Child Health Outcomes (ECHO) Programme (355 females/419 males, gestational ages 23-42 weeks). We identified 407 cytosine-guanine dinucleotides (CpGs) in females and 794 in males where placental methylation levels were associated with gestational age. After cell-type adjustment, 55 CpGs in females and 826 in males were significant. These were enriched for biological processes critical to the immune system in females and transmembrane transport in males. Our findings are distinct between the sexes: in females, associations with gestational age are largely explained by differences in placental cellular composition, whereas in males, gestational age is directly associated with numerous alterations in methylation levels.
- Published
- 2023
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32. Accelerated epigenetic age at birth and child emotional and behavioura development in early childhood: a meta-analysis of four prospective cohort studies in ECHO.
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Song AY, Bulka CM, Niemiec SS, Kechris K, Boyle KE, Marsit CJ, O'Shea TM, Fry RC, Lyall K, Fallin MD, Volk HE, and Ladd-Acosta C
- Subjects
- Child, Preschool, Humans, Infant, Newborn, DNA Methylation, Epigenesis, Genetic, Prospective Studies, Autism Spectrum Disorder, Premature Birth
- Abstract
Background: 'Epigenetic clocks' have been developed to accurately predict chronologic gestational age and have been associated with child health outcomes in prior work. Methods: We meta-analysed results from four prospective U.S cohorts investigating the association between epigenetic age acceleration estimated using blood DNA methylation collected at birth and preschool age Childhood Behavior Checklist (CBCL) scores. Results: Epigenetic ageing was not significantly associated with CBCL total problem scores (β = 0.33, 95% CI: -0.95, 0.28) and DSM-oriented pervasive development problem scores (β = -0.23, 95% CI: -0.61, 0.15). No associations were observed for other DSM-oriented subscales. Conclusions: The meta-analysis results suggest that epigenetic gestational age acceleration is not associated with child emotional and behavioural functioning for preschool age group. These findings may relate to our study population, which includes two cohorts enriched for ASD and one preterm birth cohort.; future work should address the role of epigenetic age in child health in other study populations. Abbreviations: DNAm: DNA methylation; CBCL: Child Behavioral Checklist; ECHO: Environmental Influences on Child Health Outcomes; EARLI: Early Autism Risk Longitudinal Investigation; MARBLES: Markers of Autism Risk in Babies - Learning Early Signs; ELGAN: Extremely Low Gestational Age Newborns; ASD: autism spectrum disorder; BMI: body mass index; DSM: Diagnostic and Statistical Manual of Mental Disorders.
- Published
- 2023
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33. Using Epigenetic Clocks to Characterize Biological Aging in Studies of Children and Childhood Exposures: a Systematic Review.
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Musci RJ, Raghunathan RS, Johnson SB, Klein L, Ladd-Acosta C, Ansah R, Hassoun R, and Voegtline KM
- Subjects
- Child, Humans, Epigenomics, Aging genetics, Epigenesis, Genetic, Biological Clocks genetics
- Abstract
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., (© 2023. Society for Prevention Research.)
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- 2023
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34. Evaluation of pediatric epigenetic clocks across multiple tissues.
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Fang F, Zhou L, Perng W, Marsit CJ, Knight AK, Cardenas A, Aung MT, Hivert MF, Aris IM, Goodrich JM, Smith AK, Gaylord A, Fry RC, Oken E, O'Connor G, Ruden DM, Trasande L, Herbstman JB, Camargo CA Jr, Bush NR, Dunlop AL, Dabelea DM, Karagas MR, Breton CV, Ober C, Everson TM, Page GP, and Ladd-Acosta C
- Subjects
- Pregnancy, Infant, Humans, Child, Female, Epigenomics, Epigenesis, Genetic, Placenta, DNA Methylation
- Abstract
Background: Epigenetic clocks are promising tools for assessing biological age. We assessed the accuracy of pediatric epigenetic clocks in gestational and chronological age determination., Results: Our study used data from seven tissue types on three DNA methylation profiling microarrays and found that the Knight and Bohlin clocks performed similarly for blood cells, while the Lee clock was superior for placental samples. The pediatric-buccal-epigenetic clock performed the best for pediatric buccal samples, while the Horvath clock is recommended for children's blood cell samples. The NeoAge clock stands out for its unique ability to predict post-menstrual age with high correlation with the observed age in infant buccal cell samples., Conclusions: Our findings provide valuable guidance for future research and development of epigenetic clocks in pediatric samples, enabling more accurate assessments of biological age., (© 2023. BioMed Central Ltd., part of Springer Nature.)
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- 2023
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35. Association between acetaminophen metabolites and CYP2E1 DNA methylation level in neonate cord blood in the Boston Birth Cohort.
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Li Y, Hong X, Liang L, Wang X, and Ladd-Acosta C
- Subjects
- Infant, Newborn, Pregnancy, Female, Humans, Fetal Blood, Acetaminophen, Birth Cohort, DNA Methylation, Cytochrome P-450 CYP2E1 genetics
- Abstract
Background: Acetaminophen is a commonly used medication by pregnant women and is known to cross the placenta. However, little is known about the biological mechanisms that regulate acetaminophen in the developing offspring. Cytochrome 2E1 (CYP2E1) is the primary enzyme responsible for the conversion of acetaminophen to its toxic metabolite. Ex vivo studies have shown that the CYP2E1 gene expression in human fetal liver and placenta is largely controlled by DNA methylation (DNAm) at CpG sites located in the gene body of CYP2E1 at the 5' end. To date, no population studies have examined the association between acetaminophen metabolite and fetal DNAm of CYP2E1 at birth., Methods: We utilized data from the Boston Birth Cohort (BBC) which represents an urban, low-income, racially and ethnically diverse population in Boston, Massachusetts. Acetaminophen metabolites were measured in the cord plasma of newborns enrolled in BBC between 2003 and 2013 using liquid chromatography-tandem mass spectrometry. DNAm at 28 CpG sites of CYP2E1 was measured by Illumina Infinium MethylationEPIC BeadChip. We used linear regression to identify differentially methylated CpG sites and the "DiffVar" method to identify differences in methylation variation associated with the detection of acetaminophen, adjusting for cell heterogeneity and batch effects. The false discovery rate (FDR) was calculated to account for multiple comparisons., Results: Among the 570 newborns included in this study, 96 (17%) had detectable acetaminophen in cord plasma. We identified 7 differentially methylated CpGs (FDR < 0.05) associated with the detection of acetaminophen and additional 4 CpGs showing a difference in the variation of methylation (FDR < 0.05). These CpGs were all located in the gene body of CYP2E1 at the 5' end and had a 3-6% lower average methylation level among participants with detectable acetaminophen compared to participants without. The CpG sites we identified overlap with previously identified DNase hypersensitivity and open chromatin regions in the ENCODE project, suggesting potential regulatory functions., Conclusions: In a US birth cohort, we found detection of cord biomarkers of acetaminophen was associated with DNAm level of CYP2E1 in cord blood. Our findings suggest that DNA methylation of CYP2E1 may be an important regulator of acetaminophen levels in newborns., (© 2023. BioMed Central Ltd., part of Springer Nature.)
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- 2023
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36. 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.
- Author
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Xu R, Hong X, Ladd-Acosta C, Buckley JP, Choi G, Wang G, Hou W, Wang X, Liang L, and Ji H
- Subjects
- Adult, Pregnancy, Female, Humans, Infant, Newborn, Child, Folic Acid, Micronutrients, Cytochrome P-450 CYP1A1 genetics, Bayes Theorem, Basic Helix-Loop-Helix Transcription Factors genetics, Basic Helix-Loop-Helix Transcription Factors metabolism, Repressor Proteins genetics, Repressor Proteins metabolism, Smoking, Vitamins, Biomarkers, DNA Methylation, Receptors, Aryl Hydrocarbon genetics
- Abstract
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., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)- Published
- 2023
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37. A Comparative Analysis of the Full and Short Versions of the Social Responsiveness Scale in Estimating an Established Autism Risk Factor Association in ECHO: Do we Get the Same Estimates?
- Author
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Patti MA, Ning X, Hosseini M, Croen LA, Joseph RM, Karagas MR, Ladd-Acosta C, Landa R, Messinger DS, Newschaffer CJ, Nguyen R, Ozonoff S, O'Shea TM, Schmidt RJ, Trevino CO, and Lyall K
- Abstract
Purpose: Prior work developed a shortened 16-item version of the Social Responsiveness Scale (SRS), a quantitative measure of social communication and autism spectrum disorder (ASD)-related traits. However, its properties for use in risk factor estimation have not been fully tested compared to the full SRS. We compared the associations between gestational age (previously established risk factor for ASD) and the 65-item "full" and 16-item "short" versions of the SRS to test the shortened version's ability to capture associations in epidemiologic analyses of ASD risk factors., Methods: We used data from participants in the Environmental influences on Child Health Outcomes (ECHO) Program (n = 2,760). SRS scores were collected via maternal/caregiver report when children were aged 2.5-18 years. We compared estimates of associations between gestational age and preterm birth between the full and short SRS using multivariable linear regression, quantile regression, and prediction methods., Results: Overall, associations based on full and short SRS scores were highly comparable. For example, we observed positive associations between preterm birth with both full ([Formula: see text]=2.8; 95% CI [1.7, 4.0]) and short ([Formula: see text]=2.9; 95% CI [1.6, 4.3]) SRS scores. Quantile regression analyses indicated similar direction and magnitude of associations across the distribution of SRS scores between gestational age with both short and full SRS scores., Conclusion: The comparability in estimates obtained for full and short SRS scores with an "established" ASD risk factor suggests ability of the shortened SRS in assessing associations with potential ASD-related risk factors and has implications for large-scale research studies seeking to reduce participant burden., (© 2023. The Author(s).)
- Published
- 2023
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38. The intersection of genome, epigenome and social experience in autism spectrum disorder: Exploring modifiable pathways for intervention.
- Author
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Strathearn L, Momany A, Kovács EH, Guiler W, and Ladd-Acosta C
- Subjects
- Child, Humans, Epigenesis, Genetic, Epigenome, DNA Methylation, Oxytocin, Autism Spectrum Disorder genetics
- Abstract
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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)
- Published
- 2023
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39. Analysis of Pregnancy Complications and Epigenetic Gestational Age of Newborns.
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Ladd-Acosta C, Vang E, Barrett ES, Bulka CM, Bush NR, Cardenas A, Dabelea D, Dunlop AL, Fry RC, Gao X, Goodrich JM, Herbstman J, Hivert MF, Kahn LG, Karagas MR, Kennedy EM, Knight AK, Mohazzab-Hosseinian S, Morin A, Niu Z, O'Shea TM, Palmore M, Ruden D, Schmidt RJ, Smith AK, Song A, Spindel ER, Trasande L, Volk H, Weisenberger DJ, and Breton CV
- Subjects
- Pregnancy, Child, Humans, Infant, Newborn, Female, Adolescent, Young Adult, Adult, Middle Aged, Infant, Cohort Studies, Gestational Age, Epigenesis, Genetic, Diabetes, Gestational epidemiology, Pre-Eclampsia, Hypertension, Pregnancy-Induced
- Abstract
Importance: Preeclampsia, gestational hypertension, and gestational diabetes, the most common pregnancy complications, are associated with substantial morbidity and mortality in mothers and children. Little is known about the biological processes that link the occurrence of these pregnancy complications with adverse child outcomes; altered biological aging of the growing fetus up to birth is one molecular pathway of increasing interest., Objective: To evaluate whether exposure to each of these 3 pregnancy complications (gestational diabetes, gestational hypertension, and preeclampsia) is associated with accelerated or decelerated gestational biological age in children at birth., Design, Setting, and Participants: Children included in these analyses were born between 1998 and 2018 and spanned multiple geographic areas of the US. Pregnancy complication information was obtained from maternal self-report and/or medical record data. DNA methylation measures were obtained from blood biospecimens collected from offspring at birth. The study used data from the national Environmental Influences on Child Health Outcomes (ECHO) multisite cohort study collected and recorded as of the August 31, 2021, data lock date. Data analysis was performed from September 2021 to December 2022., Exposures: Three pregnancy conditions were examined: gestational hypertension, preeclampsia, and gestational diabetes., Main Outcomes and Measures: Accelerated or decelerated biological gestational age at birth, estimated using existing epigenetic gestational age clock algorithms., Results: A total of 1801 child participants (880 male [48.9%]; median [range] chronological gestational age at birth, 39 [30-43] weeks) from 12 ECHO cohorts met the analytic inclusion criteria. Reported races included Asian (49 participants [2.7%]), Black (390 participants [21.7%]), White (1026 participants [57.0%]), and other races (92 participants [5.1%]) (ie, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, multiple races, and other race not specified). In total, 524 participants (29.0%) reported Hispanic ethnicity. Maternal ages ranged from 16 to 45 years of age with a median of 29 in the analytic sample. A range of maternal education levels, from less than high school (260 participants [14.4%]) to Bachelor's degree and above (629 participants [34.9%]), were reported. In adjusted regression models, prenatal exposure to maternal gestational diabetes (β, -0.423; 95% CI, -0.709 to -0.138) and preeclampsia (β, -0.513; 95% CI, -0.857 to -0.170), but not gestational hypertension (β, 0.003; 95% CI, -0.338 to 0.344), were associated with decelerated epigenetic aging among exposed neonates vs those who were unexposed. Modification of these associations, by sex, was observed with exposure to preeclampsia (β, -0.700; 95% CI, -1.189 to -0.210) and gestational diabetes (β, -0.636; 95% CI, -1.070 to -0.200), with associations observed among female but not male participants., Conclusions and Relevance: This US cohort study of neonate biological changes related to exposure to maternal pregnancy conditions found evidence that preeclampsia and gestational diabetes delay biological maturity, especially in female offspring.
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- 2023
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40. DNA methylation signatures as biomarkers of socioeconomic position.
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Rajaprakash M, Dean LT, Palmore M, Johnson SB, Kaufman J, Fallin DM, and Ladd-Acosta C
- Abstract
This review article provides a framework for the use of deoxyribonucleic acid (DNA) methylation (DNAm) biomarkers to study the biological embedding of socioeconomic position (SEP) and summarizes the latest developments in the area. It presents the emerging literature showing associations between individual- and neighborhood-level SEP exposures and DNAm across the life course. In contrast to questionnaire-based methods of assessing SEP, we suggest that DNAm biomarkers may offer an accessible metric to study questions about SEP and health outcomes, acting as a personal dosimeter of exposure. However, further work remains in standardizing SEP measures across studies and evaluating consistency across domains, tissue types, and time periods. Meta-analyses of epigenetic associations with SEP are offered as one approach to confirm the replication of DNAm loci across studies. The development of DNAm biomarkers of SEP would provide a method for examining its impact on health outcomes in a more robust way, increasing the rigor of epidemiological studies., (© The Author(s) 2023. Published by Oxford University Press.)
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- 2022
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41. Epigenetics as a Biomarker for Early-Life Environmental Exposure.
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Schrott R, Song A, and Ladd-Acosta C
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- Humans, Child, Prospective Studies, Epigenomics, Environmental Exposure adverse effects
- Abstract
Purpose of Review: There is interest in evaluating the developmental origins of health and disease (DOHaD) which emphasizes the role of prenatal and early-life environments on non-communicable health outcomes throughout the life course. The ability to rigorously assess and identify early-life risk factors for later health outcomes, including those with childhood onset, in large population samples is often limited due to measurement challenges such as impractical costs associated with prospective studies with a long follow-up duration, short half-lives for some environmental toxicants, and lack of biomarkers that capture inter-individual differences in biologic response to external environments., Recent Findings: Epigenomic patterns, and DNA methylation in particular, have emerged as a potential objective biomarker to address some of these study design and exposure measurement challenges. In this article, we summarize the literature to date on epigenetic changes associated with specific prenatal and early-life exposure domains as well as exposure mixtures in human observational studies and their biomarker potential. Additionally, we highlight evidence for other types of epigenetic patterns to serve as exposure biomarkers. Evidence strongly supports epigenomic biomarkers of exposure that are detectable across the lifespan and across a range of exposure domains. Current and future areas of research in this field seek to expand these lines of evidence to other environmental exposures, to determine their specificity, and to develop predictive algorithms and methylation scores that can be used to evaluate early-life risk factors for health outcomes across the life span., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
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- 2022
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42. Associations between accelerated parental biologic age, autism spectrum disorder, social traits, and developmental and cognitive outcomes in their children.
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Song AY, Bakulski K, Feinberg JI, Newschaffer C, Croen LA, Hertz-Picciotto I, Schmidt RJ, Farzadegan H, Lyall K, Fallin MD, Volk HE, and Ladd-Acosta C
- Subjects
- Child, Male, Pregnancy, Female, Humans, Prospective Studies, Parents, Cognition, Epigenesis, Genetic, Autism Spectrum Disorder epidemiology, Autism Spectrum Disorder genetics, Biological Products
- Abstract
Parental age is a known risk factor for autism spectrum disorder (ASD), however, studies to identify the biologic changes underpinning this association are limited. In recent years, "epigenetic clock" algorithms have been developed to estimate biologic age and to evaluate how the epigenetic aging impacts health and disease. In this study, we examined the relationship between parental epigenetic aging and their child's prospective risk of ASD and autism related quantitative traits in the Early Autism Risk Longitudinal Investigation study. Estimates of epigenetic age were computed using three robust clock algorithms and DNA methylation measures from the Infinium HumanMethylation450k platform for maternal blood and paternal blood specimens collected during pregnancy. Epigenetic age acceleration was defined as the residual of regressing chronological age on epigenetic age while accounting for cell type proportions. Multinomial logistic regression and linear regression models were completed adjusting for potential confounders for both maternal epigenetic age acceleration (n = 163) and paternal epigenetic age acceleration (n = 80). We found accelerated epigenetic aging in mothers estimated by Hannum's clock was significantly associated with lower cognitive ability and function in offspring at 12 months, as measured by Mullen Scales of Early Learning scores (β = -1.66, 95% CI: -3.28, -0.04 for a one-unit increase). We also observed a marginal association between accelerated maternal epigenetic aging by Horvath's clock and increased odds of ASD in offspring at 36 months of age (aOR = 1.12, 95% CI: 0.99, 1.26). By contrast, fathers accelerated aging was marginally associated with decreased ASD risk in their offspring (aOR = 0.83, 95% CI: 0.68, 1.01). Our findings suggest epigenetic aging could play a role in parental age risks on child brain development., (© 2022 International Society for Autism Research and Wiley Periodicals LLC.)
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- 2022
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43. Cardiometabolic Pregnancy Complications in Association With Autism-Related Traits as Measured by the Social Responsiveness Scale in ECHO.
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Lyall K, Ning X, Aschner JL, Avalos LA, Bennett DH, Bilder DA, Bush NR, Carroll KN, Chu SH, Croen LA, Dabelea D, Daniels JL, Duarte C, Elliott AJ, Fallin MD, Ferrara A, Hertz-Picciotto I, Hipwell AE, Jensen ET, Johnson SL, Joseph RM, Karagas M, Kelly RS, Lester BM, Margolis A, McEvoy CT, Messinger D, Neiderhiser JM, O'Connor TG, Oken E, Sathyanarayana S, Schmidt RJ, Sheinkopf SJ, Talge NM, Turi KN, Wright RJ, Zhao Q, Newschaffer C, Volk HE, Ladd-Acosta C, and Environmental Influences On Child Health Outcomes OBOPCF
- Subjects
- Child, Female, Humans, Infant, Newborn, Pregnancy, Autism Spectrum Disorder epidemiology, Autistic Disorder, Cardiovascular Diseases complications, Diabetes, Gestational, Premature Birth
- Abstract
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 (β = 4.64, 95% confidence interval: 3.27, 6.01) and gestational diabetes (β = 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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
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- 2022
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44. Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies.
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Ray D, Vergara C, Taub MA, Wojcik G, Ladd-Acosta C, Beaty TH, and Duggal P
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- Benchmarking, Female, Genetic Association Studies, Humans, Models, Genetic, Mothers, Parent-Child Relations, Polymorphism, Single Nucleotide, Cleft Lip genetics, Cleft Palate genetics
- Abstract
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., (© 2022 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.)
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- 2022
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45. Prenatal Exposure to Ambient Air Pollution and Epigenetic Aging at Birth in Newborns.
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Song AY, Feinberg JI, Bakulski KM, Croen LA, Fallin MD, Newschaffer CJ, Hertz-Picciotto I, Schmidt RJ, Ladd-Acosta C, and Volk HE
- Abstract
In utero air pollution exposure has been associated with adverse birth outcomes, yet effects of air pollutants on regulatory mechanisms in fetal growth and critical windows of vulnerability during pregnancy are not well understood. There is evidence that epigenetic alterations may contribute to these effects. DNA methylation (DNAm) based age estimators have been developed and studied extensively with health outcomes in recent years. Growing literature suggests environmental factors, such as air pollution and smoking, can influence epigenetic aging. However, little is known about the effect of prenatal air pollution exposure on epigenetic aging. In this study, we leveraged existing data on prenatal air pollution exposure and cord blood DNAm from 332 mother-child pairs in the Early Autism Risk Longitudinal Investigation (EARLI) and Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), two pregnancy cohorts enrolling women who had a previous child diagnosed with autism spectrum disorder, to assess the relationship of prenatal exposure to air pollution and epigenetic aging at birth. DNAm age was computed using existing epigenetic clock algorithms for cord blood tissue-Knight and Bohlin. Epigenetic age acceleration was defined as the residual of regressing chronological gestational age on DNAm age, accounting for cell type proportions. Multivariable linear regression models and distributed lag models (DLMs), adjusting for child sex, maternal race/ethnicity, study sites, year of birth, maternal education, were completed. In the single-pollutant analysis, we observed exposure to PM
2.5, PM10, and O3 during preconception period and pregnancy period were associated with decelerated epigenetic aging at birth. For example, pregnancy average PM10 exposure (per 10 unit increase) was associated with epigenetic age deceleration at birth (weeks) for both Knight and Bohlin clocks ( β = -0.62, 95% CI: -1.17, -0.06; β = -0.32, 95% CI: -0.63, -0.01, respectively). Weekly DLMs revealed that increasing PM2.5 during the first trimester and second trimester were associated with decelerated epigenetic aging and that increasing PM10 during the preconception period was associated with decelerated epigenetic aging, using the Bohlin clock estimate. Prenatal ambient air pollution exposure, particularly in early and mid-pregnancy, was associated with decelerated epigenetic aging at birth., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Song, Feinberg, Bakulski, Croen, Fallin, Newschaffer, Hertz-Picciotto, Schmidt, Ladd-Acosta and Volk.)- Published
- 2022
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46. Association between atopic diseases and neurodevelopmental disabilities in a longitudinal birth cohort.
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Qu X, Lee LC, Ladd-Acosta C, Hong X, Ji Y, Kalb LG, Volk HE, and Wang X
- Subjects
- Birth Cohort, Child, Female, Humans, Infant, Newborn, Risk Factors, Asthma complications, Asthma epidemiology, Attention Deficit Disorder with Hyperactivity complications, Attention Deficit Disorder with Hyperactivity epidemiology, Autism Spectrum Disorder complications, Autism Spectrum Disorder epidemiology, Dermatitis, Atopic complications, Dermatitis, Atopic epidemiology
- Abstract
Reports on the association between the prevalence of atopic diseases and neurodevelopmental disabilities (NDs) have been inconsistent in the literature. We investigated whether autism spectrum disorder (ASD), attention deficit-hyperactivity disorders (ADHD), and other NDs are more prevalent in children with asthma, atopic dermatitis (AD) and allergic rhinitis (AR) compared to those without specific atopic conditions. A total of 2580 children enrolled at birth were followed prospectively, of which 119 have ASD, 423 have ADHD, 765 have other NDs, and 1273 have no NDs. Atopic diseases and NDs were defined based on physician diagnoses in electronic medical records. Logistic regressions adjusting for maternal and child characteristics estimated the associations between NDs (i.e., ASD, ADHD, and other NDs) and asthma, AD and AR, respectively. Children with asthma, AD or AR had a greater likelihood of having ADHD or other NDs compared with children without specific atopic conditions. The association between ASD and asthma diminished after adjusting for maternal and child factors. Either mothers or children having atopic conditions and both mothers and children with atopic conditions were associated with a higher prevalence of ADHD in children, compared with neither mothers nor children having atopic conditions. Children diagnosed with multiple atopic diseases were more likely to have NDs compared with those without or with only one type of atopic disease. In conclusion, in this U.S. urban birth cohort, children with atopic diseases had a higher co-morbidity of NDs. The findings have implications for etiologic research that searches for common early life antecedents of NDs and atopic conditions. Findings from this study also should raise awareness among health care providers and parents about the possible co-occurrence of both NDs and atopic conditions, which calls for coordinated efforts to screen, prevent and manage NDs and atopic conditions., (© 2022 International Society for Autism Research and Wiley Periodicals LLC.)
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- 2022
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47. Individual and Combined Association Between Prenatal Polysubstance Exposure and Childhood Risk of Attention-Deficit/Hyperactivity Disorder.
- Author
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Garrison-Desany HM, Hong X, Maher BS, Beaty TH, Wang G, Pearson C, Liang L, Wang X, and Ladd-Acosta C
- Subjects
- Analgesics, Opioid, Child, Cohort Studies, Female, Humans, Infant, Newborn, Male, Mothers, Pregnancy, Attention Deficit Disorder with Hyperactivity diagnosis, Attention Deficit Disorder with Hyperactivity epidemiology, Attention Deficit Disorder with Hyperactivity etiology, Cannabis, Substance-Related Disorders complications, Substance-Related Disorders epidemiology
- Abstract
Importance: Polysubstance use among pregnant women has increased because of the opioid epidemic and the increasing legalization of cannabis along with persistent tobacco and alcohol consumption. Previous research on prenatal substance use and the child's risk of attention-deficit/hyperactivity disorder (ADHD) has mostly focused on single-substance exposures; simultaneous examination of multiple substance use and assessment of their synergistic health consequences is needed., Objectives: To assess the consequences of the use of specific substances during pregnancy, investigate whether the interaction of multiple prenatal substance exposures is associated with increases in the risk of childhood ADHD, and estimate the aggregate burden of polysubstance exposure during gestation., Design, Setting, and Participants: This cohort study analyzed data from the Boston Birth Cohort from 1998 to 2019. The sample of the present study comprised a multiethnic urban cohort of mother-child pairs who were predominantly low income. A total of 3138 children who were enrolled shortly after birth at Boston Medical Center were included and followed up from age 6 months to 21 years., Exposures: Substance use during pregnancy was identified based on self-reported tobacco smoking, alcohol consumption, and use of cannabis, cocaine, or opioids in any trimester of pregnancy. Diagnostic codes for neonatal opioid withdrawal syndrome or neonatal abstinence syndrome from the International Classification of Diseases, Ninth Revision, and the International Classification of Diseases, Tenth Revision, were also used to identify opioid exposure during gestation., Main Outcomes and Measures: ADHD diagnosis in the child's electronic medical record., Results: Among 3138 children (1583 boys [50.4%]; median age, 12 years [IQR, 9-14 years]; median follow-up, 10 years [IQR, 7-12 years]) in the final analytic sample, 486 (15.5%) had an ADHD diagnosis and 2652 (84.5%) were neurotypical. The median postnatal follow-up duration was 12 years (IQR, 9-14 years). Among mothers, 46 women (1.5%) self-identified as Asian (non-Pacific Islander), 701 (22.3%) as Hispanic, 1838 (58.6%) as non-Hispanic Black, 227 (7.2%) as non-Hispanic White, and 326 (10.4%) as other races and/or ethnicities (including American Indian or Indigenous, Cape Verdean, Pacific Islander, multiracial, other, or unknown). A total of 759 women (24.2%) reported the use of at least 1 substance during pregnancy, with tobacco being the most frequently reported (580 women [18.5%]). Cox proportional hazards models revealed that opioid exposure (60 children) had the highest adjusted hazard ratio (HR) for ADHD (2.19; 95% CI, 1.10-4.37). After including main statistical effects of all individual substances in an elastic net regression model, the HR of opioids was reduced to 1.60, and evidence of a statistical interaction between opioids and both cannabis and alcohol was found, producing 1.42 and 1.15 times higher risk of ADHD, respectively. The interaction between opioids and smoking was also associated with a higher risk of ADHD (HR, 1.17)., Conclusions and Relevance: The findings of this study suggest that it is important to consider prenatal concurrent exposure to multiple substances and their possible interactions when counseling women regarding substance use during pregnancy, the future risk of ADHD for their children, and strategies for cessation and treatment programs.
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- 2022
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48. Maternal tobacco smoking and offspring autism spectrum disorder or traits in ECHO cohorts.
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Hertz-Picciotto I, Korrick SA, Ladd-Acosta C, Karagas MR, Lyall K, Schmidt RJ, Dunlop AL, Croen LA, Dabelea D, Daniels JL, Duarte CS, Fallin MD, Karr CJ, Lester B, Leve LD, Li Y, McGrath M, Ning X, Oken E, Sagiv SK, Sathyanaraya S, Tylavsky F, Volk HE, Wakschlag LS, Zhang M, O'Shea TM, and Musci RJ
- Subjects
- Child, Female, Humans, Infant, Newborn, Mothers psychology, Odds Ratio, Pregnancy, Tobacco Smoking, United States, Autism Spectrum Disorder epidemiology, Autism Spectrum Disorder etiology, Autistic Disorder complications, Prenatal Exposure Delayed Effects epidemiology
- Abstract
Given inconsistent evidence on preconception or prenatal tobacco use and offspring autism spectrum disorder (ASD), this study assessed associations of maternal smoking with ASD and ASD-related traits. Among 72 cohorts in the Environmental Influences on Child Health Outcomes consortium, 11 had ASD diagnosis and prenatal tobaccosmoking (n = 8648). and 7 had Social Responsiveness Scale (SRS) scores of ASD traits (n = 2399). Cohorts had diagnoses alone (6), traits alone (2), or both (5). Diagnoses drew from parent/caregiver report, review of records, or standardized instruments. Regression models estimated smoking-related odds ratios (ORs) for diagnoses and standardized mean differences for SRS scores. Cohort-specific ORs were meta-analyzed. Overall, maternal smoking was unassociated with child ASD (adjusted OR, 1.08; 95% confidence interval [CI], 0.72-1.61). However, heterogeneity across studies was strong: preterm cohorts showed reduced ASD risk for exposed children. After excluding preterm cohorts (biased by restrictions on causal intermediate and exposure opportunity) and small cohorts (very few ASD cases in either smoking category), the adjusted OR for ASD from maternal smoking was 1.44 (95% CI, 1.02-2.03). Children of smoking (versus non-smoking) mothers had more ASD traits (SRS T-score + 2.37 points, 95% CI, 0.73-4.01 points), with results homogeneous across cohorts. Maternal preconception/prenatal smoking was consistently associated with quantitative ASD traits and modestly associated with ASD diagnosis among sufficiently powered United States cohorts of non-preterm children. Limitations resulting from self-reported smoking and unmeasured confounders preclude definitive conclusions. Nevertheless, counseling on potential and known risks to the child from maternal smoking is warranted for pregnant women and pregnancy planners. LAY SUMMARY: Evidence on the association between maternal prenatal smoking and the child's risk for autism spectrum disorder has been conflicting, with some studies reporting harmful effects, and others finding reduced risks. Our analysis of children in the ECHO consortium found that maternal prenatal tobacco smoking is consistently associated with an increase in autism-related symptoms in the general population and modestly associated with elevated risk for a diagnosis of autism spectrum disorder when looking at a combined analysis from multiple studies that each included both pre- and full-term births. However, this study is not proof of a causal connection. Future studies to clarify the role of smoking in autism-like behaviors or autism diagnoses should collect more reliable data on smoking and measure other exposures or lifestyle factors that might have confounded our results., (© 2022 The Authors. Autism Research published by International Society for Autism Research and Wiley Periodicals LLC.)
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- 2022
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49. Maternal blood metal concentrations and whole blood DNA methylation during pregnancy in the Early Autism Risk Longitudinal Investigation (EARLI).
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Aung MT, M Bakulski K, Feinberg JI, F Dou J, D Meeker J, Mukherjee B, Loch-Caruso R, Ladd-Acosta C, Volk HE, Croen LA, Hertz-Picciotto I, Newschaffer CJ, and Fallin MD
- Subjects
- Basic Helix-Loop-Helix Transcription Factors genetics, Epigenome, Female, Genes, Homeobox, Homeodomain Proteins genetics, Humans, Autistic Disorder genetics, Autistic Disorder metabolism, DNA Methylation, Metals blood, Metals metabolism, Metals toxicity, Pregnancy
- Abstract
The maternal epigenome may be responsive to prenatal metals exposures. We tested whether metals are associated with concurrent differential maternal whole blood DNA methylation. In the Early Autism Risk Longitudinal Investigation cohort, we measured first or second trimester maternal blood metals concentrations (cadmium, lead, mercury, manganese, and selenium) using inductively coupled plasma mass spectrometry. DNA methylation in maternal whole blood was measured on the Illumina 450 K array. A subset sample of 97 women had both measures available for analysis, all of whom did not report smoking during pregnancy. Linear regression was used to test for site-specific associations between individual metals and DNA methylation, adjusting for cell type composition and confounding variables. Discovery gene ontology analysis was conducted on the top 1,000 sites associated with each metal. We observed hypermethylation at 11 DNA methylation sites associated with lead (FDR False Discovery Rate q -value <0.1), near the genes CYP24A1, ASCL2, FAT1, SNX31, NKX6-2, LRC4C, BMP7, HOXC11, PCDH7, ZSCAN18 , and VIPR2 . Lead-associated sites were enriched (FDR q -value <0.1) for the pathways cell adhesion, nervous system development, and calcium ion binding. Manganese was associated with hypermethylation at four DNA methylation sites (FDR q -value <0.1), one of which was near the gene ARID2 . Manganese-associated sites were enriched for cellular metabolism pathways (FDR q -value<0.1). Effect estimates for DNA methylation sites associated ( p < 0.05) with cadmium, lead, and manganese were highly correlated (Pearson ρ > 0.86). DNA methylation sites associated with lead and manganese may be potential biomarkers of exposure or implicate downstream gene pathways.
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- 2022
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50. Evaluating the interrelations between the autism polygenic score and psychiatric family history in risk for autism.
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Schendel D, Munk Laursen T, Albiñana C, Vilhjalmsson B, Ladd-Acosta C, Fallin MD, Benke K, Lee B, Grove J, Kalkbrenner A, Ejlskov L, Hougaard D, Bybjerg-Grauholm J, Baekvad-Hansen M, Børglum AD, Werge T, Nordentoft M, Mortensen PB, and Agerbo E
- Subjects
- Case-Control Studies, Humans, Multifactorial Inheritance genetics, Risk Factors, Siblings, Autism Spectrum Disorder genetics, Autistic Disorder genetics
- Abstract
Psychiatric family history or a high autism polygenic risk score (PRS) have been separately linked to autism spectrum disorder (ASD) risk. The study aimed to simultaneously consider psychiatric family history and individual autism genetic liability (PRS) in autism risk. We performed a case-control study of all Denmark singleton births, May 1981-December 2005, in Denmark at their first birthday and a known mother. Cases were diagnosed with ASD before 2013 and controls comprised a random sample of 30,000 births without ASD, excluding persons with non-Denmark-born parents, missing ASD PRS, non-European ancestry. Adjusted odds ratios (aOR) were estimated for ASD by PRS decile and by psychiatric history in parents or full siblings (8 mutually-exclusive categories) using logistic regression. Adjusted ASD PRS z-score least-squares means were estimated by psychiatric family history category. ASD risk (11,339 ASD cases; 20,175 controls) from ASD PRS was not substantially altered after accounting for psychiatric family history (e.g., ASD PRS 10th decile aOR: 2.35 (95% CI 2.11-2.63) before vs 2.11 (95% CI 1.91-2.40) after adjustment) nor from psychiatric family history after accounting for ASD PRS (e.g., ASD family history aOR: 6.73 (95% CI 5.89-7.68) before vs 6.32 (95% CI 5.53-7.22) after adjustment). ASD risk from ASD PRS varied slightly by psychiatric family history. While ASD risk from psychiatric family history was not accounted for by ASD PRS and vice versa, risk overlap between the two factors will likely increase as measures of genetic risk improve. The two factors are best viewed as complementary measures of family-based autism risk. LAY SUMMARY: Autism risk from a history of mental disorders in the immediate family was not explained by a measure of individual genetic risk (autism polygenic risk score) and vice versa. That is, genetic risk did not appear to overlap family history risk. As genetic measures for autism improve then the overlap in autism risk from family history versus genetic factors will likely increase, but further study may be needed to fully determine the components of risk and how they are inter-related between these key family factors. Meanwhile, the two factors may be best viewed as complementary measures of autism family-based risk., (© 2021 International Society for Autism Research and Wiley Periodicals LLC.)
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- 2022
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