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1. Prevalence and Clinical Profile of Severe Acute Respiratory Syndrome Coronavirus 2 Infection among Farmworkers, California, USA, June-November 2020

Author

Lewnard, Joseph A, Mora, Ana M, Nkwocha, Oguchi, Kogut, Katherine, Rauch, Stephen A, Morga, Norma, Hernandez, Samantha, Wong, Marcus P, Huen, Karen, Andrejko, Kristin, Jewell, Nicholas P, Parra, Kimberly L, Holland, Nina, Harris, Eva, Cuevas, Maximiliano, Eskenazi, Brenda, and CHAMACOS-Project-19 Study Team

Subjects
Adult, serosurvey, Clinical Sciences, coronavirus, infection prevalence, Microbiology, California, Vaccine Related, respiratory infections, Clinical Research, Biodefense, Prevalence, Humans, viruses, Prospective Studies, Lung, essential workers, SARS, Farmers, CHAMACOS-Project-19 Study Team, SARS-CoV-2, Prevention, Salinas Valley, COVID-19, Pneumonia, United States, zoonoses, Infectious Diseases, Emerging Infectious Diseases, Good Health and Well Being, coronavirus disease, Medical Microbiology, Pneumonia & Influenza, Public Health and Health Services, Infection, farmworkers, severe acute respiratory syndrome coronavirus 2
Abstract

During the ongoing coronavirus disease (COVID-19) pandemic, farmworkers in the United States are considered essential personnel and continue in-person work. We conducted prospective surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and antibody prevalence among farmworkers in Salinas Valley, California, during June 15-November 30, 2020. We observed 22.1% (1,514/6,864) positivity for SARS-CoV-2 infection among farmworkers compared with 17.2% (1,255/7,305) among other adults from the same communities (risk ratio 1.29, 95% CI 1.20-1.37). In a nested study enrolling 1,115 farmworkers, prevalence of current infection was 27.7% among farmworkers reporting >1 COVID-19 symptom and 7.2% among farmworkers without symptoms (adjusted odds ratio 4.16, 95% CI 2.85-6.06). Prevalence of SARS-CoV-2 antibodies increased from 10.5% (95% CI 6.0%-18.4%) during July 16-August 31 to 21.2% (95% CI 16.6%-27.4%) during November 1-30. High SARS-CoV-2 infection prevalence among farmworkers underscores the need for vaccination and other preventive interventions.

Published
2021

2. Additional file 3 of Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age

Author

Merid, Simon, Novoloaca, Alexei, Sharp, Gemma, Küpers, Leanne, Kho, Alvin, Roy, Ritu, Gao, Lu, Annesi-Maesano, Isabella, Jain, Pooja, Plusquin, Michelle, Kogevinas, Manolis, Allard, Catherine, Vehmeijer, Florianne, Kazmi, Nabila, Salas, Lucas, Rezwan, Faisal, Hongmei Zhang, Sebert, Sylvain, Czamara, Darina, Rifas-Shiman, Sheryl, Melton, Phillip, Lawlor, Debbie, Pershagen, Göran, Breton, Carrie, Huen, Karen, Baiz, Nour, Gagliardi, Luigi, Nawrot, Tim, Corpeleijn, Eva, Perron, Patrice, Duijts, Liesbeth, Nohr, Ellen, Mariona Bustamante, Ewart, Susan, Karmaus, Wilfried, Shanshan Zhao, Page, Christian, Herceg, Zdenko, Marjo-Riitta Jarvelin, Lahti, Jari, Baccarelli, Andrea, Anderson, Denise, Priyadarshini Kachroo, Relton, Caroline, Bergström, Anna, Eskenazi, Brenda, Soomro, Munawar, Vineis, Paolo, Snieder, Harold, Bouchard, Luigi, Jaddoe, Vincent, Sørensen, Thorkild, Vrijheid, Martine, S. Arshad, Holloway, John, Håberg, Siri, Magnus, Per, Dwyer, Terence, Binder, Elisabeth, DeMeo, Dawn, Vonk, Judith, Newnham, John, Kelan Tantisira, Kull, Inger, Wiemels, Joseph, Heude, Barbara, Sunyer, Jordi, Nystad, Wenche, Munthe-Kaas, Monica, Räikkönen, Katri, Oken, Emily, Rae-Chi Huang, Weiss, Scott, Antó, Josep, Bousquet, Jean, Kumar, Ashish, Söderhäll, Cilla, Almqvist, Catarina, Cardenas, Andres, Gruzieva, Olena, Xu, Cheng-Jian, Reese, Sarah, Kere, Juha, Brodin, Petter, Solomon, Olivia, Wielscher, Matthias, Holland, Nina, Ghantous, Akram, Marie-France Hivert, Felix, Janine, Koppelman, Gerard, London, Stephanie, and Melén, Erik

Abstract

Figure S1. Forest plot for the top 10 Bonferroni-significant CpGs from the meta-analysis on the association between continuous GA and offspring DNA methylation at birth adjusted for estimated cell proportions. Figure S2. Sensitivity analysis: Correlation of the point estimates for the no complications model main association of DNA methylation with gestational age (y-axis representing 3648 participants from 17 cohorts) with point estimates for a meta-analysis after excluding three cohorts (MoBa1, MoBa2 and ALSPAC) that were included in a previous publication1,2 (x-axis representing 2190 participants from 14 cohorts). Figure S3. Correlations between methylation and gene expression levels for selected four pairs. First, we created residuals for mRNA expression and residuals for DNA methylation and used linear regression models to evaluate correlations between expression residuals and methylation residuals. These residual models were adjusted for covariates, estimated white blood cell proportions, and technical variation. Figure S4. Sensitivity analysis: Correlation of the point estimates for the no complications model main association of DNA methylation with gestational age (y-axis representing 3648 participants from 17 cohorts) with point estimates for a meta-analysis after excluding Non-European three cohorts (CBC, CHS and CHAMACOS) (x-axis representing 3290 participants from 14 cohorts).

Published
2020
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3. Additional file 2 of Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age

Author

Merid, Simon, Novoloaca, Alexei, Sharp, Gemma, Küpers, Leanne, Kho, Alvin, Roy, Ritu, Gao, Lu, Annesi-Maesano, Isabella, Jain, Pooja, Plusquin, Michelle, Kogevinas, Manolis, Allard, Catherine, Vehmeijer, Florianne, Kazmi, Nabila, Salas, Lucas, Rezwan, Faisal, Hongmei Zhang, Sebert, Sylvain, Czamara, Darina, Rifas-Shiman, Sheryl, Melton, Phillip, Lawlor, Debbie, Pershagen, Göran, Breton, Carrie, Huen, Karen, Baiz, Nour, Gagliardi, Luigi, Nawrot, Tim, Corpeleijn, Eva, Perron, Patrice, Duijts, Liesbeth, Nohr, Ellen, Mariona Bustamante, Ewart, Susan, Karmaus, Wilfried, Shanshan Zhao, Page, Christian, Herceg, Zdenko, Marjo-Riitta Jarvelin, Lahti, Jari, Baccarelli, Andrea, Anderson, Denise, Priyadarshini Kachroo, Relton, Caroline, Bergström, Anna, Eskenazi, Brenda, Soomro, Munawar, Vineis, Paolo, Snieder, Harold, Bouchard, Luigi, Jaddoe, Vincent, Sørensen, Thorkild, Vrijheid, Martine, S. Arshad, Holloway, John, Håberg, Siri, Magnus, Per, Dwyer, Terence, Binder, Elisabeth, DeMeo, Dawn, Vonk, Judith, Newnham, John, Kelan Tantisira, Kull, Inger, Wiemels, Joseph, Heude, Barbara, Sunyer, Jordi, Nystad, Wenche, Munthe-Kaas, Monica, Räikkönen, Katri, Oken, Emily, Rae-Chi Huang, Weiss, Scott, Antó, Josep, Bousquet, Jean, Kumar, Ashish, Söderhäll, Cilla, Almqvist, Catarina, Cardenas, Andres, Gruzieva, Olena, Xu, Cheng-Jian, Reese, Sarah, Kere, Juha, Brodin, Petter, Solomon, Olivia, Wielscher, Matthias, Holland, Nina, Ghantous, Akram, Marie-France Hivert, Felix, Janine, Koppelman, Gerard, London, Stephanie, and Melén, Erik

Abstract

Supplementary information.

Published
2020
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4. Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight

Author

Küpers, Leanne K, Monnereau, Claire, Sharp, Gemma C, Yousefi, Paul, Salas, Lucas A, Ghantous, Akram, Page, Christian M, Reese, Sarah E, Wilcox, Allen J, Czamara, Darina, Starling, Anne P, Novoloaca, Alexei, Lent, Samantha, Roy, Ritu, Hoyo, Cathrine, Breton, Carrie V, Allard, Catherine, Just, Allan C, Bakulski, Kelly M, Holloway, John W, Everson, Todd M, Xu, Cheng-Jian, Huang, Rae-Chi, van der Plaat, Diana A, Wielscher, Matthias, Merid, Simon Kebede, Ullemar, Vilhelmina, Rezwan, Faisal I, Lahti, Jari, van Dongen, Jenny, Langie, Sabine AS, Richardson, Tom G, Magnus, Maria C, Nohr, Ellen A, Xu, Zongli, Duijts, Liesbeth, Zhao, Shanshan, Zhang, Weiming, Plusquin, Michelle, DeMeo, Dawn L, Solomon, Olivia, Heimovaara, Joosje H, Jima, Dereje D, Gao, Lu, Bustamante, Mariona, Perron, Patrice, Wright, Robert O, Hertz-Picciotto, Irva, Zhang, Hongmei, Karagas, Margaret R, Gehring, Ulrike, Marsit, Carmen J, Beilin, Lawrence J, Vonk, Judith M, Jarvelin, Marjo-Riitta, Bergström, Anna, Örtqvist, Anne K, Ewart, Susan, Villa, Pia M, Moore, Sophie E, Willemsen, Gonneke, Standaert, Arnout RL, Håberg, Siri E, Sørensen, Thorkild IA, Taylor, Jack A, Räikkönen, Katri, Yang, Ivana V, Kechris, Katerina, Nawrot, Tim S, Silver, Matt J, Gong, Yun Yun, Richiardi, Lorenzo, Kogevinas, Manolis, Litonjua, Augusto A, Eskenazi, Brenda, Huen, Karen, Mbarek, Hamdi, Maguire, Rachel L, Dwyer, Terence, Vrijheid, Martine, Bouchard, Luigi, Baccarelli, Andrea A, Croen, Lisa A, Karmaus, Wilfried, Anderson, Denise, de Vries, Maaike, Sebert, Sylvain, Kere, Juha, Karlsson, Robert, Arshad, Syed Hasan, Hämäläinen, Esa, Routledge, Michael N, Boomsma, Dorret I, Feinberg, Andrew P, Newschaffer, Craig J, Govarts, Eva, Moisse, Matthieu, Fallin, M Daniele, Melén, Erik, and Prentice, Andrew M

Subjects
Adult, Male, Adolescent, Reproductive health and childbirth, Body Mass Index, Fetal Development, Fetus, Folic Acid, Genetic, Pregnancy, Infant Mortality, Genetics, 2.1 Biological and endogenous factors, Humans, Birth Weight, Aetiology, Child, Pediatric, Genome, Human Genome, Smoking, Infant, DNA, DNA Methylation, Newborn, Good Health and Well Being, Prenatal Exposure Delayed Effects, CpG Islands, Female, Epigenesis, Human, Genome-Wide Association Study
Abstract

Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (PBonferroni

Published
2019

5. Additional file 1: Figure S1. of CpG Methylation across the adipogenic PPARγ gene and its relationship with birthweight and child BMI at 9 years

Author

Volberg, Vitaly, Yousefi, Paul, Huen, Karen, Harley, Kim, Eskenazi, Brenda, and Holland, Nina

Abstract

PPARγ Promoter and Gene Body CpG Sites. Figure S1 shows the distribution of 23 CpG sites (red squares) measured by the Illumina Methylation 450 K Array. Blue squares indicate all other CpG sites (N = 183). Numbers in the DNA sequence, such as “2100” are shorthand for number of base pairs. (PPTX 295 kb)

Published
2017
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6. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis

Author

Joubert, Bonnie R, Felix, Janine F, Yousefi, Paul, Bakulski, Kelly M, Just, Allan C, Breton, Carrie, Reese, Sarah E, Markunas, Christina A, Richmond, Rebecca C, Xu, Cheng-Jian, Küpers, Leanne K, Oh, Sam S, Hoyo, Cathrine, Gruzieva, Olena, Söderhäll, Cilla, Salas, Lucas A, Baïz, Nour, Zhang, Hongmei, Lepeule, Johanna, Ruiz, Carlos, Ligthart, Symen, Wang, Tianyuan, Taylor, Jack A, Duijts, Liesbeth, Sharp, Gemma C, Jankipersadsing, Soesma A, Nilsen, Roy M, Vaez, Ahmad, Fallin, M Daniele, Hu, Donglei, Litonjua, Augusto A, Fuemmeler, Bernard F, Huen, Karen, Kere, Juha, Kull, Inger, Munthe-Kaas, Monica Cheng, Gehring, Ulrike, Bustamante, Mariona, Saurel-Coubizolles, Marie José, Quraishi, Bilal M, Ren, Jie, Tost, Jörg, Gonzalez, Juan R, Peters, Marjolein J, Håberg, Siri E, Xu, Zongli, van Meurs, Joyce B, Gaunt, Tom R, Kerkhof, Marjan, Corpeleijn, Eva, Feinberg, Andrew P, Eng, Celeste, Baccarelli, Andrea A, Benjamin Neelon, Sara E, Bradman, Asa, Merid, Simon Kebede, Bergström, Anna, Herceg, Zdenko, Hernandez-Vargas, Hector, Brunekreef, Bert, Pinart, Mariona, Heude, Barbara, Ewart, Susan, Yao, Jin, Lemonnier, Nathanaël, Franco, Oscar H, Wu, Michael C, Hofman, Albert, McArdle, Wendy, Van der Vlies, Pieter, Falahi, Fahimeh, Gillman, Matthew W, Barcellos, Lisa F, Kumar, Ashish, Wickman, Magnus, Guerra, Stefano, Charles, Marie-Aline, Holloway, John, Auffray, Charles, Tiemeier, Henning W, Smith, George Davey, Postma, Dirkje, Hivert, Marie-France, Eskenazi, Brenda, Vrijheid, Martine, Arshad, Hasan, Antó, Josep M, Dehghan, Abbas, Karmaus, Wilfried, Annesi-Maesano, Isabella, Sunyer, Jordi, Ghantous, Akram, Pershagen, Göran, Holland, Nina, Murphy, Susan K, DeMeo, Dawn L, Burchard, Esteban G, Ladd-Acosta, Christine, Snieder, Harold, and Nystad, Wenche

Subjects
Cleft Lip, Reproductive health and childbirth, Medical and Health Sciences, White People, Genetic, Pregnancy, Tobacco, Genetics, Humans, 2.2 Factors relating to the physical environment, 2.1 Biological and endogenous factors, Aetiology, Child, Preschool, Genetic Association Studies, Pediatric, Genetics & Heredity, Tobacco Smoke and Health, Prevention, Smoking, Human Genome, Chromosome Mapping, Infant, DNA Methylation, Biological Sciences, Newborn, Asthma, Cleft Palate, Good Health and Well Being, Respiratory, Female, Epigenesis
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 pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2× 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.

Published
2016

7. Estimation of blood cellular heterogeneity in newborns and children for epigenome-wide association studies

Author

Yousefi, Paul, Huen, Karen, Quach, Hong, Motwani, Girish, Hubbard, Alan, Eskenazi, Brenda, and Holland, Nina

Subjects
Male, Bioengineering, Toxicology, Medical and Health Sciences, 450K, Cohort Studies, Leukocyte Count, Genetic, Reference Values, Humans, Longitudinal Studies, Child, Pediatric, Blood Cells, epigenetics, Age Factors, Infant, birth cohort, differential cell count, DNA Methylation, Biological Sciences, Newborn, Blood Cell Count, minfi, Female, Environmental Sciences, Epigenesis, Genome-Wide Association Study
Abstract

Confounding by cellular heterogeneity has become a major concern for epigenome-wide association studies (EWAS) in peripheral blood samples from population and clinical studies. Adjusting for white blood cell percentage estimates produced by the minfi implementation of the Houseman algorithm (minfi) during statistical analysis is now an established method to account for this bias in adults. However, minfi has not been benchmarked against white blood cell counts in children that may differ substantially from the reference dataset used in its estimation. We compared estimates of white blood cell type percentages produced by two methods, minfi and differential cell count (DCC), in a birth cohort at two time points (birth and 12 years of age). We found that both minfi and DCC had similar trends as children aged, and neither count method differed by sex among newborns (P > 0.10). However, minfi estimates did not correlate well with DCC in samples from newborns (ρ = -0.05 for granulocytes; ρ = -0.03 for lymphocytes). In older children, correlation improved substantially (ρ = 0.77 for granulocytes; ρ = 0.75 for lymphocytes), likely due to increasing similarity with minfi's adult reference data as children aged. Our findings suggest that the minfi method may provide suitable estimates of white blood cell composition for samples from adults and older children, but may not currently be appropriate for EWAS involving newborns or young children.

Published
2015

8. Sex differences in DNA methylation assessed by 450 K BeadChip in newborns

Author

Yousefi, Paul, Huen, Karen, Davé, Veronica, Barcellos, Lisa, Eskenazi, Brenda, and Holland, Nina

Subjects
Pediatric, Male, Epigenomics, Bioinformatics, Human Genome, Infant, Gender, Cord blood, DNA Methylation, Biological Sciences, Newborn, Medical and Health Sciences, Genetic, Information and Computing Sciences, Genetics, Humans, CpG Islands, Female, Epigenetics, Gene ontology, Birth cohort, Epigenesis, Genome-Wide Association Study, EWAS
Abstract

BackgroundDNA methylation is an important epigenetic mark that can potentially link early life exposures to adverse health outcomes later in life. Host factors like sex and age strongly influence biological variation of DNA methylation, but characterization of these relationships is still limited, particularly in young children.MethodsIn a sample of 111 Mexican-American subjects (58 girls , 53 boys), we interrogated DNA methylation differences by sex at birth using the 450 K BeadChip in umbilical cord blood specimens, adjusting for cell composition.ResultsWe observed that ~3% of CpG sites were differentially methylated between girls and boys at birth (FDR P

Published
2015

11. Associations between perinatal factors and adiponectin and leptin in 9-year-old Mexican-American children

Author

Volberg, Vitaly, Harley, Kim G, Aguilar, Raul S, Rosas, Lisa G, Huen, Karen, Yousefi, Paul, Davé, Veronica, Phan, Nguyet, Lustig, Robert H, Eskenazi, Brenda, and Holland, Nina

Subjects
Leptin, Male, obesity, Mothers, Nutritional Status, Medical and Health Sciences, Oral and gastrointestinal, Body Mass Index, Endocrinology & Metabolism, Adipokines, Pregnancy, Surveys and Questionnaires, Mexican Americans, Humans, 2.1 Biological and endogenous factors, Longitudinal Studies, Aetiology, Child, Metabolic and endocrine, Nutrition, Cancer, Pediatric, Prevention, Nutrition Surveys, United States, lipid profile, Maternal Exposure, Prenatal Exposure Delayed Effects, Female, growth rate, Adiponectin, Child Nutritional Physiological Phenomena
Abstract

What is already known about this subjectMexican-American children are at particularly high risk of obesity. Features of the perinatal environment, including maternal nutrition, anthropometry, glucose tolerance and growth rate during infancy are implicated in programming of obesity in the offspring.What this study addsGreater rate of weight or length gain in the first 6 months of life is associated with lower 9-year child adiponectin levels, adjusting for 9-year child BMI. Nine-year-old child adipokine levels are strongly related to those of their mothers'.ObjectivesTo (i) determine whether perinatal factors (including maternal anthropometry and nutrition and early life growth measures) are associated with adiponectin and leptin levels in 9-year-old children, and (ii) assess relationships between adiponectin, leptin and concurrent lipid profile in these children.MethodsWe measured plasma adiponectin and leptin for 146 mothers-9-year-old child pairs from the ongoing longitudinal birth cohort followed by the Center for the Health Assessment of Mothers and Children of Salinas. Data on perinatal factors, including sociodemographics, maternal anthropometry and nutrition, and early life child growth were collected during pregnancy, birth and 6-month visits.ResultsGreater rate of weight and length gain during the first 6 months of life were associated with lower adiponectin in 9-year-olds (β = -2.0, P = 0.04; β = -8.2, P = 0.02, respectively) adjusting for child body mass index (BMI). We found no associations between child adipokine levels and either maternal calorie, protein, total fat, saturated fat, fibre, sugar-sweetened beverage consumption during pregnancy or children's concurrent sugar-sweetened beverage and fast food intake. Lipid profile in 9-year-old children closely reflected adiponectin but not leptin levels after adjustment for child BMI. Additionally, we report that child adipokine levels were closely related to their mothers' levels at the 9-year visit.ConclusionOverall, our results support the hypothesis that early life factors may contribute to altered adipokine levels in children.

Published
2013

12. Additional file 2 of DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

Author

Vehmeijer, Florianne O. L., Küpers, Leanne K., Sharp, Gemma C., Salas, Lucas A., Lent, Samantha, Dereje D. Jima, Tindula, Gwen, Reese, Sarah, Cancan Qi, Gruzieva, Olena, Page, Christian, Rezwan, Faisal I., Melton, Philip E., Nohr, Ellen, Geòrgia Escaramís, Rzehak, Peter, Heiskala, Anni, Gong, Tong, Tuominen, Samuli T., Gao, Lu, Ross, Jason P., Starling, Anne P., Holloway, John W., Yousefi, Paul, Aasvang, Gunn Marit, Beilin, Lawrence J., Bergström, Anna, Binder, Elisabeth, Chatzi, Leda, Corpeleijn, Eva, Czamara, Darina, Eskenazi, Brenda, Ewart, Susan, Ferre, Natalia, Grote, Veit, Gruszfeld, Dariusz, Håberg, Siri E., Hoyo, Cathrine, Huen, Karen, Karlsson, Robert, Kull, Inger, Langhendries, Jean-Paul, Lepeule, Johanna, Magnus, Maria C., Maguire, Rachel L., Molloy, Peter L., Monnereau, Claire, Mori, Trevor A., Oken, Emily, Räikkönen, Katri, Rifas-Shiman, Sheryl, Ruiz-Arenas, Carlos, Sebert, Sylvain, Ullemar, Vilhelmina, Verduci, Elvira, Vonk, Judith M., Xu, Cheng-Jian, Yang, Ivana V., Hongmei Zhang, Weiming Zhang, Karmaus, Wilfried, Dabelea, Dana, Muhlhausler, Beverly S., Breton, Carrie V., Lahti, Jari, Almqvist, Catarina, Marjo-Riitta Jarvelin, Koletzko, Berthold, Vrijheid, Martine, Sørensen, Thorkild I. A., Rae-Chi Huang, Arshad, Syed Hasan, Nystad, Wenche, Melén, Erik, Koppelman, Gerard H., London, Stephanie J., Holland, Nina, Mariona Bustamante, Murphy, Susan K., Marie-France Hivert, Baccarelli, Andrea, Relton, Caroline L., Snieder, Harold, Jaddoe, Vincent W. V., and Felix, Janine F.

Subjects
2. Zero hunger
Abstract

Additional file 2: Supplementary Methods. Study-specific funding, acknowledgements and methods in alphabetical order, including references.

13. Additional file 1: of DNA methylation and socioeconomic status in a Mexican-American birth cohort

Author

Coker, Eric, Gunier, Robert, Huen, Karen, Holland, Nina, and Eskenazi, Brenda

Subjects
2. Zero hunger
Abstract

Figure S1. Minimal sufficient adjustment for measuring the direct effect of SES on LINE-1 DNA methylation: diet quality index, maternal urinary phthalate concentration, maternal age, maternal smoking during pregnancy, and the number of years living in the U.S. DAG analysis performed using DAGitty v.2.3 online platform. Figure S2. Correlations (Pearsonâ s) for study variables, including LINE-1 and Alu methylation. Table S1. Summary statistics (mean (95%CI)) for cell type concentration overall and stratified by SES indicators. Table S2. Results from linear regression mixed effects (LMER) models of crude and adjusted associations between maternal, household, and neighborhood indicators of SES and diet quality index and Alu DNA methylation. (DOC 665Â kb)

14. Additional file 5 of DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

Author

Vehmeijer, Florianne O. L., Küpers, Leanne K., Sharp, Gemma C., Salas, Lucas A., Lent, Samantha, Dereje D. Jima, Tindula, Gwen, Reese, Sarah, Cancan Qi, Gruzieva, Olena, Page, Christian, Rezwan, Faisal I., Melton, Philip E., Nohr, Ellen, Geòrgia Escaramís, Rzehak, Peter, Heiskala, Anni, Gong, Tong, Tuominen, Samuli T., Gao, Lu, Ross, Jason P., Starling, Anne P., Holloway, John W., Yousefi, Paul, Aasvang, Gunn Marit, Beilin, Lawrence J., Bergström, Anna, Binder, Elisabeth, Chatzi, Leda, Corpeleijn, Eva, Czamara, Darina, Eskenazi, Brenda, Ewart, Susan, Ferre, Natalia, Grote, Veit, Gruszfeld, Dariusz, Håberg, Siri E., Hoyo, Cathrine, Huen, Karen, Karlsson, Robert, Kull, Inger, Langhendries, Jean-Paul, Lepeule, Johanna, Magnus, Maria C., Maguire, Rachel L., Molloy, Peter L., Monnereau, Claire, Mori, Trevor A., Oken, Emily, Räikkönen, Katri, Rifas-Shiman, Sheryl, Ruiz-Arenas, Carlos, Sebert, Sylvain, Ullemar, Vilhelmina, Verduci, Elvira, Vonk, Judith M., Xu, Cheng-Jian, Yang, Ivana V., Hongmei Zhang, Weiming Zhang, Karmaus, Wilfried, Dabelea, Dana, Muhlhausler, Beverly S., Breton, Carrie V., Lahti, Jari, Almqvist, Catarina, Marjo-Riitta Jarvelin, Koletzko, Berthold, Vrijheid, Martine, Sørensen, Thorkild I. A., Rae-Chi Huang, Arshad, Syed Hasan, Nystad, Wenche, Melén, Erik, Koppelman, Gerard H., London, Stephanie J., Holland, Nina, Mariona Bustamante, Murphy, Susan K., Marie-France Hivert, Baccarelli, Andrea, Relton, Caroline L., Snieder, Harold, Jaddoe, Vincent W. V., and Felix, Janine F.

Subjects
2. Zero hunger
Abstract

Additional file 5: Supplementary Information. Figures S1 – 6.

15. Additional file 5 of DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

Author

Vehmeijer, Florianne O. L., Küpers, Leanne K., Sharp, Gemma C., Salas, Lucas A., Lent, Samantha, Dereje D. Jima, Tindula, Gwen, Reese, Sarah, Cancan Qi, Gruzieva, Olena, Page, Christian, Rezwan, Faisal I., Melton, Philip E., Nohr, Ellen, Geòrgia Escaramís, Rzehak, Peter, Heiskala, Anni, Gong, Tong, Tuominen, Samuli T., Gao, Lu, Ross, Jason P., Starling, Anne P., Holloway, John W., Yousefi, Paul, Aasvang, Gunn Marit, Beilin, Lawrence J., Bergström, Anna, Binder, Elisabeth, Chatzi, Leda, Corpeleijn, Eva, Czamara, Darina, Eskenazi, Brenda, Ewart, Susan, Ferre, Natalia, Grote, Veit, Gruszfeld, Dariusz, Håberg, Siri E., Hoyo, Cathrine, Huen, Karen, Karlsson, Robert, Kull, Inger, Langhendries, Jean-Paul, Lepeule, Johanna, Magnus, Maria C., Maguire, Rachel L., Molloy, Peter L., Monnereau, Claire, Mori, Trevor A., Oken, Emily, Räikkönen, Katri, Rifas-Shiman, Sheryl, Ruiz-Arenas, Carlos, Sebert, Sylvain, Ullemar, Vilhelmina, Verduci, Elvira, Vonk, Judith M., Xu, Cheng-Jian, Yang, Ivana V., Hongmei Zhang, Weiming Zhang, Karmaus, Wilfried, Dabelea, Dana, Muhlhausler, Beverly S., Breton, Carrie V., Lahti, Jari, Almqvist, Catarina, Marjo-Riitta Jarvelin, Koletzko, Berthold, Vrijheid, Martine, Sørensen, Thorkild I. A., Rae-Chi Huang, Arshad, Syed Hasan, Nystad, Wenche, Melén, Erik, Koppelman, Gerard H., London, Stephanie J., Holland, Nina, Mariona Bustamante, Murphy, Susan K., Marie-France Hivert, Baccarelli, Andrea, Relton, Caroline L., Snieder, Harold, Jaddoe, Vincent W. V., and Felix, Janine F.

Subjects
2. Zero hunger
Abstract

Additional file 5: Supplementary Information. Figures S1 – 6.

16. Additional file 1: of DNA methylation and socioeconomic status in a Mexican-American birth cohort

Author

Coker, Eric, Gunier, Robert, Huen, Karen, Holland, Nina, and Eskenazi, Brenda

Subjects
2. Zero hunger
Abstract

Figure S1. Minimal sufficient adjustment for measuring the direct effect of SES on LINE-1 DNA methylation: diet quality index, maternal urinary phthalate concentration, maternal age, maternal smoking during pregnancy, and the number of years living in the U.S. DAG analysis performed using DAGitty v.2.3 online platform. Figure S2. Correlations (Pearsonâ s) for study variables, including LINE-1 and Alu methylation. Table S1. Summary statistics (mean (95%CI)) for cell type concentration overall and stratified by SES indicators. Table S2. Results from linear regression mixed effects (LMER) models of crude and adjusted associations between maternal, household, and neighborhood indicators of SES and diet quality index and Alu DNA methylation. (DOC 665Â kb)

17. Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age

Author

Merid, Simon Kebede, Novoloaca, Alexei, Sharp, Gemma C., Küpers, Leanne K., Kho, Alvin T., Roy, Ritu, Gao, Lu, Annesi-Maesano, Isabella, Jain, Pooja, Plusquin, Michelle, Kogevinas, Manolis, Allard, Catherine, Vehmeijer, Florianne O., Kazmi, Nabila, Salas, Lucas A., Rezwan, Faisal I., Zhang, Hongmei, Sebert, Sylvain, Czamara, Darina, Rifas-Shiman, Sheryl L., Melton, Phillip E., Lawlor, Debbie A., Pershagen, Göran, Breton, Carrie V., Huen, Karen, Baiz, Nour, Gagliardi, Luigi, Nawrot, Tim S., Corpeleijn, Eva, Perron, Patrice, Duijts, Liesbeth, Nohr, Ellen Aagaard, Bustamante, Mariona, Ewart, Susan L., Karmaus, Wilfried, Zhao, Shanshan, Page, Christian M., Herceg, Zdenko, Jarvelin, Marjo-Riitta, Lahti, Jari, Baccarelli, Andrea A., Anderson, Denise, Kachroo, Priyadarshini, Relton, Caroline L., Bergström, Anna, Eskenazi, Brenda, Soomro, Munawar Hussain, Vineis, Paolo, Snieder, Harold, Bouchard, Luigi, Jaddoe, Vincent W., Sørensen, Thorkild I. A., Vrijheid, Martine, Arshad, S. Hasan, Holloway, John W., Håberg, Siri E., Magnus, Per, Dwyer, Terence, Binder, Elisabeth B., DeMeo, Dawn L., Vonk, Judith M., Newnham, John, Tantisira, Kelan G., Kull, Inger, Wiemels, Joseph L., Heude, Barbara, Sunyer, Jordi, Nystad, Wenche, Munthe-Kaas, Monica C., Räikkönen, Katri, Oken, Emily, Huang, Rae-Chi, Weiss, Scott T., Antó, Josep Maria, Bousquet, Jean, Kumar, Ashish, Söderhäll, Cilla, Almqvist, Catarina, Cardenas, Andres, Gruzieva, Olena, Xu, Cheng-Jian, Reese, Sarah E., Kere, Juha, Brodin, Petter, Solomon, Olivia, Wielscher, Matthias, Holland, Nina, Ghantous, Akram, Hivert, Marie-France, Felix, Janine F., Koppelman, Gerard H., London, Stephanie J., and Melén, Erik

Subjects
3. Good health

18. Additional file 2 of DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

Author

Vehmeijer, Florianne O. L., Küpers, Leanne K., Sharp, Gemma C., Salas, Lucas A., Lent, Samantha, Dereje D. Jima, Tindula, Gwen, Reese, Sarah, Cancan Qi, Gruzieva, Olena, Page, Christian, Rezwan, Faisal I., Melton, Philip E., Nohr, Ellen, Geòrgia Escaramís, Rzehak, Peter, Heiskala, Anni, Gong, Tong, Tuominen, Samuli T., Gao, Lu, Ross, Jason P., Starling, Anne P., Holloway, John W., Yousefi, Paul, Aasvang, Gunn Marit, Beilin, Lawrence J., Bergström, Anna, Binder, Elisabeth, Chatzi, Leda, Corpeleijn, Eva, Czamara, Darina, Eskenazi, Brenda, Ewart, Susan, Ferre, Natalia, Grote, Veit, Gruszfeld, Dariusz, Håberg, Siri E., Hoyo, Cathrine, Huen, Karen, Karlsson, Robert, Kull, Inger, Langhendries, Jean-Paul, Lepeule, Johanna, Magnus, Maria C., Maguire, Rachel L., Molloy, Peter L., Monnereau, Claire, Mori, Trevor A., Oken, Emily, Räikkönen, Katri, Rifas-Shiman, Sheryl, Ruiz-Arenas, Carlos, Sebert, Sylvain, Ullemar, Vilhelmina, Verduci, Elvira, Vonk, Judith M., Xu, Cheng-Jian, Yang, Ivana V., Hongmei Zhang, Weiming Zhang, Karmaus, Wilfried, Dabelea, Dana, Muhlhausler, Beverly S., Breton, Carrie V., Lahti, Jari, Almqvist, Catarina, Marjo-Riitta Jarvelin, Koletzko, Berthold, Vrijheid, Martine, Sørensen, Thorkild I. A., Rae-Chi Huang, Arshad, Syed Hasan, Nystad, Wenche, Melén, Erik, Koppelman, Gerard H., London, Stephanie J., Holland, Nina, Mariona Bustamante, Murphy, Susan K., Marie-France Hivert, Baccarelli, Andrea, Relton, Caroline L., Snieder, Harold, Jaddoe, Vincent W. V., and Felix, Janine F.

Subjects
2. Zero hunger
Abstract

Additional file 2: Supplementary Methods. Study-specific funding, acknowledgements and methods in alphabetical order, including references.

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