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Aguilar-Lacasaña S, Fontes Marques I, de Castro M, Dadvand P, Escribà X, Fossati S, González JR, Nieuwenhuijsen M, Alfano R, Annesi-Maesano I, Brescianini S, Burrows K, Calas L, Elhakeem A, Heude B, Hough A, Isaevska E, W V Jaddoe V, Lawlor DA, Monaghan G, Nawrot T, Plusquin M, Richiardi L, Watmuff A, Yang TC, Vrijheid M, F Felix J, Bustamante M. Green space exposure and blood DNA methylation at birth and in childhood - A multi-cohort study. ENVIRONMENT INTERNATIONAL 2024; 188:108684. [PMID: 38776651 DOI: 10.1016/j.envint.2024.108684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/21/2024] [Accepted: 04/21/2024] [Indexed: 05/25/2024]
Abstract
Green space exposure has been associated with improved mental, physical and general health. However, the underlying biological mechanisms remain largely unknown. The aim of this study was to investigate the association between green space exposure and cord and child blood DNA methylation. Data from eight European birth cohorts with a total of 2,988 newborns and 1,849 children were used. Two indicators of residential green space exposure were assessed: (i) surrounding greenness (satellite-based Normalized Difference Vegetation Index (NDVI) in buffers of 100 m and 300 m) and (ii) proximity to green space (having a green space ≥ 5,000 m2 within a distance of 300 m). For these indicators we assessed two exposure windows: (i) pregnancy, and (ii) the period from pregnancy to child blood DNA methylation assessment, named as cumulative exposure. DNA methylation was measured with the Illumina 450K or EPIC arrays. To identify differentially methylated positions (DMPs) we fitted robust linear regression models between pregnancy green space exposure and cord blood DNA methylation and between cumulative green space exposure and child blood DNA methylation. Two sensitivity analyses were conducted: (i) without adjusting for cellular composition, and (ii) adjusting for air pollution. Cohort results were combined through fixed-effect inverse variance weighted meta-analyses. Differentially methylated regions (DMRs) were identified from meta-analysed results using the Enmix-combp and DMRcate methods. There was no statistical evidence of pregnancy or cumulative exposures associating with any DMP (False Discovery Rate, FDR, p-value < 0.05). However, surrounding greenness exposure was inversely associated with four DMRs (three in cord blood and one in child blood) annotated to ADAMTS2, KCNQ1DN, SLC6A12 and SDK1 genes. Results did not change substantially in the sensitivity analyses. Overall, we found little evidence of the association between green space exposure and blood DNA methylation. Although we identified associations between surrounding greenness exposure with four DMRs, these findings require replication.
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Affiliation(s)
- Sofia Aguilar-Lacasaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain; Universitat de Barcelona, Barcelona, Spain.
| | - Irene Fontes Marques
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Xavier Escribà
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Serena Fossati
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Juan R González
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Rossella Alfano
- Centre for Environmental Sciences, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium
| | - Isabella Annesi-Maesano
- Desbrest Institute of Epidemiology and Public Health (IDESP), Montpellier University and Inserm, Montpellier, Service des Maladies Allergiques et Respiratoires, CHU, Montpellier, France
| | - Sonia Brescianini
- Centre for Behavioural Science and Mental Health, Istituto Superiore di Sanità, Rome, Italy
| | - Kimberley Burrows
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lucinda Calas
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France
| | - Ahmed Elhakeem
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France
| | - Amy Hough
- Born in Bradford, Wolfson Centre for Applied Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Elena Isaevska
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Medical Sciences, University of Turin, CPO-Piemonte, Turin, Italy
| | - Vincent W V Jaddoe
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Deborah A Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Genevieve Monaghan
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tim Nawrot
- Centre for Environmental Sciences, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium; Department of Public Health, Leuven University (KU Leuven), Leuven, Belgium
| | - Michelle Plusquin
- Centre for Environmental Sciences, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium
| | - Lorenzo Richiardi
- Department of Medical Sciences, University of Turin, CPO-Piemonte, Turin, Italy
| | - Aidan Watmuff
- Born in Bradford, Wolfson Centre for Applied Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, UK
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
| | - Janine F Felix
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública, Spain
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102
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Shen S, Sobczyk MK, Paternoster L, Brown SJ. From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics. J Invest Dermatol 2024; 144:1189-1199.e8. [PMID: 38782533 DOI: 10.1016/j.jid.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 05/25/2024]
Abstract
Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.
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Affiliation(s)
- Silvia Shen
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Institute for Evolution and Ecology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| | - Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sara J Brown
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, NHS Lothian, Edinburgh, United Kingdom
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103
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Liu Y, Liu S, Zhen D, Huang J, He F. Ultrasensitive Detection of Tumor Suppressor Gene Methylation by Piezoelectric Sensing Based on Enrichment of Transcription Activator-Like Effectors. Anal Chem 2024; 96:8534-8542. [PMID: 38743638 DOI: 10.1021/acs.analchem.4c00484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The detection of DNA methylation at cytosine/guanine dinucleotide (CpG) islands in promoter regions of tumor suppressor genes has great potential for early cancer screening, diagnosis, and prognosis monitoring. Nevertheless, achieving accurate, sensitive, cost-effective, and quantitative detection of target methylated DNA remains challenging. Herein, we propose a novel piezoelectric sensor (series piezoelectric quartz crystal (SPQC)) based on transcription activator-like effectors (TALEs) for detecting DNA methylation of Ras association domain family 1 isoform A (RASSF1A) tumor suppressor genes (R-5mC). The sensor employs TALEs-Ni magnetic beads to specifically recognize and separate the R-5mC, thereby improving the detection selectivity. The TALEs-Ni magnetic beads-R-5mC complex is sheared by a nucleic acid enzyme (DNAzyme) to release the single-stranded DNA (ST). ST initiates a catalyzed hairpin assembly (CHA) reaction on the surface of the electrode, which in turn triggers the hybridization chain reaction (HCR) and silver staining for enhanced detection sensitivity. The strategy exhibits a linear response in the detection of R-5mC in the range of 1 fM to 1 nM with a detection limit of 0.79 fM. R-5mC as low as 0.01% can be detected, even in the presence of large numbers of unmethylated DNA. The detection of R-5mC in circulating cell-free DNA (cfDNA) derived from clinical plasma specimens of lung cancer patients yielded satisfactory results.
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Affiliation(s)
- Yu Liu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P.R. China
| | - Shuyi Liu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P.R. China
| | - Deshuai Zhen
- Hunan Key Laboratory of Typical Environment Pollution and Health Hazards, College of Public Health, University of South China, Hengyang 421001, PR China
| | - Ji Huang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P.R. China
| | - Fengjiao He
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P.R. China
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104
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Peng Q, Liu X, Li W, Jing H, Li J, Gao X, Luo Q, Breeze CE, Pan S, Zheng Q, Li G, Qian J, Yuan L, Yuan N, You C, Du S, Zheng Y, Yuan Z, Tan J, Jia P, Wang J, Zhang G, Lu X, Shi L, Guo S, Liu Y, Ni T, Wen B, Zeng C, Jin L, Teschendorff AE, Liu F, Wang S. Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits. Nat Genet 2024; 56:846-860. [PMID: 38641644 DOI: 10.1038/s41588-023-01494-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/02/2023] [Indexed: 04/21/2024]
Abstract
Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.
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Affiliation(s)
- Qianqian Peng
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Wenran Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Han Jing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jiarui Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xingjian Gao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | | | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Guochao Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Jiaqiang Qian
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liyun Yuan
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Chenglong You
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyu Yuan
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Guoqing Zhang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Xianping Lu
- Shenzhen Chipscreen Biosciences Co. Ltd., Shenzhen, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Shicheng Guo
- Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, China
| | - Bo Wen
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- The Fifth People's Hospital of Shanghai and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Changqing Zeng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Kingdom of Saudi Arabia.
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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105
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Christiansen C, Potier L, Martin TC, Villicaña S, Castillo-Fernandez JE, Mangino M, Menni C, Tsai PC, Campbell PJ, Mullin S, Ordoñana JR, Monteagudo O, Sachdev PS, Mather KA, Trollor JN, Pietilainen KH, Ollikainen M, Dalgård C, Kyvik K, Christensen K, van Dongen J, Willemsen G, Boomsma DI, Magnusson PKE, Pedersen NL, Wilson SG, Grundberg E, Spector TD, Bell JT. Enhanced resolution profiling in twins reveals differential methylation signatures of type 2 diabetes with links to its complications. EBioMedicine 2024; 103:105096. [PMID: 38574408 PMCID: PMC11004697 DOI: 10.1016/j.ebiom.2024.105096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) susceptibility is influenced by genetic and environmental factors. Previous findings suggest DNA methylation as a potential mechanism in T2D pathogenesis and progression. METHODS We profiled DNA methylation in 248 blood samples from participants of European ancestry from 7 twin cohorts using a methylation sequencing platform targeting regulatory genomic regions encompassing 2,048,698 CpG sites. FINDINGS We find and replicate 3 previously unreported T2D differentially methylated CpG positions (T2D-DMPs) at FDR 5% in RGL3, NGB and OTX2, and 20 signals at FDR 25%, of which 14 replicated. Integrating genetic variation and T2D-discordant monozygotic twin analyses, we identify both genetic-based and genetic-independent T2D-DMPs. The signals annotate to genes with established GWAS and EWAS links to T2D and its complications, including blood pressure (RGL3) and eye disease (OTX2). INTERPRETATION The results help to improve our understanding of T2D disease pathogenesis and progression and may provide biomarkers for its complications. FUNDING Funding acknowledgements for each cohort can be found in the Supplementary Note.
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Affiliation(s)
| | - Louis Potier
- APHP, Paris Cité University, INSERM, Paris, France
| | | | | | | | | | | | - Pei-Chien Tsai
- King's College London, UK; Department of Biomedical Sciences, Chang Gung University, Taoyuan City, Taiwan; Molecular Infectious Disease Research Center, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
| | - Purdey J Campbell
- Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Shelby Mullin
- Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia; School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
| | | | | | | | | | | | - Kirsi H Pietilainen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland; HealthyWeightHub, Abdominal Center, Helsinki University Hospital and University of Helsinki, Finland
| | - Miina Ollikainen
- Minerva Foundation Institute for Medical Research, Helsinki, Finland; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Finland
| | | | | | | | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | | | | | - Scott G Wilson
- King's College London, UK; Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia; School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
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106
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Lee HS, Kim B, Park T. Genome- and epigenome-wide association studies identify susceptibility of CpG sites and regions for metabolic syndrome in a Korean population. Clin Epigenetics 2024; 16:60. [PMID: 38685121 PMCID: PMC11059751 DOI: 10.1186/s13148-024-01671-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/13/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND While multiple studies have investigated the relationship between metabolic syndrome (MetS) and its related traits (fasting glucose, triglyceride, HDL cholesterol, blood pressure, waist circumference) and DNA methylation, our understanding of the epigenetic mechanisms in MetS remains limited. Therefore, we performed an epigenome-wide meta-analysis of blood DNA methylation to identify differentially methylated probes (DMPs) and differentially methylated regions (DMRs) associated with MetS and its components using two independent cohorts comprising a total of 2,334 participants. We also investigated the specific genetic effects on DNA methylation, identified methylation quantitative trait loci (meQTLs) through genome-wide association studies and further utilized Mendelian randomization (MR) to assess how these meQTLs subsequently influence MetS status. RESULTS We identified 40 DMPs and 27 DMRs that are significantly associated with MetS. In addition, we identified many novel DMPs and DMRs underlying inflammatory and steroid hormonal processes. The most significant associations were observed in 3 DMPs (cg19693031, cg26974062, cg02988288) and a DMR (chr1:145440444-145441553) at the TXNIP, which are involved in lipid metabolism. These CpG sites were identified as coregulators of DNA methylation in MetS, TG and FAG levels. We identified a total of 144 cis-meQTLs, out of which only 13 were found to be associated with DMPs for MetS. Among these, we confirmed the identified causal mediators of genetic effects at CpG sites cg01881899 at ABCG1 and cg00021659 at the TANK genes for MetS. CONCLUSIONS This study observed whether specific CpGs and methylated regions act independently or are influenced by genetic effects for MetS and its components in the Korean population. These associations between the identified DNA methylation and MetS, along with its individual components, may serve as promising targets for the development of preventive interventions for MetS.
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Affiliation(s)
- Ho-Sun Lee
- Forensic Toxicology Division, Daegu Institute, National Forensic Service, Chilgok-gun, 39872, Gyeongsangbuk-do, Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea.
| | - Boram Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea
| | - Taesung Park
- Forensic Toxicology Division, Daegu Institute, National Forensic Service, Chilgok-gun, 39872, Gyeongsangbuk-do, Korea
- Department of Statistics, Seoul National University, Seoul, 08826, Korea
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107
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Lee SM, Loo CE, Prasasya RD, Bartolomei MS, Kohli RM, Zhou W. Low-input and single-cell methods for Infinium DNA methylation BeadChips. Nucleic Acids Res 2024; 52:e38. [PMID: 38407446 PMCID: PMC11040145 DOI: 10.1093/nar/gkae127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 02/27/2024] Open
Abstract
The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection P-value calculation achieved higher sensitivities for low-input datasets and was validated in over 100 000 public diverse methylome profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.
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Affiliation(s)
- Sol Moe Lee
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA 19104, USA
| | - Christian E Loo
- Graduate Group in Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rexxi D Prasasya
- Department of Cell and Developmental Biology, Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Marisa S Bartolomei
- Department of Cell and Developmental Biology, Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Rahul M Kohli
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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108
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Pett L, Li Z, Abrishamcar S, Hodge K, Everson T, Christensen G, Gearing M, Kobor MS, Konwar C, MacIsaac JL, Dever K, Wingo AP, Levey A, Lah JJ, Wingo TS, Hüls A. The association between neighborhood deprivation and DNA methylation in an autopsy cohort. Aging (Albany NY) 2024; 16:6694-6716. [PMID: 38663907 PMCID: PMC11087100 DOI: 10.18632/aging.205764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 03/18/2024] [Indexed: 05/08/2024]
Abstract
Previous research has found that living in a disadvantaged neighborhood is associated with poor health outcomes. Living in disadvantaged neighborhoods may alter inflammation and immune response in the body, which could be reflected in epigenetic mechanisms such as DNA methylation (DNAm). We used robust linear regression models to conduct an epigenome-wide association study examining the association between neighborhood deprivation (Area Deprivation Index; ADI), and DNAm in brain tissue from 159 donors enrolled in the Emory Goizueta Alzheimer's Disease Research Center (Georgia, USA). We found one CpG site (cg26514961, gene PLXNC1) significantly associated with ADI after controlling for covariates and multiple testing (p-value=5.0e-8). Effect modification by APOE ε4 was statistically significant for the top ten CpG sites from the EWAS of ADI, indicating that the observed associations between ADI and DNAm were mainly driven by donors who carried at least one APOE ε4 allele. Four of the top ten CpG sites showed a significant concordance between brain tissue and tissues that are easily accessible in living individuals (blood, buccal cells, saliva), including DNAm in cg26514961 (PLXNC1). Our study identified one CpG site (cg26514961, PLXNC1 gene) that was significantly associated with neighborhood deprivation in brain tissue. PLXNC1 is related to immune response, which may be one biological pathway how neighborhood conditions affect health. The concordance between brain and other tissues for our top CpG sites could make them potential candidates for biomarkers in living individuals.
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Affiliation(s)
- Lindsay Pett
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Zhenjiang Li
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Sarina Abrishamcar
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Kenyaita Hodge
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Todd Everson
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Grace Christensen
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Marla Gearing
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA 30322, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Michael S. Kobor
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BC, Canada
| | - Chaini Konwar
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Julia L. MacIsaac
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BC, Canada
| | - Kristy Dever
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BC, Canada
| | - Aliza P. Wingo
- Division of Mental Health, Atlanta VA Medical Center, Decatur, GA 30033, USA
- Department of Psychiatry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Allan Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - James J. Lah
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Thomas S. Wingo
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
| | - Anke Hüls
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
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109
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Jiang F, Zhao J, Sun J, Chen W, Zhao Y, Zhou S, Yuan S, Timofeeva M, Law PJ, Larsson SC, Chen D, Houlston RS, Dunlop MG, Theodoratou E, Li X. Impact of ambient air pollution on colorectal cancer risk and survival: insights from a prospective cohort and epigenetic Mendelian randomization study. EBioMedicine 2024; 103:105126. [PMID: 38631091 PMCID: PMC11035091 DOI: 10.1016/j.ebiom.2024.105126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 03/20/2024] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND This study investigates the associations between air pollution and colorectal cancer (CRC) risk and survival from an epigenomic perspective. METHODS Using a newly developed Air Pollutants Exposure Score (APES), we utilized a prospective cohort study (UK Biobank) to investigate the associations of individual and combined air pollution exposures with CRC incidence and survival, followed by an up-to-date systematic review with meta-analysis to verify the associations. In epigenetic two-sample Mendelian randomization analyses, we examine the associations between genetically predicted DNA methylation related to air pollution and CRC risk. Further genetic colocalization and gene-environment interaction analyses provided different insights to disentangle pathogenic effects of air pollution via epigenetic modification. FINDINGS During a median 12.97-year follow-up, 5767 incident CRC cases among 428,632 participants free of baseline CRC and 533 deaths in 2401 patients with CRC were documented in the UK Biobank. A higher APES score was associated with an increased CRC risk (HR, 1.03, 95% CI = 1.01-1.06; P = 0.016) and poorer survival (HR, 1.13, 95% CI = 1.03-1.23; P = 0.010), particularly among participants with insufficient physical activity and ever smokers (Pinteraction > 0.05). A subsequent meta-analysis of seven observational studies, including UK Biobank data, corroborated the association between PM2.5 exposure (per 10 μg/m3 increment) and elevated CRC risk (RR,1.42, 95% CI = 1.12-1.79; P = 0.004; I2 = 90.8%). Genetically predicted methylation at PM2.5-related CpG site cg13835894 near TMBIM1/PNKD and cg16235962 near CXCR5, and NO2-related cg16947394 near TMEM110 were associated with an increased CRC risk. Gene-environment interaction analysis confirmed the epigenetic modification of aforementioned CpG sites with CRC risk and survival. INTERPRETATION Our study suggests the association between air pollution and CRC incidence and survival, underscoring the possible modifying roles of epigenomic factors. Methylation may partly mediate pathogenic effects of air pollution on CRC, with annotation to epigenetic alterations in protein-coding genes TMBIM1/PNKD, CXCR5 and TMEM110. FUNDING Xue Li is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001), the National Nature Science Foundation of China (No. 82204019) and Healthy Zhejiang One Million People Cohort (K-20230085). ET is supported by a Cancer Research UK Career Development Fellowship (C31250/A22804). MGD is supported by the MRC Human Genetics Unit Centre Grant (U127527198).
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Affiliation(s)
- Fangyuan Jiang
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianhui Zhao
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Sun
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenxi Chen
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuyuan Zhao
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Siyun Zhou
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Maria Timofeeva
- Danish Institute for Advanced Study (DIAS), Epidemiology, Biostatistics and Biodemography Research Unit, Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Philip J Law
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden; Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala, Sweden
| | - Dong Chen
- Department of Colorectal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China
| | - Richard S Houlston
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Malcolm G Dunlop
- Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Xue Li
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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110
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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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111
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Czamara D, Dieckmann L, Lahti-Pulkkinen M, Cruceanu C, Henrich W, Plagemann A, Räikkönen K, Braun T, Binder EB, Lahti J, Entringer S. Sex differences in DNA methylation across gestation: a large scale, cross-cohort, multi-tissue analysis. Cell Mol Life Sci 2024; 81:177. [PMID: 38600394 PMCID: PMC11006734 DOI: 10.1007/s00018-024-05208-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/12/2024]
Abstract
Biological sex is a key variable influencing many physiological systems. Disease prevalence as well as treatment success can be modified by sex. Differences emerge already early in life and include pregnancy complications and adverse birth outcomes. The placenta is a critical organ for fetal development and shows sex-based differences in the expression of hormones and cytokines. Epigenetic regulation, such as DNA methylation (DNAm), may underlie the previously reported placental sexual dimorphism. We associated placental DNAm with fetal sex in three cohorts. Individual cohort results were meta-analyzed with random-effects modelling. CpG-sites differentially methylated with sex were further investigated regarding pathway enrichment, overlap with methylation quantitative trait loci (meQTLs), and hits from phenome-wide association studies (PheWAS). We evaluated the consistency of findings across tissues (CVS, i.e. chorionic villus sampling from early placenta, and cord blood) as well as with gene expression. We identified 10,320 epigenome-wide significant sex-differentially methylated probes (DMPs) spread throughout the epigenome of the placenta at birth. Most DMPs presented with lower DNAm levels in females. DMPs mapped to genes upregulated in brain, were enriched for neurodevelopmental pathways and significantly overlapped with meQTLs and PheWAS hits. Effect sizes were moderately correlated between CVS and placenta at birth, but only weakly correlated between birth placenta and cord blood. Sex differential gene expression in birth placenta was less pronounced and implicated genetic regions only marginally overlapped with those associated with differential DNAm. Our study provides an integrative perspective on sex-differential DNAm in perinatal tissues underscoring the possible link between placenta and brain.
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Affiliation(s)
- Darina Czamara
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany.
| | - Linda Dieckmann
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Munich, Germany
| | - Marius Lahti-Pulkkinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Cristiana Cruceanu
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Wolfgang Henrich
- Department of Obstetrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Andreas Plagemann
- Department of Obstetrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany
- Department of Experimental Obstetrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, HUS Helsinki University Hospital, Helsinki, Finland
| | - Thorsten Braun
- Department of Obstetrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany
- Department of Experimental Obstetrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Elisabeth B Binder
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Emory University, Atlanta, GA, USA
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sonja Entringer
- Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany.
- Department of Pediatrics, Health and Disease Research Program, School of Medicine, University of California, Irvine, CA, USA.
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112
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Qin Q, Zhou Y, Guo J, Chen Q, Tang W, Li Y, You J, Li Q. Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response. Genome Med 2024; 16:47. [PMID: 38566132 PMCID: PMC10985907 DOI: 10.1186/s13073-024-01318-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 03/20/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes. METHODS We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts. RESULTS We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment. CONCLUSIONS Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.
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Affiliation(s)
- Qingqing Qin
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, China
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
- Department of Pediatrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, China
| | - Ying Zhou
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, China
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
- Department of Pediatrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, China
| | - Jintao Guo
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, China
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
- Department of Pediatrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, China
| | - Qinwei Chen
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, China
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
| | - Weiwei Tang
- Department of Medical Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University and Institute of Hematology, Xiamen University, Xiamen, 361003, China
- Xiamen Key Laboratory of Antitumor Drug Transformation Research, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, The School of Clinical Medicine of Fujian, Medical University, Xiamen, 361003, China
| | - Yuchen Li
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, China
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
- Department of Pediatrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, China
| | - Jun You
- Department of Gastrointestinal Oncology Surgery, The First Affiliated Hospital of Xiamen University, Cancer Center, Xiamen, 361003, China
| | - Qiyuan Li
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, China.
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China.
- Department of Pediatrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, China.
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113
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Hatton AA, Cheng FF, Lin T, Shen RJ, Chen J, Zheng Z, Qu J, Lyu F, Harris SE, Cox SR, Jin ZB, Martin NG, Fan D, Montgomery GW, Yang J, Wray NR, Marioni RE, Visscher PM, McRae AF. Genetic control of DNA methylation is largely shared across European and East Asian populations. Nat Commun 2024; 15:2713. [PMID: 38548728 PMCID: PMC10978881 DOI: 10.1038/s41467-024-47005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 03/15/2024] [Indexed: 04/01/2024] Open
Abstract
DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.
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Affiliation(s)
- Alesha A Hatton
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Fei-Fei Cheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- School of Life Sciences, Westlake University, Hangzhou, 310030, Zhejiang, China
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ren-Juan Shen
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, 100008, Beijing, China
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jie Chen
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jia Qu
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Fan Lyu
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Zi-Bing Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, 100008, Beijing, China
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Nicholas G Martin
- Queensland Institute of Medical Research Berghofer, Brisbane, QLD, 4006, Australia
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, 100191, Beijing, China
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, 310030, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, Zhejiang, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
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114
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Abrishamcar S, Zhuang B, Thomas M, Gladish N, MacIsaac J, Jones M, Simons E, Moraes T, Mandhane P, Brook J, Subbarao P, Turvey S, Chen E, Miller G, Kobor M, Huels A. Association between Maternal Perinatal Stress and Depression on Infant DNA Methylation in the First Year of Life. RESEARCH SQUARE 2024:rs.3.rs-3962429. [PMID: 38562779 PMCID: PMC10984027 DOI: 10.21203/rs.3.rs-3962429/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Maternal stress and depression during pregnancy and the first year of the infant's life affect a large percentage of mothers. Maternal stress and depression have been associated with adverse fetal and childhood outcomes as well as differential child DNA methylation (DNAm). However, the biological mechanisms connecting maternal stress and depression to poor health outcomes in children are still largely unknown. Here we aim to determine whether prenatal stress and depression are associated with changes in cord blood mononuclear cell DNAm (CBMC-DNAm) in newborns (n = 119) and whether postnatal stress and depression are associated with changes in peripheral blood mononuclear cell DNAm (PBMC-DNAm) in children of 12 months of age (n = 113) from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. Stress was measured using the 10-item Perceived Stress Scale (PSS) and depression was measured using the Center for Epidemiologic Studies Depression Questionnaire (CESD). Both stress and depression were measured at 18 weeks and 36 weeks of pregnancy and six months and 12 months postpartum. We conducted epigenome-wide association studies (EWAS) using robust linear regression followed by a sensitivity analysis in which we bias-adjusted for inflation and unmeasured confounding using the bacon and cate methods. To investigate the cumulative effect of maternal stress and depression, we created composite prenatal and postnatal adversity scores. We identified a significant association between prenatal stress and differential CBMC-DNAm at 8 CpG sites and between prenatal depression and differential CBMC-DNAm at 2 CpG sites. Additionally, we identified a significant association between postnatal stress and differential PBMC-DNAm at 8 CpG sites and between postnatal depression and differential PBMC-DNAm at 11 CpG sites. Using our composite scores, we further identified 2 CpG sites significantly associated with prenatal adversity and 7 CpG sites significantly associated with postnatal adversity. Several of the associated genes, including PLAGL1, HYMAI, BRD2, and ERC2 have been implicated in adverse fetal outcomes and neuropsychiatric disorders. This suggested that differential DNAm may play a role in the relationship between maternal mental health and child health.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Anke Huels
- Rollins School of Public Health, Emory University
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Lu Y, Oliva M, Pierce BL, Liu J, Chen LS. Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits. Nat Commun 2024; 15:2383. [PMID: 38493154 PMCID: PMC10944527 DOI: 10.1038/s41467-024-46675-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.
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Affiliation(s)
- Yihao Lu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Meritxell Oliva
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Jin Liu
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
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116
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Mulder RH, Neumann A, Felix JF, Suderman M, Cecil CAM. What makes clocks tick? Characterizing developmental dynamics of adult epigenetic clock sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.12.584597. [PMID: 38559237 PMCID: PMC10979995 DOI: 10.1101/2024.03.12.584597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
DNA methylation (DNAm) at specific sites can be used to calculate 'epigenetic clocks', which in adulthood are used as indicators of age(ing). However, little is known about how these clock sites 'behave' during development and what factors influence their variability in early life. This knowledge could be used to optimize healthy aging well before the onset of age-related conditions. Here, we leveraged results from two longitudinal population-based cohorts (N=5,019 samples from 2,348 individuals) to characterize trajectories of adult clock sites from birth to early adulthood. We find that clock sites (i) diverge widely in their developmental trajectories, often showing non-linear change over time; (ii) are substantially more likely than non-clock sites to vary between individuals already from birth, differences that are predictive of DNAm variation at later ages; and (iii) show enrichment for genetic and prenatal environmental exposures, supporting an early-origins perspective to epigenetic aging.
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Affiliation(s)
- Rosa H. Mulder
- Department of Child and Adolescent Psychiatry / Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alexander Neumann
- Department of Child and Adolescent Psychiatry / Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Janine F. Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Charlotte A. M. Cecil
- Department of Child and Adolescent Psychiatry / Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
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117
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Guirette M, Lan J, McKeown NM, Brown MR, Chen H, de Vries PS, Kim H, Rebholz CM, Morrison AC, Bartz TM, Fretts AM, Guo X, Lemaitre RN, Liu CT, Noordam R, de Mutsert R, Rosendaal FR, Wang CA, Beilin LJ, Mori TA, Oddy WH, Pennell CE, Chai JF, Whitton C, van Dam RM, Liu J, Tai ES, Sim X, Neuhouser ML, Kooperberg C, Tinker LF, Franceschini N, Huan T, Winkler TW, Bentley AR, Gauderman WJ, Heerkens L, Tanaka T, van Rooij J, Munroe PB, Warren HR, Voortman T, Chen H, Rao DC, Levy D, Ma J. Genome-Wide Interaction Analysis With DASH Diet Score Identified Novel Loci for Systolic Blood Pressure. Hypertension 2024; 81:552-560. [PMID: 38226488 PMCID: PMC10922535 DOI: 10.1161/hypertensionaha.123.22334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/28/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND The Dietary Approaches to Stop Hypertension (DASH) diet score lowers blood pressure (BP). We examined interactions between genotype and the DASH diet score in relation to systolic BP. METHODS We analyzed up to 9 420 585 single nucleotide polymorphisms in up to 127 282 individuals of 6 population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (n=35 660) and UK Biobank (n=91 622) and performed European population-specific and cross-population meta-analyses. RESULTS We identified 3 loci in European-specific analyses and an additional 4 loci in cross-population analyses at Pinteraction<5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency, 0.03) and the DASH diet score (Pinteraction=4e-8; P for heterogeneity, 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (Pinteraction=9.4e-7) and 0.20±0.06 mm Hg (Pinteraction=0.001) in Cohorts for Heart and Aging Research in Genomic Epidemiology and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with cis-expression quantitative trait loci (eQTL) variants (P=4e-273) and cis-DNA methylation quantitative trait loci variants (P=1e-300). Although the closest gene for rs117878928 is MTHFS, the highest narrow sense heritability accounted by single nucleotide polymorphisms potentially interacting with the DASH diet score in this locus was for gene ST20 at 15q25.1. CONCLUSIONS We demonstrated gene-DASH diet score interaction effects on systolic BP in several loci. Studies with larger diverse populations are needed to validate our findings.
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Affiliation(s)
- Mélanie Guirette
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA (M.G., J.L., J.M.)
| | - Jessie Lan
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA (M.G., J.L., J.M.)
| | - Nicola M McKeown
- Programs of Nutrition, Department of Health Sciences, Sargent College of Health & Rehabilitation Sciences, Boston University, MA (N.M.M.)
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston (M.R.B., H.C., P.S.d.V., A.C.M.)
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston (M.R.B., H.C., P.S.d.V., A.C.M.)
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston (M.R.B., H.C., P.S.d.V., A.C.M.)
| | - Hyunju Kim
- Department of Epidemiology (H.K., A.M.F.), Cardiovascular Health Research Unit, University of Washington, Seattle, WA
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (C.M.R.)
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston (M.R.B., H.C., P.S.d.V., A.C.M.)
| | - Traci M Bartz
- Departments of Biostatistics and Medicine (T.M.B.), Cardiovascular Health Research Unit, University of Washington, Seattle, WA
| | - Amanda M Fretts
- Department of Epidemiology (H.K., A.M.F.), Cardiovascular Health Research Unit, University of Washington, Seattle, WA
| | - Xiuqing Guo
- The Lundquist Institute at Harbor-University of California, Los Angeles, Torrance, CA (X.G.)
| | - Rozenn N Lemaitre
- Department of Medicine (R.N.L.), Cardiovascular Health Research Unit, University of Washington, Seattle, WA
| | - Ching-Ti Liu
- Biostatistics, Boston University School of Public Health, MA (C.-T.L.)
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics (R.N.), Leiden University Medical Center, the Netherlands
| | - Renée de Mutsert
- Department of Clinical Epidemiology (R.d.M., F.R.R.), Leiden University Medical Center, the Netherlands
| | - Frits R Rosendaal
- Department of Clinical Epidemiology (R.d.M., F.R.R.), Leiden University Medical Center, the Netherlands
| | - Carol A Wang
- School of Medicine and Public Health, University of Newcastle, NSW, Australia (C.A.W., C.E.P)
- Mothers' and Babies' Research Program, Hunter Medical Research Institute, NSW, Australia (C.A.W., C.E.P.)
| | - Lawrence J Beilin
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Crawley (L.J.B., T.A.M.)
| | - Trevor A Mori
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Crawley (L.J.B., T.A.M.)
| | - Wendy H Oddy
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia (W.H.O.)
| | - Craig E Pennell
- School of Medicine and Public Health, University of Newcastle, NSW, Australia (C.A.W., C.E.P)
- Mothers' and Babies' Research Program, Hunter Medical Research Institute, NSW, Australia (C.A.W., C.E.P.)
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System (J.F.C., C.W., R.M.v.D., E.S.T., X.S.)
| | - Clare Whitton
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System (J.F.C., C.W., R.M.v.D., E.S.T., X.S.)
- School of Population Health, Curtin University, Perth, Western Australia, Australia (C.W.)
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System (J.F.C., C.W., R.M.v.D., E.S.T., X.S.)
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University (R.M.v.D.)
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research (J.L.)
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System (J.F.C., C.W., R.M.v.D., E.S.T., X.S.)
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore (E.S.T.)
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System (J.F.C., C.W., R.M.v.D., E.S.T., X.S.)
| | - Marian L Neuhouser
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA (M.L.N., C.K., L.F.T.)
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA (M.L.N., C.K., L.F.T.)
| | - Lesley F Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA (M.L.N., C.K., L.F.T.)
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill (N.F.)
| | - TianXiao Huan
- Framingham Heart Study and Population Sciences Branch, National Heart, Lung, and Blood Institute, MA (T.H., D.L.)
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Germany (T.W.W.)
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD (A.R.B.)
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California (W.J.G.)
| | - Luc Heerkens
- Division of Human Nutrition and Health, Wageningen University & Research, the Netherlands (L.H.)
| | - Toshiko Tanaka
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD (T.T.)
| | - Jeroen van Rooij
- Department of Internal Medicine (J.v.R.), Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Patricia B Munroe
- Centre of Clinical Pharmacology & Precision Medicine, William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, United Kingdom (P.B.M., H.R.W.)
| | - Helen R Warren
- Centre of Clinical Pharmacology & Precision Medicine, William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, United Kingdom (P.B.M., H.R.W.)
| | - Trudy Voortman
- Department of Epidemiology (T.V.), Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Honglei Chen
- Department of Epidemiology and Biostatistics College of Human Medicine, Michigan State University, East Lansing (H.C.)
| | - D C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R.)
| | - Daniel Levy
- Framingham Heart Study and Population Sciences Branch, National Heart, Lung, and Blood Institute, MA (T.H., D.L.)
| | - Jiantao Ma
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA (M.G., J.L., J.M.)
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118
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Wang Z, Fu G, Ma G, Wang C, Wang Q, Lu C, Fu L, Zhang X, Cong B, Li S. The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction. Hum Genet 2024; 143:401-421. [PMID: 38507014 DOI: 10.1007/s00439-024-02659-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/13/2024] [Indexed: 03/22/2024]
Abstract
As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.
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Affiliation(s)
- Zhonghua Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Guangping Fu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Guanju Ma
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Chunyan Wang
- Physical Examination Center of Shijiazhuang People's Hospital, Shijiazhuang, 050011, Hebei, China
| | - Qian Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Chaolong Lu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Lihong Fu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Xiaojing Zhang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Shujin Li
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China.
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119
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Yagound B, Sarma RR, Edwards RJ, Richardson MF, Rodriguez Lopez CM, Crossland MR, Brown GP, DeVore JL, Shine R, Rollins LA. Is developmental plasticity triggered by DNA methylation changes in the invasive cane toad ( Rhinella marina)? Ecol Evol 2024; 14:e11127. [PMID: 38450317 PMCID: PMC10917582 DOI: 10.1002/ece3.11127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
Many organisms can adjust their development according to environmental conditions, including the presence of conspecifics. Although this developmental plasticity is common in amphibians, its underlying molecular mechanisms remain largely unknown. Exposure during development to either 'cannibal cues' from older conspecifics, or 'alarm cues' from injured conspecifics, causes reduced growth and survival in cane toad (Rhinella marina) tadpoles. Epigenetic modifications, such as changes in DNA methylation patterns, are a plausible mechanism underlying these developmental plastic responses. Here we tested this hypothesis, and asked whether cannibal cues and alarm cues trigger the same DNA methylation changes in developing cane toads. We found that exposure to both cannibal cues and alarm cues was associated with local changes in DNA methylation patterns. These DNA methylation changes affected genes putatively involved in developmental processes, but in different genomic regions for different conspecific-derived cues. Genetic background explains most of the epigenetic variation among individuals. Overall, the molecular mechanisms triggered by exposure to cannibal cues seem to differ from those triggered by alarm cues. Studies linking epigenetic modifications to transcriptional activity are needed to clarify the proximate mechanisms that regulate developmental plasticity in cane toads.
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Affiliation(s)
- Boris Yagound
- Evolution & Ecology Research Centre, Biological, Earth and Environmental SciencesUniversity of New South WalesSydneyNew South WalesAustralia
| | - Roshmi R. Sarma
- Evolution & Ecology Research Centre, Biological, Earth and Environmental SciencesUniversity of New South WalesSydneyNew South WalesAustralia
- Centre for Integrative Ecology, School of Life and Environmental SciencesDeakin UniversityGeelongVictoriaAustralia
| | - Richard J. Edwards
- Evolution & Ecology Research Centre, School of Biotechnology and Biomedical SciencesUniversity of New South WalesSydneyNew South WalesAustralia
- Minderoo OceanOmics Centre at UWA, Oceans InstituteDeakin UniversityGeelongVictoriaAustralia
| | - Mark F. Richardson
- Centre for Integrative Ecology, School of Life and Environmental SciencesDeakin UniversityGeelongVictoriaAustralia
- Minderoo OceanOmics Centre at UWA, Oceans InstituteDeakin UniversityGeelongVictoriaAustralia
- Deakin Genomics Research and Discovery FacilityDeakin University, Locked BagGeelongVICAustralia
| | - Carlos M. Rodriguez Lopez
- Deakin Genomics Research and Discovery FacilityDeakin University, Locked BagGeelongVICAustralia
- School of Agriculture, Food and Wine, Waite Research InstituteThe University of AdelaideGlen OsmondSouth AustraliaAustralia
- Environmental Epigenetics and Genetics Group, Department of HorticultureCollege of Agriculture, Food and Environment, University of KentuckyLexingtonKentuckyUSA
| | - Michael R. Crossland
- School of Agriculture, Food and Wine, Waite Research InstituteThe University of AdelaideGlen OsmondSouth AustraliaAustralia
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
| | - Gregory P. Brown
- School of Agriculture, Food and Wine, Waite Research InstituteThe University of AdelaideGlen OsmondSouth AustraliaAustralia
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
- Department of Biological SciencesMacquarie UniversitySydneyNew South WalesAustralia
| | - Jayna L. DeVore
- School of Agriculture, Food and Wine, Waite Research InstituteThe University of AdelaideGlen OsmondSouth AustraliaAustralia
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
- UMR 241 EIOUniversity of French Polynesia, IFREMER, ILM, IRDFaa’aTahitiFrench Polynesia
| | - Richard Shine
- School of Agriculture, Food and Wine, Waite Research InstituteThe University of AdelaideGlen OsmondSouth AustraliaAustralia
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
- Department of Biological SciencesMacquarie UniversitySydneyNew South WalesAustralia
| | - Lee A. Rollins
- Evolution & Ecology Research Centre, Biological, Earth and Environmental SciencesUniversity of New South WalesSydneyNew South WalesAustralia
- Centre for Integrative Ecology, School of Life and Environmental SciencesDeakin UniversityGeelongVictoriaAustralia
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120
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Hubers N, Hagenbeek FA, Pool R, Déjean S, Harms AC, Roetman PJ, van Beijsterveldt CEM, Fanos V, Ehli EA, Vermeiren RRJM, Bartels M, Hottenga JJ, Hankemeier T, van Dongen J, Boomsma DI. Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32955. [PMID: 37534875 DOI: 10.1002/ajmg.b.32955] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 06/13/2023] [Accepted: 07/11/2023] [Indexed: 08/04/2023]
Abstract
The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.
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Affiliation(s)
- Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Sébastien Déjean
- Toulouse Mathematics Institute, UMR 5219, University of Toulouse, CNRS, Toulouse, France
| | - Amy C Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
- The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Peter J Roetman
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Vassilios Fanos
- Department of Surgical Sciences, University of Cagliari and Neonatal Intensive Care Unit, Cagliari, Italy
| | - Erik A Ehli
- Avera Institute for Human Genetics, Sioux Falls, South Dakota, USA
| | - Robert R J M Vermeiren
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Youz, Parnassia Group, the Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
- The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
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121
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Hu X, Chen S, Ye S, Chen W, Zhou Y. New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era. Front Immunol 2024; 15:1342837. [PMID: 38487541 PMCID: PMC10937589 DOI: 10.3389/fimmu.2024.1342837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/19/2024] [Indexed: 03/17/2024] Open
Abstract
Diabetic kidney disease (DKD) is becoming the leading cause of chronic kidney disease, especially in the industrialized world. Despite mounting evidence has demonstrated that immunity and inflammation are highly involved in the pathogenesis and progression of DKD, the underlying mechanisms remain incompletely understood. Substantial molecules, signaling pathways, and cell types participate in DKD inflammation, by integrating into a complex regulatory network. Most of the studies have focused on individual components, without presenting their importance in the global or system-based processes, which largely hinders clinical translation. Besides, conventional technologies failed to monitor the different behaviors of resident renal cells and immune cells, making it difficult to understand their contributions to inflammation in DKD. Recently, the advancement of omics technologies including genomics, epigenomics, transcriptomics, proteomics, and metabolomics has revolutionized biomedical research, which allows an unbiased global analysis of changes in DNA, RNA, proteins, and metabolites in disease settings, even at single-cell and spatial resolutions. They help us to identify critical regulators of inflammation processes and provide an overview of cell heterogeneity in DKD. This review aims to summarize the application of multiple omics in the field of DKD and emphasize the latest evidence on the interplay of inflammation and DKD revealed by these technologies, which will provide new insights into the role of inflammation in the pathogenesis of DKD and lead to the development of novel therapeutic approaches and diagnostic biomarkers.
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Affiliation(s)
- Xinrong Hu
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Sixiu Chen
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Siyang Ye
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Wei Chen
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Yi Zhou
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
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Camerota M, Lester BM, Castellanos FX, Carter BS, Check J, Helderman J, Hofheimer JA, McGowan EC, Neal CR, Pastyrnak SL, Smith LM, O'Shea TM, Marsit CJ, Everson TM. Epigenome-wide association study identifies neonatal DNA methylation associated with two-year attention problems in children born very preterm. Transl Psychiatry 2024; 14:126. [PMID: 38418845 PMCID: PMC10902402 DOI: 10.1038/s41398-024-02841-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Prior research has identified epigenetic predictors of attention problems in school-aged children but has not yet investigated these in young children, or children at elevated risk of attention problems due to preterm birth. The current study evaluated epigenome-wide associations between neonatal DNA methylation and attention problems at age 2 years in children born very preterm. Participants included 441 children from the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) Study, a multi-site study of infants born < 30 weeks gestational age. DNA methylation was measured from buccal swabs collected at NICU discharge using the Illumina MethylationEPIC Bead Array. Attention problems were assessed at 2 years of adjusted age using the attention problems subscale of the Child Behavior Checklist (CBCL). After adjustment for multiple testing, DNA methylation at 33 CpG sites was associated with child attention problems. Differentially methylated CpG sites were located in genes previously linked to physical and mental health, including several genes associated with ADHD in prior epigenome-wide and genome-wide association studies. Several CpG sites were located in genes previously linked to exposure to prenatal risk factors in the NOVI sample. Neonatal epigenetics measured at NICU discharge could be useful in identifying preterm children at risk for long-term attention problems and related psychiatric disorders, who could benefit from early prevention and intervention efforts.
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Affiliation(s)
- Marie Camerota
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.
- Brown Center for the Study of Children at Risk, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA.
| | - Barry M Lester
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Brown Center for the Study of Children at Risk, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
- Department of Pediatrics, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Brian S Carter
- Department of Pediatrics-Neonatology, Children's Mercy Hospital, Kansas City, MO, USA
| | - Jennifer Check
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer Helderman
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Julie A Hofheimer
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Elisabeth C McGowan
- Department of Pediatrics, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
| | - Charles R Neal
- Department of Pediatrics, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - Steven L Pastyrnak
- Department of Pediatrics, Spectrum Health-Helen DeVos Hospital, Grand Rapids, MI, USA
| | - Lynne M Smith
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Thomas Michael O'Shea
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Carmen J Marsit
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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Casazza W, Inkster AM, Del Gobbo GF, Yuan V, Delahaye F, Marsit C, Park YP, Robinson WP, Mostafavi S, Dennis JK. Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions. iScience 2024; 27:109047. [PMID: 38357671 PMCID: PMC10865402 DOI: 10.1016/j.isci.2024.109047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/19/2023] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.
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Affiliation(s)
- William Casazza
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Amy M. Inkster
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Giulia F. Del Gobbo
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Victor Yuan
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | | | - Carmen Marsit
- Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yongjin P. Park
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Wendy P. Robinson
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Sara Mostafavi
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Paul Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Jessica K. Dennis
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
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Lange de Luna J, Nounu A, Neumeyer S, Sinke L, Wilson R, Hellbach F, Matías-García PR, Delerue T, Winkelmann J, Peters A, Thorand B, Beekman M, Heijmans BT, Slagboom E, Gieger C, Linseisen J, Waldenberger M. Epigenome-wide association study of dietary fatty acid intake. Clin Epigenetics 2024; 16:29. [PMID: 38365790 PMCID: PMC10874013 DOI: 10.1186/s13148-024-01643-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/09/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Dietary intake of n-3 polyunsaturated fatty acids (PUFA) may have a protective effect on the development of cardiovascular diseases, diabetes, depression and cancer, while a high intake of n-6 PUFA was often reported to be associated with inflammation-related traits. The effect of PUFAs on health outcomes might be mediated by DNA methylation (DNAm). The aim of our study is to identify the impact of PUFA intake on DNAm in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 cohort and the Leiden Longevity Study (LLS). RESULTS DNA methylation levels were measured in whole blood from the population-based KORA FF4 study (N = 1354) and LLS (N = 448), using the Illumina MethylationEPIC BeadChip and Illumina HumanMethylation450 array, respectively. We assessed associations between DNAm and intake of eight and four PUFAs in KORA and LLS, respectively. Where possible, results were meta-analyzed. Below the Bonferroni correction threshold (p < 7.17 × 10-8), we identified two differentially methylated positions (DMPs) associated with PUFA intake in the KORA study. The DMP cg19937480, annotated to gene PRDX1, was positively associated with docosahexaenoic acid (DHA) in model 1 (beta: 2.00 × 10-5, 95%CI: 1.28 × 10-5-2.73 × 10-5, P value: 6.98 × 10-8), while cg05041783, annotated to gene MARK2, was positively associated with docosapentaenoic acid (DPA) in our fully adjusted model (beta: 9.80 × 10-5, 95%CI: 6.25 × 10-5-1.33 × 10-4, P value: 6.75 × 10-8). In the meta-analysis, we identified the CpG site (cg15951061), annotated to gene CDCA7L below Bonferroni correction (1.23 × 10-7) associated with eicosapentaenoic acid (EPA) intake in model 1 (beta: 2.00 × 10-5, 95% CI: 1.27 × 10-5-2.73 × 10-5, P value = 5.99 × 10-8) and we confirmed the association of cg19937480 with DHA in both models 1 and 2 (beta: 2.07 × 10-5, 95% CI: 1.31 × 10-5-2.83 × 10-5, P value = 1.00 × 10-7 and beta: 2.19 × 10-5, 95% CI: 1.41 × 10-5-2.97 × 10-5, P value = 5.91 × 10-8 respectively). CONCLUSIONS Our study identified three CpG sites associated with PUFA intake. The mechanisms of these sites remain largely unexplored, highlighting the novelty of our findings. Further research is essential to understand the links between CpG site methylation and PUFA outcomes.
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Affiliation(s)
- Julia Lange de Luna
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, LMU Munich, 80539, Munich, Germany
| | - Aayah Nounu
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Sonja Neumeyer
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Fabian Hellbach
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital of Augsburg, 86156, Augsburg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, LMU Munich, 80539, Munich, Germany
| | - Pamela R Matías-García
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Thomas Delerue
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Klinikum Rechts Der Isar, Chair Neurogenetics, Technical University of Munich, Munich, Germany
- Klinikum Rechts Der Isar, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, LMU Munich, 80539, Munich, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, LMU Munich, 80539, Munich, Germany
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Jakob Linseisen
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital of Augsburg, 86156, Augsburg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, LMU Munich, 80539, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany.
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125
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Liu J, Zhong X. Epiallelic variation of non-coding RNA genes and their phenotypic consequences. Nat Commun 2024; 15:1375. [PMID: 38355746 PMCID: PMC10867003 DOI: 10.1038/s41467-024-45771-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024] Open
Abstract
Epigenetic variations contribute greatly to the phenotypic plasticity and diversity. Current functional studies on epialleles have predominantly focused on protein-coding genes, leaving the epialleles of non-coding RNA (ncRNA) genes largely understudied. Here, we uncover abundant DNA methylation variations of ncRNA genes and their significant correlations with plant adaptation among 1001 natural Arabidopsis accessions. Through genome-wide association study (GWAS), we identify large numbers of methylation QTL (methylQTL) that are independent of known DNA methyltransferases and enriched in specific chromatin states. Proximal methylQTL closely located to ncRNA genes have a larger effect on DNA methylation than distal methylQTL. We ectopically tether a DNA methyltransferase MQ1v to miR157a by CRISPR-dCas9 and show de novo establishment of DNA methylation accompanied with decreased miR157a abundance and early flowering. These findings provide important insights into the genetic basis of epigenetic variations and highlight the contribution of epigenetic variations of ncRNA genes to plant phenotypes and diversity.
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Affiliation(s)
- Jie Liu
- Department of Biology, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Xuehua Zhong
- Department of Biology, Washington University in St. Louis, St. Louis, MO, 63130, USA.
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126
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Vosberg DE, Jurisica I, Pausova Z, Paus T. Intrauterine growth and the tangential expansion of the human cerebral cortex in times of food scarcity and abundance. Nat Commun 2024; 15:1205. [PMID: 38350995 PMCID: PMC10864407 DOI: 10.1038/s41467-024-45409-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
Tangential growth of the human cerebral cortex is driven by cell proliferation during the first and second trimester of pregnancy. Fetal growth peaks in mid-gestation. Here, we explore how genes associated with fetal growth relate to cortical growth. We find that both maternal and fetal genetic variants associated with higher birthweight predict larger cortical surface area. The relative dominance of the maternal vs. fetal variants in these associations show striking variations across birth years (1943 to 1966). The birth-year patterns vary as a function of the epigenetic status near genes differentially methylated in individuals exposed (or not) to famine during the Dutch Winter of 1944/1945. Thus, it appears that the two sets of molecular processes contribute to early cortical development to a different degree in times of food scarcity or its abundance.
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Affiliation(s)
- Daniel E Vosberg
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Research Institute of the Hospital for Sick Children, Toronto, ON, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Departments of Medical Biophysics and Computer Science, and the Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Zdenka Pausova
- Research Institute of the Hospital for Sick Children, Toronto, ON, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
- ECOGENE-21, Chicoutimi, Quebec, Canada
| | - Tomáš Paus
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada.
- Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.
- ECOGENE-21, Chicoutimi, Quebec, Canada.
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.
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Dieckmann L, Czamara D. Epigenetics of prenatal stress in humans: the current research landscape. Clin Epigenetics 2024; 16:20. [PMID: 38308342 PMCID: PMC10837967 DOI: 10.1186/s13148-024-01635-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/25/2024] [Indexed: 02/04/2024] Open
Abstract
Fetal exposure to prenatal stress can have significant consequences on short- and long-term health. Epigenetic mechanisms, especially DNA methylation (DNAm), are a possible process how these adverse environmental events could be biologically embedded. We evaluated candidate gene as well as epigenome-wide association studies associating prenatal stress and DNAm changes in peripheral tissues; however, most of these findings lack robust replication. Prenatal stress-associated epigenetic changes have also been linked to child health including internalizing problems, neurobehavioral outcomes and stress reactivity. Future studies should focus on refined measurement and definition of prenatal stress and its timing, ideally also incorporating genomic as well as longitudinal information. This will provide further opportunities to enhance our understanding of the biological embedding of prenatal stress exposure.
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Affiliation(s)
- Linda Dieckmann
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Munich, Germany
| | - Darina Czamara
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany.
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128
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Ikram MA, Kieboom BCT, Brouwer WP, Brusselle G, Chaker L, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, de Knegt RJ, Luik AI, van Meurs J, Pardo LM, Rivadeneira F, van Rooij FJA, Vernooij MW, Voortman T, Terzikhan N. The Rotterdam Study. Design update and major findings between 2020 and 2024. Eur J Epidemiol 2024; 39:183-206. [PMID: 38324224 DOI: 10.1007/s10654-023-01094-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/14/2023] [Indexed: 02/08/2024]
Abstract
The Rotterdam Study is a population-based cohort study, started in 1990 in the district of Ommoord in the city of Rotterdam, the Netherlands, with the aim to describe the prevalence and incidence, unravel the etiology, and identify targets for prediction, prevention or intervention of multifactorial diseases in mid-life and elderly. The study currently includes 17,931 participants (overall response rate 65%), aged 40 years and over, who are examined in-person every 3 to 5 years in a dedicated research facility, and who are followed-up continuously through automated linkage with health care providers, both regionally and nationally. Research within the Rotterdam Study is carried out along two axes. First, research lines are oriented around diseases and clinical conditions, which are reflective of medical specializations. Second, cross-cutting research lines transverse these clinical demarcations allowing for inter- and multidisciplinary research. These research lines generally reflect subdomains within epidemiology. This paper describes recent methodological updates and main findings from each of these research lines. Also, future perspective for coming years highlighted.
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Affiliation(s)
- M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands.
| | - Brenda C T Kieboom
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Willem Pieter Brouwer
- Department of Hepatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Guy Brusselle
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Pulmonology, University Hospital Ghent, Ghent, Belgium
| | - Layal Chaker
- Department of Epidemiology, and Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - André Goedegebure
- Department of Otorhinolaryngology and Head & Neck Surgery, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, and Department of Neurology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Rob J de Knegt
- Department of Hepatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Luba M Pardo
- Department of Dermatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Fernando Rivadeneira
- Department of Medicine, and Department of Oral & Maxillofacial Surgery, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, and Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
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129
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Ying K, Liu H, Tarkhov AE, Sadler MC, Lu AT, Moqri M, Horvath S, Kutalik Z, Shen X, Gladyshev VN. Causality-enriched epigenetic age uncouples damage and adaptation. NATURE AGING 2024; 4:231-246. [PMID: 38243142 PMCID: PMC11070280 DOI: 10.1038/s43587-023-00557-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 12/12/2023] [Indexed: 01/21/2024]
Abstract
Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.
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Affiliation(s)
- Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- T. H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Hanna Liu
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
- Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA
| | - Andrei E Tarkhov
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marie C Sadler
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ake T Lu
- Altos Labs, San Diego, CA, USA
- Departments of Human Genetics and Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Steve Horvath
- Altos Labs, San Diego, CA, USA
- Departments of Human Genetics and Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Xia Shen
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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130
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Bogan SN, Yi SV. Potential Role of DNA Methylation as a Driver of Plastic Responses to the Environment Across Cells, Organisms, and Populations. Genome Biol Evol 2024; 16:evae022. [PMID: 38324384 PMCID: PMC10899001 DOI: 10.1093/gbe/evae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/09/2024] [Accepted: 01/23/2024] [Indexed: 02/09/2024] Open
Abstract
There is great interest in exploring epigenetic modifications as drivers of adaptive organismal responses to environmental change. Extending this hypothesis to populations, epigenetically driven plasticity could influence phenotypic changes across environments. The canonical model posits that epigenetic modifications alter gene regulation and subsequently impact phenotypes. We first discuss origins of epigenetic variation in nature, which may arise from genetic variation, spontaneous epimutations, epigenetic drift, or variation in epigenetic capacitors. We then review and synthesize literature addressing three facets of the aforementioned model: (i) causal effects of epigenetic modifications on phenotypic plasticity at the organismal level, (ii) divergence of epigenetic patterns in natural populations distributed across environmental gradients, and (iii) the relationship between environmentally induced epigenetic changes and gene expression at the molecular level. We focus on DNA methylation, the most extensively studied epigenetic modification. We find support for environmentally associated epigenetic structure in populations and selection on stable epigenetic variants, and that inhibition of epigenetic enzymes frequently bears causal effects on plasticity. However, there are pervasive confounding issues in the literature. Effects of chromatin-modifying enzymes on phenotype may be independent of epigenetic marks, alternatively resulting from functions and protein interactions extrinsic of epigenetics. Associations between environmentally induced changes in DNA methylation and expression are strong in plants and mammals but notably absent in invertebrates and nonmammalian vertebrates. Given these challenges, we describe emerging approaches to better investigate how epigenetic modifications affect gene regulation, phenotypic plasticity, and divergence among populations.
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Affiliation(s)
- Samuel N Bogan
- Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, USA
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA
| | - Soojin V Yi
- Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, USA
- Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, CA, USA
- Neuroscience Research Institute, University of California, Santa Barbara, CA, USA
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131
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Ryan CP, Belsky DW. Epigenetic clock work ticks forward. NATURE AGING 2024; 4:170-172. [PMID: 38291215 DOI: 10.1038/s43587-024-00570-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Affiliation(s)
- C P Ryan
- Robert N. Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
| | - D W Belsky
- Robert N. Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA.
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.
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132
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Zhang H, Kalla R, Chen J, Zhao J, Zhou X, Adams A, Noble A, Ventham NT, Wellens J, Ho GT, Dunlop MG, Nowak JK, Ding Y, Liu Z, Satsangi J, Theodoratou E, Li X. Altered DNA methylation within DNMT3A, AHRR, LTA/TNF loci mediates the effect of smoking on inflammatory bowel disease. Nat Commun 2024; 15:595. [PMID: 38238335 PMCID: PMC10796384 DOI: 10.1038/s41467-024-44841-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024] Open
Abstract
This work aims to investigate how smoking exerts effect on the development of inflammatory bowel disease (IBD). A prospective cohort study and a Mendelian randomization study are first conducted to evaluate the association between smoking behaviors, smoking-related DNA methylation and the risks of Crohn's disease (CD) and ulcerative colitis (UC). We then perform both genome-wide methylation analysis and co-localization analysis to validate the observed associations. Compared to never smoking, current and previous smoking habits are associated with increased CD (P = 7.09 × 10-10) and UC (P < 2 × 10-16) risk, respectively. DNA methylation alteration at cg17742416 [DNMT3A] is linked to both CD (P = 7.30 × 10-8) and UC (P = 1.04 × 10-4) risk, while cg03599224 [LTA/TNF] is associated with CD risk (P = 1.91 × 10-6), and cg14647125 [AHRR] and cg23916896 [AHRR] are linked to UC risk (P = 0.001 and 0.002, respectively). Our study identifies biological mechanisms and pathways involved in the effects of smoking on the pathogenesis of IBD.
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Affiliation(s)
- Han Zhang
- Department of Big Data in Health Science School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rahul Kalla
- Edinburgh IBD Science Unit, Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | - Jie Chen
- Department of Big Data in Health Science School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianhui Zhao
- Department of Big Data in Health Science School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xuan Zhou
- Department of Big Data in Health Science School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Alex Adams
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Alexandra Noble
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Nicholas T Ventham
- Academic Coloproctology, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Judith Wellens
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK
- Department of Chronic Diseases and Metabolism, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
| | - Gwo-Tzer Ho
- Edinburgh IBD Science Unit, Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | - Malcolm G Dunlop
- Cancer Research UK Scotland Centre and Medical Research Council Human Genetics Unit, University of Edinburgh, Edinburgh, UK
| | - Jan Krzysztof Nowak
- Department of Paediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland
| | - Yuan Ding
- Department of Hepatobiliary and Pancreatic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhanju Liu
- Center for IBD Research, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Jack Satsangi
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK.
| | - Evropi Theodoratou
- Cancer Research UK Scotland Centre and Medical Research Council Human Genetics Unit, University of Edinburgh, Edinburgh, UK.
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.
| | - Xue Li
- Department of Big Data in Health Science School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK.
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133
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Hellbach F, Freuer D, Meisinger C, Peters A, Winkelmann J, Costeira R, Hauner H, Baumeister SE, Bell JT, Waldenberger M, Linseisen J. Usual dietary intake and change in DNA methylation over years: EWAS in KORA FF4 and KORA fit. Front Nutr 2024; 10:1295078. [PMID: 38249614 PMCID: PMC10799384 DOI: 10.3389/fnut.2023.1295078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Changes in DNA methylation can increase or suppress the expression of health-relevant genes. We investigated for the first time the relationship between habitual food consumption and changes in DNA methylation. Methods The German KORA FF4 and KORA Fit studies were used to study the change in methylation over a median follow-up of 4 years. Only subjects participating in both surveys and with available dietary and methylation data were included in the analysis (n = 465). DNA methylation was measured using the Infinium MethylationEPIC BeadChip (Illumina), resulting in 735,527 shared CpGs across both studies. Generalized estimating equation models with an interaction term of exposure and time point were used to analyze the association of 34 food groups, folic acid, and two dietary patterns with changes in DNA methylation over time. Results The results were corrected for genomic inflation. Significant interaction terms indicate different effects between both time points. We observed only a few significant associations between food intake and change in DNA methylation, except for cream and spirit consumption. The annotated genes include CLN3, PROM1, DLEU7, TLL2, and UGT1A10. Discussion We identified weak associations between food consumption and DNA methylation change. The differential results for cream and spirits, both consumed in low quantities, require replication in independent studies.
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Affiliation(s)
- Fabian Hellbach
- Department of Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
- Medical Faculty, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilian University Munich, Munich, Germany
| | - Dennis Freuer
- Department of Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
| | - Christa Meisinger
- Department of Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
| | - Annette Peters
- Medical Faculty, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilian University Munich, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Technical University of Munich, Institute of Human Genetics, Klinikum Rechts der Isar, Munich, Germany
| | - Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Hans Hauner
- Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- School of Medicine, Institute of Nutritional Medicine, Technical University of Munich, Munich, Germany
| | - Sebastian-Edgar Baumeister
- Medical Faculty, Institute of Health Services Research in Dentistry, University of Münster, Münster, Germany
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Jakob Linseisen
- Department of Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
- Medical Faculty, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilian University Munich, Munich, Germany
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134
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van Dongen J, Hubers N, Boomsma DI. New insights into the (epi)genetics of twinning. Hum Reprod 2024; 39:35-42. [PMID: 38052159 PMCID: PMC10767898 DOI: 10.1093/humrep/dead131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 05/24/2023] [Indexed: 12/07/2023] Open
Abstract
Spontaneous dizygotic (DZ) twins, i.e. twins conceived without the use of ARTs, run in families and their prevalence varies widely around the globe. In contrast, monozygotic (MZ) twins occur at a constant rate across time and geographical regions and, with some rare exceptions, do not cluster in families. The leading hypothesis for MZ twins, which arise when a zygote splits during preimplantation stages of development, is random occurrence. We have found the first series of genes underlying the liability of being the mother of DZ twins and have shown that being an MZ twin is strongly associated with a stable DNA methylation signature in child and adult somatic tissues. Because identical twins keep this molecular signature across the lifespan, this discovery opens up completely new possibilities for the retrospective diagnosis of whether a person is an MZ twin whose co-twin may have vanished in the early stages of pregnancy. Here, we summarize the gene finding results for mothers of DZ twins based on genetic association studies followed by meta-analysis, and further present the striking epigenetic results for MZ twins.
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Affiliation(s)
- Jenny van Dongen
- Netherlands Twin Register, Department of Biological Psychology, Amsterdam Reproduction and Development Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Nikki Hubers
- Netherlands Twin Register, Department of Biological Psychology, Amsterdam Reproduction and Development Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Amsterdam Reproduction and Development Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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135
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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Johnson WC, Van Den Berg D, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Barr RG, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. Am J Hum Genet 2024; 111:133-149. [PMID: 38181730 PMCID: PMC10806864 DOI: 10.1016/j.ajhg.2023.11.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024] Open
Abstract
Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA; Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany; Computational Health Center, Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Brielin C Brown
- New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA
| | - Daniel C Nachun
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Russell P Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D Smith
- Northwest Genomics Center, University of Washington, Seattle, WA, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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136
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Paus T. Population Neuroscience: Principles and Advances. Curr Top Behav Neurosci 2024; 68:3-34. [PMID: 38589637 DOI: 10.1007/7854_2024_474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.
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Affiliation(s)
- Tomáš Paus
- Department of Psychiatry and Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
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137
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Dai Y, Hsu YC, Fernandes BS, Zhang K, Li X, Enduru N, Liu A, Manuel AM, Jiang X, Zhao Z. Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach. J Alzheimers Dis 2024; 97:1807-1827. [PMID: 38306043 PMCID: PMC11649026 DOI: 10.3233/jad-231020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Background The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, McWilliams School of
Biomedical Informatics, The University of Texas Health Science Center at Houston,
Houston, TX, USA
| | - Yu-Chun Hsu
- Center for Secure Artificial Intelligence for Healthcare,
School of Biomedical Informatics, The University of Texas Health Science Center at
Houston, Houston, TX, USA
| | - Brisa S. Fernandes
- Center for Precision Health, McWilliams School of
Biomedical Informatics, The University of Texas Health Science Center at Houston,
Houston, TX, USA
| | - Kai Zhang
- Center for Secure Artificial Intelligence for Healthcare,
School of Biomedical Informatics, The University of Texas Health Science Center at
Houston, Houston, TX, USA
| | - Xiaoyang Li
- Center for Precision Health, McWilliams School of
Biomedical Informatics, The University of Texas Health Science Center at Houston,
Houston, TX, USA
- Department of Biostatistics and Data Science, School of
Public Health, The University of Texas Health Science Center at Houston, Houston,
TX, USA
| | - Nitesh Enduru
- Center for Precision Health, McWilliams School of
Biomedical Informatics, The University of Texas Health Science Center at Houston,
Houston, TX, USA
- Department of Epidemiology, Human Genetics and
Environmental Sciences, School of Public Health, The University of Texas Health
Science Center at Houston, Houston, TX, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of
Biomedical Informatics, The University of Texas Health Science Center at Houston,
Houston, TX, USA
- Department of Epidemiology, Human Genetics and
Environmental Sciences, School of Public Health, The University of Texas Health
Science Center at Houston, Houston, TX, USA
| | - Astrid M. Manuel
- Center for Precision Health, McWilliams School of
Biomedical Informatics, The University of Texas Health Science Center at Houston,
Houston, TX, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence for Healthcare,
School of Biomedical Informatics, The University of Texas Health Science Center at
Houston, Houston, TX, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of
Biomedical Informatics, The University of Texas Health Science Center at Houston,
Houston, TX, USA
- Department of Epidemiology, Human Genetics and
Environmental Sciences, School of Public Health, The University of Texas Health
Science Center at Houston, Houston, TX, USA
- Department of Biomedical Informatics, Vanderbilt University
Medical enter, Nashville, TN, USA
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138
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Fang F, Quach B, Lawrence KG, van Dongen J, Marks JA, Lundgren S, Lin M, Odintsova VV, Costeira R, Xu Z, Zhou L, Mandal M, Xia Y, Vink JM, Bierut LJ, Ollikainen M, Taylor JA, Bell JT, Kaprio J, Boomsma DI, Xu K, Sandler DP, Hancock DB, Johnson EO. Trans-ancestry epigenome-wide association meta-analysis of DNA methylation with lifetime cannabis use. Mol Psychiatry 2024; 29:124-133. [PMID: 37935791 PMCID: PMC11078760 DOI: 10.1038/s41380-023-02310-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9436 participants (7795 European and 1641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 ( p < 5.85 × 10 - 7 ) : cg22572071 near gene ADGRF1, cg15280358 in ADAM12, cg00813162 in ACTN1, and cg01101459 near LINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated to APOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes.
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Affiliation(s)
- Fang Fang
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA.
| | - Bryan Quach
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Kaitlyn G Lawrence
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jesse A Marks
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Sara Lundgren
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Mingkuan Lin
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ricardo Costeira
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Zongli Xu
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Linran Zhou
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Meisha Mandal
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Yujing Xia
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jacqueline M Vink
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Laura J Bierut
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, St. Louis, MO, USA
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jordana T Bell
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Dana B Hancock
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Eric O Johnson
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
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139
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Luo M, Walton E, Neumann A, Thio CHL, Felix JF, van IJzendoorn MH, Pappa I, Cecil CAM. DNA methylation at birth and lateral ventricular volume in childhood: a neuroimaging epigenetics study. J Child Psychol Psychiatry 2024; 65:77-90. [PMID: 37469193 PMCID: PMC10953396 DOI: 10.1111/jcpp.13866] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/05/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Lateral ventricular volume (LVV) enlargement has been repeatedly linked to schizophrenia; yet, what biological factors shape LVV during early development remain unclear. DNA methylation (DNAm), an essential process for neurodevelopment that is altered in schizophrenia, is a key molecular system of interest. METHODS In this study, we conducted the first epigenome-wide association study of neonatal DNAm in cord blood with LVV in childhood (measured using T1-weighted brain scans at 10 years), based on data from a large population-based birth cohort, the Generation R Study (N = 840). Employing both probe-level and methylation profile score (MPS) approaches, we further examined whether epigenetic modifications identified at birth in cord blood are: (a) also observed cross-sectionally in childhood using peripheral blood DNAm at age of 10 years (Generation R, N = 370) and (b) prospectively associated with LVV measured in young adulthood in an all-male sample from the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 114). RESULTS At birth, DNAm levels at four CpGs (annotated to potassium channel tetramerization domain containing 3, KCTD3; SHH signaling and ciliogenesis regulator, SDCCAG8; glutaredoxin, GLRX) prospectively associated with childhood LVV after genome-wide correction; these genes have been implicated in brain development and psychiatric traits including schizophrenia. An MPS capturing a broader epigenetic profile of LVV - but not individual top hits - showed significant cross-sectional associations with LVV in childhood in Generation R and prospectively associated with LVV in early adulthood within ALSPAC. CONCLUSIONS This study finds suggestive evidence that DNAm at birth prospectively associates with LVV at different life stages, albeit with small effect sizes. The prediction of MPS on LVV in a childhood sample and an independent male adult sample further underscores the stability and reproducibility of DNAm as a potential marker for LVV. Future studies with larger samples and comparable time points across development are needed to further elucidate how DNAm associates with this clinically relevant brain structure and risk for neuropsychiatric disorders, and what factors explain the identified DNAm profile of LVV at birth.
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Affiliation(s)
- Mannan Luo
- Department of Psychology, Education and Child StudiesErasmus University RotterdamRotterdamThe Netherlands
- Generation R Study Group, Erasmus MCUniversity Medical Center RotterdamRotterdamThe Netherlands
| | | | - Alexander Neumann
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MCUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Chris H. L. Thio
- Department of EpidemiologyUniversity Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Janine F. Felix
- Generation R Study Group, Erasmus MCUniversity Medical Center RotterdamRotterdamThe Netherlands
- Department of Pediatrics, Erasmus MCUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Marinus H. van IJzendoorn
- Department of Psychology, Education and Child StudiesErasmus University RotterdamRotterdamThe Netherlands
- Research Department of Clinical, Educational and Health Psychology, Faculty of Brain Sciences, UCLUniversity of LondonLondonUK
| | - Irene Pappa
- Generation R Study Group, Erasmus MCUniversity Medical Center RotterdamRotterdamThe Netherlands
- Clinical Child and Family StudiesVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Charlotte A. M. Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MCUniversity Medical Center RotterdamRotterdamThe Netherlands
- Department of Epidemiology, Erasmus MCUniversity Medical Center RotterdamRotterdamThe Netherlands
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CenterLeidenThe Netherlands
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140
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Drouard G, Wang Z, Heikkinen A, Foraster M, Julvez J, Kanninen KM, van Kamp I, Pirinen M, Ollikainen M, Kaprio J. Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.12.23299868. [PMID: 38168348 PMCID: PMC10760270 DOI: 10.1101/2023.12.12.23299868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Zhiyang Wang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Aino Heikkinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Maria Foraster
- PHAGEX Research Group, Blanquerna School of Health Science, Universitat Ramon Llull (URL), Barcelona, Spain
| | - Jordi Julvez
- Clinical and epidemiological Neuroscience (NeuroÈpia), Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- ISGlobal, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Spain
| | - Katja M. Kanninen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Irene van Kamp
- National Institute for Public Health and the Environment, centre for Sustainability, Environment and Health, Netherlands
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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141
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Koch Z, Li A, Evans DS, Cummings S, Ideker T. Somatic mutation as an explanation for epigenetic aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.08.569638. [PMID: 38106096 PMCID: PMC10723383 DOI: 10.1101/2023.12.08.569638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
DNA methylation marks have recently been used to build models known as "epigenetic clocks" which predict calendar age. As methylation of cytosine promotes C-to-T mutations, we hypothesized that the methylation changes observed with age should reflect the accrual of somatic mutations, and the two should yield analogous aging estimates. In analysis of multimodal data from 9,331 human individuals, we find that CpG mutations indeed coincide with changes in methylation, not only at the mutated site but also with pervasive remodeling of the methylome out to ±10 kilobases. This one-to-many mapping enables mutation-based predictions of age that agree with epigenetic clocks, including which individuals are aging faster or slower than expected. Moreover, genomic loci where mutations accumulate with age also tend to have methylation patterns that are especially predictive of age. These results suggest a close coupling between the accumulation of sporadic somatic mutations and the widespread changes in methylation observed over the course of life.
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Affiliation(s)
- Zane Koch
- Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla CA, 92093, USA
| | - Adam Li
- Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla CA, 92093, USA
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, 94158
| | - Steven Cummings
- California Pacific Medical Center Research Institute, San Francisco CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, 94158
| | - Trey Ideker
- Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla CA, 92093, USA
- Department of Medicine, University of California San Diego, La Jolla California, 92093, USA
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142
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Long E, Zhang J. Evidence for the role of selection for reproductively advantageous alleles in human aging. SCIENCE ADVANCES 2023; 9:eadh4990. [PMID: 38064565 PMCID: PMC10708185 DOI: 10.1126/sciadv.adh4990] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023]
Abstract
The antagonistic pleiotropy hypothesis posits that natural selection for pleiotropic mutations that confer earlier or more reproduction but impair the post-reproductive life causes aging. This hypothesis of the evolutionary origin of aging is supported by case studies but lacks unambiguous genomic evidence. Here, we genomically test this hypothesis using the genotypes, reproductive phenotypes, and death registry of 276,406 U.K. Biobank participants. We observe a strong, negative genetic correlation between reproductive traits and life span. Individuals with higher polygenetic scores for reproduction (PGSR) have lower survivorships to age 76 (SV76), and PGSR increased over birth cohorts from 1940 to 1969. Similar trends are seen from individual genetic variants examined. The antagonistically pleiotropic variants are often associated with cis-regulatory effects across multiple tissues or on multiple target genes. These and other findings support the antagonistic pleiotropy hypothesis of aging in humans and point to potential molecular mechanisms of the reproduction-life-span antagonistic pleiotropy.
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Affiliation(s)
- Erping Long
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
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143
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Lukacsovich D, O’Shea D, Huang H, Zhang W, Young JI, Steven Chen X, Dietrich ST, Kunkle B, Martin ER, Wang L. MIAMI-AD (Methylation in Aging and Methylation in AD): an integrative knowledgebase that facilitates explorations of DNA methylation across sex, aging, and Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.04.23299412. [PMID: 38105943 PMCID: PMC10723513 DOI: 10.1101/2023.12.04.23299412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) is a common neurodegenerative disorder with a significant impact on aging populations. DNA methylation (DNAm) alterations have been implicated in both the aging processes and the development of AD. Given that AD affects more women than men, it is also important to explore DNAm changes that occur specifically in each sex. We created MIAMI-AD, a comprehensive knowledge base containing manually curated summary statistics from 97 published tables in 37 studies, all of which included at least 100 participants. MIAMI-AD enables easy browsing, querying, and downloading DNAm associations at multiple levels - at individual CpG, gene, genomic regions, or genome-wide, in one or multiple studies. Moreover, it also offers tools to perform integrative analyses, such as comparing DNAm associations across different phenotypes or tissues, as well as interactive visualizations. Using several use case examples, we demonstrated that MIAMI-AD facilitates our understanding of age-associated CpGs in AD and the sex-specific roles of DNAm in AD. This open-access resource is freely available to the research community, and all the underlying data can be downloaded. MIAMI-AD (https://miami-ad.org/) facilitates integrative explorations to better understand the interplay between DNAm across aging, sex, and AD.
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Affiliation(s)
- David Lukacsovich
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Deirdre O’Shea
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, 33433
| | - Hanchen Huang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Wei Zhang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Juan I. Young
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - X. Steven Chen
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Sven-Thorsten Dietrich
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Brian Kunkle
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Eden R. Martin
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Lily Wang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
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144
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Bakulski KM, Blostein F, London SJ. Linking Prenatal Environmental Exposures to Lifetime Health with Epigenome-Wide Association Studies: State-of-the-Science Review and Future Recommendations. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:126001. [PMID: 38048101 PMCID: PMC10695268 DOI: 10.1289/ehp12956] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND The prenatal environment influences lifetime health; epigenetic mechanisms likely predominate. In 2016, the first international consortium paper on cigarette smoking during pregnancy and offspring DNA methylation identified extensive, reproducible exposure signals. This finding raised expectations for epigenome-wide association studies (EWAS) of other exposures. OBJECTIVE We review the current state-of-the-science for DNA methylation associations across prenatal exposures in humans and provide future recommendations. METHODS We reviewed 134 prenatal environmental EWAS of DNA methylation in newborns, focusing on 51 epidemiological studies with meta-analysis or replication testing. Exposures spanned cigarette smoking, alcohol consumption, air pollution, dietary factors, psychosocial stress, metals, other chemicals, and other exogenous factors. Of the reproducible DNA methylation signatures, we examined implementation as exposure biomarkers. RESULTS Only 19 (14%) of these prenatal EWAS were conducted in cohorts of 1,000 or more individuals, reflecting the still early stage of the field. To date, the largest perinatal EWAS sample size was 6,685 participants. For comparison, the most recent genome-wide association study for birth weight included more than 300,000 individuals. Replication, at some level, was successful with exposures to cigarette smoking, folate, dietary glycemic index, particulate matter with aerodynamic diameter < 10 μ m and < 2.5 μ m , nitrogen dioxide, mercury, cadmium, arsenic, electronic waste, PFAS, and DDT. Reproducible effects of a more limited set of prenatal exposures (smoking, folate) enabled robust methylation biomarker creation. DISCUSSION Current evidence demonstrates the scientific premise for reproducible DNA methylation exposure signatures. Better powered EWAS could identify signatures across many exposures and enable comprehensive biomarker development. Whether methylation biomarkers of exposures themselves cause health effects remains unclear. We expect that larger EWAS with enhanced coverage of epigenome and exposome, along with improved single-cell technologies and evolving methods for integrative multi-omics analyses and causal inference, will expand mechanistic understanding of causal links between environmental exposures, the epigenome, and health outcomes throughout the life course. https://doi.org/10.1289/EHP12956.
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Affiliation(s)
| | - Freida Blostein
- University of Michigan, Ann Arbor, Michigan, USA
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Stephanie J. London
- National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research Triangle Park, North Carolina, USA
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145
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Doherty T, Yao Z, Khleifat AAL, Tantiangco H, Tamburin S, Albertyn C, Thakur L, Llewellyn DJ, Oxtoby NP, Lourida I, Ranson JM, Duce JA. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimers Dement 2023; 19:5922-5933. [PMID: 37587767 DOI: 10.1002/alz.13428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/26/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.
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Affiliation(s)
- Thomas Doherty
- Eisai Europe Ltd, Hatfield, UK
- University of Westminster, London, UK
| | | | - Ahmad A L Khleifat
- Institute of Psychiatry, Psychology & Neuroscience, Department of Basic and Clinical Neuroscience, King's College London, London, UK
| | | | - Stefano Tamburin
- University of Verona, Department of Neurosciences, Biomedicine & Movement Sciences, Verona, Italy
| | - Chris Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | | | - James A Duce
- The ALBORADA Drug Discovery Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
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146
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Mozhui K, Kim H, Villani F, Haghani A, Sen S, Horvath S. Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits. Epigenetics 2023; 18:2252631. [PMID: 37691384 PMCID: PMC10496549 DOI: 10.1080/15592294.2023.2252631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/31/2023] [Accepted: 08/16/2023] [Indexed: 09/12/2023] Open
Abstract
DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more 'aged methylome' for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein-protein interaction enrichment analyses identified the hepatic nuclear factor, Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.
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Affiliation(s)
- Khyobeni Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Hyeonju Kim
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Flavia Villani
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Amin Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego, CA, USA
| | - Saunak Sen
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
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Kotsakis Ruehlmann A, Sammallahti S, Cortés Hidalgo AP, Bakulski KM, Binder EB, Campbell ML, Caramaschi D, Cecil CAM, Colicino E, Cruceanu C, Czamara D, Dieckmann L, Dou J, Felix JF, Frank J, Håberg SE, Herberth G, Hoang TT, Houtepen LC, Hüls A, Koen N, London SJ, Magnus MC, Mancano G, Mulder RH, Page CM, Räikkönen K, Röder S, Schmidt RJ, Send TS, Sharp G, Stein DJ, Streit F, Tuhkanen J, Witt SH, Zar HJ, Zenclussen AC, Zhang Y, Zillich L, Wright R, Lahti J, Brunst KJ. Epigenome-wide meta-analysis of prenatal maternal stressful life events and newborn DNA methylation. Mol Psychiatry 2023; 28:5090-5100. [PMID: 36899042 DOI: 10.1038/s41380-023-02010-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 02/08/2023] [Accepted: 02/21/2023] [Indexed: 03/12/2023]
Abstract
Prenatal maternal stressful life events are associated with adverse neurodevelopmental outcomes in offspring. Biological mechanisms underlying these associations are largely unknown, but DNA methylation likely plays a role. This meta-analysis included twelve non-overlapping cohorts from ten independent longitudinal studies (N = 5,496) within the international Pregnancy and Childhood Epigenetics consortium to examine maternal stressful life events during pregnancy and DNA methylation in cord blood. Children whose mothers reported higher levels of cumulative maternal stressful life events during pregnancy exhibited differential methylation of cg26579032 in ALKBH3. Stressor-specific domains of conflict with family/friends, abuse (physical, sexual, and emotional), and death of a close friend/relative were also associated with differential methylation of CpGs in APTX, MyD88, and both UHRF1 and SDCCAG8, respectively; these genes are implicated in neurodegeneration, immune and cellular functions, regulation of global methylation levels, metabolism, and schizophrenia risk. Thus, differences in DNA methylation at these loci may provide novel insights into potential mechanisms of neurodevelopment in offspring.
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Affiliation(s)
- Anna Kotsakis Ruehlmann
- University of Cincinnati College of Medicine, Department of Environmental and Public Health Sciences, Cincinnati, OH, USA
| | - Sara Sammallahti
- Erasmus MC, University Medical Center Rotterdam, Department of Adolescent and Child Psychiatry and Psychology, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Andrea P Cortés Hidalgo
- Erasmus MC, University Medical Center Rotterdam, Department of Adolescent and Child Psychiatry and Psychology, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Kelly M Bakulski
- University of Michigan, School of Public Health, Department of Epidemiology, Ann Arbor, MI, USA
| | - Elisabeth B Binder
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, Munich, Germany
| | - Megan Loraine Campbell
- University of Cape Town, Department of Psychiatry and Mental Health, Cape Town, South Africa
| | - Doretta Caramaschi
- School of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Charlotte A M Cecil
- Erasmus MC, University Medical Center Rotterdam, Department of Adolescent and Child Psychiatry and Psychology, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Elena Colicino
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Cristiana Cruceanu
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, Munich, Germany
| | - Darina Czamara
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, Munich, Germany
| | - Linda Dieckmann
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - John Dou
- University of Michigan, School of Public Health, Department of Epidemiology, Ann Arbor, MI, USA
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunda Herberth
- Helmholtz Centre for Environmental Research - UFZ, Department of Environmental Immunology, Leipzig, Germany
| | - Thanh T Hoang
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, 27709, NC, USA
| | - Lotte C Houtepen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Anke Hüls
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Nastassja Koen
- Department of Psychiatry and Mental Health, University of Cape Town, South Africa; and UCT Neuroscience Institute, Cape Town, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Cape Town, South Africa
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, 27709, NC, USA
| | - Maria C Magnus
- Norwegian Institute of Public Health, Centre for Fertility and Health, Oslo, Norway
| | - Giulia Mancano
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Rosa H Mulder
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Christian M Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Katri Räikkönen
- University of Helsinki, Faculty of Medicine, Department of Psychology and Logopedics, Helsinki, Finland
| | - Stefan Röder
- Helmholtz Centre for Environmental Research - UFZ, Department of Environmental Immunology, Leipzig, Germany
| | - Rebecca J Schmidt
- University of California-Davis, School of Medicine, Department of Public Health Sciences, Davis, CA, USA
| | - Tabea S Send
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gemma Sharp
- School of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, South Africa; and UCT Neuroscience Institute, Cape Town, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Cape Town, South Africa
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Johanna Tuhkanen
- University of Helsinki, Faculty of Medicine, Department of Psychology and Logopedics, Helsinki, Finland
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heather J Zar
- Department of Paediatrics & Child Health & SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - Ana C Zenclussen
- Helmholtz Centre for Environmental Research - UFZ, Department of Environmental Immunology, Leipzig, Germany
| | - Yining Zhang
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Lea Zillich
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Rosalind Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Kravis Children's Hospital, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jari Lahti
- University of Helsinki, Faculty of Medicine, Department of Psychology and Logopedics, Helsinki, Finland
| | - Kelly J Brunst
- University of Cincinnati College of Medicine, Department of Environmental and Public Health Sciences, Cincinnati, OH, USA.
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148
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Harris HA, Friedman C, Starling AP, Dabelea D, Johnson SL, Fuemmeler BF, Jima D, Murphy SK, Hoyo C, Jansen PW, Felix JF, Mulder RH. An epigenome-wide association study of child appetitive traits and DNA methylation. Appetite 2023; 191:107086. [PMID: 37844693 PMCID: PMC11156223 DOI: 10.1016/j.appet.2023.107086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023]
Abstract
The etiology of childhood appetitive traits is poorly understood. Early-life epigenetic processes may be involved in the developmental programming of appetite regulation in childhood. One such process is DNA methylation (DNAm), whereby a methyl group is added to a specific part of DNA, where a cytosine base is next to a guanine base, a CpG site. We meta-analyzed epigenome-wide association studies (EWASs) of cord blood DNAm and early-childhood appetitive traits. Data were from two independent cohorts: the Generation R Study (n = 1,086, Rotterdam, the Netherlands) and the Healthy Start study (n = 236, Colorado, USA). DNAm at autosomal methylation sites in cord blood was measured using the Illumina Infinium HumanMethylation450 BeadChip. Parents reported on their child's food responsiveness, emotional undereating, satiety responsiveness and food fussiness using the Children's Eating Behaviour Questionnaire at age 4-5 years. Multiple regression models were used to examine the association of DNAm (predictor) at the individual site- and regional-level (using DMRff) with each appetitive trait (outcome), adjusting for covariates. Bonferroni-correction was applied to adjust for multiple testing. There were no associations of DNAm and any appetitive trait when examining individual CpG-sites. However, when examining multiple CpGs jointly in so-called differentially methylated regions, we identified 45 associations of DNAm with food responsiveness, 7 associations of DNAm with emotional undereating, 13 associations of DNAm with satiety responsiveness, and 9 associations of DNAm with food fussiness. This study shows that DNAm in the newborn may partially explain variation in appetitive traits expressed in early childhood and provides preliminary support for early programming of child appetitive traits through DNAm. Investigating differential DNAm associated with appetitive traits could be an important first step in identifying biological pathways underlying the development of these behaviors.
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Affiliation(s)
- Holly A Harris
- Department of Child & Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Erasmus University Rotterdam, Department of Psychology, Education & Child Studies, Rotterdam, the Netherlands.
| | - Chloe Friedman
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Anne P Starling
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Susan L Johnson
- Department of Pediatrics, Section of Nutrition, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Bernard F Fuemmeler
- Virginia Commonwealth University, Massey Comprehensive Cancer Center, Richmond, VA, USA.
| | - Dereje Jima
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA; Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA.
| | - Susan K Murphy
- Duke University Medical Center, Department of Obstetrics and Gynecology, Reproductive Sciences, Durham, NC, USA.
| | - Cathrine Hoyo
- Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA.
| | - Pauline W Jansen
- Department of Child & Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Erasmus University Rotterdam, Department of Psychology, Education & Child Studies, Rotterdam, the Netherlands.
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Rosa H Mulder
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
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149
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Zhang J, Wang Y, Zhang Y, Yao J. Genome-wide association study in Alzheimer's disease: a bibliometric and visualization analysis. Front Aging Neurosci 2023; 15:1290657. [PMID: 38094504 PMCID: PMC10716290 DOI: 10.3389/fnagi.2023.1290657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/08/2023] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Thousands of research studies concerning genome-wide association studies (GWAS) in Alzheimer's disease (AD) have been published in the last decades. However, a comprehensive understanding of the current research status and future development trends of GWAS in AD have not been clearly shown. In this study, we tried to gain a systematic overview of GWAS in AD by bibliometric and visualization analysis. METHODS The literature search terms are: ("genome-wide analysis" or "genome-wide association study" or "whole-genome analysis") AND ("Alzheimer's Disease" or "Alzheimer Disease"). Relevant publications were extracted from the Web of Science Core Collection (WoSCC) database. Collected data were further analyzed using VOSviewer, CiteSpace and R package Bibliometrix. The countries, institutions, authors and scholar collaborations were investigated. The co-citation analysis of publications was visualized. In addition, research hotspots and fronts were examined. RESULTS A total of 1,350 publications with 59,818 citations were identified. The number of publications and citations presented a significant rising trend since 2013. The United States was the leading country with an overwhelming number of publications (775) and citations (42,237). The University of Washington and Harvard University were the most prolific institutions with 101 publications each. Bennett DA was the most influential researcher with the highest local H-index. Neurobiology of Aging was the journal with the highest number of publications. Aβ, tau, immunity, microglia and DNA methylation were research hotspots. Disease and causal variants were research fronts. CONCLUSION The most frequently studied AD pathogenesis and research hotspots are (1) Aβ and tau, (2) immunity and microglia, with TREM2 as a potential immunotherapy target, and (3) DNA methylation. The research fronts are (1) looking for genetic similarities between AD and other neurological diseases and syndromes, and (2) searching for causal variants of AD. These hotspots suggest noteworthy directions for future studies on AD pathogenesis and genetics, in which basic research regarding immunity is promising for clinical conversion. The current under-researched directions are (1) GWAS in AD biomarkers based on large sample sizes, (2) studies of causal variants of AD, and (3) GWAS in AD based on non-European populations, which need to be strengthened in the future.
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Affiliation(s)
- Junyao Zhang
- Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinuo Wang
- Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Zhang
- Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junyan Yao
- Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Anesthesiology and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
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150
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Guirette M, Lan J, McKeown N, Brown MR, Chen H, DE Vries PS, Kim H, Rebholz CM, Morrison AC, Bartz TM, Fretts AM, Guo X, Lemaitre RN, Liu CT, Noordam R, DE Mutsert R, Rosendaal FR, Wang CA, Beilin L, Mori TA, Oddy WH, Pennell CE, Chai JF, Whitton C, VAN Dam RM, Liu J, Tai ES, Sim X, Neuhouser ML, Kooperberg C, Tinker L, Franceschini N, Huan T, Winkler TW, Bentley AR, Gauderman WJ, Heerkens L, Tanaka T, van Rooij J, Munroe PB, Warren HR, Voortman T, Chen H, Rao DC, Levy D, Ma J. Genome-Wide Interaction Analysis with DASH Diet Score Identified Novel Loci for Systolic Blood Pressure. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23298402. [PMID: 37986948 PMCID: PMC10659476 DOI: 10.1101/2023.11.10.23298402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Objective We examined interactions between genotype and a Dietary Approaches to Stop Hypertension (DASH) diet score in relation to systolic blood pressure (SBP). Methods We analyzed up to 9,420,585 biallelic imputed single nucleotide polymorphisms (SNPs) in up to 127,282 individuals of six population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE; n=35,660) and UK Biobank (n=91,622) and performed European population-specific and cross-population meta-analyses. Results We identified three loci in European-specific analyses and an additional four loci in cross-population analyses at P for interaction < 5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency = 0.03) and the DASH diet score (P for interaction = 4e-8; P for heterogeneity = 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (P for interaction = 9.4e-7) and 0.20±0.06 mm Hg (P for interaction = 0.001) in CHARGE and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with cis-expression quantitative trait loci (eQTL) variants (P = 4e-273) and cis-DNA methylation quantitative trait loci (mQTL) variants (P = 1e-300). While the closest gene for rs117878928 is MTHFS, the highest narrow sense heritability accounted by SNPs potentially interacting with the DASH diet score in this locus was for gene ST20 at 15q25.1. Conclusion We demonstrated gene-DASH diet score interaction effects on SBP in several loci. Studies with larger diverse populations are needed to validate our findings.
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Affiliation(s)
- Mélanie Guirette
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Jessie Lan
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Nicola McKeown
- Programs of Nutrition, Department of Health Sciences, Sargent College of Health & Rehabilitation Sciences, Boston University, Boston, MA, USA
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S DE Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hyunju Kim
- Cardiovascular Health Research Unit, Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Departments of Biostatistics and Medicine, University of Washington, Seattle, WA, USA
| | - Amanda M Fretts
- Cardiovascular Health Research Unit, Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Xiuqing Guo
- The Lundquist Institute at Harbor-UCLA, Torrance, CA, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ching-Ti Liu
- Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Renée DE Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Carol A Wang
- School of Medicine and Public Health, University of Newcastle, NSW, Australia
- Hunter Medical Research Institute, NSW, Australia
| | - Lawrence Beilin
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Crawley, Western Australia, Australia
| | - Trevor A Mori
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Crawley, Western Australia, Australia
| | - Wendy H Oddy
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia Saw Swee Hock, School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Craig E Pennell
- School of Medicine and Public Health, University of Newcastle, NSW, Australia
- Hunter Medical Research Institute, NSW, Australia
| | - Jin Fang Chai
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Clare Whitton
- School of Population Health, Curtin University, Perth, Western Australia, Australia
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - Rob M VAN Dam
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - E Shyong Tai
- School of Population Health, Curtin University, Perth, Western Australia, Australia
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Xueling Sim
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Marian L Neuhouser
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Lesley Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Tianxiao Huan
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg; Regensburg, Germany
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California; CA, USA
| | - Luc Heerkens
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, the Netherlands
| | - Toshiko Tanaka
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
| | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Patricia B Munroe
- Centre of Clinical Pharmacology & Precision Medicine, William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Helen R Warren
- Centre of Clinical Pharmacology & Precision Medicine, William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Honglei Chen
- Department of Epidemiology and Biostatistics College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
| | - D C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel Levy
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA, USA
| | - Jiantao Ma
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
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