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Strong associations between neural tube defects (NTDs) and monozygotic (MZ) twinning have long been noted, and it has been suggested that NTD cases who do not present as MZ twins may be the survivors of MZ twinning events. We have recently shown that MZ twins carry a strong, distinctive DNA methylation signature and have developed an algorithm based on genomewide DNA methylation array data that distinguishes MZ twins from dizygotic twins and other relatives at well above chance level. We have applied this algorithm to published methylation data from five fetal tissues (placental chorionic villi, kidney, spinal cord, brain and muscle) collected from spina bifida cases (n = 22), anencephalic cases (n = 15) and controls (n = 19). We see no difference in signature between cases and controls, providing no support for a common etiological role of MZ twinning in NTDs. The strong associations therefore continue to await elucidation.

Amyotrophic Lateral Sclerosis (ALS) is recognised to be a complex neurodegenerative disease involving both genetic and non-genetic risk factors. The underlying causes and risk factors for the majority of cases remain unknown; however, ever-larger genetic data studies and methodologies promise an enhanced understanding. Recent analyses using published summary statistics from the largest ALS genome-wide association study (GWAS) (20,806 ALS cases and 59,804 healthy controls) identified that schizophrenia (SCZ), cognitive performance (CP) and educational attainment (EA) related traits were genetically correlated with ALS. To provide additional evidence for these correlations, we built single and multi-trait genetic predictors using GWAS summary statistics for ALS and these traits, (SCZ, CP, EA) in an independent Australian cohort (846 ALS cases and 665 healthy controls). We compared methods for generating the risk predictors and found that the combination of traits improved the prediction (Nagelkerke-R-2) of the case-control logistic regression. The combination of ALS, SCZ, CP, and EA, using the SBayesR predictor method gave the highest prediction (Nagelkerke-R-2) of 0.027 (P value = 4.6 x 10(-8)), with the odds-ratio for estimated disease risk between the highest and lowest deciles of individuals being 3.15 (95% CI 1.96-5.05). These results support the genetic correlation between ALS, SCZ, CP and EA providing a better understanding of the complexity of ALS. Refereed/Peer-reviewed

Background Amyotrophic lateral sclerosis (ALS) is a complex, late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies (GWAS) have identified ten risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Given association analysis data alone cannot determine the most plausible risk gene for this locus, we undertook a comprehensive suite of in silico, in vivo and in vitro studies to address this. Methods The Functional Mapping and Annotation (FUMA) pipeline and five tools (conditional and joint analysis (GCTA-COJO), Stratified Linkage Disequilibrium Score Regression (S-LDSC), Polygenic Priority Scoring (PoPS), Summary-based Mendelian Randomisation (SMR-HEIDI) and transcriptome-wide association study (TWAS) analyses) were used to perform bioinformatic integration of GWAS data (Ncases = 20,806, Ncontrols = 59,804) with ‘omics reference datasets including the blood (eQTLgen consortium N = 31,684) and brain (N = 2581). This was followed up by specific expression studies in ALS case-control cohorts (microarray Ntotal = 942, protein Ntotal = 300) and gene knockdown (KD) studies of human neuronal iPSC cells and zebrafish-morpholinos (MO). Results SMR analyses implicated both TNIP1 and GPX3 (p < 1.15 × 10−6), but there was no simple SNP/expression relationship. Integrating multiple datasets using PoPS supported GPX3 but not TNIP1. In vivo expression analyses from blood in ALS cases identified that lower GPX3 expression correlated with a more progressed disease (ALS functional rating score, p = 5.5 × 10−3, adjusted R2 = 0.042, Beffect = 27.4 ± 13.3 ng/ml/ALSFRS unit) with microarray and protein data suggesting lower expression with risk allele (recessive model p = 0.06, p = 0.02 respectively). Validation in vivo indicated gpx3 KD caused significant motor deficits in zebrafish-MO (mean difference vs. control ± 95% CI, vs. control, swim distance = 112 ± 28 mm, time = 1.29 ± 0.59 s, speed = 32.0 ± 2.53 mm/s, respectively, p for all gpx3 expression, with no phenotype identified with tnip1 KD or gpx3 overexpression. Conclusions These results support GPX3 as a lead ALS risk gene in this locus, with more data needed to confirm/reject a role for TNIP1. This has implications for understanding disease mechanisms (GPX3 acts in the same pathway as SOD1, a well-established ALS-associated gene) and identifying new therapeutic approaches. Few previous examples of in-depth investigations of risk loci in ALS exist and a similar approach could be applied to investigate future expected GWAS findings.

DNA methylation has fundamental roles in gene programming and aging that may help predict mortality. However, no large-scale study has investigated whether site-specific DNA methylation predicts all-cause mortality. We used the Illumina-HumanMethylation450-BeadChip to identify blood DNA methylation sites associated with all-cause mortality for 12, 300 participants in 12 Cohorts of the Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium. Over an average 10-year follow-up, there were 2,561 deaths across the cohorts. Nine sites mapping to three intergenic and six gene-specific regions were associated with mortality (P < 9.3x10-7) independently of age and other mortality predictors. Six sites (cg14866069, cg23666362, cg20045320, cg07839457, cg07677157, cg09615688)—mapping respectively to BMPR1B, MIR1973, IFITM3, NLRC5, and two intergenic regions—were associated with reduced mortality risk. The remaining three sites (cg17086398, cg12619262, cg18424841)—mapping respectively to SERINC2, CHST12, and an intergenic region—were associated with increased mortality risk. DNA methylation at each site predicted 5%¬¬–15% of all deaths. We also assessed the causal association of those sites to age-related chronic diseases by using Mendelian randomization, identifying weak causal relationship between cg18424841 and cg09615688 with coronary heart disease. Of the nine sites, three (cg20045320, cg07839457, cg07677157) were associated with lower incidence of heart disease risk and two (cg20045320, cg07839457) with smoking and inflammation in prior CHARGE analyses. Methylation of cg20045320, cg07839457, and cg17086398 was associated with decreased expression of nearby genes (IFITM3, IRF, NLRC5, MT1, MT2, MARCKSL1) linked to immune responses and cardiometabolic diseases. These sites may serve as useful clinical tools for mortality risk assessment and preventative care.

ENIGMA-CNV Working Group: van der Meer, Dennis; Sonderby, Ida E; Kaufmann, Tobias; Walters, G Bragi; Abdellaoui, Abdel; Ames, David; Amunts, Katrin; Andersson, Micael; Armstrong, Nicola J; Bernard, Manon; Blackburn, Nicholas B; Blangero, John; Boomsma, Dorret I; Brodaty, Henry; Brouwer, Rachel M; Bulow, Robin; Cahn, Wiepke; Calhoun, Vince D; Caspers, Svenja; Cavalleri, Gianpiero L; Ching, Christopher R K; Cichon, Sven; Ciufolini, Simone; Corvin, Aiden; Crespo-Facorro, Benedicto; Curran, Joanne E; Dalvie, Shareefa; Dazzan, Paola; de Geus, Eco J C; de Zubicaray, Greig I; de Zwarte, Sonja M C; Delanty, Norman; den Braber, Anouk; Desrivieres, Sylvane; Di Forti, Marta; Doherty, Joanne L; Donohoe, Gary; Ehrlich, Stefan; Eising, Else; Espeseth, Thomas; Fisher, Simon E; Fladby, Tormod; Frei, Oleksandr; Frouin, Vincent; Fukunaga, Masaki; Gareau, Thomas; Glahn, David C; Grabe, Hans J; Groenewold, Nynke A; Gustafsson, Omar; Haavik, Jan; Haberg, Asta K; Hashimoto, Ryota; Hehir-Kwa, Jayne Y; Hibar, Derrek P; Hillegers, Manon H J; Hoffmann, Per; Holleran, Laurena; Hottenga, Jouke-Jan; Hulshoff Pol, Hilleke E; Ikeda, Masashi; Jacquemont, Sebastien; Jahanshad, Neda; Jockwitz, Christiane; Johansson, Stefan; Jonsson, Erik G; Kikuchi, Masataka; Knowles, Emma E M; Kwok, John B; Le Hellard, Stephanie; Linden, David E J; Liu, Jingyu; Lundervold, Arvid; Lundervold, Astri J; Martin, Nicholas G; Mather, Karen A; Mathias, Samuel R; McMahon, Katie L; McRae, Allan F; Medland, Sarah E; Moberget, Torgeir; Moreau, Clara; Morris, Derek W; Muhleisen, Thomas W; Murray, Robin M; Nordvik, Jan E; Nyberg, Lars; Olde Loohuis, Loes M; Ophoff, Roel A; Owen, Michael J; Paus, Tomas; Pausova, Zdenka; Peralta, Juan M; Pike, Bruce; Prieto, Carlos; Quinlan, Erin Burke; Reinbold, Celine S; Reis Marques, Tiago; Rucker, James J H; Sachdev, Perminder S; Sando, Sigrid B; Schofield, Peter R; Schork, Andrew J; Schumann, Gunter; Shin, Jean; Shumskaya, Elena; Silva, Ana I; Sisodiya, Sanjay M; Steen, Vidar M; Stein, Dan J; Strike, Lachlan T; Tamnes, Christian K; Teumer, Alexander; Thalamuthu, Anbupalam; Tordesillas-Gutierrez, Diana; Uhlmann, Anne; Ulfarsson, Magnus O; van 't Ent, Dennis; van den Bree, Marianne B M; Vassos, Evangelos; Wen, Wei; Wittfeld, Katharina; Wright, Margaret J; Zayats, Tetyana; Dale, Anders M; Djurovic, Srdjan; Agartz, Ingrid; Westlye, Lars T; Stefansson, Hreinn; Stefansson, Kari; Thompson, Paul M; Andreassen, Ole A., [Importance] Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities., [Objective] To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance., [Design, Setting, and Participants] In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019., [Main Outcomes and Measures] The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort., [Results] Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = −0.41; SE, 0.08; P = 4.9 × 10−8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10−7), and a smaller nucleus accumbens (Cohen d = −0.27; SE, 0.07; P = 7.3 × 10−5). There was also a significant negative dose response on cortical thickness (β = −0.24; SE, 0.05; P = 6.8 × 10−7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks., [Conclusions and Relevance] These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders., This study is supported in part by grants U54 EB20403, R01MH116147, and R56AG058854 from the National Institutes of Health, grant 609020 from the European Union Seventh Framework Programme, and grants 223273 and 276082 from the Research Council Norway. Part of this work was performed using the Service for Sensitive Data (TSD), which is developed and operated by the TSD Service Groupand owned by the University of Oslo.

An improved understanding of etiological mechanisms in Parkinson’s disease (PD) is urgently needed because the number of affected individuals is projected to increase rapidly as populations age. We present results from a blood-based methylome-wide association study of PD involving meta-analysis of 229 K CpG probes in 1,132 cases and 999 controls from two independent cohorts. We identify two previously unreported epigenome-wide significant associations with PD, including cg06690548 on chromosome 4. We demonstrate that cg06690548 hypermethylation in PD is associated with down-regulation of the SLC7A11 gene and show this is consistent with an environmental exposure, as opposed to medications or genetic factors with effects on DNA methylation or gene expression. These findings are notable because SLC7A11 codes for a cysteine-glutamate anti-porter regulating levels of the antioxidant glutathione, and it is a known target of the environmental neurotoxin β-methylamino-L-alanine (BMAA). Our study identifies the SLC7A11 gene as a plausible biological target in PD., Parkinson’s disease (PD) is a common neurodegenerative disorder with a complex etiology involving genetics and the environment. Here, Vallerga et al. identify two CpG probes associated with PD in a blood cell type-corrected epigenome-wide meta-analysis, implicating the SLC7A11 gene as a plausible biological target.

Publisher's version (útgefin grein), Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = −0.71 to −1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = −0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10−6, 1.7 × 10− 9, 3.5 × 10−12 and 1.0 × 10−4, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes., 1000BRAINS: 1000BRAINS is a population-based cohort based on the Heinz-Nixdorf Recall Study and is supported in part by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support in terms of the Heinz Nixdorf Study. The HNR study is also supported by the German Ministry of Education and Science (FKZ 01EG940), and the German Research Council (DFG, ER 155/6-1). The authors are supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 7202070 (Human Brain Project SGA1; Katrin Amunts, Sven Cichon). This work was further supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e:Med Program (grant 01ZX1314A to M.M.N. and S.C.), and by the Swiss National Science Foundation (SNSF, grant 156791 to S.C.). 16p.11.2 European Consortium: B.D. is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant Nr 32003B_159780) and Foundation Synapsis. LREN is very grateful to the Roger De Spoelberch and Partridge Foundations for their generous financial support. This work was supported by grants from the Simons Foundation (SFARI274424) and the Swiss National Science Foundation (31003A_160203) to A.R. and S.J. Betula: The relevant Betula data collection and analyses were supported by a grant from the Knut & Alice Wallenberg (KAW) to L. Nyberg. Brainscale: the Brainscale study was supported by the Netherlands Organization for Scientific Research MagW 480-04-004 (Dorret Boomsma), 51.02.060 (Hilleke Hulshoff Pol), 668.772 (Dorret Boomsma & Hilleke Hulshoff Pol); NWO/SPI 56-464-14192 (Dorret Boomsma), the European Research Council (ERC-230374) (Dorret Boomsma), High Potential Grant Utrecht University (Hilleke Hulshoff Pol), NWO Brain and Cognition 433-09-220 (Hilleke Hulshoff Pol). Brain Imaging Genetics (BIG): This work makes use of the BIG database, first established in Nijmegen, The Netherlands, in 2007. This resource is now part of Cognomics (www.cognomics.nl), a joint initiative by researchers of the Donders Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud university medical centre and the Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative has received supported from the participating departments and centres and from external grants, i.e., the Biobanking and Biomolecular Resources Research Infrastructure (the Netherlands) (BBMRI-NL), the Hersenstichting Nederland, and the Netherlands Organisation for Scientific Research (NWO). The research leading to these results also receives funding from the NWO Gravitation grant ‘Language in Interaction’, the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreements n° 602450 (IMAGEMEND), n°278948 (TACTICS), and n°602805 (Aggressotype) as well as from the European Community’s Horizon 2020 programme under grant agreement n° 643051 (MiND) and from ERC-2010-AdG 268800-NEUROSCHEMA. In addition, the work was supported by a grant for the ENIGMA Consortium (grant number U54 EB020403) from the BD2K Initiative of a cross-NIH partnership. COBRE: This work was supported by a NIH COBRE Phase I grant (1P20RR021938, Lauriello, PI and 2P20GM103472, Calhoun, PI) awarded to the Mind Research Network. We wish to express our gratitude to numerous investigators who were either external consultants to the Cores and projects, mentors on the projects, members of the external advisory committee and members of the internal advisory committee. Decode: The research leading to these results has received financial contribution from the European Union’s Seventh Framework Programme (EU-FP7/2007–2013), EU-FP7 funded grant no. 602450 (IMAGEMEND) as well as support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no.115300 (EUAIMS). DemGene: Norwegian Health Association and Research Council of Norway. Dublin: Work was supported by Science Foundation Ireland (SFI grant 12/IP/1359 to Gary Donohoe and SFI08/IN.1/B1916-Corvin to Aidan C Corvin) and the European Research Council (ERC-StG-2015-677467). EPIGEN-UK (SMS, CL): The work was partly undertaken at UCLH/UCL, which received a proportion of funding from the UK Department of Health’s NIHR Biomedical Research Centres funding scheme. We are grateful to the Wolfson Trust and the Epilepsy Society for supporting the Epilepsy Society MRI scanner, and the Epilepsy Society for supporting CL. Haavik: The work at the K.G.Jebsen center for neuropsychiatric disorders at the University of Bergen, Norway, was supported by Stiftelsen K.G. Jebsen, European Community’s Seventh Framework Program under grant agreement no 602805 and the H2020 Research and Innovation Program under grant agreement numbers 643051 and 667302. HUNT: The HUNT Study is a collaboration between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. HUNT-MRI was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, and the Norwegian National Advisory Unit for functional MRI. IMAGEN: The work received support from the European Union-funded FP6Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), the FP7 projects IMAGEMEND (602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative Medicine Initiative Project EU-AIMS (115300), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the Swedish Research Council FORMAS, the Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-1, SM 80/7-2, SFB 940/1). Further support was provided by grants from: ANR (project AF12-NEUR0008-01—WM2NA, and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. MCIC: This work was supported primarily by the Department of Energy DE-FG02-99ER62764 through its support of the Mind Research Network and the consortium as well as by the National Association for Research in Schizophrenia and Affective Disorders (NARSAD) Young Investigator Award (to SE) as well as through the Blowitz-Ridgeway and Essel Foundations, and through NWO ZonMw TOP 91211021, the DFG research fellowship (to SE), the Mind Research Network, National Institutes of Health through NCRR 5 month-RR001066 (MGH General Clinical Research Center), NIMH K08 MH068540, the Biomedical Informatics Research Network with NCRR Supplements to P41 RR14075 (MGH), M01 RR 01066 (MGH), NIBIB R01EB006841 (MRN), R01EB005846 (MRN), 2R01 EB000840 (MRN), 1RC1MH089257 (MRN), as well as grant U24 RR021992. NCNG: this sample collection was supported by grants from the Bergen Research Foundation and the University of Bergen, the Dr Einar Martens Fund, the K.G. Jebsen Foundation, the Research Council of Norway, to SLH, VMS and TE. The Bergen group was supported by grants from the Western Norway Regional Health Authority (Grant 911593 to AL, Grant 911397 and 911687 to AJL). NESDA: Funding for NESDA was obtained from the Netherlands Organization for Scientific Research (Geestkracht program grant 10-000-1002); the Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University’s Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health.Computing was supported by BiG Grid, the Dutch e-Science Grid, which is financially supported by NWO. NTR: The NTR study was supported by the Netherlands Organization for Scientific Research (NWO), MW904-61-193 (Eco de Geus & Dorret Boomsma), MaGW-nr: 400-07- 080 (Dennis van ‘t Ent), MagW 480-04-004 (Dorret Boomsma), NWO/SPI 56-464-14192 (Dorret Boomsma), the European Research Council, ERC-230374 (Dorret Boomsma), and Amsterdam Neuroscience. OATS: OATS (Older Australian Twins Study) was facilitated by access to Twins Research Australia, which is funded by a National Health and Medical Research Council (NHMRC) Enabling Grant 310667. OATS is also supported via a NHMRC/Australian Research Council Strategic Award (401162) and a NHMRC Project Grant (1045325). DNA extraction was performed by Genetic Repositories Australia, which was funded by a NHMRC Enabling Grant (401184). OATS genotyping was partly funded by a Commonwealth Scientific and Industrial Research Organisation Flagship Collaboration Fund Grant. PAFIP: PAFIP data were collected at the Hospital Universitario Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: Carlos III Health Institute PIE14/00031 and SAF2013-46292-R and SAF2015-71526-REDT. We wish to acknowledge IDIVAL Neuroimaging Unit for imaging acquirement and analysis.We want to particularly acknowledge the patients and the BioBankValdecilla (PT13/0010/0024) integrated in the Spanish National Biobanks Network for its collaboration. QTIM: The QTIM study was supported by grants from the US National Institute of Child Health and Human Development (R01 HD050735) and the Australian National Health and Medical Research Council (NHMRC) (486682, 1009064). Genotyping was supported by NHMRC (389875). Lachlan Strike is supported by an Australian Postgraduate Award (APA). AFM is supported by NHMRC CDF 1083656. We thank the twins and siblings for their participation, the many research assistants, as well as the radiographers, for their contribution to data collection and processing of the samples. SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, 01ZZ0403 and 01ZZ0701), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg- West Pomerania. Whole-body MR imaging was supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. StrokeMRI: StrokeMRI has been supported by the Research Council of Norway (249795), the South-Eastern Norway Regional Health Authority (2014097, 2015044, 2015073) and the Norwegian ExtraFoundation for Health and Rehabilitation. TOP: TOP is supported by the Research Council of Norway (223273, 213837, 249711), the South East Norway Health Authority (2017-112), the Kristian Gerhard Jebsen Stiftelsen (SKGJ‐MED‐008) and the European Community’s Seventh Framework Programme (FP7/2007–2013), grant agreement no. 602450 (IMAGEMEND). We acknowledge the technical support and service from the Genomics Core Facility at the Department of Clinical Science, the University of Bergen

The identification of biomarkers that discriminate individual ageing trajectories is a principal target of ageing research. Some of the most promising predictors of biological ageing have been developed using DNA methylation. One recent candidate, which tracks age-related phenotypes in addition to chronological age, is ‘DNAm PhenoAge’. Here, we performed a phenome-wide association analysis of this biomarker in a cohort of older adults to assess its relationship with a comprehensive set of both historical, and contemporaneously-measured, phenotypes. Higher than expected DNAm PhenoAge compared with chronological age, known as epigenetic age acceleration, was found to associate with a number of blood, cognitive, physical fitness and lifestyle variables, and with mortality. Notably, DNAm PhenoAge, assessed at age 70, was associated with cognitive ability at age 11, and with educational attainment. Adjusting for age 11 cognitive ability attenuated the majority of the cross-sectional later-life associations between DNAm PhenoAge and health outcomes. These results highlight the importance of early life factors on healthy older ageing.

Epigenetic mechanisms have been associated with genes involved in Posttraumatic stress disorder (PTSD). PTSD often co-occurs with other health conditions such as depression, cardiovascular disorder and respiratory illnesses. PTSD and migraine have previously been reported to be symptomatically positively correlated with each other, but little is known about the genes involved. The aim of this study was to understand the comorbidity between PTSD and migraine using a monozygotic twin disease discordant study design in six pairs of monozygotic twins discordant for PTSD and 15 pairs of monozygotic twins discordant for migraine. DNA from peripheral blood was run on Illumina EPIC arrays and analyzed. Multiple testing correction was performed using the Bonferroni method and 10% false discovery rate (FDR). We validated 11 candidate genes previously associated with PTSD including DOCK2, DICER1, and ADCYAP1. In the epigenome-wide scan, seven novel CpGs were significantly associated with PTSD within/near IL37, WNT3, ADNP2, HTT, SLFN11, and NQO2, with all CpGs except the IL37 CpG hypermethylated in PTSD. These results were significantly enriched for genes whose DNA methylation was previously associated with migraine (p-value = 0.036). At 10% FDR, 132 CpGs in 99 genes associated with PTSD were also associated with migraine in the migraine twin samples. Genes associated with PTSD were overrepresented in vascular smooth muscle, axon guidance and oxytocin signaling pathways, while genes associated with both PTSD and migraine were enriched for AMPK signaling and longevity regulating pathways. In conclusion, these results suggest that common genes and pathways are likely involved in PTSD and migraine, explaining at least in part the co-morbidity between the two disorders.

Background DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed ‘epigenetic clocks’. The deviation of predicted age from the actual age (‘age acceleration residual’, AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association. Methods In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues. Results We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91–1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79–1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor. Conclusions This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age. Electronic supplementary material The online version of this article (10.1186/s13073-019-0667-1) contains supplementary material, which is available to authorized users.

Introduction: The gut microbiota has important roles in maintaining human health. The microbiota and its metabolic byproducts could play a role in the pathogenesis of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Areas covered: The authors evaluate the methods of assessing the gut microbiota, and also review how the gut microbiota affects the various physiological functions of the gut. The authors then consider how gut dysbiosis could theoretically affect the pathogenesis of ALS. They present the current evidence regarding the composition of the gut microbiota in ALS and in rodent models of ALS. Finally, the authors review therapies that could improve gut dysbiosis in the context of ALS. Expert opinion: Currently reported studies suggest some instances of gut dysbiosis in ALS patients and mouse models; however, these studies are limited, and more information with well-controlled larger datasets is required to make a definitive judgment about the role of the gut microbiota in ALS pathogenesis. Overall this is an emerging field that is worthy of further investigation. The authors advocate for larger studies using modern metagenomic techniques to address the current knowledge gaps.

Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease. Results We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. Conclusions We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

ENIGMA-CNV working group., Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers—the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function., 1000BRAINS: The 1000BRAINS study was funded by the Institute of Neuroscience and Medicine, Research Center Juelich, Germany. We thank the Heinz Nixdorf Foundation (Germany) for the generous support of the Heinz Nixdorf Recall Study on which 1000BRAINS is based. We also thank the scientists and the study staff of the Heinz Nixdorf Recall Study and 1000BRAINS. Funding was also granted by the Initiative and Networking Fund of the Helmholtz Association (Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 945539 (Human Brain Project SGA3; Amunts, Caspers, Cichon). Brainscale: The Brainscale study was supported by the Netherlands Organization for Scientific Research MagW 480-04-004 (Dorret I. Boomsma), 51.02.060 (Hilleke E. Hulshoff Pol), 668.772 (Dorret I. Boomsma and Hilleke E. Hulshoff Pol); NWO/SPI 56-464-14192 (Dorret I. Boomsma), the European Research Council (ERC-230374) (Dorret I. Boomsma), High Potential Grant Utrecht University (Hilleke E.Hulshoff Pol) and NWO Brain and Cognition 433-09-220 (Hilleke E.Hulshoff Pol). Betula: The Betula study was funded by the Knut and Alice Wallenberg (KAW) foundation (Nyberg). The Freesurfer segmentations on the Betula sample were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N (in Umeå, Sweden), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. Brain Imaging Genetics (BIG): This work makes use of the BIG database, first established in Nijmegen, The Netherlands, in 2007. This resource is now part of Cognomics (www.cognomics.nl), a joint initiative by researchers from the Donders Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud University Medical Centre and the Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative has received support from the participating departments and centres and from external grants, that is, the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL), the Hersenstichting Nederland and the Netherlands Organization for Scientific Research (NWO). The research leading to these results also receives funding from the NWO Gravitation grant ‘Language in Interaction’, the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nos. 602450 (IMAGEMEND), 278948 (TACTICS) and 602805 (Aggressotype), as well as from the European Community’s Horizon 2020 programme under grant agreement no. 643051 (MiND) and from ERC-2010-AdG 268800-NEUROSCHEMA. In addition, the work was supported by a grant for the ENIGMA Consortium (grant number U54 EB020403) from the BD2K Initiative of a cross-NIH partnership. deCODE genetics: deCODE genetics acknowledges support from the Innovative Medicines Initiative Joint Undertaking under grant agreement nos. 115008 (NEWMEDS) and 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union’s Seventh Framework Programme (EU-FP7/2007-2013), EU-FP7-funded grant agreement no. 602450 (IMAGEMEND) and EU-funded FP7-People-2011-IAPP grant agreement no. 286213 (PsychDPC). Dublin: This work was supported by Science Foundation Ireland (SFI grant 12/IP/1359 to Gary Donohoe and grant SFI08/IN.1/B1916-Corvin to Aidan C. Corvin). ECHO-DEFINE: The ECHO study acknowledges funding from a Medical Research Council (MRC) Centre Grant to Michael J. Owen (G0801418), the Wellcome Trust (Institutional Strategic Support Fund (ISSF) to van den Bree and Clinical Research Training Fellowship to Joanne L. Doherty), the Waterloo Foundation (WF 918-1234 to van den Bree), the Baily Thomas Charitable Fund (2315/1 to van den Bree), National Institute of Mental Health (NIMH 5UO1MH101724 to van den Bree and Michael J. Owen), the IMAGINE-2 study (funded by the MRC (MR/T033045/1) to van den Bree, Jeremy Hall and Michael J. Owen), the IMAGINE-ID study (funded by MRC (MR/N022572/1) to Jeremy Hall, van den Bree and Owen). The DEFINE study was supported by a Wellcome Trust Strategic Award (100202/Z/12/Z) to Michael J. Owen. ENIGMA: ENIGMA is supported in part by NIH grants U54 EB20403, R01MH116147 and R56AG058854. NIA T32AG058507; NIH/NIMH 5T32MH073526. EPIGEN-Dublin: The EPIGEN-Dublin cohort was supported by a Science Foundation Ireland Research Frontiers Programme award (08/RFP/GEN1538). EPIGEN-UK (Sisodiya): The work was partly undertaken at UCLH/UCL, which received a proportion of funding from the UK Department of Health’s NIHR Biomedical Research Centres funding scheme. We are grateful to the Wolfson Trust and the Epilepsy Society for supporting the Epilepsy Society MRI scanner. GAP: This work was supported by the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and the Institute of Psychiatry, Psychology and Neuroscience, King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. GOBS: The GOBS study data collection was supported in part by the National Institutes of Health (NIH) grants: R01 MH078143, R01 MH078111 and R01 MH083824, with work conducted in part in facilities constructed under the support of NIH grant C06 RR020547. GSP: Data were in part provided by the Brain Genomics Superstruct Project (GSP) of Harvard University and Massachusetts General Hospital (MGH) (Principal Investigators: Randy Buckner, Jordan Smoller and Joshua Roffman), with support from the Center for Brain Science Neuroinformatics Research Group, Athinoula A. Martinos Center for Biomedical Imaging, Center for Genomic Medicine and Stanley Center for Psychiatric Research. Twenty individual investigators at Harvard and MGH generously contributed data to the overall project. We would like to thank Randy Buckner for insightful comments and feedback on this work. HUBIN: The HUBIN study was financed by the Swedish Research Council (K2010-62X-15078-07-2, K2012-61X-15078-09-3, 521-2014-3487 K2015-62X-15077-12-3, 2017-00949), the regional agreement on medical training and clinical research between Stockholm County Council and the Karolinska Institutet. HUNT: The HUNT study is a collaboration between HUNT Research Centre (Faculty of Medicine and Movement Sciences, NTNU—Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority and the Norwegian Institute of Public Health. HUNT-MRI was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, and the Norwegian National Advisory Unit for functional MRI. IMAGEN: This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), Human Brain Project (HBP SGA 2, 785907),the FP7 projects IMAGEMEND(602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative Medicine Initiative Project EUAIMS (115300-2), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the Swedish Research Council FORMAS, the Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152, 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL 01EE1406A, 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG grants, SM 80/7-2, SFB 940/2), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1). Further support was provided by grants from: ANR (project AF12-NEUR0008-01—WM2NA, ANR-12-SAMA-0004), the Eranet Neuron (ANR-18-NEUR00002-01), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1) and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. Lifespan: The study is funded by the Research Council of Norway (230345, 288083 and 223273). NCNG: NCNG sample collection was supported by grants from the Bergen Research Foundation and the University of Bergen, the Dr Einar Martens Fund, the Research Council of Norway, to le Hellard, Steen and Espeseth. The Bergen group was supported by grants from the Western Norway Regional Health Authority (Grant 911593 to Arvid Lundervold, Grant 911397 and 911687 to Astri Johansen Lundervold). NTR: The NTR cohort was supported by the Netherlands Organization for Scientific Research (NWO) and The Netherlands Organisation for Health Research and Development (ZonMW) grants 904-61-090, 985-10-002, 912-10-020, 904-61-193, 480-04-004,463-06-001, 451-04-034, 400-05-717, Addiction-31160008, 016-115-035, 481-08-011, 056-32-010, Middelgroot-911-09-032, OCW_NWO Gravity programme—024.001.003, NWO-Groot 480-15-001/674, Center for Medical Systems Biology (CSMB, NWO Genomics), NBIC/BioAssist/RK(2008.024), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL, 184.021.007 and 184.033.111); Spinozapremie (NWO-56-464-14192), KNAW Academy Professor Award (PAH/6635) and University Research Fellow grant (URF) to Dorret I. Boomsma; Amsterdam Public Health research institute (former EMGO+), Neuroscience Amsterdam research institute (former NCA); the European Science Foundation (ESF, EU/QLRT-2001-01254), the European Community’s Seventh Framework Programme (FP7- HEALTH-F4-2007-2013, grant 01413: ENGAGE and grant 602768: ACTION); the European Research Council (ERC Starting 284167, ERC Consolidator 771057, ERC Advanced 230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the National Institutes of Health (NIH, R01D0042157-01A1, R01MH58799-03, MH081802, DA018673, R01 DK092127-04, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by NWO through grant 2018/EW/00408559, BiG Grid, the Dutch e-Science Grid and SURFSARA. OATS: The OATS study has been funded by a National Health & Medical Research Council (NHMRC) and Australian Research Council (ARC) Strategic Award Grant of the Ageing Well, Ageing Productively Programme (ID No. 401162) and NHMRC Project Grants (ID Nos. 1045325 and 1085606). This research was facilitated through Twins Research Australia, a national resource in part supported by an NHMRC Centre for Research Excellence Grant (ID No.: 1079102). We thank the participants for their time and generosity in contributing to this research. We acknowledge the contribution of the OATS research team (https://cheba.unsw.edu.au/project/older-australian-twins-study) to this study. OATS genotyping was partly funded by a Commonwealth Scientific and Industrial Research Organization Flagship Collaboration Fund Grant. Osaka: Osaka study was supported by the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS: Grant Number JP18dm0207006), Brain/MINDS& beyond studies (Grant Number JP20dm0307002) and Health and Labour Sciences Research Grants for Comprehensive Research on Persons with Disabilities (Grant Number JP20dk0307081) from the Japan Agency for Medical Research and Development (AMED), Grants-in-Aid for Scientific Research (KAKENHI; Grant Numbers JP25293250 and JP16H05375). Some computations were performed at the Research Center for Computational Science, Okazaki, Japan. PAFIP: The PAFIP study was supported by Instituto de Salud Carlos III, FIS 00/3095, 01/3129, PI020499, PI060507, PI10/00183, the SENY Fundació Research Grant CI2005-0308007 and the FundaciónMarqués de Valdecilla API07/011. Biological samples from our cohort were stored at the Valdecilla Biobank and genotyping services were conducted at the Spanish ‘Centro Nacional de Genotipado’ (CEGEN-ISCIII). MCIC/COBRE: The study is funded by the National Institutes of Health studies R01EB006841, P20GM103472 and P30GM122734 and Department of Energy DE-FG02-99ER62764. PING: Data collection and sharing for the Paediatric Imaging, Neurocognition and Genetics (PING) Study (National Institutes of Health Grant RC2DA029475) were funded by the National Institute on Drug Abuse and the Eunice Kennedy Shriver National Institute of Child Health & Human Development. A full list of PING investigators is at http://pingstudy.ucsd.edu/investigators.html. QTIM: The QTIM study was supported by the National Institute of Child Health and Human Development (R01 HD050735) and the National Health and Medical Research Council (NHMRC 486682, 1009064), Australia. Genotyping was supported by NHMRC (389875). Medland is supported in part by an NHMRC fellowship (APP1103623). SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grant nos. 01ZZ9603, 01ZZ0103 and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide single-nucleotide polymorphism typing in SHIP and MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. StrokeMRI: StrokeMRI was supported by the Norwegian ExtraFoundation for Health and Rehabilitation(2015/FO5146), the Research Council of Norway (249795, 262372), the South-Eastern Norway Regional Health Authority (2014097, 2015044, 2015073) and the Department of Psychology, University of Oslo. Sydney MAS: The Sydney Memory and Aging Study (Sydney MAS) is funded by National and HealthMedical Research Council (NHMRC) Programme and Project Grants (ID350833, ID568969 and ID109308). We also thank the Sydney MAS participants and the Research Team. SYS: The SYS Study is supported by Canadian Institutes of Health Research. TOP: Centre of Excellence: RCN #23273 and RCN #226971. Part of this work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT) (tsd-drift@usit.uio.no). The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2013-COFUND) under grant agreement no. 609020—Scientia Fellows; the Research Council of Norway (RCN) #276082—A lifespan perspective on mental illness: toward precision medicine using multimodal brain imaging and genetics. Ida E. Sønderby and Rune Bøen are supported by South-Eastern Norway Regional Health Authority (#2020060). Ida E. Sønderby and Ole A. Andreassen have received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement no. 847776 (CoMorMent project) and the KG Jebsen Foundation (SKGJ-MED-021). UCLA_UMCU: The UCLA_UMCU cohort comprises of six studies which were supported by National Alliance for Research in Schizophrenia and Affective Disorders (NARSAD) (20244 to Prof. Hillegers), The Netherlands Organisation for Health Research and Development (ZonMw) (908-02-123 to Prof. Hulshoff Pol), and Netherlands Organisation for Scientific Research (NWO 9120818 and NWO-VIDI 917-46-370 to Prof. Hulshoff Pol). The GROUP study was funded through the Geestkracht programme of the Dutch Health Research Council (ZonMw, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly and Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ inGeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord-Holland-Noord. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia Psycho-medical Center, The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGzE, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh, voor Geestelijke Gezondheid, Mondriaan, Virenzeriagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-JozefKortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Center Utrecht and the mental health institutions: Altrecht, GGZ Centraal and Delta.). UK Biobank: This work made use of data sharing from UK Biobank (under project code 27412). Others: Work by Pierre Vanderhaeghen was funded by Grants of the European Research Council (ERC Adv Grant GENDEVOCORTEX), the EOS Programme, the Belgian FWO, the AXA Research Fund and the Belgian Queen Elizabeth Foundation. Ikuo K. Suzuki was supported by a postdoctoral fellowship of the FRS/FNRS.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability of around 50%. DNA methylation patterns can serve as biomarkers of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study (EWAS) meta-analysis in 10,462 samples (7,344 ALS patients and 3,118 controls), representing the largest case-control study of DNA methylation for any disease to date. We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We show that DNA-methylation-based proxies for HDL-cholesterol, BMI, white blood cell (WBC) proportions and alcohol intake were independently associated with ALS. Integration of these results with our latest GWAS showed that cholesterol biosynthesis was causally related to ALS. Finally, we found that DNA methylation levels at several DMPs and blood cell proportion estimates derived from DNA methylation data, are associated with survival rate in patients, and could represent indicators of underlying disease processes.

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore – disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

Context Circulating concentrations of free triiodothyronine (fT3), free thyroxine (fT4), and thyrotropin (TSH) are partly heritable traits. Recent studies have advanced knowledge of their genetic architecture. Epigenetic modifications, such as DNA methylation (DNAm), may be important in pituitary-thyroid axis regulation and action, but data are limited. Objective To identify novel associations between fT3, fT4, and TSH and differentially methylated positions (DMPs) in the genome in subjects from 2 Australian cohorts. Method We performed an epigenome-wide association study (EWAS) of thyroid function parameters and DNAm using participants from: Brisbane Systems Genetics Study (median age 14.2 years, n = 563) and the Raine Study (median age 17.0 years, n = 863). Plasma fT3, fT4, and TSH were measured by immunoassay. DNAm levels in blood were assessed using Illumina HumanMethylation450 BeadChip arrays. Analyses employed generalized linear mixed models to test association between DNAm and thyroid function parameters. Data from the 2 cohorts were meta-analyzed. Results We identified 2 DMPs with epigenome-wide significant (P Conclusions This study has demonstrated associations between blood-based DNAm and both fT3 and TSH. This may provide insight into mechanisms underlying thyroid hormone action and/or pituitary-thyroid axis function.

Background Studies evaluating the relationship between chronic inflammation and cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals’ persisting levels of inflammation. DNA methylation (DNAm) has shown utility in indexing environmental exposures and could be leveraged to provide proxy signatures of chronic inflammation. Method We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 875 older adults (Lothian Birth Cohort 1936; mean age: 70 years) to develop a DNAm-based predictor. The predictor was tested in an independent cohort (Generation Scotland; N = 7028 [417 with measured IL-6], mean age: 51 years). Results A weighted score from 35 CpG sites optimally predicted IL-6 in the independent test set (Generation Scotland; R2 = 4.4%, p = 2.1 × 10−5). In the independent test cohort, both measured IL-6 and the DNAm proxy increased with age (serum IL-6: n = 417, β = 0.02, SE = 0.004, p = 1.3 × 10−7; DNAm IL-6 score: N = 7028, β = 0.02, SE = 0.0009, p < 2 × 10−16). Serum IL-6 did not associate with cognitive ability (n = 417, β = −0.06, SE = 0.05, p = .19); however, an inverse association was identified between the DNAm score and cognitive functioning (N = 7028, β = −0.16, SE = 0.02, pFDR < 2 × 10−16). Conclusions These results suggest methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for further insight into the relationship between inflammation and pertinent health outcomes.

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = -0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10

We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case–control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3 × 10−22). The maximum AUC achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3 × 10−34) when cell-type proportion was included in the predictor.

Amyotrophic lateral sclerosis (ALS) is a complex late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies have identified eleven risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Current association analysis data alone cannot determine the most plausible risk gene in this locus. Here, we undertake a comprehensive suite of studies to provide objective evidence to support or reject the relevance of these two genes. We use bioinformatic integration of genetic association data with omics reference data sets, in-vitro and in-vivo approaches to narrow down the likely candidate. TNIP1 and GPX3 are implicated in-silico (rs10463311 is an eQTL for these two genes). In-vivo expression analyses in ALS cases identify that GPX3 expression decreases with increased disability and rate of progression. Validation in-vivo, indicate GPX3 loss-of-function causes motor deficits in zebrafish embryos. Taken together, these results support GPX3 as being the ALS risk gene in this locus which has implications for understanding disease mechanisms and targeted therapeutic approaches.

Importance: Depressive disorders arise from a combination of genetic and environmental risk factors. Epigenetic disruption provides a plausible mechanism through which gene-environment interactions lead to depression. Large-scale, epigenome-wide studies on depression are missing, hampering the identification of potentially modifiable biomarkers.Objective: To identify epigenetic mechanisms underlying depression in middle-aged and elderly persons, using DNA methylation in blood.Design, Setting, and Participants: To date, the first cross-ethnic meta-analysis of epigenome-wide association studies (EWAS) within the framework of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was conducted. The discovery EWAS included 7948 individuals of European origin from 9 population-based cohorts. Participants who were assessed for both depressive symptoms and whole-blood DNA methylation were included in the study. Results of EWAS were pooled using sample-size weighted meta-analysis. Replication of the top epigenetic sites was performed in 3308 individuals of African American and European origin from 2 population-based cohorts.Main Outcomes and Measures: Whole-blood DNA methylation levels were assayed with Illumina-Infinium Human Methylation 450K BeadChip and depressive symptoms were assessed by questionnaire.Results: The discovery cohorts consisted of 7948 individuals (4104 [51.6%] women) with a mean (SD) age of 65.4 (5.8) years. The replication cohort consisted of 3308 individuals (2456 [74.2%] women) with a mean (SD) age of 60.3 (6.4) years. The EWAS identified methylation of 3 CpG sites to be significantly associated with increased depressive symptoms: cg04987734 (P = 1.57 × 10−08; n = 11 256; CDC42BPB gene), cg12325605 (P = 5.24 × 10−09; n = 11 256; ARHGEF3 gene), and an intergenic CpG site cg14023999 (P = 5.99 × 10−08; n = 11 256; chromosome = 15q26.1). The predicted expression of the CDC42BPB gene in the brain (basal ganglia) (effect, 0.14; P = 2.7 × 10−03) and of ARHGEF3 in fibroblasts (effect, −0.48; P = 9.8 × 10−04) was associated with major depression.Conclusions and Relevance: This study identifies 3 methylated sites associated with depressive symptoms. All 3 findings point toward axon guidance as the common disrupted pathway in depression. The findings provide new insights into the molecular mechanisms underlying the complex pathophysiology of depression. Further research is warranted to determine the utility of these findings as biomarkers of depression and evaluate any potential role in the pathophysiology of depression and their downstream clinical effects.

Background Migraine is a common heritable neurovascular disorder typically characterised by episodic attacks of severe pulsating headache and nausea, often accompanied by visual, auditory or other sensory symptoms. Although genome-wide association studies have identified over 40 single nucleotide polymorphisms associated with migraine, there remains uncertainty about the casual genes involved in disease pathogenesis and how their function is regulated. Results We performed an epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in 67 migraine cases and 67 controls with a matching age and sex distribution. Association analyses between migraine and methylation probe expression, after adjustment for cell type proportions, indicated an excess of small P values, but there was no significant single-probe association after correction for multiple testing (P

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

IntroductionAdvanced age is associated with cognitive and physical decline, and is a major risk factor for a multitude of disorders including neurodegenerative diseases such as Alzheimer’s disease. There is also a gap in life-expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in DNA methylation in an unrelated cohort of 2,586 individuals between the ages of 18 and 87 years.MethodsGenome-wide DNA methylation was measured on the Illumina HumanMethylationEPIC beadchip in a subset of unrelated individuals from the Generation Scotland cohort. Mixed linear model-based analyses were performed to investigate the relationship between DNA methylation and an interaction term between age and sex, as well as chronological age.ResultsAt a genome-wide significance level of P < 3.6 × 10−8, 14 loci were associated with the age-by-sex interaction term, the majority of which were X-linked (n = 12). Seven of these loci were annotated to genes. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation x age r = 0.02), but decreased across female adult age range (DNA methylation x age r = −0.61). The seven age-by-sex-associated genes were enriched among differentially-expressed genes in lung, liver, testis and blood.ConclusionThe majority of differences in age-associated DNA methylation trajectories between sexes are present on the X-chromosome. Several of these differences occur within genes which have implicated in multiple cancers, schizophrenia and systemic lupus erythematosus.

Background Major challenges in understanding the functional consequences of genetic risk factors for human disease are which tissues and cell types are affected and the limited availability of suitable tissue. The aim of this study was to evaluate tissue-specific genotype-epigenetic characteristics in DNA samples from both endometrium and blood collected from women at different stages of the menstrual cycle and relate results to genetic risk factors for reproductive traits and diseases. Results We analysed DNA methylation (DNAm) data from endometrium and blood samples from 66 European women. Methylation profiles were compared between stages of the menstrual cycle, and changes in methylation overlaid with changes in transcription and genotypes. We observed large changes in methylation (27,262 DNAm probes) across the menstrual cycle in endometrium that were not observed in blood. Individual genotype data was tested for association with methylation at 443,016 and 443,101 DNAm probes in endometrium and blood respectively to identify methylation quantitative trait loci (mQTLs). A total of 4546 sentinel cis-mQTLs (P

Monozygotic (MZ) twins provide a natural system for investigating developmental plasticity and the potential epigenetic origins of disease. A major difference in the intrauterine environment between MZ pairs is whether they share a common placenta or have separate placentas. Using DNA methylation measured at >400,000 points in the genome on the Illumina HumanMethylation450 array, we demonstrate that the co-twins of MZ pairs (average age of 14) that shared a common placenta (n = 18 pairs) have more similar DNA methylation levels in blood throughout the genome relative to those with separate placentas (n = 16 pairs). Functional annotation of the genomic regions that show significantly different correlation between monochorionic (MC) and dichorionic (DC) MZ pairs found an over-representation of genes involved in the regulation of transcription, neuronal development, and cellular differentiation. These results support the idea that prenatal environmental exposures may have a lasting effect on an individual's epigenetic landscape, and the potential for these changes to have functional consequences.

BACKGROUNDEpigenetic age acceleration (an older methylation age compared to chronological age) correlates strongly with various age-related morbidities and mortality. Chronic systemic inflammation is thought to be a hallmark of ageing but the relationship between an increased epigenetic age and this likely key phenotype of ageing has not yet been extensively investigated.METHODSWe modelled the trajectories of the inflammatory biomarkers C-reactive protein (CRP; measured using both a high- and low-sensitivity assay), and interleukin-6 (IL-6) over the 8th decade in the Lothian Birth Cohort 1936. We additionally investigated the association between CRP and imputed leukocyte counts. Using linear mixed models we examined the cross-sectional and longitudinal association between the inflammatory biomarkers and two measures of epigenetic age acceleration, derived from the Horvath and Hannum epigenetic clocks.RESULTSLow-sensitivity CRP declined, high-sensitivity CRP did not change, and IL-6 increased over time. CRP levels inversely associated with total counts of CD8+T cells and CD4+T cells, and positively associated with senescent CD8+T cells, plasmablasts and granulocytes. Cross-sectionally, the Hannum, but not the Horvath, measure of age acceleration was positively associated with low-sensitivity CRP, high-sensitivity CRP, IL-6 and a restricted measure of CRP (≤10mg/L) likely reflecting levels relevant to chronic inflammation.CONCLUSIONSWe found a divergent relationship between inflammation and immune system parameters in older age. We additionally report the Hannum measure of epigenetic age acceleration associated with an elevated inflammatory profile cross-sectionally, but not longitudinally.

Importance: Genome-wide association studies (GWASs) in European populations have identified more than 100 schizophrenia-associated loci. A schizophrenia GWAS in a unique Indian population offers novel findings. Objective: To discover and functionally evaluate genetic loci for schizophrenia in a GWAS of a unique Indian population. Design, Setting, and Participants: This GWAS included a sample of affected individuals, family members, and unrelated cases and controls. Three thousand ninety-two individuals were recruited and diagnostically ascertained via medical records, hospitals, clinics, and clinical networks in Chennai and surrounding regions. Affected participants fulfilled DSM-IV diagnostic criteria for schizophrenia. Unrelated control participants had no personal or family history of psychotic disorder. Recruitment, genotyping, and analysis occurred in consecutive phases beginning January 1, 2001. Recruitment was completed on February 28, 2018, and genotyping and analysis are ongoing. Main Outcomes and Measures: Associations of single-nucleotide polymorphisms and gene expression with schizophrenia. Results: The study population included 1321 participants with schizophrenia, 885 family controls, and 886 unrelated controls. Among participants with schizophrenia, mean (SD) age was 39.1 (11.4) years, and 52.7% were male. This sample demonstrated uniform ethnicity, a degree of inbreeding, and negligible rates of substance abuse. A novel genome-wide significant association was observed between schizophrenia and a chromosome 8q24.3 locus (rs10866912, allele A; odds ratio [OR], 1.27 [95% CI, 1.17-1.38]; P = 4.35 × 10) that attracted support in the schizophrenia Psychiatric Genomics Consortium 2 data (rs10866912, allele A; OR, 1.04 [95% CI, 1.02-1.06]; P = 7.56 × 10). This locus has undergone natural selection, with the risk allele A declining in frequency from India (approximately 72%) to Europe (approximately 43%). rs10866912 directly modifies the abundance of the nicotinate phosphoribosyltransferase gene (NAPRT1) transcript in brain cortex (normalized effect size, 0.79; 95% CI, 0.6-1.0; P = 5.8 × 10). NAPRT1 encodes a key enzyme for niacin metabolism. In Indian lymphoblastoid cell lines, (risk) allele A of rs10866912 was associated with NAPRT1 downregulation (AA: 0.74, n = 21; CC: 1.56, n = 17; P =.004). Preliminary zebrafish data further suggest that partial loss of function of NAPRT1 leads to abnormal brain development. Conclusions and Relevance: Bioinformatic analyses and cellular and zebrafish gene expression studies implicate NAPRT1 as a novel susceptibility gene. Given this gene's role in niacin metabolism and the evidence for niacin deficiency provoking schizophrenialike symptoms in neuropsychiatric diseases such as pellagra and Hartnup disease, these results suggest that the rs10866912 genotype and niacin status may have implications for schizophrenia susceptibility and treatment.

BackgroundGenome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes, and could have clinical applications. Here, penalised regression models were used to develop DNAm predictors for body mass index (BMI), smoking status, alcohol consumption, and educational attainment in a cohort of 5,100 individuals. Using an independent test cohort comprising 906 individuals, the proportion of phenotypic variance explained in each trait was examined for DNAm-based and genetic predictors. Receiver operator characteristic curves were generated to investigate the predictive performance of DNAm-based predictors, using dichotomised phenotypes. The relationship between DNAm scores and all-cause mortality (n = 214 events) was assessed via Cox proportional-hazards models.ResultsThe DNAm-based predictors explained different proportions of the phenotypic variance for BMI (12%), smoking (60%), alcohol consumption (12%) and education (3%). The combined genetic and DNAm predictors explained 20% of the variance in BMI, 61% in smoking, 13% in alcohol consumption, and 6% in education. DNAm predictors for smoking, alcohol, and education but not BMI predicted mortality in univariate models. The predictors showed moderate discrimination of obesity (AUC=0.67) and alcohol consumption (AUC=0.75), and excellent discrimination of current smoking status (AUC=0.98). There was poorer discrimination of college-educated individuals (AUC=0.59).ConclusionsDNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.List of abbreviationsDNAmDNA methylationBMIBody mass indexAUCArea under the curveCpGCytosine phosphate Guanine dinucleotideEWASEpigenome-wide association studyGS:SFHSGeneration Scotland: The Scottish family health studyLBC1936Lothian birth cohort 1936LASSOLeast absolute shrinkage and selector operatorHRHazard ratioCIConfidence intervalSTRADLStratifying resilience and depression longitudinally

BackgroundThe relationship between obesity and adverse health is well established, but little is known about the contribution of DNA methylation to obesity-related health outcomes. Additionally, it is of interest whether such contributions are independent of those attributed by the most widely used clinical measure of body mass – the Body Mass Index (BMI).MethodWe tested whether an epigenetic BMI score accounts for inter-individual variation in health-related, cognitive, psychosocial and lifestyle outcomes in the Lothian Birth Cohort 1936 (n=903). Weights for the epigenetic BMI score were derived using penalised regression on methylation data from unrelated Generation Scotland participants (n=2566).ResultsThe Epigenetic BMI score was associated with variables related to poor physical health (R2 ranges from 0.02-0.10), metabolic syndrome (R2 ranges from 0.01-0.09), lower crystallised intelligence (R2=0.01), lower health-related quality of life (R2=0.02), physical inactivity (R2=0.02), and social deprivation (R2=0.02). The epigenetic BMI score (per SD) was also associated with self-reported type 2 diabetes (OR 2.25, 95 % CI 1.74, 2.94), cardiovascular disease (OR 1.44, 95 % CI 1.23, 1.69) and high blood pressure (OR 1.21, 95% CI 1.13, 1.48; all at pConclusionsOur results show that regression models with epigenetic and phenotypic BMI scores as predictors account for a greater proportion of all outcome variables than either predictor alone, demonstrating independent and additive effects of epigenetic and phenotypic BMI scores.

Alzheimer’s disease (AD) is a public health priority for the 21st century. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. Using self-report of parental history of Alzheimer’s dementia for case ascertainment in a genome-wide association study of over 300,000 participants from UK Biobank (32,222 maternal cases, 16,613 paternal cases) and meta-analysing with published consortium data (n=74,046 with 25,580 cases across the discovery and replication analyses), six new AD-associated loci (P−8) are identified. Three contain genes relevant for AD and neurodegeneration: ADAM10, ADAMTS4, and ACE. Suggestive loci include drug targets such as VKORC1 (warfarin dose) and BZRAP1 (benzodiazepine receptor). We report evidence that association of SNPs and AD at the PVR gene is potentially mediated by both gene expression and DNA methylation in the prefrontal cortex. Our discovered loci may help to elucidate the biological mechanisms underlying AD and, given that many are existing drug targets for other diseases and disorders, warrant further exploration for potential precision medicine applications.

Age-related changes in DNA methylation have been observed in many cross-sectional studies, but longitudinal evidence is still very limited. Here, we aimed to characterize longitudinal age-related methylation patterns (Illumina HumanMethylation450 array) using 1011 blood samples collected from 385 old Swedish twins (mean age of 69 at baseline) up to five times over 20 years. We identified 1316 age-associated methylation sites (p−7) using a longitudinal epigenome-wide association study design. We measured how estimated cellular compositions changed with age and how much they confounded the age effect. We validated the results in two independent longitudinal cohorts, where 118 CpGs were replicated in PIVUS (p−5) and 594 were replicated in LBC (p−5). Functional annotation of age-associated CpGs showed enrichment in CCCTC-binding factor (CTCF) and other unannotated transcription factor binding sites. We further investigated genetic influences on methylation (methylation quantitative trait loci) and found no interaction between age and genetic effects in the 1316 age-associated CpGs. Moreover, in the same CpGs, methylation differences within twin pairs increased over time, where monozygotic twins had smaller intra-pair differences than dizygotic twins. We show that age-related methylation changes persist in a longitudinal perspective, and are fairly stable across cohorts. Moreover, the changes are under genetic influence, although this effect is independent of age. In addition, inter-individual methylation variations increase over time, especially in age-associated CpGs, indicating the increase of environmental contributions on DNA methylation with age.

Several studies have investigated the relationship between genetic variation and DNA methylation with respect to type 2 diabetes, but it is unknown if DNA methylation is a mediator in the disease pathway or if it is altered in response to disease state. This study uses genotypic information as a causal anchor to help decipher the likely role of DNA methylation measured in peripheral blood in the etiology of type 2 diabetes. Illumina HumanMethylation450 BeadChip data were generated on 1,018 young individuals from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. In stage 1, 118 unique associations between published type 2 diabetes single nucleotide polymorphisms (SNPs) and genome-wide methylation (methylation quantitative trait loci [mQTLs]) were identified. In stage 2, a further 226 mQTLs were identified between 202 additional independent non–type 2 diabetes SNPs and CpGs identified in stage 1. Where possible, associations were replicated in independent cohorts of similar age. We discovered that around half of known type 2 diabetes SNPs are associated with variation in DNA methylation and postulated that methylation could either be on a causal pathway to future disease or could be a noncausal biomarker. For one locus (KCNQ1), we were able to provide further evidence that methylation is likely to be on the causal pathway to disease in later life.

Background: Gene discovery has provided remarkable biological insights into amyotrophic lateral sclerosis (ALS). One challenge for clinical application of genetic testing is critical evaluation of the significance of reported variants. Methods: We use whole exome sequencing (WES) to develop a clinically relevant approach to identify a subset of ALS patients harboring likely pathogenic mutations. In parallel, we assess if DNA methylation can be used to screen for pathogenicity of novel variants since a methylation signature has been shown to associate with the pathogenic C9orf72 expansion, but has not been explored for other ALS mutations. Australian patients identified with ALS-relevant variants were cross-checked with population databases and case reports to critically assess whether they were “likely causal,” “uncertain significance,” or “unlikely causal.”. Results: Published ALS variants were identified in >10% of patients; however, in only 3% of patients (4/120) could these be confidently considered pathogenic (in SOD1 and TARDBP). We found no evidence for a differential DNA methylation signature in these mutation carriers. Conclusions: The use of WES in a typical ALS clinic demonstrates a critical approach to variant assessment with the capability to combine cohorts to enhance the largely unknown genetic basis of ALS Refereed/Peer-reviewed

Background The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. Methods and Findings We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. Conclusions We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases., Daniel Levy and colleagues examine mechanisms that could link body mass index to cardiovascular diseases via gene expression., Author Summary Why Was This Study Done? Genetic sequence variants explain only a modest proportion of the variation in body mass index (BMI) and cardiometabolic disease in the general population. There is limited understanding of the link of DNA methylation—a well-characterized epigenetic modification—with BMI and cardiometabolic disease in the general population. What Did the Researchers Do and Find? We conducted a cross-sectional analysis of the association of BMI with leukocyte DNA methylation at over 400,000 sites in the genome among 7,798 community-dwelling adults. We identified associations between BMI and methylation at 83 replicated sites (including 50 novel sites) and concurrent differences in expression in whole blood of genes overrepresented in lipid metabolism pathways. Using genetic sequence variants to model exposure to differential DNA methylation and tissue-specific gene expression, we found differential methylation and expression of SREBF1 to be implicated in BMI, adiposity-related traits, and coronary artery disease. Using genetic sequence variants to model exposure to differences in BMI, we found a substantial proportion of the differentially methylated sites (16 of 83) to be downstream of BMI. What Do These Findings Mean? Evidence is accumulating that epigenetic modifications, such as DNA methylation, are related to obesity-related diseases in the general population. We provide support for a role of genomic regulation of a lipid metabolism transcription factor, SREBF1, in adiposity and coronary artery disease. Mendelian randomization approaches can help prioritize relevant loci for future functional studies, but the cross-sectional observational nature of our study limits definitive causal inference.

Supplemental Digital Content is available in the text., Background— Genome-wide association studies have identified loci influencing circulating lipid concentrations in humans; further information on novel contributing genes, pathways, and biology may be gained through studies of epigenetic modifications. Methods and Results— To identify epigenetic changes associated with lipid concentrations, we assayed genome-wide DNA methylation at cytosine–guanine dinucleotides (CpGs) in whole blood from 2306 individuals from 2 population-based cohorts, with replication of findings in 2025 additional individuals. We identified 193 CpGs associated with lipid levels in the discovery stage (P

Background Ankylosing spondylitis (AS) is an immune-mediated arthritis particularly targeting the spine and pelvis and is characterised by inflammation, osteoproliferation and frequently ankylosis. Current treatments that predominately target inflammatory pathways have disappointing efficacy in slowing disease progression. Thus, a better understanding of the causal association and pathological progression from inflammation to bone formation, particularly whether inflammation directly initiates osteoproliferation, is required. Methods The proteoglycan-induced spondylitis (PGISp) mouse model of AS was used to histopathologically map the progressive axial disease events, assess molecular changes during disease progression and define disease progression using unbiased clustering of semi-quantitative histology. PGISp mice were followed over a 24-week time course. Spinal disease was assessed using a novel semi-quantitative histological scoring system that independently evaluated the breadth of pathological features associated with PGISp axial disease, including inflammation, joint destruction and excessive tissue formation (osteoproliferation). Matrix components were identified using immunohistochemistry. Results Disease initiated with inflammation at the periphery of the intervertebral disc (IVD) adjacent to the longitudinal ligament, reminiscent of enthesitis, and was associated with upregulated tumor necrosis factor and metalloproteinases. After a lag phase, established inflammation was temporospatially associated with destruction of IVDs, cartilage and bone. At later time points, advanced disease was characterised by substantially reduced inflammation, excessive tissue formation and ectopic chondrocyte expansion. These distinct features differentiated affected mice into early, intermediate and advanced disease stages. Excessive tissue formation was observed in vertebral joints only if the IVD was destroyed as a consequence of the early inflammation. Ectopic excessive tissue was predominantly chondroidal with chondrocyte-like cells embedded within collagen type II- and X-rich matrix. This corresponded with upregulation of mRNA for cartilage markers Col2a1, sox9 and Comp. Osteophytes, though infrequent, were more prevalent in later disease. Conclusions The inflammation-driven IVD destruction was shown to be a prerequisite for axial disease progression to osteoproliferation in the PGISp mouse. Osteoproliferation led to vertebral body deformity and fusion but was never seen concurrent with persistent inflammation, suggesting a sequential process. The findings support that early intervention with anti-inflammatory therapies will be needed to limit destructive processes and consequently prevent progression of AS. Electronic supplementary material The online version of this article (doi:10.1186/s13075-015-0805-0) contains supplementary material, which is available to authorized users.

To identify common genetic variants that predispose to caffeine-induced insomnia and to test whether genes whose expression changes in the presence of caffeine are enriched for association with caffeine-induced insomnia.A hypothesis-free, genome-wide association study.Community-based sample of Australian twins from the Australian Twin Registry.After removal of individuals who said that they do not drink coffee, a total of 2,402 individuals from 1,470 families in the Australian Twin Registry provided both phenotype and genotype information.A dichotomized scale based on whether participants reported ever or never experiencing caffeine-induced insomnia. A factor score based on responses to a number of questions regarding normal sleep habits was included as a covariate in the analysis. More than 2 million common single nucleotide polymorphisms (SNPs) were tested for association with caffeine-induced insomnia. No SNPs reached the genome-wide significance threshold. In the analysis that did not include the insomnia factor score as a covariate, the most significant SNP identified was an intronic SNP in the PRIMA1 gene (P = 1.4 × 10⁻⁶, odds ratio = 0.68 [0.53 - 0.89]). An intergenic SNP near the GBP4 gene on chromosome 1 was the most significant upon inclusion of the insomnia factor score into the model (P = 1.9 × 10⁻⁶, odds ratio = 0.70 [0.62 - 0.78]). A previously identified association with a polymorphism in the ADORA2A gene was replicated.Several genes have been identified in the study as potentially influencing caffeine-induced insomnia. They will require replication in another sample. The results may have implications for understanding the biologic mechanisms underlying insomnia.