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1. Comprehensive evaluation of smoking exposures and their interactions on DNA methylationResearch in context

Author

Thanh T. Hoang, Yunsung Lee, Daniel L. McCartney, Elin T.G. Kersten, Christian M. Page, Paige M. Hulls, Mikyeong Lee, Rosie M. Walker, Charles E. Breeze, Brian D. Bennett, Adam B. Burkholder, James Ward, Anne Lise Brantsæter, Ida H. Caspersen, Alison A. Motsinger-Reif, Marie Richards, Julie D. White, Shanshan Zhao, Rebecca C. Richmond, Maria C. Magnus, Gerard H. Koppelman, Kathryn L. Evans, Riccardo E. Marioni, Siri E. Håberg, Stephanie J. London, Bastiaan Heijmans, Peter ’t Hoen, Joyce van Meurs, Rick Jansen, Lude Franke, Dorret Boomsma, René Pool, Jenny van Dongen, Jouke Hottenga, Marleen van Greevenbroek, Coen Stehouwer, Carla van der Kallen, Casper Schalkwijk, Cisca Wijmenga, Sasha Zhernakova, Ettje Tigchelaar, P. Eline Slagboom, Marian Beekman, Joris Deelen, Diana Van Heemst, Jan Veldink, Leonard van den Berg, Cornelia van Duijn, Bert Hofman, Aaron Isaacs, André Uitterlinden, P. Mila Jhamai, Michael Verbiest, H. Eka Suchiman, Marijn Verkerk, Ruud van der Breggen, Jeroen van Rooij, Nico Lakenberg, Hailiang Mei, Maarten van Iterson, Michiel van Galen, Jan Bot, Dasha Zhernakova, Peter van ‘t Hof, Patrick Deelen, Irene Nooren, Matthijs Moed, Martijn Vermaat, René Luijk, Marc Bonder, Freerk van Dijk, Wibowo Arindrarto, Szymon Kielbasa, Morris Swertz, and Erik van Zwet

Subjects
Illumina EPIC array, Epigenomics, Secondhand smoke exposure, Dietary intake, Sex difference, Smoking cessation, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: Smoking impacts DNA methylation, but data are lacking on smoking-related differential methylation by sex or dietary intake, recent smoking cessation (

Published
2024
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2. A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object

Author

Anna Niehues, Casper de Visser, Fiona Hagenbeek, Naama Karu, Alida Kindt, Purva Kulkarni, René Pool, Dorret Boomsma, Jenny van Dongen, Alain van Gool, and Peter 't Hoen

Subjects
Nextflow, ISA-API, RO-Crate, Singularity, metadata, Science
Abstract

In current biomedical and complex trait research, increasing numbers of large molecular profiling (omics) data sets are being generated. At the same time, many studies fail to be reproduced (Baker 2016, Kim 2018). In order to improve study reproducibility and data reuse, including integration of data sets of different types and origins, it is imperative to work with omics data that is findable, accessible, interoperable, and reusable (FAIR, Wilkinson 2016) at the source. The data analysis, integration and stewardship pillar of the Netherlands X-omics Initiative aims to facilitate multi-omics research by providing tools to create, analyze and integrate FAIR omics data. We here report a joint activity of X-omics and the Netherlands Twin Register demonstrating the FAIRification of a multi-omics data set and the development of a FAIR multi-omics data analysis workflow.The implementation of FAIR principles (Wilkinson 2016) can improve scientific transparency and facilitate data reuse. However, Kim (2018) showed in a case study that the availability of data and code are required but not sufficient to reproduce data analyses. They highlighted the importance of interoperable and open formats, and structured metadata. In order to increase research reproducibility on the data analysis level, additional practices such as version-control, code licensing, and documentation have been proposed. These include recommendations for FAIR software by the Netherlands eScience Center and the Dutch Data Archiving and Networked Services (DANS), and FAIR principles for research software proposed by the Research Data Alliance (Chue Hong 2022). Data analysis in biomedical research usually comprises multiple steps often resulting in complex data analysis workflows and requiring additional practices, such as containerization, to ensure transparency and reproducibility (Goble 2020, Stoudt 2021).We apply these practices to a multi-omics data set that comprises genome-wide DNA methylation profiles, targeted metabolomics, and behavioral data of two cohorts that participated in the ACTION Biomarker Study (ACTION, Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies, see consortium members in Suppl. material 1) (Boomsma 2015, Bartels 2018, Hagenbeek 2020, van Dongen 2021, Hagenbeek 2022). The ACTION-NTR cohort consists of twins that are either longitudinally concordant or discordant for childhood aggression. The ACTION-Curium-LUMC cohort consists of children referred to the Dutch LUMC Curium academic center for child and youth psychiatry. With the joint analysis of multi-omics data and behavioral data, we aim to identify substructures in the ACTION-NTR cohort and link them to aggressive behavior. First, the individuals are clustered using Similarity Network Fusion (SNF, Wang 2014), and latent feature dimensions are uncovered using different unsupervised methods including Multi-Omics Factor Analysis (MOFA) (Argelaguet 2018) and Multiple Correspondence Analysis (MCA, Lê 2008, Husson 2017). In a second step, we determine correlations between -omics and phenotype dimensions, and use them to explain the subgroups of individuals from the ACTION-NTR cohort. In order to validate the results, we project data of the ACTION-Curium-LUMC cohort onto the latent dimensions and determine if correlations between omics and phenotype data can be reproduced.Integration of data across cohorts and across data types, requires interoperability. We applied different practices to make the data FAIR, including conversion of files to community-standard formats, and capturing experimental metadata using the ISA (Investigation, Study, Assay) metadata framework (Johnson 2021) and ontology-based annotations. All data analysis steps including pre-processing of different omics data types were implemented in either R or Python and combined in a modular Nextflow (Di Tommaso 2017) workflow, where the environment for each step is provided as a Singularity (Kurtzer 2017) container. The analysis workflow is packaged in a Research Object Crate (RO-Crate) (Soiland-Reyes 2022). The RO-Crate is a FAIR digital object that contains the Nextflow workflow including ontology-based annotations of each analysis step. Since omics data is considered to be potentially personally identifiable, the packaged workflow contains a minimal synthetic data set resembling the original data structure. Finally, the code is made available on GitHub and the workflow is registered at Workflowhub (Goble 2021). Since our Nextflow workflow is set up in a modular manner, the individual analysis steps can be reused in other workflows. We demonstrate this replicability by applying different sub-workflows to data from two different cohorts.

Published
2022
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3. Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Author

Ilja M. Nolte, M. Loretto Munoz, Vinicius Tragante, Azmeraw T. Amare, Rick Jansen, Ahmad Vaez, Benedikt von der Heyde, Christy L. Avery, Joshua C. Bis, Bram Dierckx, Jenny van Dongen, Stephanie M. Gogarten, Philippe Goyette, Jussi Hernesniemi, Ville Huikari, Shih-Jen Hwang, Deepali Jaju, Kathleen F. Kerr, Alexander Kluttig, Bouwe P. Krijthe, Jitender Kumar, Sander W. van der Laan, Leo-Pekka Lyytikäinen, Adam X. Maihofer, Arpi Minassian, Peter J. van der Most, Martina Müller-Nurasyid, Michel Nivard, Erika Salvi, James D. Stewart, Julian F. Thayer, Niek Verweij, Andrew Wong, Delilah Zabaneh, Mohammad H. Zafarmand, Abdel Abdellaoui, Sulayma Albarwani, Christine Albert, Alvaro Alonso, Foram Ashar, Juha Auvinen, Tomas Axelsson, Dewleen G. Baker, Paul I. W. de Bakker, Matteo Barcella, Riad Bayoumi, Rob J. Bieringa, Dorret Boomsma, Gabrielle Boucher, Annie R. Britton, Ingrid Christophersen, Andrea Dietrich, George B. Ehret, Patrick T. Ellinor, Markku Eskola, Janine F. Felix, John S. Floras, Oscar H. Franco, Peter Friberg, Maaike G. J. Gademan, Mark A. Geyer, Vilmantas Giedraitis, Catharina A. Hartman, Daiane Hemerich, Albert Hofman, Jouke-Jan Hottenga, Heikki Huikuri, Nina Hutri-Kähönen, Xavier Jouven, Juhani Junttila, Markus Juonala, Antti M. Kiviniemi, Jan A. Kors, Meena Kumari, Tatiana Kuznetsova, Cathy C. Laurie, Joop D. Lefrandt, Yong Li, Yun Li, Duanping Liao, Marian C. Limacher, Henry J. Lin, Cecilia M. Lindgren, Steven A. Lubitz, Anubha Mahajan, Barbara McKnight, Henriette Meyer zu Schwabedissen, Yuri Milaneschi, Nina Mononen, Andrew P. Morris, Mike A. Nalls, Gerjan Navis, Melanie Neijts, Kjell Nikus, Kari E. North, Daniel T. O'Connor, Johan Ormel, Siegfried Perz, Annette Peters, Bruce M. Psaty, Olli T. Raitakari, Victoria B. Risbrough, Moritz F. Sinner, David Siscovick, Johannes H. Smit, Nicholas L. Smith, Elsayed Z. Soliman, Nona Sotoodehnia, Jan A. Staessen, Phyllis K. Stein, Adrienne M. Stilp, Katarzyna Stolarz-Skrzypek, Konstantin Strauch, Johan Sundström, Cees A. Swenne, Ann-Christine Syvänen, Jean-Claude Tardif, Kent D. Taylor, Alexander Teumer, Timothy A. Thornton, Lesley E. Tinker, André G. Uitterlinden, Jessica van Setten, Andreas Voss, Melanie Waldenberger, Kirk C. Wilhelmsen, Gonneke Willemsen, Quenna Wong, Zhu-Ming Zhang, Alan B. Zonderman, Daniele Cusi, Michele K. Evans, Halina K. Greiser, Pim van der Harst, Mohammad Hassan, Erik Ingelsson, Marjo-Riitta Järvelin, Stefan Kääb, Mika Kähönen, Mika Kivimaki, Charles Kooperberg, Diana Kuh, Terho Lehtimäki, Lars Lind, Caroline M. Nievergelt, Chris J. O'Donnell, Albertine J. Oldehinkel, Brenda Penninx, Alexander P. Reiner, Harriëtte Riese, Arie M. van Roon, John D. Rioux, Jerome I. Rotter, Tamar Sofer, Bruno H. Stricker, Henning Tiemeier, Tanja G. M. Vrijkotte, Folkert W. Asselbergs, Bianca J. J. M. Brundel, Susan R. Heckbert, Eric A. Whitsel, Marcel den Hoed, Harold Snieder, and Eco J. C. de Geus

Subjects
Science
Abstract

Heart rate variability (HRV) describes the individual variation in cardiac cycle duration and is a measure of vagal control of heart rate. Here, the authors identify seventeen single-nucleotide polymorphisms associated with HRV, lending new insight into the vagal regulation of heart rhythm.

Published
2017
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4. Linkage of health insurance data to the Netherlands Twin Register

Author

Gerard van Grootheest, Adelaide Ariel, Tina Glasner, Bep Verkerk, Toos van Beijsterveldt, Jan van der Laan, Mark de Groot, Sipke Visser, Bart Bakker, and Dorret Boomsma

Subjects
Demography. Population. Vital events, HB848-3697
Abstract

ABSTRACT Objectives We compared four different approaches to record linkage, demonstrating its potential in enriching the Netherlands Twin Register (NTR) with healthcare data from a large health insurance provider (AHD), which covers approximately 26% of the population. Challenges included the fact that young twins share most of their linkage variables and overlapping identification numbers were absent. We extensively validated the linkage results and complemented them with an indicator of representativeness. Approach Subjects born since 1986 were selected in the NTR (N=30,383) and AHD (N=1,532,675). Linkage variables were cleaned and harmonised and linkage keys were chosen that were strong enough to uniquely identify most subjects in either dataset. The four linkages were deterministic, probabilistic, probabilistic with Jaro-Winkler distance, and probabilistic using encrypted identifiers. Validation took place by reviewing linkage variables and information like family relations; linked record pairs were categorised into correct, possible or false links. Furthermore, the consistency of linked data was reviewed focusing on Attention Deficit Hyperactivity Disorder (ADHD), a prevalent condition among children. Finally, we quantified the impact of the linkage on representativeness of the target population. Results Probabilistic linkage with distance calculation linked 7944 NTR subjects to the AHD with 82% correct, 17% possible, and 1% false links. It encompassed nearly all links identified by any of the other methods, which were more conservative. Information about medication was available in the NTR dataset for 40% of the linked subjects. Validation of the information about ADHD inside linked records revealed few discrepancies between the two datasets. Linkage had a modest effect on the representativeness of the population. The current linkage has confirmed a number of known ADHD cases and identified 146 new subjects using methylphenidate and 101 receiving psychiatric care for ADHD, who were not previously recognized as ADHD cases within the NTR. Conclusion Record linkage with sources of data such as a health insurance database can be an efficient way of data collection in cohort research. Our study shows that record linkage is also possible for twin pairs. Although a minority of all NTR subjects were covered by the AHD, the linkage resulted in an enrichment of the NTR dataset. Exact information about the dosage and frequency of drugs was obtained without contacting subjects with detailed questionnaires. With a total of almost eight thousand retrieved records, the size of the linked dataset is sufficiently large for epidemiological research of non-rare conditions.

Published
2017
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5. Erratum: Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Author

Ilja M. Nolte, M. Loretto Munoz, Vinicius Tragante, Azmeraw T. Amare, Rick Jansen, Ahmad Vaez, Benedikt von der Heyde, Christy L. Avery, Joshua C. Bis, Bram Dierckx, Jenny van Dongen, Stephanie M. Gogarten, Philippe Goyette, Jussi Hernesniemi, Ville Huikari, Shih-Jen Hwang, Deepali Jaju, Kathleen F. Kerr, Alexander Kluttig, Bouwe P. Krijthe, Jitender Kumar, Sander W. van der Laan, Leo-Pekka Lyytikäinen, Adam X. Maihofer, Arpi Minassian, Peter J. van der Most, Martina Müller-Nurasyid, Michel Nivard, Erika Salvi, James D. Stewart, Julian F. Thayer, Niek Verweij, Andrew Wong, Delilah Zabaneh, Mohammad H. Zafarmand, Abdel Abdellaoui, Sulayma Albarwani, Christine Albert, Alvaro Alonso, Foram Ashar, Juha Auvinen, Tomas Axelsson, Dewleen G. Baker, Paul I. W. de Bakker, Matteo Barcella, Riad Bayoumi, Rob J. Bieringa, Dorret Boomsma, Gabrielle Boucher, Annie R. Britton, Ingrid Christophersen, Andrea Dietrich, George B. Ehret, Patrick T. Ellinor, Markku Eskola, Janine F. Felix, John S. Floras, Oscar H. Franco, Peter Friberg, Maaike G. J. Gademan, Mark A. Geyer, Vilmantas Giedraitis, Catharina A. Hartman, Daiane Hemerich, Albert Hofman, Jouke-Jan Hottenga, Heikki Huikuri, Nina Hutri-Kähönen, Xavier Jouven, Juhani Junttila, Markus Juonala, Antti M. Kiviniemi, Jan A. Kors, Meena Kumari, Tatiana Kuznetsova, Cathy C. Laurie, Joop D. Lefrandt, Yong Li, Yun Li, Duanping Liao, Marian C. Limacher, Henry J. Lin, Cecilia M. Lindgren, Steven A. Lubitz, Anubha Mahajan, Barbara McKnight, Henriette Meyer zu Schwabedissen, Yuri Milaneschi, Nina Mononen, Andrew P. Morris, Mike A. Nalls, Gerjan Navis, Melanie Neijts, Kjell Nikus, Kari E. North, Daniel T. O'Connor, Johan Ormel, Siegfried Perz, Annette Peters, Bruce M. Psaty, Olli T. Raitakari, Victoria B. Risbrough, Moritz F. Sinner, David Siscovick, Johannes H. Smit, Nicholas L. Smith, Elsayed Z. Soliman, Nona Sotoodehnia, Jan A. Staessen, Phyllis K. Stein, Adrienne M. Stilp, Katarzyna Stolarz-Skrzypek, Konstantin Strauch, Johan Sundström, Cees A. Swenne, Ann-Christine Syvänen, Jean-Claude Tardif, Kent D. Taylor, Alexander Teumer, Timothy A. Thornton, Lesley E. Tinker, André G. Uitterlinden, Jessica van Setten, Andreas Voss, Melanie Waldenberger, Kirk C. Wilhelmsen, Gonneke Willemsen, Quenna Wong, Zhu-Ming Zhang, Alan B Zonderman, Daniele Cusi, Michele K. Evans, Halina K. Greiser, Pim van der Harst, Mohammad Hassan, Erik Ingelsson, Marjo-Riitta Järvelin, Stefan Kääb, Mika Kähönen, Mika Kivimaki, Charles Kooperberg, Diana Kuh, Terho Lehtimäki, Lars Lind, Caroline M. Nievergelt, Chris J. O'Donnell, Albertine J. Oldehinkel, Brenda Penninx, Alexander P. Reiner, Harriëtte Riese, Arie M. van Roon, John D. Rioux, Jerome I. Rotter, Tamar Sofer, Bruno H. Stricker, Henning Tiemeier, Tanja G. M. Vrijkotte, Folkert W. Asselbergs, Bianca J. J.M Brundel, Susan R. Heckbert, Eric A. Whitsel, Marcel den Hoed, Harold Snieder, and Eco J. C. de Geus

Subjects
Science
Abstract

Nature Communications 8: Article number: 15805 (2017); Published: 14 June 2017; Updated: 2 August 2017 In Supplementary Fig. 10 of this Article, images for panels a and b were inadvertently omitted. The correct version of Supplementary Fig. 10 is provided as Supplementary Information associated withthis Erratum.

Published
2017
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6. Genome-wide prediction of functional gene-gene interactions inferred from patterns of genetic differentiation in mice and men.

Author

Zoltán Bochdanovits, David Sondervan, Sophie Perillous, Toos van Beijsterveldt, Dorret Boomsma, and Peter Heutink

Subjects
Medicine, Science
Abstract

The human genome encodes a limited number of genes yet contributes to individual differences in a vast array of heritable traits. A possible explanation for the capacity our genome to generate this virtually unlimited range of phenotypic variation in complex traits is to assume functional interactions between genes. Therefore we searched two mammalian genomes to identify potential epistatic interactions by looking for co-adapted genes marked by excess two-locus genetic differentiation between populations/lineages using publicly available SNP genotype data. The practical motivation for this effort is to reduce the number of pair-wise tests that need to be performed in genome-wide association studies aimed at detecting GxG interactions, by focusing on pairs predicted to be more likely to jointly affect variation in complex traits. Hence, this approach generates a list of candidate interactions that can be empirically tested. In both the mouse and human data we observed two-locus genetic differentiation in excess of what can be expected from chance alone based on simulations. In an attempt to validate our hypothesis that pairs of genes showing excess genetic divergence represent potential functional interactions, we selected a small set of gene combinations postulated to be interacting based on our analyses and looked for a combined effect of the selected genes on variation in complex traits in both mice and man. In both cases the individual effect of the genes were not significant, instead we observed marginally significant interaction effects. These results show that genome wide searches for gene-gene interactions based on population genetic data are feasible and can generate interesting candidate gene pairs to be further tested for their contribution to phenotypic variation in complex traits.

Published
2008
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7. Combined genome scans for body stature in 6,602 European twins: evidence for common Caucasian loci.

Author

Markus Perola, Sampo Sammalisto, Tero Hiekkalinna, Nick G Martin, Peter M Visscher, Grant W Montgomery, Beben Benyamin, Jennifer R Harris, Dorret Boomsma, Gonneke Willemsen, Jouke-Jan Hottenga, Kaare Christensen, Kirsten Ohm Kyvik, Thorkild I A Sørensen, Nancy L Pedersen, Patrik K E Magnusson, Tim D Spector, Elisabeth Widen, Karri Silventoinen, Jaakko Kaprio, Aarno Palotie, Leena Peltonen, and GenomEUtwin Project

Subjects
Genetics, QH426-470
Abstract

Twin cohorts provide a unique advantage for investigations of the role of genetics and environment in the etiology of variation in common complex traits by reducing the variance due to environment, age, and cohort differences. The GenomEUtwin (http://www.genomeutwin.org) consortium consists of eight twin cohorts (Australian, Danish, Dutch, Finnish, Italian, Norwegian, Swedish, and United Kingdom) with the total resource of hundreds of thousands of twin pairs. We performed quantitative trait locus (QTL) analysis of one of the most heritable human complex traits, adult stature (body height) using genome-wide scans performed for 3,817 families (8,450 individuals) derived from twin cohorts from Australia, Denmark, Finland, Netherlands, Sweden, and United Kingdom with an approximate ten-centimorgan microsatellite marker map. The marker maps for different studies differed and they were combined and related to the sequence positions using software developed by us, which is publicly available (https://apps.bioinfo.helsinki.fi/software/cartographer.aspx). Variance component linkage analysis was performed with age, sex, and country of origin as covariates. The covariate adjusted heritability was 81% for stature in the pooled dataset. We found evidence for a major QTL for human stature on 8q21.3 (multipoint logarithm of the odds 3.28), and suggestive evidence for loci on Chromosomes X, 7, and 20. Some evidence of sex heterogeneity was found, however, no obvious female-specific QTLs emerged. Several cohorts contributed to the identified loci, suggesting an evolutionarily old genetic variant having effects on stature in European-based populations. To facilitate the genetic studies of stature we have also set up a website that lists all stature genome scans published and their most significant loci (http://www.genomeutwin.org/stature_gene_map.htm).

Published
2007
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8. Grit and self-control predict school performance: strongly genetic, weakly causal

Author

Sofieke Kevenaar, Elsje van Bergen, Albertine Oldehinkel, Dorret Boomsma, and Conor V. Dolan

Abstract

Background: The non-cognitive skills self-control and grit have often been cited as predictors of school performance, but little research has investigated whether this relationship is causal. We investigated the causal nature of this association in a classical twin design, with mono- and dizygotic twin pairs. Specifically, we evaluated the direct impact of self-control and grit on school performance, while controlling for genetic or environmental influences common to all three traits (i.e., confounding). Methods: Teachers of 4,891 Dutch 12-year-old twin pairs (of which 3,837 were complete pairs) completed a survey about school performance (school grades), self-control (ASEBA self-control scale), and the perseverance aspect of grit. We regressed school performance on self-control and grit within the twin model to establish the phenotypic, putatively causal, regression relationship. We modelled genetic or environmental confounding (influences common to the three phenotypes) to determine their influence. In all analyses, we corrected for sex, rater effects of the teacher, and parental socioeconomic status. Results: Self-control and grit explained 28.4% of the school-performance variance in the phenotypic regression analysis (assuming no confounding). However, allowing for genetic confounding (due to genetic pleiotropy) revealed that the association was largely attributable to genetic influences that the three traits share. In the presence of genetic pleiotropy, the phenotypic regression of school performance on self-control and grit accounted for only 4.4%. Conclusions: The association between self-control and grit as predictors of school performance is attributable to both genetic pleiotropy, and to a lesser extent, direct effects of self-control and grit on school performance, which are putatively causal.

Published
2023
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9. Self-control and grit are associated with school performance mainly because of shared genetic effects

Author

Sofieke Kevenaar, Conor V. Dolan, Dorret Boomsma, and Elsje van Bergen

Abstract

Background: By combining the classical twin design with regression analysis, we investigated the role of two non-cognitive factors, self-control and grit, in the prediction of school performance. We did so at the phenotypic, genetic, and environmental level. Methods: Teachers filled out a survey on the twins’ school performance (school grades for reading, literacy, and math), self-control (ASEBA self-control scale), and grit (the perseverance aspect) for 4,891 Dutch 12-years-old twin pairs (3,837 pairs with data for both and 1,054 pairs with data for one of the twins). We employed regression analyses to first assess the contributions of self-control and grit to school performance at the phenotypic level, and next at the genetic and environmental level, while correcting for rater (teacher) effects, parental SES, and sex. Results: Higher SES was associated with better school performance, self-control, and grit. On average, girls had more self-control and grit than boys. Corrected for sex, SES, and teacher rater effects, genetic factors accounted for 74%, 69%, and 58% of the phenotypic variance of school performance, self-control, and grit, respectively. Phenotypically, self-control and grit explained 28.3% of the variance in school performance. We found that this phenotypic result largely reflected genetic influences. Conclusions: Children who have better self-control and are grittier tend to do better in school. Individual differences in these three traits are not correlated because of shared environmental influences, but mainly because of shared genetic factors.

Published
2023
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10. Maximising the Value of Twin Studies in Health and Behaviour

Author

Fiona A. Hagenbeek, Jana S. Hirzinger, Sophie Breunig, Susanne Bruins, Dmitry V. Kuznetsov, Kirsten Schut, Veronika V. Odintsova, and Dorret Boomsma

Abstract

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes, and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

Published
2022
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11. Are genetic and environmental risk factors for psychopathology amplified in children with below-average intelligence? A population-based twin study

Author

Susanne Bruins, Elsje van Bergen, Maurits Masselink, Stefania A. Barzeva, Catharina Hartman, Roy Otten, Nanda Rommelse, Conor V. Dolan, and Dorret Boomsma

Abstract

There is a negative association between intelligence and psychopathology, implying that individuals with intellectual disability are at greater risk for psychopathology. We analyzed data on intelligence and psychopathology in seven-year-old twin pairs (npairs ≥ 616) to determine the degree to which genetic and environmental factors common to intelligence and psychopathology explain the association, and to examine whether genetic and environmental effects on psychopathology are moderated by intelligence. We found that intelligence correlated negatively with internalizing, externalizing, and neurodevelopmental psychopathology. The associations were mainly due to common genetic factors. Intelligence moderated genetic and environmental effects on internalizing psychopathology (anxiety and negative affect), but not those on externalizing and neurodevelopmental psychopathology. The heritability of anxiety and negative affect was greatest in individuals with below-average intelligence. We discuss mechanisms through which this effect could arise, and we end with some recommendations for future research.

Published
2022
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12. Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect

Author

Susanne Bruins, Jouke-Jan Hottenga, Michael C. Neale, René Pool, Dorret Boomsma, and Conor V. Dolan

Abstract

One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.

Published
2022
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13. A large-scale genome-wide association study meta-analysis of cannabis use disorder

Author

Emma C Johnson, Ditte Demontis, Thorgeir E Thorgeirsson, Raymond K Walters, Renato Polimanti, Alexander S Hatoum, Sandra Sanchez-Roige, Sarah E Paul, Frank R Wendt, Toni-Kim Clarke, Dongbing Lai, Gunnar W Reginsson, Hang Zhou, June He, David A A Baranger, Daniel F Gudbjartsson, Robbee Wedow, Daniel E Adkins, Amy E Adkins, Jeffry Alexander, Silviu-Alin Bacanu, Tim B Bigdeli, Joseph Boden, Sandra A Brown, Kathleen K Bucholz, Jonas Bybjerg-Grauholm, Robin P Corley, Louisa Degenhardt, Danielle M Dick, Benjamin W Domingue, Louis Fox, Alison M Goate, Scott D Gordon, Laura M Hack, Dana B Hancock, Sarah M Hartz, Ian B Hickie, David M Hougaard, Kenneth Krauter, Penelope A Lind, Jeanette N McClintick, Matthew B McQueen, Jacquelyn L Meyers, Grant W Montgomery, Ole Mors, Preben B Mortensen, Merete Nordentoft, John F Pearson, Roseann E Peterson, Maureen D Reynolds, John P Rice, Valgerdur Runarsdottir, Nancy L Saccone, Richard Sherva, Judy L Silberg, Ralph E Tarter, Thorarinn Tyrfingsson, Tamara L Wall, Bradley T Webb, Thomas Werge, Leah Wetherill, Margaret J Wright, Stephanie Zellers, Mark J Adams, Laura J Bierut, Jason D Boardman, William E Copeland, Lindsay A Farrer, Tatiana M Foroud, Nathan A Gillespie, Richard A Grucza, Kathleen Mullan Harris, Andrew C Heath, Victor Hesselbrock, John K Hewitt, Christian J Hopfer, John Horwood, William G Iacono, Eric O Johnson, Kenneth S Kendler, Martin A Kennedy, Henry R Kranzler, Pamela A F Madden, Hermine H Maes, Brion S Maher, Nicholas G Martin, Matthew McGue, Andrew M McIntosh, Sarah E Medland, Elliot C Nelson, Bernice Porjesz, Brien P Riley, Michael C Stallings, Michael M Vanyukov, Scott Vrieze, Lea K Davis, Ryan Bogdan, Joel Gelernter, Howard J Edenberg, Kari Stefansson, Anders D Børglum, Arpana Agrawal, Raymond Walters, Emma Johnson, Jeanette McClintick, Alexander Hatoum, Frank Wendt, Mark Adams, Amy Adkins, Fazil Aliev, Anthony Batzler, Sarah Bertelsen, Joanna Biernacka, Tim Bigdeli, Li-Shiun Chen, Yi-Ling Chou, Franziska Degenhardt, Anna Docherty, Alexis Edwards, Pierre Fontanillas, Jerome Foo, Josef Frank, Ina Giegling, Scott Gordon, Laura Hack, Annette Hartmann, Sarah Hartz, Stefanie Heilmann-Heimbach, Stefan Herms, Colin Hodgkinson, Per Hoffman, Jouke Hottenga, Martin Kennedy, Mervi Alanne-Kinnunen, Bettina Konte, Jari Lahti, Marius Lahti-Pulkkinen, Lannie Ligthart, Anu Loukola, Brion Maher, Hamdi Mbarek, Andrew McIntosh, Matthew McQueen, Jacquelyn Meyers, Yuri Milaneschi, Teemu Palviainen, John Pearson, Roseann Peterson, Samuli Ripatti, Euijung Ryu, Nancy Saccone, Jessica Salvatore, Melanie Schwandt, Fabian Streit, Jana Strohmaier, Nathaniel Thomas, Jen-Chyong Wang, Bradley Webb, Amanda Wills, Jason Boardman, Danfeng Chen, Doo-Sup Choi, William Copeland, Robert Culverhouse, Norbert Dahmen, Benjamin Domingue, Sarah Elson, Mark Frye, Wolfgang Gäbel, Caroline Hayward, Marcus Ising, Margaret Keyes, Falk Kiefer, John Kramer, Samuel Kuperman, Susanne Lucae, Michael Lynskey, Wolfgang Maier, Karl Mann, Satu Männistö, Bertram Müller-Myhsok, Alison Murray, John Nurnberger, Aarno Palotie, Ulrich Preuss, Katri Räikkönen, Maureen Reynolds, Monika Ridinger, Norbert Scherbaum, Marc Schuckit, Michael Soyka, Jens Treutlein, Stephanie Witt, Norbert Wodarz, Peter Zill, Daniel Adkins, Dorret Boomsma, Laura Bierut, Sandra Brown, Kathleen Bucholz, Sven Cichon, E. Jane Costello, Harriet de Wit, Nancy Diazgranados, Danielle Dick, Johan Eriksson, Lindsay Farrer, Tatiana Foroud, Nathan Gillespie, Alison Goate, David Goldman, Richard Grucza, Dana Hancock, Andrew Heath, John Hewitt, Christian Hopfer, William Iacono, Eric Johnson, Jaakko Kaprio, Victor Karpyak, Kenneth Kendler, Henry Kranzler, Paul Lichtenstein, Penelope Lind, Matt McGue, James MacKillop, Pamela Madden, Hermine Maes, Patrik Magnusson, Nicholas Martin, Sarah Medland, Grant Montgomery, Elliot Nelson, Markus Nöthen, Abraham Palmer, Nancy Pederson, Brenda Penninx, John Rice, Marcella Rietschel, Brien Riley, Richard Rose, Dan Rujescu, Pei-Hong Shen, Judy Silberg, Michael Stallings, Ralph Tarter, Michael Vanyukov, Tamara Wall, John Whitfield, Hongyu Zhao, Benjamin Neale, Howard Edenberg, Technology Centre, Department of Psychology and Logopedics, Developmental Psychology Research Group, University Management, HUSLAB, Genetic Epidemiology, Institute for Molecular Medicine Finland, Department of Public Health, Centre of Excellence in Complex Disease Genetics, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Biostatistics Helsinki, Faculty of Arts, Research Programme of Molecular Medicine, Aarno Palotie / Principal Investigator, Genomics of Neurological and Neuropsychiatric Disorders, Research Programs Unit, Diabetes and Obesity Research Program, Department of General Practice and Primary Health Care, Johan Eriksson / Principal Investigator, Clinicum, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Digital Health, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, and APH - Methodology

Subjects
Risk, Marijuana Abuse, medicine.medical_specialty, Alcohol abuse, Disease, Polymorphism, Single Nucleotide, 3124 Neurology and psychiatry, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, medicine, Humans, 030212 general & internal medicine, Psychiatry, Borderline personality disorder, Biological Psychiatry, business.industry, Articles, Mental illness, medicine.disease, Mental health, 030227 psychiatry, 3. Good health, Substance abuse, Psychiatry and Mental health, Translational science, business, Genome-Wide Association Study, Psychopathology
Abstract

Background: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50–70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. Methods: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. Findings: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07–1·15, p=1·84 × 10 −9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86–0·93, p=6·46 × 10 −9). Cannabis use disorder and cannabis use were genetically correlated (r g 0·50, p=1·50 × 10 −21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. Interpretation: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. Funding: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Published
2020
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14. Non-right handedness is associated with language and reading impairments

Author

Filippo Abbondanza, Philip Dale, Carol Wang, Marianna E. Hayiou-Thomas, Umar Toseeb, Tanner koomar, Karen Wigg, Yu Feng, Kaitlyn M Price, Elizabeth Kerr, Sharon Guger, Maureen Lovett, Lisa J Strug, Elsje van Bergen, Conor V. Dolan, Bruce Tomblin, Kristina Moll, Gerd Schulte-Körne, Nina Neuhoff, Andreas Warnke, Simon Fisher, Cathy L Barr, Jacob J Michaelson, Dorret Boomsma, Margaret J. Snowling, Charles Hulme, Andrew Whitehouse, Craig E Pennell, Dianne Newbury, John Frederick Stein, Joel B Talcott, Dorothy Vera Margaret Bishop, and Silvia Paracchini

Abstract

Worldwide, the majority of people prefer using the right hand for most motor tasks, including writing. Because of the link between handedness and language hemispheric dominance, handedness has been studied for association with language-related disorders. No clear pattern has emerged from these studies, and inconsistencies have been attributed to small sample sizes, publication bias, and heterogeneous criteria for the definition of handedness and disorders.Here, we assessed the frequency of non-right handedness (NRH) in 10 distinct cohorts not analysed before in this context. We identified N = 2,499 cases with reading and/or language impairment and N = 4,428 unique controls on the basis of a priori defined criteria. Overall, NRH was more frequent and more variable in the cases (8-24%) than in the controls (8-16%). Meta-analysis in the eight cohorts that met the inclusion criteria showed an increase of NRH in individuals with language/reading impairment compared to controls (OR = 1.21, CI = 1.06 - 1.37, p = 0.009). No moderator effects were detected for type of cohort (epidemiological versus clinical) and type of impairment (language versus reading). Our results support a genuine but modest association between NRH and reading and language impairments, suggesting shared biological pathways underlying brain lateralization, handedness, and cognitive functions.

Published
2022
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15. DNA methylation in peripheral tissues and Left-handedness

Author

Veronika Odintsova, Matthew Sudermann, Fiona Hagenbeek, Doretta Caramaschi, Jouke-Jan Hottenga, Rene Pool, Biobank-based Integrative Omics Study Consortium BIOS, Conor Dolan, Lannie Ligthart, Catharina van Beijsterveldt, Gonneke Willemsen, Eco de Geus, Jeffrey Beck, Erik Ehli, Gabriel Cuellar-Partida, David Evans, Sarah Medland, Caroline Relton, Dorret Boomsma, and Jenny van Dongen

Subjects
Pathology, medicine.medical_specialty, DNA methylation, medicine, Biology, Left handedness, Peripheral
Abstract

Handedness has low heritability and epigenetic mechanisms have been proposed as an etiological mechanism. To examine this hypothesis, we performed an epigenome-wide association study (EWAS) of left-handedness. In a meta-analysis of 3,914 adults of whole-blood DNA methylation, we observed that CpG sites located in proximity of handedness-associated genetic variants were more strongly associated with left-handedness than other CpG sites (P = 0.04), but did not identify any differentially methylated positions. We identified differentially methylated regions at 20q11.23 (P = 0.00004) and 2p25.1 (P = 0.03), which were less methylated in left-handed adults. In longitudinal analyses of DNA methylation in peripheral blood and buccal cells from children (N = 1,737), we observed moderately stable associations across age (correlation range [0.355–0.578]) but inconsistent across tissues (correlation range [-0.384-0.318]). We conclude that DNA methylation in peripheral tissues captures little of the variance in handedness. Future investigations should consider other more targeted sources of tissue, such as the brain.

Published
2021
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16. 1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans

Author

Ida E. Sønderby, Dennis van der Meer, Clara Moreau, Tobias Kaufmann, G. Bragi Walters, Maria Ellegaard, Abdel Abdellaoui, David Ames, Katrin Amunts, Micael Andersson, Nicola J Armstrong, Manon Bernard, Nicholas B Blackburn, John Blangero, Dorret Boomsma, Henry Brodaty, Rachel M. Brouwer, Robin Bülow, Rune Boen, Wiepke Cahn, Vince D Calhoun, Svenja Caspers, Christopher RK Ching, Sven Cichon, Simone Ciufolini, Benedicto Crespo-Facorro, Joanne E Curran, Anders M Dale, Shareefa Dalvie, Paola Dazzan, Eco de Geus, Greig Ian de Zubicaray, Sonja M. C. de Zwarte, Sylvane Desrivieres, Joanne L Doherty, Gary Donohoe, Bogdan Draganski, Stefan Ehrlich, Else Eising, Thomas Espeseth, Kim Fejgin, Simon Fisher, Tormod Fladby, Oleksandr Frei, Vincent Frouin, Masaki Fukunaga, Thomas Gareau, Tian Ge, David C. Glahn, Hans J. Grabe, Nynke A. Groenewold, Omar Gustafsson, Jan Haakvik, Asta Håberg, Jeremy Hall, Ryota Hashimoto, Jayne Y Hehir-Kwa, Derrek P. Hibar, Manon H.J. Hillegers, Per Hoffmann, Laurena Holleran, Avram J Holmes, Georg Homuth, Jouke-Jan Hottenga, Hilleke E. Hulshoff Pol, Masashi Ikeda, Neda Jahanshad, Christiane Jockwitz, Stefan Johansson, Erik G. Jönsson, Niklas R Jørgensen, Masataka Kikuchi, Emma EM Knowles, Kuldeep Kumar, Stephanie Le Hellard, Costin Leu, David Linden, Jingyu Liu, Arvid Lundervold, Astri J. Lundervold, Anne Manuela Maillard, Nicholas G Martin, Sandra Martin-Brevet, Karen A Mather, Samuel R. Mathias, Katie Louise McMahon, Allan F McRae, Sarah Medland, Andreas Meyer-Lindenberg, Torgeir Moberget, Claudia Modenato, Jennifer Monereo Sanchez, Derek Morris, Thomas W Mühleisen, Robin Murray, Jacob Nielsen, Jan E Nordvik, Lars Nyberg, Loes M Olde Loohuis, Roel A Ophoff, Michael J Owen, Tomáš Paus, Zdenka Pausova, Juan M Peralta, Bruce Pike, Carlos Prieto, Erin Burke Quinlan, Céline S Reinbold, Tiago Reis Marques, James J Rucker, Perminder S Sachdev, Sigrid S Sando, Peter R Schofield, Andrew J Schork, Gunter Schumann, Jean Shin, Elena Shumskaya, Ana I Silva, Sanjay M. Sisodiya, Vidar M Steen, Dan J. Stein, Lachlan Strike, Ikuo K Suzuki, Christian K. Tamnes, Alexander Teumer, Anbupalam Thalamuthu, Diana Tordesillas-Gutiérrez, Anne Uhlmann, Magnus O Ulfarsson, Dennis van 't Ent, Marianne BM van den Bree, Pierre Vanderhaeghen, Evangelos Vassos, Wei Wen, Katharina Wittfeld, Margaret J. Wright, Ingrid Agartz, Srdjan Djurovic, Lars T. Westlye, Hreinn Stefansson, Kári Stefánsson, Sebastien Jacquemont, Paul Thompson, Ole A. Andreassen, and for the p. European Consortium

Abstract

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 MRI 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 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.

Published
2021
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18. Additional file 7: of Epigenome-wide association study of serum cotinine in current smokers reveals novel genetically driven loci

Author

Gupta, Richa, Dongen, Jenny, Fu, Yu, Abdellaoui, Abdel, Tyndale, Rachel, Velagapudi, Vidya, Dorret Boomsma, Korhonen, Tellervo, Kaprio, Jaakko, Loukola, Anu, and Ollikainen, Miina

Abstract

Figure S3. Mediation analysis to assess whether DNA methylation is a causal mediator to the observed association between genetic variants and cotinine levels. (PDF 588 kb)

Published
2019
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19. MOESM3 of Genome-wide analysis of DNA methylation in buccal cells: a study of monozygotic twins and mQTLs

Author

Dongen, Jenny Van, Ehli, Erik, Jansen, Rick, Beijsterveldt, Catharina Van, Willemsen, Gonneke, Hottenga, Jouke, Kallsen, Noah, Peyton, Shanna, Breeze, Charles, Kluft, Cornelis, Heijmans, Bastiaan, Bartels, Meike, Davies, Gareth, and Dorret Boomsma

Abstract

Additional file 3. DHS enrichment for methylation sites with large MZ twin correlation, adjusted for cellular composition.

Published
2018
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20. Additional file 1: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Abstract

Table S1. Inclusion and exclusion criteria. (DOCX 36Â kb)

Published
2018
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21. Additional file 3: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Abstract

Table S3. Questionnaires baseline. (DOCX 37Â kb)

Published
2018
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22. Additional file 1: Table S1. of Does the sex of one’s co-twin affect height and BMI in adulthood? A study of dizygotic adult twins from 31 cohorts

Author

Bogl, Leonie, Jelenkovic, Aline, Vuoksimaa, Eero, Ahrenfeldt, Linda, Pietiläinen, Kirsi, Stazi, Maria, Fagnani, Corrado, D’Ippolito, Cristina, Yoon-Mi Hur, Hoe-Uk Jeong, Silberg, Judy, Lindon Eaves, Maes, Hermine, Gombojav Bayasgalan, Danshiitsoodol Narandalai, Cutler, Tessa, Kandler, Christian, Jang, Kerry, Christensen, Kaare, Skytthe, Axel, Kyvik, Kirsten, Cozen, Wendy, Hwang, Amie, Mack, Thomas, Derom, Catherine, Vlietinck, Robert, Nelson, Tracy, Whitfield, Keith, Corley, Robin, Huibregtse, Brooke, McAdams, Tom, Eley, Thalia, Gregory, Alice, Krueger, Robert, McGue, Matt, Shandell Pahlen, Willemsen, Gonneke, Bartels, Meike, Beijsterveldt, Toos Van, Zengchang Pang, Qihua Tan, Dongfeng Zhang, Martin, Nicholas, Medland, Sarah, Montgomery, Grant, Hjelmborg, Jacob, Rebato, Esther, Swan, Gary, Krasnow, Ruth, Busjahn, Andreas, Lichtenstein, Paul, Öncel, Sevgi, Aliev, Fazil, Baker, Laura, Tuvblad, Catherine, Siribaddana, Sisira, Hotopf, Matthew, Athula Sumathipala, Fruhling Rijsdijk, Magnusson, Patrik, Pedersen, Nancy, Aslan, Anna, Ordoñana, Juan, Sánchez-Romera, Juan, Colodro-Conde, Lucia, Duncan, Glen, Buchwald, Dedra, Tarnoki, Adam, Tarnoki, David, Yokoyama, Yoshie, Hopper, John, Loos, Ruth, Dorret Boomsma, Sørensen, Thorkild, Silventoinen, Karri, and Kaprio, Jaakko

Abstract

Sample size, mean, standard deviation and range for age by cohort in females from same- and opposite-sex dizygotic twin pairs. Table S2. Sample size, mean, standard deviation, and range for age by cohort in males from same- and opposite-sex dizygotic twin pairs. Table S3. Sample size, mean, and standard deviation for height (cm) and BMI (kg/m2) by cohort in females from same- and opposite-sex dizygotic twin pairs. Table S4. Sample size, mean, and standard deviation for height (cm) and BMI (kg/m2) by cohort in males from same- and opposite-sex dizygotic twin pairs. (DOCX 45 kb)

Published
2017
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23. Additional file 3: Figure S1. of Integrative DNA methylome analysis of pan-cancer biomarkers in cancer discordant monozygotic twin-pairs

Author

Roos, Leonie, Dongen, Jenny Van, Bell, Christopher, Burri, Andrea, Deloukas, Panos, Dorret Boomsma, Spector, Tim, and Bell, Jordana

Abstract

Variability at three top-ranked pan-cancer DMPs in 9 cancer discordant and 480 healthy MZ twin-pairs. Histogram with density overlay of absolute differences of 480 healthy MZ twin-pairs with median (blue) and density and median of differences between 9 cancer discordant MZ twin-pairs (red). At [A] cg0244695 near SASH1, [B] cg26079695 in COL11A2, and [C] cg27094856 in AXL. (PDF 143Â kb)

Published
2016
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24. Additional file 2: Figure S1. of Integrative DNA methylome analysis of pan-cancer biomarkers in cancer discordant monozygotic twin-pairs

Author

Roos, Leonie, Dongen, Jenny Van, Bell, Christopher, Burri, Andrea, Deloukas, Panos, Dorret Boomsma, Spector, Tim, and Bell, Jordana

Abstract

Replication of four top-ranked pan-cancer DMPs in 9 cancer discordant MZ twin-pairs. Direction of association at [A] cg0244695 near SASH1, [B] cg26079695 in COL11A2, [C] cg27094856 in AXL, and [D] cg21046959 in LINC00340. Results are plotted using normalised unadjusted beta values of cancer-affected individuals (left) and healthy individuals (right). The lines connect co-twins in twin-pairs and indicate a consistent direction of effect. (PDF 88Â kb)

Published
2016
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25. Additional file 14: of Blood lipids influence DNA methylation in circulating cells

Author

Dekkers, Koen, Iterson, Maarten Van, Slieker, Roderick, Moed, Matthijs, Bonder, Marc, Galen, Michiel Van, Hailiang Mei, Zhernakova, Daria, Berg, Leonard Van Den, Deelen, Joris, Dongen, Jenny Van, Heemst, Diana Van, Hofman, Albert, Hottenga, Jouke, Kallen, Carla Van Der, Schalkwijk, Casper, Stehouwer, Coen, Tigchelaar, Ettje, AndrĂŠ Uitterlinden, Willemsen, Gonneke, Zhernakova, Alexandra, Franke, Lude, Hoen, Peter, Jansen, Rick, Meurs, Joyce Van, Dorret Boomsma, Duijn, Cornelia Van, Greevenbroek, Marleen Van, Veldink, Jan, Wijmenga, Cisca, Zwet, Erik Van, P. Slagboom, J. Jukema, and Heijmans, Bastiaan

Subjects
body regions, nervous system, fungi
Abstract

DNA methylation was associated with gene expression. (PDF 21974Â kb)

Published
2016
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26. Additional file 4: of Blood lipids influence DNA methylation in circulating cells

Author

Dekkers, Koen, Iterson, Maarten Van, Slieker, Roderick, Moed, Matthijs, Bonder, Marc, Galen, Michiel Van, Hailiang Mei, Zhernakova, Daria, Berg, Leonard Van Den, Deelen, Joris, Dongen, Jenny Van, Heemst, Diana Van, Hofman, Albert, Hottenga, Jouke, Kallen, Carla Van Der, Schalkwijk, Casper, Stehouwer, Coen, Tigchelaar, Ettje, AndrĂŠ Uitterlinden, Willemsen, Gonneke, Zhernakova, Alexandra, Franke, Lude, Hoen, Peter, Jansen, Rick, Meurs, Joyce Van, Dorret Boomsma, Duijn, Cornelia Van, Greevenbroek, Marleen Van, Veldink, Jan, Wijmenga, Cisca, Zwet, Erik Van, P. Slagboom, J. Jukema, and Heijmans, Bastiaan

Abstract

The EWAS effect size estimates were not sensitive to the exclusion of non-fasted samples, the exclusion of samples with imputed cell counts, or the addition of current smoking behavior, lipid-lowering medication, or BMI as covariates. (PDF 273Â kb)

Published
2016
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28. Additional file 12: of Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Author

Slieker, Roderick, Iterson, Maarten Van, RenĂŠ Luijk, Beekman, Marian, Zhernakova, Daria, Moed, Matthijs, Hailiang Mei, Galen, Michiel Van, Deelen, Patrick, Bonder, Marc, Zhernakova, Alexandra, AndrĂŠ Uitterlinden, Tigchelaar, Ettje, Stehouwer, Coen, Schalkwijk, Casper, Kallen, Carla Van Der, Hofman, Albert, Heemst, Diana Van, Eco De Geus, Dongen, Jenny Van, Deelen, Joris, Berg, Leonard Van Den, Meurs, Joyce Van, Jansen, Rick, Hoen, Peter, Franke, Lude, Wijmenga, Cisca, Veldink, Jan, Swertz, Morris, Greevenbroek, Marleen Van, Duijn, Cornelia Van, Dorret Boomsma, P. Slagboom, and Heijmans, Bastiaan

Abstract

Description of external datasets used. (PDF 79 kb)

Published
2016
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29. Additional file 3: of Blood lipids influence DNA methylation in circulating cells

Author

Dekkers, Koen, Iterson, Maarten Van, Slieker, Roderick, Moed, Matthijs, Bonder, Marc, Galen, Michiel Van, Hailiang Mei, Zhernakova, Daria, Berg, Leonard Van Den, Deelen, Joris, Dongen, Jenny Van, Heemst, Diana Van, Hofman, Albert, Hottenga, Jouke, Kallen, Carla Van Der, Schalkwijk, Casper, Stehouwer, Coen, Tigchelaar, Ettje, AndrĂŠ Uitterlinden, Willemsen, Gonneke, Zhernakova, Alexandra, Franke, Lude, Hoen, Peter, Jansen, Rick, Meurs, Joyce Van, Dorret Boomsma, Duijn, Cornelia Van, Greevenbroek, Marleen Van, Veldink, Jan, Wijmenga, Cisca, Zwet, Erik Van, P. Slagboom, J. Jukema, and Heijmans, Bastiaan

Subjects
animal structures, embryonic structures, fungi, lipids (amino acids, peptides, and proteins), human activities
Abstract

Estimates and 95Â % confidence intervals for the EWAS-identified CpGs for each cohort contributing to the meta-analysis for TG, LDL-C, and HDL-C. (PDF 297Â kb)

Published
2016
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30. Additional file 2: of Blood lipids influence DNA methylation in circulating cells

Author

Dekkers, Koen, Iterson, Maarten Van, Slieker, Roderick, Moed, Matthijs, Bonder, Marc, Galen, Michiel Van, Hailiang Mei, Zhernakova, Daria, Berg, Leonard Van Den, Deelen, Joris, Dongen, Jenny Van, Heemst, Diana Van, Hofman, Albert, Hottenga, Jouke, Kallen, Carla Van Der, Schalkwijk, Casper, Stehouwer, Coen, Tigchelaar, Ettje, Uitterlinden, André, Willemsen, Gonneke, Zhernakova, Alexandra, Franke, Lude, Peter ’T Hoen, Jansen, Rick, Meurs, Joyce Van, Dorret Boomsma, Duijn, Cornelia Van, Greevenbroek, Marleen Van, Veldink, Jan, Wijmenga, Cisca, Zwet, Erik Van, P. Slagboom, J. Jukema, and Heijmans, Bastiaan

Abstract

Quantile–quantile plots depicting the associations between TG, LDL-C, and HDL-C and genome-wide DNA methylation when compared with a uniform distribution expected by chance for a uncorrected meta-analysis z-values and b meta-analysis z-values corrected for genome-wide inflation using genomic control (λ = 1.0). (PDF 14800 kb)

Published
2016
Full Text
View/download PDF

31. Additional file 9: of Blood lipids influence DNA methylation in circulating cells

Author

Dekkers, Koen, Iterson, Maarten Van, Slieker, Roderick, Moed, Matthijs, Bonder, Marc, Galen, Michiel Van, Hailiang Mei, Zhernakova, Daria, Berg, Leonard Van Den, Deelen, Joris, Dongen, Jenny Van, Heemst, Diana Van, Hofman, Albert, Hottenga, Jouke, Kallen, Carla Van Der, Schalkwijk, Casper, Stehouwer, Coen, Tigchelaar, Ettje, AndrĂŠ Uitterlinden, Willemsen, Gonneke, Zhernakova, Alexandra, Franke, Lude, Hoen, Peter, Jansen, Rick, Meurs, Joyce Van, Dorret Boomsma, Duijn, Cornelia Van, Greevenbroek, Marleen Van, Veldink, Jan, Wijmenga, Cisca, Zwet, Erik Van, P. Slagboom, J. Jukema, and Heijmans, Bastiaan

Abstract

Comparison between EWAS and MR estimates (with 95Â % confidence intervals). (PDF 109Â kb)

Published
2016
Full Text
View/download PDF

32. Additional file 1: Figure S1. of Integrative DNA methylome analysis of pan-cancer biomarkers in cancer discordant monozygotic twin-pairs

Author

Roos, Leonie, Dongen, Jenny Van, Bell, Christopher, Burri, Andrea, Deloukas, Panos, Dorret Boomsma, Spector, Tim, and Bell, Jordana

Abstract

Pan-cancer top-ranked results in 41 discordant MZ twin-pairs. Direction of association at [A] cg26079695 in COL11A2, [B] cg27094856 in AXL, and [C] cg21046959 in LINC00340. Results are plotted using normalised unadjusted beta values of cancer-affected individuals (left) and healthy individuals (right). The lines connect co-twins in twin-pairs and indicate a consistent direction of effect. (PDF 78Â kb)

Published
2016
Full Text
View/download PDF

33. Additional file 5: Figure S1. of Integrative DNA methylome analysis of pan-cancer biomarkers in cancer discordant monozygotic twin-pairs

Author

Roos, Leonie, Dongen, Jenny Van, Bell, Christopher, Burri, Andrea, Deloukas, Panos, Dorret Boomsma, Spector, Tim, and Bell, Jordana

Abstract

Differential methylation with respect to age and time of cancer diagnosis. [A] Time to diagnosis compared to age at blood sample collection, the line represent the least squares regression fit. [B] Unadjusted DNA methylation values at cg02444695 (near SASH1) in affected individuals (red) and healthy co-twins (blue), shown with respect to age at blood sample collection (years) with smooth (LOESS) lines fitted for both groups. [Câ F] Unadjusted DNA methylation values in affected individuals (red) and healthy co-twins (blue), shown with respect to time of diagnosis (years) with smooth (LOESS) lines fitted for both groups with blood samples collected 5 to 11Â years before cancer diagnosis. The orange and black vertical lines represent the time of diagnosis and time window of the main study respectively. At [C] cg0244695 near SASH1, [D] cg26079695 in COL11A2, [E] cg27094856 in AXL, and [F] cg21046959 in LINC00340. (PDF 185Â kb)

Published
2016
Full Text
View/download PDF

34. The Dopaminergic Reward System And Leisure Time Exercise Behavior

Author

Charlotte Huppertz, Meike Bartels, Maria Groen-Blokhuis, Marleen de Moor, Niels van der Aa, Abdel Abdellaoui, Catharina van Beijsterveldt, Erik Ehli, Jouke-Jan Hottenga, Gonneke Willemsen, Xiangjun Xiao, Paul Scheet, Gareth Davies, Dorret Boomsma, James Hudziak, and Eco de Geus

Subjects
Physical Therapy, Sports Therapy and Rehabilitation, Orthopedics and Sports Medicine
Published
2014
Full Text
View/download PDF

35. Combined genome scans for body stature in 6602 European twins: evidence for common Caucasian loci

Author

Markus Perola, Sampo Sammalisto, Tero Hiekkalinna, Nick Martin, Peter M. Visscher, Grant W. Montgomery, Beben Benyamin, Jennifer R Harris, Dorret Boomsma, Gonneke Willemsen, Jouke-Jan Hottenga, Kaare Christensen, Kirsten Ohm Kyvik, Thorkild I. A. Sørensen, Nancy L. Pedersen, Patrik KE Magnusson, Tim D Spector, Elisabeth Widen, Karri Silventoinen, Jaakko Kaprio, Aarno Palotie, and Leena Peltonen

Subjects
Cancer Research, Genetics, Molecular Biology, Genetics (clinical), Ecology, Evolution, Behavior and Systematics
Published
2005
Full Text
View/download PDF

36. Additional file 2: Figure S1. of Does the sex of one’s co-twin affect height and BMI in adulthood? A study of dizygotic adult twins from 31 cohorts

Author

Bogl, Leonie, Jelenkovic, Aline, Vuoksimaa, Eero, Ahrenfeldt, Linda, Pietiläinen, Kirsi, Stazi, Maria, Fagnani, Corrado, D’Ippolito, Cristina, Yoon-Mi Hur, Hoe-Uk Jeong, Silberg, Judy, Lindon Eaves, Maes, Hermine, Gombojav Bayasgalan, Danshiitsoodol Narandalai, Cutler, Tessa, Kandler, Christian, Jang, Kerry, Christensen, Kaare, Skytthe, Axel, Kyvik, Kirsten, Cozen, Wendy, Hwang, Amie, Mack, Thomas, Derom, Catherine, Vlietinck, Robert, Nelson, Tracy, Whitfield, Keith, Corley, Robin, Huibregtse, Brooke, McAdams, Tom, Eley, Thalia, Gregory, Alice, Krueger, Robert, McGue, Matt, Shandell Pahlen, Willemsen, Gonneke, Bartels, Meike, Beijsterveldt, Toos Van, Zengchang Pang, Qihua Tan, Dongfeng Zhang, Martin, Nicholas, Medland, Sarah, Montgomery, Grant, Hjelmborg, Jacob, Rebato, Esther, Swan, Gary, Krasnow, Ruth, Busjahn, Andreas, Lichtenstein, Paul, Öncel, Sevgi, Aliev, Fazil, Baker, Laura, Tuvblad, Catherine, Siribaddana, Sisira, Hotopf, Matthew, Athula Sumathipala, Fruhling Rijsdijk, Magnusson, Patrik, Pedersen, Nancy, Aslan, Anna, Ordoñana, Juan, Sánchez-Romera, Juan, Colodro-Conde, Lucia, Duncan, Glen, Buchwald, Dedra, Tarnoki, Adam, Tarnoki, David, Yokoyama, Yoshie, Hopper, John, Loos, Ruth, Dorret Boomsma, Sørensen, Thorkild, Silventoinen, Karri, and Kaprio, Jaakko

Subjects
10. No inequality
Abstract

The differences in height (cm) between opposite sex (OS) and same sex (SS) plotted against the mean heights (cm) of the cohorts for females and males. The figure examines whether the difference in height between OS and SS twins is greater in taller cohorts. The Spearman correlations are r = −0.16 and p = 0.39 for females and r = 0.10 and p = 0.59 for males. Figure S2. The differences in BMI (kg/m2) between opposite-sex (OS) and same-sex (SS) twins plotted against the mean BMIs (kg/m2) of the cohorts for females and males. The figure examines whether the difference in BMI between OS and SS twins is greater in heavier cohorts. The Spearman correlations are r = −0.39 and p = 0.029 in females and r = 0.10 and p = 0.61 for BMI in males. (DOCX 159 kb)

37. Additional file 1: of Blood lipids influence DNA methylation in circulating cells

Author

Dekkers, Koen, Iterson, Maarten Van, Slieker, Roderick, Moed, Matthijs, Bonder, Marc, Galen, Michiel Van, Hailiang Mei, Zhernakova, Daria, Berg, Leonard Van Den, Deelen, Joris, Dongen, Jenny Van, Heemst, Diana Van, Hofman, Albert, Hottenga, Jouke, Kallen, Carla Van Der, Schalkwijk, Casper, Stehouwer, Coen, Tigchelaar, Ettje, AndrĂŠ Uitterlinden, Willemsen, Gonneke, Zhernakova, Alexandra, Franke, Lude, Hoen, Peter, Jansen, Rick, Meurs, Joyce Van, Dorret Boomsma, Duijn, Cornelia Van, Greevenbroek, Marleen Van, Veldink, Jan, Wijmenga, Cisca, Zwet, Erik Van, P. Slagboom, J. Jukema, and Heijmans, Bastiaan

Subjects
3. Good health
Abstract

Full list of authors of the BIOS Consortium. (PDF 68Â kb)

38. Additional file 1: of Epigenome-wide association study of serum cotinine in current smokers reveals novel genetically driven loci

Author

Gupta, Richa, Dongen, Jenny, Fu, Yu, Abdellaoui, Abdel, Tyndale, Rachel, Velagapudi, Vidya, Dorret Boomsma, Korhonen, Tellervo, Kaprio, Jaakko, Loukola, Anu, and Ollikainen, Miina

Subjects
population characteristics, social sciences, 3. Good health
Abstract

Figure S1. Manhattan and QQ plots showing epigenome-wide association results from secondary analysis when accounting for the rate of nicotine metabolism using a GRS. (A) QQ plot showing observed versus expected − log10(P) for association at all loci. (B) Manhattan plot showing chromosomal locations of − log10(P) for association at each locus. All CpG sites with FDR

39. Additional file 4: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Subjects
3. Good health
Abstract

Table S4. Biomarkers baseline. (DOCX 24Â kb)

40. Additional file 1: of Blood lipids influence DNA methylation in circulating cells

Author

Dekkers, Koen, Iterson, Maarten Van, Slieker, Roderick, Moed, Matthijs, Bonder, Marc, Galen, Michiel Van, Hailiang Mei, Zhernakova, Daria, Berg, Leonard Van Den, Deelen, Joris, Dongen, Jenny Van, Heemst, Diana Van, Hofman, Albert, Hottenga, Jouke, Kallen, Carla Van Der, Schalkwijk, Casper, Stehouwer, Coen, Tigchelaar, Ettje, AndrĂŠ Uitterlinden, Willemsen, Gonneke, Zhernakova, Alexandra, Franke, Lude, Hoen, Peter, Jansen, Rick, Meurs, Joyce Van, Dorret Boomsma, Duijn, Cornelia Van, Greevenbroek, Marleen Van, Veldink, Jan, Wijmenga, Cisca, Zwet, Erik Van, P. Slagboom, J. Jukema, and Heijmans, Bastiaan

Subjects
3. Good health
Abstract

Full list of authors of the BIOS Consortium. (PDF 68Â kb)

41. Additional file 5: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Subjects
3. Good health
Abstract

Table S5. MRI settings. (DOCX 15Â kb)

42. Additional file 5: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Subjects
3. Good health
Abstract

Table S5. MRI settings. (DOCX 15Â kb)

43. Additional file 4: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Subjects
3. Good health
Abstract

Table S4. Biomarkers baseline. (DOCX 24Â kb)

44. Additional file 2: Figure S1. of Does the sex of one’s co-twin affect height and BMI in adulthood? A study of dizygotic adult twins from 31 cohorts

Author

Bogl, Leonie, Jelenkovic, Aline, Vuoksimaa, Eero, Ahrenfeldt, Linda, Pietiläinen, Kirsi, Stazi, Maria, Fagnani, Corrado, D’Ippolito, Cristina, Yoon-Mi Hur, Hoe-Uk Jeong, Silberg, Judy, Lindon Eaves, Maes, Hermine, Gombojav Bayasgalan, Danshiitsoodol Narandalai, Cutler, Tessa, Kandler, Christian, Jang, Kerry, Christensen, Kaare, Skytthe, Axel, Kyvik, Kirsten, Cozen, Wendy, Hwang, Amie, Mack, Thomas, Derom, Catherine, Vlietinck, Robert, Nelson, Tracy, Whitfield, Keith, Corley, Robin, Huibregtse, Brooke, McAdams, Tom, Eley, Thalia, Gregory, Alice, Krueger, Robert, McGue, Matt, Shandell Pahlen, Willemsen, Gonneke, Bartels, Meike, Beijsterveldt, Toos Van, Zengchang Pang, Qihua Tan, Dongfeng Zhang, Martin, Nicholas, Medland, Sarah, Montgomery, Grant, Hjelmborg, Jacob, Rebato, Esther, Swan, Gary, Krasnow, Ruth, Busjahn, Andreas, Lichtenstein, Paul, Öncel, Sevgi, Aliev, Fazil, Baker, Laura, Tuvblad, Catherine, Siribaddana, Sisira, Hotopf, Matthew, Athula Sumathipala, Fruhling Rijsdijk, Magnusson, Patrik, Pedersen, Nancy, Aslan, Anna, Ordoñana, Juan, Sánchez-Romera, Juan, Colodro-Conde, Lucia, Duncan, Glen, Buchwald, Dedra, Tarnoki, Adam, Tarnoki, David, Yokoyama, Yoshie, Hopper, John, Loos, Ruth, Dorret Boomsma, Sørensen, Thorkild, Silventoinen, Karri, and Kaprio, Jaakko

Subjects
10. No inequality
Abstract

The differences in height (cm) between opposite sex (OS) and same sex (SS) plotted against the mean heights (cm) of the cohorts for females and males. The figure examines whether the difference in height between OS and SS twins is greater in taller cohorts. The Spearman correlations are r = −0.16 and p = 0.39 for females and r = 0.10 and p = 0.59 for males. Figure S2. The differences in BMI (kg/m2) between opposite-sex (OS) and same-sex (SS) twins plotted against the mean BMIs (kg/m2) of the cohorts for females and males. The figure examines whether the difference in BMI between OS and SS twins is greater in heavier cohorts. The Spearman correlations are r = −0.39 and p = 0.029 in females and r = 0.10 and p = 0.61 for BMI in males. (DOCX 159 kb)

45. Additional file 2: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Subjects
3. Good health
Abstract

Table S2. Neuropsychological tests baseline. (DOCX 48Â kb)

46. Additional file 1: of Epigenome-wide association study of serum cotinine in current smokers reveals novel genetically driven loci

Author

Gupta, Richa, Dongen, Jenny, Fu, Yu, Abdellaoui, Abdel, Tyndale, Rachel, Velagapudi, Vidya, Dorret Boomsma, Korhonen, Tellervo, Kaprio, Jaakko, Loukola, Anu, and Ollikainen, Miina

Subjects
population characteristics, social sciences, 3. Good health
Abstract

Figure S1. Manhattan and QQ plots showing epigenome-wide association results from secondary analysis when accounting for the rate of nicotine metabolism using a GRS. (A) QQ plot showing observed versus expected − log10(P) for association at all loci. (B) Manhattan plot showing chromosomal locations of − log10(P) for association at each locus. All CpG sites with FDR

47. Additional file 2: of The EMIF-AD PreclinAD study: study design and baseline cohort overview

Author

Konijnenberg, Elles, Carter, Stephen, Kate, Mara Ten, Braber, Anouk Den, Tomassen, Jori, Chinenye Amadi, Wesselman, Linda, Hoang-Ton Nguyen, Kreeke, Jacoba Van De, Yaqub, Maqsood, Demuru, Matteo, Mulder, Sandra, Hillebrand, Arjan, Bouwman, Femke, Teunissen, Charlotte, SernĂŠ, Erik, Moll, Annette, Verbraak, Frank, Hinz, Rainer, Pendleton, Neil, Lammertsma, Adriaan, Berckel, Bart Van, Barkhof, Frederik, Dorret Boomsma, Scheltens, Philip, Herholz, Karl, and Visser, Pieter

Subjects
3. Good health
Abstract

Table S2. Neuropsychological tests baseline. (DOCX 48Â kb)

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