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Zeng K, Du J, Chen YZ, Wang DY, Sun ML, Li YZ, Wang DY, Liu SH, Zhu XM, Lv P, Du Z, Liu K, Yao J. Metabolomics efficiently discriminates monozygotic twins in peripheral blood. Int J Legal Med 2024:10.1007/s00414-024-03269-1. [PMID: 38858273 DOI: 10.1007/s00414-024-03269-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 06/03/2024] [Indexed: 06/12/2024]
Abstract
Monozygotic (MZ) twins cannot be distinguished using conventional forensic STR typing because they present identical STR genotypings. However, MZ twins do not always live in the same environment and often have different dietary and other lifestyle habits. Metabolic profiles are deyermined by individual characteristics and are also influenced by the environment in which they live. Therefore, they are potential markers capable of identifying MZ twins. Moreover, the production of proteins varies from organism to organism and is influenced by both the physiological state of the body and the external environment. Hence, we used metabolomics and proteomics to identify metabolites and proteins in peripheral blood to discriminate MZ twins. We identified 1749 known metabolites and 622 proteins in proteomic analysis. The metabolic profiles of four pairs of MZ twins revealed minor differences in intra-MZ twins and major differences in inter-MZ twins. Each pair of MZ twins exhibited distinct characteristics, and four metabolites-methyl picolinate, acesulfame, paraxanthine, and phenylbenzimidazole sulfonic acid-were observed in all four MZ twin pairs. These four differential exogenous metabolites conincidently show that the different external environments and life styles can be well distinguished by metabolites, considering that twins do not all have the same eating habits and living environments. Moreover, MZ twins showed different protein profiles in serum but not in whole blood. Thus, our results indicate that differential metabolites provide potential biomarkers for the personal identification of MZ twins in forensic medicine.
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Affiliation(s)
- Kuo Zeng
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
- Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Beijing, China
| | - Jiang Du
- Department of Pathology, School of Basic Medicine, China Medical University, Shenyang, P.R. China
| | - Yun-Zhou Chen
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Dan-Yang Wang
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Mao-Ling Sun
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Yu-Zhang Li
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Dong-Yi Wang
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Shu-Han Liu
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Xiu-Mei Zhu
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Peng Lv
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Zhe Du
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China
| | - Kun Liu
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China.
| | - Jun Yao
- School of Forensic Medicine, China Medical University, No.77, Puhe Road, Shenbei New District, Shenyang, 110122, P.R. China.
- Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, P.R. China.
- China Medical University Center of Forensic Investigation, Shenyang, P.R. China.
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2
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Hubers N, Hagenbeek FA, Pool R, Déjean S, Harms AC, Roetman PJ, van Beijsterveldt CEM, Fanos V, Ehli EA, Vermeiren RRJM, Bartels M, Hottenga JJ, Hankemeier T, van Dongen J, Boomsma DI. Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32955. [PMID: 37534875 DOI: 10.1002/ajmg.b.32955] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 06/13/2023] [Accepted: 07/11/2023] [Indexed: 08/04/2023]
Abstract
The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.
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Affiliation(s)
- Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Sébastien Déjean
- Toulouse Mathematics Institute, UMR 5219, University of Toulouse, CNRS, Toulouse, France
| | - Amy C Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
- The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Peter J Roetman
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Vassilios Fanos
- Department of Surgical Sciences, University of Cagliari and Neonatal Intensive Care Unit, Cagliari, Italy
| | - Erik A Ehli
- Avera Institute for Human Genetics, Sioux Falls, South Dakota, USA
| | - Robert R J M Vermeiren
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Youz, Parnassia Group, the Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
- The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
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3
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Drouard G, Hagenbeek FA, Whipp AM, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. BMC Med 2023; 21:508. [PMID: 38129841 PMCID: PMC10740308 DOI: 10.1186/s12916-023-03198-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. METHODS Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. RESULTS We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. CONCLUSIONS Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Fiona A Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce M Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
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4
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Drouard G, Hagenbeek FA, Whipp A, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291995. [PMID: 37425750 PMCID: PMC10327285 DOI: 10.1101/2023.06.28.23291995] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Background The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. Methods Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks. Results We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. Conclusions Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Fiona A. Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - BIOS Consortium
- Biobank-based Integrative Omics Study Consortium. Lists of authors and their affiliations appear in the supplementary material (see Additional file 1)
| | | | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J. C. de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M. Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Hagenbeek FA, Hirzinger JS, Breunig S, Bruins S, Kuznetsov DV, Schut K, Odintsova VV, Boomsma DI. Maximizing the value of twin studies in health and behaviour. Nat Hum Behav 2023:10.1038/s41562-023-01609-6. [PMID: 37188734 DOI: 10.1038/s41562-023-01609-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
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, and 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 we 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.
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Affiliation(s)
- Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
| | - Jana S Hirzinger
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Sophie Breunig
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychology & Neuroscience, University of Colorado Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Susanne Bruins
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dmitry V Kuznetsov
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Faculty of Sociology, Bielefeld University, Bielefeld, Germany
| | - Kirsten Schut
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Nightingale Health Plc, Helsinki, Finland
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Department of Psychiatry, University Medical Center of Groningen, University of Groningen, Groningen, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands.
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6
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Niehues A, de Visser C, Hagenbeek F, Karu N, Kindt A, Kulkarni P, Pool R, Boomsma D, van Dongen J, van Gool A, 't Hoen P. A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object. RESEARCH IDEAS AND OUTCOMES 2022. [DOI: 10.3897/rio.8.e94042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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.
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