@article {387, title = {A multi-omics data analysis workflow packaged as a FAIR Digital Object}, journal = {GigaScience}, volume = {13}, year = {2024}, month = {01}, pages = {giad115}, abstract = {

Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object.We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub.Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

}, issn = {2047-217X}, doi = {10.1093/gigascience/giad115}, url = {https://doi.org/10.1093/gigascience/giad115}, author = {Niehues, Anna and de~Visser, Casper and Hagenbeek, Fiona A and Kulkarni, Purva and Pool, Ren{\'e} and Karu, Naama and Kindt, Alida S D and Singh, Gurnoor and Vermeiren, Robert R J M and Boomsma, Dorret I and van~Dongen, Jenny and {\textquoteright}t~Hoen, Peter A C and van~Gool, Alain J} } @article {370, title = {Integrative multi-omics analysis of childhood aggressive behavior}, journal = {Behavior Genetics}, volume = {53}, year = {2023}, pages = {101{\textendash}117}, abstract = {

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42\%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 27

}, keywords = {childhood aggression, DNA methylation, Genetic nurturing, metabolomics, Multi-omics, polygenic scores}, doi = {10.1007/s10519-022-10126-7}, author = {Hagenbeek, Fiona A and van Dongen, Jenny and Pool, Ren{\'e} and Roetman, Peter J and Harms, Amy C and Hottenga, Jouke Jan and Kluft, Cornelis and Colins, Olivier F and van Beijsterveldt, Catharina E M and Fanos, Vassilios and Ehli, Erik A and Hankemeier, Thomas and Vermeiren, Robert R J M and Bartels, Meike and D{\'e}jean, S{\'e}bastien and Boomsma, Dorret I} } @article {384, title = {Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder}, journal = {American Journal of Medical Genetics Part B: Neuropsychiatric Genetics}, year = {2023}, pages = {e32955}, 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 = 25

}, keywords = {ADHD, DNA methylation, genetic nurture, metabolites, Multi-omics, polygenic scores}, doi = {10.1002/ajmg.b.32955}, author = {Hubers, Nikki and Hagenbeek, Fiona A and Pool, Ren{\'e} and D{\'e}jean, S{\'e}bastien and Harms, Amy C and Roetman, Peter J and van Beijsterveldt, Catharina E M and Fanos, Vassilios and Ehli, Erik A and Vermeiren, Robert R J M and Bartels, Meike and Hottenga, Jouke Jan and Hankemeier, Thomas and van Dongen, Jenny and Boomsma, Dorret I} } @unpublished {383, title = {Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins}, journal = { medRxiv}, year = {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.

}, doi = {10.1101/2023.06.28.23291995}, author = {Drouard, Gabin and Hagenbeek, Fiona A and Whipp, Alyce and Pool, Ren{\'e} and Hottenga, Jouke-Jan and Jansen, Rick and Hubers, Nikki and Afonin, Aleksei and Willemsen, Gonneke and de Geus, Eco J C and Ripatti, Samuli and Pirinen, Matti and Kanninen, Katja M and Boomsma, Dorret I and van Dongen, Jenny and Kaprio, Jaakko and BIOS Consortium and BBMRI-NL Metabolomics Consortium} } @article {376, title = {DNA methylation in peripheral tissues and left-handedness}, journal = {Nature Scientific Reports}, volume = {12}, year = {2022}, pages = {5606}, 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 of left-handedness. In a meta-analysis of 3914 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. In longitudinal analyses of DNA methylation in peripheral blood and buccal cells from children (N = 1737), we observed moderately stable associations across age (correlation range [0.355-0.578]), but inconsistent across tissues (correlation range [- 0.384 to 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.

}, doi = {10.1038/s41598-022-08998-0}, author = {Odintsova, Veronika V and Suderman, Matthew and Hagenbeek, Fiona A and Caramaschi, Doretta and Hottenga, Jouke-Jan and Pool, Ren{\'e} and BIOS Consortium and Dolan, Conor V and Ligthart, Lannie and van Beijsterveldt, Catharina E M and Willemsen, Gonneke and de Geus, Eco J C and Beck, Jeffrey J and Ehli, Erik A and Cuellar-Partida, Gabriel and Evans, David M and Medland, Sarah E and Relton, Caroline L and Boomsma, Dorret I and van Dongen, Jenny} } @article {380, title = {Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention}, journal = {Nature Genetics}, volume = {54}, year = {2022}, pages = {1332{\textendash}1344}, abstract = {

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.

}, doi = {10.1038/s41588-022-01165-1}, author = {Wang, Zhe and Emmerich, Andrew and Pillon, Nicolas J and Moore, Tim and Hemerich, Daiane and Cornelis, Marilyn C and Mazzaferro, Eugenia and Broos, Siacia and Ahluwalia, Tarunveer S and Bartz, Traci M and Bentley, Amy R and Bielak, Lawrence F and Chong, Mike and Chu, Audrey Y and Berry, Diane and Dorajoo, Rajkumar and Dueker, Nicole D and Kasbohm, Elisa and Feenstra, Bjarke and Feitosa, Mary F and Gieger, Christian and Graff, Mariaelisa and Hall, Leanne M and Haller, Toomas and Hartwig, Fernando P and Hillis, David A and Huikari, Ville and Heard-Costa, Nancy and Holzapfel, Christina and Jackson, Anne U and Johansson, \AAsa and J{\o}rgensen, Anja Moltke and Kaakinen, Marika A and Karlsson, Robert and Kerr, Kathleen F and Kim, Boram and Koolhaas, Chantal M and Kutalik, Zoltan and Lagou, Vasiliki and Lind, Penelope A and Lorentzon, Mattias and Lyytik{\"a}inen, Leo-Pekka and Mangino, Massimo and Metzendorf, Christoph and Monroe, Kristine R and Pacolet, Alexander and P{\'e}russe, Louis and Pool, Ren{\'e} and Richmond, Rebecca C and Rivera, Natalia V and Robiou-du-Pont, Sebastien and Schraut, Katharina E and Schulz, Christina-Alexandra and Stringham, Heather M and Tanaka, Toshiko and Teumer, Alexander and Turman, Constance and van der Most, Peter J and Vanmunster, Mathias and van Rooij, Frank J A and van Vliet-Ostaptchouk, Jana V and Zhang, Xiaoshuai and Zhao, Jing-Hua and Zhao, Wei and Balkhiyarova, Zhanna and Balslev-Harder, Marie N and Baumeister, Sebastian E and Beilby, John and Blangero, John and Boomsma, Dorret I and Brage, Soren and Braund, Peter S and Brody, Jennifer A and Bruinenberg, Marcel and Ekelund, Ulf and Liu, Ching-Ti and Cole, John W and Collins, Francis S and Cupples, L Adrienne and Esko, T{\~o}nu and Enroth, Stefan and Faul, Jessica D and Fernandez-Rhodes, Lindsay and Fohner, Alison E and Franco, Oscar H and Galesloot, Tessel E and Gordon, Scott D and Grarup, Niels and Hartman, Catharina A and Heiss, Gerardo and Hui, Jennie and Illig, Thomas and Jago, Russell and James, Alan and Joshi, Peter K and Jung, Taeyeong and K{\"a}h{\"o}nen, Mika and Kilpel{\"a}inen, Tuomas O and Koh, Woon-Puay and Kolcic, Ivana and Kraft, Peter P and Kuusisto, Johanna and Launer, Lenore J and Li, Aihua and Linneberg, Allan and Luan, Jian{\textquoteright}an and Vidal, Pedro Marques and Medland, Sarah E and Milaneschi, Yuri and Moscati, Arden and Musk, Bill and Nelson, Christopher P and Nolte, Ilja M and Pedersen, Nancy L and Peters, Annette and Peyser, Patricia A and Power, Christine and Raitakari, Olli T and Reedik, M{\"a}gi and Reiner, Alex P and Ridker, Paul M and Rudan, Igor and Ryan, Kathy and Sarzynski, Mark A and Scott, Laura J and Scott, Robert A and Sidney, Stephen and Siggeirsdottir, Kristin and Smith, Albert V and Smith, Jennifer A and Sonestedt, Emily and Str{\o}m, Marin and Tai, E Shyong and Teo, Koon K and Thorand, Barbara and T{\"o}njes, Anke and Tremblay, Angelo and Uitterlinden, Andr{\'e} G and Vangipurapu, Jagadish and van Schoor, Natasja and V{\"o}lker, Uwe and Willemsen, Gonneke and Williams, Kayleen and Wong, Quenna and Xu, Huichun and Young, Kristin L and Yuan, Jian Min and Zillikens, M Carola and Zonderman, Alan B and Ameur, Adam and Bandinelli, Stefania and Bis, Joshua C and Boehnke, Michael and Bouchard, Claude and Chasman, Daniel I and Smith, George Davey and de Geus, Eco J C and Deldicque, Louise and D{\"o}rr, Marcus and Evans, Michele K and Ferrucci, Luigi and Fornage, Myriam and Fox, Caroline and Garland, Jr, Theodore and Gudnason, Vilmundur and Gyllensten, Ulf and Hansen, Torben and Hayward, Caroline and Horta, Bernardo L and Hypponen, Elina and Jarvelin, Marjo-Riitta and Johnson, W Craig and Kardia, Sharon L R and Kiemeney, Lambertus A and Laakso, Markku and Langenberg, Claudia and Lehtim{\"a}ki, Terho and Marchand, Loic Le and Lifelines Cohort Study and Magnusson, Patrik K E and Martin, Nicholas G and Melbye, Mads and Metspalu, Andres and Meyre, David and North, Kari E and Ohlsson, Claes and Oldehinkel, Albertine J and Orho-Melander, Marju and Pare, Guillaume and Park, Taesung and Pedersen, Oluf and Penninx, Brenda W J H and Pers, Tune H and Polasek, Ozren and Prokopenko, Inga and Rotimi, Charles N and Samani, Nilesh J and Sim, Xueling and Snieder, Harold and S{\o}rensen, Thorkild I A and Spector, Tim D and Timpson, Nicholas J and van Dam, Rob M and van der Velde, Nathalie and van Duijn, Cornelia M and Vollenweider, Peter and V{\"o}lzke, Henry and Voortman, Trudy and Waeber, G{\'e}rard and Wareham, Nicholas J and Weir, David R and Wichmann, Heinz-Erich and Wilson, James F and Hevener, Andrea L and Krook, Anna and Zierath, Juleen R and Thomis, Martine A I and Loos, Ruth J F and Hoed, Marcel den} } @article {371, title = {Heritability of urinary amines, organic acids, and steroid hormones in children}, journal = {Metabolites}, volume = {12}, year = {2022}, pages = {474}, abstract = {

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7-12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The monozygotic (MZ) and dizygotic (DZ) correlations for the urinary metabolites indicated a role for non-additive genetic factors for 50 amines, 13 organic acids, and 6 steroids. The average broad-sense heritability for these amines, organic acids, and steroids was 0.49 (range: 0.25-0.64), 0.50 (range: 0.33-0.62), and 0.64 (range: 0.43-0.81), respectively. For 6 amines, 7 organic acids, and 4 steroids the twin correlations indicated a role for shared environmental factors and the average narrow-sense heritability was 0.50 (range: 0.37-0.68), 0.50 (range; 0.23-0.61), and 0.47 (range: 0.32-0.70) for these amines, organic acids, and steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

}, keywords = {amines, children, classical twin design, heritability, metabolites, organic acids, steroid hormones, urine}, doi = {10.3390/metabo12060474}, author = {Hagenbeek, Fiona A and van Dongen, Jenny and Pool, Ren{\'e} and Harms, Amy C and Roetman, Peter J and Fanos, Vassilios and van Keulen, Britt J and Walker, Brian R and Karu, Naama and Hulshoff Pol, Hilleke E and Rotteveel, Joost and Finken, Martijn J J and Vermeiren, Robert R J M and Kluft, Cornelis and Bartels, Meike and Hankemeier, Thomas and Boomsma, Dorret I} } @article {345, title = {Genetic meta-analysis of twin birth weight shows high genetic correlation with singleton birth weight}, journal = {Human Molecular Genetics}, volume = {30}, year = {2021}, pages = {1894{\textendash}1905}, abstract = {

Birth weight (BW) is an important predictor of newborn survival and health and has associations with many adult health outcomes, including cardiometabolic disorders, autoimmune diseases and mental health. On average, twins have a lower BW than singletons as a result of a different pattern of fetal growth and shorter gestational duration. Therefore, investigations into the genetics of BW often exclude data from twins, leading to a reduction in sample size and remaining ambiguities concerning the genetic contribution to BW in twins. In this study, we carried out a genome-wide association meta-analysis of BW in 42 212 twin individuals and found a positive correlation of beta values (Pearson\&$\#$39;s r = 0.66, 95\% confidence interval [CI]: 0.47-0.77) with 150 previously reported genome-wide significant variants for singleton BW. We identified strong positive genetic correlations between BW in twins and numerous anthropometric traits, most notably with BW in singletons (genetic correlation [rg] = 0.92, 95\% CI: 0.66-1.18). Genetic correlations of BW in twins with a series of health-related traits closely resembled those previously observed for BW in singletons. Polygenic scores constructed from a genome-wide association study on BW in the UK Biobank demonstrated strong predictive power in a target sample of Dutch twins and singletons. Together, our results indicate that a similar genetic architecture underlies BW in twins and singletons and that future genome-wide studies might benefit from including data from large twin registers.

}, doi = {10.1093/hmg/ddab121}, author = {Beck, Jeffrey J and Pool, Ren{\'e} and van de Weijer, Margot and Chen, Xu and Krapohl, Eva and Gordon, Scott D and Nygaard, Marianne and Debrabant, Birgit and Palviainen, Teemu and van der Zee, Matthijs D and Baselmans, Bart and Finnicum, Casey T and Yi, Lu and Lundstr{\"o}m, Sebastian and van Beijsterveldt, Toos and Christiansen, Lene and Heikkil{\"a}, Kauko and Kittelsrud, Julie and Loukola, Anu and Ollikainen, Miina and Christensen, Kaare and Martin, Nicholas G and Plomin, Robert and Nivard, Michel and Bartels, Meike and Dolan, Conor and Willemsen, Gonneke and de Geus, Eco and Almqvist, Catarina and Magnusson, Patrik K E and Mbarek, Hamdi and Ehli, Erik A and Boomsma, Dorret I and Hottenga, Jouke-Jan} } @article {359, title = {Predicting complex traits and exposures from polygenic scores and blood and buccal DNA methylation profiles}, journal = {Frontiers in Psychiatry}, volume = {12}, year = {2021}, pages = {688464}, abstract = {

We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6\%) of which 54.4\% was captured by the MS. The two predictors captured 16\% of former vs. never smoking initiation variance (MS:15.5\%, PGS: 0.5\%), 17.7\% of prenatal maternal smoking variance (MS:16.9\%, PGS: 0.8\%), 11.9\% of BMI variance (MS: 6.4\%, PGS 5.5\%), and 1.9\% of birth weight variance (MS: 0.4\%, PGS: 1.5\%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6\%), where the MSs contributed 1.5\%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

}, keywords = {birth weight, BMI, DNA methylation, maternal smoking, methylation scores, multi-omics prediction, polygenic scores, smoking}, doi = {10.3389/fpsyt.2021.688464}, author = {Odintsova, Veronika V and Rebattu, Valerie and Hagenbeek, Fiona A and Pool, Ren{\'e} and Beck, Jeffrey J and Ehli, Erik A and van Beijsterveldt, Catharina E M and Ligthart, Lannie and Willemsen, Gonneke and de Geus, Eco J C and Hottenga, Jouke-Jan and Boomsma, Dorret I and van Dongen, Jenny} } @article {339, title = {Genetics and not shared environment explains familial resemblance in adult metabolomics data}, journal = {Twin Research and Human Genetics}, volume = {23}, year = {2020}, pages = {145{\textendash}155}, abstract = {

Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term \&$\#$39;metabolomics\&$\#$39; refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

}, keywords = {classical twin design, enrichment analysis, heritability, metabolite classes, shared environment}, doi = {10.1017/thg.2020.53}, author = {Pool, Ren{\'e} and Hagenbeek, Fiona A and Hendriks, Anne M and van Dongen, Jenny and Willemsen, Gonneke and de Geus, Eco and BBMRI Metabolomics Consortium and Willems van Dijk, Ko and Verhoeven, Aswin and Suchiman, H Eka and Beekman, Marian and Slagboom, P Eline and Harms, Amy C and Hankemeier, Thomas and Boomsma, Dorret I} } @article {335, title = {Heritability estimates for 361 blood metabolites across 40 genome-wide association studies}, journal = {Nature Communications}, volume = {11}, year = {2020}, pages = {39}, abstract = {

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50\% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify \>800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.

}, doi = {10.1038/s41467-019-13770-6}, author = {Hagenbeek, Fiona A and Pool, Ren{\'e} and van Dongen, Jenny and Draisma, Harmen H M and Jan Hottenga, Jouke and Willemsen, Gonneke and Abdellaoui, Abdel and Fedko, Iryna O and den Braber, Anouk and Visser, Pieter Jelle and de Geus, Eco J C N and Willems van Dijk, Ko and Verhoeven, Aswin and Suchiman, H Eka and Beekman, Marian and Slagboom, P Eline and van Duijn, Cornelia M and BBMRI Metabolomics Consortium and Harms, Amy C and Hankemeier, Thomas and Bartels, Meike and Nivard, Michel G and Boomsma, Dorret I} } @article {334, title = {Urinary Amine and organic acid metabolites evaluated as markers for childhood aggression: The ACTION biomarker study}, journal = {Frontiers in Psychiatry}, volume = {11}, year = {2020}, pages = {165}, abstract = {

Biomarkers are of interest as potential diagnostic and predictive instruments in personalized medicine. We present the first urinary metabolomics biomarker study of childhood aggression. We aim to examine the association of urinary metabolites and neurotransmitter ratios involved in key metabolic and neurotransmitter pathways in a large cohort of twins (N = 1,347) and clinic-referred children (N = 183) with an average age of 9.7 years. This study is part of ACTION (Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies), in which we developed a standardized protocol for large-scale collection of urine samples in children. Our analytical design consisted of three phases: a discovery phase in twins scoring low or high on aggression (N = 783); a replication phase in twin pairs discordant for aggression (N = 378); and a validation phase in clinical cases and matched twin controls (N = 367). In the discovery phase, 6 biomarkers were significantly associated with childhood aggression, of which the association of O-phosphoserine ($\beta$ = 0.36; SE = 0.09; p = 0.004), and gamma-L-glutamyl-L-alanine ($\beta$ = 0.32; SE = 0.09; p = 0.01) remained significant after multiple testing. Although non-significant, the directions of effect were congruent between the discovery and replication analyses for six biomarkers and two neurotransmitter ratios and the concentrations of 6 amines differed between low and high aggressive twins. In the validation analyses, the top biomarkers and neurotransmitter ratios, with congruent directions of effect, showed no significant associations with childhood aggression. We find suggestive evidence for associations of childhood aggression with metabolic dysregulation of neurotransmission, oxidative stress, and energy metabolism. Although replication is required, our findings provide starting points to investigate causal and pleiotropic effects of these dysregulations on childhood aggression.

}, keywords = {amines, biomarkers, childhood aggression, metabolites, metabolomics, neurotransmitters, organic acids, oxidative stress}, doi = {10.3389/fpsyt.2020.00165}, author = {Hagenbeek, Fiona A and Roetman, Peter J and Pool, Ren{\'e} and Kluft, Cornelis and Harms, Amy C and van Dongen, Jenny and Colins, Olivier F and Talens, Simone and van Beijsterveldt, Catharina E M and Vandenbosch, Marjolein M L J Z and de Zeeuw, Eveline L and D{\'e}jean, S{\'e}bastien and Fanos, Vassilios and Ehli, Erik A and Davies, Gareth E and Hottenga, Jouke Jan and Hankemeier, Thomas and Bartels, Meike and Vermeiren, Robert R J M and Boomsma, Dorret I} } @article {328, title = {Genomics of human aggression}, journal = {Psychiatric Genetics}, volume = {29}, year = {2019}, pages = {170{\textendash}190}, abstract = {

There are substantial differences, or variation, between humans in aggression, with its molecular genetic basis mostly unknown. This review summarizes knowledge on the genetic contribution to variation in aggression with the following three foci: (1) a comprehensive overview of reviews on the genetics of human aggression, (2) a systematic review of genome-wide association studies (GWASs), and (3) an automated tool for the selection of literature based on supervised machine learning. The phenotype definition \‘aggression\’ (or \‘aggressive behaviour\’, or \‘aggression-related traits\’) included anger, antisocial behaviour, conduct disorder, and oppositional defiant disorder. The literature search was performed in multiple databases, manually and using a novel automated selection tool, resulting in 18 reviews and 17 GWASs of aggression. Heritability estimates of aggression in children and adults are around 50\%, with relatively small fluctuations around this estimate. In 17 GWASs, 817 variants were reported as suggestive (P \≤ 1.0E\−05), including 10 significant associations (P \≤ 5.0E\−08). Nominal associations (P \≤ 1E\−05) were found in gene-based tests for genes involved in immune, endocrine, and nervous systems. Associations were not replicated across GWASs. A complete list of variants and their position in genes and chromosomes are available online. The automated literature search tool produced literature not found by regular search strategies. Aggression in humans is heritable, but its genetic basis remains to be uncovered. No sufficiently large GWASs have been carried out yet. With increases in sample size, we expect aggression to behave like other complex human traits for which GWAS has been successful.

}, doi = {10.1097/YPG.0000000000000239}, author = {Odintsova, Veronika V and Roetman, Peter J and Ip, Hill F and Pool, Ren{\'e} and Van der Laan, Camiel M and Tona, Klodiana-Daphne and Vermeiren, Robert R J M and Boomsma, Dorret I} } @article {323, title = {The Netherlands Twin Register: Longitudinal research based on twin and twin-family designs}, journal = {Twin Research and Human Genetics}, volume = {22}, year = {2019}, pages = {623{\textendash}636}, abstract = {

The Netherlands Twin Register (NTR) is a national register in which twins, multiples and their parents, siblings, spouses and other family members participate. Here we describe the NTR resources that were created from more than 30 years of data collections; the development and maintenance of the newly developed database systems, and the possibilities these resources create for future research. Since the early 1980s, the NTR has enrolled around 120,000 twins and a roughly equal number of their relatives. The majority of twin families have participated in survey studies, and subsamples took part in biomaterial collection (e.g., DNA) and dedicated projects, for example, for neuropsychological, biomarker and behavioral traits. The recruitment into the NTR is all inclusive without any restrictions on enrollment. These resources - the longitudinal phenotyping, the extended pedigree structures and the multigeneration genotyping - allow for future twin-family research that will contribute to gene discovery, causality modeling, and studies of genetic and cultural inheritance.

}, keywords = {biobank, endophenotyping, longitudinal phenotyping, multigeneration pedigree, twins, zygosity}, doi = {10.1017/thg.2019.93}, author = {Ligthart, Lannie and van Beijsterveldt, Catharina E M and Kevenaar, Sofieke T and de Zeeuw, Eveline and van Bergen, Elsje and Bruins, Susanne and Pool, Ren{\'e} and Helmer, Quinta and van Dongen, Jenny and Hottenga, Jouke-Jan and Van{\textquoteright}t Ent, Dennis and Dolan, Conor V and Davies, Gareth E and Ehli, Erik A and Bartels, Meike and Willemsen, Gonneke and de Geus, Eco J C and Boomsma, Dorret I} } @article {129, title = {Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry.}, journal = {Am J Med Genet B Neuropsychiatr Genet}, year = {2016}, month = {2016 Feb 23}, abstract = {

Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published between 1990 and 2015) that reported statistically significant associations of biochemical biomarkers with aggression, DSM-IV disorders involving aggression, and their subtypes. The markers are of different types, including inflammation markers, neurotransmitters, lipoproteins, and hormones from various classes. Most studies focused on only a limited portfolio of biomarkers, frequently a specific class only. When integrating the data, it is clear that compounds from several biological pathways have been found to be associated with aggressive behavior, indicating complexity and the need for a broad approach. In the second part of the paper, using examples from the aggression literature and psychiatric metabolomics studies, we argue that a better understanding of aggression would benefit from a more holistic approach such as provided by metabolomics. \© 2016 Wiley Periodicals, Inc.

}, issn = {1552-485X}, doi = {10.1002/ajmg.b.32435}, author = {Fiona Hagenbeek and Kluft, Cornelis and Thomas Hankemeier and Meike Bartels and Draisma, Harmen H M and Christel Middeldorp and Berger, Ruud and Antonio Noto and Lussu, Milena and Pool, Ren{\'e} and Faa, Gavino and Dorret I. Boomsma} }