@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 {379, title = {Multivariate genetic structure of externalizing behavior and structural brain development in a longitudinal adolescent twin sample}, journal = {International Journal of Molecular Sciences}, volume = {23}, year = {2022}, pages = {3176}, abstract = {

Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers, and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and the brain volumes and white matter integrity (fractional anisotropy [FA] and mean diffusivity [MD]) with magnetic resonance imaging in the BrainSCALE cohort, which consisted of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years for the twins. Genetic covariance modeling based on the classical twin design, extended to also include siblings of twins, showed that genes influence externalizing behavior and changes therein (h2 up to 88\%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to -0.20), with sizeable genetic correlations (FA ra up to +0.42; MD ra up to -0.33). The cortical gray matter (CGM; rph up to -0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genetically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence of the same genetic factor.

}, keywords = {Adolescence, externalizing behavior, Genetic correlation, gray matter volume, heritability, longitudinal, magnetic resonance imaging, white matter integrity}, doi = {10.3390/ijms23063176}, author = {Teeuw, Jalmar and Klein, Marieke and Mota, Nina Roth and Brouwer, Rachel M and van {\textquoteright}t Ent, Dennis and Al-Hassaan, Zyneb and Franke, Barbara and Boomsma, Dorret I and Hulshoff Pol, Hilleke E} } @article {352, title = {Measurement and genetic architecture of lifetime depression in the Netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report)}, journal = {Psychological Medicine}, volume = {51}, year = {2020}, pages = {1{\textendash}10}, abstract = {

BACKGROUND: Major depressive disorder (MDD) is a common mood disorder, with a heritability of around 34\%. Molecular genetic studies made significant progress and identified genetic markers associated with the risk of MDD; however, progress is slowed down by substantial heterogeneity as MDD is assessed differently across international cohorts. Here, we used a standardized online approach to measure MDD in multiple cohorts in the Netherlands and evaluated whether this approach can be used in epidemiological and genetic association studies of depression.

METHODS: Within the Biobank Netherlands Internet Collaboration (BIONIC) project, we collected MDD data in eight cohorts involving 31 936 participants, using the online Lifetime Depression Assessment Self-report (LIDAS), and estimated the prevalence of current and lifetime MDD in 22 623 unrelated individuals. In a large Netherlands Twin Register (NTR) twin-family dataset (n $\approx$ 18 000), we estimated the heritability of MDD, and the prediction of MDD in a subset (n = 4782) through Polygenic Risk Score (PRS).

RESULTS: Estimates of current and lifetime MDD prevalence were 6.7\% and 18.1\%, respectively, in line with population estimates based on validated psychiatric interviews. In the NTR heritability estimates were 0.34/0.30 (s.e. = 0.02/0.02) for current/lifetime MDD, respectively, showing that the LIDAS gives similar heritability rates for MDD as reported in the literature. The PRS predicted risk of MDD (OR 1.23, 95\% CI 1.15-1.3

}, keywords = {LIDAS, Lifetime Depression Assessment Self-report, major depressive disorder, online assessment tool, prevalence}, doi = {10.1017/S0033291720000100}, author = {Fedko, Iryna O and Hottenga, Jouke-Jan and Helmer, Quinta and Mbarek, Hamdi and Huider, Floris and Amin, Najaf and Beulens, Joline W and Bremmer, Marijke A and Elders, Petra J and Galesloot, Tessel E and Kiemeney, Lambertus A and van Loo, Hanna M and Picavet, H Susan J and Rutters, Femke and van der Spek, Ashley and van de Wiel, Anne M and van Duijn, Cornelia and de Geus, Eco J C and Feskens, Edith J M and Hartman, Catharina A and Oldehinkel, Albertine J and Smit, Jan H and Verschuren, W M Monique and Penninx, Brenda W J H and Boomsma, Dorret I and Bot, Mariska} } @article {321, title = {Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight}, journal = {Nature Communications}, volume = {10}, year = {2019}, pages = {1893}, abstract = {

Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10\% increase in methylation (PBonferroni \< 1.06 x 10-7). In additional analyses in 7,278 participants, \<1.3\% of birthweight-associated differential methylation is also observed in childhood and adolescence, but not adulthood. Birthweight-related CpGs overlap with some Bonferroni-significant CpGs that were previously reported to be related to maternal smoking (55/91

}, doi = {10.1038/s41467-019-09671-3}, author = {K{\"u}pers, Leanne K and Monnereau, Claire and Sharp, Gemma C and Yousefi, Paul and Salas, Lucas A and Ghantous, Akram and Page, Christian M and Reese, Sarah E and Wilcox, Allen J and Czamara, Darina and Starling, Anne P and Novoloaca, Alexei and Lent, Samantha and Roy, Ritu and Hoyo, Cathrine and Breton, Carrie V and Allard, Catherine and Just, Allan C and Bakulski, Kelly M and Holloway, John W and Everson, Todd M and Xu, Cheng-Jian and Huang, Rae-Chi and van der Plaat, Diana A and Wielscher, Matthias and Merid, Simon Kebede and Ullemar, Vilhelmina and Rezwan, Faisal I and Lahti, Jari and van Dongen, Jenny and Langie, Sabine A S and Richardson, Tom G and Magnus, Maria C and Nohr, Ellen A and Xu, Zongli and Duijts, Liesbeth and Zhao, Shanshan and Zhang, Weiming and Plusquin, Michelle and DeMeo, Dawn L and Solomon, Olivia and Heimovaara, Joosje H and Jima, Dereje D and Gao, Lu and Bustamante, Mariona and Perron, Patrice and Wright, Robert O and Hertz-Picciotto, Irva and Zhang, Hongmei and Karagas, Margaret R and Gehring, Ulrike and Marsit, Carmen J and Beilin, Lawrence J and Vonk, Judith M and Jarvelin, Marjo-Riitta and Bergstr{\"o}m, Anna and {\"O}rtqvist, Anne K and Ewart, Susan and Villa, Pia M and Moore, Sophie E and Willemsen, Gonneke and Standaert, Arnout R L and H\aaberg, Siri E and S{\o}rensen, Thorkild I A and Taylor, Jack A and R{\"a}ikk{\"o}nen, Katri and Yang, Ivana V and Kechris, Katerina and Nawrot, Tim S and Silver, Matt J and Gong, Yun Yun and Richiardi, Lorenzo and Kogevinas, Manolis and Litonjua, Augusto A and Eskenazi, Brenda and Huen, Karen and Mbarek, Hamdi and Maguire, Rachel L and Dwyer, Terence and Vrijheid, Martine and Bouchard, Luigi and Baccarelli, Andrea A and Croen, Lisa A and Karmaus, Wilfried and Anderson, Denise and de Vries, Maaike and Sebert, Sylvain and Kere, Juha and Karlsson, Robert and Arshad, Syed Hasan and H{\"a}m{\"a}l{\"a}inen, Esa and Routledge, Michael N and Boomsma, Dorret I and Feinberg, Andrew P and Newschaffer, Craig J and Govarts, Eva and Moisse, Matthieu and Fallin, M Daniele and Mel{\'e}n, Erik and Prentice, Andrew M and Kajantie, Eero and Almqvist, Catarina and Oken, Emily and Dabelea, Dana and Boezen, H Marike and Melton, Phillip E and Wright, Rosalind J and Koppelman, Gerard H and Trevisi, Letizia and Hivert, Marie-France and Sunyer, Jordi and Munthe-Kaas, Monica C and Murphy, Susan K and Corpeleijn, Eva and Wiemels, Joseph and Holland, Nina and Herceg, Zdenko and Binder, Elisabeth B and Davey Smith, George and Jaddoe, Vincent W V and Lie, Rolv T and Nystad, Wenche and London, Stephanie J and Lawlor, Debbie A and Relton, Caroline L and Snieder, Harold and Felix, Janine F} } @article {263, title = {Maternal and paternal cannabis use during pregnancy and the risk of psychotic-like experiences in the offspring}, journal = {Schizophrenia Research}, volume = {202}, year = {2018}, pages = {322 - 327}, abstract = {

Cannabis use continues to increase among pregnant women. Gestational cannabis exposure has been associated with various adverse outcomes. However, it remains unclear whether cannabis use during pregnancy increases the risk for offspring psychotic-like experiences. In this prospective cohort, we examined the relationship between parental cannabis use during pregnancy and offspring psychotic-like experiences. Comparisons were made between maternal and paternal cannabis use during pregnancy to investigate causal influences of intra-uterine cannabis exposure during foetal neurodevelopmental. This study was embedded in the Generation R birth cohort and included N = 3692 participants. Maternal cannabis exposure was determined using self-reports and cannabis metabolite levels from urine. Paternal cannabis use during pregnancy was obtained by maternal report. Maternal cannabis use increased the risk of psychotic-like experiences in the offspring (ORadjusted = 1.38, 95\% CI 1.03\–1.85). Estimates were comparable for maternal cannabis use exclusively before pregnancy versus continued cannabis use during pregnancy. Paternal cannabis use was similarly associated with offspring psychotic-like experiences (ORadjusted = 1.44, 95\% CI 1.14\–1.82). We demonstrated that both maternal and paternal cannabis use were associated with more offspring psychotic-like experiences at age ten years. This may suggest that common aetiologies, rather than solely causal intra-uterine mechanisms, underlie the association between parental cannabis use and offspring psychotic-like experiences. These common backgrounds most likely reflect genetic vulnerabilities and shared familial mechanisms, shedding a potential new light on the debated causal path from cannabis use to psychotic-like phenomena. Our findings indicate that diagnostic screening and preventative measures need to be adapted for young people at risk for severe mental illness.

}, keywords = {Child psychiatry, Epidemiology, Gestational exposure, Marijuana, Psychosis, Substance use}, issn = {0920-9964}, doi = {https://doi.org/10.1016/j.schres.2018.06.067}, url = {http://www.sciencedirect.com/science/article/pii/S0920996418304110}, author = {Koen Bolhuis and Steven A. Kushner and Selda Yalniz and Manon H.J. Hillegers and Vincent W.V. Jaddoe and Henning Tiemeier and Hanan El Marroun} } @article {262, title = {Monozygotic twin differences in school performance are stable and systematic}, journal = {Developmental Science}, volume = {21}, year = {2018}, pages = {e12694}, abstract = {

Abstract School performance is one of the most stable and heritable psychological characteristics. Notwithstanding, monozygotic twins (MZ), who have identical genotypes, differ in school performance. These MZ differences result from non-shared environments that do not contribute to the similarity within twin pairs. Because to date few non-shared environmental factors have been reliably associated with MZ differences in school performance, they are thought to be idiosyncratic and due to chance, suggesting that the effect of non-shared environments on MZ differences are age- and trait-specific. In a sample of 2768 MZ twin pairs, we found first that MZ differences in school performance were moderately stable from age 12 through 16, with differences at the ages 12 and 14 accounting for 20\% of the variance in MZ differences at age 16. Second, MZ differences in school performance correlated positively with MZ differences across 16 learning-related variables, including measures of intelligence, personality and school attitudes, with the twin who scored higher on one also scoring higher on the other measures. Finally, MZ differences in the 16 learning-related variables accounted for 22\% of the variance in MZ differences in school performance at age 16. These findings suggest that, unlike for other psychological domains, non-shared environmental factors affect school performance in systematic ways that have long-term and generalist influence. Our findings should motivate the search for non-shared environmental factors responsible for the stable and systematic effects on children\’s differences in school performance. A video abstract of this article can be viewed at: https://youtu.be/0bw2Fl\_HGq0

}, keywords = {difference scores, learning, Monozygotic twin, non-shared environment, school performance}, doi = {10.1111/desc.12694}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/desc.12694}, author = {Sophie von Stumm and Robert Plomin} } @article {291, title = {Multi-polygenic score approach to trait prediction}, volume = {23}, year = {2017}, month = {08/2017}, pages = {1368}, abstract = {

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9\% variance in educational achievement, 4.8\% in general cognitive ability and 5.4\% in BMI in an independent test set, predicting 1.1\%, 1.1\%, and 1.6\% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene\–environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

}, url = {https://doi.org/10.1038/mp.2017.163}, author = {Krapohl, E and Patel, H and Newhouse, S and Curtis, C J and von Stumm, S and Dale, P S and Zabaneh, D and Breen, G and O{\textquoteright}Reilly, P F and Plomin, R} } @article {95, title = {Methylation as an epigenetic source of random genetic effects in the classical twin design}, journal = {Advances in Genomics and Genetics, Dove Medical Press}, volume = {5}, year = {2015}, month = {09/2015}, pages = {305{\textendash}315}, abstract = {

The epigenetic effects of cytosine methylation on gene expression are an acknowledged source of phenotypic variance. The discordant monozygotic (MZ) twin design has been used to demonstrate the role of methylation in disease. Application of the classical twin design, featuring both monozygotic and dizygotic twins, has demonstrated that individual differences in methylation levels are attributable to genetic and environmental (including stochastic) factors, with the latter explaining most of the variance. What implications epigenetic sources of variance have for the twin modeling of (non-epigenetic) phenotypes such as height and IQ is an open question. One possibility is that epigenetic effects are absorbed by the variance component attributable to unshared environmental. Another possibility is that such effects form an independent source of variance distinguishable in principle from standard genetic and environmental sources. In the present paper, we conceptualized epigenetic processes as giving rise to randomness in the effects of polygenetic influences. This means that the regression coefficient in the regression of the phenotype on the polygenic factor, as specified in the twin model, varies over individuals. We investigate the consequences of ignoring this randomness in the standard twin model.

}, keywords = {classical twin design, epigenetics, heritability, methylation, parameter randomness}, doi = {http://dx.doi.org/10.2147/AGG.S46909}, url = {https://www.dovepress.com/methylation-as-an-epigenetic-source-of-random-genetic-effects-in-the-c-peer-reviewed-article-AGG}, author = {Conor V Dolan and Michel G. Nivard and Jenny van Dongen and van der Sluis,Sophie and Dorret I. Boomsma} }