@unpublished {466, title = {Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and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 {463, title = {DNA methylation in peripheral tissues and left-handedness}, journal = {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 {459, title = {Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people}, journal = {Proceedings of the National Academy of Sciences (PNAS)}, volume = {119}, year = {2022}, pages = {e2202764119}, abstract = {

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80\% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 \× 10\−8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26\% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.

}, keywords = {genome-wide association study, language, meta-analysis, reading}, doi = {10.1073/pnas.2202764119}, author = {Eising, Else and Mirza-Schreiber, Nazanin and de Zeeuw, Eveline L and Wang, Carol A and Truong, Dongnhu T and Allegrini, Andrea G and Shapland, Chin Yang and Zhu, Gu and Wigg, Karen G and Gerritse, Margot L and Molz, Barbara and Alag{\"o}z, G{\"o}kberk and Gialluisi, Alessandro and Abbondanza, Filippo and Rimfeld, Kaili and van Donkelaar, Marjolein and Liao, Zhijie and Jansen, Philip R and Andlauer, Till F M and Bates, Timothy C and Bernard, Manon and Blokland, Kirsten and Bonte, Milene and B{\o}rglum, Anders D and Bourgeron, Thomas and Brandeis, Daniel and Ceroni, Fabiola and Cs{\'e}pe, Val{\'e}ria and Dale, Philip S and de Jong, Peter F and DeFries, John C and D{\'e}monet, Jean-Fran\c cois and Demontis, Ditte and Feng, Yu and Gordon, Scott D and Guger, Sharon L and Hayiou-Thomas, Marianna E and Hern{\'a}ndez-Cabrera, Juan A and Hottenga, Jouke-Jan and Hulme, Charles and Kere, Juha and Kerr, Elizabeth N and Koomar, Tanner and Landerl, Karin and Leonard, Gabriel T and Lovett, Maureen W and Lyytinen, Heikki and Martin, Nicholas G and Martinelli, Angela and Maurer, Urs and Michaelson, Jacob J and Moll, Kristina and Monaco, Anthony P and Morgan, Angela T and N{\"o}then, Markus M and Pausova, Zdenka and Pennell, Craig E and Pennington, Bruce F and Price, Kaitlyn M and Rajagopal, Veera M and Ramus, Franck and Richer, Louis and Simpson, Nuala H and Smith, Shelley D and Snowling, Margaret J and Stein, John and Strug, Lisa J and Talcott, Joel B and Tiemeier, Henning and van der Schroeff, Marc P and Verhoef, Ellen and Watkins, Kate E and Wilkinson, Margaret and Wright, Margaret J and Barr, Cathy L and Boomsma, Dorret I and Carreiras, Manuel and Franken, Marie-Christine J and Gruen, Jeffrey R and Luciano, Michelle and M{\"u}ller-Myhsok, Bertram and Newbury, Dianne F and Olson, Richard K and Paracchini, Silvia and Paus, Tom{\'a}\v s and Plomin, Robert and Reilly, Sheena and Schulte-K{\"o}rne, Gerd and Tomblin, J Bruce and van Bergen, Elsje and Whitehouse, Andrew J O and Willcutt, Erik G and St Pourcain, Beate and Francks, Clyde and Fisher, Simon E} } @article {454, title = {1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans}, journal = {Translational Psychiatry}, volume = {11}, year = {2021}, pages = {182}, 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 magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

}, doi = { 10.1038/s41398-021-01213-0}, author = {S{\o}nderby, Ida E and van der Meer, Dennis and Moreau, Clara and Kaufmann, Tobias and Walters, G Bragi and Ellegaard, Maria and Abdellaoui, Abdel and Ames, David and Amunts, Katrin and Andersson, Micael and Armstrong, Nicola J and Bernard, Manon and Blackburn, Nicholas B and Blangero, John and Boomsma, Dorret I and Brodaty, Henry and Brouwer, Rachel M and B{\"u}low, Robin and B{\o}en, Rune and Cahn, Wiepke and Calhoun, Vince D and Caspers, Svenja and Ching, Christopher R K and Cichon, Sven and Ciufolini, Simone and Crespo-Facorro, Benedicto and Curran, Joanne E and Dale, Anders M and Dalvie, Shareefa and Dazzan, Paola and de Geus, Eco J C and de Zubicaray, Greig I and de Zwarte, Sonja M C and Desrivieres, Sylvane and Doherty, Joanne L and Donohoe, Gary and Draganski, Bogdan and Ehrlich, Stefan and Eising, Else and Espeseth, Thomas and Fejgin, Kim and Fisher, Simon E and Fladby, Tormod and Frei, Oleksandr and Frouin, Vincent and Fukunaga, Masaki and Gareau, Thomas and Ge, Tian and Glahn, David C and Grabe, Hans J and Groenewold, Nynke A and G{\'u}stafsson, {\'O}mar and Haavik, Jan and Haberg, Asta K and Hall, Jeremy and Hashimoto, Ryota and Hehir-Kwa, Jayne Y and Hibar, Derrek P and Hillegers, Manon H J and Hoffmann, Per and Holleran, Laurena and Holmes, Avram J and Homuth, Georg and Hottenga, Jouke-Jan and Hulshoff Pol, Hilleke E and Ikeda, Masashi and Jahanshad, Neda and Jockwitz, Christiane and Johansson, Stefan and J{\"o}nsson, Erik G and J{\o}rgensen, Niklas R and Kikuchi, Masataka and Knowles, Emma E M and Kumar, Kuldeep and Le Hellard, Stephanie and Leu, Costin and Linden, David E J and Liu, Jingyu and Lundervold, Arvid and Lundervold, Astri Johansen and Maillard, Anne M and Martin, Nicholas G and Martin-Brevet, Sandra and Mather, Karen A and Mathias, Samuel R and McMahon, Katie L and McRae, Allan F and Medland, Sarah E and Meyer-Lindenberg, Andreas and Moberget, Torgeir and Modenato, Claudia and S{\'a}nchez, Jennifer Monereo and Morris, Derek W and M{\"u}hleisen, Thomas W and Murray, Robin M and Nielsen, Jacob and Nordvik, Jan E and Nyberg, Lars and Loohuis, Loes M Olde and Ophoff, Roel A and Owen, Michael J and Paus, Tomas and Pausova, Zdenka and Peralta, Juan M and Pike, G Bruce and Prieto, Carlos and Quinlan, Erin B and Reinbold, C{\'e}line S and Marques, Tiago Reis and Rucker, James J H and Sachdev, Perminder S and Sando, Sigrid B and Schofield, Peter R and Schork, Andrew J and Schumann, Gunter and Shin, Jean and Shumskaya, Elena and Silva, Ana I and Sisodiya, Sanjay M and Steen, Vidar M and Stein, Dan J and Strike, Lachlan T and Suzuki, Ikuo K and Tamnes, Christian K and Teumer, Alexander and Thalamuthu, Anbupalam and Tordesillas-Guti{\'e}rrez, Diana and Uhlmann, Anne and Ulfarsson, Magnus O and van {\textquoteright}t Ent, Dennis and van den Bree, Marianne B M and Vanderhaeghen, Pierre and Vassos, Evangelos and Wen, Wei and Wittfeld, Katharina and Wright, Margaret J and Agartz, Ingrid and Djurovic, Srdjan and Westlye, Lars T and Stefansson, Hreinn and Stefansson, Kari and Jacquemont, S{\'e}bastien and Thompson, Paul M and Andreassen, Ole A and ENIGMA-CNV working group} } @article {437, title = {Genetic association study of childhood aggression across raters, instruments, and age}, journal = {Translational Psychiatry}, volume = {11}, year = {2021}, pages = {413}, abstract = {

Childhood aggressive behavior (AGG) has a substantial heritability of around 50\%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31\% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E-06), PCDH7 (P = 2.0E-06), and IPO13 (P = 2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44\%) and in retrospectively assessed childhood aggression (variance explained = 0.20\%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range [Formula: see text]: 0.19-1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = \ -0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range [Formula: see text]: 0.46-0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

}, doi = {10.1038/s41398-021-01480-x}, author = {Ip, Hill F and Van der Laan, Camiel M and Krapohl, Eva M L and Brikell, Isabell and S{\'a}nchez-Mora, Cristina and Nolte, Ilja M and St Pourcain, Beate and Bolhuis, Koen and Palviainen, Teemu and Zafarmand, Hadi and Colodro-Conde, Luc{\'\i}a and Gordon, Scott and Zayats, Tetyana and Aliev, Fazil and Jiang, Chang and Wang, Carol A and Saunders, Gretchen and Karhunen, Ville and Hammerschlag, Anke R and Adkins, Daniel E and Border, Richard and Peterson, Roseann E and Prinz, Joseph A and Thiering, Elisabeth and Sepp{\"a}l{\"a}, Ilkka and Vilor-Tejedor, Nat{\`a}lia and Ahluwalia, Tarunveer S and Day, Felix R and Hottenga, Jouke-Jan and Allegrini, Andrea G and Rimfeld, Kaili and Chen, Qi and Lu, Yi and Martin, Joanna and Soler Artigas, Mar{\'\i}a and Rovira, Paula and Bosch, Rosa and Espa{\~n}ol, Gemma and Ramos Quiroga, Josep Antoni and Neumann, Alexander and Ensink, Judith and Grasby, Katrina and Morosoli, Jos{\'e} J and Tong, Xiaoran and Marrington, Shelby and Middeldorp, Christel and Scott, James G and Vinkhuyzen, Anna and Shabalin, Andrey A and Corley, Robin and Evans, Luke M and Sugden, Karen and Alemany, Silvia and Sass, L{\ae}rke and Vinding, Rebecca and Ruth, Kate and Tyrrell, Jess and Davies, Gareth E and Ehli, Erik A and Hagenbeek, Fiona A and de Zeeuw, Eveline and van Beijsterveldt, Toos C E M and Larsson, Henrik and Snieder, Harold and Verhulst, Frank C and Amin, Najaf and Whipp, Alyce M and Korhonen, Tellervo and Vuoksimaa, Eero and Rose, Richard J and Uitterlinden, Andre G and Heath, Andrew C and Madden, Pamela and Haavik, Jan and Harris, Jennifer R and Helgeland, {\O}yvind and Johansson, Stefan and Knudsen, Gun Peggy S and Njolstad, Pal Rasmus and Lu, Qing and Rodriguez, Alina and Henders, Anjali K and Mamun, Abdullah and Najman, Jackob M and Brown, Sandy and Hopfer, Christian and Krauter, Kenneth and Reynolds, Chandra and Smolen, Andrew and Stallings, Michael and Wadsworth, Sally and Wall, Tamara L and Silberg, Judy L and Miller, Allison and Keltikangas-J{\"a}rvinen, Liisa and Hakulinen, Christian and Pulkki-R\aaback, Laura and Havdahl, Alexandra and Magnus, Per and Raitakari, Olli T and Perry, John R B and Llop, Sabrina and Lopez-Espinosa, Maria-Jose and B{\o}nnelykke, Klaus and Bisgaard, Hans and Sunyer, Jordi and Lehtim{\"a}ki, Terho and Arseneault, Louise and Standl, Marie and Heinrich, Joachim and Boden, Joseph and Pearson, John and Horwood, L John and Kennedy, Martin and Poulton, Richie and Eaves, Lindon J and Maes, Hermine H and Hewitt, John and Copeland, William E and Costello, Elizabeth J and Williams, Gail M and Wray, Naomi and Jarvelin, Marjo-Riitta and McGue, Matt and Iacono, William and Caspi, Avshalom and Moffitt, Terrie E and Whitehouse, Andrew and Pennell, Craig E and Klump, Kelly L and Burt, S Alexandra and Dick, Danielle M and Reichborn-Kjennerud, Ted and Martin, Nicholas G and Medland, Sarah E and Vrijkotte, Tanja and Kaprio, Jaakko and Tiemeier, Henning and Davey Smith, George and Hartman, Catharina A and Oldehinkel, Albertine J and Casas, Miquel and Ribas{\'e}s, Marta and Lichtenstein, Paul and Lundstr{\"o}m, Sebastian and Plomin, Robert and Bartels, Meike and Nivard, Michel G and Boomsma, Dorret I} } @article {433, 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 {446, 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 {441, 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 {414, 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 {193, title = {Heritability of Behavioral Problems in 7-Year Olds Based on Shared and Unique Aspects of Parental Views}, journal = {Behavior Genetics}, volume = {47}, year = {2017}, month = {Mar}, pages = {152{\textendash}163}, abstract = {

In studies of child psychopathology, phenotypes of interest are often obtained by parental ratings. When behavioral ratings are obtained in the context of a twin study, this allows for the decomposition of the phenotypic variance, into a genetic and a non-genetic part. If a phenotype is assessed by a single rater, heritability is based on the child\&$\#$39;s behavior as expressed in the presence of that particular rater, whereas heritability based on assessments by multiple raters allows for the estimation of the heritability of the phenotype based on rater agreement, as well as the heritability of the rater specific view of the behavior. The aim of this twin study was to quantify the rater\ common and rater specific contributions to the variation in children\&$\#$39;s behavioral problems. We estimated the heritability of maternal and paternal ratings of the Child Behavior Checklist (CBCL) 6\–18 empirical emotional and behavioral problem scales in a large sample of 12,310 7-year old Dutch twin pairs. Between 30 and 59{\%} of variation in the part of the phenotype parents agree upon was explained by genetic effects. Common environmental effects that make children in the same family similar explained less variance, ranging between 0 and 32{\%}. For unique views of their children\&$\#$39;s behavioral problems, heritability ranged between 0 and 20{\%} for maternal and between 0 and 22{\%} for paternal views. Between 7 and 24{\%} of the variance was accounted for by common environmental factors specific to mother and father\&$\#$39;s views. The proportion of rater shared and rater specific heritability can be translated into genetic correlations between parental views and inform the design and interpretation of results of molecular genetic studies. Genetic correlations were nearly or above 0.7 for all CBCL based psychopathology scales. Such large genetic correlations suggest two practical guidelines for genome-wide association studies (GWAS): when studies have collected data from either fathers or mothers, the shared genetic aetiology in parental ratings indicates that is possible to analyze paternal and maternal assessments in a single GWAS or meta-analysis. Secondly, if a study has collected information from both parents, a gain in statistical power may be realized in GWAS by the simultaneous analysis of the data.

}, issn = {1573-3297}, doi = {10.1007/s10519-016-9823-1}, url = {https://doi.org/10.1007/s10519-016-9823-1}, author = {Fedko, Iryna O. and Laura W. Wesseldijk and Michel G. Nivard and Hottenga, Jouke-Jan and Catharina E. M. van Beijsterveldt and Christel Middeldorp and Meike Bartels and Dorret I. Boomsma} } @article {128, title = {Genetic and environmental influences interact with age and sex in shaping the human methylome}, journal = {Nat Commun}, volume = {7}, year = {2016}, month = {2016/04/07}, abstract = {

The methylome is subject to genetic and environmental effects. Their impact may depend on sex and age, resulting in sex- and age-related physiological variation and disease susceptibility. Here we estimate the total heritability of DNA methylation levels in whole blood and estimate the variance explained by common single nucleotide polymorphisms at 411,169 sites in 2,603 individuals from twin families, to establish a catalogue of between-individual variation in DNA methylation. Heritability estimates vary across the genome (mean=19\%) and interaction analyses reveal thousands of sites with sex-specific heritability as well as sites where the environmental variance increases with age. Integration with previously published data illustrates the impact of genome and environment across the lifespan at methylation sites associated with metabolic traits, smoking and ageing. These findings demonstrate that our catalogue holds valuable information on locations in the genome where methylation variation between people may reflect disease-relevant environmental exposures or genetic variation.

}, url = {http://dx.doi.org/10.1038/ncomms11115}, author = {Jenny van Dongen and Michel G. Nivard and Gonneke Willemsen and Hottenga, Jouke-Jan and Helmer, Quinta and Conor V Dolan and Ehli, Erik A. and Gareth E Davies and van Iterson, Maarten and Breeze, Charles E. and Beck, Stephan and BIOS Consortium and Suchiman, H. Eka and Jansen, Rick and Joyce B.J. Van Meurs and Bastiaan T. Heijmans and Slagboom, P. Eline and Dorret I. Boomsma} }