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1. Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms

Author

Jami, Eshim S, Hammerschlag, Anke R, Ip, Hill F, Allegrini, Andrea G, Benyamin, Beben, Border, Richard, Diemer, Elizabeth W, Jiang, Chang, Karhunen, Ville, Lu, Yi, Lu, Qing, Mallard, Travis T, Mishra, Pashupati P, Nolte, Ilja M, Palviainen, Teemu, Peterson, Roseann E, Sallis, Hannah M, Shabalin, Andrey A, Tate, Ashley E, Thiering, Elisabeth, Vilor-Tejedor, Natàlia, Wang, Carol, Zhou, Ang, Adkins, Daniel E, Alemany, Silvia, Ask, Helga, Chen, Qi, Corley, Robin P, Ehli, Erik A, Evans, Luke M, Havdahl, Alexandra, Hagenbeek, Fiona A, Hakulinen, Christian, Henders, Anjali K, Hottenga, Jouke Jan, Korhonen, Tellervo, Mamun, Abdullah, Marrington, Shelby, Neumann, Alexander, Rimfeld, Kaili, Rivadeneira, Fernando, Silberg, Judy L, van Beijsterveldt, Catharina E, Vuoksimaa, Eero, Whipp, Alyce M, Tong, Xiaoran, Andreassen, Ole A, Boomsma, Dorret I, Brown, Sandra A, Burt, S Alexandra, Copeland, William, Dick, Danielle M, Harden, K Paige, Harris, Kathleen Mullan, Hartman, Catharina A, Heinrich, Joachim, Hewitt, John K, Hopfer, Christian, Hypponen, Elina, Jarvelin, Marjo-Riitta, Kaprio, Jaakko, Keltikangas-Järvinen, Liisa, Klump, Kelly L, Krauter, Kenneth, Kuja-Halkola, Ralf, Larsson, Henrik, Lehtimäki, Terho, Lichtenstein, Paul, Lundström, Sebastian, Maes, Hermine H, Magnus, Per, Munafò, Marcus R, Najman, Jake M, Njølstad, Pål R, Oldehinkel, Albertine J, Pennell, Craig E, Plomin, Robert, Reichborn-Kjennerud, Ted, Reynolds, Chandra, Rose, Richard J, Smolen, Andrew, Snieder, Harold, Stallings, Michael, Standl, Marie, Sunyer, Jordi, Tiemeier, Henning, Wadsworth, Sally J, Wall, Tamara L, Whitehouse, Andrew JO, Williams, Gail M, Ystrøm, Eivind, Nivard, Michel G, Bartels, Meike, and Middeldorp, Christel M

Subjects
Biological Psychology, Psychology, Serious Mental Illness, Brain Disorders, Pediatric, Human Genome, Genetics, Behavioral and Social Science, Depression, Mental Health, Mental Illness, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Mental health, Adolescent, Adult, Aggression, Anxiety, Attention Deficit Disorder with Hyperactivity, Autistic Disorder, Bipolar Disorder, Child, Child, Preschool, Genome-Wide Association Study, Humans, Loneliness, Polymorphism, Single Nucleotide, Schizophrenia, Sleep Initiation and Maintenance Disorders, depression, anxiety, repeated measures, genetic epidemiology, molecular genetics, Medical and Health Sciences, Psychology and Cognitive Sciences, Developmental & Child Psychology, Clinical sciences, Paediatrics, Applied and developmental psychology
Abstract

ObjectiveTo investigate the genetic architecture of internalizing symptoms in childhood and adolescence.MethodIn 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument.ResultsThe meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, neffective = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (|rg| > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range |rg| = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa.ConclusionGenetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

Published
2022

3. Smoking remains associated with education after controlling for social background and genetic factors in a study of 18 twin cohorts

Author

Silventoinen, Karri, Piirtola, Maarit, Jelenkovic, Aline, Sund, Reijo, Tarnoki, Adam D, Tarnoki, David L, Medda, Emanuela, Nisticò, Lorenza, Toccaceli, Virgilia, Honda, Chika, Inui, Fujio, Tomizawa, Rie, Watanabe, Mikio, Sakai, Norio, Gatz, Margaret, Butler, David A, Lee, Jooyeon, Lee, Soo Ji, Sung, Joohon, Franz, Carol E, Kremen, William S, Lyons, Michael J, Derom, Catherine A, Vlietinck, Robert F, Loos, Ruth JF, Tynelius, Per, Rasmussen, Finn, Martin, Nicholas G, Medland, Sarah E, Montgomery, Grant W, Brandt, Ingunn, Nilsen, Thomas S, Harris, Jennifer R, Tyler, Jessica, Hopper, John L, Magnusson, Patrik KE, Pedersen, Nancy L, Dahl Aslan, Anna K, Ordoñana, Juan R, Sánchez-Romera, Juan F, Colodro-Conde, Lucia, Rebato, Esther, Zhang, Dongfeng, Pang, Zengchang, Tan, Qihua, Silberg, Judy L, Maes, Hermine H, Boomsma, Dorret I, Sørensen, Thorkild IA, Korhonen, Tellervo, and Kaprio, Jaakko

Subjects
Biological Psychology, Psychology, Genetics, Pediatric, Prevention, Tobacco, Tobacco Smoke and Health, Quality Education, Child, Educational Status, Humans, Smoking, Smoking Cessation, Twins, Dizygotic, Twins, Monozygotic
Abstract

We tested the causality between education and smoking using the natural experiment of discordant twin pairs allowing to optimally control for background genetic and childhood social factors. Data from 18 cohorts including 10,527 monozygotic (MZ) and same-sex dizygotic (DZ) twin pairs discordant for education and smoking were analyzed by linear fixed effects regression models. Within twin pairs, education levels were lower among the currently smoking than among the never smoking co-twins and this education difference was larger within DZ than MZ pairs. Similarly, education levels were higher among former smoking than among currently smoking co-twins, and this difference was larger within DZ pairs. Our results support the hypothesis of a causal effect of education on both current smoking status and smoking cessation. However, the even greater intra-pair differences within DZ pairs, who share only 50% of their segregating genes, provide evidence that shared genetic factors also contribute to these associations.

Published
2022

5. Mental health and music engagement: review, framework, and guidelines for future studies

Author

Gustavson, Daniel E, Coleman, Peyton L, Iversen, John R, Maes, Hermine H, Gordon, Reyna L, and Lense, Miriam D

Subjects
Neurosciences, Brain Disorders, Clinical Research, Schizophrenia, Behavioral and Social Science, Mental Health, Depression, Mental health, Good Health and Well Being, Anxiety, Humans, Mental Disorders, Music, Music Therapy, Quality of Life, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Is engaging with music good for your mental health? This question has long been the topic of empirical clinical and nonclinical investigations, with studies indicating positive associations between music engagement and quality of life, reduced depression or anxiety symptoms, and less frequent substance use. However, many earlier investigations were limited by small populations and methodological limitations, and it has also been suggested that aspects of music engagement may even be associated with worse mental health outcomes. The purpose of this scoping review is first to summarize the existing state of music engagement and mental health studies, identifying their strengths and weaknesses. We focus on broad domains of mental health diagnoses including internalizing psychopathology (e.g., depression and anxiety symptoms and diagnoses), externalizing psychopathology (e.g., substance use), and thought disorders (e.g., schizophrenia). Second, we propose a theoretical model to inform future work that describes the importance of simultaneously considering music-mental health associations at the levels of (1) correlated genetic and/or environmental influences vs. (bi)directional associations, (2) interactions with genetic risk factors, (3) treatment efficacy, and (4) mediation through brain structure and function. Finally, we describe how recent advances in large-scale data collection, including genetic, neuroimaging, and electronic health record studies, allow for a more rigorous examination of these associations that can also elucidate their neurobiological substrates.

Published
2021

6. Correspondence Between Perceived Pubertal Development and Hormone Levels in 9-10 Year-Olds From the Adolescent Brain Cognitive Development Study

Author

Herting, Megan M, Uban, Kristina A, Gonzalez, Marybel Robledo, Baker, Fiona C, Kan, Eric C, Thompson, Wesley K, Granger, Douglas A, Albaugh, Matthew D, Anokhin, Andrey P, Bagot, Kara S, Banich, Marie T, Barch, Deanna M, Baskin-Sommers, Arielle, Breslin, Florence J, Casey, BJ, Chaarani, Bader, Chang, Linda, Clark, Duncan B, Cloak, Christine C, Constable, R Todd, Cottler, Linda B, Dagher, Rada K, Dapretto, Mirella, Dick, Anthony S, Dosenbach, Nico, Dowling, Gayathri J, Dumas, Julie A, Edwards, Sarah, Ernst, Thomas, Fair, Damien A, Feldstein-Ewing, Sarah W, Freedman, Edward G, Fuemmeler, Bernard F, Garavan, Hugh, Gee, Dylan G, Giedd, Jay N, Glaser, Paul EA, Goldstone, Aimee, Gray, Kevin M, Hawes, Samuel W, Heath, Andrew C, Heitzeg, Mary M, Hewitt, John K, Heyser, Charles J, Hoffman, Elizabeth A, Huber, Rebekah S, Huestis, Marilyn A, Hyde, Luke W, Infante, M Alejandra, Ivanova, Masha Y, Jacobus, Joanna, Jernigan, Terry L, Karcher, Nicole R, Laird, Angela R, LeBlanc, Kimberly H, Lisdahl, Krista, Luciana, Monica, Luna, Beatriz, Maes, Hermine H, Marshall, Andrew T, Mason, Michael J, McGlade, Erin C, Morris, Amanda S, Nagel, Bonnie J, Neigh, Gretchen N, Palmer, Clare E, Paulus, Martin P, Potter, Alexandra S, Puttler, Leon I, Rajapakse, Nishadi, Rapuano, Kristina, Reeves, Gloria, Renshaw, Perry F, Schirda, Claudiu, Sher, Kenneth J, Sheth, Chandni, Shilling, Paul D, Squeglia, Lindsay M, Sutherland, Matthew T, Tapert, Susan F, Tomko, Rachel L, Yurgelun-Todd, Deborah, Wade, Natasha E, Weiss, Susan RB, Zucker, Robert A, and Sowell, Elizabeth R

Subjects
Paediatrics, Biomedical and Clinical Sciences, Pediatric, Mental Health, Women's Health, Basic Behavioral and Social Science, Behavioral and Social Science, Clinical Research, Social Determinants of Health, Neurosciences, Metabolic and endocrine, Good Health and Well Being, Adolescent, Adolescent Development, Child, Child Development, Cross-Sectional Studies, Dehydroepiandrosterone, Estradiol, Female, Gonadal Steroid Hormones, Humans, Male, Puberty, Self Report, Sexual Maturation, Socioeconomic Factors, Testosterone, adolescent brain cognitive development, salivary hormones, pubertal development scale, puberty, testosterone, dehydroepiandrosterone, estradiol, Clinical Sciences, Nutrition and Dietetics, Clinical sciences
Abstract

AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study-a multi-site sample of 9-10 year-olds (n = 11,875)-and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child's weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9-10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.

Published
2021

7. A large-scale genome-wide association study meta-analysis of cannabis use disorder.

Author

Johnson, Emma C, Demontis, Ditte, Thorgeirsson, Thorgeir E, Walters, Raymond K, Polimanti, Renato, Hatoum, Alexander S, Sanchez-Roige, Sandra, Paul, Sarah E, Wendt, Frank R, Clarke, Toni-Kim, Lai, Dongbing, Reginsson, Gunnar W, Zhou, Hang, He, June, Baranger, David AA, Gudbjartsson, Daniel F, Wedow, Robbee, Adkins, Daniel E, Adkins, Amy E, Alexander, Jeffry, Bacanu, Silviu-Alin, Bigdeli, Tim B, Boden, Joseph, Brown, Sandra A, Bucholz, Kathleen K, Bybjerg-Grauholm, Jonas, Corley, Robin P, Degenhardt, Louisa, Dick, Danielle M, Domingue, Benjamin W, Fox, Louis, Goate, Alison M, Gordon, Scott D, Hack, Laura M, Hancock, Dana B, Hartz, Sarah M, Hickie, Ian B, Hougaard, David M, Krauter, Kenneth, Lind, Penelope A, McClintick, Jeanette N, McQueen, Matthew B, Meyers, Jacquelyn L, Montgomery, Grant W, Mors, Ole, Mortensen, Preben B, Nordentoft, Merete, Pearson, John F, Peterson, Roseann E, Reynolds, Maureen D, Rice, John P, Runarsdottir, Valgerdur, Saccone, Nancy L, Sherva, Richard, Silberg, Judy L, Tarter, Ralph E, Tyrfingsson, Thorarinn, Wall, Tamara L, Webb, Bradley T, Werge, Thomas, Wetherill, Leah, Wright, Margaret J, Zellers, Stephanie, Adams, Mark J, Bierut, Laura J, Boardman, Jason D, Copeland, William E, Farrer, Lindsay A, Foroud, Tatiana M, Gillespie, Nathan A, Grucza, Richard A, Harris, Kathleen Mullan, Heath, Andrew C, Hesselbrock, Victor, Hewitt, John K, Hopfer, Christian J, Horwood, John, Iacono, William G, Johnson, Eric O, Kendler, Kenneth S, Kennedy, Martin A, Kranzler, Henry R, Madden, Pamela AF, Maes, Hermine H, Maher, Brion S, Martin, Nicholas G, McGue, Matthew, McIntosh, Andrew M, Medland, Sarah E, Nelson, Elliot C, Porjesz, Bernice, Riley, Brien P, Stallings, Michael C, Vanyukov, Michael M, Vrieze, Scott, Psychiatric Genomics Consortium Substance Use Disorders Workgroup, Davis, Lea K, Bogdan, Ryan, Gelernter, Joel, and Edenberg, Howard J

Subjects
Psychiatric Genomics Consortium Substance Use Disorders Workgroup, Humans, Marijuana Abuse, Risk, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

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

Published
2020

8. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium

Author

Polimanti, Renato, Walters, Raymond K, Johnson, Emma C, McClintick, Jeanette N, Adkins, Amy E, Adkins, Daniel E, Bacanu, Silviu-Alin, Bierut, Laura J, Bigdeli, Tim B, Brown, Sandra, Bucholz, Kathleen K, Copeland, William E, Costello, E Jane, Degenhardt, Louisa, Farrer, Lindsay A, Foroud, Tatiana M, Fox, Louis, Goate, Alison M, Grucza, Richard, Hack, Laura M, Hancock, Dana B, Hartz, Sarah M, Heath, Andrew C, Hewitt, John K, Hopfer, Christian J, Johnson, Eric O, Kendler, Kenneth S, Kranzler, Henry R, Krauter, Kenneth, Lai, Dongbing, Madden, Pamela AF, Martin, Nicholas G, Maes, Hermine H, Nelson, Elliot C, Peterson, Roseann E, Porjesz, Bernice, Riley, Brien P, Saccone, Nancy, Stallings, Michael, Wall, Tamara L, Webb, Bradley T, Wetherill, Leah, Edenberg, Howard J, Agrawal, Arpana, and Gelernter, Joel

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Drug Abuse (NIDA only), Substance Misuse, Neurosciences, Brain Disorders, Human Genome, Genetics, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Analgesics, Opioid, Behavior, Addictive, Female, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Genome-Wide Association Study, Genomics, Humans, Male, Multifactorial Inheritance, Opioid-Related Disorders, Psychiatric Genomics Consortium Substance Use Disorders Workgroup, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.

Published
2020

9. Genetic and environmental influences on human height from infancy through adulthood at different levels of parental education

Author

Jelenkovic, Aline, Sund, Reijo, Yokoyama, Yoshie, Latvala, Antti, Sugawara, Masumi, Tanaka, Mami, Matsumoto, Satoko, Freitas, Duarte L, Maia, José Antonio, Knafo-Noam, Ariel, Mankuta, David, Abramson, Lior, Ji, Fuling, Ning, Feng, Pang, Zengchang, Rebato, Esther, Saudino, Kimberly J, Cutler, Tessa L, Hopper, John L, Ullemar, Vilhelmina, Almqvist, Catarina, Magnusson, Patrik KE, Cozen, Wendy, Hwang, Amie E, Mack, Thomas M, Nelson, Tracy L, Whitfield, Keith E, Sung, Joohon, Kim, Jina, Lee, Jooyeon, Lee, Sooji, Llewellyn, Clare H, Fisher, Abigail, Medda, Emanuela, Nisticò, Lorenza, Toccaceli, Virgilia, Baker, Laura A, Tuvblad, Catherine, Corley, Robin P, Huibregtse, Brooke M, Derom, Catherine A, Vlietinck, Robert F, Loos, Ruth JF, Burt, S Alexandra, Klump, Kelly L, Silberg, Judy L, Maes, Hermine H, Krueger, Robert F, McGue, Matt, Pahlen, Shandell, Gatz, Margaret, Butler, David A, Harris, Jennifer R, Brandt, Ingunn, Nilsen, Thomas S, Harden, K Paige, Tucker-Drob, Elliot M, Franz, Carol E, Kremen, William S, Lyons, Michael J, Lichtenstein, Paul, Bartels, Meike, Beijsterveldt, Catharina EM van, Willemsen, Gonneke, Öncel, Sevgi Y, Aliev, Fazil, Jeong, Hoe-Uk, Hur, Yoon-Mi, Turkheimer, Eric, Boomsma, Dorret I, Sørensen, Thorkild IA, Kaprio, Jaakko, and Silventoinen, Karri

Subjects
Biological Psychology, Health Sciences, Psychology, Pediatric, Nutrition, Genetics, Prevention, 2.1 Biological and endogenous factors, Aetiology, Quality Education, Adolescent, Adult, Body Height, Child, Child, Preschool, Environment, Female, Gene-Environment Interaction, Genetic Background, Humans, Infant, Infant, Newborn, Male, Parenting, Parents, Quantitative Trait Loci, Quantitative Trait, Heritable, Young Adult
Abstract

Genetic factors explain a major proportion of human height variation, but differences in mean stature have also been found between socio-economic categories suggesting a possible effect of environment. By utilizing a classical twin design which allows decomposing the variation of height into genetic and environmental components, we tested the hypothesis that environmental variation in height is greater in offspring of lower educated parents. Twin data from 29 cohorts including 65,978 complete twin pairs with information on height at ages 1 to 69 years and on parental education were pooled allowing the analyses at different ages and in three geographic-cultural regions (Europe, North America and Australia, and East Asia). Parental education mostly showed a positive association with offspring height, with significant associations in mid-childhood and from adolescence onwards. In variance decomposition modeling, the genetic and environmental variance components of height did not show a consistent relation to parental education. A random-effects meta-regression analysis of the aggregate-level data showed a trend towards greater shared environmental variation of height in low parental education families. In conclusion, in our very large dataset from twin cohorts around the globe, these results provide only weak evidence for the study hypothesis.

Published
2020

10. Correspondence Between Perceived Pubertal Development and Hormone Levels in 9-10 Year-Olds From the Adolescent Brain Cognitive Development Study.

Author

Herting, Megan M, Uban, Kristina A, Gonzalez, Marybel Robledo, Baker, Fiona C, Kan, Eric C, Thompson, Wesley K, Granger, Douglas A, Albaugh, Matthew D, Anokhin, Andrey P, Bagot, Kara S, Banich, Marie T, Barch, Deanna M, Baskin-Sommers, Arielle, Breslin, Florence J, Casey, BJ, Chaarani, Bader, Chang, Linda, Clark, Duncan B, Cloak, Christine C, Constable, R Todd, Cottler, Linda B, Dagher, Rada K, Dapretto, Mirella, Dick, Anthony S, Dosenbach, Nico, Dowling, Gayathri J, Dumas, Julie A, Edwards, Sarah, Ernst, Thomas, Fair, Damien A, Feldstein-Ewing, Sarah W, Freedman, Edward G, Fuemmeler, Bernard F, Garavan, Hugh, Gee, Dylan G, Giedd, Jay N, Glaser, Paul EA, Goldstone, Aimee, Gray, Kevin M, Hawes, Samuel W, Heath, Andrew C, Heitzeg, Mary M, Hewitt, John K, Heyser, Charles J, Hoffman, Elizabeth A, Huber, Rebekah S, Huestis, Marilyn A, Hyde, Luke W, Infante, M Alejandra, Ivanova, Masha Y, Jacobus, Joanna, Jernigan, Terry L, Karcher, Nicole R, Laird, Angela R, LeBlanc, Kimberly H, Lisdahl, Krista, Luciana, Monica, Luna, Beatriz, Maes, Hermine H, Marshall, Andrew T, Mason, Michael J, McGlade, Erin C, Morris, Amanda S, Nagel, Bonnie J, Neigh, Gretchen N, Palmer, Clare E, Paulus, Martin P, Potter, Alexandra S, Puttler, Leon I, Rajapakse, Nishadi, Rapuano, Kristina, Reeves, Gloria, Renshaw, Perry F, Schirda, Claudiu, Sher, Kenneth J, Sheth, Chandni, Shilling, Paul D, Squeglia, Lindsay M, Sutherland, Matthew T, Tapert, Susan F, Tomko, Rachel L, Yurgelun-Todd, Deborah, Wade, Natasha E, Weiss, Susan RB, Zucker, Robert A, and Sowell, Elizabeth R

Subjects
Humans, Dehydroepiandrosterone, Testosterone, Estradiol, Gonadal Steroid Hormones, Cross-Sectional Studies, Adolescent Development, Child Development, Puberty, Sexual Maturation, Socioeconomic Factors, Adolescent, Child, Female, Male, Self Report, adolescent brain cognitive development, dehydroepiandrosterone, estradiol, pubertal development scale, puberty, salivary hormones, testosterone, Pediatric, Behavioral and Social Science, Prevention, Pediatric Research Initiative, Clinical Research, Mental Health, Basic Behavioral and Social Science, Contraception/Reproduction, Mental health, Clinical Sciences, Nutrition and Dietetics
Abstract

AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study-a multi-site sample of 9-10 year-olds (n = 11,875)-and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child's weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9-10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.

Published
2020

12. Educational attainment of same-sex and opposite-sex dizygotic twins: An individual-level pooled study of 19 twin cohorts

Author

Silventoinen, Karri, Bogl, Leonie H., Jelenkovic, Aline, Vuoksimaa, Eero, Latvala, Antti, Li, Weilong, Tan, Qihua, Zhang, Dongfeng, Pang, Zengchang, Ordoñana, Juan R., Sánchez-Romera, Juan F., Colodro-Conde, Lucia, Willemsen, Gonneke, Bartels, Meike, van Beijsterveldt, Catharina E.M., Rebato, Esther, Corley, Robin P., Huibregtse, Brooke M., Hopper, John L., Tyler, Jessica, Duncan, Glen E., Buchwald, Dedra, Silberg, Judy L., Maes, Hermine H., Kandler, Christian, Cozen, Wendy, Hwang, Amie E., Mack, Thomas M., Nelson, Tracy L., Whitfield, Keith E., Medda, Emanuela, Nisticò, Lorenza, Toccaceli, Virgilia, Krueger, Robert F., McGue, Matt, Pahlen, Shandell, Martin, Nicholas G., Medland, Sarah E., Montgomery, Grant W., Heikkilä, Kauko, Derom, Catherine A., Vlietinck, Robert F., Loos, Ruth J.F., Magnusson, Patrik K.E., Pedersen, Nancy L., Dahl Aslan, Anna K., Hotopf, Matthew, Sumathipala, Athula, Rijsdijk, Fruhling, Siribaddana, Sisira H., Rose, Richard J., Sørensen, Thorkild I.A., Boomsma, Dorret I., and Kaprio, Jaakko

Published
2021
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15. A large-scale genome-wide association study meta-analysis of cannabis use disorder

Author

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

Published
2020
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17. Genetic association study of childhood aggression across raters, instruments, and age

Author

Ip, Hill F., van der Laan, Camiel M., Krapohl, Eva M. L., Brikell, Isabell, Sánchez-Mora, Cristina, Nolte, Ilja M., St Pourcain, Beate, Bolhuis, Koen, Palviainen, Teemu, Zafarmand, Hadi, Colodro-Conde, Lucía, Gordon, Scott, Zayats, Tetyana, Aliev, Fazil, Jiang, Chang, Wang, Carol A., Saunders, Gretchen, Karhunen, Ville, Hammerschlag, Anke R., Adkins, Daniel E., Border, Richard, Peterson, Roseann E., Prinz, Joseph A., Thiering, Elisabeth, Seppälä, Ilkka, Vilor-Tejedor, Natàlia, Ahluwalia, Tarunveer S., Day, Felix R., Hottenga, Jouke-Jan, Allegrini, Andrea G., Rimfeld, Kaili, Chen, Qi, Lu, Yi, Martin, Joanna, Soler Artigas, María, Rovira, Paula, Bosch, Rosa, Español, Gemma, Ramos Quiroga, Josep Antoni, Neumann, Alexander, Ensink, Judith, Grasby, Katrina, Morosoli, José J., Tong, Xiaoran, Marrington, Shelby, Middeldorp, Christel, Scott, James G., Vinkhuyzen, Anna, Shabalin, Andrey A., Corley, Robin, Evans, Luke M., Sugden, Karen, Alemany, Silvia, Sass, Lærke, Vinding, Rebecca, Ruth, Kate, Tyrrell, Jess, Davies, Gareth E., Ehli, Erik A., Hagenbeek, Fiona A., De Zeeuw, Eveline, Van Beijsterveldt, Toos C.E.M., Larsson, Henrik, Snieder, Harold, Verhulst, Frank C., Amin, Najaf, Whipp, Alyce M., Korhonen, Tellervo, Vuoksimaa, Eero, Rose, Richard J., Uitterlinden, André G., Heath, Andrew C., Madden, Pamela, Haavik, Jan, Harris, Jennifer R., Helgeland, Øyvind, Johansson, Stefan, Knudsen, Gun Peggy S., Njolstad, Pal Rasmus, Lu, Qing, Rodriguez, Alina, Henders, Anjali K., Mamun, Abdullah, Najman, Jackob M., Brown, Sandy, Hopfer, Christian, Krauter, Kenneth, Reynolds, Chandra, Smolen, Andrew, Stallings, Michael, Wadsworth, Sally, Wall, Tamara L., Silberg, Judy L., Miller, Allison, Keltikangas-Järvinen, Liisa, Hakulinen, Christian, Pulkki-Råback, Laura, Havdahl, Alexandra, Magnus, Per, Raitakari, Olli T., Perry, John R. B., Llop, Sabrina, Lopez-Espinosa, Maria-Jose, Bønnelykke, Klaus, Bisgaard, Hans, Sunyer, Jordi, Lehtimäki, Terho, Arseneault, Louise, Standl, Marie, Heinrich, Joachim, Boden, Joseph, Pearson, John, Horwood, L. John, Kennedy, Martin, Poulton, Richie, Eaves, Lindon J., Maes, Hermine H., Hewitt, John, Copeland, William E., Costello, Elizabeth J., Williams, Gail M., Wray, Naomi, Järvelin, Marjo-Riitta, McGue, Matt, Iacono, William, Caspi, Avshalom, Moffitt, Terrie E., Whitehouse, Andrew, Pennell, Craig E., Klump, Kelly L., Burt, S. Alexandra, Dick, Danielle M., Reichborn-Kjennerud, Ted, Martin, Nicholas G., Medland, Sarah E., Vrijkotte, Tanja, Kaprio, Jaakko, Tiemeier, Henning, Davey Smith, George, Hartman, Catharina A., Oldehinkel, Albertine J., Casas, Miquel, Ribasés, Marta, Lichtenstein, Paul, Lundström, Sebastian, Plomin, Robert, Bartels, Meike, Nivard, Michel G., and Boomsma, Dorret I.

Published
2021
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18. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

Author

Hagler, Donald J., Jr., Hatton, SeanN., Cornejo, M. Daniela, Makowski, Carolina, Fair, Damien A., Dick, Anthony Steven, Sutherland, Matthew T., Casey, B.J., Barch, Deanna M., Harms, Michael P., Watts, Richard, Bjork, James M., Garavan, Hugh P., Hilmer, Laura, Pung, Christopher J., Sicat, Chelsea S., Kuperman, Joshua, Bartsch, Hauke, Xue, Feng, Heitzeg, Mary M., Laird, Angela R., Trinh, Thanh T., Gonzalez, Raul, Tapert, Susan F., Riedel, Michael C., Squeglia, Lindsay M., Hyde, Luke W., Rosenberg, Monica D., Earl, Eric A., Howlett, Katia D., Baker, Fiona C., Soules, Mary, Diaz, Jazmin, de Leon, Octavio Ruiz, Thompson, Wesley K., Neale, Michael C., Herting, Megan, Sowell, Elizabeth R., Alvarez, Ruben P., Hawes, Samuel W., Sanchez, Mariana, Bodurka, Jerzy, Breslin, Florence J., Morris, Amanda Sheffield, Paulus, Martin P., Simmons, W. Kyle, Polimeni, Jonathan R., van der Kouwe, Andre, Nencka, Andrew S., Gray, Kevin M., Pierpaoli, Carlo, Matochik, John A., Noronha, Antonio, Aklin, Will M., Conway, Kevin, Glantz, Meyer, Hoffman, Elizabeth, Little, Roger, Lopez, Marsha, Pariyadath, Vani, Weiss, Susan RB., Wolff-Hughes, Dana L., DelCarmen-Wiggins, Rebecca, Feldstein Ewing, Sarah W., Miranda-Dominguez, Oscar, Nagel, Bonnie J., Perrone, Anders J., Sturgeon, Darrick T., Goldstone, Aimee, Pfefferbaum, Adolf, Pohl, Kilian M., Prouty, Devin, Uban, Kristina, Bookheimer, Susan Y., Dapretto, Mirella, Galvan, Adriana, Bagot, Kara, Giedd, Jay, Infante, M. Alejandra, Jacobus, Joanna, Patrick, Kevin, Shilling, Paul D., Desikan, Rahul, Li, Yi, Sugrue, Leo, Banich, Marie T., Friedman, Naomi, Hewitt, John K., Hopfer, Christian, Sakai, Joseph, Tanabe, Jody, Cottler, Linda B., Nixon, Sara Jo, Chang, Linda, Cloak, Christine, Ernst, Thomas, Reeves, Gloria, Kennedy, David N., Heeringa, Steve, Peltier, Scott, Schulenberg, John, Sripada, Chandra, Zucker, Robert A., Iacono, William G., Luciana, Monica, Calabro, Finnegan J., Clark, Duncan B., Lewis, David A., Luna, Beatriz, Schirda, Claudiu, Brima, Tufikameni, Foxe, John J., Freedman, Edward G., Mruzek, Daniel W., Mason, Michael J., Huber, Rebekah, McGlade, Erin, Prescot, Andrew, Renshaw, Perry F., Yurgelun-Todd, Deborah A., Allgaier, Nicholas A., Dumas, Julie A., Ivanova, Masha, Potter, Alexandra, Florsheim, Paul, Larson, Christine, Lisdahl, Krista, Charness, Michael E., Fuemmeler, Bernard, Hettema, John M., Maes, Hermine H., Steinberg, Joel, Anokhin, Andrey P., Glaser, Paul, Heath, Andrew C., Madden, Pamela A., Baskin-Sommers, Arielle, Constable, R. Todd, Grant, Steven J., Dowling, Gayathri J., Brown, Sandra A., Jernigan, Terry L., and Dale, Anders M.

Published
2019
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19. Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models.

Author

Singh, Madhurbain, Verhulst, Brad, Vinh, Philip, Zhou, Yi, Castro-de-Araujo, Luis F. S., Hottenga, Jouke-Jan, Pool, René, de Geus, Eco J. C., Vink, Jacqueline M., Boomsma, Dorret I., Maes, Hermine H. M., Dolan, Conor V., and Neale, Michael C.

Subjects
PANEL analysis, CAUSAL inference, TIME management
Abstract

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., "distal") effects and the bidirectional cross-sectional (i.e., "proximal") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders

Author

Walters, Raymond K., Polimanti, Renato, Johnson, Emma C., McClintick, Jeanette N., Adams, Mark J., Adkins, Amy E., Aliev, Fazil, Bacanu, Silviu-Alin, Batzler, Anthony, Bertelsen, Sarah, Biernacka, Joanna M., Bigdeli, Tim B., Chen, Li-Shiun, Clarke, Toni-Kim, Chou, Yi-Ling, Degenhardt, Franziska, Docherty, Anna R., Edwards, Alexis C., Fontanillas, Pierre, Foo, Jerome C., Fox, Louis, Frank, Josef, Giegling, Ina, Gordon, Scott, Hack, Laura M., Hartmann, Annette M., Hartz, Sarah M., Heilmann-Heimbach, Stefanie, Herms, Stefan, Hodgkinson, Colin, Hoffmann, Per, Jan Hottenga, Jouke, Kennedy, Martin A., Alanne-Kinnunen, Mervi, Konte, Bettina, Lahti, Jari, Lahti-Pulkkinen, Marius, Lai, Dongbing, Ligthart, Lannie, Loukola, Anu, Maher, Brion S., Mbarek, Hamdi, McIntosh, Andrew M., McQueen, Matthew B., Meyers, Jacquelyn L., Milaneschi, Yuri, Palviainen, Teemu, Pearson, John F., Peterson, Roseann E., Ripatti, Samuli, Ryu, Euijung, Saccone, Nancy L., Salvatore, Jessica E., Sanchez-Roige, Sandra, Schwandt, Melanie, Sherva, Richard, Streit, Fabian, Strohmaier, Jana, Thomas, Nathaniel, Wang, Jen-Chyong, Webb, Bradley T., Wedow, Robbee, Wetherill, Leah, Wills, Amanda G., 23andMe Research Team, Boardman, Jason D., Chen, Danfeng, Choi, Doo-Sup, Copeland, William E., Culverhouse, Robert C., Dahmen, Norbert, Degenhardt, Louisa, Domingue, Benjamin W., Elson, Sarah L., Frye, Mark A., Gäbel, Wolfgang, Hayward, Caroline, Ising, Marcus, Keyes, Margaret, Kiefer, Falk, Kramer, John, Kuperman, Samuel, Lucae, Susanne, Lynskey, Michael T., Maier, Wolfgang, Mann, Karl, Männistö, Satu, Müller-Myhsok, Bertram, Murray, Alison D., Nurnberger, John I., Palotie, Aarno, Preuss, Ulrich, Räikkönen, Katri, Reynolds, Maureen D, Ridinger, Monika, Scherbaum, Norbert, Schuckit, Marc A., Soyka, Michael, Treutlein, Jens, Witt, Stephanie, Wodarz, Norbert, Zill, Peter, Adkins, Daniel E., Boden, Joseph M., Boomsma, Dorret I., Bierut, Laura J., Brown, Sandra A., Bucholz, Kathleen K., Cichon, Sven, Costello, E. Jane, de Wit, Harriet, Diazgranados, Nancy, Dick, Danielle M., Eriksson, Johan G., Farrer, Lindsay A., Foroud, Tatiana M., Gillespie, Nathan A., Goate, Alison M., Goldman, David, Grucza, Richard A., Hancock, Dana B., Harris, Kathleen Mullan, Heath, Andrew C., Hesselbrock, Victor, Hewitt, John K., Hopfer, Christian J., Horwood, John, Iacono, William, Johnson, Eric O., Kaprio, Jaakko A., Karpyak, Victor M., Kendler, Kenneth S., Kranzler, Henry R., Krauter, Kenneth, Lichtenstein, Paul, Lind, Penelope A., McGue, Matt, MacKillop, James, Madden, Pamela A. F., Maes, Hermine H., Magnusson, Patrik, Martin, Nicholas G., Medland, Sarah E., Montgomery, Grant W., Nelson, Elliot C., Nöthen, Markus M., Palmer, Abraham A., Pedersen, Nancy L., Penninx, Brenda W. J. H., Porjesz, Bernice, Rice, John P., Rietschel, Marcella, Riley, Brien P., Rose, Richard, Rujescu, Dan, Shen, Pei-Hong, Silberg, Judy, Stallings, Michael C., Tarter, Ralph E., Vanyukov, Michael M., Vrieze, Scott, Wall, Tamara L., Whitfield, John B., Zhao, Hongyu, Neale, Benjamin M., Gelernter, Joel, Edenberg, Howard J., and Agrawal, Arpana

Published
2018
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24. A Genetic Epidemiological Mega Analysis of Smoking Initiation in Adolescents

Author

Maes, Hermine H., Prom-Wormley, Elizabeth, Eaves, Lindon J., Rhee, Soo Hyun, Hewitt, John K., Young, Susan, Corley, Robin, McGue, Matt, Iacono, William G., Legrand, Lisa, Samek, Diana R., Murrelle, E. Lenn, Silberg, Judy L., Miles, Donna R., Schieken, Richard M., Beunen, Gaston P., Thomis, Martine, Rose, Richard J., Dick, Danielle M., Boomsma, Dorret I., Bartels, Meike, Vink, Jacqueline M., Lichtenstein, Paul, White, Victoria, Kaprio, Jaakko, and Neale, Michael C.

Published
2017

30. Genetic and Cultural Transmission of Alcohol Use Disorder in Swedish Twin Pedigrees.

Author

Maes, Hermine H., Neale, Michael C., Ohlsson, Henrik, Sundquist, Jan, Sundquist, Kristina, and Kendler, Kenneth S.

Abstract

Objective: Using Swedish nationwide registry data, we investigated the contribution of genetic and environmental risk factors to the etiology of alcohol use disorder (AUD) by extended twin pedigree modeling. Method: AUD was defined using public inpatient, outpatient, prescription, and criminal records. Three-generational pedigrees were selected for index individuals born between 1980 and 1990, obtained from the national twin and genealogical registers, whose parents were twins. Relatives of the twins included in the pedigrees were their parents, siblings, spouses, and children. Genetic structural equation modeling was applied to the population-based data on AUD, using OpenMx, with age used as a covariate. Results: Analyses including up to 162,469 individuals in 18,971 pedigrees estimated AUD prevalence at 5%-12% in men and 2%-5% in women. Results indicated substantial heritability (about 50%-60%), of which a portion upwards of 5% was attributable to the consequences of assortative mating. Contributions of shared environmental factors to AUD, which represent a mix of within- and cross-generational effects, appeared to be moderate (about 10%-20%). Unique environment accounted for the remaining variance (about 20%-30%). Sex differences in the magnitude of the variance components suggested higher heritability in men and correspondingly higher shared environmental contributions in women. Conclusions: Using objective registry data, we found that AUD is highly heritable. Furthermore, shared environmental factors contributed significantly to the liability of AUD in both men and women. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Analysis and Interpretation of Twin Studies Including Measures of the Shared Environment

Author

Turkheimer, Eric, D'Onofrio, Brian M., and Maes, Hermine H.

Abstract

Recent reports using a classical behavior genetic research design in which twin data are combined with a measured characteristic of their shared family environment have made striking claims about estimating environmental influences on behavior with genetic effects controlled. Such claims are overstated for two related reasons. First, when a variable is measured at the family level in a way that makes it necessarily equivalent for twins reared together, it is not possible to partition it into genetic and environmental components. Second, although structural equation modeling and DeFries--Fulker analysis are sound tools for the analysis of many types of twin data, they do not control for genetic or environmental confounds when estimating the effect of measured family-level variables.

Published
2005
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37. The Concept of Resistance to Substance Use and a Research Approach: The Resist! Project.

Author

Vanyukov, Michael M., Maes, Hermine H. M., Iacono, William G., Kirisci, Levent, Samek, Diana R., Silberg, Judy L., Zimmerman, Emily B., and Prom-Wormley, Elizabeth C.

Subjects
*SUBSTANCE abuse, *MENTAL illness, *TWIN studies, *ADOLESCENT development, PSYCHIATRIC research
Abstract

Illicit substance use is dangerous in both acute and chronic forms, frequently resulting in lethal poisoning, addiction, and other negative consequences. Similar to research in other psychiatric conditions, whose ultimate goal is to enable effective prevention and treatment, studies in substance use are focused on factors elevating the risk for the disorder. The rapid growth of the substance use problem despite the effort invested in fighting it, however, suggests the need in changing the research approach. Instead of attempting to identify risk factors, whose neutralization is often infeasible if not impossible, it may be more promising to systematically reverse the perspective to the factors enhancing the aspect of liability to disorder that shares the same dimension but is opposite to risk, that is, resistance to substance use. Resistance factors, which enable the majority of the population to remain unaffected despite the ubiquity of psychoactive substances, may be more amenable to translation. While the resistance aspect of liability is symmetric to risk, the resistance approach requires substantial changes in sampling (high-resistance rather than high-risk) and using quantitative indices of liability. This article provides an overview and a practical approach to research in resistance to substance use/addiction, currently implemented in a NIH-funded project. The project benefits from unique opportunities afforded by the data originating from two longitudinal twin studies, the Virginia Twin Study of Adolescent and Behavioral Development and the Minnesota Twin Family Study. The methodology described is also applicable to other psychiatric disorders. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

Author

Gillespie, Nathan A., Gentry, Amanda Elswick, Kirkpatrick, Robert M., Reynolds, Chandra A., Mathur, Ravi, Kendler, Kenneth S., Maes, Hermine H., Webb, Bradley T., and Peterson, Roseann E.

Subjects
STRUCTURAL equation modeling, AUTOREGRESSIVE models, GENOME-wide association studies, OLD age, MIDDLE age, VARIANCES
Abstract

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike. Author summary: We used a new method called genomic structural equation modeling to analyse data from 165,717 men and 193,073 women from the UKBiobank. Our results revealed that genetic influences on BMI were very stable between ages 40 and 73. In other words, there did not appear to be any age-dependent genetic influences on BMI during this period. The same results were seen in men and women. [ABSTRACT FROM AUTHOR]

Published
2022
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