Browsing by Author "Mallard, Travis T."

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    Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research
    (Springer Nature, 2025) Cai, Na; Verhulst, Brad; Andreassen, Ole A.; Buitelaar, Jan; Edenberg, Howard J.; Hettema, John M.; Gandal, Michael; Grotzinger, Andrew; Jonas, Katherine; Lee, Phil; Mallard, Travis T.; Mattheisen, Manuel; Neale, Michael C.; Nurnberger, John I., Jr.; Peyrot, Wouter J.; Tucker-Drob, Elliot M.; Smoller, Jordan W.; Kendler, Kenneth S.; Biochemistry and Molecular Biology, School of Medicine
    Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.
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    Correction: Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research
    (Springer Nature, 2025) Cai, Na; Verhulst, Brad; Andreassen, Ole A.; Buitelaar, Jan; Edenberg, Howard J.; Hettema, John M.; Gandal, Michael; Grotzinger, Andrew; Jonas, Katherine; Lee, Phil; Mallard, Travis T.; Mattheisen, Manuel; Neale, Michael C.; Nurnberger, John I., Jr.; Peyrot, Wouter J.; Tucker-Drob, Elliot M.; Smoller, Jordan W.; Kendler, Kenneth S.; Biochemistry and Molecular Biology, School of Medicine
    Correction to: Molecular Psychiatry 10.1038/s41380-024-02878-x, published online 27 December 2024 In this article the author’s name Wouter J. Peyrot was incorrectly written as Wouter Peyrout. The original article has been corrected.
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    Item-Level Genome-Wide Association Study of the Alcohol Use Disorders Identification Test in Three Population-Based Cohorts
    (American Psychiatric Association, 2022) Mallard, Travis T.; Savage, Jeanne E.; Johnson, Emma C.; Huang, Yuye; Edwards, Alexis C.; Hottenga, Jouke J.; Grotzinger, Andrew D.; Gustavson, Daniel E.; Jennings, Mariela V.; Anokhin, Andrey; Dick, Danielle M.; Edenberg, Howard J.; Kramer, John R.; Lai, Dongbing; Meyers, Jacquelyn L.; Pandey, Ashwini K.; Harden, Kathryn Paige; Nivard, Michel G.; de Geus, Eco J. C.; Boomsma, Dorret I.; Agrawal, Arpana; Davis, Lea K.; Clarke, Toni-Kim; Palmer, Abraham A.; Sanchez-Roige, Sandra; Biochemistry and Molecular Biology, School of Medicine
    Objective: Genome-wide association studies (GWASs) of the Alcohol Use Disorders Identification Test (AUDIT), a 10-item screen for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which may have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders and/or medical conditions. The authors used genomic structural equation modeling to elucidate the genetics of alcohol consumption and problematic consequences of alcohol use as measured by AUDIT. Methods: To explore these unexpected differences in genetic correlations, the authors conducted the first item-level and the largest GWAS of AUDIT items (N=160,824) and applied a multivariate framework to mitigate previous biases. Results: The authors identified novel patterns of similarity (and dissimilarity) among the AUDIT items and found evidence of a correlated two-factor structure at the genetic level ("consumption" and "problems," rg=0.80). Moreover, by applying empirically derived weights to each of the AUDIT items, the authors constructed an aggregate measure of alcohol consumption that was strongly associated with alcohol dependence (rg=0.67), moderately associated with several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by conducting polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, the authors identified novel genetic associations between alcohol consumption, alcohol misuse, and health. Conclusions: This work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.
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    Mapping Pathways by which Genetic Risk Influences Adolescent Externalizing Behavior: The Interplay between Externalizing Polygenic Risk Scores, Parental Knowledge, and Peer Substance Use
    (Springer, 2021) Kuo, Sally I-Chun; Salvatore, Jessica E.; Barr, Peter B.; Aliev, Fazil; Anokhin, Andrey; Bucholz, Kathleen K.; Chan, Grace; Edenberg, Howard J.; Hesselbrock, Victor; Kamarajan, Chella; Kramer, John R.; Lai, Dongbing; Mallard, Travis T.; Nurnberger, John I., Jr.; Pandey, Gayathri; Plawecki, Martin H.; Sanchez-Roige, Sandra; Waldman, Irwin; Palmer, Abraham A.; Externalizing Consortium; Dick, Danielle M.; Biochemistry and Molecular Biology, School of Medicine
    Genetic predispositions and environmental influences both play an important role in adolescent externalizing behavior; however, they are not always independent. To elucidate gene-environment interplay, we examined the interrelationships between externalizing polygenic risk scores, parental knowledge, and peer substance use in impacting adolescent externalizing behavior across two time-points in a high-risk longitudinal sample of 1,200 adolescents (764 European and 436 African ancestry; Mage = 12.99) from the Collaborative Study on the Genetics of Alcoholism. Results from multivariate path analysis indicated that externalizing polygenic scores were directly associated with adolescent externalizing behavior but also indirectly via peer substance use, in the European ancestry sample. No significant polygenic association nor indirect effects of genetic risk were observed in the African ancestry group, likely due to more limited power. Our findings underscore the importance of gene-environment interplay and suggest peer substance use may be a mechanism through which genetic risk influences adolescent externalizing behavior.
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    Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals
    (Springer Nature, 2023) Zhou, Hang; Kember, Rachel L.; Deak, Joseph D.; Xu, Heng; Toikumo, Sylvanus; Yuan, Kai; Lind, Penelope A.; Farajzadeh, Leila; Wang, Lu; Hatoum, Alexander S.; Johnson, Jessica; Lee, Hyunjoon; Mallard, Travis T.; Xu, Jiayi; Johnston, Keira J. A.; Johnson, Emma C.; Galimberti, Marco; Dao, Cecilia; Levey, Daniel F.; Overstreet, Cassie; Byrne, Enda M.; Gillespie, Nathan A.; Gordon, Scott; Hickie, Ian B.; Whitfield, John B.; Xu, Ke; Zhao, Hongyu; Huckins, Laura M.; Davis, Lea K.; Sanchez-Roige, Sandra; Madden, Pamela A. F.; Heath, Andrew C.; Medland, Sarah E.; Martin, Nicholas G.; Ge, Tian; Smoller, Jordan W.; Hougaard, David M.; Børglum, Anders D.; Demontis, Ditte; Krystal, John H.; Gaziano, J. Michael; Edenberg, Howard J.; Agrawal, Arpana; Million Veteran Program; Justice, Amy C.; Stein, Murray B.; Kranzler, Henry R.; Gelernter, Joel; Biochemistry and Molecular Biology, School of Medicine
    Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.
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    The Landscape of Shared and Divergent Genetic Influences across 14 Psychiatric Disorders
    (medRxiv, 2025-01-15) Grotzinger, Andrew D.; Werme, Josefin; Peyrot, Wouter J.; Frei, Oleksandr; de Leeuw, Christiaan; Bicks, Lucy K.; Guo, Qiuyu; Margolis, Michael P.; Coombes, Brandon J.; Batzler, Anthony; Pazdernik, Vanessa; Biernacka, Joanna M.; Andreassen, Ole A.; Anttila, Verneri; Børglum, Anders D.; Cai, Na; Demontis, Ditte; Edenberg, Howard J.; Faraone, Stephen V.; Franke, Barbara; Gandal, Michael J.; Gelernter, Joel; Hettema, John M.; Jonas, Katherine G.; Knowles, James A.; Koenen, Karestan C.; Maihofer, Adam X.; Mallard, Travis T.; Mattheisen, Manuel; Mitchell, Karen S.; Neale, Benjamin M.; Nievergelt, Caroline M.; Nurnberger, John I.; O'Connell, Kevin S.; Robinson, Elise B.; Sanchez-Roige, Sandra S.; Santangelo, Susan L.; Stefansson, Hreinn; Stefansson, Kari; Stein, Murray B.; Strom, Nora I.; Thornton, Laura M.; Tucker-Drob, Elliot M.; Verhulst, Brad; Waldman, Irwin D.; Walters, G. Bragi; Wray, Naomi R.; Anxiety Disorders Working Group; Attention-Deficit/Hyperactivity Disorder (ADHD) Working Group; Autism Spectrum Disorders Working Group; Bipolar Disorder Working Group; Eating Disorders Working Group; Major Depressive Disorder Working Group; Nicotine Dependence GenOmics (iNDiGO) Consortium; Obsessive-Compulsive Disorder Working Group; Post-Traumatic Stress Disorder Working Group; Schizophrenia Working Group; Substance Use Disorders Working Group; Tourette Syndrome Working Group; Lee, Phil H.; Kendler, Kenneth S.; Smoller, Jordan W.; Biochemistry and Molecular Biology, School of Medicine
    Psychiatric disorders display high levels of comorbidity and genetic overlap 1,2. Genomic methods have shown that even for schizophrenia and bipolar disorder, two disorders long-thought to be etiologically distinct 3, the majority of genetic signal is shared 4. Furthermore, recent cross-disorder analyses have uncovered over a hundred pleiotropic loci shared across eight disorders 5. However, the full scope of shared and disorder-specific genetic basis of psychopathology remains largely uncharted. Here, we address this gap by triangulating across a suite of cutting-edge statistical genetic and functional genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders (1,056,201 cases). Our analyses identify and characterize five underlying genomic factors 6 that explain the majority of the genetic variance of the individual disorders (~66% on average) and are associated with 268 pleiotropic loci. We observed particularly high levels of polygenic overlap 7 and local genetic correlation 8 and very few disorder-specific loci 9 for two factors defined by: (i) schizophrenia and bipolar disorder ("SB factor"), and by (ii) major depression, PTSD, and anxiety ("internalizing factor"). At the functional level, we applied multiple methods 10-12 which demonstrated that the shared genetic signal across the SB factor was substantially enriched in genes expressed in excitatory neurons, whereas the internalizing factor was associated with oligodendrocyte biology. By comparison, the genetic signal shared across all 14 disorders was enriched for broad biological processes (e.g., transcriptional regulation). These results indicate increasing differentiation of biological function at different levels of shared cross-disorder risk, from quite general vulnerability to more specific pathways associated with subsets of disorders. These observations may inform a more neurobiologically valid psychiatric nosology and implicate novel targets for therapeutic developments designed to treat commonly occurring comorbid presentations.
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    Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis
    (Springer Nature, 2022) Tielbeek, Jorim J.; Uffelmann, Emil; Williams, Benjamin S.; Colodro-Conde, Lucía; Gagnon, Éloi; Mallard, Travis T.; Levitt, Brandt E.; Jansen, Philip R.; Johansson, Ada; Sallis, Hannah M.; Pistis, Giorgio; Saunders, Gretchen R. B.; Allegrini, Andrea G.; Rimfeld, Kaili; Konte, Bettina; Klein, Marieke; Hartmann, Annette M.; Salvatore, Jessica E.; Nolte, Ilja M.; Demontis, Ditte; Malmberg, Anni L. K.; Burt, S. Alexandra; Savage, Jeanne E.; Sugden, Karen; Poulton, Richie; Mullan Harris, Kathleen; Vrieze, Scott; McGue, Matt; Iacono, William G.; Roth Mota, Nina; Mill, Jonathan; Viana, Joana F.; Mitchell, Brittany L.; Morosoli, Jose J.; Andlauer, Till F. M.; Ouellet-Morin, Isabelle; Tremblay, Richard E.; Côté, Sylvana M.; Gouin, Jean-Philippe; Brendgen, Mara R.; Dionne, Ginette; Vitaro, Frank; Lupton, Michelle K.; Martin, Nicholas G.; COGA Consortium; Spit for Science Working Group; Castelao, Enrique; Räikkönen, Katri; Eriksson, Johan G.; Lahti, Jari; Hartman, Catharina A.; Oldehinkel, Albertine J.; Snieder, Harold; Liu, Hexuan; Preisig, Martin; Whipp, Alyce; Vuoksimaa, Eero; Lu, Yi; Jern, Patrick; Rujescu, Dan; Giegling, Ina; Palviainen, Teemu; Kaprio, Jaakko; Harden, Kathryn Paige; Munafò, Marcus R.; Morneau-Vaillancourt, Geneviève; Plomin, Robert; Viding, Essi; Boutwell, Brian B.; Aliev, Fazil; Dick, Danielle M.; Popma, Arne; Faraone, Stephen V.; Børglum, Anders D.; Medland, Sarah E.; Franke, Barbara; Boivin, Michel; Pingault, Jean-Baptiste; Glennon, Jeffrey C.; Barnes, J. C.; Fisher, Simon E.; Moffitt, Terrie E.; Caspi, Avshalom; Polderman, Tinca J. C.; Posthuma, Danielle; Medical and Molecular Genetics, School of Medicine
    Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, p = 6.32 × 10-10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). Polygenic risk score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg = 0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg = 0.54), cognitive ability (intelligence rg = -0.40), educational attainment (years of schooling rg = -0.46) and reproductive traits (age at first birth rg = -0.58, father's age at death rg = -0.54). Our findings provide a starting point toward identifying critical biosocial risk mechanisms for the development of ASB.