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Singh M, Verhulst B, Vinh P, Zhou YD, Castro-de-Araujo LFS, Hottenga JJ, Pool R, de Geus EJC, Vink JM, Boomsma DI, Maes HHM, Dolan CV, Neale MC. Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models. MULTIVARIATE BEHAVIORAL RESEARCH 2024; 59:342-370. [PMID: 38358370 PMCID: PMC11014768 DOI: 10.1080/00273171.2023.2283634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
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.
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Affiliation(s)
- Madhurbain Singh
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
- Department of Biological Psychology, Vrije Universiteit Amsterdam
| | - Brad Verhulst
- Department of Psychiatry and Behavioral Sciences, Texas A&M University
| | - Philip Vinh
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
| | - Yi Daniel Zhou
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
- Department of Psychiatry, Virginia Commonwealth University
| | | | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam
- Amsterdam Public Health Research Institute
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam
- Amsterdam Public Health Research Institute
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam
- Amsterdam Public Health Research Institute
| | | | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam
- Amsterdam Public Health Research Institute
| | - Hermine H M Maes
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
| | - Conor V Dolan
- Department of Biological Psychology, Vrije Universiteit Amsterdam
- Amsterdam Public Health Research Institute
| | - Michael C Neale
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
- Department of Biological Psychology, Vrije Universiteit Amsterdam
- Department of Psychiatry, Virginia Commonwealth University
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Lyu X, Garrison SM. Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance. Twin Res Hum Genet 2023; 26:1-12. [PMID: 37799059 DOI: 10.1017/thg.2023.40] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of nonclassical ACE models, with a focus on same-sex and opposite-sex twin groups. The ACE model is a statistical model that posits that additive genetic factors (A), common environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E) account for individual differences in a phenotype. By extending Visscher's (2004) least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (HSS) influences the statistical power of additive genetic estimates (A), AIC-based model performance, and the frequency of negative estimates. We found that larger HSS and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to .95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than .5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with nontwin kin pairs or situations where zygosity is unavailable, as well as areas for future research.
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Affiliation(s)
- Xuanyu Lyu
- Department of Psychology, Wake Forest University, Winston Salem, North Carolina, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, Colorado, USA
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA
| | - S Mason Garrison
- Department of Psychology, Wake Forest University, Winston Salem, North Carolina, USA
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Castro-de-Araujo LF, de Araujo JAP, Morais Xavier ÉF, Kanaan RAA. Feedback-loop between psychotic symptoms and brain volume: A cross-lagged panel model study. J Psychiatr Res 2023; 162:150-155. [PMID: 37156129 DOI: 10.1016/j.jpsychires.2023.05.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/10/2023]
Abstract
Brain structural changes are known to be associated with psychotic symptoms, with worse symptoms consistently associated with brain volume loss in some areas. It is not clear whether volume and symptoms interfere with each other over the course of psychosis. In this paper, we analyse the temporal relationships between psychosis symptom severity and total gray matter volume. We applied a cross-lagged panel model to a public dataset from the NUSDAST cohorts. The subjects were assessed at three-time points: baseline, 24 months, and 48 months. Psychosis symptoms were measured by SANS and SAPS scores. The cohort contained 673 subjects with schizophrenia, healthy subjects and their siblings. There were significant effects of symptom severity on total gray matter volume and vice-versa. The worse the psychotic symptoms, the smaller the total gray volume, and the smaller the volume, the worse the symptomatology. There is a bidirectional temporal relationship between symptoms of psychosis and brain volume.
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Affiliation(s)
- Luis Fs Castro-de-Araujo
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, P.O. Box 980126, Richmond, VA, 23298-0126, USA; Deptartment of Psychiatry, The University of Melbourne, Austin Health, Victoria, Australia.
| | | | - Érika Fialho Morais Xavier
- Center of Data and Knowledge Integration for Health (CIDACS), Fiocruz, R. Mundo, 121. Salvador, Bahia, Brazil
| | - Richard A A Kanaan
- Deptartment of Psychiatry, The University of Melbourne, Austin Health, Victoria, Australia
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Castro-de-Araujo LFS, Singh M, Zhou Y, Vinh P, Verhulst B, Dolan CV, Neale MC. MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives. Behav Genet 2023; 53:63-73. [PMID: 36322200 PMCID: PMC9823046 DOI: 10.1007/s10519-022-10122-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non-familial sources. Our model extends the MRDoC model (Minică et al. in Behav Genet 48:337-349, https://doi.org/10.1007/s10519-018-9904-4 , 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al. in Behav Genet 23:29-50, https://doi.org/10.1007/BF01067552 , 1993).
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Affiliation(s)
- Luis F. S. Castro-de-Araujo
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
- Department of Psychiatry, Austin Health, The University of Melbourne, Melbourne, VIC Australia
| | - Madhurbain Singh
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Yi Zhou
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Philip Vinh
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Brad Verhulst
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, 2900 E 29th Street, Bryan, TX 77802 USA
| | - Conor V. Dolan
- Department of Biological Psychology, Vrije Universiteit. Amsterdam, Transitorium 2B03, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
- Department of Biological Psychology, Vrije Universiteit. Amsterdam, Transitorium 2B03, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
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Simulated nonlinear genetic and environmental dynamics of complex traits. Dev Psychopathol 2022; 35:662-677. [PMID: 35236532 PMCID: PMC9440154 DOI: 10.1017/s0954579421001796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Genetic studies of complex traits often show disparities in estimated heritability depending on the method used, whether by genomic associations or twin and family studies. We present a simulation of individual genomes with dynamic environmental conditions to consider how linear and nonlinear effects, gene-by-environment interactions, and gene-by-environment correlations may work together to govern the long-term development of complex traits and affect estimates of heritability from common methods. Our simulation studies demonstrate that the genetic effects estimated by genome wide association studies in unrelated individuals are inadequate to characterize gene-by-environment interaction, while including related individuals in genome-wide complex trait analysis (GCTA) allows gene-by-environment interactions to be recovered in the heritability. These theoretical findings provide an explanation for the "missing heritability" problem and bridge the conceptual gap between the most common findings of GCTA and twin studies. Future studies may use the simulation model to test hypotheses about phenotypic complexity either in an exploratory way or by replicating well-established observations of specific phenotypes.
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