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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 Behav Res 2024:1-29. [PMID: 38358370 DOI: 10.1080/00273171.2023.2283634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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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Mikhail ME, Burt SA, Neale MC, Keel PK, Katzman DK, Klump KL. Changes in affect longitudinally mediate associations between emotion regulation strategy use and disordered eating. Int J Eat Disord 2024. [PMID: 38332591 DOI: 10.1002/eat.24162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/26/2024] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND Trait-level emotion regulation (ER) difficulties are associated with eating disorders (EDs) transdiagnostically. However, little research has examined whether within-person fluctuations in ER longitudinally predict ED behaviors in daily life or the mechanisms of ER effects. Investigating daily ER could help us better understand why people experience ED behaviors at a given time. We examined whether day-to-day changes in adaptive (e.g., cognitive reappraisal) and maladaptive (e.g., rumination) ER longitudinally predicted core ED behaviors (binge eating, purging, dieting) and whether changes in affect mediated effects. METHOD Female participants (N = 688) ages 15-30 from the Michigan State University Twin Registry reported their adaptive and maladaptive ER use, negative affect (NA), positive affect (PA), binge eating, purging, and dieting on 49 consecutive days. Using structural equation modeling, we examined whether within-person fluctuations in ER predicted same- and next-day ED behaviors and whether changes in affect mediated longitudinal ER effects. RESULTS Greater maladaptive ER predicted increased likelihood of same-day binge eating and next-day binge eating and purging. The association between maladaptive ER and next-day binge eating and purging was mediated by increased next-day NA. In contrast, dieting was more closely related to changes in PA. Adaptive ER did not predict reduced likelihood of any ED behavior. CONCLUSIONS Maladaptive ER may longitudinally increase risk for binge eating and purging by amplifying NA. Interventions focused on decreasing maladaptive ER and subsequent NA might help disrupt binge eating-purging cycles. Conversely, results add to evidence that PA fluctuations may play a unique role in maintaining restrictive behaviors. PUBLIC SIGNIFICANCE Little is known about how daily changes in emotion regulation may impact disordered eating. We found that maladaptive emotion regulation (e.g., rumination) was associated with a higher likelihood of binge eating and purging on the next day because it predicted increased next-day negative affect. In contrast, dieting was more closely tied to fluctuations in positive affect. Targeting daily emotion regulation and affective processes may help disrupt cycles of disordered eating.
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Affiliation(s)
- Megan E Mikhail
- Department of Psychology, Michigan State University, East Lansing, Michigan, USA
| | - S Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, Michigan, USA
| | - Michael C Neale
- Department of Psychiatry, Human Genetics, and Psychology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Pamela K Keel
- Department of Psychology, Florida State University, Tallahassee, Florida, USA
| | - Debra K Katzman
- Department of Pediatrics, University of Toronto, Toronto, Canada
| | - Kelly L Klump
- Department of Psychology, Michigan State University, East Lansing, Michigan, USA
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Gillespie NA, Elman JA, McKenzie RE, Tu XM, Xian H, Reynolds CA, Panizzon MS, Lyons MJ, Eglit GML, Neale MC, Rissman RA, Franz C, Kremen WS. The heritability of blood-based biomarkers related to risk of Alzheimer's disease in a population-based sample of early old-age men. Alzheimers Dement 2024; 20:356-365. [PMID: 37622539 PMCID: PMC10843753 DOI: 10.1002/alz.13407] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 08/26/2023]
Abstract
INTRODUCTION Despite their increased application, the heritability of Alzheimer's disease (AD)-related blood-based biomarkers remains unexplored. METHODS Plasma amyloid beta 40 (Aβ40), Aβ42, the Aβ42/40 ratio, total tau (t-tau), and neurofilament light (NfL) data came from 1035 men 60 to 73 years of age (μ = 67.0, SD = 2.6). Twin models were used to calculate heritability and the genetic and environmental correlations between them. RESULTS Additive genetics explained 44% to 52% of Aβ42, Aβ40, t-tau, and NfL. The Aβ42/40 ratio was not heritable. Aβ40 and Aβ42 were genetically near identical (rg = 0.94). Both Aβ40 and Aβ42 were genetically correlated with NfL (rg = 0.35 to 0.38), but genetically unrelated to t-tau. DISCUSSION Except for Aβ42/40, plasma biomarkers are heritable. Aβ40 and Aβ42 share mostly the same genetic influences, whereas genetic influences on plasma t-tau and NfL are largely unique in early old-age men. The absence of genetic associations between the Aβs and t-tau is not consistent with the amyloid cascade hypothesis.
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Affiliation(s)
- Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behaviour GeneticsDepartment of PsychiatryVirginia Commonwealth UniversityRichmondVirginiaUSA
- QIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
| | - Jeremy A. Elman
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Ruth E. McKenzie
- Department of PsychologyBoston UniversityBostonMassachusettsUSA
- School of Education and Social PolicyMerrimack CollegeNorth AndoverMassachusettsUSA
| | - Xin M. Tu
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
- Department of Family Medicine and Public HealthUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Hong Xian
- Department of Epidemiology and BiostatisticsSaint. Louis UniversitySt. LouisMissouriUSA
- Research Service, VA St. Louis Healthcare SystemSt. LouisMissouriUSA
| | | | - Matthew S. Panizzon
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Michael J. Lyons
- Department of Psychological and Brain SciencesBoston UniversityBostonMassachusettsUSA
| | - Graham M. L. Eglit
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Sam and Rose Stein Institute for Research on AgingUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behaviour GeneticsDepartment of PsychiatryVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Robert A. Rissman
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Carol Franz
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - William S. Kremen
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
- Department of NeurosciencesUniversity of California, San DiegoLa JollaCaliforniaUSA
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Castro-de-Araujo LF, Singh M, Zhou Y, Vinh P, Maes HH, Verhulst B, Dolan CV, Neale MC. Power, measurement error, and pleiotropy robustness in twin-design extensions to Mendelian Randomization. Res Sq 2023:rs.3.rs-3411642. [PMID: 37886585 PMCID: PMC10602165 DOI: 10.21203/rs.3.rs-3411642/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Mendelian Randomization (MR) has become an important tool for causal inference in the health sciences. It takes advantage of the random segregation of alleles to control for background confounding factors. In brief, the method works by using genetic variants as instrumental variables, but it depends on the assumption of exclusion restriction, i.e., that the variants affect the outcome exclusively via the exposure variable. Equivalently, the assumption states that there is no horizontal pleiotropy from the variant to the outcome. This assumption is unlikely to hold in nature, so several extensions to MR have been developed to increase its robustness against horizontal pleiotropy, though not eliminating the problem entirely (Sanderson et al. 2022). The Direction of Causation (DoC) model, which affords information from the cross-twin cross-trait correlations to estimate causal paths, was extended with polygenic scores to explicitly model horizontal pleiotropy and a causal path (MR-DoC, Minică et al 2018). MR-DoC was further extended to accommodate bidirectional causation (MR-DoC2 ; Castro-de-Araujo et al. 2023). In the present paper, we compared the power of the DoC model, MR-DoC, and MR-DoC2. We investigated the effect of phenotypic measurement error and the effect of misspecification of unshared (individual-specific) environmental factors on the parameter estimates.
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Affiliation(s)
| | | | - Yi Zhou
- Virginia Commonwealth University
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Williams ME, Elman JA, Bell TR, Dale AM, Eyler LT, Fennema-Notestine C, Franz CE, Gillespie NA, Hagler DJ, Lyons MJ, McEvoy LK, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Kremen WS. Higher cortical thickness/volume in Alzheimer's-related regions: protective factor or risk factor? Neurobiol Aging 2023; 129:185-194. [PMID: 37343448 PMCID: PMC10676195 DOI: 10.1016/j.neurobiolaging.2023.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/18/2023] [Accepted: 05/03/2023] [Indexed: 06/23/2023]
Abstract
Some evidence suggests a biphasic pattern of changes in cortical thickness wherein higher, rather than lower, thickness is associated with very early Alzheimer's disease (AD) pathology. We examined whether integrating information from AD brain signatures based on mean diffusivity (MD) can aid in the interpretation of cortical thickness/volume as a risk factor for future AD-related changes. Participants were 572 men in the Vietnam Era Twin Study of Aging who were cognitively unimpaired at baseline (mean age = 56 years; range = 51-60). Individuals with both high thickness/volume signatures and high MD signatures at baseline had lower cortical thickness/volume in AD signature regions and lower episodic memory performance 12 years later compared to those with high thickness/volume and low MD signatures at baseline. Groups did not differ in level of young adult cognitive reserve. Our findings are in line with a biphasic model in which increased cortical thickness may precede future decline and establish the value of examining cortical MD alongside cortical thickness to identify subgroups with differential risk for poorer brain and cognitive outcomes.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Tyler R Bell
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
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Williams ME, Gillespie NA, Bell TR, Dale AM, Elman JA, Eyler LT, Fennema-Notestine C, Franz CE, Hagler DJ, Lyons MJ, McEvoy LK, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Kremen WS. Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer's Disease Neuroimaging Signatures Across Midlife and Early Old Age. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:918-927. [PMID: 35738479 PMCID: PMC9827615 DOI: 10.1016/j.bpsc.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/04/2022] [Accepted: 06/07/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Composite scores of magnetic resonance imaging-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed AD signatures, have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared with thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities and whether diffusion- and thickness/volume-based signatures each capture unique AD-related phenotypic or genetic information remains unknown. METHODS Our validated thickness/volume signature, our novel mean diffusivity (MD) signature, and a magnetic resonance imaging-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from 3 waves of the Vietnam Era Twin Study of Aging (VETSA) (baseline/wave 1: mean age [years] = 56.1, SD = 2.6, range = 51.1-60.2). Subsequent waves occurred at approximately 5.7-year intervals. RESULTS MD and thickness/volume signatures were highly heritable (56%-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance. CONCLUSIONS Cortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California.
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Tyler R Bell
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, California; Department of Neuroscience, University of California San Diego, San Diego, California
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Lisa T Eyler
- Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, California
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Radiology, University of California San Diego, San Diego, California
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, San Diego, California
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, San Diego, California
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, California
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
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van Dongen J, Willemsen G, de Geus EJC, Boomsma DI, Neale MC. Effects of smoking on genome-wide DNA methylation profiles: A study of discordant and concordant monozygotic twin pairs. eLife 2023; 12:e83286. [PMID: 37643467 PMCID: PMC10501767 DOI: 10.7554/elife.83286] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 08/08/2023] [Indexed: 08/31/2023] Open
Abstract
Background Smoking-associated DNA methylation levels identified through epigenome-wide association studies (EWASs) are generally ascribed to smoking-reactive mechanisms, but the contribution of a shared genetic predisposition to smoking and DNA methylation levels is typically not accounted for. Methods We exploited a strong within-family design, that is, the discordant monozygotic twin design, to study reactiveness of DNA methylation in blood cells to smoking and reversibility of methylation patterns upon quitting smoking. Illumina HumanMethylation450 BeadChip data were available for 769 monozygotic twin pairs (mean age = 36 years, range = 18-78, 70% female), including pairs discordant or concordant for current or former smoking. Results In pairs discordant for current smoking, 13 differentially methylated CpGs were found between current smoking twins and their genetically identical co-twin who never smoked. Top sites include multiple CpGs in CACNA1D and GNG12, which encode subunits of a calcium voltage-gated channel and G protein, respectively. These proteins interact with the nicotinic acetylcholine receptor, suggesting that methylation levels at these CpGs might be reactive to nicotine exposure. All 13 CpGs have been previously associated with smoking in unrelated individuals and data from monozygotic pairs discordant for former smoking indicated that methylation patterns are to a large extent reversible upon smoking cessation. We further showed that differences in smoking level exposure for monozygotic twins who are both current smokers but differ in the number of cigarettes they smoke are reflected in their DNA methylation profiles. Conclusions In conclusion, by analysing data from monozygotic twins, we robustly demonstrate that DNA methylation level in human blood cells is reactive to cigarette smoking. Funding We acknowledge funding from the National Institute on Drug Abuse grant DA049867, the Netherlands Organization for Scientific Research (NWO): Biobanking and Biomolecular Research Infrastructure (BBMRI-NL, NWO 184.033.111) and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995); epigenetic data were generated at the Human Genomics Facility (HuGe-F) at ErasmusMC Rotterdam. Cotinine assaying was sponsored by the Neuroscience Campus Amsterdam. DIB acknowledges the Royal Netherlands Academy of Science Professor Award (PAH/6635).
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Affiliation(s)
- Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Amsterdam Public Health Research InstituteAmsterdamNetherlands
- Amsterdam Reproduction and Development (AR&D) Research InstituteAmsterdamNetherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Amsterdam Public Health Research InstituteAmsterdamNetherlands
| | - Eco JC de Geus
- Department of Biological Psychology, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Amsterdam Public Health Research InstituteAmsterdamNetherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Amsterdam Public Health Research InstituteAmsterdamNetherlands
- Amsterdam Reproduction and Development (AR&D) Research InstituteAmsterdamNetherlands
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth UniversityRichmondUnited States
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Bruins S, Hottenga JJ, Neale MC, Pool R, Boomsma DI, Dolan CV. Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect. Multivariate Behav Res 2023:1-13. [PMID: 37439516 DOI: 10.1080/00273171.2023.2228763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.
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Affiliation(s)
- Susanne Bruins
- Department of Biological Psychology, Vrije Universiteit
- Amsterdam Public Health research institute
| | | | - Michael C Neale
- Department of Biological Psychology, Vrije Universiteit
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit
- Amsterdam Public Health research institute
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit
- Amsterdam Public Health research institute
- Amsterdam Reproduction and Development research institute
| | - Conor V Dolan
- Department of Biological Psychology, Vrije Universiteit
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Gross R, Thaweethai T, Rosenzweig EB, Chan J, Chibnik LB, Cicek MS, Elliott AJ, Flaherman VJ, Foulkes AS, Witvliet MG, Gallagher R, Gennaro ML, Jernigan TL, Karlson EW, Katz SD, Kinser PA, Kleinman LC, Lamendola-Essel MF, Milner JD, Mohandas S, Mudumbi PC, Newburger JW, Rhee KE, Salisbury AL, Snowden JN, Stein CR, Stockwell MS, Tantisira KG, Thomason ME, Truong DT, Warburton D, Wood JC, Ahmed S, Akerlundh A, Alshawabkeh AN, Anderson BR, Aschner JL, Atz AM, Aupperle RL, Baker FC, Balaraman V, Banerjee D, Barch DM, Baskin-Sommers A, Bhuiyan S, Bind MAC, Bogie AL, Buchbinder NC, Bueler E, Bükülmez H, Casey B, Chang L, Clark DB, Clifton RG, Clouser KN, Cottrell L, Cowan K, D’Sa V, Dapretto M, Dasgupta S, Dehority W, Dummer KB, Elias MD, Esquenazi-Karonika S, Evans DN, Faustino EVS, Fiks AG, Forsha D, Foxe JJ, Friedman NP, Fry G, Gaur S, Gee DG, Gray KM, Harahsheh AS, Heath AC, Heitzeg MM, Hester CM, Hill S, Hobart-Porter L, Hong TK, Horowitz CR, Hsia DS, Huentelman M, Hummel KD, Iacono WG, Irby K, Jacobus J, Jacoby VL, Jone PN, Kaelber DC, Kasmarcak TJ, Kluko MJ, Kosut JS, Laird AR, Landeo-Gutierrez J, Lang SM, Larson CL, Lim PPC, Lisdahl KM, McCrindle BW, McCulloh RJ, Mendelsohn AL, Metz TD, Morgan LM, Müller-Oehring EM, Nahin ER, Neale MC, Ness-Cochinwala M, Nolan SM, Oliveira CR, Oster ME, Payne RM, Raissy H, Randall IG, Rao S, Reeder HT, Rosas JM, Russell MW, Sabati AA, Sanil Y, Sato AI, Schechter MS, Selvarangan R, Shakti D, Sharma K, Squeglia LM, Stevenson MD, Szmuszkovicz J, Talavera-Barber MM, Teufel RJ, Thacker D, Udosen MM, Warner MR, Watson SE, Werzberger A, Weyer JC, Wood MJ, Yin HS, Zempsky WT, Zimmerman E, Dreyer BP. Researching COVID to enhance recovery (RECOVER) pediatric study protocol: Rationale, objectives and design. medRxiv 2023:2023.04.27.23289228. [PMID: 37214806 PMCID: PMC10197716 DOI: 10.1101/2023.04.27.23289228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Importance The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. Observations We describe the protocol for the Pediatric Observational Cohort Study of the NIH's RE searching COV ID to E nhance R ecovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of five cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study ( n =10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n=6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n=6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n=600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. Conclusions and Relevance RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions. Clinical Trialsgov Identifier Clinical Trial Registration: http://www.clinicaltrials.gov . Unique identifier: NCT05172011.
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Affiliation(s)
- Rachel Gross
- Department of Pediatrics, New York University Grossman School of Medicine, New York, NY, USA
| | - Tanayott Thaweethai
- Department of Biostatistics, Massachusetts General Hospital, Boston, MA, USA
| | - Erika B. Rosenzweig
- Department of Pediatrics, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - James Chan
- Department of Biostatistics, Massachusetts General Hospital, Boston, MA, USA
| | - Lori B. Chibnik
- Department of Biostatistics, Massachusetts General Hospital, Boston, MA, USA
| | - Mine S. Cicek
- Department of Laboratory Medicine and Pathology, Mayo Clinic Hospital, Rochester, MN, USA
| | - Amy J. Elliott
- Avera Research Institute, Avera Health, Sioux Falls, SD, USA
| | - Valerie J. Flaherman
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Andrea S. Foulkes
- Department of Biostatistics, Massachusetts General Hospital, Boston, MA, USA
| | | | - Richard Gallagher
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Maria Laura Gennaro
- Public Health Research Institute and Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Terry L. Jernigan
- Center for Human Development, Cognitive Science, Psychiatry, Radiology, University of California San Diego, La Jolla, CA, USA
| | | | - Stuart D. Katz
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Patricia A. Kinser
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University School of Nursing, Richmond, VA, USA
| | - Lawrence C. Kleinman
- Department of Pediatrics, Division of Population Health, Quality, and Implementation Sciences (POPQuIS), Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | | | - Joshua D. Milner
- Department of Pediatrics, Columbia University Medical Center: Columbia University Irving Medical Center, New York, NY, USA
| | - Sindhu Mohandas
- Department of Infectious Diseases, Children’s Hospital Los Angeles and the Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Praveen C. Mudumbi
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, Boston, MA, USA
| | - Kyung E. Rhee
- Department of Pediatrics, University of California San Diego School of Medicine, San Diego, CA, USA
| | - Amy L. Salisbury
- School of Nursing, Virginia Commonwealth University, Richmond, VA, USA
| | - Jessica N. Snowden
- Departments of Pediatrics and Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Cheryl R. Stein
- Department of Child and Adolescent Psychiatry, Hassenfeld Children’s Hospital at NYU Langone, New York, NY, USA
| | - Melissa S. Stockwell
- Department of Pediatrics, Division of Child and Adolescent Health, Columbia University Vagelos College of Physicians and Surgeons and NewYork-Presbyterian, New York, NY, USA
| | - Kelan G. Tantisira
- Division of Pediatric Respiratory Medicine, University of California San Diego, San Diego, CA, USA
| | - Moriah E. Thomason
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Dongngan T. Truong
- Division of Pediatric Cardiology, University of Utah and Primary Children’s Hospital, Salt Lake City, UT, USA
| | - David Warburton
- Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - John C. Wood
- Department of Pediatrics and Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Shifa Ahmed
- Department of Biostatistics, Massachusetts General Hospital, Boston, MA, USA
| | - Almary Akerlundh
- Department of Pulmonary Research, Rady Children’s Hospital-San Diego, San Diego, CA, USA
| | | | - Brett R. Anderson
- Division of Pediatric Cardiology, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Judy L. Aschner
- Department of Pediatrics, Hackensack University Medical Center, Hackensack, NJ, USA
| | - Andrew M. Atz
- Department of Pediatrics, Medical University of South Carolina, Charleston, SC, USA
| | - Robin L. Aupperle
- Oxley College of Health Sciences, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Fiona C. Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Venkataraman Balaraman
- Department of Pediatrics, Kapiolani Medical Center for Women and Children, Honolulu, HI, USA
| | - Dithi Banerjee
- Department of Pathology and Laboratory Medicine, Children’s Mercy Hospital, Kansas City, MO, USA
| | - Deanna M. Barch
- Department of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | | | - Sultana Bhuiyan
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Marie-Abele C. Bind
- Department of Biostatistics, Massachusetts General Hospital, Boston, MA, USA
| | - Amanda L. Bogie
- Department of Pediatrics, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Natalie C. Buchbinder
- Center for Human Development, University of California San Diego, San Diego, CA, USA
| | - Elliott Bueler
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Hülya Bükülmez
- Department of Pediatrics, Division of Rheumatology, The MetroHealth System, Case Western Reserve University, Cleveland, OH, USA
| | - B.J. Casey
- Department of Neuroscience and Behavior, Barnard College - Columbia University, New York, NY, USA
| | - Linda Chang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Duncan B. Clark
- Departments of Psychiatry and Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Katharine N. Clouser
- Department of Pediatrics, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - Lesley Cottrell
- Department of Pediatrics, West Virginia University, Morgantown, WV, USA
| | - Kelly Cowan
- Department of Pediatrics, Robert Larner M.D. College of Medicine at the University of Vermont, Burlington, VT, USA
| | - Viren D’Sa
- Department of Pediatrics, Rhode Island Hospital, Providence, RI, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Soham Dasgupta
- Department of Pediatrics, Norton Children’s Hospital, University of Louisville, Louisville, KY, USA
| | - Walter Dehority
- Department of Pediatrics, Division of Infectious Diseases, University of New Mexico, Albuquerque, NM, USA
| | - Kirsten B. Dummer
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Matthew D. Elias
- Division of Cardiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shari Esquenazi-Karonika
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Danielle N. Evans
- Arkansas Children’s Research Institute, Arkansas Children’s Hospital, Little Rock, AR, USA
| | | | - Alexander G. Fiks
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Daniel Forsha
- Department of Cardiology, Children’s Mercy Kansas City, Ward Family Heart Center, Kansas City, MO, USA, Kansas City, MO, USA
| | - John J. Foxe
- Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Naomi P. Friedman
- Institute for Behavioral Genetics and Department of Psychology and Neuroscience, University of Colorado Boulder, Bolder, CO, USA
| | - Greta Fry
- Pennington Biomedical Research Center Clinic, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Sunanda Gaur
- Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Dylan G. Gee
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Kevin M. Gray
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Ashraf S. Harahsheh
- Department of Pediatrics, Division of Cardiology, George Washington University School of Medicine & Health Sciences, Washington, DC, USA
| | - Andrew C. Heath
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Mary M. Heitzeg
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Christina M. Hester
- Division of Practice-Based Research, Innovation, & Evaluation, American Academy of Family Physicians, Leawood, KS, USA
| | - Sophia Hill
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Laura Hobart-Porter
- Departments of Pediatrics and Physical Medicine & Rehabilitation, Section of Pediatric Rehabilitation, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Travis K.F. Hong
- Department of Pediatrics, Kapiolani Medical Center for Women and Children, Honolulu, HI, USA
| | - Carol R. Horowitz
- Center for Health Equity and Community Engaged Research and Department of Population Health Science and Policy, New York, NY, USA
| | - Daniel S. Hsia
- Clinical Trials Unit, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Matthew Huentelman
- Division of Neurogenomics, Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Kathy D. Hummel
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - William G. Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Katherine Irby
- Department of Pediatrics, Arkansas Children’s Hospital, University of Arkansas Medical School, Little Rock, AR, USA
| | - Joanna Jacobus
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Vanessa L. Jacoby
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Pei-Ni Jone
- Department of Pediatrics, Pediatric Cardiology, Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - David C. Kaelber
- Departments of Pediatrics, Internal Medicine, and Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Tyler J. Kasmarcak
- Department of Pediatric Clinical Research, Medical University of South Carolina, Charleston, SC, USA
| | - Matthew J. Kluko
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA
| | - Jessica S. Kosut
- Department of Pediatrics, Kapiolani Medical Center for Women and Children, Honolulu, HI, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Jeremy Landeo-Gutierrez
- Department of Pediatrics, Respiratory Medicine Division, University of California San Diego, San Diego, CA, USA
| | - Sean M. Lang
- Heart Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Christine L. Larson
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Peter Paul C. Lim
- Department of Pediatric Infectious Disease, Avera McKennan University Health Center, University of South Dakota, Sioux Falls, SD, USA
| | - Krista M. Lisdahl
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Brian W. McCrindle
- Department of Pediatrics, University of Toronto, Labatt Family Heart Center, The Hospital for Sick Children, Toronto, ON, Canada
| | - Russell J. McCulloh
- Department of Pediatrics, University of Nebraska Medical Center, Omaha, NE, USA
| | - Alan L. Mendelsohn
- Department of Pediatrics, Division of Developmental-Behavioral Pediatrics, New York University Grossman School of Medicine, New York, NY, USA
| | - Torri D. Metz
- Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, UT, USA
| | - Lerraughn M. Morgan
- Department of Pediatrics, Valley Children’s Healthcare, Department of Pediatrics, Madera, CA, Madera, CA, USA
| | | | - Erica R. Nahin
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Michael C. Neale
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Manette Ness-Cochinwala
- Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Sheila M. Nolan
- Department of Pediatrics, New York Medical College, Valhalla, NY, USA
| | - Carlos R. Oliveira
- Department of Pediatrics, Section of Infectious Diseases and Global Health, Yale University School of Medicine, New Haven, CT, USA
| | - Matthew E. Oster
- Department of Pediatric Cardiology, Children’s Healthcare of Atlanta, Atlanta, GA, USA
| | - R. Mark Payne
- Department of Pediatrics, Division of Pediatric Cardiology, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Hengameh Raissy
- Department of Pediatrics, University of New Mexico, Health Sciences Center, Albuquerque, NM, USA
| | - Isabelle G. Randall
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Suchitra Rao
- Department of Pediatrics, Division of Infectious Diseases, Epidemiology and Hospital Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Harrison T. Reeder
- Department of Biostatistics, Massachusetts General Hospital, Boston, MA, USA
| | - Johana M. Rosas
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Mark W. Russell
- Department of Pediatrics, University of Michigan Health System, Ann Arbor, MI, USA
| | - Arash A. Sabati
- Department of Pediatric Cardiology, Phoenix Children’s Hospital, Phoenix, AZ, USA
| | - Yamuna Sanil
- Division of Pediatric Cardiology, Children’s Hospital of Michigan, Detroit, MI, USA
| | - Alice I. Sato
- Department of Pediatric Infectious Disease, University of Nebraska Medical Center, Omaha, NE, USA
| | - Michael S. Schechter
- Department of Pediatrics, Children’s Hospital of Richmond at Virginia Commonwealth University, Richmond, VA, USA
| | - Rangaraj Selvarangan
- Department of Pathology and Laboratory Medicine, Children’s Mercy Hospital, Kansas City, MO, USA
| | - Divya Shakti
- Department of Pediatrics, Pediatric Cardiology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Kavita Sharma
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lindsay M. Squeglia
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Michelle D. Stevenson
- Department of Pediatrics, University of Louisville School of Medicine, Louisville, KY, USA
| | | | - Maria M. Talavera-Barber
- Department of Pediatrics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD, USA
| | - Ronald J. Teufel
- Department of Pediatrics, Medical University of South Carolina, Charleston, SC, USA
| | - Deepika Thacker
- Nemours Cardiac Center, Nemours Childrens Health, Delaware, Wilmington, DE, USA
| | - Mmekom M. Udosen
- RECOVER Neurocognitive and Wellbeing/Mental Health Team, NYU Grossman School of Medicine, New York, NY, USA
| | - Megan R. Warner
- Department of Pulmonary Research, Rady Children’s Hospital-San Diego, San Diego, CA, USA
| | - Sara E. Watson
- Department of Pediatrics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Alan Werzberger
- Department of Pediatrics, Columbia University Medical Center: Columbia University Irving Medical Center, New York, NY, USA
| | - Jordan C. Weyer
- Center for Individualized Medicine, Mayo Clinic Hospital, Rochester, MN, USA
| | - Marion J. Wood
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - H. Shonna Yin
- Departments of Pediatrics and Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - William T. Zempsky
- Department of Pediatrics, Connecticut Children’s Medical Center, Hartford, CT, USA
| | - Emily Zimmerman
- Department of Communication Sciences & Disorders, Northeastern University, Boston, MA, USA
| | - Benard P. Dreyer
- Department of Pediatrics, New York University Grossman School of Medicine, New York, NY, USA
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Bell TR, Beck A, Gillespie NA, Reynolds CA, Elman JA, Williams ME, Gustavson DE, Lyons MJ, Neale MC, Kremen WS, Franz CE. A Traitlike Dimension of Subjective Memory Concern Over 30 Years Among Adult Male Twins. JAMA Psychiatry 2023:2804641. [PMID: 37163244 PMCID: PMC10173101 DOI: 10.1001/jamapsychiatry.2023.1004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Importance Subjective memory concern has long been considered a state-related indicator of impending cognitive decline or dementia. The possibility that subjective memory concern may itself be a heritable trait is largely ignored, yet such an association would substantially confound its use in clinical or research settings. Objective To assess the heritability and traitlike dimensions of subjective memory concern and its clinical correlates. Design, Setting, and Participants This longitudinal twin cohort study was conducted from 1967 to 2019 among male adults with a mean (SD) age of 37.75 (2.52) years to follow-up at mean ages of 56.15 (2.72), 61.50 (2.43), and 67.35 (2.57) years (hereafter, 38, 56, 62, and 67 years, respectively) in the Vietnam Era Twin Study of Aging. The study included a national community-dwelling sample with health, education, and lifestyle characteristics comparable to a general sample of US men in this age cohort. Participants were monozygotic and dizygotic twins randomly recruited from the Vietnam Era Twin Registry. Data were analyzed from May 2021 to December 2022. Main Outcomes and Measures Measures included subjective memory concern at 4 time points; objective memory, depressive symptoms, and anxiety at the last 3 time points; negative emotionality (trait neuroticism) at age 56 years; polygenic risk scores (PRSs) for neuroticism, depression, and Alzheimer disease; APOE genotype; and parental history of dementia. Primary outcomes were heritability and correlations between subjective memory concern and other measures. Results The sample included 1555 male adults examined at age 38 years, 520 at age 56 years (due to late introduction of subjective memory concern questions), 1199 at age 62 years, and 1192 at age 67 years. Phenotypically, subjective memory concerns were relatively stable over time. At age 56 years, subjective memory concern had larger correlations with depressive symptoms (r, 0.32; 95% CI, 0.21 to 0.42), anxiety (r, 0.36; 95% CI, 0.18 to 0.51), and neuroticism (r, 0.34; 95% CI, 0.26 to 0.41) than with objective memory (r, -0.24; 95% CI, -0.33 to -0.13). Phenotypic results were similar at ages 62 and 67 years. A best-fitting autoregressive twin model indicated that genetic influences on subjective memory concern accumulated and persisted over time (h2 = 0.26-0.34 from age 38-67 years). At age 56 years, genetic influences for subjective memory concern were moderately correlated with genetic influences for anxiety (r, 0.36; 95% CI, 0.18 to 0.51), negative emotionality (r, 0.51; 95% CI, 0.44-0.57), and depressive symptoms (r, 0.20; 95% CI, 0.10 to 0.29) as well as objective memory (r, -0.22; 95% CI, -0.30 to -0.14). Similar genetic correlations were seen at ages 62 and 67 years. The neuroticism PRS was associated with subjective memory concern at age 38 years (r, 0.10; 95% CI, 0.03. to 0.18) and age 67 years (r, 0.09; 95% CI, 0.01 to 0.16). Subjective memory concern was not associated with any Alzheimer disease risk measures. Conclusions and Relevance This cohort study found stable genetic influences underlying subjective memory concern dating back to age 38 years. Subjective memory concern had larger correlations with affect-related measures than with memory-related measures. Improving the utility of subjective memory concern as an indicator of impending cognitive decline and dementia may depend on isolating its statelike component from its traitlike component.
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Affiliation(s)
- Tyler R Bell
- Center for Behavior Genetics of Aging, Department of Psychiatry, University of California, San Diego, La Jolla
| | - Asad Beck
- Graduate Program in Neuroscience, University of Washington, Seattle
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond
| | | | - Jeremy A Elman
- Center for Behavior Genetics of Aging, Department of Psychiatry, University of California, San Diego, La Jolla
| | - McKenna E Williams
- Center for Behavior Genetics of Aging, Department of Psychiatry, University of California, San Diego, La Jolla
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego
| | | | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond
| | - William S Kremen
- Center for Behavior Genetics of Aging, Department of Psychiatry, University of California, San Diego, La Jolla
| | - Carol E Franz
- Center for Behavior Genetics of Aging, Department of Psychiatry, University of California, San Diego, La Jolla
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11
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Zhou Y, Pat N, Neale MC. Associations between resting state functional brain connectivity and childhood anhedonia: A reproduction and replication study. PLoS One 2023; 18:e0277158. [PMID: 37141274 PMCID: PMC10159190 DOI: 10.1371/journal.pone.0277158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/28/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND Previously, a study using a sample of the Adolescent Brain Cognitive Development (ABCD)® study from the earlier 1.0 release found differences in several resting state functional MRI (rsfMRI) brain connectivity measures associated with children reporting anhedonia. Here, we aim to reproduce, replicate, and extend the previous findings using data from the later ABCD study 4.0 release, which includes a significantly larger sample. METHODS To reproduce and replicate the previous authors' findings, we analyzed data from the ABCD 1.0 release (n = 2437), from an independent subsample from the newer ABCD 4.0 release (excluding individuals from the 1.0 release) (n = 6456), and from the full ABCD 4.0 release sample (n = 8866). Additionally, we assessed whether using a multiple linear regression approach could improve replicability by controlling for the effects of comorbid psychiatric conditions and sociodemographic covariates. RESULTS While the previously reported associations were reproducible, effect sizes for most rsfMRI measures were drastically reduced in replication analyses (including for both t-tests and multiple linear regressions) using the ABCD 4.0 (excluding 1.0) sample. However, 2 new rsfMRI measures (the Auditory vs. Right Putamen and the Retrosplenial-Temporal vs. Right-Thalamus-Proper measures) exhibited replicable associations with anhedonia and stable, albeit small, effect sizes across the ABCD samples, even after accounting for sociodemographic covariates and comorbid psychiatric conditions using a multiple linear regression approach. CONCLUSION The most statistically significant associations between anhedonia and rsfMRI connectivity measures found in the ABCD 1.0 sample tended to be non-replicable and inflated. Contrastingly, replicable associations exhibited smaller effects with less statistical significance in the ABCD 1.0 sample. Multiple linear regressions helped assess the specificity of these findings and control the effects of confounding covariates.
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Affiliation(s)
- Yi Zhou
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Narun Pat
- Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States of America
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Maes HH, Neale MC, Ohlsson H, Sundquist J, Sundquist K, Kendler KS. Genetic and Cultural Transmission of Alcohol Use Disorder in Swedish Twin Pedigrees. J Stud Alcohol Drugs 2023; 84:368-377. [PMID: 36971731 PMCID: PMC10364785 DOI: 10.15288/jsad.22-00097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 01/11/2023] [Indexed: 07/20/2023] Open
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.
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Affiliation(s)
- Hermine H. Maes
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
- Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia
| | - Michael C. Neale
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Henrik Ohlsson
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Jan Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | | | - Kenneth S. Kendler
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
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13
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Garduno AC, Laughlin GA, Bergstrom J, Tu XM, Cummins KM, Franz CE, Elman JA, Lyons MJ, Reynolds CA, Neale MC, Gillespie NA, Xian H, McKenzie RE, Toomey R, Kremen WS, Panizzon MS, McEvoy LK. Alcohol use and cognitive aging in middle-aged men: The Vietnam Era Twin Study of Aging. J Int Neuropsychol Soc 2023; 29:235-245. [PMID: 35465863 PMCID: PMC9592679 DOI: 10.1017/s1355617722000169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVE To determine associations of alcohol use with cognitive aging among middle-aged men. METHOD 1,608 male twins (mean 57 years at baseline) participated in up to three visits over 12 years, from 2003-2007 to 2016-2019. Participants were classified into six groups based on current and past self-reported alcohol use: lifetime abstainers, former drinkers, very light (1-4 drinks in past 14 days), light (5-14 drinks), moderate (15-28 drinks), and at-risk drinkers (>28 drinks in past 14 days). Linear mixed-effects regressions modeled cognitive trajectories by alcohol group, with time-based models evaluating rate of decline as a function of baseline alcohol use, and age-based models evaluating age-related differences in performance by current alcohol use. Analyses used standardized cognitive domain factor scores and adjusted for sociodemographic and health-related factors. RESULTS Performance decreased over time in all domains. Relative to very light drinkers, former drinkers showed worse verbal fluency performance, by -0.21 SD (95% CI -0.35, -0.07), and at-risk drinkers showed faster working memory decline, by 0.14 SD (95% CI 0.02, -0.20) per decade. There was no evidence of protective associations of light/moderate drinking on rate of decline. In age-based models, light drinkers displayed better memory performance at advanced ages than very light drinkers (+0.14 SD; 95% CI 0.02, 0.20 per 10-years older age); likely attributable to residual confounding or reverse association. CONCLUSIONS Alcohol consumption showed minimal associations with cognitive aging among middle-aged men. Stronger associations of alcohol with cognitive aging may become apparent at older ages, when cognitive abilities decline more rapidly.
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Affiliation(s)
- Alexis C Garduno
- Division of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Gail A Laughlin
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Jaclyn Bergstrom
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Xin M Tu
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Kevin M Cummins
- Department of Public Health, California State University, Fullerton, CA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Hong Xian
- Department of Statistics, St Louis University, St Louis, MO, USA
- Research Service, VA St Louis Healthcare System, St Louis, MO, USA
| | - Ruth E McKenzie
- Department of Psychology, Boston University, Boston, MA, USA
- Department of Applied Human Development and Community Studies, Merrimack College, North Andover, MA, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Linda K McEvoy
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
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14
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Maes HHM, Lapato DM, Schmitt JE, Luciana M, Banich MT, Bjork JM, Hewitt JK, Madden PA, Heath AC, Barch DM, Thompson WK, Iacono WG, Neale MC. Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®. Behav Genet 2023; 53:1-24. [PMID: 36357558 PMCID: PMC9823057 DOI: 10.1007/s10519-022-10123-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 10/11/2022] [Indexed: 11/12/2022]
Abstract
Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.
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Affiliation(s)
- Hermine H. M. Maes
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA ,grid.224260.00000 0004 0458 8737Massey Cancer Center, Virginia Commonwealth University, Richmond, VA USA
| | - Dana M. Lapato
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA
| | - J. Eric Schmitt
- grid.25879.310000 0004 1936 8972Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Monica Luciana
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Marie T. Banich
- grid.266190.a0000000096214564Department of Psychology and Neuroscience, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA
| | - James M. Bjork
- grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
| | - John K. Hewitt
- grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute for Behavioral Genetics, University of Colorado, Boulder, USA
| | - Pamela A. Madden
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Andrew C. Heath
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Deanna M. Barch
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Wes K. Thompson
- grid.266100.30000 0001 2107 4242Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California at San Diego, La Jolla, CA USA
| | - William G. Iacono
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Michael C. Neale
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
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15
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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: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA ,grid.1008.90000 0001 2179 088XDepartment of Psychiatry, Austin Health, The University of Melbourne, Melbourne, VIC Australia
| | - Madhurbain Singh
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Yi Zhou
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Philip Vinh
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Brad Verhulst
- grid.264756.40000 0004 4687 2082Department of Psychiatry and Behavioral Sciences, Texas A&M University, 2900 E 29th Street, Bryan, TX 77802 USA
| | - Conor V. Dolan
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit. Amsterdam, Transitorium 2B03, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
| | - Michael C. Neale
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA ,grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit. Amsterdam, Transitorium 2B03, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
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16
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Singh M, Dolan CV, Neale MC. Integrating Cross-Lagged Panel Models with Instrumental Variables to Extend the Temporal Generalizability of Causal Inference. Multivariate Behav Res 2023; 58:148-149. [PMID: 36622870 PMCID: PMC10149576 DOI: 10.1080/00273171.2022.2160954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Affiliation(s)
- Madhurbain Singh
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Conor V. Dolan
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
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17
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Mikhail ME, Ackerman LS, Culbert KM, Burt SA, Neale MC, Keel PK, Katzman DK, Klump KL. A cotwin control study of associations between financial hardship and binge eating phenotypes during COVID-19. Int J Eat Disord 2023; 56:132-142. [PMID: 36300949 PMCID: PMC9851975 DOI: 10.1002/eat.23841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/20/2022] [Accepted: 10/14/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND COVID-19 was associated with significant financial hardship and increased binge eating (BE). However, it is largely unknown whether financial stressors contributed to BE during the pandemic. We used a longitudinal, cotwin control design that controls for genetic/environmental confounds by comparing twins in the same family to examine whether financial hardship during COVID-19 was associated with BE. METHODS Female twins (N = 158; Mage = 22.13) from the Michigan State University Twin Registry rated financial stressors (e.g., inability to afford necessities) daily for 49 consecutive days during COVID-19. We first examined whether financial hardship was associated with BE phenotypes across the full sample. We then examined whether cotwins who differed on financial hardship also differed in BE. RESULTS Participants who experienced greater mean financial hardship across the study had significantly greater dimensional BE symptoms, and participants who experienced greater financial hardship on a given day reported significantly more emotional eating that day. These results were replicated in cotwin control analyses. Twins who experienced more financial hardship than their cotwin across the study reported greater dimensional BE symptoms than their cotwin, and participants who experienced more financial hardship than their cotwin on a given day reported greater emotional eating that day. Results were identical when restricting analyses to monozygotic twins, suggesting associations were not due to genetic confounds. CONCLUSIONS Results suggest that BE-related symptoms may be elevated in women who experienced financial hardship during COVID-19 independent of potential genetic/environmental confounds. However, additional research in larger samples is needed. PUBLIC SIGNIFICANCE Little is known regarding how financial difficulties during the COVID-19 pandemic may have contributed to increased binge eating (BE). We found preliminary evidence that financial hardship during COVID-19 may be associated with greater rates of BE-related symptoms even when comparing twins from the same family. While additional research is needed, results suggest that people who experienced financial hardship during COVID-19 may be at increased risk for BE.
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Affiliation(s)
- Megan E. Mikhail
- Department of PsychologyMichigan State UniversityEast LansingMichiganUSA
| | | | - Kristen M. Culbert
- Department of PsychologyMichigan State UniversityEast LansingMichiganUSA
| | - S. Alexandra Burt
- Department of PsychologyMichigan State UniversityEast LansingMichiganUSA
| | - Michael C. Neale
- Departments of Psychiatry, Human Genetics, and PsychologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Pamela K. Keel
- Department of PsychologyFlorida State UniversityTallahasseeFloridaUSA
| | - Debra K. Katzman
- Department of PediatricsUniversity of TorontoTorontoOntarioCanada
| | - Kelly L. Klump
- Department of PsychologyMichigan State UniversityEast LansingMichiganUSA
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18
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Tang R, Panizzon MS, Elman JA, Gillespie NA, Hauger RL, Rissman RA, Lyons MJ, Neale MC, Reynolds CA, Franz CE, Kremen WS. Association of neurofilament light chain with renal function: mechanisms and clinical implications. Alzheimers Res Ther 2022; 14:189. [PMID: 36527130 PMCID: PMC9756450 DOI: 10.1186/s13195-022-01134-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/03/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Blood-based neurofilament light chain (NfL) is a promising biomarker of neurodegeneration across multiple neurodegenerative diseases. However, blood-based NfL is highly associated with renal function in older adults, which leads to the concern that blood-based NfL levels may be influenced by renal function, rather than neurodegeneration alone. Despite growing interest in using blood-based NfL as a biomarker of neurodegeneration in research and clinical practices, whether renal function should always be accounted for in these settings remains unclear. Moreover, the mechanisms underlying this association between blood-based measures of NfL and renal function remain elusive. In this study, we first evaluated the effect of renal function on the associations of plasma NfL with other measures of neurodegeneration. We then examined the extent of genetic and environmental contributions to the association between plasma NfL and renal function. METHODS In a sample of 393 adults (mean age=75.22 years, range=54-90), we examined the associations of plasma NfL with cerebrospinal fluid (CSF) NfL and brain volumetric measures before and after adjusting for levels of serum creatinine (an index of renal function). In an independent sample of 969 men (mean age=67.57 years, range=61-73) that include monozygotic and dizygotic twin pairs, we replicated the same analyses and leveraged biometrical twin modeling to examine the genetic and environmental influences on the plasma NfL and creatinine association. RESULTS Plasma NfL's associations with cerebrospinal fluid NfL and brain volumetric measures did not meaningfully change after adjusting for creatinine levels. Both plasma NfL and creatinine were significantly heritable (h2=0.54 and 0.60, respectively). Their phenotypic correlation (r=0.38) was moderately explained by shared genetic influences (genetic correlation=0.46) and unique environmental influences (unique environmental correlation=0.27). CONCLUSIONS Adjusting for renal function is unnecessary when assessing associations between plasma NfL and other measures of neurodegeneration but is necessary if plasma NfL is compared to a cutoff for classifying neurodegeneration-positive versus neurodegeneration-negative individuals. Blood-based measures of NfL and renal function are heritable and share common genetic influences.
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Affiliation(s)
- Rongxiang Tang
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA.
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, 92093, USA
| | - Nathan A Gillespie
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, 23284, USA
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, 92093, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA, 92093, USA
| | - Robert A Rissman
- Department of Neurosciences, University of California San Diego, CA, 92093, La Jolla, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02212, USA
| | - Michael C Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, 92521, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, 92093, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, 92093, USA
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19
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Gillespie NA, Rissman RA, Elman JA, Reynolds CA, Panizzon MS, Lyons MJ, Neale MC, Franz CE, Kremen WS. The etiology of blood‐based biomarkers for Alzheimer’s Disease in a population‐based sample of mid to late‐age males. Alzheimers Dement 2022. [DOI: 10.1002/alz.060480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Robert A. Rissman
- Department of Neurosciences, University of California San Diego CA USA
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20
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Ditmars HL, Logue MW, Toomey R, McKenzie RE, Franz CE, Panizzon MS, Reynolds CA, Cuthbert KN, Vandiver R, Gustavson DE, Eglit GML, Elman JA, Sanderson-Cimino M, Williams ME, Andreassen OA, Dale AM, Eyler LT, Fennema-Notestine C, Gillespie NA, Hauger RL, Jak AJ, Neale MC, Tu XM, Whitsel N, Xian H, Kremen WS, Lyons MJ. Associations Between Depression and Cardiometabolic Health: A 27-Year Longitudinal Study - Corrigendum. Psychol Med 2022; 52:3018. [PMID: 36177891 DOI: 10.1017/s0033291722003105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Hillary L Ditmars
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Mark W Logue
- Research Service, VA Boston Healthcare System, Boston, MA, USA
- Biomedical Genetics Program, Boston University School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Ruth E McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- School of Education and Social Policy, Merrimack College, North Andover, MA, USA
| | - Carol E Franz
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Kristy N Cuthbert
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Richard Vandiver
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Daniel E Gustavson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Graham M L Eglit
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, San Diego, CA, USA
| | - Jeremy A Elman
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Mark Sanderson-Cimino
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- San Diego State University/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - McKenna E Williams
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- San Diego State University/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Nathan A Gillespie
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Richard L Hauger
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Amy J Jak
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Michael C Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Xin M Tu
- Department of Family Medicine and Public Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Nathan Whitsel
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Hong Xian
- Department of Epidemiology & Biostatistics, Saint Louis University College for Public Health & Social Justice, Saint Louis, MO, USA
| | - William S Kremen
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
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21
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Mikhail ME, Fowler N, Burt SA, Neale MC, Keel PK, Katzman DK, Klump KL. A daily diary study of emotion regulation as a moderator of negative affect-binge eating associations. Int J Eat Disord 2022; 55:1305-1315. [PMID: 35779074 PMCID: PMC9529946 DOI: 10.1002/eat.23768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/19/2022] [Accepted: 06/20/2022] [Indexed: 11/12/2022]
Abstract
BACKGROUND While negative affect (NA) typically increases risk for binge eating, the ultimate impact of NA may depend on a person's ability to regulate their emotions. In this daily, longitudinal study, we examined whether emotion regulation (ER) modified the strength of NA-dysregulated eating associations. METHODS Women (N = 311) from the Michigan State University Twin Registry first reported dimensional binge eating symptoms and broad ER difficulties (e.g., limited emotional awareness, difficulty controlling emotional impulses). Participants then rated use of adaptive (cognitive reappraisal, social sharing, situation modification, and acceptance) and maladaptive (rumination, expressive suppression, and self-criticism) ER strategies, emotional eating (EE), objective binge eating (OBE), and NA once daily for 49 consecutive days. RESULTS There were several main effects of ER on binge-eating pathology in both between-person (i.e., comparing women who differed on average) and within-person (i.e., examining fluctuations in variables day-to-day) analyses. Between-person, greater broad ER difficulties, greater maladaptive strategy use, and lower adaptive strategy use were all associated with greater binge-eating pathology. Within-person, greater maladaptive strategy use was associated with greater odds of OBE on that day and on the following day. However, neither broad ER difficulties nor use of specific strategies moderated associations between NA and dysregulated eating in between- or within-person analyses. CONCLUSIONS While ER is independently associated with risk for dysregulated eating, it may not fully mitigate the impact of NA. Additional strategies (e.g., decreasing environmental stressors and increasing social support) may be needed to minimize NA and its impact on dysregulated eating. PUBLIC SIGNIFICANCE Negative affect (NA; e.g., sadness, guilt) increases dysregulated eating risk. Because NA is sometimes unavoidable, we examined whether emotion regulation (ER; i.e., how a person responds to their emotions) might impact whether NA leads to dysregulated eating. Although more effective ER was associated with less dysregulated eating overall, ER did not impact the association between NA and dysregulated eating. Other approaches may therefore be needed to mitigate NA-dysregulated eating associations.
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Affiliation(s)
| | - Natasha Fowler
- Department of Behavioral Neuroscience, Oregon Health and Science University, USA
| | | | - Michael C. Neale
- Departments of Psychiatry, Human Genetics, and Psychology, Virginia Commonwealth University, USA
| | | | | | - Kelly L. Klump
- Department of Psychology, Michigan State University, USA
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22
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Ditmars HL, Logue MW, Toomey R, McKenzie RE, Franz CE, Panizzon MS, Reynolds CA, Cuthbert KN, Vandiver R, Gustavson DE, Eglit GML, Elman JA, Sanderson-Cimino M, Williams ME, Andreassen OA, Dale AM, Eyler LT, Fennema-Notestine C, Gillespie NA, Hauger RL, Jak AJ, Neale MC, Tu XM, Whitsel N, Xian H, Kremen WS, Lyons MJ. Associations between depression and cardiometabolic health: A 27-year longitudinal study. Psychol Med 2022; 52:3007-3017. [PMID: 33431106 PMCID: PMC8547283 DOI: 10.1017/s003329172000505x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Clarifying the relationship between depression symptoms and cardiometabolic and related health could clarify risk factors and treatment targets. The objective of this study was to assess whether depression symptoms in midlife are associated with the subsequent onset of cardiometabolic health problems. METHODS The study sample comprised 787 male twin veterans with polygenic risk score data who participated in the Harvard Twin Study of Substance Abuse ('baseline') and the longitudinal Vietnam Era Twin Study of Aging ('follow-up'). Depression symptoms were assessed at baseline [mean age 41.42 years (s.d. = 2.34)] using the Diagnostic Interview Schedule, Version III, Revised. The onset of eight cardiometabolic conditions (atrial fibrillation, diabetes, erectile dysfunction, hypercholesterolemia, hypertension, myocardial infarction, sleep apnea, and stroke) was assessed via self-reported doctor diagnosis at follow-up [mean age 67.59 years (s.d. = 2.41)]. RESULTS Total depression symptoms were longitudinally associated with incident diabetes (OR 1.29, 95% CI 1.07-1.57), erectile dysfunction (OR 1.32, 95% CI 1.10-1.59), hypercholesterolemia (OR 1.26, 95% CI 1.04-1.53), and sleep apnea (OR 1.40, 95% CI 1.13-1.74) over 27 years after controlling for age, alcohol consumption, smoking, body mass index, C-reactive protein, and polygenic risk for specific health conditions. In sensitivity analyses that excluded somatic depression symptoms, only the association with sleep apnea remained significant (OR 1.32, 95% CI 1.09-1.60). CONCLUSIONS A history of depression symptoms by early midlife is associated with an elevated risk for subsequent development of several self-reported health conditions. When isolated, non-somatic depression symptoms are associated with incident self-reported sleep apnea. Depression symptom history may be a predictor or marker of cardiometabolic risk over decades.
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Affiliation(s)
- Hillary L. Ditmars
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
| | - Mark W. Logue
- Research Service, VA Boston Healthcare System, Boston, MA
- Biomedical Genetics Program, Boston University School of Medicine, Boston, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
| | - Ruth E. McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
- School of Education and Social Policy, Merrimack College, North Andover, MA, USA
| | - Carol E. Franz
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA
| | - Matthew S. Panizzon
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA
| | - Chandra A. Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA
| | - Kristy N. Cuthbert
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
| | - Richard Vandiver
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
| | | | - Graham M. L. Eglit
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA
- VA San Diego Healthcare System, San Diego, CA
| | - Jeremy A. Elman
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA
| | - Mark Sanderson-Cimino
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- San Diego State University/UC San Diego Joint Doctoral Program in Clinical Psychology
| | - McKenna E. Williams
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- San Diego State University/UC San Diego Joint Doctoral Program in Clinical Psychology
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine University of Oslo Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital Oslo, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA
| | - Lisa T. Eyler
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Christine Fennema-Notestine
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Department of Radiology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Nathan A. Gillespie
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Richard L. Hauger
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA
| | - Amy J. Jak
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA
| | - Michael C. Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Xin M. Tu
- Department of Family Medicine and Public Health, VA San Diego Healthcare System, San Diego, CA
| | - Nathan Whitsel
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Hong Xian
- Department of Epidemiology & Biostatistics, Saint Louis University College for Public Health & Social Justice
| | - William S. Kremen
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
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23
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Neale MC, Hatemi PK, Prom-Wormley EC, Neale BM, Heath AC, Maes HH. Reverend Dr. Lindon Eaves: A Career Remembrance. Behav Genet 2022. [PMID: 35568773 DOI: 10.1007/s10519-022-10103-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.,Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.,Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Peter K Hatemi
- Political Science, Microbiology, The Pennsylvania State University, State College, PA, USA
| | - Elizabeth C Prom-Wormley
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.,Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA.,Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, USA
| | - Benjamin M Neale
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington School of Medicine, St Louis, MI, USA
| | - Hermine H Maes
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA. .,Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA. .,Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA. .,Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA.
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24
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Gillespie NA, Hatton SN, Hagler DJ, Dale AM, Elman JA, McEvoy LK, Eyler LT, Fennema-Notestine C, Logue MW, McKenzie RE, Puckett OK, Tu XM, Whitsel N, Xian H, Reynolds CA, Panizzon MS, Lyons MJ, Neale MC, Kremen WS, Franz C. The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years. Front Aging Neurosci 2022; 14:831002. [PMID: 35493948 PMCID: PMC9051484 DOI: 10.3389/fnagi.2022.831002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/15/2022] [Indexed: 01/27/2023] Open
Abstract
Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.
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Affiliation(s)
- Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behaviour Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States,QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia,*Correspondence: Nathan A. Gillespie,
| | - Sean N. Hatton
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Donald J. Hagler
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Anders M. Dale
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States,Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States,Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
| | - Jeremy A. Elman
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Linda K. McEvoy
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Lisa T. Eyler
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, United States
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Mark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, United States,Department of Psychiatry and Biomedical Genetics Section, Boston University School of Medicine, Boston, MA, United States,Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ruth E. McKenzie
- Department of Psychology, Boston University, Boston, MA, United States,School of Education and Social Policy, Merrimack College, North Andover, MA, United States
| | - Olivia K. Puckett
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Xin M. Tu
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Nathan Whitsel
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Hong Xian
- Department of Epidemiology and Biostatistics, Saint. Louis University, St. Louis, MO, United States,Research Service, VA St. Louis Healthcare System, St. Louis, MO, United States
| | - Chandra A. Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
| | - Matthew S. Panizzon
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behaviour Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States,Department of Biological Psychology, Free University of Amsterdam, Amsterdam, Netherlands
| | - William S. Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, United States,William S. Kremen,
| | - Carol Franz
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Carol Franz,
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25
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Whitsel N, Reynolds CA, Buchholz EJ, Pahlen S, Pearce RC, Hatton SN, Elman JA, Gillespie NA, Gustavson DE, Puckett OK, Dale AM, Eyler LT, Fennema-Notestine C, Hagler DJ, Hauger RL, McEvoy LK, McKenzie R, Neale MC, Panizzon MS, Sanderson-Cimino M, Toomey R, Tu XM, Williams MKE, Bell T, Xian H, Lyons MJ, Kremen WS, Franz CE. Long-term associations of cigarette smoking in early mid-life with predicted brain aging from mid- to late life. Addiction 2022; 117:1049-1059. [PMID: 34605095 PMCID: PMC8904283 DOI: 10.1111/add.15710] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 09/03/2021] [Accepted: 09/15/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND AND AIMS Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later. DESIGN Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated. SETTING Population-based United States sample. PARTICIPANTS/CASES Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves. MEASUREMENTS Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19). FINDINGS In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD. CONCLUSIONS Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.
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Affiliation(s)
- Nathan Whitsel
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Erik J Buchholz
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Shandell Pahlen
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Rahul C Pearce
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Sean N Hatton
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Daniel E Gustavson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Olivia K Puckett
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Anders M Dale
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Donald J Hagler
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Linda K McEvoy
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Ruth McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Mark Sanderson-Cimino
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, La Jolla, CA, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Xin M Tu
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Mc Kenna E Williams
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, La Jolla, CA, USA
| | - Tyler Bell
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Hong Xian
- Department of Epidemiology and Biostatistics, St Louis University, St Louis, MO, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
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Eglit GML, Elman JA, Panizzon MS, Sanderson-Cimino M, Williams ME, Dale AM, Eyler LT, Fennema-Notestine C, Gillespie NA, Gustavson DE, Hatton SN, Hagler DJ, Hauger RL, Jak AJ, Logue MW, McEvoy LK, McKenzie RE, Neale MC, Puckett O, Reynolds CA, Toomey R, Tu XM, Whitsel N, Xian H, Lyons MJ, Franz CE, Kremen WS. Paradoxical cognitive trajectories in men from earlier to later adulthood. Neurobiol Aging 2022; 109:229-238. [PMID: 34785406 PMCID: PMC8715388 DOI: 10.1016/j.neurobiolaging.2021.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 10/07/2021] [Accepted: 10/07/2021] [Indexed: 01/03/2023]
Abstract
Because longitudinal studies of aging typically lack cognitive data from earlier ages, it is unclear how general cognitive ability (GCA) changes throughout the life course. In 1173 Vietnam Era Twin Study of Aging (VETSA) participants, we assessed young adult GCA at average age 20 and current GCA at 3 VETSA assessments beginning at average age 56. The same GCA index was used throughout. Higher young adult GCA and better GCA maintenance were associated with stronger specific cognitive abilities from age 51 to 73. Given equivalent GCA at age 56, individuals who had higher age 20 GCA outperformed those whose GCA remained stable in terms of memory, executive function, and working memory abilities from age 51 to 73. Thus, paradoxically, despite poorer maintenance of GCA, high young adult GCA still conferred benefits. Advanced predicted brain age and the combination of elevated vascular burden and APOE-ε4 status were associated with poorer maintenance of GCA. These findings highlight the importance of distinguishing between peak and current GCA for greater understanding of cognitive aging.
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Affiliation(s)
- Graham M L Eglit
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA.
| | - Jeremy A Elman
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Mathew S Panizzon
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Mark Sanderson-Cimino
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA; San Diego State University/University of California, San Diego Joint Doctoral Program, San Diego, CA, USA
| | - McKenna E Williams
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA; San Diego State University/University of California, San Diego Joint Doctoral Program, San Diego, CA, USA
| | - Anders M Dale
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Daniel E Gustavson
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Sean N Hatton
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Donald J Hagler
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Richard L Hauger
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Amy J Jak
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Mark W Logue
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA; Psychiatry and Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Linda K McEvoy
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Ruth E McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA; School of Education and Social Policy, Merrimack College, North Andover, MA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Olivia Puckett
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Xin M Tu
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Nathan Whitsel
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Hong Xian
- Department of Epidemiology and Biostatistics, St. Louis University, St. Louis, MO, USA
| | - Michael J Lyons
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Carol E Franz
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
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27
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Bustamante D, Amstadter AB, Pritikin JN, Brick TR, Neale MC. Associations Between Traumatic Stress, Brain Volumes and Post-traumatic Stress Disorder Symptoms in Children: Data from the ABCD Study. Behav Genet 2021; 52:75-91. [PMID: 34860306 PMCID: PMC8860798 DOI: 10.1007/s10519-021-10092-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 11/07/2021] [Indexed: 11/26/2022]
Abstract
Reduced volumes in brain regions of interest (ROIs), primarily from adult samples, are associated with posttraumatic stress disorder (PTSD). We extended this work to children using data from the Adolescent Brain Cognitive Development (ABCD) Study® (N = 11,848; Mage = 9.92). Structural equation modeling and an elastic-net (EN) machine-learning approach were used to identify potential effects of traumatic events (TEs) on PTSD symptoms (PTSDsx) directly, and indirectly via the volumes 300 subcortical and cortical ROIs. We then estimated the genetic and environmental variation in the phenotypes. TEs were directly associated with PTSDsx (r = 0.92) in children, but their indirect effects (r < 0.0004)-via the volumes of EN-identified subcortical and cortical ROIs-were negligible at this age. Additive genetic factors explained a modest proportion of the variance in TEs (23.4%) and PTSDsx (21.3%), and accounted for most of the variance of EN-identified volumes of four of the five subcortical (52.4-61.8%) three of the nine cortical ROIs (46.4-53.3%) and cerebral white matter in the left hemisphere (57.4%). Environmental factors explained most of the variance in TEs (C = 61.6%, E = 15.1%), PTSDsx (residual-C = 18.4%, residual-E = 21.8%), right lateral ventricle (C = 15.2%, E = 43.1%) and six of the nine EN-identified cortical ROIs (C = 4.0-13.6%, E = 56.7-74.8%). There is negligible evidence that the volumes of brain ROIs are associated with the indirect effects of TEs on PTSDsx at this age. Overall, environmental factors accounted for more of the variation in TEs and PTSDsx. Whereas additive genetic factors accounted for most of the variability in the volumes of a minority of cortical and in most of subcortical ROIs.
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Affiliation(s)
- Daniel Bustamante
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA.
- Integrative Life Sciences Doctoral Program, Virginia Commonwealth University, Richmond, VA, USA.
| | - Ananda B Amstadter
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA
- Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Joshua N Pritikin
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA
- Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Timothy R Brick
- Department of Human Development and Family Studies, and Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA
- Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
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Williams ME, Gillespie NA, Dale AM, Elman JA, Eyler LT, Fennema‐Notestine C, Hagler DJ, McEvoy LK, Neale MC, Panizzon MS, Sanderson‐Cimino ME, Franz CE, Kremen WS. Genetic and environmental influences on Alzheimer’s disease neuroimaging signatures. Alzheimers Dement 2021. [DOI: 10.1002/alz.054708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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29
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Wade NE, Ortigara JM, Sullivan RM, Tomko RL, Breslin FJ, Baker FC, Fuemmeler BF, Delrahim Howlett K, Lisdahl KM, Marshall AT, Mason MJ, Neale MC, Squeglia LM, Wolff-Hughes DL, Tapert SF, Bagot KS. Passive Sensing of Preteens' Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy. JMIR Ment Health 2021; 8:e29426. [PMID: 34661541 PMCID: PMC8561413 DOI: 10.2196/29426] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/11/2021] [Accepted: 06/12/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Concerns abound regarding childhood smartphone use, but studies to date have largely relied on self-reported screen use. Self-reporting of screen use is known to be misreported by pediatric samples and their parents, limiting the accurate determination of the impact of screen use on social, emotional, and cognitive development. Thus, a more passive, objective measurement of smartphone screen use among children is needed. OBJECTIVE This study aims to passively sense smartphone screen use by time and types of apps used in a pilot sample of children and to assess the feasibility of passive sensing in a larger longitudinal sample. METHODS The Adolescent Brain Cognitive Development (ABCD) study used passive, objective phone app methods for assessing smartphone screen use over 4 weeks in 2019-2020 in a subsample of 67 participants (aged 11-12 years; 31/67, 46% female; 23/67, 34% White). Children and their parents both reported average smartphone screen use before and after the study period, and they completed a questionnaire regarding the acceptability of the study protocol. Descriptive statistics for smartphone screen use, app use, and protocol feasibility and acceptability were reviewed. Analyses of variance were run to assess differences in categorical app use by demographics. Self-report and parent report were correlated with passive sensing data. RESULTS Self-report of smartphone screen use was partly consistent with objective measurement (r=0.49), although objective data indicated that children used their phones more than they reported. Passive sensing revealed the most common types of apps used were for streaming (mean 1 hour 57 minutes per day, SD 1 hour 32 minutes), communication (mean 48 minutes per day, SD 1 hour 17 minutes), gaming (mean 41 minutes per day, SD 41 minutes), and social media (mean 36 minutes per day, SD 1 hour 7 minutes). Passive sensing of smartphone screen use was generally acceptable to children (43/62, 69%) and parents (53/62, 85%). CONCLUSIONS The results of passive, objective sensing suggest that children use their phones more than they self-report. Therefore, use of more robust methods for objective data collection is necessary and feasible in pediatric samples. These data may then more accurately reflect the impact of smartphone screen use on behavioral and emotional functioning. Accordingly, the ABCD study is implementing a passive sensing protocol in the full ABCD cohort. Taken together, passive assessment with a phone app provided objective, low-burden, novel, informative data about preteen smartphone screen use.
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Affiliation(s)
- Natasha E Wade
- University of California, San Diego, La Jolla, CA, United States
| | | | - Ryan M Sullivan
- University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Rachel L Tomko
- Medical University of South Carolina, Charleston, SC, United States
| | | | | | | | | | | | | | | | - Michael C Neale
- Virginia Commonwealth University, Richmond, VA, United States
| | | | | | - Susan F Tapert
- University of California, San Diego, La Jolla, CA, United States
| | - Kara S Bagot
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
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30
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Franz CE, Hatton SN, Elman JA, Warren T, Gillespie NA, Whitsel NA, Puckett OK, Dale AM, Eyler LT, Fennema-Notestine C, Hagler DJ, Hauger RL, McKenzie R, Neale MC, Panizzon MS, Pearce RC, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Williams M, Xian H, Lyons MJ, Kremen WS. Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age. Neurobiol Aging 2021; 108:80-89. [PMID: 34547718 PMCID: PMC8862767 DOI: 10.1016/j.neurobiolaging.2021.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 07/23/2021] [Accepted: 08/12/2021] [Indexed: 01/18/2023]
Abstract
We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.
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Affiliation(s)
- Carol E Franz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA.
| | - Sean N Hatton
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Teresa Warren
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA; QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Nathan A Whitsel
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Olivia K Puckett
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA; Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Ruth McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Rahul C Pearce
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Mark Sanderson-Cimino
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Xin M Tu
- Department of Family Medicine, University of California San Diego, San Diego, CA, USA
| | - McKenna Williams
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Hong Xian
- Department of Epidemiology & Biostatistics, St. Louis University, St. Louis, MO, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA
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31
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Wright ML, Fettweis JM, Eaves LJ, Silberg JL, Neale MC, Serrano MG, Jimenez NR, Prom-Wormley E, Girerd PH, Borzelleca JF, Jefferson KK, Strauss JF, York TP, Buck GA. Vaginal microbiome Lactobacillus crispatus is heritable among European American women. Commun Biol 2021; 4:872. [PMID: 34354222 PMCID: PMC8342574 DOI: 10.1038/s42003-021-02394-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 06/18/2021] [Indexed: 02/07/2023] Open
Abstract
The diversity and dominant bacterial taxa in the vagina are reported to be influenced by multiple intrinsic and extrinsic factors, including but not limited to pregnancy, contraceptive use, pathogenic states, socioeconomic status, and ancestry. However, the extent to which host genetic factors influence variation in the vaginal microbiota is unclear. We used a biometrical genetic approach to determine whether host genetic factors contribute to inter-individual differences in taxa from a sample of 332 twins who self-identified as being of African (44 pairs) or European ancestry (122 pairs). Lactobacillus crispatus, a major determinant of vaginal health, was identified as heritable among European American women (narrow-sense heritability = 34.7%, P-value = 0.018). Heritability of L. crispatus is consistent with the reduced prevalence of adverse reproductive disorders, including bacterial vaginosis and preterm birth, among women of European ancestry. Wright et al. apply biometric genetic approach to identify the extent to which host genetic factors influence species-level variation in the vaginal microbiota. Their study suggests that Lactobacillus crispatus, a major determinant of vaginal health, is heritable among European American women, consistent with the reduced prevalence of adverse reproductive disorders in them.
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Affiliation(s)
- Michelle L Wright
- School of Nursing, The University of Texas at Austin, Austin, TX, USA.,Department of Women's Health, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Jennifer M Fettweis
- Department of Microbiology and Immunology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Department of Obstetrics and Gynecology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA
| | - Lindon J Eaves
- Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Judy L Silberg
- Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Mid-Atlantic Twin Registry, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael C Neale
- Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Myrna G Serrano
- Department of Microbiology and Immunology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA
| | - Nicole R Jimenez
- Department of Microbiology and Immunology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA
| | - Elizabeth Prom-Wormley
- Family Medicine and Population Health, Division of Epidemiology, Virginia Commonwealth University, Richmond, VA, USA
| | - Philippe H Girerd
- Department of Obstetrics and Gynecology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA
| | - Joseph F Borzelleca
- Department of Obstetrics and Gynecology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Kimberly K Jefferson
- Department of Microbiology and Immunology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA
| | - Jerome F Strauss
- Department of Obstetrics and Gynecology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA
| | - Timothy P York
- Department of Obstetrics and Gynecology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA. .,Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
| | - Gregory A Buck
- Department of Microbiology and Immunology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.,Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA.,Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, VA, USA
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32
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Williams ME, Elman JA, McEvoy LK, Andreassen OA, Dale AM, Eglit GML, Eyler LT, Fennema-Notestine C, Franz CE, Gillespie NA, Hagler DJ, Hatton SN, Hauger RL, Jak AJ, Logue MW, Lyons MJ, McKenzie RE, Neale MC, Panizzon MS, Puckett OK, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Whitsel N, Xian H, Kremen WS. 12-year prediction of mild cognitive impairment aided by Alzheimer's brain signatures at mean age 56. Brain Commun 2021; 3:fcab167. [PMID: 34396116 PMCID: PMC8361427 DOI: 10.1093/braincomms/fcab167] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/26/2021] [Accepted: 05/10/2021] [Indexed: 01/22/2023] Open
Abstract
Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo 0316, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0372, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92093, USA
| | - Graham M L Eglit
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, CA 92093, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sean N Hatton
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92093, USA
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA 92093, USA
| | - Amy J Jak
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- VA San Diego Healthcare System, San Diego, CA 92093, USA
| | - Mark W Logue
- National Center for PTSD: Behavioral Science Division, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry and the Biomedical Genetics Section, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02212, USA
| | - Ruth E McKenzie
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- School of Education and Social Policy, Merrimack College, North Andover, MA 01845, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Olivia K Puckett
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA 92521, USA
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02212, USA
| | - Xin M Tu
- Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093, USA
| | - Nathan Whitsel
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Hong Xian
- Department of Biostatistics, St. Louis University, St. Louis, MO 63103, USA
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA 92093, USA
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Hwang LD, Mitchell BL, Medland SE, Martin NG, Neale MC, Evans DM. Correction to: The Augmented Classical Twin Design: Incorporating Genome-Wide Identity by Descent Sharing Into Twin Studies in Order to Model Violations of the Equal Environments Assumption. Behav Genet 2021; 51:441-442. [PMID: 34043138 DOI: 10.1007/s10519-021-10065-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Level 7, 37 Kent St, Brisbane, Australia.,QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Brittany L Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Biomedical Science, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Biomedical Science, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia
| | - Michael C Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Level 7, 37 Kent St, Brisbane, Australia. .,QIMR Berghofer Medical Research Institute, Brisbane, Australia. .,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. .,Translational Research Institute, Woolloongabba, QLD, 4102, Australia.
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Verhulst B, Clark SL, Chen J, Maes HH, Chen X, Neale MC. Clarifying the Genetic Influences on Nicotine Dependence and Quantity of Use in Cigarette Smokers. Behav Genet 2021; 51:375-384. [PMID: 33884518 DOI: 10.1007/s10519-021-10056-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 03/30/2021] [Indexed: 11/29/2022]
Abstract
Nicotine dependence and smoking quantity are both robustly associated with the CHRNA5-A3-B4 gene cluster in the 15q25 region, and SNP rs16969968 in particular. The purpose of this paper is to use structural equation modeling techniques (SEM) to disentangle the complex pattern of relationships between rs16969968, nicotine quantity (as measured by the number of cigarettes an individual smokes per day; CPD) and nicotine dependence (as measured by the Fagerström Test for Nicotine Dependence; FTND). CPD is an indicator, but also a potential cause, of FTND, complicating the interpretation of associations between these constructs and requires a more detailed investigation than standard GWAS or general linear regression models can provide. FTND items and genotypes were collected in four samples, with a combined sample size of 5,373 respondents. A mega-analysis was conducted using a multiple group SEM approach to test competing hypotheses regarding the relationships between the SNP rs16969968, FTND and CPD. In the best fitting model, the FTND items loaded onto two correlated factors. The first, labeled "maintenance," assesses the motivation to maintain constant levels of nicotine through out the day. The second was labeled "urgency" as its items concern the urgency to restore nicotine levels after abstinence. We focus our attention on the "maintenance" factor, of which CPD was an indicator. The best fitting model included a negative feedback loop between the Maintenance factor and CPD. Accordingly, the motivation to maintain higher levels of nicotine increased the quantity of nicotine consumed, which subsequently decreases the maintenance motivation. The fact that the Maintenance-CPD feedback model fits the data best implies that there are at least two biological pathways that lead from rs16969968 to smoking behaviors. The model is consistent with a supply and demand system, which allows individuals to achieve a homeostatic equilibrium for their nicotine concentration.
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Affiliation(s)
- Brad Verhulst
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, 8447 Riverside Pkwy, Bryan, TX, 77807, USA.
| | - Shaunna L Clark
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, 8447 Riverside Pkwy, Bryan, TX, 77807, USA
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada, Reno, USA
| | | | - Xiangning Chen
- Nevada Institute of Personalized Medicine, University of Nevada, Reno, USA
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Sanderson-Cimino M, Panizzon MS, Elman JA, Tu X, Gustavson DE, Puckett O, Cross K, Notestine R, Hatton SN, Eyler LT, McEvoy LK, Hagler DJ, Neale MC, Gillespie NA, Lyons MJ, Franz CE, Fennema-Notestine C, Kremen WS. Periventricular and deep abnormal white matter differ in associations with cognitive performance at midlife. Neuropsychology 2021; 35:252-264. [PMID: 33970659 PMCID: PMC8500190 DOI: 10.1037/neu0000718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Objective: Abnormal white matter (AWM) on magnetic resonance imaging is associated with cognitive performance in older adults. We explored cognitive associations with AWM during late-midlife. Method: Participants were community-dwelling men (n = 242; M = 61.90 years; range = 56-66). Linear-mixed effects regression models examined associations of total, periventricular, and deep AWM with cognitive performance, controlling for multiple comparisons. Models considering specific cognitive domains controlled for current general cognitive ability (GCA). We hypothesized that total AWM would be associated with worse processing speed, executive function, and current GCA; deep AWM would correlate with GCA and periventricular AWM would relate to specific cognitive abilities. We also assessed the potential influence of cognitive reserve by examining a moderation effect of early life (mean age of 20) cognition. Results: Greater total and deep AWM were associated with poorer current GCA. Periventricular AWM was associated with worse executive function, working memory, and episodic memory. When periventricular and deep AWM were modeled simultaneously, both retained their respective significant associations with cognitive performance. Cognitive reserve did not moderate associations. Conclusions: Our findings suggest that AWM contributes to poorer cognitive function in late-midlife. Examining only total AWM may obscure the potential differential impact of regional AWM. Separating total AWM into subtypes while controlling for current GCA revealed a dissociation in relationships with cognitive performance; deep AWM was associated with nonspecific cognitive ability whereas periventricular AWM was associated with specific frontal-related abilities and memory. Management of vascular or other risk factors that may increase the risk of AWM should begin during or before early late-midlife. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Mark Sanderson-Cimino
- Joint Doctoral Program in Clinical Psychology, San Diego State/University of California
- Center for Behavior Genetics of Aging, University of California
| | - Matthew S. Panizzon
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | - Jeremy A. Elman
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | - Xin Tu
- Family Medicine and Public Health, University of California
| | - Daniel E. Gustavson
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Department of Medicine, Vanderbilt University Medical Center
| | - Olivia Puckett
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | | | - Randy Notestine
- Department of Psychiatry University of California
- Computational and Applied Statistics Laboratory (CASL) at the San Diego Supercomputer Center
| | - Sean N Hatton
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Department of Neurosciences, University of California
| | - Lisa T. Eyler
- Department of Psychiatry University of California
- Mental Illness Research, Education, And Clinical Center, Veterans Affairs San Diego Healthcare System
| | - Linda K. McEvoy
- Department of Radiology, University of California, San Diego
| | | | - Michael C. Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University
| | - Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University
| | - Carol E. Franz
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
| | - Christine Fennema-Notestine
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Department of Radiology, University of California, San Diego
| | - William S. Kremen
- Center for Behavior Genetics of Aging, University of California
- Department of Psychiatry University of California
- Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System
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36
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Pritikin JN, Neale MC, Prom-Wormley EC, Clark SL, Verhulst B. GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS. Behav Genet 2021; 51:343-357. [PMID: 33604756 DOI: 10.1007/s10519-021-10043-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 01/18/2021] [Indexed: 12/12/2022]
Abstract
Most genome-wide association study (GWAS) analyses test the association between single-nucleotide polymorphisms (SNPs) and a single trait or outcome. While valuable second-step analyses of these associations (e.g., calculating genetic correlations between traits) are common, single-step multivariate analyses of GWAS data are rarely performed. This is unfortunate because multivariate analyses can reveal information which is irrevocably obscured in multi-step analysis. One simple example is the distinction between variance common to a set of measures, and variance specific to each. Neither GWAS of sum- or factor-scores, nor GWAS of the individual measures will deliver a clean picture of loci associated with each measure's specific variance. While multivariate GWAS opens up a broad new landscape of feasible and informative analyses, its adoption has been slow, likely due to the heavy computational demands and difficulties specifying models it requires. Here we describe GW-SEM 2.0, which is designed to simplify model specification and overcome the inherent computational challenges associated with multivariate GWAS. In addition, GW-SEM 2.0 allows users to accurately model ordinal items, which are common in behavioral and psychological research, within a GWAS context. This new release enhances computational efficiency, allows users to select the fit function that is appropriate for their analyses, expands compatibility with standard genomic data formats, and outputs results for seamless reading into other standard post-GWAS processing software. To demonstrate GW-SEM's utility, we conducted (1) a series of GWAS using three substance use frequency items from data in the UK Biobank, (2) a timing study for several predefined GWAS functions, and (3) a Type I Error rate study. Our multivariate GWAS analyses emphasize the utility of GW-SEM for identifying novel patterns of associations that vary considerably between genomic loci for specific substances, highlighting the importance of differentiating between substance-specific use behaviors and polysubstance use. The timing studies demonstrate that the analyses take a reasonable amount of time and show the cost of including additional items. The Type I Error rate study demonstrates that hypothesis tests for genetic associations with latent variable models follow the hypothesized uniform distribution. Taken together, we suggest that GW-SEM may provide substantially deeper insights into the underlying genomic architecture for multivariate behavioral and psychological systems than is currently possible with standard GWAS methods. The current release of GW-SEM 2.0 is available on CRAN (stable release) and GitHub (beta release), and tutorials are available on our github wiki ( https://jpritikin.github.io/gwsem/ ).
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Affiliation(s)
- Joshua N Pritikin
- The Department of Psychiatry, Virginia Commonwealth University, Richmond, USA
- The Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, USA
| | - Michael C Neale
- The Department of Psychiatry, Virginia Commonwealth University, Richmond, USA
- The Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, USA
- The Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, USA
| | - Elizabeth C Prom-Wormley
- The Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, USA
| | - Shaunna L Clark
- The Department of Psychiatry and Behavioral Sciences, Texas A&M University, College Station, USA
| | - Brad Verhulst
- The Department of Psychiatry and Behavioral Sciences, Texas A&M University, College Station, USA.
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Elman JA, Puckett OK, Beck A, Fennema-Notestine C, Cross LK, Dale AM, Eglit GML, Eyler LT, Gillespie NA, Granholm EL, Gustavson DE, Hagler DJ, Hatton SN, Hauger R, Jak AJ, Logue MW, McEvoy LK, McKenzie RE, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Whitsel N, Williams ME, Xian H, Lyons MJ, Franz CE, Kremen WS. MRI-assessed locus coeruleus integrity is heritable and associated with multiple cognitive domains, mild cognitive impairment, and daytime dysfunction. Alzheimers Dement 2021; 17:1017-1025. [PMID: 33580733 PMCID: PMC8248066 DOI: 10.1002/alz.12261] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 10/12/2020] [Accepted: 11/10/2020] [Indexed: 12/22/2022]
Abstract
Introduction The locus coeruleus (LC) undergoes extensive neurodegeneration in early Alzheimer's disease (AD). The LC is implicated in regulating the sleep–wake cycle, modulating cognitive function, and AD progression. Methods Participants were 481 men (ages 62 to 71.7) from the Vietnam Era Twin Study of Aging. LC structural integrity was indexed by neuromelanin‐sensitive magnetic resonance imaging (MRI) contrast‐to‐noise ratio (LCCNR). We examined LCCNR, cognition, amnestic mild cognitive impairment (aMCI), and daytime dysfunction. Results Heritability of LCCNR was .48. Participants with aMCI showed greater daytime dysfunction. Lower LCCNR was associated with poorer episodic memory, general verbal fluency, semantic fluency, and processing speed, as well as increased odds of aMCI and greater daytime dysfunction. Discussion Reduced LC integrity is associated with widespread differences across cognitive domains, daytime sleep‐related dysfunction, and risk for aMCI. These findings in late‐middle‐aged adults highlight the potential of MRI‐based measures of LC integrity in early identification of AD risk.
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Affiliation(s)
- Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA
| | - Olivia K Puckett
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA
| | - Asad Beck
- Graduate Program in Neuroscience, University of Washington, Seattle, Washington, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Latonya K Cross
- Department of Psychology, University of Hawaii Hilo, Hilo, Hawaii, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, California, USA.,Department of Neuroscience, University of California San Diego, La Jolla, California, USA
| | - Graham M L Eglit
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, California, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Eric L Granholm
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,VA San Diego Healthcare System, San Diego, California, USA
| | - Daniel E Gustavson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Sean N Hatton
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA.,Department of Neuroscience, University of California San Diego, La Jolla, California, USA
| | - Richard Hauger
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,VA San Diego Healthcare System, San Diego, California, USA
| | - Amy J Jak
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,VA San Diego Healthcare System, San Diego, California, USA
| | - Mark W Logue
- National Center for PTSD: Behavioral Science Division, VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Psychiatry and the Biomedical Genetics Section, Boston University School of Medicine, Boston, Massachusetts, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Ruth E McKenzie
- School of Education and Public Policy, Merrimack College, Andover, Massachusetts, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, California, USA
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA.,Joint Doctoral Program in Clinical Psychology, San Diego State/University of California, San Diego, California, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
| | - Xin M Tu
- Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA
| | - Nathan Whitsel
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA
| | - McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA.,Joint Doctoral Program in Clinical Psychology, San Diego State/University of California, San Diego, California, USA
| | - Hong Xian
- Department of Epidemiology & Biostatistics, St. Louis University, St. Louis, Missouri, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, California, USA.,VA San Diego Healthcare System, San Diego, California, USA
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Hwang LD, Mitchell BL, Medland SE, Martin NG, Neale MC, Evans DM. The Augmented Classical Twin Design: Incorporating Genome-Wide Identity by Descent Sharing Into Twin Studies in Order to Model Violations of the Equal Environments Assumption. Behav Genet 2021; 51:223-236. [PMID: 33582897 DOI: 10.1007/s10519-021-10044-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/21/2021] [Indexed: 01/09/2023]
Abstract
The Classical Twin Method (CTM) compares the similarity of monozygotic (MZ) twins with that of dizygotic (DZ) twins to make inferences about the relative importance of genes and environment in the etiology of individual differences. The design has been applied to thousands of traits across the biomedical, behavioral and social sciences and is arguably the most widely used natural experiment known to science. The fundamental assumption of the CTM is that trait relevant environmental covariation within MZ pairs is the same as that found within DZ pairs, so that zygosity differences in within-pair variance must be due to genetic factors uncontaminated by the environment. This equal environments assumption (EEA) has been, and still is hotly contested, and has been mentioned as a possible contributing factor to the missing heritability conundrum. In this manuscript, we introduce a new model for testing the EEA, which we call the Augmented Classical Twin Design which uses identity by descent (IBD) sharing between DZ twin pairs to estimate separate environmental variance components for MZ and DZ twin pairs, and provides a test of whether these are equal. We show through simulation that given large samples of DZ twin pairs, the model provides unbiased estimates of variance components and valid tests of the EEA under strong assumptions (e.g. no epistatic variance, IBD sharing in DZ twins estimated accurately etc.) which may not hold in reality. Sample sizes in excess of 50,000 DZ twin pairs with genome-wide genetic data are likely to be required in order to detect substantial violations of the EEA with moderate power. Consequently, we recommend that the Augmented Classical Twin Design only be applied to datasets with very large numbers of DZ twin pairs (> 50,000 DZ twin pairs), and given the strong assumptions relating to the absence of epistatic variance, appropriate caution be exercised regarding interpretation of the results.
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Affiliation(s)
- Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Level 7, 37 Kent St, Brisbane, Australia.,QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Brittany L Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Biomedical Science, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Biomedical Science, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia
| | - Michael C Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Level 7, 37 Kent St, Brisbane, Australia. .,QIMR Berghofer Medical Research Institute, Brisbane, Australia. .,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. .,Translational Research Institute, Woolloongabba, QLD, 4102, Australia.
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Kirkpatrick RM, Pritikin JN, Hunter MD, Neale MC. Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx. Behav Genet 2021; 51:331-342. [PMID: 33439421 DOI: 10.1007/s10519-020-10037-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 12/07/2020] [Indexed: 11/29/2022]
Abstract
There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance-covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available-genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, "mxGREML", designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature's current limitations, and our plans for its further development.
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Affiliation(s)
- Robert M Kirkpatrick
- Virginia Commonwealth University, Richmond, USA. .,Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23298-0126, USA.
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Maes HH, Neale MC, Lonn SL, Lichtenstein P, Sundquist J, Sundquist K, Kendler KS. Modeling Etiology of Smoking During Pregnancy in Swedish Twins, Full-, and Half-Siblings, Reared Together and Apart. Nicotine Tob Res 2021; 22:1736-1743. [PMID: 32386311 DOI: 10.1093/ntr/ntaa076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 05/07/2020] [Indexed: 02/03/2023]
Abstract
INTRODUCTION Using Swedish nationwide registry data, we investigated the contribution of genetic and environmental risk factors to the etiology of smoking status across stages of pregnancy with increasing degrees of social and psychological pressure to reduce or quit smoking, by twin and sibling modeling. AIMS AND METHODS Smoking status was available before, and during early and late pregnancy from the Medical Birth Register. Twin, full-, and half-sibling pairs, both reared together and apart, born between 1960 and 1990 were obtained from national twin and genealogical registers. Genetic structural equation modeling in OpenMx was applied to the population-based data to estimate shared genetic and/or environmental covariance across stages of pregnancy, accounting for maternal birth cohort and age at pregnancy. RESULTS Analyses of paired data on 258 749 individuals suggested that risk factors for smoking status changed across stages of pregnancy. Results predicted substantial heritability (60-70%) and moderate contributions of shared environmental factors (10-15%) for smoking status. Whilst the same shared environmental factors were amplified from before pregnancy to late pregnancy, new primarily unique environmental factors explained ~10% of the variance during early pregnancy which was carried forward to late pregnancy. CONCLUSIONS Using registry data on women across pregnancy, we replicated that smoking status is highly heritable. Furthermore, we found support for increased impact of shared environmental factors during pregnancy of factors already present prior to pregnancy, and an independent set of mostly new unique environmental factors that may be triggered by increased social pressure to reduce or quit smoking during pregnancy. IMPLICATIONS As new factors partially explain smoking status during pregnancy and the effects of familial factors increase across pregnancy, efforts to prevent or reduce smoking during pregnancy should receive continued attention, with a focus on both the individual and the family unit.
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Affiliation(s)
- Hermine H Maes
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA.,Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA.,Massey Cancer Center, Virginia Commonwealth University, Richmond, VA
| | - Michael C Neale
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA.,Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Sara Larsson Lonn
- Family Medicine and Clinical Epidemiology, Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology, Karolinska Institute, Stockholm, Sweden
| | - Jan Sundquist
- Family Medicine and Clinical Epidemiology, Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Kristina Sundquist
- Family Medicine and Clinical Epidemiology, Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Kenneth S Kendler
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA.,Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
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Abstract
The measurement of many human traits, states, and disorders begins with a set of items on a questionnaire. The response format for these questions is often simply binary (e.g., yes/no) or ordered (e.g., high, medium or low). During data analysis, these items are frequently summed or used to estimate factor scores. In clinical applications, such assessments are often non-normally distributed in the general population because many respondents are unaffected, and therefore asymptomatic. As a result, in many cases these measures violate the statistical assumptions required for subsequent analyses. To reduce the influence of the non-normality and quasi-continuous assessment, variables are frequently recoded into binary (affected-unaffected) or ordinal (mild-moderate-severe) diagnoses. Ordinal data therefore present challenges at multiple levels of analysis. Categorizing continuous variables into ordered categories typically results in a loss of statistical power, which represents an incentive to the data analyst to assume that the data are normally distributed, even when they are not. Despite prior zeitgeists suggesting that, e.g., variables with more than 10 ordered categories may be regarded as continuous and analyzed as if they were, we show via simulation studies that this is not generally the case. In particular, using Pearson product-moment correlations instead of maximum likelihood estimates of polychoric correlations biases the estimated correlations towards zero. This bias is especially severe when a plurality of the observations fall into a single observed category, such as a score of zero. By contrast, estimating the ordinal correlation by maximum likelihood yields no estimation bias, although standard errors are (appropriately) larger. We also illustrate how odds ratios depend critically on the proportion or prevalence of affected individuals in the population, and therefore are sub-optimal for studies where comparisons of association metrics are needed. Finally, we extend these analyses to the classical twin model and demonstrate that treating binary data as continuous will underestimate genetic and common environmental variance components, and overestimate unique environment (residual) variance. These biases increase as prevalence declines. While modeling ordinal data appropriately may be more computationally intensive and time consuming, failing to do so will likely yield biased correlations and biased parameter estimates from modeling them.
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Affiliation(s)
- Brad Verhulst
- Department of Psychiatry, Texas A&M University, College Station, USA.
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Maes HH, Neale MC, Kirkpatrick RM, Kendler KS. Using Multimodel Inference/Model Averaging to Model Causes of Covariation Between Variables in Twins. Behav Genet 2021; 51:82-96. [PMID: 33150523 PMCID: PMC7855182 DOI: 10.1007/s10519-020-10026-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 10/10/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To explore and apply multimodel inference to test the relative contributions of latent genetic, environmental and direct causal factors to the covariation between two variables with data from the classical twin design by estimating model-averaged parameters. METHODS Behavior genetics is concerned with understanding the causes of variation in phenotypes and the causes of covariation between two or more phenotypes. Two variables may correlate as a result of genetic, shared environmental or unique environmental factors contributing to variation in both variables. Two variables may also correlate because one or both directly cause variation in the other. Furthermore, covariation may result from any combination of these sources, leading to 25 different identified structural equation models. OpenMx was used to fit all these models to account for covariation between two variables collected in twins. Multimodel inference and model averaging were used to summarize the key sources of covariation, and estimate the magnitude of these causes of covariance. Extensions of these models to test heterogeneity by sex are discussed. RESULTS We illustrate the application of multimodel inference by fitting a comprehensive set of bivariate models to twin data from the Virginia Twin Study of Psychiatric and Substance Use Disorders. Analyses of body mass index and tobacco consumption data show sufficient power to reject distinct models, and to estimate the contribution of each of the five potential sources of covariation, irrespective of selecting the best fitting model. Discrimination between models on sample size, type of variable (continuous versus binary or ordinal measures) and the effect size of sources of variance and covariance. CONCLUSIONS We introduce multimodel inference and model averaging approaches to the behavior genetics community, in the context of testing models for the causes of covariation between traits in term of genetic, environmental and causal explanations.
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Affiliation(s)
- Hermine H Maes
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, PO Box 980033, Richmond, VA, 23298-0033, USA.
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA.
| | - Michael C Neale
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, PO Box 980033, Richmond, VA, 23298-0033, USA
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Robert M Kirkpatrick
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Kenneth S Kendler
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, PO Box 980033, Richmond, VA, 23298-0033, USA
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
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Eglit GM, Elman JA, Panizzon MS, Sanderson‐Cimino ME, Williams ME, Dale AM, Eyler LT, Fennema‐Notestine C, Gillespie NA, Gustavson DE, Hatton SN, Hauger RL, Jak AJ, Logue MW, McEvoy LK, McKenzie R, Neale MC, Puckett OK, Reynolds CA, Toomey R, Tu XM, Whitsell N, Xian H, Lyons MJ, Franz CE, Kremen WS. Paradoxical cognitive reserve: Cognitive trajectories from earlier to later adulthood. Alzheimers Dement 2020. [DOI: 10.1002/alz.047686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Graham M.L. Eglit
- Center for Behavior Genetics of Aging University of California San Diego La Jolla CA USA
- Department of Psychiatry University of California San Diego La Jolla CA USA
- Veterans Affairs San Diego Healthcare System San Diego CA USA
| | | | | | | | - McKenna E. Williams
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San Diego CA USA
| | | | | | | | | | | | | | | | - Amy J. Jak
- University of California San Diego La Jolla CA USA
| | | | | | | | | | | | | | | | - Xin M. Tu
- University of California San Diego La Jolla CA USA
| | | | - Hong Xian
- St. Louis University St. Louis MO USA
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Williams ME, Elman JA, McEvoy LK, Dale AM, Fennema‐Notestine C, Franz CE, Gillespie NA, Hagler DJ, Lyons MJ, Neale MC, Panizzon MS, Puckett OK, Kremen WS. Cortical thickness and mean diffusivity AD signatures at average age 56 predict 12‐year progression to mild cognitive impairment. Alzheimers Dement 2020. [DOI: 10.1002/alz.043486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- McKenna E. Williams
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San Diego CA USA
| | | | | | | | | | | | | | | | | | | | | | | | - William S. Kremen
- University of California, San Diego La Jolla CA USA
- VA San Diego Healthcare System San Diego CA USA
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Quach BC, Bray MJ, Gaddis NC, Liu M, Palviainen T, Minica CC, Zellers S, Sherva R, Aliev F, Nothnagel M, Young KA, Marks JA, Young H, Carnes MU, Guo Y, Waldrop A, Sey NYA, Landi MT, McNeil DW, Drichel D, Farrer LA, Markunas CA, Vink JM, Hottenga JJ, Iacono WG, Kranzler HR, Saccone NL, Neale MC, Madden P, Rietschel M, Marazita ML, McGue M, Won H, Winterer G, Grucza R, Dick DM, Gelernter J, Caporaso NE, Baker TB, Boomsma DI, Kaprio J, Hokanson JE, Vrieze S, Bierut LJ, Johnson EO, Hancock DB. Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits. Nat Commun 2020; 11:5562. [PMID: 33144568 PMCID: PMC7642344 DOI: 10.1038/s41467-020-19265-z] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 09/24/2020] [Indexed: 12/31/2022] Open
Abstract
Cigarette smoking is the leading cause of preventable morbidity and mortality. Genetic variation contributes to initiation, regular smoking, nicotine dependence, and cessation. We present a Fagerström Test for Nicotine Dependence (FTND)-based genome-wide association study in 58,000 European or African ancestry smokers. We observe five genome-wide significant loci, including previously unreported loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416), and extend loci reported for other smoking traits to nicotine dependence. Using the heaviness of smoking index from UK Biobank (N = 33,791), rs2714700 is consistently associated; rs1862416 is not associated, likely reflecting nicotine dependence features not captured by the heaviness of smoking index. Both variants influence nearby gene expression (rs2714700/MAGI2-AS3 in hippocampus; rs1862416/TENM2 in lung), and expression of genes spanning nicotine dependence-associated variants is enriched in cerebellum. Nicotine dependence (SNP-based heritability = 8.6%) is genetically correlated with 18 other smoking traits (rg = 0.40-1.09) and co-morbidities. Our results highlight nicotine dependence-specific loci, emphasizing the FTND as a composite phenotype that expands genetic knowledge of smoking.
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Affiliation(s)
- Bryan C Quach
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
| | - Michael J Bray
- Department of Psychiatry, Washington University, St. Louis, MO, 63130, USA
| | - Nathan C Gaddis
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290, Helsinki, Finland
| | - Camelia C Minica
- Department of Biological Psychology, Vrije Universiteit, 1081 BT, Amsterdam, The Netherlands
| | - Stephanie Zellers
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Richard Sherva
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, 02118, USA
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, 23284, USA
- Faculty of Business, Karabuk University, 78050, Kılavuzlar/Karabük Merkez/Karabük, Turkey
| | - Michael Nothnagel
- Cologne Center for Genomics, University of Cologne, 50931, Köln, Germany
- University Hospital Cologne, 50931, Köln, Germany
| | - Kendra A Young
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jesse A Marks
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
| | - Hannah Young
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Megan U Carnes
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
| | - Yuelong Guo
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
- GeneCentric Therapeutics, Research Triangle Park, NC, 27709, USA
| | - Alex Waldrop
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
| | - Nancy Y A Sey
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Maria T Landi
- Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, 20892, USA
| | - Daniel W McNeil
- Department of Psychology, West Virginia University, Morgantown, WV, 26505, USA
- Department of Dental Practice and Rural Health, West Virginia University, Morgantown, WV, 26505, USA
| | - Dmitriy Drichel
- Cologne Center for Genomics, University of Cologne, 50931, Köln, Germany
- University Hospital Cologne, 50931, Köln, Germany
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, Boston, MA, 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Christina A Markunas
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
| | - Jacqueline M Vink
- Behavioural Science Institute, Radboud University, 6500 HE, Nijmegen, The Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, 1081 BT, Amsterdam, The Netherlands
| | - William G Iacono
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- VISN 4 MIRECC, Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
| | - Nancy L Saccone
- Department of Genetics, Washington University, St. Louis, MO, 63130, USA
- Division of Biostatistics, Washington University, St. Louis, MO, 63130, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23284, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Pamela Madden
- Department of Psychiatry, Washington University, St. Louis, MO, 63130, USA
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, 68159, Mannheim, Germany
| | - Mary L Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Matthew McGue
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Georg Winterer
- Experimental & Clinical Research Center, Department of Anesthesiology and Operative Intensive Care Medicine, Charité - University Medicine Berlin, 10117, Berlin, Germany
| | - Richard Grucza
- Departments of Family and Community Medicine and Health and Clinical Outcomes Research, Saint Louis University, St. Louis, MO, 63130, USA
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, 23284, USA
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, 23284, USA
- Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, 06511, USA
| | - Neil E Caporaso
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, 20892, USA
| | - Timothy B Baker
- Center for Tobacco Research and Intervention, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53726, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, 1081 BT, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, 00290, Helsinki, Finland
| | - John E Hokanson
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University, St. Louis, MO, 63130, USA
| | - Eric O Johnson
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA
- Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA
| | - Dana B Hancock
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, 27709, USA.
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Garcia SC, Mikhail ME, Keel PK, Burt SA, Neale MC, Boker S, Klump KL. Increased rates of eating disorders and their symptoms in women with major depressive disorder and anxiety disorders. Int J Eat Disord 2020; 53:1844-1854. [PMID: 32844425 PMCID: PMC7669595 DOI: 10.1002/eat.23366] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Individuals with eating disorders (EDs) have increased rates of major depressive disorder (MDD) and anxiety disorders. Yet, few studies have investigated rates of EDs and their symptoms in individuals presenting with MDD/anxiety disorders. Identifying potential disordered eating in people with MDD/anxiety disorders is important because even subclinical disordered eating is associated with reduced quality of life, and undiagnosed eating pathology may hinder treatment progress for both MDD/anxiety disorders and comorbid EDs. METHOD We compared rates of EDs (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified feeding and eating disorders) and their symptoms in 130 women with, and 405 women without, lifetime MDD or an anxiety disorder (generalized anxiety disorder, obsessive-compulsive disorder, social phobia, specific phobia, panic disorder, agoraphobia, or post-traumatic stress disorder) recruited from the population-based Michigan State University Twin Registry. Lifetime ED and MDD/anxiety diagnoses, and lifetime clinically significant disordered eating behaviors (e.g., binge eating, excessive exercise) were assessed using the Structured Clinical Interview for DSM-IV (SCID). RESULTS Among participants with lifetime MDD or any anxiety disorder, 13% met criteria for a lifetime ED and 39% reported engaging in at least one lifetime clinically significant disordered eating behavior (e.g., binge eating) on the SCID. In contrast, only 3% of participants without a history of MDD/an anxiety disorder met criteria for a lifetime ED, and only 11% reported lifetime clinically significant disordered eating behavior. DISCUSSION Our findings suggest that women with MDD and anxiety disorders have elevated rates of EDs, and it is therefore imperative to screen for disordered eating in these populations.
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Affiliation(s)
- Susana C. Garcia
- Department of Psychology, John Jay College of Criminal Justice, New York, New York
| | - Megan E. Mikhail
- Department of Psychology, Michigan State University, East Lansing, Michigan
| | - Pamela K. Keel
- Department of Psychology, Florida State University, Tallahassee, Florida
| | - S. Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, Michigan
| | - Michael C. Neale
- Departments of Psychiatry, Human Genetics, and Psychology, Virginia Commonwealth University, Richmond, Virginia
| | - Steven Boker
- Department of Psychology, University of Virginia, Charlottesville, Virginia
| | - Kelly L. Klump
- Department of Psychology, Michigan State University, East Lansing, Michigan
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Schmitt JE, Raznahan A, Clasen LS, Wallace GL, Pritikin JN, Lee NR, Giedd JN, Neale MC. The Dynamic Associations Between Cortical Thickness and General Intelligence are Genetically Mediated. Cereb Cortex 2020; 29:4743-4752. [PMID: 30715232 DOI: 10.1093/cercor/bhz007] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 12/04/2018] [Indexed: 11/14/2022] Open
Abstract
The neural substrates of intelligence represent a fundamental but largely uncharted topic in human developmental neuroscience. Prior neuroimaging studies have identified modest but highly dynamic associations between intelligence and cortical thickness (CT) in childhood and adolescence. In a separate thread of research, quantitative genetic studies have repeatedly demonstrated that most measures of intelligence are highly heritable, as are many brain regions associated with intelligence. In the current study, we integrate these 2 streams of prior work by examining the genetic contributions to CT-intelligence relationships using a genetically informative longitudinal sample of 813 typically developing youth, imaged with high-resolution MRI and assessed with Wechsler Intelligence Scales (IQ). In addition to replicating the phenotypic association between multimodal association cortex and language centers with IQ, we find that CT-IQ covariance is nearly entirely genetically mediated. Moreover, shared genetic factors drive the rapidly evolving landscape of CT-IQ relationships in the developing brain.
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Affiliation(s)
- J Eric Schmitt
- Departments of Radiology and Psychiatry, Division of Neuroradiology, Brain Behavior Laboratory, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Building 10, Room 4D18, 10 Center Drive, Bethesda, MD, USA
| | - Liv S Clasen
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Building 10, Room 4D18, 10 Center Drive, Bethesda, MD, USA
| | - Greg L Wallace
- Department of Speech, Language, and Hearing Sciences, The George Washington University, 2115 G Street NW, Hall of Government, Room 226, Washington, DC, USA
| | - Joshua N Pritikin
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980126, Richmond, VA, USA
| | - Nancy Raitano Lee
- Department of Psychology, Drexel University, 3201 Chestnut Street, Stratton Hall, Room 123E, Philadelphia, PA, USA
| | - Jay N Giedd
- Department of Psychiatry, University of California at San Diego, 9500 Gilman Drive #0949, La Jolla, CA, USA
| | - Michael C Neale
- Departments of Psychiatry and Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980126, Richmond, VA, USA
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Wang B, Wu T, Neale MC, Verweij R, Liu G, Su S, Snieder H. Genetic and Environmental Influences on Blood Pressure and Body Mass Index in the National Academy of Sciences-National Research Council World War II Veteran Twin Registry. Hypertension 2020; 76:1428-1434. [PMID: 32981367 PMCID: PMC7535104 DOI: 10.1161/hypertensionaha.120.15232] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Supplemental Digital Content is available in the text. Blood pressure (BP) and obesity phenotypes may covary due to shared genetic or environmental factors or both. Furthermore, it is possible that the heritability of BP differs according to obesity status—a form of G×E interaction. This hypothesis has never been tested in White twins. The present study included 15 924 White male twin pairs aged between 15 and 33 years from the National Academy of Sciences–National Research Council World War II Veteran Twin Registry. Systolic and diastolic BPs, as well as height and weight, were measured at the induction physical examination. Body mass index (BMI) was used as the index of general obesity. Quantitative genetic modeling was performed using Mx software. Univariate analysis showed that narrow sense heritabilities (95% CI) for systolic BP, diastolic BP, height, and BMI were 0.401 (0.381–0.420), 0.297 (0.280–0.320), 0.866 (0.836–0.897), and 0.639 (0.614–0.664), respectively. Positive phenotypic correlations of BMI with systolic BP (r=0.13) and diastolic BP (r=0.08) were largely due to genetic factors (70% and 86%, respectively). The gene-BMI interaction analysis did not show any support for a modifying effect of BMI on genetic and environmental influences of systolic BP and diastolic BP. Our results suggest that correlations between BP and BMI are mainly explained by common genes influencing both. Higher BMI levels have no influence on the penetrance of genetic vulnerability to elevated BP. These conclusions may prove valuable for gene-finding studies.
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Affiliation(s)
- Bin Wang
- From the Department of Epidemiology, University of Groningen, University Medical Center Groningen, the Netherlands (B.W., T.W., R.V., G.L., H.S.)
| | - Ting Wu
- From the Department of Epidemiology, University of Groningen, University Medical Center Groningen, the Netherlands (B.W., T.W., R.V., G.L., H.S.)
| | - Michael C Neale
- Department of Human and Molecular Genetics (M.C.N.), Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond.,Department of Psychiatry (M.C.N.), Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond
| | - Renske Verweij
- From the Department of Epidemiology, University of Groningen, University Medical Center Groningen, the Netherlands (B.W., T.W., R.V., G.L., H.S.)
| | - Gaifen Liu
- From the Department of Epidemiology, University of Groningen, University Medical Center Groningen, the Netherlands (B.W., T.W., R.V., G.L., H.S.).,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, China (G.L.)
| | - Shaoyong Su
- Georgia Prevention Institute, Medical College of Georgia, Augusta University (S.S., H.S.)
| | - Harold Snieder
- From the Department of Epidemiology, University of Groningen, University Medical Center Groningen, the Netherlands (B.W., T.W., R.V., G.L., H.S.).,Georgia Prevention Institute, Medical College of Georgia, Augusta University (S.S., H.S.)
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49
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Schmitt JE, Raznahan A, Liu S, Neale MC. The Heritability of Cortical Folding: Evidence from the Human Connectome Project. Cereb Cortex 2020; 31:702-715. [PMID: 32959043 DOI: 10.1093/cercor/bhaa254] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/09/2020] [Accepted: 08/10/2020] [Indexed: 12/13/2022] Open
Abstract
The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.
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Affiliation(s)
- J Eric Schmitt
- Departments of Radiology and Psychiatry, Division of Neuroradiology, Brain Behavior Laboratory, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Siyuan Liu
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Michael C Neale
- Departments of Psychiatry and Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298-980126, USA
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50
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Hofer E, Roshchupkin GV, Adams HHH, Knol MJ, Lin H, Li S, Zare H, Ahmad S, Armstrong NJ, Satizabal CL, Bernard M, Bis JC, Gillespie NA, Luciano M, Mishra A, Scholz M, Teumer A, Xia R, Jian X, Mosley TH, Saba Y, Pirpamer L, Seiler S, Becker JT, Carmichael O, Rotter JI, Psaty BM, Lopez OL, Amin N, van der Lee SJ, Yang Q, Himali JJ, Maillard P, Beiser AS, DeCarli C, Karama S, Lewis L, Harris M, Bastin ME, Deary IJ, Veronica Witte A, Beyer F, Loeffler M, Mather KA, Schofield PR, Thalamuthu A, Kwok JB, Wright MJ, Ames D, Trollor J, Jiang J, Brodaty H, Wen W, Vernooij MW, Hofman A, Uitterlinden AG, Niessen WJ, Wittfeld K, Bülow R, Völker U, Pausova Z, Bruce Pike G, Maingault S, Crivello F, Tzourio C, Amouyel P, Mazoyer B, Neale MC, Franz CE, Lyons MJ, Panizzon MS, Andreassen OA, Dale AM, Logue M, Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, Lind PA, Pizzagalli F, Stein JL, Thompson PM, Medland SE, Sachdev PS, Kremen WS, Wardlaw JM, Villringer A, van Duijn CM, Grabe HJ, Longstreth WT, Fornage M, Paus T, Debette S, Ikram MA, Schmidt H, Schmidt R, Seshadri S. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nat Commun 2020; 11:4796. [PMID: 32963231 PMCID: PMC7508833 DOI: 10.1038/s41467-020-18367-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022] Open
Abstract
Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging. Cortex morphology varies with age, cognitive function, and in neurological and psychiatric diseases. Here the authors report 160 genome-wide significant associations with thickness, surface area and volume of the total cortex and 34 cortical regions from a GWAS meta-analysis in 22,824 adults.
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Affiliation(s)
- Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.,Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Gennady V Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Hieab H H Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Habil Zare
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA.,Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, USA
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | | | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA.,QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Michelle Luciano
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Aniket Mishra
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Rui Xia
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xueqiu Jian
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Yasaman Saba
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Lukas Pirpamer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Stephan Seiler
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - James T Becker
- Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Oscar L Lopez
- Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jayandra J Himali
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Pauline Maillard
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Charles DeCarli
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - Sherif Karama
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Lindsay Lewis
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Mat Harris
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Faculty of Medicine, CRC 1052 Obesity Mechanisms, University of Leipzig, Leipzig, Germany
| | - Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Faculty of Medicine, CRC 1052 Obesity Mechanisms, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, Australia.,School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - John B Kwok
- School of Medical Sciences, University of New South Wales, Sydney, Australia.,Brain and Mind Centre - The University of Sydney, Camperdown, NSW, Australia
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia.,Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, Australia
| | - David Ames
- National Ageing Research Institute, Royal Melbourne Hospital, Parkvill, VIC, Australia.,Academic Unit for Psychiatry of Old Age, University of Melbourne, St George's Hospital, Kew, VIC, Australia
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Katharina Wittfeld
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Zdenka Pausova
- Hospital for Sick Children, Toronto, ON, Canada.,Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinial Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Sophie Maingault
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Fabrice Crivello
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Christophe Tzourio
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France.,Pole de santé publique, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Philippe Amouyel
- Centre Hospitalier Universitaire de Bordeaux, France; Inserm U1167, Lille, France.,Department of Epidemiology and Public Health, Pasteur Institute of Lille, Lille, France.,Department of Public Health, Lille University Hospital, Lille, France
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Mark Logue
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,National Center for PTSD at Boston VA Healthcare System, Boston, MA, USA.,Department of Psychiatry and Department of Medicine-Biomedical Genetics Section, Boston University School of Medicine, Boston, MA, USA
| | - Katrina L Grasby
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jodie N Painter
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Lucía Colodro-Conde
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Janita Bralten
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.,Neuroscience Biomarkers, Janssen Research and Development, LLC, San Diego, CA, USA
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jason L Stein
- Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Joanna M Wardlaw
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hans J Grabe
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - William T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA
| | - Myriam Fornage
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tomas Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.,Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Stephanie Debette
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France.,CHU de Bordeaux, Department of Neurology, F-33000, Bordeaux, France
| | - M Arfan Ikram
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Helena Schmidt
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA. .,Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
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