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Kulkarni AP, Hwang G, Cook CJ, Mohanty R, Guliani A, Nair VA, Bendlin BB, Meyerand E, Prabhakaran V. Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study. Hum Brain Mapp 2023; 44:5238-5293. [PMID: 36537283 PMCID: PMC10543121 DOI: 10.1002/hbm.25947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 09/07/2023] Open
Abstract
We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].
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Affiliation(s)
- Arman P. Kulkarni
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Gyujoon Hwang
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Cole J. Cook
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Akhil Guliani
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Elizabeth Meyerand
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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Madrid-Valero JJ, Gregory AM. Behaviour genetics and sleep: A narrative review of the last decade of quantitative and molecular genetic research in humans. Sleep Med Rev 2023; 69:101769. [PMID: 36933344 DOI: 10.1016/j.smrv.2023.101769] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/05/2023]
Abstract
During the last decade quantitative and molecular genetic research on sleep has increased considerably. New behavioural genetics techniques have marked a new era for sleep research. This paper provides a summary of the most important findings from the last ten years, on the genetic and environmental influences on sleep and sleep disorders and their associations with health-related variables (including anxiety and depression) in humans. In this review we present a brief summary of the main methods in behaviour genetic research (such as twin and genome-wide association studies). We then discuss key research findings on: genetic and environmental influences on normal sleep and sleep disorders, as well as on the association between sleep and health variables (highlighting a substantial role for genes in individual differences in sleep and their associations with other variables). We end by discussing future lines of enquiry and drawing conclusions, including those focused on problems and misconceptions associated with research of this type. In this last decade our knowledge about genetic and environmental influences on sleep and its disorders has expanded. Both, twin and genome-wide association studies show that sleep and sleep disorders are substantially influenced by genetic factors and for the very first time multiple specific genetic variants have been associated with sleep traits and disorders.
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Affiliation(s)
- Juan J Madrid-Valero
- Department of Health Psychology, Faculty of Health Sciences, University of Alicante, Spain.
| | - Alice M Gregory
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
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Does lower educational attainment increase the risk of osteoarthritis surgery? a Swedish twin study. BMC Musculoskelet Disord 2023; 24:72. [PMID: 36707830 PMCID: PMC9883874 DOI: 10.1186/s12891-023-06163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 01/16/2023] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Previous studies have reported an inverse association between educational attainment and different osteoarthritis (OA) outcomes. However, none of the previous studies have accounted for potential confounding by early-life environment and genetics. Thus, we aimed to examine the association between educational attainment and knee and hip OA surgery using twin data. METHODS From the Swedish Twin Registry (STR), we identified dizygotic (DZ) and monozygotic (MZ) twins. All twins in the STR aged 35 to 64 years were followed from January the 1st 1987 or the date they turned 35 years until OA surgery, relocation outside Sweden, death or the end of 2016 (18,784 DZ and 8,657 MZ complete twin pairs). Associations between educational attainment and knee and hip OA surgery were estimated in models matched on twin pairs, using Weibull within-between (WB) shared frailty model. RESULTS For knee OA surgery, the analysis matched on MZ twins yielded a within-estimate hazard ratio (HR) per 3 years of education, of 1.06 (95% CI: 0.81, 1.32), suggesting no association between the outcome and the individual´s education. Rather, there seemed to be a so called familial effect of education, with a between-pair estimate of HR = 0.71 (95% CI: 0.41, 1.01). For hip OA surgery, the within- and between-pair estimates for MZ twins were 0.92 (95% CI: 0.69, 1.14) and 1.15 (95% CI: 0.87, 1.42), respectively. CONCLUSIONS Our results suggest that the inverse associations between education and knee/hip OA surgery observed in cohort studies are potentially confounded by unobserved familial factors like genetics and/or early life exposures.
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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: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [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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Evans DM, Medland SE, Prom-Wormley E. Introduction to the Special Issue on Statistical Genetic Methods for Human Complex Traits. Behav Genet 2021; 51:165-169. [PMID: 33864530 DOI: 10.1007/s10519-021-10057-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia. .,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Elizabeth Prom-Wormley
- The Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, USA
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