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Shared polygenic risk for ADHD, executive dysfunction and other psychiatric disorders. Transl Psychiatry 2020; 10:182. [PMID: 32518222 PMCID: PMC7283259 DOI: 10.1038/s41398-020-00872-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 12/14/2022] Open
Abstract
Many psychiatric disorders are associated with impaired executive functioning (EF). The associated EF component varies by psychiatric disorders, and this variation might be due to genetic liability. We explored the genetic association between five psychiatric disorders and EF in clinically-recruited attention deficit hyperactivity disorder (ADHD) children using polygenic risk score (PRS) methodology. Genome-wide association study (GWAS) summary data for ADHD, major depressive disorder (MDD), schizophrenia (SZ), bipolar disorder (BIP) and autism were used to calculate the PRSs. EF was evaluated by the Stroop test for inhibitory control, the trail-making test for cognitive flexibility, and the digital span test for working memory in a Chinese ADHD cohort (n = 1147). Exploratory factor analysis of the three measures identified one principal component for EF (EF-PC). Linear regression models were used to analyze the association between each PRS and the EF measures. The role of EF measures in mediating the effects of the PRSs on ADHD symptoms was also analyzed. The result showed the PRSs for MDD, ADHD and BIP were all significantly associated with the EF-PC. For each EF component, the association results were different for the PRSs of the five psychiatric disorders: the PRSs for ADHD and MDD were associated with inhibitory control (adjusted P = 0.0183 and 0.0313, respectively), the PRS for BIP was associated with working memory (adjusted P = 0.0416), and the PRS for SZ was associated with cognitive flexibility (adjusted P = 0.0335). All three EF measures were significantly correlated with ADHD symptoms. In mediation analyses, the ADHD and MDD PRSs, which were associated with inhibitory control, had significant indirect effects on ADHD symptoms through the mediation of inhibitory control. These findings indicate that the polygenic risks for several psychiatric disorders influence specific executive dysfunction in children with ADHD. The results helped to clarify the relationship between risk genes of each mental disorder and the intermediate cognitive domain, which may further help elucidate the risk genes and motivate efforts to develop EF measures as a diagnostic marker and future treatment target.
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102
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Chandler HL, Hodgetts CJ, Caseras X, Murphy K, Lancaster TM. Polygenic risk for Alzheimer's disease shapes hippocampal scene-selectivity. Neuropsychopharmacology 2020; 45:1171-1178. [PMID: 31896120 PMCID: PMC7234982 DOI: 10.1038/s41386-019-0595-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 11/27/2019] [Accepted: 12/05/2019] [Indexed: 01/16/2023]
Abstract
Preclinical models of Alzheimer's disease (AD) suggest APOE modulates brain function in structures vulnerable to AD pathophysiology. However, genome-wide association studies now demonstrate that AD risk is shaped by a broader polygenic architecture, estimated via polygenic risk scoring (AD-PRS). Despite this breakthrough, the effect of AD-PRS on brain function in young individuals remains unknown. In a large sample (N = 608) of young, asymptomatic individuals, we measure the impact of both (i) APOE and (ii) AD-PRS on a vulnerable cortico-limbic scene-processing network heavily implicated in AD pathophysiology. Integrity of this network, which includes the hippocampus (HC), is fundamental for maintaining cognitive function during ageing. We show that AD-PRS, not APOE, selectively influences activity within the HC in response to scenes, while other perceptual nodes remained intact. This work highlights the impact of polygenic contributions to brain function beyond APOE, which could aid potential therapeutic/interventional strategies in the detection and prevention of AD.
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Affiliation(s)
- Hannah L Chandler
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Carl J Hodgetts
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics & Genomics, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Kevin Murphy
- CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, CF24 3AA, UK
| | - Thomas M Lancaster
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK.
- MRC Centre for Neuropsychiatric Genetics & Genomics, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK.
- UK Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK.
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Bellou E, Stevenson-Hoare J, Escott-Price V. Polygenic risk and pleiotropy in neurodegenerative diseases. Neurobiol Dis 2020; 142:104953. [PMID: 32445791 PMCID: PMC7378564 DOI: 10.1016/j.nbd.2020.104953] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 05/12/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022] Open
Abstract
In this paper we explore the phenomenon of pleiotropy in neurodegenerative diseases, focusing on Alzheimer's disease (AD). We summarize the various techniques developed to investigate pleiotropy among traits, elaborating in the polygenic risk scores (PRS) analysis. PRS was designed to assess a cumulative effect of a large number of SNPs for association with a disease and, later for disease risk prediction. Since genetic predictions rely on heritability, we discuss SNP-based heritability from genome-wide association studies and its contribution to the prediction accuracy of PRS. We review work examining pleiotropy in neurodegenerative diseases and related phenotypes and biomarkers. We conclude that the exploitation of pleiotropy may aid in the identification of novel genes and provide further insights in the disease mechanisms, and along with PRS analysis, may be advantageous for precision medicine.
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104
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Mollon J, Curran JE, Mathias SR, Knowles EEM, Carlisle P, Fox PT, Olvera RL, Göring HHH, Rodrigue A, Almasy L, Duggirala R, Blangero J, Glahn DC. Neurocognitive impairment in type 2 diabetes: evidence for shared genetic aetiology. Diabetologia 2020; 63:977-986. [PMID: 32016567 PMCID: PMC7150650 DOI: 10.1007/s00125-020-05101-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 01/15/2020] [Indexed: 01/02/2023]
Abstract
AIMS/HYPOTHESIS Type 2 diabetes is associated with cognitive impairments, but it is unclear whether common genetic factors influence both type 2 diabetes risk and cognition. METHODS Using data from 1892 Mexican-American individuals from extended pedigrees, including 402 with type 2 diabetes, we examined possible pleiotropy between type 2 diabetes and cognitive functioning, as measured by a comprehensive neuropsychological test battery. RESULTS Negative phenotypic correlations (ρp) were observed between type 2 diabetes and measures of attention (Continuous Performance Test [CPT d']: ρp = -0.143, p = 0.001), verbal memory (California Verbal Learning Test [CVLT] recall: ρp = -0.111, p = 0.004) and face memory (Penn Face Memory Test [PFMT]: ρp = -0.127, p = 0.002; PFMT Delayed: ρp = -0.148, p = 2 × 10-4), replicating findings of cognitive impairment in type 2 diabetes. Negative genetic correlations (ρg) were also observed between type 2 diabetes and measures of attention (CPT d': ρg = -0.401, p = 0.001), working memory (digit span backward test: ρg = -0.380, p = 0.005), and face memory (PFMT: ρg = -0.476, p = 2 × 10-4; PFMT Delayed: ρg = -0.376, p = 0.005), suggesting that the same genetic factors underlying risk for type 2 diabetes also influence poor cognitive performance in these domains. Performance in these domains was also associated with type 2 diabetes risk using an endophenotype ranking value approach. Specifically, on measures of attention (CPT d': β = -0.219, p = 0.005), working memory (digit span backward: β = -0.326, p = 0.035), and face memory (PFMT: β = -0.171, p = 0.023; PFMT Delayed: β = -0.215, p = 0.005), individuals with type 2 diabetes showed the lowest performance, while unaffected/unrelated individuals showed the highest performance, and those related to an individual with type 2 diabetes performed at an intermediate level. CONCLUSIONS/INTERPRETATION These findings suggest that cognitive impairment may be a useful endophenotype of type 2 diabetes and, therefore, help to elucidate the pathophysiological underpinnings of this chronic disease. DATA AVAILABILITY The data analysed in this study is available in dbGaP: www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001215.v2.p2.
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Affiliation(s)
- Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, BCH 3428, Boston, MA, 02115, USA.
| | - Joanne E Curran
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, BCH 3428, Boston, MA, 02115, USA
| | - Emma E M Knowles
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, BCH 3428, Boston, MA, 02115, USA
| | - Phoebe Carlisle
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Rene L Olvera
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Harald H H Göring
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Amanda Rodrigue
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, BCH 3428, Boston, MA, 02115, USA
| | - Laura Almasy
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi Duggirala
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - John Blangero
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, BCH 3428, Boston, MA, 02115, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
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105
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Alloza C, Blesa-Cábez M, Bastin ME, Madole JW, Buchanan CR, Janssen J, Gibson J, Deary IJ, Tucker-Drob EM, Whalley HC, Arango C, McIntosh AM, Cox SR, Lawrie SM. Psychotic-like experiences, polygenic risk scores for schizophrenia, and structural properties of the salience, default mode, and central-executive networks in healthy participants from UK Biobank. Transl Psychiatry 2020; 10:122. [PMID: 32341335 PMCID: PMC7186224 DOI: 10.1038/s41398-020-0794-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/11/2020] [Accepted: 03/25/2020] [Indexed: 02/07/2023] Open
Abstract
Schizophrenia is a highly heritable disorder with considerable phenotypic heterogeneity. Hallmark psychotic symptoms can be considered as existing on a continuum from non-clinical to clinical populations. Assessing genetic risk and psychotic-like experiences (PLEs) in non-clinical populations and their associated neurobiological underpinnings can offer valuable insights into symptom-associated brain mechanisms without the potential confounds of the effects of schizophrenia and its treatment. We leveraged a large population-based cohort (UKBiobank, N = 3875) including information on PLEs (obtained from the Mental Health Questionnaire (MHQ); UKBiobank Category: 144; N auditory hallucinations = 55, N visual hallucinations = 79, N persecutory delusions = 16, N delusions of reference = 13), polygenic risk scores for schizophrenia (PRSSZ) and multi-modal brain imaging in combination with network neuroscience. Morphometric (cortical thickness, volume) and water diffusion (fractional anisotropy) properties of the regions and pathways belonging to the salience, default-mode, and central-executive networks were computed. We hypothesized that these anatomical concomitants of functional dysconnectivity would be negatively associated with PRSSZ and PLEs. PRSSZ was significantly associated with a latent measure of cortical thickness across the salience network (r = -0.069, p = 0.010) and PLEs showed a number of significant associations, both negative and positive, with properties of the salience and default mode networks (involving the insular cortex, supramarginal gyrus, and pars orbitalis, pFDR < 0.050); with the cortical thickness of the insula largely mediating the relationship between PRSSZ and auditory hallucinations. Generally, these results are consistent with the hypothesis that higher genetic liability for schizophrenia is related to subtle disruptions in brain structure and may predispose to PLEs even among healthy participants. In addition, our study suggests that networks engaged during auditory hallucinations show structural associations with PLEs in the general population.
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Affiliation(s)
- C Alloza
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK.
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
- Ciber del Area de Salud Mental (CIBERSAM), Madrid, Spain.
| | - M Blesa-Cábez
- MRC Centre for Reproductive Health, The University of Edinburgh, Edinburgh, UK
| | - M E Bastin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - J W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - C R Buchanan
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE), Edinburgh, UK
| | - J Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Area de Salud Mental (CIBERSAM), Madrid, Spain
| | - J Gibson
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - E M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - H C Whalley
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - C Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Area de Salud Mental (CIBERSAM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - A M McIntosh
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - S R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE), Edinburgh, UK
| | - S M Lawrie
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
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106
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Bralten J, Widomska J, Witte WD, Yu D, Mathews CA, Scharf JM, Buitelaar J, Crosbie J, Schachar R, Arnold P, Lemire M, Burton CL, Franke B, Poelmans G. Shared genetic etiology between obsessive-compulsive disorder, obsessive-compulsive symptoms in the population, and insulin signaling. Transl Psychiatry 2020; 10:121. [PMID: 32341337 PMCID: PMC7186226 DOI: 10.1038/s41398-020-0793-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 03/11/2020] [Accepted: 03/30/2020] [Indexed: 11/25/2022] Open
Abstract
Obsessive-compulsive symptoms (OCS) in the population have been linked to obsessive-compulsive disorder (OCD) in genetic and epidemiological studies. Insulin signaling has been implicated in OCD. We extend previous work by assessing genetic overlap between OCD, population-based OCS, and central nervous system (CNS) and peripheral insulin signaling. We conducted genome-wide association studies (GWASs) in the population-based Philadelphia Neurodevelopmental Cohort (PNC, 650 children and adolescents) of the total OCS score and six OCS factors from an exploratory factor analysis of 22 questions. Subsequently, we performed polygenic risk score (PRS)-based analysis to assess shared genetic etiologies between clinical OCD (using GWAS data from the Psychiatric Genomics Consortium), the total OCS score and OCS factors. We then performed gene-set analyses with a set of OCD-linked genes centered around CNS insulin-regulated synaptic function and PRS-based analyses for five peripheral insulin signaling-related traits. For validation purposes, we explored data from the independent Spit for Science population cohort (5,047 children and adolescents). In the PNC, we found a significant shared genetic etiology between OCD and 'guilty taboo thoughts'. In the Spit for Science cohort, we additionally observed genetic sharing between 'symmetry/counting/ordering' and 'contamination/cleaning'. The CNS insulin-linked gene-set also associated with 'symmetry/counting/ordering' in the PNC. Further, we identified genetic sharing between peripheral insulin signaling-related traits: type 2 diabetes with 'aggressive taboo thoughts', and levels of fasting insulin and 2 h glucose with OCD. In conclusion, OCD, OCS in the population and insulin-related traits share genetic risk factors, indicating a common etiological mechanism underlying somatic and psychiatric disorders.
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Affiliation(s)
- Janita Bralten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Joanna Widomska
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ward De Witte
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dongmei Yu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Carol A Mathews
- Department of Psychiatry and Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Jeremiah M Scharf
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jan Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Karakter Child and Adolescent Psychiatry, Nijmegen, The Netherlands
| | - Jennifer Crosbie
- Neurosciences & Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Russell Schachar
- Neurosciences & Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Paul Arnold
- Genetics & Genome Biology, Hospital for Sick Children, Toronto, ON, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
- Departments of Psychiatry & Medical Genetics; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Mathieu Lemire
- Neurosciences & Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
| | - Christie L Burton
- Neurosciences & Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Geert Poelmans
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.
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Abstract
PURPOSE OF REVIEW Recent advances in genetic technologies allowed researchers to identify large numbers of candidate risk genes associated with autism spectrum disorder (ASD). Both strongly penetrant rare variants and the accumulation of common variants with much weaker penetrance contribute to the cause of ASD. To identify the highly confident candidate genes, software and resources have been applied, and functional evaluation of the variants has provided further insights for ASD pathophysiology. These studies ultimately identify the molecular and circuit alteration underlying the behavioral abnormalities in ASD. In this review, we introduce the recent genetic and genomic findings and functional approaches for ASD variants providing a deeper understanding of the etiology of ASD. RECENT FINDINGS Integrated meta-analysis that recruited a larger number of ASD cases has helped to prioritize ASD candidate genes or genetic loci into highly confidence candidate genes for further investigation. Not only coding but also noncoding variants have been recently implicated to confer the risk of ASD. Functional approaches of genes or variants revealed the disruption of specific molecular pathways. Further studies combining ASD genetics and genomics with recent techniques in engineered mouse models show molecular and circuit mechanisms underlying the behavioral deficits in ASD. SUMMARY Advances in ASD genetics and the following functional studies provide significant insights into ASD pathophysiology at molecular and circuit levels.
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108
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Reliability and validity of the UK Biobank cognitive tests. PLoS One 2020; 15:e0231627. [PMID: 32310977 PMCID: PMC7170235 DOI: 10.1371/journal.pone.0231627] [Citation(s) in RCA: 143] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/21/2020] [Indexed: 11/19/2022] Open
Abstract
UK Biobank is a health resource with data from over 500,000 adults. The cognitive assessment in UK Biobank is brief and bespoke, and is administered without supervision on a touchscreen computer. Psychometric information on the UK Biobank cognitive tests are limited. Despite the non-standard nature of these tests and the limited psychometric information, the UK Biobank cognitive data have been used in numerous scientific publications. The present study examined the validity and short-term test-retest reliability of the UK Biobank cognitive tests. A sample of 160 participants (mean age = 62.59, SD = 10.24) was recruited who completed the UK Biobank cognitive assessment and a range of well-validated cognitive tests (‘reference tests’). Fifty-two participants returned 4 weeks later to repeat the UK Biobank tests. Correlations were calculated between UK Biobank tests and reference tests. Two measures of general cognitive ability were created by entering scores on the UK Biobank cognitive tests, and scores on the reference tests, respectively, into separate principal component analyses and saving scores on the first principal component. Four-week test-retest correlations were calculated for UK Biobank tests. UK Biobank cognitive tests showed a range of correlations with their respective reference tests, i.e. those tests that are thought to assess the same underlying cognitive ability (mean Pearson r = 0.53, range = 0.22 to 0.83, p≤.005). The measure of general cognitive ability based on the UK Biobank cognitive tests correlated at r = 0.83 (p < .001) with a measure of general cognitive ability created using the reference tests. Four-week test-retest reliability of the UK Biobank tests were moderate-to-high (mean Pearson r = 0.55, range = 0.40 to 0.89, p≤.003). Despite the brief, non-standard nature of the UK Biobank cognitive tests, some tests showed substantial concurrent validity and test-retest reliability. These psychometric results provide currently-lacking information on the validity of the UK Biobank cognitive tests.
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109
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Maglanoc LA, Kaufmann T, van der Meer D, Marquand AF, Wolfers T, Jonassen R, Hilland E, Andreassen OA, Landrø NI, Westlye LT. Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning. Biol Psychiatry 2020; 87:717-726. [PMID: 31858985 DOI: 10.1016/j.biopsych.2019.10.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/07/2019] [Accepted: 10/18/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge. METHODS In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia. RESULTS Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits. CONCLUSIONS These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.
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Affiliation(s)
- Luigi A Maglanoc
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Rune Jonassen
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Eva Hilland
- Department of Psychology, University of Oslo, Oslo, Norway; Division of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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110
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Genetic liability to schizophrenia is negatively associated with educational attainment in UK Biobank. Mol Psychiatry 2020; 25:703-705. [PMID: 30610204 DOI: 10.1038/s41380-018-0328-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 06/22/2018] [Accepted: 07/09/2018] [Indexed: 11/08/2022]
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Beesley LJ, Salvatore M, Fritsche LG, Pandit A, Rao A, Brummett C, Willer CJ, Lisabeth LD, Mukherjee B. The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities. Stat Med 2020; 39:773-800. [PMID: 31859414 PMCID: PMC7983809 DOI: 10.1002/sim.8445] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 09/10/2019] [Accepted: 11/16/2019] [Indexed: 01/03/2023]
Abstract
Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.
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Affiliation(s)
| | | | | | - Anita Pandit
- University of Michigan, Department of Biostatistics
| | - Arvind Rao
- University of Michigan, Department of Computational Medicine and Bioinformatics
| | - Chad Brummett
- University of Michigan, Department of Anesthesiology
| | - Cristen J. Willer
- University of Michigan, Department of Computational Medicine and Bioinformatics
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Gardner RM, Dalman C, Rai D, Lee BK, Karlsson H. The Association of Paternal IQ With Autism Spectrum Disorders and Its Comorbidities: A Population-Based Cohort Study. J Am Acad Child Adolesc Psychiatry 2020; 59:410-421. [PMID: 31026573 DOI: 10.1016/j.jaac.2019.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 04/09/2019] [Accepted: 04/17/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Original case descriptions of autism noted that parents of the affected children tended to be highly educated and intelligent, a characterization that has endured publicly. Recent genetic studies indicate that risk for autism spectrum disorders (ASD) is associated with high intelligence. We examined the association between paternal intelligence and ASD, considering co-occurring intellectual disability (ID) and attention-deficit/hyperactivity disorder (ADHD). METHOD We used a register-based cohort study design including 360,151 individuals with fathers conscripted to the Swedish military, resident in Stockholm, Sweden, born from 1984 to 2008, and followed until December 31, 2011, for diagnosis of ASD, ADHD, and/or ID. Risk of neurodevelopmental disorders relative to paternal IQ (rated on a 9-point scale) was assessed using a score of 5 (average intelligence) as the referent in models accounting for potentially nonlinear relationships and clustering of siblings. RESULTS We observed an association between high paternal IQ and offspring risk of ASD without ID/ADHD in models adjusted for individual and family characteristics (ORIQ=9 1.32, 95% CI 1.15-1.52), an association that appeared to be driven largely by the fathers' score on the technical comprehension portion of the test (ORTechnical IQ = 9 1.53, 95% CI 1.31-1.78). Conversely, low paternal IQ was associated with ASD+ID (ORIQ = 11.78, 95% CI 1.27-2.49) and ASD+ADHD (ORIQ = 11.40, 95% CI 1.16-1.70); low paternal IQ was strongly associated with ID (ORIQ = 1 4.46, 95% CI 3.62-5.49) and present also for ADHD (ORIQ = 11.56, 95% CI 1.42-1.72)] without co-occurring ASD or ID. CONCLUSION The relationship between paternal IQ and offspring risk of ASD was nonmonotonic and varied by the presence of co-occurring disorders, probably reflecting phenotypic diversity among affected individuals.
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Affiliation(s)
| | - Christina Dalman
- Karolinska Institutet, Stockholm, Sweden; Centre for Epidemiology and Community Medicine of the Stockholm County Council, Stockholm, Sweden
| | - Dheeraj Rai
- Centre for Academic Mental Health, School of Social and Community Medicine, University of Bristol, UK
| | - Brian K Lee
- Dornsife School of Public Health, Drexel University, Philadelphia, PA
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Barbu MC, Spiliopoulou A, Colombo M, McKeigue P, Clarke TK, Howard DM, Adams MJ, Shen X, Lawrie SM, McIntosh AM, Whalley HC. Expression quantitative trait loci-derived scores and white matter microstructure in UK Biobank: a novel approach to integrating genetics and neuroimaging. Transl Psychiatry 2020; 10:55. [PMID: 32066731 PMCID: PMC7026054 DOI: 10.1038/s41398-020-0724-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 01/09/2020] [Accepted: 01/13/2020] [Indexed: 01/01/2023] Open
Abstract
Expression quantitative trait loci (eQTL) are genetic variants associated with gene expression. Using genome-wide genotype data, it is now possible to impute gene expression using eQTL mapping efforts. This approach can be used to analyse previously unexplored relationships between gene expression and heritable in vivo measures of human brain structural connectivity. Using large-scale eQTL mapping studies, we computed 6457 gene expression scores (eQTL scores) using genome-wide genotype data in UK Biobank, where each score represents a genetic proxy measure of gene expression. These scores were then tested for associations with two diffusion tensor imaging measures, fractional anisotropy (NFA = 14,518) and mean diffusivity (NMD = 14,485), representing white matter structural integrity. We found FDR-corrected significant associations between 8 eQTL scores and structural connectivity phenotypes, including global and regional measures (βabsolute FA = 0.0339-0.0453; MD = 0.0308-0.0381) and individual tracts (βabsolute FA = 0.0320-0.0561; MD = 0.0295-0.0480). The loci within these eQTL scores have been reported to regulate expression of genes involved in various brain-related processes and disorders, such as neurite outgrowth and Parkinson's disease (DCAKD, SLC35A4, SEC14L4, SRA1, NMT1, CPNE1, PLEKHM1, UBE3C). Our findings indicate that eQTL scores are associated with measures of in vivo brain connectivity and provide novel information not previously found by conventional genome-wide association studies. Although the role of expression of these genes regarding white matter microstructural integrity is not yet clear, these results suggest it may be possible, in future, to map potential trait- and disease-associated eQTL to in vivo brain connectivity and better understand the mechanisms of psychiatric disorders and brain traits, and their associated imaging findings.
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Affiliation(s)
- Miruna C. Barbu
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Athina Spiliopoulou
- grid.4305.20000 0004 1936 7988Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Marco Colombo
- grid.4305.20000 0004 1936 7988Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Paul McKeigue
- grid.4305.20000 0004 1936 7988Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Toni-Kim Clarke
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - David M. Howard
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK ,grid.13097.3c0000 0001 2322 6764Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Mark J. Adams
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Stephen M. Lawrie
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Andrew M. McIntosh
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Cognitive Ageing and Cognitive Epidemiology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C. Whalley
- grid.4305.20000 0004 1936 7988Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Salvatore JE, Barr PB, Stephenson M, Aliev F, Kuo SIC, Su J, Agrawal A, Almasy L, Bierut L, Bucholz K, Chan G, Edenberg HJ, Johnson EC, McCutcheon VV, Meyers JL, Schuckit M, Tischfield J, Wetherill L, Dick DM. Sibling comparisons elucidate the associations between educational attainment polygenic scores and alcohol, nicotine and cannabis. Addiction 2020; 115:337-346. [PMID: 31659820 PMCID: PMC7034661 DOI: 10.1111/add.14815] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 05/30/2019] [Accepted: 09/02/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND AIMS The associations between low educational attainment and substance use disorders (SUDs) may be related to a common genetic vulnerability. We aimed to elucidate the associations between polygenic scores for educational attainment and clinical criterion counts for three SUDs (alcohol, nicotine and cannabis). DESIGN Polygenic association and sibling comparison methods. The latter strengthens inferences in observational research by controlling for confounding factors that differ between families. SETTING Six sites in the United States. PARTICIPANTS European ancestry participants aged 25 years and older from the Collaborative Study on the Genetics of Alcoholism (COGA). Polygenic association analyses included 5582 (54% female) participants. Sibling comparisons included 3098 (52% female) participants from 1226 sibling groups nested within the overall sample. MEASUREMENTS Outcomes included criterion counts for DSM-5 alcohol use disorder (AUDSX), Fagerström nicotine dependence (NDSX) and DSM-5 cannabis use disorder (CUDSX). We derived polygenic scores for educational attainment (EduYears-GPS) using summary statistics from a large (> 1 million) genome-wide association study of educational attainment. FINDINGS In polygenic association analyses, higher EduYears-GPS predicted lower AUDSX, NDSX and CUDSX [P < 0.01, effect sizes (R2 ) ranging from 0.30 to 1.84%]. These effects were robust in sibling comparisons, where sibling differences in EduYears-GPS predicted all three SUDs (P < 0.05, R2 0.13-0.20%). CONCLUSIONS Individuals who carry more alleles associated with educational attainment tend to meet fewer clinical criteria for alcohol, nicotine and cannabis use disorders, and these effects are robust to rigorous controls for potentially confounding factors that differ between families (e.g. socio-economic status, urban-rural residency and parental education).
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Affiliation(s)
- Jessica E. Salvatore
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA 23284-2018
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth, University, Box 980126, Richmond, VA 23298
| | - Peter B. Barr
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA 23284-2018
| | - Mallory Stephenson
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA 23284-2018
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA 23284-2018
- Department of Business Administration, Karabuk University, 78050 Karabuk, Turkey
| | - Sally I-Chun Kuo
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA 23284-2018
| | - Jinni Su
- Department of Psychology, Arizona State University, Box 871104, Tempe, AZ 85287-1104
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid, CB 8134, St., Louis, MO 63110
| | - Laura Almasy
- Department of Genetics, University of Pennsylvania, 415 Curie Boulevard Philadelphia, PA, 19104-6145
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, 3615, Civic Center Blvd, ARC 1016-C, Philadelphia, PA 19104
| | - Laura Bierut
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid, CB 8134, St., Louis, MO 63110
| | - Kathleen Bucholz
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid, CB 8134, St., Louis, MO 63110
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, 263 Farmington, Avenue, Farmington, CT 06030-2103
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University, 635 Barnhill Dr.,, Indianapolis, IN 46202
| | - Emma C. Johnson
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid, CB 8134, St., Louis, MO 63110
| | - Vivia V. McCutcheon
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid, CB 8134, St., Louis, MO 63110
| | - Jacquelyn L. Meyers
- Department of Psychiatry, SUNY Downstate Medical Center, 450 Clarkson Avenue Brooklyn, NY 11203
| | - Marc Schuckit
- Department of Psychiatry, University of California-San Diego, 9500 Gilman Drive La Jolla,, CA 92093
| | - Jay Tischfield
- Department of Genetics and the Human Genetics Institute of New Jersey, 145 Bevier Road, Piscataway, NJ 08854-8082
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, Indiana University, 410 W. 10th Street, Indianapolis, IN 46202
| | - Danielle M. Dick
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA 23284-2018
- Department of Human & Molecular Genetics, Virginia Commonwealth University, Box, 980033, Richmond, VA, USA 23298
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Box, 842018 Richmond, VA, 23284
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Richards AL, Pardiñas AF, Frizzati A, Tansey KE, Lynham AJ, Holmans P, Legge SE, Savage JE, Agartz I, Andreassen OA, Blokland GAM, Corvin A, Cosgrove D, Degenhardt F, Djurovic S, Espeseth T, Ferraro L, Gayer-Anderson C, Giegling I, van Haren NE, Hartmann AM, Hubert JJ, Jönsson EG, Konte B, Lennertz L, Olde Loohuis LM, Melle I, Morgan C, Morris DW, Murray RM, Nyman H, Ophoff RA, van Os J, Petryshen TL, Quattrone D, Rietschel M, Rujescu D, Rutten BPF, Streit F, Strohmaier J, Sullivan PF, Sundet K, Wagner M, Escott-Price V, Owen MJ, Donohoe G, O’Donovan MC, Walters JTR. The Relationship Between Polygenic Risk Scores and Cognition in Schizophrenia. Schizophr Bull 2020; 46:336-344. [PMID: 31206164 PMCID: PMC7442352 DOI: 10.1093/schbul/sbz061] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Cognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases. METHODS We combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases. RESULTS PRS for both population IQ (P = 4.39 × 10-28) and EA (P = 1.27 × 10-26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS. CONCLUSIONS Cognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.
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Affiliation(s)
- Alexander L Richards
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Aura Frizzati
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Katherine E Tansey
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Amy J Lynham
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Peter Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Sophie E Legge
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Jeanne E Savage
- Complex Trait Genetics Lab, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Ole A Andreassen
- CoE NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gabriella A M Blokland
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands,Department of Psychiatry, Harvard Medical School, Boston, MA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
| | - Donna Cosgrove
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics Center, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland
| | - Franziska Degenhardt
- Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany,Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Srdjan Djurovic
- CoE NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Thomas Espeseth
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Laura Ferraro
- Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Palermo, Italy
| | - Charlotte Gayer-Anderson
- Department of Health Service and Population Research, Institute of Psychiatry, King’s College London, London, UK
| | - Ina Giegling
- Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Neeltje E van Haren
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands,Department of Child and Adolescent Psychiatry/Psychology, Sophia Children’s Hospital, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Annette M Hartmann
- Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - John J Hubert
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Erik G Jönsson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden,CoE NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Bettina Konte
- Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Leonhard Lennertz
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Loes M Olde Loohuis
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA
| | - Ingrid Melle
- CoE NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Craig Morgan
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College, London, UK
| | - Derek W Morris
- Centre for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
| | - Robin M Murray
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Håkan Nyman
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | | | - Jim van Os
- Department of Psychiatry and Medical Psychology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands,Department of Psychiatry, Utrecht University Medical Centre, Utrecht, The Netherlands,King’s Health Partners Department of Psychosis Studies, King’s College London, Institute of Psychiatry, London, UK
| | | | | | - Tracey L Petryshen
- Department of Psychiatry, Harvard Medical School, Boston, MA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Diego Quattrone
- Social, Genetics and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Dan Rujescu
- Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Fabian Streit
- Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Jana Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Kjetil Sundet
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Michael Wagner
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Valentina Escott-Price
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Gary Donohoe
- Centre for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
| | - Michael C O’Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK,To whom correspondence should be addressed; tel: 44 (0)29-20688-434, fax: 44 (0)29-20687-068, e-mail:
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Mozersky JT, Antes AL, Baldwin K, Jenkerson M, DuBois JM. How do clinical research coordinators learn Good Clinical Practice? A mixed-methods study of factors that predict uptake of knowledge. Clin Trials 2020; 17:166-175. [PMID: 31984765 DOI: 10.1177/1740774519893301] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Good Clinical Practice is an international standard for the design and conduct of clinical trials to ensure ethical and scientific integrity. Recent National Institutes of Health policy mandates Good Clinical Practice training for all investigators and staff involved in National Institutes of Health-funded clinical trials, yet approaches to Good Clinical Practice training vary widely. There are limited data on Good Clinical Practice knowledge among the clinical trial workforce and no evidence regarding effective methods to learn Good Clinical Practice. METHODS We used an exploratory sequential mixed-methods design. We conducted 18 exploratory qualitative interviews with clinical research coordinators to help inform the development of the quantitative survey. We then administered a validated 32-item, multiple-choice test of Good Clinical Practice knowledge with a survey of work and training experiences to 625 clinical research coordinators at three academic medical centers in the United States. Variables that were significantly associated with Good Clinical Practice knowledge were entered into a multiple regression analysis to identify unique predictors of Good Clinical Practice knowledge. We controlled for verbal-numerical reasoning and learning orientation. RESULTS During qualitative interviews, clinical research coordinators reported that formal Good Clinical Practice training had value but they simultaneously emphasized the importance of experience, day-to-day practice, and observing colleagues and mentors as essential to supplement formal training. In our quantitative survey, five variables predicted a total of 22% of variance in Good Clinical Practice knowledge scores: years of experience as a clinical research coordinator, working on diverse types of trials, supporting industry-funded trials, being certified in clinical research coordination, and aggregated hours of online and face-to-face training (in that order). CONCLUSION The duration and richness of experience as a clinical research coordinator were the strongest predictors of Good Clinical Practice knowledge, a finding consistent with our exploratory qualitative interview results. Our findings suggest that formal online and face-to-face training has a minimal influence on Good Clinical Practice knowledge. The type of training-whether online or face to face-does not make a significant difference in Good Clinical Practice knowledge scores. Much of the variance in Good Clinical Practice knowledge remains unexplained, calling for further research in this area.
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Affiliation(s)
- Jessica T Mozersky
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Alison L Antes
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Kari Baldwin
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Michelle Jenkerson
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - James M DuBois
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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Mallet J, Le Strat Y, Dubertret C, Gorwood P. Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis. J Clin Med 2020; 9:E341. [PMID: 31991840 PMCID: PMC7074036 DOI: 10.3390/jcm9020341] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 01/14/2020] [Accepted: 01/23/2020] [Indexed: 12/26/2022] Open
Abstract
Schizophrenia is a multifactorial disease associated with widespread cognitive impairment. Although cognitive deficits are one of the factors most strongly associated with functional impairment in schizophrenia (SZ), current treatment strategies hardly tackle these impairments. To develop more efficient treatment strategies in patients, a better understanding of their pathogenesis is needed. Recent progress in genetics, driven by large genome-wide association studies (GWAS) and the use of polygenic risk scores (PRS), has provided new insights about the genetic architecture of complex human traits, including cognition and SZ. Here, we review the recent findings examining the genetic links between SZ and cognitive functions in population-based samples as well as in participants with SZ. The performed meta-analysis showed a negative correlation between the polygenetic risk score of schizophrenia and global cognition (p < 0.001) when the samples rely on general and healthy participants, while no significant correlation was detected when the three studies devoted to schizophrenia patients were meta-analysed (p > 0.05). Our review and meta-analysis therefore argues against universal pleiotropy for schizophrenia alleles and cognition, since cognition in SZ patients would be underpinned by the same genetic factors than in the general population, and substantially independent of common variant liability to the disorder.
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Affiliation(s)
- Jasmina Mallet
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Yann Le Strat
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Caroline Dubertret
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Philip Gorwood
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
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Abstract
PURPOSE OF REVIEW We review recent progress in uncovering the complex genetic architecture of cognition, arising primarily from genome-wide association studies (GWAS). We explore the genetic correlations between cognitive performance and neuropsychiatric disorders, the genetic and environmental factors associated with age-related cognitive decline, and speculate about the future role of genomics in the understanding of cognitive processes. RECENT FINDINGS Improvements in genomic methods, and the increasing availability of large datasets via consortia cooperation, have led to a greater understanding of the role played by common and rare variants in the genomics of cognition, the highly polygenic basis of cognitive function and dysfunction, and the multiple biological processes involved. Recent research has aided in our understanding of the complex biological nature of genomics of cognition. Further development of data banks and techniques to analyze this data hold significant promise for understanding cognitive ability, and for treating cognitively related disability.
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The genome-wide risk alleles for psychiatric disorders at 3p21.1 show convergent effects on mRNA expression, cognitive function, and mushroom dendritic spine. Mol Psychiatry 2020; 25:48-66. [PMID: 31723243 DOI: 10.1038/s41380-019-0592-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/30/2019] [Accepted: 10/31/2019] [Indexed: 12/13/2022]
Abstract
Schizophrenia and bipolar disorder (BPD) are believed to share clinical features, etiological factors, and disease pathologies (such as impaired cognitive functions and dendritic spine pathology). Meanwhile, there is growing evidence of shared genetic risk between schizophrenia and BPD, despite that our knowledge of the functional risk variations and biological mechanisms is still limited. Here, we conduct summary data-based Mendelian randomization (SMR) analyses through combining the statistical data from genome-wide association studies (GWAS) of both schizophrenia and BPD and multiple expression quantitative trait loci (eQTL) datasets of the human brain dorsolateral prefrontal cortex (DLPFC) tissues. These integrative investigations identify a lead risk locus at the chromosome 3p21.1 region, which contains numerous single-nucleotide polymorphisms (SNPs) in varied linkage disequilibrium (LD) and encompasses more than 20 genes. Further analyses suggest that many SNPs at 3p21.1 are significantly associated with both schizophrenia and BPD, and even depression, and the psychiatric risk alleles at 3p21.1 are correlated with mRNA expression of multiple genes such as NEK4, GNL3, and PBRM1. We also identify a 335-bp functional Alu polymorphism rs71052682 in significant LD with the psychiatric GWAS risk SNP rs2251219, and confirm the regulatory effects of this Alu polymorphism on transcription activities. We then explore the involvement of the 3p21.1 locus in the common clinical features and etiology of these illnesses. We reveal that psychiatric risk alleles at 3p21.1 in low-to-high LD consistently predict worse cognitive functions in humans, and manipulating the gene expression (NEK4, GNL3, and PBRM1) linked with higher genetic risk could reduce the density of mushroom dendritic spines in rat primary cortical neurons, mirroring the spine pathology in the prefrontal cortex of psychiatric patients. Our results find that, although the risk alleles at 3p21.1 are in low-to-moderate LD spanning a large genomic area, their underlying biological mechanisms in psychiatric disorders likely converge. These results provide essential insights into the neural mechanisms underlying the chromosome 3p21.1 risk locus in the shared pathological and etiological features of both schizophrenia and BPD.
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120
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Tielbeek JJ, Boutwell BB. Exploring the Genomic Architectures of Health, Physical Traits and Antisocial Behavioral Outcomes: A Brief Report. Front Psychiatry 2020; 11:539. [PMID: 32670102 PMCID: PMC7330713 DOI: 10.3389/fpsyt.2020.00539] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 05/26/2020] [Indexed: 11/13/2022] Open
Abstract
A widely replicated finding across the behavioral sciences is that antisocial behaviors correlate with an array of health problems. Less clear, however, is the precise nature of this association. There is reason to suspect that a direct causal link exists between incarceration-a consequence of some antisocial behaviors-and certain negative health outcomes, for instance. However, it might be the case that broader phenotypes like antisocial behavior may correlate with certain health and physiological traits at a genomic level. We explore this possibility from a theoretical vantage point, while also presenting some preliminary data from existing secondary sources. Tentatively, no significant genetic correlations emerged across a host of health, physiological, and wellbeing outcomes after correction for multiple testing. However, more work is needed exploring this topic. We propose that future studies should make use of larger, more diverse samples and examine the genetic overlap between homogeneous clusters of antisocial behavioral subtypes and disease traits or symptoms.
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Affiliation(s)
- Jorim J Tielbeek
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Brian B Boutwell
- Department of Criminal Justice and Legal Studies, School of Applied Sciences, University of Mississippi, University, MS, United States.,John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, United States
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121
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Harrison JR, Mistry S, Muskett N, Escott-Price V. From Polygenic Scores to Precision Medicine in Alzheimer's Disease: A Systematic Review. J Alzheimers Dis 2020; 74:1271-1283. [PMID: 32250305 PMCID: PMC7242840 DOI: 10.3233/jad-191233] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/12/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Late-onset Alzheimer's disease (AD) is highly heritable. The effect of many common genetic variants, single nucleotide polymorphisms (SNPs), confer risk. Variants are clustered in areas of biology, notably immunity and inflammation, cholesterol metabolism, endocytosis, and ubiquitination. Polygenic scores (PRS), which weight the sum of an individual's risk alleles, have been used to draw inferences about the pathological processes underpinning AD. OBJECTIVE This paper aims to systematically review how AD PRS are being used to study a range of outcomes and phenotypes related to neurodegeneration. METHODS We searched the literature from July 2008-July 2018 following PRISMA guidelines. RESULTS 57 studies met criteria. The AD PRS can distinguish AD cases from controls. The ability of AD PRS to predict conversion from mild cognitive impairment (MCI) to AD was less clear. There was strong evidence of association between AD PRS and cognitive impairment. AD PRS were correlated with a number of biological phenotypes associated with AD pathology, such as neuroimaging changes and amyloid and tau measures. Pathway-specific polygenic scores were also associated with AD-related biologically relevant phenotypes. CONCLUSION PRS can predict AD effectively and are associated with cognitive impairment. There is also evidence of association between AD PRS and other phenotypes relevant to neurodegeneration. The associations between pathway specific polygenic scores and phenotypic changes may allow us to define the biology of the disease in individuals and indicate who may benefit from specific treatments. Longitudinal cohort studies are required to test the ability of PGS to delineate pathway-specific disease activity.
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Affiliation(s)
- Judith R. Harrison
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Hadyn Ellis Building, Cardiff University, Cardiff, UK
| | - Sumit Mistry
- MRC Centre for Neuropsychiatric Genetics and Genomics, Hadyn Ellis Building, Cardiff University, Cardiff, UK
| | - Natalie Muskett
- Cardiff University Medical School, University Hospital of Wales, Cardiff, UK
| | - Valentina Escott-Price
- Dementia Research Institute & the MRC Centre for Neuropsychiatric Genetics and Genomics, Hadyn Ellis Building, Cardiff University, Cardiff, UK
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Gampawar P, Schmidt R, Schmidt H. Leukocyte Telomere Length Is Related to Brain Parenchymal Fraction and Attention/Speed in the Elderly: Results of the Austrian Stroke Prevention Study. Front Psychiatry 2020; 11:100. [PMID: 32180739 PMCID: PMC7059269 DOI: 10.3389/fpsyt.2020.00100] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 02/06/2020] [Indexed: 12/14/2022] Open
Abstract
There are controversial results if leukocyte telomere length (LTL) is related to structural brain changes and cognitive decline in aging. Here, we investigated the association between LTL and 1) global MRI correlates of brain aging such as brain parenchymal fraction (BPF) and white matter hyperintensities (WMH) load and Fazekas score as well as 2) global (g-factor) and domain-specific cognition such as attention/speed, conceptualization, memory, and visuopractical skills. In total, 909 participants of the Austrian Stroke Prevention Study with LTL, MRI, and cognitive tests were included. There were 388 (42.7%) men, and the mean age was 65.9 years. Longer LTL was significantly associated with larger BPF (β = 0.43, p < 0.001), larger WMH load (β = 0.03, p = 0.04), and score (β = 0.05, p = 0.04) after adjusting for age, sex, vascular risk factors, and ApoE4 carrier status. The effect on BPF was more significant in the subgroups of women (β = 0.51, p = 0.001), age >65 years (β = 0.58, p = 0.002), BMI ≥ 25 (β = 0.40, p = 0.004), education ≤10 years (β = 0.42, p = 0.002), hypertensives (β = 0.51, p = 0.001), cardiovascular disease (CVD) (β = 0.58, p = 0.005), non-diabetics (β = 0.42, p < 0.001), and Apoe4 non-carriers (β = 0.49, p < 0.001). The effect on WMH was significant within the hypertensives (load: β = 0.04, p = 0.02), non-diabetics (load:β = 0.03, p = 0.01; score: β = 0.06, p = 0.02), in those with education ≤10 years (load: β = 0.03, p = 0.04; score: β = 0.07, p = 0.02), in ApoE4 non-carriers (load: β = 0.03, p = 0.02; score: β = 0.07, p = 0.01) and in subjects without CVD (score: β = 0.06, p = 0.05). We only observed a significant association between LTL and the cognitive domain of attention/speed, which was confined to the subgroups of BMI ≥ 25 (β = 0.04, p = 0.05) and education ≤10 years (β = 0.04, p = 0.05). The effect of LTL on attention/speed was partly mediated in both subgroups by BPF (β = 0.02, 95% CI = 0.01:0.03) when tested by bootstrapping. Our results support a strong protective role of longer LTL on global brain volume which in turn may contribute to better cognitive functions, especially in the attention/speed domain in the elderly.
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Affiliation(s)
- Piyush Gampawar
- Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signalling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Helena Schmidt
- Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signalling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
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Valge M, Meitern R, Hõrak P. Morphometric traits predict educational attainment independently of socioeconomic background. BMC Public Health 2019; 19:1696. [PMID: 31852467 PMCID: PMC6921596 DOI: 10.1186/s12889-019-8072-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/11/2019] [Indexed: 02/08/2023] Open
Abstract
Background Aim of this study is to describe the relationship between anthropometric traits and educational attainment among Estonian schoolchildren born between 1937 and 1962. We asked whether height, cranial volume and face width (a testosterone-dependent trait), measured in childhood predict later educational attainment independently of each other, family socioeconomic position (SEP) and sex. Associations between morphometric traits and education and their interactions with biosocial variables are of scholarly importance because higher education is nearly universally associated with low fertility in women, and often with high fertility in men. Hence, morphometric traits associated with educational attainment are targeted by natural selection and describing the exact nature of these associations is relevant for understanding the current patterns of evolution of human body size. Methods Data on morphometric measurements and family background of 11,032 Estonian schoolchildren measured between seven and 19 years of age were obtained from the study performed by Juhan Aul between 1956 and 1969. Ordinal logistic regression was used for testing the effects of morphometric traits, biosocial variables and their interaction on the cumulative probability of obtaining education beyond primary level. Results Of biosocial variables, family SEP was the most important determinant of educational attainment, followed by the sex, rural vs urban origin and the number of siblings. No significant interactions with morphometric traits were detected, i.e., within each category of SEP, rural vs urban origin and sex, taller children and those with larger heads and relatively narrower faces were more likely to proceed to secondary and/or tertiary education. The effect of height on education was independent of cranial volume, indicating that taller children did not obtain more educations because their brains were larger than those of shorter children; height per se was important. Conclusions Our main finding – that adjusting for other morphometric traits and biosocial variables, morphometric traits still robustly predicted educational attainment, is relevant for understanding the current patterns of evolution of human body size. Our findings suggest that fecundity selection acting on educational attainment could be partly responsible for the concurrent selection for smaller stature and cranial volume in women and opposite trends in men.
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Affiliation(s)
- Markus Valge
- Department of Zoology, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia
| | - Richard Meitern
- Department of Zoology, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia
| | - Peeter Hõrak
- Department of Zoology, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia.
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Korologou-Linden R, Anderson EL, Jones HJ, Davey Smith G, Howe LD, Stergiakouli E. Polygenic risk scores for Alzheimer's disease, and academic achievement, cognitive and behavioural measures in children from the general population. Int J Epidemiol 2019; 48:1972-1980. [PMID: 31056667 PMCID: PMC6929531 DOI: 10.1093/ije/dyz080] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2019] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE Several studies report a polygenic component of risk for Alzheimer's disease. Understanding whether this polygenic signal is associated with educational, cognitive and behavioural outcomes in children could provide an earlier window for intervention. METHODS We examined whether polygenic risk scores (PRS) at varying P-value thresholds in children from the Avon Longitudinal Study of Parents and Children were associated with academic achievement, cognitive and behavioural measures in childhood and adolescence. RESULTS We did not detect any evidence that the genome-wide significant PRS (5x10-8) were associated with these outcomes. PRS at the highest P-value threshold examined (P ≤ 5x10-1) were associated with lower academic achievement in adolescents (Key Stage 3; β: -0.03; 95% confidence interval: -0.05, -0.003) but the effect was attenuated when single nucleotide polymorphisms (SNPs) associated with educational attainment were removed. These PRS were associated with lower IQ (β: -0.04; 95% CI: -0.07, -0.02) at age 8 years with the effect remaining after removing SNPs associated with educational attainment. CONCLUSIONS SNPs mediating the biological effects of Alzheimer's disease are unlikely to operate early in life. The evidence of association between PRS for Alzheimer's disease at liberal thresholds and cognitive measures suggest shared genetic pathways between Alzheimer's disease, academic achievement and cognition.
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Affiliation(s)
- Roxanna Korologou-Linden
- Medical Research Council Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Emma L Anderson
- Medical Research Council Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Hannah J Jones
- Medical Research Council Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, Bristol, UK
- NIHR Biomedical Research Centre at University Hospitals Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
- School of Oral and Dental Sciences, University of Bristol, Bristol, UK
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125
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Nesic MJ, Stojkovic B, Maric NP. On the origin of schizophrenia: Testing evolutionary theories in the post-genomic era. Psychiatry Clin Neurosci 2019; 73:723-730. [PMID: 31525268 DOI: 10.1111/pcn.12933] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 09/10/2019] [Accepted: 09/12/2019] [Indexed: 12/13/2022]
Abstract
Considering the relatively high heritability of schizophrenia and the fact that it significantly reduces the reproductive fitness of affected individuals, it is not clear how the disorder is still maintained in human populations at a disproportionally high prevalence. Many theories propose that the disorder is a result of a trade-off between costs and benefits of the evolution of exclusively human adaptations. There have also been suggestions that schizophrenia risk alleles are accompanied with increase in fitness of affected persons or their relatives in both past and current social contexts. The discoveries of novel schizophrenia-related genes and the advancements in comparative genomics (especially comparisons of the human genome and the genomes of related species, such as chimpanzees and extinct hominids) have finally made certain evolutionary theories testable. In this paper, we review the current understanding of the genetics of schizophrenia, the basic principles of evolution that complement our understanding of the subject, and the latest genetic studies that examine long-standing evolutionary theories of schizophrenia using novel methodologies and data. We find that the origin of schizophrenia is complex and likely governed by different evolutionary mechanisms that are not mutually exclusive. Furthermore, the most recent evidence implies that schizophrenia cannot be comprehended as a trait that has elevated fitness in human evolutionary lineage, but has been a mildly deleterious by-product of specific patterns of the evolution of the human brain. In other words, novel findings do not support previous hypotheses stating that schizophrenia risk genes have an evolutionary advantage.
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Affiliation(s)
- Milica J Nesic
- Clinic for Psychiatry, Clinical Center of Serbia, Belgrade, Serbia
| | - Biljana Stojkovic
- Institute of Zoology, Faculty of Biology, University of Belgrade, Belgrade, Serbia.,Department of Evolutionary Biology, Institute for Biological Research 'Siniša Stanković', University of Belgrade, Belgrade, Serbia
| | - Nadja P Maric
- Clinic for Psychiatry, Clinical Center of Serbia, Belgrade, Serbia.,Faculty of Medicine, University of Belgrade, Belgrade, Serbia
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Karavani E, Zuk O, Zeevi D, Barzilai N, Stefanis NC, Hatzimanolis A, Smyrnis N, Avramopoulos D, Kruglyak L, Atzmon G, Lam M, Lencz T, Carmi S. Screening Human Embryos for Polygenic Traits Has Limited Utility. Cell 2019; 179:1424-1435.e8. [PMID: 31761530 PMCID: PMC6957074 DOI: 10.1016/j.cell.2019.10.033] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/11/2019] [Accepted: 10/25/2019] [Indexed: 12/19/2022]
Abstract
The increasing proportion of variance in human complex traits explained by polygenic scores, along with progress in preimplantation genetic diagnosis, suggests the possibility of screening embryos for traits such as height or cognitive ability. However, the expected outcomes of embryo screening are unclear, which undermines discussion of associated ethical concerns. Here, we use theory, simulations, and real data to evaluate the potential gain of embryo screening, defined as the difference in trait value between the top-scoring embryo and the average embryo. The gain increases very slowly with the number of embryos but more rapidly with the variance explained by the score. Given current technology, the average gain due to screening would be ≈2.5 cm for height and ≈2.5 IQ points for cognitive ability. These mean values are accompanied by wide prediction intervals, and indeed, in large nuclear families, the majority of children top-scoring for height are not the tallest.
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Affiliation(s)
- Ehud Karavani
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Or Zuk
- Department of Statistics, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel
| | - Danny Zeevi
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nir Barzilai
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Genetics, Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Nikos C Stefanis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, 115 28 Athens, Greece; University Mental Health Research Institute, 115 27 Athens, Greece; Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, 115 21 Athens, Greece
| | - Alex Hatzimanolis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, 115 28 Athens, Greece; Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, 115 21 Athens, Greece
| | - Nikolaos Smyrnis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, 115 28 Athens, Greece; University Mental Health Research Institute, 115 27 Athens, Greece
| | - Dimitrios Avramopoulos
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Gil Atzmon
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Genetics, Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Biology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel
| | - Max Lam
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY 11004, USA; Institute of Behavioral Science, Feinstein Institutes of Medical Research, Manhasset, NY 11030, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Todd Lencz
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY 11004, USA; Institute of Behavioral Science, Feinstein Institutes of Medical Research, Manhasset, NY 11030, USA; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA.
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel.
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127
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Shen X, Adams MJ, Ritakari TE, Cox SR, McIntosh AM, Whalley HC. White Matter Microstructure and Its Relation to Longitudinal Measures of Depressive Symptoms in Mid- and Late Life. Biol Psychiatry 2019; 86:759-768. [PMID: 31443934 PMCID: PMC6906887 DOI: 10.1016/j.biopsych.2019.06.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 06/04/2019] [Accepted: 06/04/2019] [Indexed: 12/27/2022]
Abstract
BACKGROUND Studies of white matter microstructure in depression typically show alterations in individuals with depression, but they are frequently limited by small sample sizes and the absence of longitudinal measures of depressive symptoms. Depressive symptoms are dynamic, however, and understanding the neurobiology of different trajectories could have important clinical implications. METHODS We examined associations between current and longitudinal measures of depressive symptoms and white matter microstructure (fractional anisotropy and mean diffusivity [MD]) in the UK Biobank Imaging Study. Depressive symptoms were assessed on two to four occasions over 5.9 to 10.7 years (n = 18,959 individuals on at least two occasions, n = 4444 on four occasions), from which we derived four measures of depressive symptomatology: cross-sectional measure at the time of scan and three longitudinal measures, namely trajectory and mean and intrasubject variance over time. RESULTS Decreased white matter microstructure in the anterior thalamic radiation demonstrated significant associations across all four measures of depressive symptoms (MD: βs = .020-.029, pcorr < .030). The greatest effect sizes were seen between white matter microstructure and longitudinal progression (MD: βs = .030-.040, pcorr < .049). Cross-sectional symptom severity was particularly associated with decreased white matter integrity in association fibers and thalamic radiations (MD: βs = .015-.039, pcorr < .041). Greater mean and within-subject variance were mainly associated with decreased white matter microstructure within projection fibers (MD: βs = .019-.029, pcorr < .044). CONCLUSIONS These findings indicate shared and differential neurobiological associations with severity, course, and intrasubject variability of depressive symptoms. This enriches our understanding of the neurobiology underlying dynamic features of the disorder.
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Affiliation(s)
- Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Tuula E Ritakari
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
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Ritchie SJ, Cox SR, Shen X, Lombardo MV, Reus LM, Alloza C, Harris MA, Alderson HL, Hunter S, Neilson E, Liewald DCM, Auyeung B, Whalley HC, Lawrie SM, Gale CR, Bastin ME, McIntosh AM, Deary IJ. Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants. Cereb Cortex 2019; 28:2959-2975. [PMID: 29771288 PMCID: PMC6041980 DOI: 10.1093/cercor/bhy109] [Citation(s) in RCA: 416] [Impact Index Per Article: 83.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/20/2018] [Indexed: 02/07/2023] Open
Abstract
Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44-77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.
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Affiliation(s)
- Stuart J Ritchie
- Department of Psychology, The University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Department of Psychology, The University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - Michael V Lombardo
- Department of Psychology and Center for Applied Neuroscience, University of Cyprus, Nicosia, Cyprus.,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Lianne M Reus
- Department of Neurology and Alzheimer Centre, VU University Medical Centre, Amsterdam, The Netherlands
| | - Clara Alloza
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - Mathew A Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK.,Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - Helen L Alderson
- Department of Psychiatry, Queen Margaret Hospital, Dunfermline, UK
| | | | - Emma Neilson
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - David C M Liewald
- Department of Psychology, The University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
| | - Bonnie Auyeung
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | | | - Stephen M Lawrie
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - Catharine R Gale
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK.,MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, The University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK.,Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Department of Psychology, The University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
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129
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Gill D, Efstathiadou A, Cawood K, Tzoulaki I, Dehghan A. Education protects against coronary heart disease and stroke independently of cognitive function: evidence from Mendelian randomization. Int J Epidemiol 2019; 48:1468-1477. [PMID: 31562522 PMCID: PMC6857750 DOI: 10.1093/ije/dyz200] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/13/2019] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND There is evidence that education protects against cardiovascular disease. However, it is not known whether such an effect is independent of cognition. METHODS We performed two-sample Mendelian randomization (MR) analyses to investigate the effect of education and cognition, respectively, on risk of CHD and ischaemic stroke. Additionally, we used multivariable MR to adjust for the effects of cognition and education in the respective analyses to measure the effects of these traits independently of each other. RESULTS In unadjusted MR, there was evidence that education is causally associated with both CHD and stroke risk [CHD: odds ratio (OR) 0.65 per 1-standard deviation (SD; 3.6 years) increase in education; 95% confidence interval (CI) 0.61-0.70, stroke: OR 0.77; 95% CI 0.69-0.86]. This effect persisted after adjusting for cognition in multivariable MR (CHD: OR 0.76; 95% CI 0.65-0.89, stroke OR 0.74; 95% CI 0.59-0.92). Cognition had an apparent effect on CHD risk in unadjusted MR (OR per 1-SD increase 0.80; 95% CI 0.74-0.85), however after adjusting for education this was no longer observed (OR 1.03; 95% CI 0.86-1.25). Cognition did not have any notable effect on the risk of developing ischaemic stroke, with (OR 0.97; 95% CI 0.87-1.08) or without adjustment for education (OR 1.04; 95% CI 0.79-1.36). CONCLUSIONS This study provides evidence to support that education protects against CHD and ischaemic stroke risk independently of cognition, but does not provide evidence to support that cognition protects against CHD and stroke risk independently of education. These findings could have implications for education and health policy.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Anthoula Efstathiadou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | | | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council–Public Health England Centre for Environment, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council–Public Health England Centre for Environment, School of Public Health, Imperial College London, London, UK
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130
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Smeland OB, Frei O, Fan CC, Shadrin A, Dale AM, Andreassen OA. The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. Psychiatr Genet 2019; 29:152-159. [PMID: 31464996 PMCID: PMC10752571 DOI: 10.1097/ypg.0000000000000234] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies have transformed psychiatric genetics and provided novel insights into the genetic etiology of psychiatric disorders. Two major discoveries have emerged; the disorders are polygenic, with a large number of common variants each with a small effect and many genetic variants influence more than one phenotype, suggesting shared genetic etiology. These concepts have the potential to revolutionize the current classification system with diagnostic categories and facilitate development of better treatments. However, to reach clinical impact, we need larger samples and better analytical tools, as most polygenic factors remain undetected. We here present statistical approaches designed to improve the yield of existing genome-wide association studies for polygenic phenotypes. We review how these tools have informed the current knowledge on the genetic architecture of psychiatric disorders, focusing on schizophrenia, bipolar disorder and major depression, and overlap with psychological and cognitive traits. We discuss application of statistical tools for stratification and prediction.
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Affiliation(s)
- Olav B. Smeland
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Chun-Chieh Fan
- Center for Human Development, University of California, San Diego, USA
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, USA
- Department of Neuroscience, University of California, San Diego, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, California, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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131
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James SN, Wong A, Tillin T, Hardy R, Chaturvedi N, Richards M. The effect of mid-life insulin resistance and type 2 diabetes on older-age cognitive state: the explanatory role of early-life advantage. Diabetologia 2019; 62:1891-1900. [PMID: 31359084 PMCID: PMC6731197 DOI: 10.1007/s00125-019-4949-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 05/28/2019] [Indexed: 12/20/2022]
Abstract
AIMS/HYPOTHESIS Type 2 diabetes, hyperglycaemia and insulin resistance are associated with cognitive impairment and dementia, but causal inference studies using Mendelian randomisation do not confirm this. We hypothesised that early-life cognition and social/educational advantage may confound the relationship. METHODS From the population-based British 1946 birth cohort, a maximum number of 1780 participants had metabolic variables (type 2 diabetes, insulin resistance [HOMA2-IR] and HbA1c) assessed at age 60-64 years, and cognitive state (Addenbrooke's Cognitive Examination III [ACE-III]) and verbal memory assessed at age 69 years. Earlier-life measures included socioeconomic position (SEP), cognition at age 8 years and educational attainment. Polygenic risk scores (PRSs) for type 2 diabetes were calculated. We first used a PRS approach with multivariable linear regression to estimate associations between PRSs and metabolic traits and later-life cognitive state. Second, using a path model approach, we estimated the interrelationships between earlier-life measures, features of mid-life type 2 diabetes and cognitive state at age 69 years. All models were adjusted for sex. RESULTS The externally weighted PRS for type 2 diabetes was associated with mid-life metabolic traits (e.g. HOMA2-IR β = 0.08 [95% CI 0.02, 0.16]), but not with ACE-III (β = 0.04 [-0.02, 0.90]) or other cognitive outcomes. While there was an association between HOMA2-IR and subsequent ACE-III (β = -0.09 [-0.15, -0.03]), path modelling showed no direct effect (β = -0.01 [-0.06, 0.03]) after accounting for the association between childhood SEP and education with HOMA2-IR. The same pattern was observed for later-life verbal memory. CONCLUSIONS/INTERPRETATION Associations between type 2 diabetes and mid-life metabolic traits with subsequent cognitive state do not appear causal, and instead they may be explained by SEP in early life, childhood cognition and educational attainment. Therefore, glucose-lowering medication may be unlikely to combat cognitive impairment in older age.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Therese Tillin
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Rebecca Hardy
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
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132
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Avinun R. The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2019; 15:81-89. [PMID: 31558103 DOI: 10.1177/1745691619867107] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Genome-wide association studies (GWASs) have shown that pleiotropy is widespread (i.e., the same genetic variants affect multiple traits) and that complex traits are polygenic (i.e., affected by many genetic variants with very small effect sizes). However, despite the growing number of GWASs, the possible contribution of gene-environment correlations (rGEs) to pleiotropy and polygenicity has been mostly ignored. rGEs can lead to environmentally mediated pleiotropy or gene-environment-trait correlations (rGETs), given that an environment that is affected by one genetically influenced phenotype, can in turn affect a different phenotype. By adding correlations with environmentally mediated genetic variants, rGETs can contribute to polygenicity. Socioeconomic status (SES) and the experience of stressful life events may, for example, be involved in rGETs. Both are genetically influenced and have been associated with a myriad of physical and mental disorders. As a result, GWASs of these disorders may find the genetic correlates of SES and stressful life events. Consequently, some of the genetic correlates of physical and mental disorders may be modified by public policy that affects environments such as SES and stressful life events. Thus, identifying rGETs can shed light on findings from GWASs and have important implications for public health.
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Affiliation(s)
- Reut Avinun
- Department of Psychology & Neuroscience, Duke University.,Department of Psychology, The Hebrew University of Jerusalem
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133
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Toulopoulou T, Zhang X, Cherny S, Dickinson D, Berman KF, Straub RE, Sham P, Weinberger DR. Polygenic risk score increases schizophrenia liability through cognition-relevant pathways. Brain 2019; 142:471-485. [PMID: 30535067 DOI: 10.1093/brain/awy279] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/19/2018] [Indexed: 02/02/2023] Open
Abstract
Cognitive deficit is thought to represent, at least in part, genetic mechanisms of risk for schizophrenia, with recent evidence from statistical modelling of twin data suggesting direct causality from the former to the latter. However, earlier evidence was based on inferences from twin not molecular genetic data and it is unclear how much genetic influence 'passes through' cognition on the way to diagnosis. Thus, we included direct measurements of genetic risk (e.g. schizophrenia polygenic risk scores) in causation models to assess the extent to which cognitive deficit mediates some of the effect of polygenic risk scores on the disorder. Causal models of family data tested relationships among key variables and allowed parsing of genetic variance components. Polygenic risk scores were calculated from summary statistics from the current largest genome-wide association study of schizophrenia and were represented as a latent trait. Cognition was also modelled as a latent trait. Participants were 1313 members of 1078 families: 416 patients with schizophrenia, 290 unaffected siblings, and 607 controls. Modelling supported earlier findings that cognitive deficit has a putatively causal role in schizophrenia. In total, polygenic risk score explained 8.07% [confidence interval (CI) 5.45-10.74%] of schizophrenia risk in our sample. Of this, more than a third (2.71%, CI 2.41-3.85%) of the polygenic risk score influence was mediated through cognition paths, exceeding the direct influence of polygenic risk score on schizophrenia risk (1.43%, CI 0.46-3.08%). The remainder of the polygenic risk score influence (3.93%, CI 2.37-4.48%) reflected reciprocal causation between schizophrenia liability and cognition (e.g. mutual influences in a cyclical manner). Analysis of genetic variance components of schizophrenia liability indicated that 26.87% (CI 21.45-32.57%) was associated with cognition-related pathways not captured by polygenic risk score. The remaining variance in schizophrenia was through pathways other than cognition-related and polygenic risk score. Although our results are based on inference through statistical modelling and do not provide an absolute proof of causality, we find that cognition pathways mediate a significant part of the influence of cumulative genetic risk on schizophrenia. We estimate from our model that 33.51% (CI 27.34-43.82%) of overall genetic risk is mediated through influences on cognition, but this requires further studies and analyses as the genetics of schizophrenia becomes better characterized.
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Affiliation(s)
- Timothea Toulopoulou
- Department of Psychology, Bilkent University, Bilkent, Ankara, Turkey.,The State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong SAR, China.,Department of Psychology, the University of Hong Kong, Hong Kong SAR, China.,Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK
| | - Xiaowei Zhang
- Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Stacey Cherny
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK.,Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins University, USA
| | - Pak Sham
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK.,Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University, USA.,Departments of Psychiatry, Neurology, Neuroscience, The McKusick Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Johns Hopkins University, USA
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134
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Cox SR, Ritchie SJ, Fawns-Ritchie C, Tucker-Drob EM, Deary IJ. Structural brain imaging correlates of general intelligence in UK Biobank. INTELLIGENCE 2019; 76:101376. [PMID: 31787788 PMCID: PMC6876667 DOI: 10.1016/j.intell.2019.101376] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44–81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction. We used a large sample from UK Biobank (N = 29,004, age range = 44–81 years). The association between brain volume and intelligence (‘g’) was r = 0.276. Multiple global tissue measures explained twice the g variance in older than middle age. The size of the association between g and global brain measures did not vary by sex. We investigate the regional cortical, subcortical and white matter correlates of g.
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Affiliation(s)
- S R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK.,Department of Psychology, The University of Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - S J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - C Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK.,Department of Psychology, The University of Edinburgh, UK
| | - E M Tucker-Drob
- Department of Psychology, University of Texas, Austin, TX, USA
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK.,Department of Psychology, The University of Edinburgh, UK
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135
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Selzam S, Ritchie SJ, Pingault JB, Reynolds CA, O'Reilly PF, Plomin R. Comparing Within- and Between-Family Polygenic Score Prediction. Am J Hum Genet 2019; 105:351-363. [PMID: 31303263 PMCID: PMC6698881 DOI: 10.1016/j.ajhg.2019.06.006] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 06/06/2019] [Indexed: 12/14/2022] Open
Abstract
Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.
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Affiliation(s)
- Saskia Selzam
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK.
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Jean-Baptiste Pingault
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK; Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA 92521, USA
| | - Paul F O'Reilly
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK; Icahn School of Medicine, Mount Sinai, New York, NY 10029, USA
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
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136
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Lam M, Hill WD, Trampush JW, Yu J, Knowles E, Davies G, Stahl E, Huckins L, Liewald DC, Djurovic S, Melle I, Sundet K, Christoforou A, Reinvang I, DeRosse P, Lundervold AJ, Steen VM, Espeseth T, Räikkönen K, Widen E, Palotie A, Eriksson JG, Giegling I, Konte B, Hartmann AM, Roussos P, Giakoumaki S, Burdick KE, Payton A, Ollier W, Chiba-Falek O, Attix DK, Need AC, Cirulli ET, Voineskos AN, Stefanis NC, Avramopoulos D, Hatzimanolis A, Arking DE, Smyrnis N, Bilder RM, Freimer NA, Cannon TD, London E, Poldrack RA, Sabb FW, Congdon E, Conley ED, Scult MA, Dickinson D, Straub RE, Donohoe G, Morris D, Corvin A, Gill M, Hariri AR, Weinberger DR, Pendleton N, Bitsios P, Rujescu D, Lahti J, Le Hellard S, Keller MC, Andreassen OA, Deary IJ, Glahn DC, Malhotra AK, Lencz T. Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways. Am J Hum Genet 2019; 105:334-350. [PMID: 31374203 PMCID: PMC6699140 DOI: 10.1016/j.ajhg.2019.06.012] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 06/12/2019] [Indexed: 12/12/2022] Open
Abstract
Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.
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Affiliation(s)
- Max Lam
- Institute of Mental Health, Singapore, 539747, Singapore; Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY 11004, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom
| | - Joey W Trampush
- Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jin Yu
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY 11004, USA
| | - Emma Knowles
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom
| | - Eli Stahl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura Huckins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David C Liewald
- Department of Psychology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, University of Bergen, Bergen 4956, Nydalen 0424, Norway; Norsk Senter for Forskning på Mentale Lidelser, K.G. Jebsen Centre for Psychosis Research, University of Bergen, Bergen 4956, Nydalen 0424, Norway
| | - Ingrid Melle
- Norsk Senter for Forskning på Mentale Lidelser, K.G. Jebsen Centre for Psychosis Research, University of Bergen, Bergen 4956, Nydalen 0424, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo 1039, Blindern 0315, Norway
| | - Kjetil Sundet
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo 1039, Blindern 0315, Norway; Department of Psychology, University of Oslo, Oslo 1094, Blindern 0317, Norway
| | - Andrea Christoforou
- Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 7804, N-5020 Bergen, Norway
| | - Ivar Reinvang
- Department of Psychology, University of Oslo, Oslo 1094, Blindern 0317, Norway
| | - Pamela DeRosse
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY 11004, USA
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, 7807, N-5020, Norway
| | - Vidar M Steen
- Norsk Senter for Forskning på Mentale Lidelser, K.G. Jebsen Centre for Psychosis Research, University of Bergen, Bergen 4956, Nydalen 0424, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 7804, N-5020 Bergen, Norway
| | - Thomas Espeseth
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo 1039, Blindern 0315, Norway; Department of Psychology, University of Oslo, Oslo 1094, Blindern 0317, Norway
| | - Katri Räikkönen
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, 00014, Finland
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Finland; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, United Kingdom; Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, 00014, Finland
| | - Johan G Eriksson
- Department of General Practice, University of Helsinki and Helsinki University Hospital, Helsinki, 00014, Finland; National Institute for Health and Welfare, Helsinki FI-00271, Finland; Folkhälsan Research Center, Helsinki 00290, Finland
| | - Ina Giegling
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle 06108, Germany
| | - Bettina Konte
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle 06108, Germany
| | - Annette M Hartmann
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle 06108, Germany
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | | | - Katherine E Burdick
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, Bronx, NY 10468, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
| | - Antony Payton
- Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, University of Manchester, Manchester M139NT, United Kingdom
| | - William Ollier
- Centre for Epidemiology, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M139PL, United Kingdom; School of Healthcare Sciences, Manchester Metropolitan University, Manchester M15 6BH, United Kingdom
| | - Ornit Chiba-Falek
- Department of Neurology, Bryan Alzheimer Disease Research Center, Duke University Medical Center, Durham, NC 27705, USA; Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC 27705, USA
| | - Deborah K Attix
- Department of Neurology, Bryan Alzheimer Disease Research Center, Duke University Medical Center, Durham, NC 27705, USA; Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC 27705, USA; Psychiatry and Behavioral Sciences, Division of Medical Psychology, Duke University Medical Center, Durham, NC 27708, USA; Department of Neurology, Duke University Medical Center, Durham, NC 27708, USA
| | - Anna C Need
- Division of Brain Sciences, Department of Medicine, Imperial College, London W12 0NN, UK
| | | | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto M6J 1H4, Canada
| | - Nikos C Stefanis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece; University Mental Health Research Institute, Athens 115 27, Greece; Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, Athens, Greece
| | - Dimitrios Avramopoulos
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Alex Hatzimanolis
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto M6J 1H4, Canada; Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece; University Mental Health Research Institute, Athens 115 27, Greece
| | - Dan E Arking
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Nikolaos Smyrnis
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto M6J 1H4, Canada; Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Robert M Bilder
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Nelson A Freimer
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Edythe London
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA 90024, USA
| | | | - Fred W Sabb
- Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, OR, 97401, USA
| | - Eliza Congdon
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA 90024, USA
| | | | - Matthew A Scult
- Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
| | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institute of Health, Bethesda, MD 20814, USA
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD 21205, USA
| | - Gary Donohoe
- Neuroimaging, Cognition, and Genomics Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Derek Morris
- Neuroimaging, Cognition, and Genomics Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD 21205, USA
| | - Neil Pendleton
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Salford Royal NHS Foundation Trust, Manchester M13 9PL, United Kingdom
| | - Panos Bitsios
- Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, Heraklion, Crete GR-71003, Greece
| | - Dan Rujescu
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle 06108, Germany
| | - Jari Lahti
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, 00014, Finland; Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki 00014, Finland
| | - Stephanie Le Hellard
- Norsk Senter for Forskning på Mentale Lidelser, K.G. Jebsen Centre for Psychosis Research, University of Bergen, Bergen 4956, Nydalen 0424, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 7804, N-5020 Bergen, Norway
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80303, USA
| | - Ole A Andreassen
- Norsk Senter for Forskning på Mentale Lidelser, K.G. Jebsen Centre for Psychosis Research, University of Bergen, Bergen 4956, Nydalen 0424, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo 1039, Blindern 0315, Norway; Institute of Clinical Medicine, University of Oslo, Oslo 0318, Norway
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Anil K Malhotra
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY 11004, USA; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
| | - Todd Lencz
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY 11004, USA; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY 11030, USA.
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137
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Martin AR, Daly MJ, Robinson EB, Hyman SE, Neale BM. Predicting Polygenic Risk of Psychiatric Disorders. Biol Psychiatry 2019; 86:97-109. [PMID: 30737014 PMCID: PMC6599546 DOI: 10.1016/j.biopsych.2018.12.015] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2018] [Revised: 11/18/2018] [Accepted: 12/08/2018] [Indexed: 12/27/2022]
Abstract
Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Elise B Robinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Steven E Hyman
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
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138
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Mendelian randomisation analysis of the effect of educational attainment and cognitive ability on smoking behaviour. Nat Commun 2019; 10:2949. [PMID: 31270314 PMCID: PMC6610141 DOI: 10.1038/s41467-019-10679-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 05/07/2019] [Indexed: 01/21/2023] Open
Abstract
Recent analyses have shown educational attainment to be associated with a number of health outcomes. This association may, in part, be due to an effect of educational attainment on smoking behaviour. In this study, we apply a multivariable Mendelian randomisation design to determine whether the effect of educational attainment on smoking behaviour is due to educational attainment or general cognitive ability. We use individual data from the UK Biobank study (N = 120,050) and summary data from large GWA studies of educational attainment, cognitive ability and smoking behaviour. Our results show that more years of education are associated with a reduced likelihood of smoking that is not due to an effect of general cognitive ability on smoking behaviour. Given the considerable physical harms associated with smoking, the effect of educational attainment on smoking is likely to contribute to the health inequalities associated with differences in educational attainment.
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139
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Choi SW, O'Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience 2019; 8:giz082. [PMID: 31307061 PMCID: PMC6629542 DOI: 10.1093/gigascience/giz082] [Citation(s) in RCA: 741] [Impact Index Per Article: 148.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 03/13/2019] [Accepted: 06/11/2019] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required. RESULTS Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power. CONCLUSION PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set-based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.
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Affiliation(s)
- Shing Wan Choi
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, UK, SE5 8AF
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, 1 Gustave L. Levy Pl, New York City, NY 10029, USA
| | - Paul F O'Reilly
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, UK, SE5 8AF
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, 1 Gustave L. Levy Pl, New York City, NY 10029, USA
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140
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Genetic Contributions to Health Literacy. Twin Res Hum Genet 2019; 22:131-139. [PMID: 31250787 DOI: 10.1017/thg.2019.28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Higher health literacy is associated with higher cognitive function and better health. Despite its wide use in medical research, no study has investigated the genetic contributions to health literacy. Using 5783 English Longitudinal Study of Ageing (ELSA) participants (mean age = 65.49, SD = 9.55) who had genotyping data and had completed a health literacy test at wave 2 (2004-2005), we carried out a genome-wide association study (GWAS) of health literacy. We estimated the proportion of variance in health literacy explained by all common single nucleotide polymorphisms (SNPs). Polygenic profile scores were calculated using summary statistics from GWAS of 21 cognitive and health measures. Logistic regression was used to test whether polygenic scores for cognitive and health-related traits were associated with having adequate, compared to limited, health literacy. No SNPs achieved genome-wide significance for association with health literacy. The proportion of variance in health literacy accounted for by common SNPs was 8.5% (SE = 7.2%). Greater odds of having adequate health literacy were associated with a 1 standard deviation higher polygenic score for general cognitive ability [OR = 1.34, 95% CI (1.26, 1.42)], verbal-numerical reasoning [OR = 1.30, 95% CI (1.23, 1.39)], and years of schooling [OR = 1.29, 95% CI (1.21, 1.36)]. Reduced odds of having adequate health literacy were associated with higher polygenic profiles for poorer self-rated health [OR = 0.92, 95% CI (0.87, 0.98)] and schizophrenia [OR = 0.91, 95% CI (0.85, 0.96)). The well-documented associations between health literacy, cognitive function and health may partly be due to shared genetic etiology. Larger studies are required to obtain accurate estimates of SNP-based heritability and to discover specific health literacy-associated genetic variants.
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141
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Deary IJ, Harris SE, Hill WD. What genome-wide association studies reveal about the association between intelligence and physical health, illness, and mortality. Curr Opin Psychol 2019; 27:6-12. [PMID: 30071465 PMCID: PMC6624475 DOI: 10.1016/j.copsyc.2018.07.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 07/17/2018] [Indexed: 01/02/2023]
Abstract
The associations between higher intelligence test scores from early life and later good health, fewer illnesses, and longer life are recent discoveries. Researchers are mapping the extent of these associations and trying to understanding them. Part of the intelligence-health association has genetic origins. Recent advances in molecular genetic technology and statistical analyses have revealed that: intelligence and many health outcomes are highly polygenic; and that modest but widespread genetic correlations exist between intelligence and health, illness and mortality. Causal accounts of intelligence-health associations are still poorly understood. The contribution of education and socio-economic status - both of which are partly genetic in origin - to the intelligence-health associations are being explored.
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Affiliation(s)
- Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom.
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom; Medical Genetics Section, Centre for Genomic & Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, United Kingdom
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
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142
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Lee JJ, McGue M, Iacono WG, Michael AM, Chabris CF. The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling. INTELLIGENCE 2019; 75:48-58. [PMID: 32831433 DOI: 10.1016/j.intell.2019.01.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
There exists a moderate correlation between MRI-measured brain size and the general factor of IQ performance (g), but the question of whether the association reflects a theoretically important causal relationship or spurious confounding remains somewhat open. Previous small studies (n < 100) looking for the persistence of this correlation within families failed to find a tendency for the sibling with the larger brain to obtain a higher test score. We studied the within-family relationship between brain volume and intelligence in the much larger sample provided by the Human Connectome Project (n = 1,022) and found a highly significant correlation (disattenuated ρ = 0.18, p < .001). We replicated this result in the Minnesota Center for Twin and Family Research (n = 2,698), finding a highly significant within-family correlation between head circumference and intelligence (disattenuated ρ = 0.19, p < .001). We also employed novel methods of causal inference relying on summary statistics from genome-wide association studies (GWAS) of head size (n ≈ 10,000) and measures of cognition (257,000 < n < 767,000). Using bivariate LD Score regression, we found a genetic correlation between intracranial volume (ICV) and years of education (EduYears) of 0.41 (p < .001). Using the Latent Causal Variable method, we found a genetic causality proportion of 0.72 (p < .001); thus the genetic correlation arises from an asymmetric pattern, extending to sub-significant loci, of genetic variants associated with ICV also being associated with EduYears but many genetic variants associated with EduYears not being associated with ICV. This is the pattern of genetic results expected from a causal effect of brain size on intelligence. These findings give reason to take up the hypothesis that the dramatic increase in brain volume over the course of human evolution has been the result of natural selection favoring general intelligence.
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Affiliation(s)
- James J Lee
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Andrew M Michael
- Geisinger Health System, 120 Hamm Drive Suite 2A, Lewisburg, PA 17837, USA.,Duke Institute for Brain Sciences, Duke University, 308 Research Drive, LSRC M051, Durham, NC 27708, USA
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143
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Damerius LA, Burkart JM, van Noordwijk MA, Haun DB, Kosonen ZK, Galdikas BM, Saraswati Y, Kurniawan D, van Schaik CP. General cognitive abilities in orangutans (Pongo abelii and Pongo pygmaeus). INTELLIGENCE 2019. [DOI: 10.1016/j.intell.2018.10.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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144
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Frangou S, Shirali M, Adams MJ, Howard DM, Gibson J, Hall LS, Smith BH, Padmanabhan S, Murray AD, Porteous DJ, Haley CS, Deary IJ, Clarke TK, McIntosh AM. Insulin resistance: Genetic associations with depression and cognition in population based cohorts. Exp Neurol 2019; 316:20-26. [PMID: 30965038 PMCID: PMC6503941 DOI: 10.1016/j.expneurol.2019.04.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 03/26/2019] [Accepted: 04/03/2019] [Indexed: 01/07/2023]
Abstract
Insulin resistance, broadly defined as the reduced ability of insulin to exert its biological action, has been associated with depression and cognitive dysfunction in observational studies. However, it is unclear whether these associations are causal and whether they might be underpinned by other shared factors. To address this knowledge gap, we capitalized on the stability of genetic biomarkers through the lifetime, and on their unidirectional relationship with depression and cognition. Specifically, we determined the association between quantitative measures of cognitive function and depression and genetic instruments of insulin resistance traits in two large-scale population samples, the Generation Scotland: Scottish Family Health Study (GS: SFHS; N = 19,994) and in the UK Biobank (N = 331,374). In the GS:SFHS, the polygenic risk score (PRS) for fasting insulin was associated with verbal intelligence and depression while the PRS for the homeostasis model assessment of insulin resistance was associated with verbal intelligence. Despite this overlap in genetic architecture, Mendelian randomization analyses in the GS:SFHS and in the UK Biobank samples did not yield evidence for causal associations from insulin resistance traits to either depression or cognition. These findings may be due to weak genetic instruments, limited cognitive measures and insufficient power but they may also indicate the need to identify other biological mechanisms that may mediate the relationship from insulin resistance to depression and cognition. Insulin resistance (INS-R) is broadly defined as the reduced ability of insulin to exert its biological action. Measures of INS- R are associated with depression and cognitive dysfunction. We used two large population samples to estimate genetic associations. The polygenic risk of INS-R was associated with depression and cognition this association did not appear to be causal.
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Affiliation(s)
- Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Masoud Shirali
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Mark J Adams
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - David M Howard
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Jude Gibson
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Lynsey S Hall
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Blair H Smith
- Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK
| | - David J Porteous
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Generation Scotland, Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Chris S Haley
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
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145
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Cornelis MC, Wang Y, Holland T, Agarwal P, Weintraub S, Morris MC. Age and cognitive decline in the UK Biobank. PLoS One 2019; 14:e0213948. [PMID: 30883587 PMCID: PMC6422276 DOI: 10.1371/journal.pone.0213948] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 03/04/2019] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES Age-related cognitive decline is a well-known phenomenon after age 65 but little is known about earlier changes and prior studies are based on relatively small samples. We investigated the impact of age on cognitive decline in the largest population sample to date including young to old adults. METHOD Between 100,352 and 468,534 participants aged 38-73 years from UK Biobank completed at least one of seven self-administered cognitive functioning tests: prospective memory (PM), pairs matching (Pairs), fluid intelligence (FI), reaction time (RT), symbol digit substitution, trail making A and B. Up to 26,005 participants completed at least one of two follow-up assessments of PM, Pairs, FI and RT. Multivariable regression models examined the association between age (<45[reference], 45-49, 50-54, 55-59, 60-64, 65+) and cognition scores at baseline. Mixed models estimated the impact of age on cognitive decline over follow-up (~5.1 years). RESULTS FI was higher between ages 50 and 64 and lower at 65+ compared to <45 at baseline. Performance on all other baseline tests was lower with older age: with increasing age category, difference in test scores ranged from 2.5 to 7.8%(P<0.0001). Compared to <45 at baseline, RT and Pairs performance declined faster across all older age cohorts (3.0 and 1.2% change, respectively, with increasing age category, P<0.0001). Cross-sectional results yielded 8 to 12-fold higher differences in RT and Pairs with age compared to longitudinal results. CONCLUSIONS Our findings suggest that declines in cognitive abilities <65 are small. The cross-sectional differences in cognition scores for middle to older adult years may be due in part to age cohort effects.
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Affiliation(s)
- Marilyn C. Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- * E-mail:
| | - Yamin Wang
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
| | - Thomas Holland
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
| | - Puja Agarwal
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
| | - Sandra Weintraub
- Mesulam Cognitive Neurology and Alzheimer’s Disease Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Martha Clare Morris
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
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146
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Abstract
Schizophrenia (SZ) is a severe psychotic disorder that is highly heritable and common in the general population. The genetic heterogeneity of SZ is substantial, with contributions from common, rare, and de novo variants, in addition to environmental factors. Large genome-wide association studies have detected many variants that are associated with SZ, yet the pathways by which these variants influence risk remain largely unknown. SZ is also clinically heterogeneous, with patients exhibiting a broad range of deficits and symptom severity that vary over the course of illness and treatment, which has complicated efforts to identify risk variants. However, the underlying brain dysfunction forms a more stable trait marker that quantitative neurocognitive and neurophysiological endophenotypes may be able to objectively measure. These endophenotypes are less likely to be heterogeneous than the disorder and provide a neurobiological context to detect risk variants and underlying pathways among genes associated with SZ diagnosis. Furthermore, many endophenotypes are translational into animal model systems, allowing for direct evaluation of the neural circuit dysfunctions and neurobiological substrates. We review a selection of the most promising SZ endophenotypes, including prepulse inhibition, mismatch negativity, oculomotor antisaccade, letter-number sequencing, and continuous performance tests. We also highlight recent findings from large consortia that suggest the potential role of genes, particularly in the neuregulin and glutamate pathways, in several of these endophenotypes. Although endophenotypes require additional time and effort to assess, the insight into the underlying neurobiology that they provide may ultimately reveal the underlying genetic architecture for SZ and suggest novel treatment targets.
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147
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Lancaster TM, Dimitriadis SL, Tansey KE, Perry G, Ihssen N, Jones DK, Singh KD, Holmans P, Pocklington A, Davey Smith G, Zammit S, Hall J, O’Donovan MC, Owen MJ, Linden DE. Structural and Functional Neuroimaging of Polygenic Risk for Schizophrenia: A Recall-by-Genotype-Based Approach. Schizophr Bull 2019; 45:405-414. [PMID: 29608775 PMCID: PMC6403064 DOI: 10.1093/schbul/sby037] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Risk profile scores (RPS) derived from genome-wide association studies (GWAS) explain a considerable amount of susceptibility for schizophrenia (SCZ). However, little is known about how common genetic risk factors for SCZ influence the structure and function of the human brain, largely due to the constraints of imaging sample sizes. In the current study, we use a novel recall-by-genotype (RbG) methodological approach, where we sample young adults from a population cohort (Avon Longitudinal Study of Parents and Children: N genotyped = 8365) based on their SCZ-RPS. We compared 197 healthy individuals at extremes of low (N = 99) or high (N = 98) SCZ-RPS with behavioral tests, and structural and functional magnetic resonance imaging (fMRI). We first provide methodological details that will inform the design of future RbG studies for common SCZ genetic risk. We further provide an between group analysis of the RbG individuals (low vs high SCZ-RPS) who underwent structural neuroimaging data (T1-weighted scans) and fMRI data during a reversal learning task. While we found little evidence for morphometric differences between the low and high SCZ-RPS groups, we observed an impact of SCZ-RPS on blood oxygen level-dependent (BOLD) signal during reward processing in the ventral striatum (PFWE-VS-CORRECTED = .037), a previously investigated broader reward-related network (PFWE-ROIS-CORRECTED = .008), and across the whole brain (PFWE-WHOLE-BRAIN-CORRECTED = .013). We also describe the study strategy and discuss specific challenges of RbG for SCZ risk (such as SCZ-RPS related homoscedasticity). This study will help to elucidate the behavioral and imaging phenotypes that are associated with SCZ genetic risk.
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Affiliation(s)
- Thomas M Lancaster
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
| | - Stavros L Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
| | - Katherine E Tansey
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Gavin Perry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | | | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Peter Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
| | - Andrew Pocklington
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Stan Zammit
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
| | - Michael C O’Donovan
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
| | - David E Linden
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK
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148
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Abstract
After more than 10 years of accumulated efforts, genome-wide association studies (GWAS) have led to many findings, most of which have been deposited into the GWAS Catalog. Between GWAS's inception and March 2017, the GWAS Catalog has collected 2429 studies, 1818 phenotypes, and 28,462 associated SNPs. We reclassified the psychology-related phenotypes into 217 reclassified phenotypes, which accounted for 514 studies and 7052 SNPs. In total, 1223 of the SNPs reached genome-wide significance. Of these, 147 were replicated for the same psychological trait in different studies. Another 305 SNPs were replicated within one original study. The SNPs rs2075650 and rs4420638 were linked to the most replications within a single reclassified phenotype or very similar reclassified phenotypes; both were associated with Alzheimer's disease (AD). Schizophrenia was associated with 74 within-phenotype SNPs reported in independents studies. Alzheimer's disease and schizophrenia were both linked to some physical phenotypes, including cholesterol and body mass index, through common GWAS signals. Alzheimer's disease also shared risk SNPs with age-related phenotypes such as age-related macular degeneration and longevity. Smoking-related SNPs were linked to lung cancer and respiratory function. Alcohol-related SNPs were associated with cardiovascular and digestive system phenotypes and disorders. Two separate studies also identified a shared risk SNP for bipolar disorder and educational attainment. This review revealed a list of reproducible SNPs worthy of future functional investigation. Additionally, by identifying SNPs associated with multiple phenotypes, we illustrated the importance of studying the relationships among phenotypes to resolve the nature of their causal links. The insights within this review will hopefully pave the way for future evidence-based genetic studies.
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149
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Wu Q, Dalman C, Karlsson H, Lewis G, Osborn DPJ, Gardner R, Hayes JF. Childhood and Parental Asthma, Future Risk of Bipolar Disorder and Schizophrenia Spectrum Disorders: A Population-Based Cohort Study. Schizophr Bull 2019; 45. [PMID: 29534225 PMCID: PMC6403048 DOI: 10.1093/schbul/sby023] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Mounting evidence implicates early life and prenatal immune disturbances in the etiology of severe mental illnesses. Asthma is a common illness associated with chronic aberrant immune responses. We aimed to determine if asthma in childhood and parents is associated with bipolar and schizophrenia spectrum disorders. METHODS A cohort study including all children born in Sweden 1973-1995 (N > 2 million) assessing associations between childhood hospitalization for asthma, parental asthma during and pre-pregnancy, and subsequent bipolar and schizophrenia spectrum disorders. RESULTS Children with hospitalizations for asthma between 11 and 15 years had increased rates of bipolar (adjusted hazard ratio [aHR] = 1.73, 95% confidence interval [CI] = 1.21-2.47) and schizophrenia spectrum disorders (aHR = 1.62, 95% CI = 1.08-2.42). However, there was no association with asthma before aged 11. These results were supported by an analysis of siblings discordant for asthma. We found an association between both maternal and paternal asthma and bipolar disorder (aHR = 1.60, 95% CI = 1.27-2.02, and aHR = 1.44, 95% CI = 1.08-1.93, respectively), but not between parental asthma and schizophrenia spectrum disorders. CONCLUSIONS As far as we are aware, this is the first study to find increased risk of bipolar disorder in children of individuals with asthma. Asthma admissions before aged 11 do not appear to be linked to bipolar or schizophrenia spectrum disorders. Taken together, our results do not suggest a straightforward link between asthma and severe mental illness via neurodevelopmental effects of inflammation, but potentially there is shared genetic vulnerability. This finding has implications for understanding the differential pathogenic mechanisms of bipolar and schizophrenia spectrum disorders.
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Affiliation(s)
- Qiong Wu
- Division of Psychiatry, University College London, London, UK
| | - Christina Dalman
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Håkan Karlsson
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, UK
| | | | - Renee Gardner
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Joseph F Hayes
- Division of Psychiatry, University College London, London, UK,To whom correspondence should be addressed; Division of Psychiatry, University College London, 6th Floor, Maple House, 149 Tottenham Court Road, London W1T 7NF, UK; tel: +44-(0)20-7679-9736, e-mail:
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150
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Gignac GE, Zajenkowski M. People tend to overestimate their romantic partner's intelligence even more than their own. INTELLIGENCE 2019. [DOI: 10.1016/j.intell.2019.01.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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