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Jia Z, Zhang H, Lv Y, Yu L, Cui Y, Zhang L, Yang C, Liu H, Zheng T, Xia W, Xu S, Li Y. Intrauterine chromium exposure and cognitive developmental delay: The modifying effect of genetic predisposition. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174350. [PMID: 38960203 DOI: 10.1016/j.scitotenv.2024.174350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/04/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024]
Abstract
There is limited evidence on the effects of intrauterine chromium (Cr) exposure on children's cognitive developmental delay (CDD). Further, little is known about the genetic factors in modifying the association between intrauterine Cr exposure and CDD. The present study involved 2361 mother-child pairs, in which maternal plasma Cr concentrations were assessed, a polygenic risk score for the child was constructed, and the child's cognitive development was evaluated using the Bayley Scales of Infant Development. The risks of CDD conferred by intrauterine Cr exposure in children with different genetic backgrounds were evaluated by logistic regression. The additive interaction between intrauterine Cr exposure and genetic factors was evaluated by calculating the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI). According to present study, higher intrauterine Cr exposure was significantly associated with increased CDD risk [each unit increase in ln-transformed maternal plasma Cr concentration (ln-Cr): adjusted OR (95 % CI), 1.18 (1.04-1.35); highest vs lowest quartile: adjusted OR (95 % CI), 1.57 (1.10-2.23)]. The dose-response relationship of intrauterine Cr exposure and CDD for children with high genetic risk was more prominent [each unit increased ln-Cr: adjusted OR (95 % CI), 1.36 (1.09-1.70)]. Joint effects between intrauterine Cr exposure and genetic factors were found. Specifically, for high genetic risk carriers, the association between intrauterine Cr exposure and CDD was more evident [highest vs lowest quartile: adjusted OR (95 % CI), 2.33 (1.43-3.80)]. For those children with high intrauterine Cr exposure and high genetic risk, the adjusted AP was 0.39 (95 % CI, 0.07-0.72). Conclusively, intrauterine Cr exposure was a high-risk factor for CDD in children, particularly for those with high genetic risk. Intrauterine Cr exposure and one's adverse genetic background jointly contribute to an increased risk of CDD in children.
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Affiliation(s)
- Zhenxian Jia
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Hongling Zhang
- Wuchang University of Technology, Wuhan, Hubei, People's Republic of China
| | - Yiqing Lv
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Ling Yu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Yuan Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Liping Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Chenhui Yang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Hongxiu Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Tongzhang Zheng
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI 02912, United States
| | - Wei Xia
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Shunqing Xu
- School of Environmental Science and Engineering, Hainan University, Haikou 570228, People's Republic of China.
| | - Yuanyuan Li
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
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Procopio F, Liao W, Rimfeld K, Malanchini M, von Stumm S, Allegrini AG, Plomin R. Multi-polygenic score prediction of mathematics, reading, and language abilities independent of general cognitive ability. Mol Psychiatry 2024:10.1038/s41380-024-02671-w. [PMID: 39085392 DOI: 10.1038/s41380-024-02671-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 06/26/2024] [Accepted: 07/08/2024] [Indexed: 08/02/2024]
Abstract
Specific cognitive abilities (SCA) correlate genetically about 0.50, which underpins general cognitive ability (g), but it also means that there is considerable genetic specificity. If g is not controlled, then genomic prediction of specific cognitive abilities is not truly specific because they are all perfused with g. Here, we investigated the heritability of mathematics, reading, and language ability independent of g (SCA.g) using twins and DNA, and the extent to which multiple genome-wide polygenic scores (multi-PGS) can jointly predict these SCA.g as compared to SCA uncorrected for g. We created SCA and SCA.g composites from a battery of 14 cognitive tests administered at age 12 to 5,000 twin pairs in the Twins Early Development Study (TEDS). Univariate twin analyses yielded an average heritability estimate of 40% for SCA.g, compared to 53% for uncorrected SCA. Using genome-wide SNP genotypes, average SNP-based heritabilities were 26% for SCA.g and 35% for SCA. We then created multi-PGS from at least 50 PGS to predict each SCA and SCA.g using elastic net penalised regression models. Multi-PGS predicted 4.4% of the variance of SCA.g on average, compared to 11.1% for SCA uncorrected for g. The twin, SNP and PGS heritability estimates for SCA.g provide further evidence that the heritabilities of SCA are not merely a reflection of g. Although the relative reduction in heritability from SCA to SCA.g was greater for PGS heritability than for twin or SNP heritability, this decrease is likely due to the paucity of PGS for SCA. We hope that these results encourage researchers to conduct genome-wide association studies of SCA, and especially SCA.g, that can be used to predict PGS profiles of SCA strengths and weaknesses independent of g.
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Affiliation(s)
- Francesca Procopio
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Wangjingyi Liao
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey, UK
| | - Margherita Malanchini
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | | | - Andrea G Allegrini
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, UK
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics. Nat Commun 2024; 15:6064. [PMID: 39025851 PMCID: PMC11258354 DOI: 10.1038/s41467-024-50309-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA.
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yilin Yang
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT, 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, Philadelphia, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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4
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Ciulkinyte A, Mountford HS, Fontanillas P, Bates TC, Martin NG, Fisher SE, Luciano M. Genetic neurodevelopmental clustering and dyslexia. Mol Psychiatry 2024:10.1038/s41380-024-02649-8. [PMID: 39009701 DOI: 10.1038/s41380-024-02649-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 06/18/2024] [Accepted: 06/26/2024] [Indexed: 07/17/2024]
Abstract
Dyslexia is a learning difficulty with neurodevelopmental origins, manifesting as reduced accuracy and speed in reading and spelling. It is substantially heritable and frequently co-occurs with other neurodevelopmental conditions, particularly attention deficit-hyperactivity disorder (ADHD). Here, we investigate the genetic structure underlying dyslexia and a range of psychiatric traits using results from genome-wide association studies of dyslexia, ADHD, autism, anorexia nervosa, anxiety, bipolar disorder, major depressive disorder, obsessive compulsive disorder, schizophrenia, and Tourette syndrome. Genomic Structural Equation Modelling (GenomicSEM) showed heightened support for a model consisting of five correlated latent genomic factors described as: F1) compulsive disorders (including obsessive-compulsive disorder, anorexia nervosa, Tourette syndrome), F2) psychotic disorders (including bipolar disorder, schizophrenia), F3) internalising disorders (including anxiety disorder, major depressive disorder), F4) neurodevelopmental traits (including autism, ADHD), and F5) attention and learning difficulties (including ADHD, dyslexia). ADHD loaded more strongly on the attention and learning difficulties latent factor (F5) than on the neurodevelopmental traits latent factor (F4). The attention and learning difficulties latent factor (F5) was positively correlated with internalising disorders (.40), neurodevelopmental traits (.25) and psychotic disorders (.17) latent factors, and negatively correlated with the compulsive disorders (-.16) latent factor. These factor correlations are mirrored in genetic correlations observed between the attention and learning difficulties latent factor and other cognitive, psychological and wellbeing traits. We further investigated genetic variants underlying both dyslexia and ADHD, which implicated 49 loci (40 not previously found in GWAS of the individual traits) mapping to 174 genes (121 not found in GWAS of individual traits) as potential pleiotropic variants. Our study confirms the increased genetic relation between dyslexia and ADHD versus other psychiatric traits and uncovers novel pleiotropic variants affecting both traits. In future, analyses including additional co-occurring traits such as dyscalculia and dyspraxia will allow a clearer definition of the attention and learning difficulties latent factor, yielding further insights into factor structure and pleiotropic effects.
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Affiliation(s)
- Austeja Ciulkinyte
- Translational Neuroscience PhD Programme, University of Edinburgh, Edinburgh, UK
| | - Hayley S Mountford
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Timothy C Bates
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Michelle Luciano
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK.
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Kang J, Deng YT, Wu BS, Liu WS, Li ZY, Xiang S, Yang L, You J, Gong X, Jia T, Yu JT, Cheng W, Feng J. Whole exome sequencing analysis identifies genes for alcohol consumption. Nat Commun 2024; 15:5777. [PMID: 38982111 PMCID: PMC11233704 DOI: 10.1038/s41467-024-50132-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/26/2024] [Indexed: 07/11/2024] Open
Abstract
Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.
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Affiliation(s)
- Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China
| | - Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
| | - Xiaohong Gong
- School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Psychology, University of Southampton, Southampton, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China.
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China.
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200433, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200433, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.
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Wootton O, Shadrin AA, Bjella T, Smeland OB, van der Meer D, Frei O, O'Connell KS, Ueland T, Andreassen OA, Stein DJ, Dalvie S. Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia. Sci Rep 2024; 14:15356. [PMID: 38961113 PMCID: PMC11222449 DOI: 10.1038/s41598-024-66085-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024] Open
Abstract
Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.
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Affiliation(s)
- Olivia Wootton
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Bjella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dan J Stein
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Shareefa Dalvie
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
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7
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Pletenev I, Bazarevich M, Zagirova D, Kononkova A, Cherkasov A, Efimova O, Tiukacheva E, Morozov K, Ulianov K, Komkov D, Tvorogova A, Golimbet V, Kondratyev N, Razin S, Khaitovich P, Ulianov S, Khrameeva E. Extensive long-range polycomb interactions and weak compartmentalization are hallmarks of human neuronal 3D genome. Nucleic Acids Res 2024; 52:6234-6252. [PMID: 38647066 PMCID: PMC11194087 DOI: 10.1093/nar/gkae271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/21/2024] [Accepted: 04/06/2024] [Indexed: 04/25/2024] Open
Abstract
Chromatin architecture regulates gene expression and shapes cellular identity, particularly in neuronal cells. Specifically, polycomb group (PcG) proteins enable establishment and maintenance of neuronal cell type by reorganizing chromatin into repressive domains that limit the expression of fate-determining genes and sustain distinct gene expression patterns in neurons. Here, we map the 3D genome architecture in neuronal and non-neuronal cells isolated from the Wernicke's area of four human brains and comprehensively analyze neuron-specific aspects of chromatin organization. We find that genome segregation into active and inactive compartments is greatly reduced in neurons compared to other brain cells. Furthermore, neuronal Hi-C maps reveal strong long-range interactions, forming a specific network of PcG-mediated contacts in neurons that is nearly absent in other brain cells. These interacting loci contain developmental transcription factors with repressed expression in neurons and other mature brain cells. But only in neurons, they are rich in bivalent promoters occupied by H3K4me3 histone modification together with H3K27me3, which points to a possible functional role of PcG contacts in neurons. Importantly, other layers of chromatin organization also exhibit a distinct structure in neurons, characterized by an increase in short-range interactions and a decrease in long-range ones.
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Affiliation(s)
- Ilya A Pletenev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Maria Bazarevich
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Diana R Zagirova
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, Moscow 127051, Russia
| | - Anna D Kononkova
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Alexander V Cherkasov
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Olga I Efimova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Eugenia A Tiukacheva
- Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow 141700, Russia
- Department of Molecular Biology, Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow 119991, Russia
- CNRS UMR9018, Institut Gustave Roussy, Villejuif 94805, France
- Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow 119334, Russia
- Department of Cellular Genomics, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia
| | - Kirill V Morozov
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Kirill A Ulianov
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Dmitriy Komkov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia
| | - Anna V Tvorogova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia
| | - Vera E Golimbet
- Laboratory of Clinical Genetics, Mental Health Research Center, Moscow 115522, Russia
| | - Nikolay V Kondratyev
- Laboratory of Clinical Genetics, Mental Health Research Center, Moscow 115522, Russia
| | - Sergey V Razin
- Department of Molecular Biology, Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow 119991, Russia
- Department of Cellular Genomics, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia
| | - Philipp Khaitovich
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Sergey V Ulianov
- Department of Molecular Biology, Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow 119991, Russia
- Department of Cellular Genomics, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia
| | - Ekaterina E Khrameeva
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
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Annevelink CE, Westra J, Sala-Vila A, Harris WS, Tintle NL, Shearer GC. A Genome-Wide Interaction Study of Erythrocyte ω-3 Polyunsaturated Fatty Acid Species and Memory in the Framingham Heart Study Offspring Cohort. J Nutr 2024; 154:1640-1651. [PMID: 38141771 DOI: 10.1016/j.tjnut.2023.12.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 12/25/2023] Open
Abstract
BACKGROUND Cognitive decline, and more specifically Alzheimer's disease, continues to increase in prevalence globally, with few, if any, adequate preventative approaches. Several tests of cognition are utilized in the diagnosis of cognitive decline that assess executive function, short- and long-term memory, cognitive flexibility, and speech and motor control. Recent studies have separately investigated the genetic component of both cognitive health, using these measures, and circulating fatty acids. OBJECTIVES We aimed to examine the potential moderating effect of main species of ω-3 polyunsaturated fatty acids (PUFAs) on an individual's genetically conferred risk of cognitive decline. METHODS The Offspring cohort from the Framingham Heart Study was cross-sectionally analyzed in this genome-wide interaction study (GWIS). Our sample included all individuals with red blood cell ω-3 PUFA, genetic, cognitive testing (via Trail Making Tests [TMTs]), and covariate data (N = 1620). We used linear mixed effects models to predict each of the 3 cognitive measures (TMT A, TMT B, and TMT D) by each ω-3 PUFA, single nucleotide polymorphism (SNP) (0, 1, or 2 minor alleles), ω-3 PUFA by SNP interaction term, and adjusting for sex, age, education, APOE ε4 genotype status, and kinship (relatedness). RESULTS Our analysis identified 31 unique SNPs from 24 genes reaching an exploratory significance threshold of 1×10-5. Fourteen of the 24 genes have been previously associated with the brain/cognition, and 5 genes have been previously associated with circulating lipids. Importantly, 8 of the genes we identified, DAB1, SORCS2, SERINC5, OSBPL3, CPA6, DLG2, MUC19, and RGMA, have been associated with both cognition and circulating lipids. We identified 22 unique SNPs for which individuals with the minor alleles benefit substantially from increased ω-3 fatty acid concentrations and 9 unique SNPs for which the common homozygote benefits. CONCLUSIONS In this GWIS of ω-3 PUFA species on cognitive outcomes, we identified 8 unique genes with plausible biology suggesting individuals with specific polymorphisms may have greater potential to benefit from increased ω-3 PUFA intake. Additional replication in prospective settings with more diverse samples is needed.
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Affiliation(s)
- Carmen E Annevelink
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Jason Westra
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States
| | - Aleix Sala-Vila
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Cardiovascular Risk and Nutrition, Hospital del Mar Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - William S Harris
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, United States
| | - Nathan L Tintle
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Department of Population Health Nursing Science, College of Nursing, University of Illinois Chicago, Chicago, IL, United States
| | - Gregory C Shearer
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States.
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9
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Breunig S, Lawrence JM, Foote IF, Gebhardt HJ, Willcutt EG, Grotzinger AD. Examining Differences in the Genetic and Functional Architecture of Attention-Deficit/Hyperactivity Disorder Diagnosed in Childhood and Adulthood. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100307. [PMID: 38633226 PMCID: PMC11021367 DOI: 10.1016/j.bpsgos.2024.100307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 04/19/2024] Open
Abstract
Background Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with diagnostic criteria requiring symptoms to begin in childhood. We investigated whether individuals diagnosed as children differ from those diagnosed in adulthood with respect to shared and unique architecture at the genome-wide and gene expression level of analysis. Methods We used genomic structural equation modeling (SEM) to investigate differences in genetic correlations (rg) of childhood-diagnosed (ncases = 14,878) and adulthood-diagnosed (ncases = 6961) ADHD with 98 behavioral, psychiatric, cognitive, and health outcomes. We went on to apply transcriptome-wide SEM to identify functional annotations and patterns of gene expression associated with genetic risk sharing or divergence across the ADHD subgroups. Results Compared with the childhood subgroup, adulthood-diagnosed ADHD exhibited a significantly larger negative rg with educational attainment, the noncognitive skills of educational attainment, and age at first sexual intercourse. We observed a larger positive rg for adulthood-diagnosed ADHD with major depression, suicidal ideation, and a latent internalizing factor. At the gene expression level, transcriptome-wide SEM analyses revealed 22 genes that were significantly associated with shared genetic risk across the subtypes that reflected a mixture of coding and noncoding genes and included 15 novel genes relative to the ADHD subgroups. Conclusions This study demonstrated that ADHD diagnosed later in life shows much stronger genetic overlap with internalizing disorders and related traits. This may indicate the potential clinical relevance of distinguishing these subgroups or increased misdiagnosis for those diagnosed later in life. Top transcriptome-wide SEM results implicated genes related to neuronal function and clinical characteristics (e.g., sleep).
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Affiliation(s)
- Sophie Breunig
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Jeremy M. Lawrence
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Isabelle F. Foote
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Hannah J. Gebhardt
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Erik G. Willcutt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
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10
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Dai J, Chen K, Zhu Y, Xia L, Wang T, Yuan Z, Zeng P. Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110906. [PMID: 38043635 DOI: 10.1016/j.pnpbp.2023.110906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023]
Abstract
Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.
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Affiliation(s)
- Jing Dai
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yiyang Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Lei Xia
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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11
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Clapp Sullivan ML, Schwaba T, Harden KP, Grotzinger AD, Nivard MG, Tucker-Drob EM. Beyond the factor indeterminacy problem using genome-wide association data. Nat Hum Behav 2024; 8:205-218. [PMID: 38225407 PMCID: PMC10922726 DOI: 10.1038/s41562-023-01789-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/20/2023] [Indexed: 01/17/2024]
Abstract
Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.
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Affiliation(s)
| | - Ted Schwaba
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michel G Nivard
- Department of Biological Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
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12
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Brown EA, Kales S, Boyle MJ, Vitti J, Kotliar D, Schaffner S, Tewhey R, Sabeti PC. Three linked variants have opposing regulatory effects on isovaleryl-CoA dehydrogenase gene expression. Hum Mol Genet 2024; 33:270-283. [PMID: 37930192 PMCID: PMC10800014 DOI: 10.1093/hmg/ddad177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/03/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
While genome-wide association studies (GWAS) and positive selection scans identify genomic loci driving human phenotypic diversity, functional validation is required to discover the variant(s) responsible. We dissected the IVD gene locus-which encodes the isovaleryl-CoA dehydrogenase enzyme-implicated by selection statistics, multiple GWAS, and clinical genetics as important to function and fitness. We combined luciferase assays, CRISPR/Cas9 genome-editing, massively parallel reporter assays (MPRA), and a deletion tiling MPRA strategy across regulatory loci. We identified three regulatory variants, including an indel, that may underpin GWAS signals for pulmonary fibrosis and testosterone, and that are linked on a positively selected haplotype in the Japanese population. These regulatory variants exhibit synergistic and opposing effects on IVD expression experimentally. Alleles at these variants lie on a haplotype tagged by the variant most strongly associated with IVD expression and metabolites, but with no functional evidence itself. This work demonstrates how comprehensive functional investigation and multiple technologies are needed to discover the true genetic drivers of phenotypic diversity.
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Affiliation(s)
- Elizabeth A Brown
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Susan Kales
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Michael James Boyle
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
| | - Joseph Vitti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Dylan Kotliar
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Steve Schaffner
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Ryan Tewhey
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Pardis C Sabeti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
- Howard Hughes Medical Institute, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
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13
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Mahedy L, Anderson EL, Tilling K, Thornton ZA, Elmore AR, Szalma S, Simen A, Culp M, Zicha S, Harel BT, Davey Smith G, Smith EN, Paternoster L. Investigation of genetic determinants of cognitive change in later life. Transl Psychiatry 2024; 14:31. [PMID: 38238328 PMCID: PMC10796929 DOI: 10.1038/s41398-023-02726-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 12/14/2023] [Accepted: 12/22/2023] [Indexed: 01/22/2024] Open
Abstract
Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10-8 with slope 1) and rs34743896 (p = 5 × 10-10 with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.
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Affiliation(s)
- Liam Mahedy
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Emma L Anderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Zak A Thornton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Andrew R Elmore
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Sándor Szalma
- Takeda Development Center Americas, Inc., San Diego, CA, USA
| | - Arthur Simen
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Meredith Culp
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Stephen Zicha
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Brian T Harel
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK
| | - Erin N Smith
- Takeda Development Center Americas, Inc., San Diego, CA, USA
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston, NHS Foundation Trust and University of Bristol, Bristol, BS8 2BN, UK.
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14
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Shi Y, Sprooten E, Mulders P, Vrijsen J, Bralten J, Demontis D, Børglum AD, Walters GB, Stefansson K, van Eijndhoven P, Tendolkar I, Franke B, Mota NR. Multi-polygenic scores in psychiatry: From disorder specific to transdiagnostic perspectives. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32951. [PMID: 37334623 PMCID: PMC10803201 DOI: 10.1002/ajmg.b.32951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/31/2023] [Accepted: 06/05/2023] [Indexed: 06/20/2023]
Abstract
The dense co-occurrence of psychiatric disorders questions the categorical classification tradition and motivates efforts to establish dimensional constructs with neurobiological foundations that transcend diagnostic boundaries. In this study, we examined the genetic liability for eight major psychiatric disorder phenotypes under both a disorder-specific and a transdiagnostic framework. The study sample (n = 513) was deeply phenotyped, consisting of 452 patients from tertiary care with mood disorders, anxiety disorders (ANX), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders, and/or substance use disorders (SUD) and 61 unaffected comparison individuals. We computed subject-specific polygenic risk score (PRS) profiles and assessed their associations with psychiatric diagnoses, comorbidity status, as well as cross-disorder behavioral dimensions derived from a rich battery of psychopathology assessments. High PRSs for depression were unselectively associated with the diagnosis of SUD, ADHD, ANX, and mood disorders (p < 1e-4). In the dimensional approach, four distinct functional domains were uncovered, namely the negative valence, social, cognitive, and regulatory systems, closely matching the major functional domains proposed by the Research Domain Criteria (RDoC) framework. Critically, the genetic predisposition for depression was selectively reflected in the functional aspect of negative valence systems (R2 = 0.041, p = 5e-4) but not others. This study adds evidence to the ongoing discussion about the misalignment between current psychiatric nosology and the underlying psychiatric genetic etiology and underscores the effectiveness of the dimensional approach in both the functional characterization of psychiatric patients and the delineation of the genetic liability for psychiatric disorders.
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Affiliation(s)
- Yingjie Shi
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Emma Sprooten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter Mulders
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Janna Vrijsen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
- Pro Persona Mental Health Care, Depression Expertise Centre, Nijmegen, The Netherlands
| | - Janita Bralten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Ditte Demontis
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Copenhagen, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders D. Børglum
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Copenhagen, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - G. Bragi Walters
- deCODE Genetics, Reykjavík, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Kari Stefansson
- deCODE Genetics, Reykjavík, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Philip van Eijndhoven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nina Roth Mota
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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15
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Kim Y, Saunders GRB, Giannelis A, Willoughby EA, DeYoung CG, Lee JJ. Genetic and neural bases of the neuroticism general factor. Biol Psychol 2023; 184:108692. [PMID: 37783279 DOI: 10.1016/j.biopsycho.2023.108692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023]
Abstract
We applied structural equation modeling to conduct a genome-wide association study (GWAS) of the general factor measured by a neuroticism questionnaire administered to ∼380,000 participants in the UK Biobank. We categorized significant genetic variants as acting either through the neuroticism general factor, through other factors measured by the questionnaire, or through paths independent of any factor. Regardless of this categorization, however, significant variants tended to show concordant associations with all items. Bioinformatic analysis showed that the variants associated with the neuroticism general factor disproportionately lie near or within genes expressed in the brain. Enriched gene sets pointed to an underlying biological basis associated with brain development, synaptic function, and behaviors in mice indicative of fear and anxiety. Psychologists have long asked whether psychometric common factors are merely a convenient summary of correlated variables or reflect coherent causal entities with a partial biological basis, and our results provide some support for the latter interpretation. Further research is needed to determine the extent to which causes resembling common factors operate alongside other mechanisms to generate the correlational structure of personality.
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Affiliation(s)
- Yuri Kim
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Road, Minneapolis, MN 55455, USA
| | - Gretchen R B Saunders
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Road, Minneapolis, MN 55455, USA
| | - Alexandros Giannelis
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Road, Minneapolis, MN 55455, USA
| | - Emily A Willoughby
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Road, Minneapolis, MN 55455, USA
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Road, Minneapolis, MN 55455, USA
| | - James J Lee
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Road, Minneapolis, MN 55455, USA.
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16
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Malanchini M, Allegrini AG, Nivard MG, Biroli P, Rimfeld K, Cheesman R, von Stumm S, Demange PA, van Bergen E, Grotzinger AD, Raffington L, De la Fuente J, Pingault JB, Harden KP, Tucker-Drob EM, Plomin R. Genetic contributions of noncognitive skills to academic development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535380. [PMID: 37066409 PMCID: PMC10103958 DOI: 10.1101/2023.04.03.535380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Noncognitive skills such as motivation and self-regulation, are partly heritable and predict academic achievement beyond cognitive skills. However, how the relationship between noncognitive skills and academic achievement changes over development is unclear. The current study examined how cognitive and noncognitive skills contribute to academic achievement from ages 7 to 16 in a sample of over 10,000 children from England and Wales. Noncognitive skills were increasingly predictive of academic achievement across development. Twin and polygenic scores analyses found that the contribution of noncognitive genetics to academic achievement became stronger over the school years. Results from within-family analyses indicated that associations with noncognitive genetics could not simply be attributed to confounding by environmental differences between nuclear families and are consistent with a possible role for evocative/active gene-environment correlations. By studying genetic effects through a developmental lens, we provide novel insights into the role of noncognitive skills in academic development.
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Affiliation(s)
- Margherita Malanchini
- School of Biological and Behavioural Sciences, Queen Mary University of London, United Kingdom
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, United Kingdom
| | - Andrea G. Allegrini
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, United Kingdom
- Department of Clinical, Educational and Health Psychology, University College London, United Kingdom
| | - Michel G. Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Pietro Biroli
- Department of Economics, Universita’ di Bologna, Bologna, Italy
| | - Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, United Kingdom
- Royal Holloway University of London, United Kingdom
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | | | - Perline A. Demange
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Mental Health, Amsterdam, the Netherlands
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Mental Health, Amsterdam, the Netherlands
| | - Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, United States
| | - Laurel Raffington
- Max Planck Research Group Biosocial – Biology, Social Disparities, and Development; Max Planck Institute for Human Development, Berlin, Germany
| | | | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, United Kingdom
| | - K. Paige Harden
- Department of Psychology, The University of Texas at Austin, United States
| | | | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, United Kingdom
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17
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Morey R, Zheng Y, Sun D, Garrett M, Gasperi M, Maihofer A, Baird CL, Grasby K, Huggins A, Haswell C, Thompson P, Medland S, Gustavson D, Panizzon M, Kremen W, Nievergelt C, Ashley-Koch A, Logue L. Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture. RESEARCH SQUARE 2023:rs.3.rs-3253035. [PMID: 37886496 PMCID: PMC10602057 DOI: 10.21203/rs.3.rs-3253035/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Paul Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, California, USA
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18
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Hindley G, Shadrin AA, van der Meer D, Parker N, Cheng W, O'Connell KS, Bahrami S, Lin A, Karadag N, Holen B, Bjella T, Deary IJ, Davies G, Hill WD, Bressler J, Seshadri S, Fan CC, Ueland T, Djurovic S, Smeland OB, Frei O, Dale AM, Andreassen OA. Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy. Nat Hum Behav 2023; 7:1584-1600. [PMID: 37365406 PMCID: PMC10824266 DOI: 10.1038/s41562-023-01630-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
Abstract
Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.
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Affiliation(s)
- Guy Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK.
| | - Alexey A Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
| | - Dennis van der Meer
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and 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
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aihua Lin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Naz Karadag
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Børge Holen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Thomas Bjella
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - W David Hill
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Chun Chieh Fan
- Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Torill Ueland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
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19
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Chakraborty S, Kahali B. Exome-wide analysis reveals role of LRP1 and additional novel loci in cognition. HGG ADVANCES 2023; 4:100208. [PMID: 37305557 PMCID: PMC10248556 DOI: 10.1016/j.xhgg.2023.100208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
Cognitive functioning is heritable, with metabolic risk factors known to accelerate age-associated cognitive decline. Identifying genetic underpinnings of cognition is thus crucial. Here, we undertake single-variant and gene-based association analyses upon 6 neurocognitive phenotypes across 6 cognition domains in whole-exome sequencing data from 157,160 individuals of the UK Biobank cohort to expound the genetic architecture of human cognition. We report 20 independent loci associated with 5 cognitive domains while controlling for APOE isoform-carrier status and metabolic risk factors; 18 of which were not previously reported, and implicated genes relating to oxidative stress, synaptic plasticity and connectivity, and neuroinflammation. A subset of significant hits for cognition indicates mediating effects via metabolic traits. Some of these variants also exhibit pleiotropic effects on metabolic traits. We further identify previously unknown interactions of APOE variants with LRP1 (rs34949484 and others, suggestively significant), AMIGO1 (rs146766120; pAla25Thr, significant), and ITPR3 (rs111522866, significant), controlling for lipid and glycemic risks. Our gene-based analysis also suggests that APOC1 and LRP1 have plausible roles along shared pathways of amyloid beta (Aβ) and lipid and/or glucose metabolism in affecting complex processing speed and visual attention. In addition, we report pairwise suggestive interactions of variants harbored in these genes with APOE affecting visual attention. Our report based on this large-scale exome-wide study highlights the effects of neuronal genes, such as LRP1, AMIGO1, and other genomic loci, thus providing further evidence of the genetic underpinnings for cognition during aging.
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Affiliation(s)
- Shreya Chakraborty
- Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka 560012, India
- Interdisciplinary Mathematical Sciences, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Bratati Kahali
- Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka 560012, India
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20
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Fürtjes AE, Arathimos R, Coleman JRI, Cole JH, Cox SR, Deary IJ, de la Fuente J, Madole JW, Tucker‐Drob EM, Ritchie SJ. General dimensions of human brain morphometry inferred from genome-wide association data. Hum Brain Mapp 2023; 44:3311-3323. [PMID: 36987996 PMCID: PMC10171533 DOI: 10.1002/hbm.26283] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2 = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.
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Affiliation(s)
- Anna E. Fürtjes
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
| | - Ryan Arathimos
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - James H. Cole
- Department of NeuroimageingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonWC1V 6LJUK
- Dementia Research CentreInstitute of Neurology, University College LondonLondonWC1N 3BGUK
| | - Simon R. Cox
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Ian J. Deary
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Javier de la Fuente
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - James W. Madole
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Stuart J. Ritchie
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
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21
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Chen CY, Tian R, Ge T, Lam M, Sanchez-Andrade G, Singh T, Urpa L, Liu JZ, Sanderson M, Rowley C, Ironfield H, Fang T, Daly M, Palotie A, Tsai EA, Huang H, Hurles ME, Gerety SS, Lencz T, Runz H. The impact of rare protein coding genetic variation on adult cognitive function. Nat Genet 2023:10.1038/s41588-023-01398-8. [PMID: 37231097 DOI: 10.1038/s41588-023-01398-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Abstract
Compelling evidence suggests that human cognitive function is strongly influenced by genetics. Here, we conduct a large-scale exome study to examine whether rare protein-coding variants impact cognitive function in the adult population (n = 485,930). We identify eight genes (ADGRB2, KDM5B, GIGYF1, ANKRD12, SLC8A1, RC3H2, CACNA1A and BCAS3) that are associated with adult cognitive function through rare coding variants with large effects. Rare genetic architecture for cognitive function partially overlaps with that of neurodevelopmental disorders. In the case of KDM5B we show how the genetic dosage of one of these genes may determine the variability of cognitive, behavioral and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function. Our study introduces the relevance of rare coding variants for cognitive function and unveils high-impact monogenic contributions to how cognitive function is distributed in the normal adult population.
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Affiliation(s)
- Chia-Yen Chen
- Research and Development, Biogen Inc, Cambridge, MA, USA.
| | - Ruoyu Tian
- Research and Development, Biogen Inc, Cambridge, MA, USA
- Dewpoint Therapeutics, Boston, MA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Max Lam
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | | | - Tarjinder Singh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lea Urpa
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jimmy Z Liu
- Research and Development, Biogen Inc, Cambridge, MA, USA
- GlaxoSmithKline, Philadelphia, PA, USA
| | | | | | | | - Terry Fang
- Research and Development, Biogen Inc, Cambridge, MA, USA
| | - Mark Daly
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aarno Palotie
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ellen A Tsai
- Research and Development, Biogen Inc, Cambridge, MA, USA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | | | - Todd Lencz
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Heiko Runz
- Research and Development, Biogen Inc, Cambridge, MA, USA.
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22
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Lee Y, Park JY, Lee JJ, Gim J, Do AR, Jo J, Park J, Kim K, Park K, Jin H, Choi KY, Kang S, Kim H, Kim S, Moon SH, Farrer LA, Lee KH, Won S. Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians. Cereb Cortex 2023; 33:6051-6062. [PMID: 36642501 PMCID: PMC10183741 DOI: 10.1093/cercor/bhac483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 01/17/2023] Open
Abstract
This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.
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Affiliation(s)
- Younghwa Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Jun Young Park
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Jang Jae Lee
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
| | - Jungsoo Gim
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Biomedical Science, Chosun University, Gwangju, Korea
| | - Ah Ra Do
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Juhong Park
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Kangjin Kim
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Kyungtaek Park
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Heejin Jin
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
| | - Sarang Kang
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
| | - Hoowon Kim
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Neurology, Chosun University Hospital, Gwangju, Korea
| | - SangYun Kim
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Seoul, Korea
| | - Lindsay A Farrer
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Kun Ho Lee
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Biomedical Science, Chosun University, Gwangju, Korea
- Dementia Research Group, Korea Brain Research Institute, Daegu, Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
- RexSoft Inc., Seoul, Korea
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23
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Yang Z, Li D, He Y, Chen X, Li Z. Unrevealing the shared genetic mechanisms underlying C-reactive protein and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2023; 126:110785. [PMID: 37150315 DOI: 10.1016/j.pnpbp.2023.110785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 04/18/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Longitudinal observational studies and Mendelian randomization research have obtained contradictory conclusions regarding the association between C-reactive protein (CRP) level and schizophrenia risk. However, the shared genetic mechanisms underlying CRP and schizophrenia remain poorly understood. Here, we examined the global and local genetic correlations using summary statistics from large-scale genome-wide association studies (GWAS) on CRP level and schizophrenia. Furthermore, we identified their shared genetic variants by applying the conditional false discovery rate approach and performed functional analyses of shared variants to explore the shared genetic mechanisms underlying CRP level and schizophrenia. We found a significant negative genetic correlation at the whole genome level and five significant local genetic correlations between CRP level and schizophrenia. Eight-three shared genetic loci were identified, from which single-nucleotide polymorphism (SNP) presents mixed effects on the increased CRP level and schizophrenia risk. Additionally, we identified 64 and 73 candidate genes that were mapped from SNPs with"concordant effect"(ceSNPs) and"discordant effect"(deSNPs) on the CRP level and schizophrenia risk respectively. Functional analyses revealed that genes mapped from ceSNPs and deSNPs exhibited similar patterns of human brain developmental expression trajectories and biological processes, but differed in expression levels and cell-type-specific enrichment in brain tissues. Our findings demonstrated mixed effects of shared genetic architecture between CRP level and schizophrenia, proving a deeper insight into the shared genetic aetiology underlying the CRP level and schizophrenia.
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Affiliation(s)
- Zihao Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China
| | - David Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Ying He
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China; China National Technology Institute on Mental Disorders & Hunan Key Laboratory of Psychiatry and Mental Health, Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Xiaogang Chen
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China; China National Technology Institute on Mental Disorders & Hunan Key Laboratory of Psychiatry and Mental Health, Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Zongchang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China; China National Technology Institute on Mental Disorders & Hunan Key Laboratory of Psychiatry and Mental Health, Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, PR China.
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Yeung HW, Stolicyn A, Buchanan CR, Tucker‐Drob EM, Bastin ME, Luz S, McIntosh AM, Whalley HC, Cox SR, Smith K. Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Hum Brain Mapp 2023; 44:1913-1933. [PMID: 36541441 PMCID: PMC9980898 DOI: 10.1002/hbm.26182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 11/11/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.
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Affiliation(s)
- Hon Wah Yeung
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Aleks Stolicyn
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Colin R. Buchanan
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of TexasAustinTexasUSA
- Population Research Center and Center on Aging and Population SciencesUniversity of Texas at AustinAustinTexasUSA
| | - Mark E. Bastin
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
- Centre for Clinical Brain ScienceUniversity of EdinburghEdinburghUK
| | - Saturnino Luz
- Edinburgh Medical SchoolUsher Institute, The University of EdinburghEdinburghUK
| | - Andrew M. McIntosh
- Department of PsychiatryUniversity of EdinburghEdinburghUK
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular Medicine, University of EdinburghEdinburghUK
| | | | - Simon R. Cox
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Keith Smith
- Department of Physics and MathematicsNottingham Trent UniversityNottinghamUK
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25
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Soo CC, Brandenburg JT, Nebel A, Tollman S, Berkman L, Ramsay M, Choudhury A. Genome-wide association study of population-standardised cognitive performance phenotypes in a rural South African community. Commun Biol 2023; 6:328. [PMID: 36973338 PMCID: PMC10043003 DOI: 10.1038/s42003-023-04636-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/28/2023] [Indexed: 03/29/2023] Open
Abstract
Cognitive function is an indicator for global physical and mental health, and cognitive impairment has been associated with poorer life outcomes and earlier mortality. A standard cognition test, adapted to a rural-dwelling African community, and the Oxford Cognition Screen-Plus were used to capture cognitive performance as five continuous traits (total cognition score, verbal episodic memory, executive function, language, and visuospatial ability) for 2,246 adults in this population of South Africans. A novel common variant, rs73485231, reached genome-wide significance for association with episodic memory using data for ~14 million markers imputed from the H3Africa genotyping array data. Window-based replication of previously implicated variants and regions of interest support the discovery of African-specific associated variants despite the small population size and low allele frequency. This African genome-wide association study identifies suggestive associations with general cognition and domain-specific cognitive pathways and lays the groundwork for further genomic studies on cognition in Africa.
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Affiliation(s)
- Cassandra C Soo
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Almut Nebel
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Institute of Clinical Molecular Biology, Kiel University, 24105, Kiel, Germany
| | - Stephen Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Lisa Berkman
- MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard Center for Population and Development Studies, Harvard University, Cambridge, MA, USA
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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26
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Williams CM, Labouret G, Wolfram T, Peyre H, Ramus F. A General Cognitive Ability Factor for the UK Biobank. Behav Genet 2023; 53:85-100. [PMID: 36378351 DOI: 10.1007/s10519-022-10127-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022]
Abstract
UK Biobank participants do not have a high-quality measure of intelligence or polygenic scores (PGSs) of intelligence to simultaneously examine the genetic and neural underpinnings of intelligence. We created a standardized measure of general intelligence (g factor) relative to the UK population and estimated its quality. After running a GWAS of g on UK Biobank participants with a g factor of good quality and without neuroimaging data (N = 187,288), we derived a g PGS for UK Biobank participants with neuroimaging data. For individuals with at least one cognitive test, the g factor from eight cognitive tests (N = 501,650) explained 29% of the variance in cognitive test performance. The PGS for British individuals with neuroimaging data (N = 27,174) explained 7.6% of the variance in g. We provided high-quality g factor estimates for most UK Biobank participants and g factor PGSs for UK Biobank participants with neuroimaging data.
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Affiliation(s)
- Camille Michèle Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France. .,LSCP, Département d'Etudes Cognitives, École Normale Supérieure, 29 rue d'Ulm, 75005, Paris, France.
| | - Ghislaine Labouret
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France
| | - Tobias Wolfram
- Faculty of Sociology, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France.,INSERM UMR 1141, Paris Diderot University, Paris, France.,Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France
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27
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Grotzinger AD, Mallard TT, Liu Z, Seidlitz J, Ge T, Smoller JW. Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis. Nat Commun 2023; 14:946. [PMID: 36806290 PMCID: PMC9941500 DOI: 10.1038/s41467-023-36605-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/08/2023] [Indexed: 02/22/2023] Open
Abstract
Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) and parsimoniously define five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. We validate these factors by demonstrating the generalizability of the model to a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that broad aspects of cognitive function are associated with a general factor for SA and that psychiatric associations are null. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex.
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Affiliation(s)
- Andrew D Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
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28
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Huang J, Gill D, Zuber V, Matthews PM, Elliott P, Tzoulaki I, Dehghan A. Circulatory proteins relate cardiovascular disease to cognitive performance: A mendelian randomisation study. Front Genet 2023; 14:1124431. [PMID: 36873953 PMCID: PMC9981660 DOI: 10.3389/fgene.2023.1124431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
Background and objectives: Mechanistic research suggests synergistic effects of cardiovascular disease (CVD) and dementia pathologies on cognitive decline. Interventions targeting proteins relevant to shared mechanisms underlying CVD and dementia could also be used for the prevention of cognitive impairment. Methods: We applied Mendelian randomisation (MR) and colocalization analysis to investigate the causal relationships of 90 CVD-related proteins measured by the Olink CVD I panel with cognitive traits. Genetic instruments for circulatory protein concentrations were obtained using a meta-analysis of genome-wide association studies (GWAS) from the SCALLOP consortium (N = 17,747) based on three sets of criteria: 1) protein quantitative trait loci (pQTL); 2) cis-pQTL (pQTL within ±500 kb from the coding gene); and 3) brain-specific cis-expression QTL (cis-eQTL) which accounts for coding gene expression based on GTEx8. Genetic associations of cognitive performance were obtained from GWAS for either: 1) general cognitive function constructed using Principal Component Analysis (N = 300,486); or, 2) g Factor constructed using genomic structural equation modelling (N = 11,263-331,679). Findings for candidate causal proteins were replicated using a separate protein GWAS in Icelanders (N = 35,559). Results: A higher concentration of genetically predicted circulatory myeloperoxidase (MPO) was nominally associated with better cognitive performance (p < 0.05) using different selection criteria for genetic instruments. Particularly, brain-specific cis-eQTL predicted MPO, which accounts for protein-coding gene expression in brain tissues, was associated with general cognitive function (βWald = 0.22, PWald = 2.4 × 10-4). The posterior probability for colocalization (PP.H4) of MPO pQTL with the g Factor was 0.577. Findings for MPO were replicated using the Icelandic GWAS. Although we did not find evidence for colocalization, we found that higher genetically predicted concentrations of cathepsin D and CD40 were associated with better cognitive performance and a higher genetically predicted concentration of CSF-1 was associated with poorer cognitive performance. Conclusion: We conclude that these proteins are involved in shared pathways between CVD and those for cognitive reserve or affecting cognitive decline, suggesting therapeutic targets able to reduce genetic risks conferred by cardiovascular disease.
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Affiliation(s)
- Jian Huang
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Paul M. Matthews
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
- UK Dementia Research Institute at Imperial College London, London, United Kingdom
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- UK Dementia Research Institute at Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- 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, United Kingdom
- UK Dementia Research Institute at Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
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29
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Sudre G, Gildea DE, Shastri GG, Sharp W, Jung B, Xu Q, Auluck PK, Elnitski L, Baxevanis AD, Marenco S, Shaw P. Mapping the cortico-striatal transcriptome in attention deficit hyperactivity disorder. Mol Psychiatry 2023; 28:792-800. [PMID: 36380233 PMCID: PMC9918667 DOI: 10.1038/s41380-022-01844-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 10/05/2022] [Accepted: 10/12/2022] [Indexed: 11/17/2022]
Abstract
Despite advances in identifying rare and common genetic variants conferring risk for ADHD, the lack of a transcriptomic understanding of cortico-striatal brain circuitry has stymied a molecular mechanistic understanding of this disorder. To address this gap, we mapped the transcriptome of the caudate nucleus and anterior cingulate cortex in post-mortem tissue from 60 individuals with and without ADHD. Significant differential expression of genes was found in the anterior cingulate cortex and, to a lesser extent, the caudate. Significant downregulation emerged of neurotransmitter gene pathways, particularly glutamatergic, in keeping with models that implicate these neurotransmitters in ADHD. Consistent with the genetic overlap between mental disorders, correlations were found between the cortico-striatal transcriptomic changes seen in ADHD and those seen in other neurodevelopmental and mood disorders. This transcriptomic evidence points to cortico-striatal neurotransmitter anomalies in the pathogenesis of ADHD, consistent with current models of the disorder.
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Affiliation(s)
- Gustavo Sudre
- Social and Behavioral Research Branch, Division of Intramural Research, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA
| | - Derek E Gildea
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA
| | - Gauri G Shastri
- Office of the Scientific Director, Division of Intramural Research, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Wendy Sharp
- Office of the Scientific Director, Division of Intramural Research, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Benjamin Jung
- Social and Behavioral Research Branch, Division of Intramural Research, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA
| | - Qing Xu
- Human Brain Collection Core, Division of Intramural Research, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Pavan K Auluck
- Human Brain Collection Core, Division of Intramural Research, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Laura Elnitski
- Translational and Functional Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA
| | - Andreas D Baxevanis
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA
| | - Stefano Marenco
- Human Brain Collection Core, Division of Intramural Research, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA
| | - Philip Shaw
- Social and Behavioral Research Branch, Division of Intramural Research, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA.
- Office of the Scientific Director, Division of Intramural Research, National Institute of Mental Health, NIH, Bethesda, MD, 20892, USA.
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30
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Hatoum AS, Morrison CL, Mitchell EC, Lam M, Benca-Bachman CE, Reineberg AE, Palmer RHC, Evans LM, Keller MC, Friedman NP. Genome-wide Association Study Shows That Executive Functioning Is Influenced by GABAergic Processes and Is a Neurocognitive Genetic Correlate of Psychiatric Disorders. Biol Psychiatry 2023; 93:59-70. [PMID: 36150907 PMCID: PMC9722603 DOI: 10.1016/j.biopsych.2022.06.034] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 06/08/2022] [Accepted: 06/23/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND Deficits in executive functions (EFs), cognitive processes that control goal-directed behaviors, are associated with psychopathology and neurologic disorders. Little is known about the molecular bases of individual differences in EFs. Prior candidate gene studies have been underpowered in their search for dopaminergic processes involved in cognitive functioning, and existing genome-wide association studies of EFs used small sample sizes and/or focused on individual tasks that are imprecise measures of EFs. METHODS We conducted a genome-wide association study of a common EF (cEF) factor score based on multiple tasks in the UK Biobank (n = 427,037 individuals of European descent). RESULTS We found 129 independent genome-wide significant lead variants in 112 distinct loci. cEF was associated with fast synaptic transmission processes (synaptic, potassium channel, and GABA [gamma-aminobutyric acid] pathways) in gene-based analyses. cEF was genetically correlated with measures of intelligence (IQ) and cognitive processing speed, but cEF and IQ showed differential genetic associations with psychiatric disorders and educational attainment. CONCLUSIONS Results suggest that cEF is a genetically distinct cognitive construct that is particularly relevant to understanding the genetic variance in psychiatric disorders.
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Affiliation(s)
- Alexander S Hatoum
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado; Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado; Department of Psychiatry, University of Washington St. Louis Medical School, St. Louis, Missouri
| | - Claire L Morrison
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado; Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado.
| | - Evann C Mitchell
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado; Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Max Lam
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, New York; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Chelsie E Benca-Bachman
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, Georgia
| | - Andrew E Reineberg
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
| | - Rohan H C Palmer
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, Georgia
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado; Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado; Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Naomi P Friedman
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado; Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
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31
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Ciobanu LG, Stankov L, Ahmed M, Heathcote A, Clark SR, Aidman E. Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses. Front Psychol 2023; 14:1054707. [PMID: 36818106 PMCID: PMC9937787 DOI: 10.3389/fpsyg.2023.1054707] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The UK Biobank cognitive assessment data has been a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population. Given the diverse nature of this data, researchers use different approaches - from the use of a single test to composing the general intelligence score, g, across the tests. We argue that both approaches are suboptimal - one being too specific and the other one too general - and suggest a novel multifactorial solution to represent cognitive abilities. Methods Using a combined Exploratory Factor (EFA) and Exploratory Structural Equation Modeling Analyses (ESEM) we developed a three-factor model to characterize an underlying structure of nine cognitive tests selected from the UK Biobank using a Cattell-Horn-Carroll framework. We first estimated a series of probable factor solutions using the maximum likelihood method of extraction. The best solution for the EFA-defined factor structure was then tested using the ESEM approach with the aim of confirming or disconfirming the decisions made. Results We determined that a three-factor model fits the UK Biobank cognitive assessment data best. Two of the three factors can be assigned to fluid reasoning (Gf) with a clear distinction between visuospatial reasoning and verbal-analytical reasoning. The third factor was identified as a processing speed (Gs) factor. Discussion This study characterizes cognitive assessment data in the UK Biobank and delivers an alternative view on its underlying structure, suggesting that the three factor model provides a more granular solution than g that can further be applied to study different facets of cognitive functioning in relation to health outcomes and to further progress examination of its biological underpinnings.
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Affiliation(s)
- Liliana G Ciobanu
- Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - Lazar Stankov
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | - Muktar Ahmed
- Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - Andrew Heathcote
- School of Psychology, University of Newcastle, Sydney, NSW, Australia
| | - Scott Richard Clark
- Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - Eugene Aidman
- School of Psychology, The University of Sydney, Sydney, NSW, Australia.,School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia.,Decision Sciences Division, Defense Science and Technology Group, Adelaide, SA, Australia
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32
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Fürtjes AE, Cole JH, Couvy-Duchesne B, Ritchie SJ. A quantified comparison of cortical atlases on the basis of trait morphometricity. Cortex 2023; 158:110-126. [PMID: 36516597 DOI: 10.1016/j.cortex.2022.11.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/02/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences. METHODS Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas. RESULTS Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated. DISCUSSION Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.
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Affiliation(s)
- Anna E Fürtjes
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK.
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Baptiste Couvy-Duchesne
- Paris Brain Institute (ICM), Inserm (U 1127), CNRS (UMR 7225), Sorbonne University, Inria Paris, Aramis Project-team, Paris, France; Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
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33
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Lasker J. Are Piagetian scales just intelligence tests? INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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34
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Grotzinger AD, de la Fuente J, Davies G, Nivard MG, Tucker-Drob EM. Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits. Nat Commun 2022; 13:6280. [PMID: 36271044 PMCID: PMC9586980 DOI: 10.1038/s41467-022-33724-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/29/2022] [Indexed: 12/25/2022] Open
Abstract
Functional genomic methods are needed that consider multiple genetically correlated traits. Here we develop and validate Transcriptome-wide Structural Equation Modeling (T-SEM), a multivariate method for studying the effects of tissue-specific gene expression across genetically overlapping traits. T-SEM allows for modeling effects on broad dimensions spanning constellations of traits, while safeguarding against false positives that can arise when effects of gene expression are specific to a subset of traits. We apply T-SEM to investigate the biological mechanisms shared across seven distinct cognitive traits (N = 11,263-331,679), as indexed by a general dimension of genetic sharing (g). We identify 184 genes whose tissue-specific expression is associated with g, including 10 genes not identified in univariate analysis for the individual cognitive traits for any tissue type, and three genes whose expression explained a significant portion of the genetic sharing across g and different subclusters of psychiatric disorders. We go on to apply Stratified Genomic SEM to identify enrichment for g within 28 functional categories. This includes categories indexing the intersection of protein-truncating variant intolerant (PI) genes and specific neuronal cell types, which we also find to be enriched for the genetic covariance between g and a psychotic disorders factor.
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Affiliation(s)
- Andrew D Grotzinger
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA. .,Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA.
| | - Javier de la Fuente
- Department of Psychology, University of Texas at Austin, Austin, TX, USA.,Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Gail Davies
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Michel G Nivard
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, the Netherlands
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA.,Population Research Center, University of Texas at Austin, Austin, TX, USA
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35
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Fitzgerald T, Birney E. CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank. CELL GENOMICS 2022; 2:100167. [PMID: 36779085 PMCID: PMC9903682 DOI: 10.1016/j.xgen.2022.100167] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 04/11/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.
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Affiliation(s)
- Tomas Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
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36
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Kuo SS, Musket CW, Rupert PE, Almasy L, Gur RC, Prasad KM, Roalf DR, Gur RE, Nimgaonkar VL, Pogue-Geile MF. Age-dependent patterns of schizophrenia genetic risk affect cognition. Schizophr Res 2022; 246:39-48. [PMID: 35709646 PMCID: PMC11227884 DOI: 10.1016/j.schres.2022.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/15/2022] [Accepted: 05/15/2022] [Indexed: 11/15/2022]
Abstract
Cognition shares substantial genetic overlap with schizophrenia, yet it remains unclear whether such genetic effects become significant during developmental periods of elevated risk for schizophrenia, such as the peak age of onset. We introduce an investigative framework integrating epidemiological, developmental, and genetic approaches to determine whether genetic effects shared between schizophrenia and cognition are significant across periods of differing risk for schizophrenia onset, and whether these effects are shared with depression. 771 European-American participants, including 636 (ages 15-84 years) from families with at least two first-degree relatives with schizophrenia and 135 unrelated controls, were divided into three age-risk groups based on ages relative to epidemiological age of onset patterns for schizophrenia: Pre-Peak (before peak age-of-onset: 15 to 22 years), Post-Peak (after peak age-of-onset: 23-42 years), and Plateau (during plateau of age-of-onset: over 42 years). For general cognition and 11 specific cognitive traits, we estimated genetic correlations with schizophrenia and with depression within each age-risk group. Genetic effects shared between deficits in general cognition and schizophrenia were nonsignificant before peak age of onset, yet were high and significant after peak age of onset and during the plateau of onset. These age-dependent genetic effects were largely consistent across specific cognitive traits and not transdiagnostically shared with depression. Schizophrenia genetic effects appear to influence cognitive traits in an age-dependent manner, supporting late developmental and perhaps neurodegenerative models that hypothesize increased expression of schizophrenia risk genes during and after the peak age of risk. Our findings underscore the utility of cognitive traits for tracking schizophrenia genetic effects across the lifespan.
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Affiliation(s)
- Susan S Kuo
- Department of Psychology, University of Pittsburgh, United States of America; Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, United States of America; Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, United States of America
| | - Christie W Musket
- Department of Psychology, University of Pittsburgh, United States of America
| | - Petra E Rupert
- Department of Psychology, University of Pittsburgh, United States of America
| | - Laura Almasy
- Department of Genetics, University of Pennsylvania, United States of America
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Konasale M Prasad
- Department of Psychiatry, University of Pittsburgh, United States of America; Department of Bioengineering, University of Pittsburgh, United States of America; Veteran Affairs Pittsburgh Healthcare System, United States of America
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Vishwajit L Nimgaonkar
- Department of Psychiatry, University of Pittsburgh, United States of America; Department of Human Genetics, University of Pittsburgh, United States of America
| | - Michael F Pogue-Geile
- Department of Psychology, University of Pittsburgh, United States of America; Department of Psychiatry, University of Pittsburgh, United States of America.
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37
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Deary IJ, Cox SR, Hill WD. Genetic variation, brain, and intelligence differences. Mol Psychiatry 2022; 27:335-353. [PMID: 33531661 PMCID: PMC8960418 DOI: 10.1038/s41380-021-01027-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/28/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023]
Abstract
Individual differences in human intelligence, as assessed using cognitive test scores, have a well-replicated, hierarchical phenotypic covariance structure. They are substantially stable across the life course, and are predictive of educational, social, and health outcomes. From this solid phenotypic foundation and importance for life, comes an interest in the environmental, social, and genetic aetiologies of intelligence, and in the foundations of intelligence differences in brain structure and functioning. Here, we summarise and critique the last 10 years or so of molecular genetic (DNA-based) research on intelligence, including the discovery of genetic loci associated with intelligence, DNA-based heritability, and intelligence's genetic correlations with other traits. We summarise new brain imaging-intelligence findings, including whole-brain associations and grey and white matter associations. We summarise regional brain imaging associations with intelligence and interpret these with respect to theoretical accounts. We address research that combines genetics and brain imaging in studying intelligence differences. There are new, though modest, associations in all these areas, and mechanistic accounts are lacking. We attempt to identify growing points that might contribute toward a more integrated 'systems biology' account of some of the between-individual differences in intelligence.
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Affiliation(s)
- Ian J. Deary
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - Simon R. Cox
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - W. David Hill
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
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38
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Karlsson Linnér R, Mallard TT, Barr PB, Sanchez-Roige S, Madole JW, Driver MN, Poore HE, de Vlaming R, Grotzinger AD, Tielbeek JJ, Johnson EC, Liu M, Rosenthal SB, Ideker T, Zhou H, Kember RL, Pasman JA, Verweij KJH, Liu DJ, Vrieze S, Kranzler HR, Gelernter J, Harris KM, Tucker-Drob EM, Waldman ID, Palmer AA, Harden KP, Koellinger PD, Dick DM. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat Neurosci 2021; 24:1367-1376. [PMID: 34446935 PMCID: PMC8484054 DOI: 10.1038/s41593-021-00908-3] [Citation(s) in RCA: 133] [Impact Index Per Article: 44.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 07/13/2021] [Indexed: 02/07/2023]
Abstract
Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates.
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Affiliation(s)
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Peter B Barr
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Morgan N Driver
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Holly E Poore
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Ronald de Vlaming
- Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Jorim J Tielbeek
- Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology and Bioinformatics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Department of Psychiatry, VA CT Healthcare System, West Haven, CT, USA
| | - Rachel L Kember
- Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Mental Illness Research Education and Clinical Center, Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Joëlle A Pasman
- Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State University, Hershey, PA, USA
- Institute of Personalized Medicine, Penn State University, Hershey, PA, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Henry R Kranzler
- Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Mental Illness Research Education and Clinical Center, Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Department of Psychiatry, VA CT Healthcare System, West Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, West Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, West Haven, CT, USA
| | - Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Irwin D Waldman
- Department of Psychology, Emory University, Atlanta, GA, USA
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Philipp D Koellinger
- Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA.
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA.
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA.
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39
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Shapland CY, Verhoef E, Davey Smith G, Fisher SE, Verhulst B, Dale PS, St Pourcain B. Multivariate genome-wide covariance analyses of literacy, language and working memory skills reveal distinct etiologies. NPJ SCIENCE OF LEARNING 2021; 6:23. [PMID: 34413317 PMCID: PMC8377061 DOI: 10.1038/s41539-021-00101-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Several abilities outside literacy proper are associated with reading and spelling, both phenotypically and genetically, though our knowledge of multivariate genomic covariance structures is incomplete. Here, we introduce structural models describing genetic and residual influences between traits to study multivariate links across measures of literacy, phonological awareness, oral language, and phonological working memory (PWM) in unrelated UK youth (8-13 years, N = 6453). We find that all phenotypes share a large proportion of underlying genetic variation, although especially oral language and PWM reveal substantial differences in their genetic variance composition with substantial trait-specific genetic influences. Multivariate genetic and residual trait covariance showed concordant patterns, except for marked differences between oral language and literacy/phonological awareness, where strong genetic links contrasted near-zero residual overlap. These findings suggest differences in etiological mechanisms, acting beyond a pleiotropic set of genetic variants, and implicate variation in trait modifiability even among phenotypes that have high genetic correlations.
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Affiliation(s)
- Chin Yang Shapland
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, University of Bristol, Bristol, UK.
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
| | - Ellen Verhoef
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- International Max Planck Research School for Language Sciences, Nijmegen, The Netherlands
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | | | - Philip S Dale
- Speech & Hearing Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
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40
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41
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42
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Deary IJ, Hill WD, Gale CR. Intelligence, health and death. Nat Hum Behav 2021; 5:416-430. [PMID: 33795857 DOI: 10.1038/s41562-021-01078-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 02/15/2021] [Indexed: 02/06/2023]
Abstract
The field of cognitive epidemiology studies the prospective associations between cognitive abilities and health outcomes. We review research in this field over the past decade and describe how our understanding of the association between intelligence and all-cause mortality has consolidated with the appearance of new, population-scale data. To try to understand the association better, we discuss how intelligence relates to specific causes of death, diseases/diagnoses and biomarkers of health through the adult life course. We examine the extent to which mortality and health associations with intelligence might be attributable to people's differences in education, other indicators of socioeconomic status, health literacy and adult environments and behaviours. Finally, we discuss whether genetic data provide new tools to understand parts of the intelligence-health associations. Social epidemiologists, differential psychologists and behavioural and statistical geneticists, among others, contribute to cognitive epidemiology; advances will occur by building on a common cross-disciplinary knowledge base.
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Affiliation(s)
- Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | - W David Hill
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Catharine R Gale
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.,MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
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43
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Pathfinder: a gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences. Mol Psychiatry 2021; 26:7823-7837. [PMID: 34599278 PMCID: PMC8872983 DOI: 10.1038/s41380-021-01300-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/17/2021] [Accepted: 09/08/2021] [Indexed: 02/03/2023]
Abstract
Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier to finding more of this 'missing heritability' is assessment--the use of diverse measures across GWA studies as well as time and the cost of assessment. In a series of four studies, we created a 15-min (40-item), online, gamified measure of g that is highly reliable (alpha = 0.78; two-week test-retest reliability = 0.88), psychometrically valid and scalable; we called this new measure Pathfinder. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields reliable verbal and nonverbal scores, correlated substantially with standard measures of g collected at previous ages (r ranging from 0.42 at age 7 to 0.57 at age 16). Pathfinder showed substantial twin heritability (0.57, 95% CIs = 0.43, 0.68) and SNP heritability (0.37, 95% CIs = 0.04, 0.70). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical, and behavioural sciences.
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44
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Demange PA, Malanchini M, Mallard TT, Biroli P, Cox SR, Grotzinger AD, Tucker-Drob EM, Abdellaoui A, Arseneault L, van Bergen E, Boomsma DI, Caspi A, Corcoran DL, Domingue BW, Harris KM, Ip HF, Mitchell C, Moffitt TE, Poulton R, Prinz JA, Sugden K, Wertz J, Williams BS, de Zeeuw EL, Belsky DW, Harden KP, Nivard MG. Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction. Nat Genet 2021; 53:35-44. [PMID: 33414549 PMCID: PMC7116735 DOI: 10.1038/s41588-020-00754-2] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 11/19/2020] [Indexed: 01/28/2023]
Abstract
Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success.
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Affiliation(s)
- Perline A Demange
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, the Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Margherita Malanchini
- Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK
- Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Pietro Biroli
- Department of Economics, University of Zurich, Zurich, Switzerland
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | | | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Abdel Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Avshalom Caspi
- Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - David L Corcoran
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Benjamin W Domingue
- Stanford Graduate School of Education, Stanford University, Palo Alto, CA, USA
| | - Kathleen Mullan Harris
- Department of Sociology and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hill F Ip
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Colter Mitchell
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Terrie E Moffitt
- Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Richie Poulton
- Department of Psychology and Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin, New Zealand
| | - Joseph A Prinz
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Karen Sugden
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Jasmin Wertz
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | | | - Eveline L de Zeeuw
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Daniel W Belsky
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.
- Robert N. Butler Columbia Aging Center, Columbia University, New York, NY, USA.
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA.
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
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