1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Li M, Dang X, Chen Y, Chen Z, Xu X, Zhao Z, Wu D. Cognitive processing speed and accuracy are intrinsically different in genetic architecture and brain phenotypes. Nat Commun 2024; 15:7786. [PMID: 39242605 PMCID: PMC11379965 DOI: 10.1038/s41467-024-52222-8] [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: 03/10/2024] [Accepted: 08/29/2024] [Indexed: 09/09/2024] Open
Abstract
Since the birth of cognitive science, researchers have used reaction time and accuracy to measure cognitive ability. Although recognition of these two measures is often based on empirical observations, the underlying consensus is that most cognitive behaviors may be along two fundamental dimensions: cognitive processing speed (CPS) and cognitive processing accuracy (CPA). In this study, we used genomic-wide association studies (GWAS) data from 14 cognitive traits to show the presence of those two factors and revealed the specific neurobiological basis underlying them. We identified that CPS and CPA had distinct brain phenotypes (e.g. white matter microstructure), neurobiological bases (e.g. postsynaptic membrane), and developmental periods (i.e. late infancy). Moreover, those two factors showed differential associations with other health-related traits such as screen exposure and sleep status, and a significant causal relationship with psychiatric disorders such as major depressive disorder and schizophrenia. Utilizing an independent cohort from the Adolescent Brain Cognitive Development (ABCD) study, we also uncovered the distinct contributions of those two factors on the cognitive development of young adolescents. These findings reveal two fundamental factors underlying various cognitive abilities, elucidate the distinct brain structural fingerprint and genetic architecture of CPS and CPA, and hint at the complex interrelationship between cognitive ability, lifestyle, and mental health.
Collapse
Affiliation(s)
- Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, 310027, China
| | - Xixi Dang
- Department of Psychology, Hangzhou Normal University, Hangzhou, China
| | - Yiwei Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, 310027, China
| | - Zhifan Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, 310027, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, 310027, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, 310027, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, 310027, China.
- Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
- Binjiang Institute, Zhejiang University, Hangzhou, China.
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Liu Y, Shen O, Zhu H, He Y, Chang X, Sun L, Jia Y, Sun H, Wang Y, Xu Q, Guo D, Shi M, Zheng J, Zhu Z. Associations between brain imaging-derived phenotypes and cognitive functions. Cereb Cortex 2024; 34:bhae297. [PMID: 39042033 DOI: 10.1093/cercor/bhae297] [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: 05/23/2024] [Revised: 06/25/2024] [Accepted: 07/09/2024] [Indexed: 07/24/2024] Open
Abstract
We aimed to evaluate the potential causal relationship between brain imaging-derived phenotypes and cognitive functions via Mendelian randomization analyses. Genetic instruments for 470 brain imaging-derived phenotypes were selected from a genome-wide association study based on the UK Biobank (n = 33,224). Statistics for cognitive functions were obtained from the genome-wide association study based on the UK Biobank. We used the inverse variance weighted Mendelian randomization method to investigate the associations between brain imaging-derived phenotypes and cognitive functions, and reverse Mendelian randomization analyses were performed for significant brain imaging-derived phenotypes to examine the reverse causation for the identified associations. We identified three brain imaging-derived phenotypes to be associated with verbal-numerical reasoning, including cortical surface area of the left fusiform gyrus (beta, 0.18 [95% confidence interval, 0.11 to 0.25], P = 4.74 × 10-7), cortical surface area of the right superior temporal gyrus (beta, 0.25 [95% confidence interval, 0.15 to 0.35], P = 6.30 × 10-7), and orientation dispersion in the left superior longitudinal fasciculus (beta, 0.14 [95% confidence interval, 0.09 to 0.20], P = 8.37 × 10-7). The reverse Mendelian randomization analysis indicated that verbal-numerical reasoning had no effect on these three brain imaging-derived phenotypes. This Mendelian randomization study identified cortical surface area of the left fusiform gyrus, cortical surface area of the right superior temporal gyrus, and orientation dispersion in the left superior longitudinal fasciculus as predictors of verbal-numerical reasoning.
Collapse
Affiliation(s)
- Yi Liu
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Ouxi Shen
- Suzhou Industrial Park Center for Disease Control and Prevention, 200 Suhong West Road, Industrial Park District, Suzhou, Jiangsu Province 215123, China
| | - Huating Zhu
- Suzhou Industrial Park Center for Disease Control and Prevention, 200 Suhong West Road, Industrial Park District, Suzhou, Jiangsu Province 215123, China
| | - Yu He
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Xinyue Chang
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Lulu Sun
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Yiming Jia
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Hongyan Sun
- Department of Medical Imaging, The Affiliated Guangji Hospital of Soochow University, 11 Guangqian Road, Xiangcheng District, Suzhou, Jiangsu Province 215123, China
| | - Yinan Wang
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Qingyun Xu
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Daoxia Guo
- School of Nursing, Suzhou Medical College of Soochow University, 199 Renai Road, Industrial Park District, Jiangsu Province 215123, Suzhou, China
| | - Mengyao Shi
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Jin Zheng
- Department of Neurology, Minhang Hospital, Fudan University, 170 Xinsong Road, Xinzhuang Town, Shanghai 200000, China
| | - Zhengbao Zhu
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| |
Collapse
|
5
|
Ohi K, Tanaka Y, Otowa T, Shimada M, Kaiya H, Nishimura F, Sasaki T, Tanii H, Shioiri T, Hara T. Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning. Aust N Z J Psychiatry 2024; 58:603-614. [PMID: 38581251 DOI: 10.1177/00048674241242936] [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] [Indexed: 04/08/2024]
Abstract
OBJECTIVE Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques. METHODS Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated. RESULTS All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks. CONCLUSIONS These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.
Collapse
Affiliation(s)
- Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
- Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan
| | - Yuta Tanaka
- Department of Intelligence Science and Engineering, Gifu University Graduate School of Natural Science and Technology, Gifu, Japan
| | - Takeshi Otowa
- Department of Psychiatry, East Medical Center, Nagoya City University, Nagoya, Japan
| | - Mihoko Shimada
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Hisanobu Kaiya
- Panic Disorder Research Center, Warakukai Medical Corporation, Tokyo, Japan
| | - Fumichika Nishimura
- Center for Research on Counseling and Support Services, The University of Tokyo, Tokyo, Japan
| | - Tsukasa Sasaki
- Department of Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo, Japan
| | - Hisashi Tanii
- Center for Physical and Mental Health, Mie University, Mie, Japan
- Graduate School of Medicine, Department of Health Promotion and Disease Prevention, Mie University, Mie, Japan
| | - Toshiki Shioiri
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Takeshi Hara
- Department of Intelligence Science and Engineering, Gifu University Graduate School of Natural Science and Technology, Gifu, Japan
| |
Collapse
|
6
|
Rahman MS, Harrison E, Biggs H, Seikus C, Elliott P, Breen G, Kingston N, Bradley JR, Hill SM, Tom BDM, Chinnery PF. Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants. Nat Med 2024; 30:1739-1748. [PMID: 38745010 PMCID: PMC11186791 DOI: 10.1038/s41591-024-02960-5] [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: 11/21/2023] [Accepted: 03/28/2024] [Indexed: 05/16/2024]
Abstract
A leading explanation for translational failure in neurodegenerative disease is that new drugs are evaluated late in the disease course when clinical features have become irreversible. Here, to address this gap, we cognitively profiled 21,051 people aged 17-85 years as part of the Genes and Cognition cohort within the National Institute for Health and Care Research BioResource across England. We describe the cohort, present cognitive trajectories and show the potential utility. Surprisingly, when studied at scale, the APOE genotype had negligible impact on cognitive performance. Different cognitive domains had distinct genetic architectures, with one indicating brain region-specific activation of microglia and another with glycogen metabolism. Thus, the molecular and cellular mechanisms underpinning cognition are distinct from dementia risk loci, presenting different targets to slow down age-related cognitive decline. Participants can now be recalled stratified by genotype and cognitive phenotype for natural history and interventional studies of neurodegenerative and other disorders.
Collapse
Affiliation(s)
- Md Shafiqur Rahman
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Emma Harrison
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Heather Biggs
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Chloe Seikus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK
| | - Nathalie Kingston
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Dept of Haematology, Cambridge University, Cambridge, UK
| | - John R Bradley
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- National Institute for Health and Care Research BioResource, Cambridge, UK.
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK.
| |
Collapse
|
7
|
Jiang S, Sun F, Yuan P, Jiang Y, Wan X. Distinct genetic and environmental origins of hierarchical cognitive abilities in adult humans. Cell Rep 2024; 43:114060. [PMID: 38568809 DOI: 10.1016/j.celrep.2024.114060] [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: 08/13/2023] [Revised: 02/02/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
Human cognitive abilities ranging from basic perceptions to complex social behaviors exhibit substantial variation in individual differences. These cognitive functions can be categorized into a two-order hierarchy based on the levels of cognitive processes. Second-order cognition including metacognition and mentalizing monitors and regulates first-order cognitive processes. These two-order hierarchical cognitive functions exhibit distinct abilities. However, it remains unclear whether individual differences in these cognitive abilities have distinct origins. We employ the classical twin paradigm to compare the genetic and environmental contributions to the two-order cognitive abilities in the same tasks from the same population. The results reveal that individual differences in first-order cognitive abilities were primarily influenced by genetic factors. Conversely, the second-order cognitive abilities have a stronger influence from shared environmental factors. These findings suggest that the abilities of metacognition and mentalizing in adults are profoundly shaped by their environmental experiences and less determined by their biological nature.
Collapse
Affiliation(s)
- Shaohan Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Huangshan University, Huangshan 245041 China
| | - Fanru Sun
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Peijun Yuan
- State Key Laboratory of Brain and Cognitive Science and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of the Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
| | - Yi Jiang
- State Key Laboratory of Brain and Cognitive Science and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of the Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
| | - Xiaohong Wan
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| |
Collapse
|
8
|
Chen TT, Kim J, Lam M, Chuang YF, Chiu YL, Lin SC, Jung SH, Kim B, Kim S, Cho C, Shim I, Park S, Ahn Y, Okbay A, Jang H, Kim HJ, Seo SW, Park WY, Ge T, Huang H, Feng YCA, Lin YF, Myung W, Chen CY, Won HH. Shared genetic architectures of educational attainment in East Asian and European populations. Nat Hum Behav 2024; 8:562-575. [PMID: 38182883 PMCID: PMC10963262 DOI: 10.1038/s41562-023-01781-9] [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: 03/19/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Educational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345). EduYears showed a high genetic correlation and power-adjusted transferability ratio between EAS and EUR. We also found similar functional enrichment, gene expression enrichment and cross-trait genetic correlations between two populations. Cross-ancestry fine-mapping identified refined credible sets with a higher posterior inclusion probability than single population fine-mapping. Polygenic prediction analysis in four independent EAS and EUR cohorts demonstrated transferability between populations. Our study supports the need for further research on diverse ancestries to increase our understanding of the genetic basis of educational attainment.
Collapse
Affiliation(s)
- Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Jaeyoung Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Max Lam
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
- Division of Psychiatry Research, the Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Research Division Institute of Mental Health Singapore, Singapore, Singapore
| | - Yi-Fang Chuang
- Institute of Public Health and International Health Program, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Ling Chiu
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan City, Taiwan
- Department of Medical Research, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Soyeon Kim
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chamlee Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Sanghyeon Park
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Yeeun Ahn
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Tian Ge
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- 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
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yen-Chen Anne Feng
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei City, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan.
- Department of Public Health and Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea.
| | | | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| |
Collapse
|
9
|
LaBianca S, Brikell I, Helenius D, Loughnan R, Mefford J, Palmer CE, Walker R, Gådin JR, Krebs M, Appadurai V, Vaez M, Agerbo E, Pedersen MG, Børglum AD, Hougaard DM, Mors O, Nordentoft M, Mortensen PB, Kendler KS, Jernigan TL, Geschwind DH, Ingason A, Dahl AW, Zaitlen N, Dalsgaard S, Werge TM, Schork AJ. Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder. Nat Genet 2024; 56:234-244. [PMID: 38036780 DOI: 10.1038/s41588-023-01593-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/25/2023] [Indexed: 12/02/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.
Collapse
Affiliation(s)
- Sonja LaBianca
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Isabell Brikell
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
| | - Dorte Helenius
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Joel Mefford
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Clare E Palmer
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Walker
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jesper R Gådin
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Morten Krebs
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Morteza Vaez
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Marianne Giørtz Pedersen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Psychosis Research Unit, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Copenhagen Mental Health Center, Mental Health Services Capital Region of Denmark Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Terry L Jernigan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Daniel H Geschwind
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrés Ingason
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Andrew W Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Søren Dalsgaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Thomas M Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, USA.
| |
Collapse
|
10
|
Archer DB, Eissman JM, Mukherjee S, Lee ML, Choi S, Scollard P, Trittschuh EH, Mez JB, Bush WS, Kunkle BW, Naj AC, Gifford KA, Cuccaro ML, Pericak‐Vance MA, Farrer LA, Wang L, Schellenberg GD, Mayeux RP, Haines JL, Jefferson AL, Kukull WA, Keene CD, Saykin AJ, Thompson PM, Martin ER, Bennett DA, Barnes LL, Schneider JA, Crane PK, Dumitrescu L, Hohman TJ. Longitudinal change in memory performance as a strong endophenotype for Alzheimer's disease. Alzheimers Dement 2024; 20:1268-1283. [PMID: 37985223 PMCID: PMC10896586 DOI: 10.1002/alz.13508] [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: 06/19/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 11/22/2023]
Abstract
INTRODUCTION Although large-scale genome-wide association studies (GWAS) have been conducted on AD, few have been conducted on continuous measures of memory performance and memory decline. METHODS We conducted a cross-ancestry GWAS on memory performance (in 27,633 participants) and memory decline (in 22,365 participants; 129,201 observations) by leveraging harmonized cognitive data from four aging cohorts. RESULTS We found high heritability for two ancestry backgrounds. Further, we found a novel ancestry locus for memory decline on chromosome 4 (rs6848524) and three loci in the non-Hispanic Black ancestry group for memory performance on chromosomes 2 (rs111471504), 7 (rs4142249), and 15 (rs74381744). In our gene-level analysis, we found novel genes for memory decline on chromosomes 1 (SLC25A44), 11 (BSX), and 15 (DPP8). Memory performance and memory decline shared genetic architecture with AD-related traits, neuropsychiatric traits, and autoimmune traits. DISCUSSION We discovered several novel loci, genes, and genetic correlations associated with late-life memory performance and decline. HIGHLIGHTS Late-life memory has high heritability that is similar across ancestries. We discovered four novel variants associated with late-life memory. We identified four novel genes associated with late-life memory. Late-life memory shares genetic architecture with psychiatric/autoimmune traits.
Collapse
|
11
|
Iacono D, Hatch K, Murphy EK, Cole RN, Post J, Leonessa F, Perl DP. Proteomic Changes in the Hippocampus after Repeated Explosive-Driven Blasts. J Proteome Res 2024; 23:397-408. [PMID: 38096401 PMCID: PMC10775857 DOI: 10.1021/acs.jproteome.3c00628] [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: 09/26/2023] [Revised: 10/16/2023] [Accepted: 10/24/2023] [Indexed: 01/06/2024]
Abstract
Repeated blast-traumatic brain injury (blast-TBI) has been hypothesized to cause persistent and unusual neurological and psychiatric symptoms in service members returning from war zones. Blast-wave primary effects have been supposed to induce damage and molecular alterations in the brain. However, the mechanisms through which the primary effect of an explosive-driven blast wave generate brain lesions and induce brain consequences are incompletely known. Prior findings from rat brains exposed to two consecutive explosive-driven blasts showed molecular changes (hyperphosphorylated-Tau, AQP4, S100β, PDGF, and DNA-polymerase-β) that varied in magnitude and direction across different brain regions. We aimed to compare, in an unbiased manner, the proteomic profile in the hippocampus of double blast vs sham rats using mass spectrometry (MS). Data showed differences in up- and down-regulation for protein abundances in the hippocampus of double blast vs sham rats. Tandem mass tag (TMT)-MS results showed 136 up-regulated and 94 down-regulated proteins between the two groups (10.25345/C52B8VP0X). These TMT-MS findings revealed changes never described before in blast studies, such as increases in MAGI3, a scaffolding protein at cell-cell junctions, which were confirmed by Western blotting analyses. Due to the absence of behavioral and obvious histopathological changes as described in our previous publications, these proteomic data further support the existence of an asymptomatic blast-induced molecular altered status (ABIMAS) associated with specific protein changes in the hippocampus of rats repeatedly expsosed to blast waves generated by explosive-driven detonations.
Collapse
Affiliation(s)
- Diego Iacono
- DoD/USU
Brain Tissue Repository & Neuropathology Program, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
- Department
of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
- Department
of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
- Neuroscience
Program, Department of Anatomy, Physiology & Genetics, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
- The
Henry M. Jackson Foundation for the Advancement of Military Medicine
(HJF), Inc., Bethesda, Maryland 20817, United States
- Neurodegeneration
Disorders Clinic, National Institute of
Neurological Disorders and Stroke, NINDS, NIH, Bethesda, Maryland 20814, United States
| | - Kathleen Hatch
- Department
of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
| | - Erin K. Murphy
- Department
of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
| | - Robert N. Cole
- Mass
Spectrometry and Proteomics, Department of Biological Chemistry, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21205, United States
| | - Jeremy Post
- Mass
Spectrometry and Proteomics, Department of Biological Chemistry, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21205, United States
| | - Fabio Leonessa
- Department
of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
| | - Daniel P. Perl
- DoD/USU
Brain Tissue Repository & Neuropathology Program, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
- Department
of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland 20814, United States
| |
Collapse
|
12
|
Chen D, Zhou Y, Zhang Y, Zeng H, Wu L, Liu Y. Unraveling shared susceptibility loci and Mendelian genetic associations linking educational attainment with multiple neuropsychiatric disorders. Front Psychiatry 2024; 14:1303430. [PMID: 38250258 PMCID: PMC10797721 DOI: 10.3389/fpsyt.2023.1303430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Background Empirical studies have demonstrated that educational attainment (EA) is associated with neuropsychiatric disorders (NPDs), suggesting a shared etiological basis between them. However, little is known about the shared genetic mechanisms and causality behind such associations. Methods This study explored the shared genetic basis and causal relationships between EA and NPDs using the high-definition likelihood (HDL) method, cross phenotype association study (CPASSOC), transcriptome-wide association study (TWAS), and bidirectional Mendelian randomization (MR) with summary-level data for EA (N = 293,723) and NPDs (N range = 9,725 to 455,258). Results Significant genetic correlations between EA and 12 NPDs (rg range - 0.49 to 0.35; all p < 3.85 × 10-3) were observed. CPASSOC identified 37 independent loci shared between EA and NPDs, one of which was novel (rs71351952, mapped gene: ARFGEF2). Functional analyses and TWAS found shared genes were enriched in brain tissue, especially in the cerebellum and highlighted the regulatory role of neuronal signaling, purine nucleotide metabolic process, and cAMP-mediated signaling pathways. CPASSOC and TWAS supported the role of three regions of 6q16.1, 3p21.31, and 17q21.31 might account for the shared causes between EA and NPDs. MR confirmed higher genetically predicted EA lower the risk of ADHD (ORIVW: 0.50; 95% CI: 0.39 to 0.63) and genetically predicted ADHD decreased the risk of EA (Causal effect: -2.8 months; 95% CI: -3.9 to -1.8). Conclusion These findings provided evidence of shared genetics and causation between EA and NPDs, advanced our understanding of EA, and implicated potential biological pathways that might underlie both EA and NPDs.
Collapse
Affiliation(s)
- Dongze Chen
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yi Zhou
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Yali Zhang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Huatang Zeng
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Liqun Wu
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Yuyang Liu
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| |
Collapse
|
13
|
Faouzi J, Tan M, Casse F, Lesage S, Tesson C, Brice A, Mangone G, Mariani LL, Iwaki H, Colliot O, Pihlstrøm L, Corvol JC. Proxy-analysis of the genetics of cognitive decline in Parkinson's disease through polygenic scores. NPJ Parkinsons Dis 2024; 10:8. [PMID: 38177146 PMCID: PMC10767119 DOI: 10.1038/s41531-023-00619-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 12/08/2023] [Indexed: 01/06/2024] Open
Abstract
Cognitive decline is common in Parkinson's disease (PD) and its genetic risk factors are not well known to date, besides variants in the GBA and APOE genes. However, variation in complex traits is caused by numerous variants and is usually studied with genome-wide association studies (GWAS), requiring a large sample size, which is difficult to achieve for outcome measures in PD. Taking an alternative approach, we computed 100 polygenic scores (PGS) related to cognitive, dementia, stroke, and brain anatomical phenotypes and investigated their association with cognitive decline in six longitudinal cohorts. The analysis was adjusted for age, sex, genetic ancestry, follow-up duration, GBA and APOE status. Then, we meta-analyzed five of these cohorts, comprising a total of 1702 PD participants with 6156 visits, using the Montreal Cognitive Assessment as a cognitive outcome measure. After correction for multiple comparisons, we found four PGS significantly associated with cognitive decline: intelligence (p = 5.26e-13), cognitive performance (p = 1.46e-12), educational attainment (p = 8.52e-10), and reasoning (p = 3.58e-5). Survival analyses highlighted an offset of several years between the first and last quartiles of PGS, with significant differences for the PGS of cognitive performance (5 years) and educational attainment (7 years). In conclusion, we found four PGS associated with cognitive decline in PD, all associated with general cognitive phenotypes. This study highlights the common genetic factors between cognitive decline in PD and the general population, and the importance of the participant's cognitive reserve for cognitive outcome in PD.
Collapse
Affiliation(s)
- Johann Faouzi
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France
- Univ Rennes, Ensai, CNRS, CREST-UMR 9194, F-35000, Rennes, France
| | - Manuela Tan
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Fanny Casse
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Suzanne Lesage
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Christelle Tesson
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Alexis Brice
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences, Département de Génétique, F-75013, Paris, France
| | - Graziella Mangone
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences, Département de Neurologie, F-75013, Paris, France
- Department of Neurology, Movement Disorder Division, Rush University Medical Center, 1725 W. Harrison Street, Chicago, IL, 60612, USA
| | - Louise-Laure Mariani
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences, Département de Neurologie, F-75013, Paris, France
| | - Hirotaka Iwaki
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Washington, DC, USA
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences, Département de Neurologie, F-75013, Paris, France.
| |
Collapse
|
14
|
Mukhopadhyay A, Deshpande SN, Bhatia T, Thelma BK. Significance of an altered lncRNA landscape in schizophrenia and cognition: clues from a case-control association study. Eur Arch Psychiatry Clin Neurosci 2023; 273:1677-1691. [PMID: 37009928 DOI: 10.1007/s00406-023-01596-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/20/2023] [Indexed: 04/04/2023]
Abstract
Genetic etiology of schizophrenia is poorly understood despite large genome-wide association data. Long non-coding RNAs (lncRNAs) with a probable regulatory role are emerging as important players in neuro-psychiatric disorders including schizophrenia. Prioritising important lncRNAs and analyses of their holistic interaction with their target genes may provide insights into disease biology/etiology. Of the 3843 lncRNA SNPs reported in schizophrenia GWASs extracted using lincSNP 2.0, we prioritised n = 247 based on association strength, minor allele frequency and regulatory potential and mapped them to lncRNAs. lncRNAs were then prioritised based on their expression in brain using lncRBase, epigenetic role using 3D SNP and functional relevance to schizophrenia etiology. 18 SNPs were finally tested for association with schizophrenia (n = 930) and its endophenotypes-tardive dyskinesia (n = 176) and cognition (n = 565) using a case-control approach. Associated SNPs were characterised by ChIP seq, eQTL, and transcription factor binding site (TFBS) data using FeatSNP. Of the eight SNPs significantly associated, rs2072806 in lncRNA hsaLB_IO39983 with regulatory effect on BTN3A2 was associated with schizophrenia (p = 0.006); rs2710323 in hsaLB_IO_2331 with role in dysregulation of ITIH1 with tardive dyskinesia (p < 0.05); and four SNPs with significant cognition score reduction (p < 0.05) in cases. Two of these with two additional variants in eQTL were observed among controls (p < 0.05), acting likely as enhancer SNPs and/or altering TFBS of eQTL mapped downstream genes. This study highlights important lncRNAs in schizophrenia and provides a proof of concept of novel interactions of lncRNAs with protein-coding genes to elicit alterations in immune/inflammatory pathways of schizophrenia.
Collapse
Affiliation(s)
- Anirban Mukhopadhyay
- Department of Genetics, University of Delhi South Campus, Benito Juarez Marg, New Delhi, 110021, India
| | - Smita N Deshpande
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research-Dr. Ram Manohar Lohia Hospital, New Delhi, India
| | - Triptish Bhatia
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research-Dr. Ram Manohar Lohia Hospital, New Delhi, India
| | - B K Thelma
- Department of Genetics, University of Delhi South Campus, Benito Juarez Marg, New Delhi, 110021, India.
| |
Collapse
|
15
|
Waszczuk MA, Jonas KG, Bornovalova M, Breen G, Bulik CM, Docherty AR, Eley TC, Hettema JM, Kotov R, Krueger RF, Lencz T, Li JJ, Vassos E, Waldman ID. Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies. Mol Psychiatry 2023; 28:4943-4953. [PMID: 37402851 PMCID: PMC10764644 DOI: 10.1038/s41380-023-02142-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/17/2023] [Accepted: 06/16/2023] [Indexed: 07/06/2023]
Abstract
Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.
Collapse
Affiliation(s)
- Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA.
| | - Katherine G Jonas
- Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | | | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Cynthia M Bulik
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna R Docherty
- Huntsman Mental Health Institute, Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - Thalia C Eley
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - John M Hettema
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
- Department of Psychiatry, Texas A&M Health Sciences Center, Bryan, TX, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | - Robert F Krueger
- Psychology Department, University of Minnesota, Minneapolis, MN, USA
| | - Todd Lencz
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - James J Li
- Department of Psychology, University of Wisconsin, Madison, WI, USA
- Waisman Center, University of Wisconsin, Madison, WI, USA
| | - Evangelos Vassos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Irwin D Waldman
- Department of Psychology, Emory University, Atlanta, GA, USA
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
| |
Collapse
|
16
|
Furuya S, Liu J, Sun Z, Lu Q, Fletcher JM. Understanding Internal Migration: A Research Note Providing an Assessment of Migration Selection With Genetic Data. Demography 2023; 60:1631-1648. [PMID: 37937916 DOI: 10.1215/00703370-11053145] [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] [Indexed: 11/09/2023]
Abstract
Migration is selective, resulting in inequalities between migrants and nonmigrants. However, investigating migration selection is empirically challenging because combined pre- and post-migration data are rarely available. We propose an alternative approach to assessing internal migration selection by integrating genetic data, enabling an investigation of migration selection with cross-sectional data collected post-migration. Using data from the UK Biobank, we utilized standard tools from statistical genetics to conduct a genome-wide association study (GWAS) for migration distance. We then calculated genetic correlations to compare GWAS results for migration with those for other characteristics. Given that individual genetics are determined at conception, these analyses allow a unique exploration of the association between pre-migration characteristics and migration. Results are generally consistent with the healthy migrant literature: genetics correlated with longer migration distance are associated with higher socioeconomic status and better health. We also extended the analysis to 53 traits and found novel correlations between migration and several physical health, mental health, personality, and sociodemographic traits.
Collapse
Affiliation(s)
- Shiro Furuya
- Department of Sociology, Center for Demography of Health and Aging, and Center for Demography and Ecology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jihua Liu
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhongxuan Sun
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Qiongshi Lu
- Center for Demography of Health and Aging, Department of Biostatistics and Medical Informatics, and Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jason M Fletcher
- Center for Demography of Health and Aging, Center for Demography and Ecology, La Follette School of Public Affairs, Department of Population Health Science, and Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI, USA
| |
Collapse
|
17
|
Liu T, Li C, Zhang R, Millender EF, Miao H, Ormsbee M, Guo J, Westbrook A, Pan Y, Wang J, Kelly TN. A longitudinal study of polygenic score and cognitive function decline considering baseline cognitive function, lifestyle behaviors, and diabetes among middle-aged and older US adults. Alzheimers Res Ther 2023; 15:196. [PMID: 37950263 PMCID: PMC10636974 DOI: 10.1186/s13195-023-01343-1] [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: 06/20/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Genomic study of cognition decline while considering baseline cognition and lifestyle behaviors is scarce. We aimed to evaluate the impact of a polygenic score for general cognition on cognition decline rate, while considering baseline cognition and lifestyle behaviors, among the general population and people with diabetes, a patient group commonly affected by cognition impairment. METHODS We tested associations of the polygenic score for general cognition with annual changing rates of cognition measures in 8 years of follow-up among 12,090 White and 3100 Black participants of the Health and Retirement Study (HRS), a nationally representative sample of adults aged 50 years and older in the USA. Cognition measures including word recall, mental status, and total cognitive score were measured biannually. To maximize sample size and length of follow-up, we treated the 2010 wave of survey as baseline, and follow-up data until 2018 were analyzed. Baseline lifestyle behaviors, APOE status, and measured cognition were sequentially adjusted. Given racial differences in polygenic score, all analyses were conducted by race. RESULTS The polygenic score was significantly associated with annual changing rates of all cognition measures independent of lifestyle behaviors and APOE status. Together with age and sex, the polygenic score explained 29.9%, 15.9%, and 26.5% variances of annual changing rates of word recall, mental status, and total cognitive scores among Whites and explained 17.2%, 13.9%, and 18.7% variance of the three traits among Blacks. Among both White and Black participants, those in the top quartile of polygenic score had the three cognition measures increased annually, while those in the bottom quartile had the three cognition measures decreased annually. After further adjusting for the average cognition assessed in 3 visits around baseline, the polygenic score was still positively associated with annual changing rates of all cognition measures for White (P ≤ 2.89E - 19) but not for Black (P ≥ 0.07) participants. In addition, among participants with diabetes, physical activity offset the genetic susceptibility to decline of mental status (interaction P ≤ 0.01) and total cognitive scores (interaction P = 0.03). CONCLUSIONS Polygenic score predicted cognition changes in addition to measured cognition. Physical activity offset genetic risk for cognition decline among diabetes patients.
Collapse
Affiliation(s)
- Tingting Liu
- College of Nursing, Florida State University, Tallahassee, FL, 32306, USA
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112, USA.
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112, USA
| | - Eugenia Flores Millender
- College of Nursing, Florida State University, Tallahassee, FL, 32306, USA
- Center of Population Sciences for Health Equity, Florida State University College of Nursing, Tallahassee, FL, 32306, USA
| | - Hongyu Miao
- College of Nursing, Florida State University, Tallahassee, FL, 32306, USA
| | - Michael Ormsbee
- Institute of Sports Sciences and Medicine, Florida State University, Tallahassee, FL, 32306, USA
| | - Jinzhen Guo
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Adrianna Westbrook
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Yang Pan
- Division of Nephrology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Jing Wang
- College of Nursing, Florida State University, Tallahassee, FL, 32306, USA
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| |
Collapse
|
18
|
Jia Z, Zhang H, Yu L, Qiu F, Lv Y, Guan J, Gang H, Zuo J, Zheng T, Liu H, Xia W, Xu S, Li Y. Prenatal Lead Exposure, Genetic Factors, and Cognitive Developmental Delay. JAMA Netw Open 2023; 6:e2339108. [PMID: 37870833 PMCID: PMC10594149 DOI: 10.1001/jamanetworkopen.2023.39108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/04/2023] [Indexed: 10/24/2023] Open
Abstract
Importance Although the effects of lead (Pb) exposure on neurocognition in children have been confirmed, the individual associations of prenatal Pb exposure and its interaction with genetic factors on cognitive developmental delay (CDD) in children remain unclear. Objective To investigate the association of prenatal Pb exposure and its interaction with genetic factors with CDD risk. Design, Setting, and Participants Women in Wuhan, China, who had an expected delivery date between March 2014 and December 2017, were recruited for this prospective cohort study. Children were assessed for cognitive development at approximately 2 years of age (March 2016 to December 2019). Maternal venous blood, cord blood, and venous blood from children were collected in a longitudinal follow-up. Data analysis was performed from March 2022 to February 2023. Exposure Prenatal Pb exposure, and genetic risk for cognitive ability evaluated by polygenic risk score constructed with 58 genetic variations. Main Outcomes and Measures Cognitive developmental delay of children aged approximately 2 years was assessed using the Chinese revision of the Bayley Scale of Infant Development. A series of multivariable logistic regressions was estimated to determine associations between prenatal Pb exposure and CDD among children with various genetic backgrounds, adjusting for confounding variables. Results This analysis included 2361 eligible mother-child pairs (1240 boys [52.5%] and 1121 girls [47.5%]; mean [SD] ages of mothers and children, 28.9 [3.6] years and 24.8 [1.0] months, respectively), with 292 children (12.4%) having CDD. Higher maternal Pb levels were significantly associated with increased risk of CDD (highest vs lowest tertile: odds ratio, 1.55; 95% CI, 1.13-2.13), adjusting for demographic confounders. The association of CDD with maternal Pb levels was more evident among children with higher genetic risk (highest vs lowest tertile: odds ratio, 2.59; 95% CI, 1.48-4.55), adjusting for demographic confounders. Conclusions and Relevance In this cohort study, prenatal Pb exposure was associated with an increased risk of CDD in children, especially in those with a high genetic risk. These findings suggest that prenatal Pb exposure and genetic background may jointly contribute to an increased risk of CDD for children and indicate the possibility for an integrated strategy to assess CDD risk and improve children's cognitive ability.
Collapse
Affiliation(s)
- Zhenxian Jia
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | | | - Ling Yu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Feng Qiu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yiqing Lv
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jing Guan
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huiqing Gang
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jingwen Zuo
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tongzhang Zheng
- Department of Epidemiology, School of Public Health, Brown University, Providence, Rhode Island
| | - Hongxiu Liu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Xia
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shunqing Xu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuanyuan Li
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| |
Collapse
|
19
|
Arnatkeviciute A, Lemire M, Morrison C, Mooney M, Ryabinin P, Roslin NM, Nikolas M, Coxon J, Tiego J, Hawi Z, Fornito A, Henrik W, Martinot JL, Martinot MLP, Artiges E, Garavan H, Nigg J, Friedman NP, Burton C, Schachar R, Crosbie J, Bellgrove MA. Trans-ancestry meta-analysis of genome wide association studies of inhibitory control. Mol Psychiatry 2023; 28:4175-4184. [PMID: 37500827 PMCID: PMC10827666 DOI: 10.1038/s41380-023-02187-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/01/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023]
Abstract
Deficits in effective executive function, including inhibitory control are associated with risk for a number of psychiatric disorders and significantly impact everyday functioning. These complex traits have been proposed to serve as endophenotypes, however, their genetic architecture is not yet well understood. To identify the common genetic variation associated with inhibitory control in the general population we performed the first trans-ancestry genome wide association study (GWAS) combining data across 8 sites and four ancestries (N = 14,877) using cognitive traits derived from the stop-signal task, namely - go reaction time (GoRT), go reaction time variability (GoRT SD) and stop signal reaction time (SSRT). Although we did not identify genome wide significant associations for any of the three traits, GoRT SD and SSRT demonstrated significant and similar SNP heritability of 8.2%, indicative of an influence of genetic factors. Power analyses demonstrated that the number of common causal variants contributing to the heritability of these phenotypes is relatively high and larger sample sizes are necessary to robustly identify associations. In Europeans, the polygenic risk for ADHD was significantly associated with GoRT SD and the polygenic risk for schizophrenia was associated with GoRT, while in East Asians polygenic risk for schizophrenia was associated with SSRT. These results support the potential of executive function measures as endophenotypes of neuropsychiatric disorders. Together these findings provide the first evidence indicating the influence of common genetic variation in the genetic architecture of inhibitory control quantified using objective behavioural traits derived from the stop-signal task.
Collapse
Affiliation(s)
- Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Mathieu Lemire
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Claire Morrison
- Department of Psychology and Neuroscience, University of Colorado-Boulder, Boulder, CO, USA
- Institute for Behavioural Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Michael Mooney
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Peter Ryabinin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Nicole M Roslin
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Molly Nikolas
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242, USA
| | - James Coxon
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Ziarih Hawi
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Walter Henrik
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental trajectories & psychiatry" Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental trajectories & psychiatry" Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental trajectories & psychiatry" Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Etablissement Public de Santé (EPS) Barthélemy Durand, 91700, Sainte-Geneviève-des-Bois, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405, Burlington, VT, USA
| | - Joel Nigg
- Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado-Boulder, Boulder, CO, USA
- Institute for Behavioural Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Christie Burton
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Russell Schachar
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Mark A Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia.
| |
Collapse
|
20
|
Park JY, Lee JJ, Lee Y, Lee D, Gim J, Farrer L, Lee KH, Won S. Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies. Bioinformatics 2023; 39:btad534. [PMID: 37665736 PMCID: PMC10539075 DOI: 10.1093/bioinformatics/btad534] [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: 04/03/2023] [Revised: 07/25/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023] Open
Abstract
MOTIVATION Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model. RESULTS Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A. AVAILABILITY AND IMPLEMENTATION Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.
Collapse
Affiliation(s)
- Jun Young Park
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea
- Neurozen Inc., Seoul 06168, Korea
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Korea
| | - Jang Jae Lee
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Korea
| | - Younghwa Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea
| | - Dongsoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea
| | - Jungsoo Gim
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Korea
- Department of Biomedical Science, Chosun University, Gwangju 61452, Korea
| | - Lindsay Farrer
- Departments of Medicine (Biomedical Genetics), Neurology, and Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, United States
- Departments of Epidemiology and Biostatistics, Boston University School of Public Health, Boston, MA 02118, United States
| | - Kun Ho Lee
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Korea
- Department of Biomedical Science, Chosun University, Gwangju 61452, Korea
- Korea Brain Research Institute, Daegu 41068, Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- Institute of Health and Environment, Seoul National University, Seoul 08826, Korea
- RexSoft Inc, Seoul 08826, Korea
| |
Collapse
|
21
|
Jiang Z, Zhang H, Ahearn TU, Garcia-Closas M, Chatterjee N, Zhu H, Zhan X, Zhao N. The sequence kernel association test for multicategorical outcomes. Genet Epidemiol 2023; 47:432-449. [PMID: 37078108 DOI: 10.1002/gepi.22527] [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: 10/18/2022] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 04/21/2023]
Abstract
Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel set-based association analysis method, sequence kernel association test (SKAT)-MC, the sequence kernel association test for multicategorical outcomes (nominal or ordinal), which jointly evaluates the relationship between a set of variants (common and rare) and disease subtypes. Through comprehensive simulation studies, we showed that SKAT-MC effectively preserves the nominal type I error rate while substantially increases the statistical power compared to existing methods under various scenarios. We applied SKAT-MC to the Polish breast cancer study (PBCS), and identified gene FGFR2 was significantly associated with estrogen receptor (ER)+ and ER- breast cancer subtypes. We also investigated educational attainment using UK Biobank data (N = 127 , 127 $N=127,127$ ) with SKAT-MC, and identified 21 significant genes in the genome. Consequently, SKAT-MC is a powerful and efficient analysis tool for genetic association studies with multicategorical outcomes. A freely distributed R package SKAT-MC can be accessed at https://github.com/Zhiwen-Owen-Jiang/SKATMC.
Collapse
Affiliation(s)
- Zhiwen Jiang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xiang Zhan
- Department of Biostatistics, Peking University, Beijing, China
| | - Ni Zhao
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
| |
Collapse
|
22
|
Sugden K, Moffitt TE, Arpawong TE, Arseneault L, Belsky DW, Corcoran DL, Crimmins EM, Hannon E, Houts R, Mill JS, Poulton R, Ramrakha S, Wertz J, Williams BS, Caspi A. Cross-National and Cross-Generational Evidence That Educational Attainment May Slow the Pace of Aging in European-Descent Individuals. J Gerontol B Psychol Sci Soc Sci 2023; 78:1375-1385. [PMID: 37058531 PMCID: PMC10394986 DOI: 10.1093/geronb/gbad056] [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: 10/28/2022] [Indexed: 04/15/2023] Open
Abstract
OBJECTIVES Individuals with more education are at lower risk of developing multiple, different age-related diseases than their less-educated peers. A reason for this might be that individuals with more education age slower. There are 2 complications in testing this hypothesis. First, there exists no definitive measure of biological aging. Second, shared genetic factors contribute toward both lower educational attainment and the development of age-related diseases. Here, we tested whether the protective effect of educational attainment was associated with the pace of aging after accounting for genetic factors. METHODS We examined data from 5 studies together totaling almost 17,000 individuals with European ancestry born in different countries during different historical periods, ranging in age from 16 to 98 years old. To assess the pace of aging, we used DunedinPACE, a DNA methylation algorithm that reflects an individual's rate of aging and predicts age-related decline and Alzheimer's disease and related disorders. To assess genetic factors related to education, we created a polygenic score based on the results of a genome-wide association study of educational attainment. RESULTS Across the 5 studies, and across the life span, higher educational attainment was associated with a slower pace of aging even after accounting for genetic factors (meta-analysis effect size = -0.20; 95% confidence interval [CI]: -0.30 to -0.10; p = .006). Further, this effect persisted after taking into account tobacco smoking (meta-analysis effect size = -0.13; 95% CI: -0.21 to -0.05; p = .01). DISCUSSION These results indicate that higher levels of education have positive effects on the pace of aging, and that the benefits can be realized irrespective of individuals' genetics.
Collapse
Affiliation(s)
- Karen Sugden
- Psychology and Neuroscience, Duke University, Durham, North Carolina, USA
| | - Terrie E Moffitt
- Psychology and Neuroscience, Duke University, Durham, North Carolina, USA
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Thalida Em Arpawong
- Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Louise Arseneault
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Daniel W Belsky
- Department of Epidemiology and Butler Columbia Aging Center, Columbia University Mailman School of Public Health, Columbia University, New York, New York, USA
| | - David L Corcoran
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Eileen M Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Eilis Hannon
- Complex Disease Epigenetics Group, University of Exeter Medical School, Exeter, UK
| | - Renate Houts
- Psychology and Neuroscience, Duke University, Durham, North Carolina, USA
| | - Jonathan S Mill
- Complex Disease Epigenetics Group, University of Exeter Medical School, Exeter, UK
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Jasmin Wertz
- Department of Psychology, School of Philosophy, Psychology & Language Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Avshalom Caspi
- Psychology and Neuroscience, Duke University, Durham, North Carolina, USA
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| |
Collapse
|
23
|
van Kippersluis H, Biroli P, Dias Pereira R, Galama TJ, von Hinke S, Meddens SFW, Muslimova D, Slob EAW, de Vlaming R, Rietveld CA. Overcoming attenuation bias in regressions using polygenic indices. Nat Commun 2023; 14:4473. [PMID: 37491308 PMCID: PMC10368647 DOI: 10.1038/s41467-023-40069-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 07/11/2023] [Indexed: 07/27/2023] Open
Abstract
Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.
Collapse
Affiliation(s)
- Hans van Kippersluis
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
- Tinbergen Institute, Amsterdam, The Netherlands.
| | - Pietro Biroli
- Department of Economics, University of Bologna, Bologna, Italy
| | - Rita Dias Pereira
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Titus J Galama
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Center for Social and Economic Research, University of Southern California, Los Angeles, CA, USA
| | - Stephanie von Hinke
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Economics, University of Bristol, Bristol, UK
| | - S Fleur W Meddens
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Statistics Netherlands, The Hague, The Netherlands
| | - Dilnoza Muslimova
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Eric A W Slob
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Medical Research Council Biostatistics Unit, Cambridge University, Cambridge, UK
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
| | - Ronald de Vlaming
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Cornelius A Rietveld
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
| |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
Cui S, Lin Q, Gui Y, Zhang Y, Lu H, Zhao H, Wang X, Li X, Jiang F. CARE as a wearable derived feature linking circadian amplitude to human cognitive functions. NPJ Digit Med 2023; 6:123. [PMID: 37433859 DOI: 10.1038/s41746-023-00865-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 06/26/2023] [Indexed: 07/13/2023] Open
Abstract
Circadian rhythms are crucial for regulating physiological and behavioral processes. Pineal hormone melatonin is often used to measure circadian amplitude but its collection is costly and time-consuming. Wearable activity data are promising alternative, but the most commonly used measure, relative amplitude, is subject to behavioral masking. In this study, we firstly derive a feature named circadian activity rhythm energy (CARE) to better characterize circadian amplitude and validate CARE by correlating it with melatonin amplitude (Pearson's r = 0.46, P = 0.007) among 33 healthy participants. Then we investigate its association with cognitive functions in an adolescent dataset (Chinese SCHEDULE-A, n = 1703) and an adult dataset (UK Biobank, n = 92,202), and find that CARE is significantly associated with Global Executive Composite (β = 30.86, P = 0.016) in adolescents, and reasoning ability, short-term memory, and prospective memory (OR = 0.01, 3.42, and 11.47 respectively, all P < 0.001) in adults. Finally, we identify one genetic locus with 126 CARE-associated SNPs using the genome-wide association study, of which 109 variants are used as instrumental variables in the Mendelian Randomization analysis, and the results show a significant causal effect of CARE on reasoning ability, short-term memory, and prospective memory (β = -59.91, 7.94, and 16.85 respectively, all P < 0.0001). The present study suggests that CARE is an effective wearable-based metric of circadian amplitude with a strong genetic basis and clinical significance, and its adoption can facilitate future circadian studies and potential intervention strategies to improve circadian rhythms and cognitive functions.
Collapse
Affiliation(s)
- Shuya Cui
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qingmin Lin
- Developmental and Behavioral Pediatrics, Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyuan Gui
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yunting Zhang
- Developmental and Behavioral Pediatrics, Child Health Advocacy Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Lu
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Xiaolei Wang
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Xinyue Li
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
| | - Fan Jiang
- Developmental and Behavioral Pediatrics, Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China.
| |
Collapse
|
26
|
Kang M, Ang TFA, Devine SA, Sherva R, Mukherjee S, Trittschuh EH, Gibbons LE, Scollard P, Lee M, Choi SE, Klinedinst B, Nakano C, Dumitrescu LC, Durant A, Hohman TJ, Cuccaro ML, Saykin AJ, Kukull WA, Bennett DA, Wang LS, Mayeux RP, Haines JL, Pericak-Vance MA, Schellenberg GD, Crane PK, Au R, Lunetta KL, Mez JB, Farrer LA. A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores. Mol Neurodegener 2023; 18:40. [PMID: 37349795 PMCID: PMC10286470 DOI: 10.1186/s13024-023-00633-4] [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: 02/17/2023] [Accepted: 06/06/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes. METHODS We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software. RESULTS Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD. CONCLUSION Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.
Collapse
Affiliation(s)
- Moonil Kang
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street E200, Boston, MA 02118 USA
| | - Ting Fang Alvin Ang
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Sherral A. Devine
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Richard Sherva
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street E200, Boston, MA 02118 USA
| | - Shubhabrata Mukherjee
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Emily H. Trittschuh
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA USA
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Laura E. Gibbons
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Phoebe Scollard
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Michael Lee
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Seo-Eun Choi
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Brandon Klinedinst
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Connie Nakano
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Logan C. Dumitrescu
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Alaina Durant
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Timothy J. Hohman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Michael L. Cuccaro
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, Miami, FL USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, WA USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | - Richard P. Mayeux
- Department of Neurology, Columbia University School of Medicine, New York, NY USA
| | - Jonathan L. Haines
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH USA
| | | | - Gerard D. Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | - Paul K. Crane
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA USA
| | - Kathryn L. Lunetta
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA USA
| | - Jesse B. Mez
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street E200, Boston, MA 02118 USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| |
Collapse
|
27
|
Zhong Z, Wang Z, Xie X, Tian S, Wang F, Wang Q, Ni S, Pan Y, Xiao Q. Evaluation of the Genetic Diversity, Population Structure and Selection Signatures of Three Native Chinese Pig Populations. Animals (Basel) 2023; 13:2010. [PMID: 37370521 DOI: 10.3390/ani13122010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, the number of these pig populations has decreased sharply, largely due to a decrease in growth rate and poor lean meat percentage. For effective conservation of these genetic resources (such as heat tolerance, meat quality and disease resistance), the whole-genome sequencing data of 78 individuals from 3 native Chinese pig populations, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), were obtained using a 150 bp paired-end platform, and 25 individuals from two foreign breeds, including Landrace (LR) and Large White (LW), were downloaded from a public database. A total of 28,384,282 SNPs were identified, of which 27,134,233 SNPs were identified in native Chinese pig populations. Both genetic diversity statistics and linkage disequilibrium (LD) analysis indicated that indigenous pig populations displayed high genetic diversity. The result of population structure implied the uniqueness of each native Chinese pig population. The selection signatures were detected between indigenous pig populations and foreign breeds by using the population differentiation index (FST) method. A total of 359 candidate genes were identified, and some genes may affect characteristics such as immunity (IL-2, IL-21 and ZFYVE16), adaptability (APBA1), reproduction (FGF2, RNF17, ADAD1 and HIPK4), meat quality (ABCA1, ADIG, TLE4 and IRX5), and heat tolerance (VPS13A, HSPA4). Overall, the findings of this study will provide some valuable insights for the future breeding, conservation and utilization of these three Chinese indigenous pig populations.
Collapse
Affiliation(s)
- Ziqi Zhong
- Hainan Key Laboratory of Tropical Animal Reproduction & Breeding and Epidemic Disease Research, College of Animal Science and Technology, Hainan University, Haikou 570228, China
| | - Ziyi Wang
- Hainan Key Laboratory of Tropical Animal Reproduction & Breeding and Epidemic Disease Research, College of Animal Science and Technology, Hainan University, Haikou 570228, China
| | - Xinfeng Xie
- Hainan Key Laboratory of Tropical Animal Reproduction & Breeding and Epidemic Disease Research, College of Animal Science and Technology, Hainan University, Haikou 570228, China
| | - Shuaishuai Tian
- Hainan Key Laboratory of Tropical Animal Reproduction & Breeding and Epidemic Disease Research, College of Animal Science and Technology, Hainan University, Haikou 570228, China
| | - Feifan Wang
- Hainan Key Laboratory of Tropical Animal Reproduction & Breeding and Epidemic Disease Research, College of Animal Science and Technology, Hainan University, Haikou 570228, China
| | - Qishan Wang
- Hainan Yazhou Bay Seed Laboratory, Yongyou Industrial Park, Yazhou Bay Sci-Tech City, Sanya 572025, China
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou 310058, China
| | - Shiheng Ni
- Animal Husbandry Technology Extending Stations of Hainan Province, Haikou 570203, China
| | - Yuchun Pan
- Hainan Yazhou Bay Seed Laboratory, Yongyou Industrial Park, Yazhou Bay Sci-Tech City, Sanya 572025, China
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou 310058, China
| | - Qian Xiao
- Hainan Key Laboratory of Tropical Animal Reproduction & Breeding and Epidemic Disease Research, College of Animal Science and Technology, Hainan University, Haikou 570228, China
| |
Collapse
|
28
|
Shi H, Chen L, Zhang S, Li R, Wu Y, Zou H, Wang C, Cai M, Lin H. Dynamic association of ambient air pollution with incidence and mortality of pulmonary hypertension: A multistate trajectory analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 262:115126. [PMID: 37315366 PMCID: PMC10443233 DOI: 10.1016/j.ecoenv.2023.115126] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND There is little evidence regarding the association between ambient air pollution and incidence and the mortality of pulmonary hypertension (PH). METHODS We included 494,750 participants at baseline in the UK Biobank study. Exposures to PM2.5, PM10, NO2, and NOx were estimated at geocoded participants' residential addresses, utilizing pollution data provided by UK Department for Environment, Food and Rural Affairs (DEFRA). The outcomes were the incidence and mortality of PH. We used multivariate multistate models to investigate the impacts of various ambient air pollutants on both incidence and mortality of PH. RESULTS During a median follow-up of 11.75 years, 2517 participants developed incident PH, and 696 died. We observed that all ambient air pollutants were associated with increased incidence of PH with different magnitudes, with adjusted hazard ratios (HRs) [95% confidence intervals (95% CIs)] for each interquartile range (IQR) increase of 1.73 (1.65, 1.81) for PM2.5, 1.70 (1.63, 1.78) for PM10, 1.42 (1.37, 1.48) for NO2, and 1.35 (1.31, 1.40) for NOx. Furthermore, PM2.5, PM10, NO2 and NO2 influenced the transition from PH to death, and the corresponding HRs (95% CIs) were 1.35 (1.25, 1.45), 1.31 (1.21, 1.41), 1.28 (1.20, 1.37) and 1.24 (1.17, 1.32), respectively. CONCLUSION The results of our study indicate that exposure to various ambient air pollutants might play key but differential roles in both the incidence and mortality of PH.
Collapse
Affiliation(s)
- Hui Shi
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Rui Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yinglin Wu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Hongtao Zou
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| |
Collapse
|
29
|
García-Marín LM, Reyes-Pérez P, Diaz-Torres S, Medina-Rivera A, Martin NG, Mitchell BL, Rentería ME. Shared molecular genetic factors influence subcortical brain morphometry and Parkinson's disease risk. NPJ Parkinsons Dis 2023; 9:73. [PMID: 37164954 PMCID: PMC10172359 DOI: 10.1038/s41531-023-00515-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 04/28/2023] [Indexed: 05/12/2023] Open
Abstract
Parkinson's disease (PD) is a late-onset and genetically complex neurodegenerative disorder. Here we sought to identify genes and molecular pathways underlying the associations between PD and the volume of ten brain structures measured through magnetic resonance imaging (MRI) scans. We leveraged genome-wide genetic data from several cohorts, including the International Parkinson's Disease Genomics Consortium (IPDG), the UK Biobank, the Adolescent Brain Cognitive Development (ABCD) study, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Enhancing Neuroimaging Genetics through Meta-Analyses (ENIGMA), and 23andMe. We observed significant positive genetic correlations between PD and intracranial and subcortical brain volumes. Genome-wide association studies (GWAS) - pairwise analyses identified 210 genomic segments with shared aetiology between PD and at least one of these brain structures. Pathway enrichment results highlight potential links with chronic inflammation, the hypothalamic-pituitary-adrenal pathway, mitophagy, disrupted vesicle-trafficking, calcium-dependent, and autophagic pathways. Investigations for putative causal genetic effects suggest that a larger putamen volume could influence PD risk, independently of the potential causal genetic effects of intracranial volume (ICV) on PD. Our findings suggest that genetic variants influencing larger intracranial and subcortical brain volumes, possibly during earlier stages of life, influence the risk of developing PD later in life.
Collapse
Affiliation(s)
- Luis M García-Marín
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México.
| | - Paula Reyes-Pérez
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Santiago Diaz-Torres
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Nicholas G Martin
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Brittany L Mitchell
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| |
Collapse
|
30
|
Cheval B, Darrous L, Choi KW, Klimentidis YC, Raichlen DA, Alexander GE, Cullati S, Kutalik Z, Boisgontier MP. Genetic insights into the causal relationship between physical activity and cognitive functioning. Sci Rep 2023; 13:5310. [PMID: 37002254 PMCID: PMC10066390 DOI: 10.1038/s41598-023-32150-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/23/2023] [Indexed: 04/03/2023] Open
Abstract
Physical activity and cognitive functioning are strongly intertwined. However, the causal relationships underlying this association are still unclear. Physical activity can enhance brain functions, but healthy cognition may also promote engagement in physical activity. Here, we assessed the bidirectional relationships between physical activity and general cognitive functioning using Latent Heritable Confounder Mendelian Randomization (LHC-MR). Association data were drawn from two large-scale genome-wide association studies (UK Biobank and COGENT) on accelerometer-measured moderate, vigorous, and average physical activity (N = 91,084) and cognitive functioning (N = 257,841). After Bonferroni correction, we observed significant LHC-MR associations suggesting that increased fraction of both moderate (b = 0.32, CI95% = [0.17,0.47], P = 2.89e - 05) and vigorous physical activity (b = 0.22, CI95% = [0.06,0.37], P = 0.007) lead to increased cognitive functioning. In contrast, we found no evidence of a causal effect of average physical activity on cognitive functioning, and no evidence of a reverse causal effect (cognitive functioning on any physical activity measures). These findings provide new evidence supporting a beneficial role of moderate and vigorous physical activity (MVPA) on cognitive functioning.
Collapse
Affiliation(s)
- Boris Cheval
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.
- Laboratory for the Study of Emotion Elicitation and Expression (E3Lab), Department of Psychology, University of Geneva, Geneva, Switzerland.
| | - Liza Darrous
- University for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Karmel W Choi
- Department of Psychiatry, Massachusetts General Hospital, Massachusetts, Boston, MA, USA
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - David A Raichlen
- Human and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
- Department of Anthropology, University of Southern California, Los Angeles, CA, USA
| | - Gene E Alexander
- Department of Psychology, University of Arizona, Tucson, AZ, USA
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Stéphane Cullati
- Population Health Laboratory, Department of Community Health, University of Fribourg, Fribourg, Switzerland
| | - Zoltán Kutalik
- University for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Matthieu P Boisgontier
- School of Rehabilitation Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
- Bruyère Research Institute, Ottawa, ON, Canada.
| |
Collapse
|
31
|
Zheng J, Ni C, Zhang Y, Huang J, Hukportie DN, Liang B, Tang S. Association of regular glucosamine use with incident dementia: evidence from a longitudinal cohort and Mendelian randomization study. BMC Med 2023; 21:114. [PMID: 36978077 PMCID: PMC10052856 DOI: 10.1186/s12916-023-02816-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/06/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Emerging data suggests the neuroprotective and anti-neuroinflammatory effects of glucosamine. We aimed to examine the association between regular glucosamine use and risk of incident dementia, including dementia subtypes. METHODS We conducted large-scale observational and two-sample Mendelian randomization (MR) analyses. Participants in UK Biobank having accessible data for dementia incidence and who did not have dementia at baseline were included in the prospective cohort. Through the Cox proportional hazard model, we examined the risks of incident all-cause dementia, Alzheimer's disease (AD), and vascular dementia among glucosamine users and non-users. To further test the causal association between glucosamine use and dementia, we conducted a 2-sample MR utilizing summary statistics from genome-wide association studies (GWAS). The GWAS data were obtained from observational cohort participants of mostly European ancestry. RESULTS During a median follow-up of 8.9 years, there were 2458 cases of all-cause dementia, 924 cases of AD, and 491 cases of vascular dementia. In multivariable analysis, the hazard ratios (HR) of glucosamine users for all-cause dementia, AD, and vascular dementia were 0.84 (95% CI 0.75-0.93), 0.83 (95% CI 0.71-0.98), and 0.74 (95% CI 0.58-0.95), respectively. The inverse associations between glucosamine use and AD appeared to be stronger among participants aged below 60 years than those aged above 60 years (p = 0.04 for interaction). The APOE genotype did not modify this association (p > 0.05 for interaction). Single-variable MR suggested a causal relationship between glucosamine use and lower dementia risk. Multivariable MR showed that taking glucosamine continued to protect against dementia after controlling for vitamin, chondroitin supplement use and osteoarthritis (all-cause dementia HR 0.88, 95% CI 0.81-0.95; AD HR 0.78, 95% CI 0.72-0.85; vascular dementia HR 0.73, 95% CI 0.57-0.94). Single and multivariable inverse variance weighted (MV-IVW) and MR-Egger sensitivity analyses produced similar results for these estimations. CONCLUSIONS The findings of this large-scale cohort and MR analysis provide evidence for potential causal associations between the glucosamine use and lower risk for dementia. These findings require further validation through randomized controlled trials.
Collapse
Affiliation(s)
- Jiazhen Zheng
- Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
| | - Can Ni
- Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
| | - Yingchai Zhang
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong, SAR, China
| | - Jinghan Huang
- Biomedical Genetics Section, School of Medicine, Boston University, Boston, USA
- Department of Chemical Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Daniel Nyarko Hukportie
- Department of Epidemiology, School of Public Health, (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, Guangzhou, China
| | - Buwen Liang
- Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
| | - Shaojun Tang
- Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China.
- Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, SAR, China.
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Zhang Y, Fletcher J, Lu Q, Song J. Gender differences in the association between parity and cognitive function: Evidence from the UK biobank. Soc Sci Med 2023; 320:115649. [PMID: 36709690 PMCID: PMC9974636 DOI: 10.1016/j.socscimed.2022.115649] [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: 08/17/2022] [Revised: 12/29/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
While much previous work linking fertility history with late-life cognition has focused on a narrow set of cognitive measures and/or has used modest sample sizes in the analysis, our paper expands the size and scope of these linkages by analyzing cognitive function across five domains and precisely estimating gendered patterns between men and women. Results point to important gendered associations between parity and cognition: having children is likely associated with better cognitive function for fathers in all five domains. However, mothers show worse cognitive function in some domains (i.e., numeric memory, prospective memory, and fluid intelligence) than childless women. We explore the possibility of confounding in these associations and rule out the effects of genetic cognitive ability on fertility. We also find that adding controls for educational attainment differ by gender-strengthening associations between parity and cognition for men and largely eliminating them for women. The findings support previous work done on how life course contexts may link to the risk of dementia or cognitive impairment, highlighting parity as potential protective or risk factors to parents' cognitive health. The use of five cognitive domains yields variations in results, giving implications on measure selection of cognitive function and calling for replicated work covering more cognitive domains.
Collapse
Affiliation(s)
- Yan Zhang
- Center for Demography of Health and Aging, University of Wisconsin, Madison, United States.
| | - Jason Fletcher
- Center for Demography of Health and Aging, University of Wisconsin, Madison, United States.
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, United States.
| | - Jie Song
- Department of Statistics, University of Wisconsin, Madison, United States.
| |
Collapse
|
34
|
O'Hare K, Watkeys O, Badcock JC, Laurens KR, Tzoumakis S, Dean K, Harris F, Carr VJ, Green MJ. Pathways from developmental vulnerabilities in early childhood to schizotypy in middle childhood. BRITISH JOURNAL OF CLINICAL PSYCHOLOGY 2023; 62:228-242. [PMID: 36458518 PMCID: PMC10946562 DOI: 10.1111/bjc.12405] [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: 07/11/2022] [Accepted: 11/14/2022] [Indexed: 12/03/2022]
Abstract
OBJECTIVES Childhood disturbances in social, emotional, language, motor and cognitive functioning, and schizotypy have each been implicated as precursors of schizophrenia-spectrum disorders. We investigated whether relationships between early childhood developmental vulnerabilities and childhood schizotypy are mediated by educational underachievement in middle childhood. METHODS Participants were members of a large Australian (n = 19,216) population cohort followed longitudinally. Path analyses were used to model relationships between developmental vulnerabilities at age ~5 years, educational underachievement from ages ~8 to 10 years and three distinct profiles of schizotypy at age ~11 years (true, introverted and affective schizotypy). RESULTS Early childhood developmental vulnerabilities on five broad domains (related to physical, emotional, social, cognitive and communication development) were associated with schizotypy profiles in middle childhood. Educational underachievement in middle childhood was associated with all schizotypy profiles, but most strongly with the true schizotypy profile (OR = 3.92, 95% CI = 3.12, 4.91). The relationships between schizotypy profiles and early childhood developmental vulnerabilities in 'language and cognitive skills (school-based)' and 'communication skills and general knowledge' domains were fully mediated by educational underachievement in middle childhood, and the relationships with early childhood 'physical health and well-being' and 'emotional maturity' domains were partially mediated. CONCLUSION Developmental continuity from early childhood developmental vulnerabilities to schizotypy in middle childhood is mediated by educational underachievement in middle childhood. While some domains of early developmental functioning showed differential relationships with distinct schizotypy profiles, these findings support a developmental pathway to schizotypy in which cognitive vulnerability operates from early childhood through to middle childhood.
Collapse
Affiliation(s)
- Kirstie O'Hare
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
| | - Oliver Watkeys
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
| | - Johanna C. Badcock
- School of Psychological ScienceUniversity of Western AustraliaPerthWAAustralia
| | - Kristin R. Laurens
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
- School of Psychology and CounsellingQueensland University of Technology (QUT)BrisbaneQldAustralia
| | - Stacy Tzoumakis
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
- School of Criminology and Criminal JusticeGriffith UniversitySouthportQldAustralia
| | - Kimberlie Dean
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
- Justice Health and Forensic Mental Health NetworkSydneyNSWAustralia
| | - Felicity Harris
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
| | - Vaughan J. Carr
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
- Neuroscience Research AustraliaSydneyNSWAustralia
- Department of PsychiatryMonash UniversityMelbourneVic.Australia
| | - Melissa J. Green
- Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNSWAustralia
- Neuroscience Research AustraliaSydneyNSWAustralia
| |
Collapse
|
35
|
Meijer M, Franke B, Sandi C, Klein M. Epigenome-wide DNA methylation in externalizing behaviours: A review and combined analysis. Neurosci Biobehav Rev 2023; 145:104997. [PMID: 36566803 DOI: 10.1016/j.neubiorev.2022.104997] [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: 08/08/2022] [Revised: 11/24/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022]
Abstract
DNA methylation (DNAm) is one of the most frequently studied epigenetic mechanisms facilitating the interplay of genomic and environmental factors, which can contribute to externalizing behaviours and related psychiatric disorders. Previous epigenome-wide association studies (EWAS) for externalizing behaviours have been limited in sample size, and, therefore, candidate genes and biomarkers with robust evidence are still lacking. We 1) performed a systematic literature review of EWAS of attention-deficit/hyperactivity disorder (ADHD)- and aggression-related behaviours conducted in peripheral tissue and cord blood and 2) combined the most strongly associated DNAm sites observed in individual studies (p < 10-3) to identify candidate genes and biological systems for ADHD and aggressive behaviours. We observed enrichment for neuronal processes and neuronal cell marker genes for ADHD. Astrocyte and granulocytes cell markers among genes annotated to DNAm sites were relevant for both ADHD and aggression-related behaviours. Only 1 % of the most significant epigenetic findings for ADHD/ADHD symptoms were likely to be directly explained by genetic factors involved in ADHD. Finally, we discuss how the field would greatly benefit from larger sample sizes and harmonization of assessment instruments.
Collapse
Affiliation(s)
- Mandy Meijer
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Laboratory of Behavioural Genetics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Carmen Sandi
- Laboratory of Behavioural Genetics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marieke Klein
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Psychiatry, University of California, La Jolla, San Diego, CA, 92093, USA.
| |
Collapse
|
36
|
Schulz CA, Weinhold L, Schmid M, Nöthen MM, Nöthlings U. Analysis of associations between dietary patterns, genetic disposition, and cognitive function in data from UK Biobank. Eur J Nutr 2023; 62:511-521. [PMID: 36152054 PMCID: PMC9899759 DOI: 10.1007/s00394-022-02976-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 07/29/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE Research suggests that diet influences cognitive function and the risk for neurodegenerative disease. The present study aimed to determine whether a recently developed diet score, based on recommendations for dietary priorities for cardio metabolic health, was associated with fluid intelligence, and whether these associations were modified by individual genetic disposition. METHODS This research has been conducted using the UK Biobank Resource. Analyses were performed using self-report data on diet and the results for the verbal-numerical reasoning test of fluid intelligence of 104,895 individuals (46% male: mean age at recruitment 57.1 years (range 40-70)). For each participant, a diet score and a polygenic score (PGS) were constructed, which evaluated predefined cut-offs for the intake of fruit, vegetables, fish, processed meat, unprocessed meat, whole grain, and refined grain, and ranged from 0 (unfavorable) to 7 (favorable). To investigate whether the diet score was associated with fluid intelligence, and whether the association was modified by PGS, linear regression analyses were performed. RESULTS The average diet score was 3.9 (SD 1.4). After adjustment for selected confounders, a positive association was found between baseline fluid intelligence and PGS (P < 0.001). No association was found between baseline fluid intelligence and diet score (P = 0.601), even after stratification for PGS, or in participants with longitudinal data available (n = 9,482). CONCLUSION In this middle-aged cohort, no evidence was found for an association between the investigated diet score and either baseline or longitudinal fluid intelligence. However, as in previous reports, fluid intelligence was strongly associated with a PGS for general cognitive function.
Collapse
Affiliation(s)
| | - Leonie Weinhold
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Ute Nöthlings
- Institute of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
| |
Collapse
|
37
|
Alvarenga AB, Oliveira HR, Turner SP, Garcia A, Retallick KJ, Miller SP, Brito LF. Unraveling the phenotypic and genomic background of behavioral plasticity and temperament in North American Angus cattle. Genet Sel Evol 2023; 55:3. [PMID: 36658485 PMCID: PMC9850537 DOI: 10.1186/s12711-023-00777-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 01/04/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Longitudinal records of temperament can be used for assessing behavioral plasticity, such as aptness to learn, memorize, or change behavioral responses based on affective state. In this study, we evaluated the phenotypic and genomic background of North American Angus cow temperament measured throughout their lifetime around the weaning season, including the development of a new indicator trait termed docility-based learning and behavioral plasticity. The analyses included 273,695 and 153,898 records for yearling (YT) and cow at weaning (CT) temperament, respectively, 723,248 animals in the pedigree, and 8784 genotyped animals. Both YT and CT were measured when the animal was loading into/exiting the chute. Moreover, CT was measured around the time in which the cow was separated from her calf. A random regression model fitting a first-order Legendre orthogonal polynomial was used to model the covariance structure of temperament and to assess the learning and behavioral plasticity (i.e., slope of the regression) of individual cows. This study provides, for the first time, a longitudinal perspective of the genetic and genomic mechanisms underlying temperament, learning, and behavioral plasticity in beef cattle. RESULTS CT measured across years is heritable (0.38-0.53). Positive and strong genetic correlations (0.91-1.00) were observed among all CT age-group pairs and between CT and YT (0.84). Over 90% of the candidate genes identified overlapped among CT age-groups and the estimated effect of genomic markers located within important candidate genes changed over time. A small but significant genetic component was observed for learning and behavioral plasticity (heritability = 0.02 ± 0.002). Various candidate genes were identified, revealing the polygenic nature of the traits evaluated. The pathways and candidate genes identified are associated with steroid and glucocorticoid hormones, development delay, cognitive development, and behavioral changes in cattle and other species. CONCLUSIONS Cow temperament is highly heritable and repeatable. The changes in temperament can be genetically improved by selecting animals with favorable learning and behavioral plasticity (i.e., habituation). Furthermore, the environment explains a large part of the variation in learning and behavioral plasticity, leading to opportunities to also improve the overall temperament by refining management practices. Moreover, behavioral plasticity offers opportunities to improve the long-term animal and handler welfare through habituation.
Collapse
Affiliation(s)
- Amanda B. Alvarenga
- grid.169077.e0000 0004 1937 2197Department of Animal Sciences, Purdue University, West Lafayette, IN USA
| | - Hinayah R. Oliveira
- grid.169077.e0000 0004 1937 2197Department of Animal Sciences, Purdue University, West Lafayette, IN USA ,Lactanet, Guelph, ON Canada
| | - Simon P. Turner
- grid.426884.40000 0001 0170 6644Animal and Veterinary Sciences Department, Scotland’s Rural College, Edinburgh, UK
| | - Andre Garcia
- American Angus Association, Angus Genetics Inc., Saint Joseph, MO USA
| | | | - Stephen P. Miller
- American Angus Association, Angus Genetics Inc., Saint Joseph, MO USA ,grid.1020.30000 0004 1936 7371AGBU, a joint venture of NSW Department of Primary Industries and University of New England, Armidale, 2351 Australia
| | - Luiz F. Brito
- grid.169077.e0000 0004 1937 2197Department of Animal Sciences, Purdue University, West Lafayette, IN USA
| |
Collapse
|
38
|
Lanooij SD, Eisel ULM, Drinkenburg WHIM, van der Zee EA, Kas MJH. Influencing cognitive performance via social interactions: a novel therapeutic approach for brain disorders based on neuroanatomical mapping? Mol Psychiatry 2023; 28:28-33. [PMID: 35858991 PMCID: PMC9812764 DOI: 10.1038/s41380-022-01698-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 01/09/2023]
Abstract
Many psychiatric and neurological disorders present deficits in both the social and cognitive domain. In this perspectives article, we provide an overview and the potential of the existence of an extensive neurobiological substrate underlying the close relationship between these two domains. By mapping the rodent brain regions involved in the social and/or cognitive domain, we show that the vast majority of brain regions involved in the cognitive domain are also involved in the social domain. The identified neuroanatomical overlap has an evolutionary basis, as complex social behavior requires cognitive skills, and aligns with the reported functional interactions of processes underlying cognitive and social performance. Based on the neuroanatomical mapping, recent (pre-)clinical findings, and the evolutionary perspective, we emphasize that the social domain requires more focus as an important treatment target and/or biomarker, especially considering the presently limited treatment strategies for these disorders.
Collapse
Affiliation(s)
- Suzanne D. Lanooij
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Ulrich L. M. Eisel
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Wilhelmus H. I. M. Drinkenburg
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands ,grid.419619.20000 0004 0623 0341Department of Neuroscience, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Eddy A. van der Zee
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Martien J. H. Kas
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| |
Collapse
|
39
|
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.
Collapse
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
| |
Collapse
|
40
|
Savignac C, Villeneuve S, Badhwar A, Saltoun K, Shafighi K, Zajner C, Sharma V, Gagliano Taliun SA, Farhan S, Poirier J, Bzdok D. APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer's disease and related dementia. PLoS Biol 2022; 20:e3001863. [PMID: 36512526 PMCID: PMC9747055 DOI: 10.1371/journal.pbio.3001863] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/30/2022] [Indexed: 12/15/2022] Open
Abstract
Alzheimer's disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in the default network (DN). Here, we examined codependent structural variations between the MTL's most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer's disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer's disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix's fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk.
Collapse
Affiliation(s)
- Chloé Savignac
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre (BIC), MNI, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Centre for Studies in the Prevention of Alzheimer’s Disease, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
- Centre de recherche de l’Institut universitaire de gériatrie de Montréal (CRIUGM), Montreal, Quebec, Canada
| | - Karin Saltoun
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Kimia Shafighi
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Chris Zajner
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Vaibhav Sharma
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Sarah A. Gagliano Taliun
- Department of Neurosciences & Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
- Montreal Heart Institute, Montréal, Quebec, Canada
| | - Sali Farhan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Judes Poirier
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Centre for Studies in the Prevention of Alzheimer’s Disease, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre (BIC), MNI, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- School of Computer Science, McGill University, Montreal, Quebec, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| |
Collapse
|
41
|
Chen J, Fu Z, Bustillo JR, Perrone-Bizzozero NI, Lin D, Canive J, Pearlson GD, Stephen JM, Mayer AR, Potkin SG, van Erp TGM, Kochunov P, Elliot Hong L, Adhikari BM, Andreassen OA, Agartz I, Westlye LT, Sui J, Du Y, Macciardi F, Hanlon FM, Jung RE, Turner JA, Liu J, Calhoun VD. Genome-Transcriptome-Functional Connectivity-Cognition Link Differentiates Schizophrenia From Bipolar Disorder. Schizophr Bull 2022; 48:1306-1317. [PMID: 35988022 PMCID: PMC9673262 DOI: 10.1093/schbul/sbac088] [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] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) and bipolar disorder (BD) share genetic risk factors, yet patients display differential levels of cognitive impairment. We hypothesized a genome-transcriptome-functional connectivity (frontoparietal)-cognition pathway linked to SZ-versus-BD differences, and conducted a multiscale study to delineate this pathway. STUDY DESIGNS Large genome-wide studies provided single nucleotide polymorphisms (SNPs) conferring more risk for SZ than BD, and we identified their regulated genes, namely SZ-biased SNPs and genes. We then (a) computed the polygenic risk score for SZ (PRSSZ) of SZ-biased SNPs and examined its associations with imaging-based frontoparietal functional connectivity (FC) and cognitive performances; (b) examined the spatial correlation between ex vivo postmortem expressions of SZ-biased genes and in vivo, SZ-related FC disruptions across frontoparietal regions; (c) investigated SZ-versus-BD differences in frontoparietal FC; and (d) assessed the associations of frontoparietal FC with cognitive performances. STUDY RESULTS PRSSZ of SZ-biased SNPs was significantly associated with frontoparietal FC and working memory test scores. SZ-biased genes' expressions significantly correlated with SZ-versus-BD differences in FC across frontoparietal regions. SZ patients showed more reductions in frontoparietal FC than BD patients compared to controls. Frontoparietal FC was significantly associated with test scores of multiple cognitive domains including working memory, and with the composite scores of all cognitive domains. CONCLUSIONS Collectively, these multiscale findings support the hypothesis that SZ-biased genetic risk, through transcriptome regulation, is linked to frontoparietal dysconnectivity, which in turn contributes to differential cognitive deficits in SZ-versus BD, suggesting that potential biomarkers for more precise patient stratification and treatment.
Collapse
Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Juan R Bustillo
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Nora I Perrone-Bizzozero
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Jose Canive
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
- Department of Psychiatry and Neuroscience, Yale University, New Haven, CT, USA
| | | | | | - Steven G Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, Clinical Translational Neuroscience Laboratory, School of Medicine, University of California, Irvine, CA, USA
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - Bhim M Adhikari
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Lars T Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | | | - Rex E Jung
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Jessica A Turner
- Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| |
Collapse
|
42
|
Sommerer Y, Dobricic V, Schilling M, Ohlei O, Bartrés-Faz D, Cattaneo G, Demuth I, Düzel S, Franzenburg S, Fuß J, Lindenberger U, Pascual-Leone Á, Sabet SS, Solé-Padullés C, Tormos JM, Vetter VM, Wesse T, Franke A, Lill CM, Bertram L. Epigenome-Wide Association Study in Peripheral Tissues Highlights DNA Methylation Profiles Associated with Episodic Memory Performance in Humans. Biomedicines 2022; 10:2798. [PMID: 36359320 PMCID: PMC9687249 DOI: 10.3390/biomedicines10112798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
Abstract
The decline in episodic memory (EM) performance is a hallmark of cognitive aging and an early clinical sign in Alzheimer’s disease (AD). In this study, we conducted an epigenome-wide association study (EWAS) using DNA methylation (DNAm) profiles from buccal and blood samples for cross-sectional (n = 1019) and longitudinal changes in EM performance (n = 626; average follow-up time 5.4 years) collected under the auspices of the Lifebrain consortium project. The mean age of participants with cross-sectional data was 69 ± 11 years (30−90 years), with 50% being females. We identified 21 loci showing suggestive evidence of association (p < 1 × 10−5) with either or both EM phenotypes. Among these were SNCA, SEPW1 (both cross-sectional EM), ITPK1 (longitudinal EM), and APBA2 (both EM traits), which have been linked to AD or Parkinson’s disease (PD) in previous work. While the EM phenotypes were nominally significantly (p < 0.05) associated with poly-epigenetic scores (PESs) using EWASs on general cognitive function, none remained significant after correction for multiple testing. Likewise, estimating the degree of “epigenetic age acceleration” did not reveal significant associations with either of the two tested EM phenotypes. In summary, our study highlights several interesting candidate loci in which differential DNAm patterns in peripheral tissue are associated with EM performance in humans.
Collapse
Affiliation(s)
- Yasmine Sommerer
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Marcel Schilling
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - David Bartrés-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Campus Clínic August Pi i Sunyer, Casanova, 143, 08036 Barcelona, Spain
| | - Gabriele Cattaneo
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Garcilaso, 57, 08027 Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Plaça Cívica, Bellaterra, 08193 Barcelona, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Camí de les Escoles, Badalona, 08916 Barcelona, Spain
| | - Ilja Demuth
- Biology of Aging Working Group, Department of Endocrinology and Metabolic Diseases, Division of Lipid Metabolism, Charité—Universitätsmedizin Berlin (corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health Center for Regenerative Therapies, Charité—Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Sören Franzenburg
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Janina Fuß
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Álvaro Pascual-Leone
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Garcilaso, 57, 08027 Barcelona, Spain
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, 1200 Centre St., Boston, MA 02131, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215, USA
| | - Sanaz Sedghpour Sabet
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Cristina Solé-Padullés
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Campus Clínic August Pi i Sunyer, Casanova, 143, 08036 Barcelona, Spain
| | - Josep M. Tormos
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Garcilaso, 57, 08027 Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Plaça Cívica, Bellaterra, 08193 Barcelona, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Camí de les Escoles, Badalona, 08916 Barcelona, Spain
| | - Valentin Max Vetter
- Biology of Aging Working Group, Department of Endocrinology and Metabolic Diseases, Division of Lipid Metabolism, Charité—Universitätsmedizin Berlin (corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Tanja Wesse
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Christina M. Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Institute of Epidemiology and Social Medicine, University of Münster, Domagkstr. 3, 48149 Münster, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, Charing Cross Hospital, St Dunstan's Road, London W68RP, UK
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Forskningsveien 3A, 0373 Oslo, Norway
| |
Collapse
|
43
|
Lahti J, Tuominen S, Yang Q, Pergola G, Ahmad S, Amin N, Armstrong NJ, Beiser A, Bey K, Bis JC, Boerwinkle E, Bressler J, Campbell A, Campbell H, Chen Q, Corley J, Cox SR, Davies G, De Jager PL, Derks EM, Faul JD, Fitzpatrick AL, Fohner AE, Ford I, Fornage M, Gerring Z, Grabe HJ, Grodstein F, Gudnason V, Simonsick E, Holliday EG, Joshi PK, Kajantie E, Kaprio J, Karell P, Kleineidam L, Knol MJ, Kochan NA, Kwok JB, Leber M, Lam M, Lee T, Li S, Loukola A, Luck T, Marioni RE, Mather KA, Medland S, Mirza SS, Nalls MA, Nho K, O'Donnell A, Oldmeadow C, Painter J, Pattie A, Reppermund S, Risacher SL, Rose RJ, Sadashivaiah V, Scholz M, Satizabal CL, Schofield PW, Schraut KE, Scott RJ, Simino J, Smith AV, Smith JA, Stott DJ, Surakka I, Teumer A, Thalamuthu A, Trompet S, Turner ST, van der Lee SJ, Villringer A, Völker U, Wilson RS, Wittfeld K, Vuoksimaa E, Xia R, Yaffe K, Yu L, Zare H, Zhao W, Ames D, Attia J, Bennett DA, Brodaty H, Chasman DI, Goldman AL, Hayward C, Ikram MA, Jukema JW, Kardia SLR, Lencz T, Loeffler M, Mattay VS, Palotie A, Psaty BM, Ramirez A, Ridker PM, Riedel-Heller SG, Sachdev PS, Saykin AJ, Scherer M, Schofield PR, Sidney S, Starr JM, Trollor J, Ulrich W, Wagner M, Weir DR, Wilson JF, Wright MJ, Weinberger DR, Debette S, Eriksson JG, Mosley TH, Launer LJ, van Duijn CM, Deary IJ, Seshadri S, Räikkönen K. Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning. Mol Psychiatry 2022; 27:4419-4431. [PMID: 35974141 PMCID: PMC9734053 DOI: 10.1038/s41380-022-01710-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022]
Abstract
Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.
Collapse
Affiliation(s)
- Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
- Turku Institute of Advanced Studies, University of Turku, Turku, Finland.
| | - Samuli Tuominen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Qiong Yang
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Nicola J Armstrong
- Department of Mathematics and Statistics, Murdoch University, Murdoch, WA, Australia
| | - Alexa Beiser
- Department of Biostatistics, Boston University, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Katharina Bey
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Janie Corley
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Gail Davies
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
| | - Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Annette L Fitzpatrick
- Department of Family Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Alison E Fohner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Institute of Public Health Genetics, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK
| | - Myriam Fornage
- McGovern Medical School, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zachary Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Greifswald, Germany
| | - Francine Grodstein
- Channing Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Harvard School of Public Health, Boston, MA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Assocation, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Eleanor Simonsick
- Translational Gerontology Branch, National Institute on Aging, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Elizabeth G Holliday
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Institute of Social and Preventive Medicine, University of Lausanne, Lausanne, Switzerland
| | - Eero Kajantie
- National Institute for Health and Welfare, Helsinki and Oulu, Oulu, Finland
- Hospital for Children and Adolescents, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Pauliina Karell
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases, Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - John B Kwok
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Markus Leber
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Max Lam
- Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Teresa Lee
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Shuo Li
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Anu Loukola
- Helsinki Biobank, University of Helsinki Central Hospital, Helsinki, Finland
| | - Tobias Luck
- Department of Economic and Social Sciences & Institute of Social Medicine, Rehabilitation Sciences and Healthcare Research, University of Applied Sciences Nordhausen, Nordhausen, Germany
- University of Leipzig, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, Leipzig, Germany
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Sunnybrook Health Sciences Centre, University of Toronto, Randwick, NSW, Australia
| | - Sarah Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Saira S Mirza
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adrienne O'Donnell
- Department of Biostatistics, Boston University, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Christopher Oldmeadow
- Clinical Research Design, IT and Statistical Support Unit, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Jodie Painter
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Alison Pattie
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Simone Reppermund
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Richard J Rose
- Department of Psychological & Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Vijay Sadashivaiah
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Claudia L Satizabal
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Peter W Schofield
- Neuropsychiatry Service, Hunter New England Local Health District, Charlestown, NSW, Australia
| | - Katharina E Schraut
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, Royal Infirmary of Edinburgh, University of Edinburgh, Edinburgh, UK
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Jeannette Simino
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Albert V Smith
- Icelandic Heart Assocation, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Institute of Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, USA
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Stella Trompet
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Sven J van der Lee
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, Department Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Robert S Wilson
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Greifswald, Germany
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Rui Xia
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kristine Yaffe
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Habil Zare
- Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas, San Antonio, TX, USA
- University of Texas Health Sciences Center, Houston, NA, US
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - David Ames
- National Ageing Research Institute, Parkville, Melbourne, VIC, Australia
- University of Melbourne, Academic Unit for Psychiatry of Old Age, St George's Hospital, Melbourne, VIC, Australia
| | - John Attia
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia
- Clinical Research Design, IT and Statistical Support Unit, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Dementia Collaborative Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Aaron L Goldman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Todd Lencz
- Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Venkata S Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Food and Drug Administration, Washington, DC, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology and Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Heath Research Institute, Seattle, WA, USA
| | - Alfredo Ramirez
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Steffi G Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin Scherer
- Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Peter R Schofield
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, NSW, Australia
| | - Stephen Sidney
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, USA
| | - John M Starr
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Julian Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - William Ulrich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Michael Wagner
- German Center for Neurodegenerative Diseases, Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephanie Debette
- Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, University of Bordeaux, Bordeaux, France
- Bordeaux University Hospital (CHU Bordeaux), Department of Neurology, Bordeaux, France
| | - Johan G Eriksson
- Folkhälsan Research Center, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki, and Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Helsinki, Singapore
| | - Thomas H Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Public Health, Oxford University, Oxford, UK
| | - Ian J Deary
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| |
Collapse
|
44
|
Ciobanu LG, Stankov L, Schubert KO, Amare AT, Jawahar MC, Lawrence-Wood E, Mills NT, Knight M, Clark SR, Aidman E. General intelligence and executive functioning are overlapping but separable at genetic and molecular pathway levels: An analytical review of existing GWAS findings. PLoS One 2022; 17:e0272368. [PMID: 36251633 PMCID: PMC9576059 DOI: 10.1371/journal.pone.0272368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 07/18/2022] [Indexed: 11/05/2022] Open
Abstract
Understanding the genomic architecture and molecular mechanisms of cognitive functioning in healthy individuals is critical for developing tailored interventions to enhance cognitive functioning, as well as for identifying targets for treating impaired cognition. There has been substantial progress in uncovering the genetic composition of the general cognitive ability (g). However, there is an ongoing debate whether executive functioning (EF)–another key predictor of cognitive health and performance, is separable from general g. To provide an analytical review on existing findings on genetic influences on the relationship between g and EF, we re-analysed a subset of genome-wide association studies (GWAS) from the GWAS catalogue that used measures of g and EF as outcomes in non-clinical populations. We identified two sets of single nucleotide polymorphisms (SNPs) associated with g (1,372 SNPs across 12 studies), and EF (300 SNPs across 5 studies) at p<5x10-6. A comparative analysis of GWAS-identified g and EF SNPs in high linkage disequilibrium (LD), followed by pathway enrichment analyses suggest that g and EF are overlapping but separable at genetic variant and molecular pathway levels, however more evidence is required to characterize the genetic overlap/distinction between the two constructs. While not without limitations, these findings may have implications for navigating further research towards translatable genetic findings for cognitive remediation, enhancement, and augmentation.
Collapse
Affiliation(s)
- Liliana G. Ciobanu
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- * E-mail:
| | - Lazar Stankov
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | - K. Oliver Schubert
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Northern Adelaide Mental Health Services, Adelaide, SA, Australia
| | - Azmeraw T. Amare
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Frailty and Healthy Ageing, University of Adelaide, Adelaide, Australia
| | | | | | - Natalie T. Mills
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Matthew Knight
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Weapons and Combat Systems Division, Defence Science & Technology Group, Edinburgh, SA, Australia
| | - Scott R. Clark
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Eugene Aidman
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
- School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Land Division, Defence Science & Technology Group, Edinburgh, SA, Australia
| |
Collapse
|
45
|
Park JY, Lengacher CA, Reich RR, Park HY, Whiting J, Nguyen AT, Rodríguez C, Meng H, Tinsley S, Chauca K, Gordillo-Casero L, Wittenberg T, Joshi A, Lin K, Ismail-Khan R, Kiluk JV, Kip KE. Translational Genomic Research: The Association between Genetic Profiles and Cognitive Functioning or Cardiac Function Among Breast Cancer Survivors Completing Chemotherapy. Biol Res Nurs 2022; 24:433-447. [PMID: 35499926 PMCID: PMC9630728 DOI: 10.1177/10998004221094386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Introduction: Emerging evidence suggests that Chemotherapy (CT) treated breast cancer survivors (BCS) who have "risk variants" in genes may be more susceptible to cognitive impairment (CI) and/or poor cardiac phenotypes. The objective of this preliminary study was to examine whether there is a relationship between genetic variants and objective/subjective cognitive or cardiac phenotypes. Methods and Analysis: BCS were recruited from Moffitt Cancer Center, Morsani College of Medicine, AdventHealth Tampa and Sarasota Memorial Hospital. Genomic DNA were collected at baseline for genotyping analysis. A total of 16 single nucleotide polymorphisms (SNPs) from 14 genes involved in cognitive or cardiac function were evaluated. Three genetic models (additive, dominant, and recessive) were used to test correlation coefficients between genetic variants and objective/subjective measures of cognitive functioning and cardiac outcomes (heart rate, diastolic blood pressure, systolic blood pressure, respiration rate, and oxygen saturation). Results: BCS (207 participants) with a mean age of 56 enrolled in this study. The majority were non-Hispanic white (73.7%), married (63.1%), and received both CT and radiation treatment (77.3%). Three SNPs in genes related to cognitive functioning (rs429358 in APOE, rs1800497 in ANKK1, rs10119 in TOMM40) emerged with the most consistent significant relationship with cognitive outcomes. Among five candidate SNPs related to cardiac functioning, rs8055236 in CDH13 and rs1801133 in MTHER emerged with potential significant relationships with cardiac phenotype. Conclusions: These preliminary results provide initial targets to further examine whether BCS with specific genetic profiles may preferentially benefit from interventions designed to improve cognitive and cardiac functioning following CT.
Collapse
Affiliation(s)
- Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Richard R. Reich
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Hyun Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Junmin Whiting
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Anh Thy Nguyen
- Department of Epidemiology and
Biostatistics, USF College of Public Health, University of South
Florida, Tampa, FL, USA
| | | | - Hongdao Meng
- School of Aging Studies, College of
Behavioral and Community Sciences, University of South
Floridaa, Tampa, FL, USA
| | - Sara Tinsley
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | | | - Anisha Joshi
- University of South Florida College
of Nursing, Tampa, FL, USA
| | - Katherine Lin
- University of South Florida College
of Nursing, Tampa, FL, USA
| | - Roohi Ismail-Khan
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - John V. Kiluk
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kevin E. Kip
- UPMC Health Services
Division, Pittsburgh, PA, USA
| |
Collapse
|
46
|
Gao Y, Felsky D, Reyes-Dumeyer D, Sariya S, Rentería MA, Ma Y, Klein HU, Cosentino S, De Jager PL, Bennett DA, Brickman AM, Schellenberg GD, Mayeux R, Barral S. Integration of GWAS and brain transcriptomic analyses in a multiethnic sample of 35,245 older adults identifies DCDC2 gene as predictor of episodic memory maintenance. Alzheimers Dement 2022; 18:1797-1811. [PMID: 34873813 PMCID: PMC9170841 DOI: 10.1002/alz.12524] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/03/2021] [Accepted: 10/12/2021] [Indexed: 01/28/2023]
Abstract
Identifying genes underlying memory function will help characterize cognitively resilient and high-risk declining subpopulations contributing to precision medicine strategies. We estimated episodic memory trajectories in 35,245 ethnically diverse older adults representing eight independent cohorts. We conducted apolipoprotein E (APOE)-stratified genome-wide association study (GWAS) analyses and combined individual cohorts' results via meta-analysis. Three independent transcriptomics datasets were used to further interpret GWAS signals. We identified DCDC2 gene significantly associated with episodic memory (Pmeta = 3.3 x 10-8 ) among non-carriers of APOE ε4 (N = 24,941). Brain transcriptomics revealed an association between episodic memory maintenance and (1) increased dorsolateral prefrontal cortex DCDC2 expression (P = 3.8 x 10-4 ) and (2) lower burden of pathological Alzheimer's disease (AD) hallmarks (paired helical fragment tau P = .003, and amyloid beta load P = .008). Additional transcriptomics results comparing AD and cognitively healthy brain samples showed a downregulation of DCDC2 levels in superior temporal gyrus (P = .007) and inferior frontal gyrus (P = .013). Our work identified DCDC2 gene as a novel predictor of memory maintenance.
Collapse
Affiliation(s)
- Yizhe Gao
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction
and Mental Health, Toronto, ON, Canada.,Department of Psychiatry & Institute of Medical
Science, University of Toronto, Toronto, ON, Canada
| | - Dolly Reyes-Dumeyer
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Sanjeev Sariya
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA
| | - Miguel Arce Rentería
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Yiyi Ma
- Center for Translational & Computational
Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center,
New York, NY, 10032, USA
| | - Hans-Ulrich Klein
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,Center for Translational & Computational
Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center,
New York, NY, 10032, USA
| | - Stephanie Cosentino
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Philip L. De Jager
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,Center for Translational & Computational
Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center,
New York, NY, 10032, USA.,Cell Circuits Program, Broad Institute, Cambridge, MA,
USA
| | - David A. Bennett
- Rush University Medical Center, Rush Alzheimer’s
Disease Center, Chicago, IL, USA.,Rush University Medical Center, Department of Neurological
Sciences, Chicago, IL, USA
| | - Adam M. Brickman
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Gerard D. Schellenberg
- Department of Pathology and Laboratory Medicine,
University of Pennsylvania, Philadelphia, PA, USA
| | - Richard Mayeux
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Sandra Barral
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | -
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| |
Collapse
|
47
|
Mattheisen M, Grove J, Als TD, Martin J, Voloudakis G, Meier S, Demontis D, Bendl J, Walters R, Carey CE, Rosengren A, Strom NI, Hauberg ME, Zeng B, Hoffman G, Zhang W, Bybjerg-Grauholm J, Bækvad-Hansen M, Agerbo E, Cormand B, Nordentoft M, Werge T, Mors O, Hougaard DM, Buxbaum JD, Faraone SV, Franke B, Dalsgaard S, Mortensen PB, Robinson EB, Roussos P, Neale BM, Daly MJ, Børglum AD. Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups. Nat Genet 2022; 54:1470-1478. [PMID: 36163277 PMCID: PMC10848300 DOI: 10.1038/s41588-022-01171-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 06/20/2022] [Indexed: 02/02/2023]
Abstract
Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.
Collapse
Affiliation(s)
- Manuel Mattheisen
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark.
- Department of Community Health and Epidemiology & Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.
| | - Jakob Grove
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Thomas D Als
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Joanna Martin
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sandra Meier
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- Department of Community Health and Epidemiology & Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raymond Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Caitlin E Carey
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anders Rosengren
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Services Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Nora I Strom
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Mads Engel Hauberg
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wen Zhang
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Marie Bækvad-Hansen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Bru Cormand
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain
- Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia, Spain
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
- Copenhagen Research Centre for Mental Health (CORE), Mental Health Centre Copenhagen, Copenhagen, Denmark
- University Hospital, Hellerup, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Services Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
- GLOBE Institute, Center for GeoGenetics, University of Copenhagen, Copenhagen, Denmark
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Joseph D Buxbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen V Faraone
- Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, NY, USA
- Department of Neuroscience and Physiology, State University of New York Upstate Medical University, Syracuse, NY, USA
| | - Barbara Franke
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Søren Dalsgaard
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Preben B Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Elise B Robinson
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, JJ Peters VA Medical Center, Bronx, NY, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Anders D Børglum
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
| |
Collapse
|
48
|
Mitchell BL, Hansell NK, McAloney K, Martin NG, Wright MJ, Renteria ME, Grasby KL. Polygenic influences associated with adolescent cognitive skills. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
49
|
Chio A, Moglia C, Canosa A, Manera U, Grassano M, Vasta R, Palumbo F, Gallone S, Brunetti M, Barberis M, De Marchi F, Dalgard C, Chia R, Mora G, Iazzolino B, Peotta L, Traynor B, Corrado L, D'Alfonso S, Mazzini L, Calvo A. Exploring the phenotype of Italian patients with ALS with intermediate ATXN2 polyQ repeats. J Neurol Neurosurg Psychiatry 2022; 93:jnnp-2022-329376. [PMID: 36008116 PMCID: PMC9606535 DOI: 10.1136/jnnp-2022-329376] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 07/12/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To detect the clinical characteristics of patients with amyotrophic lateral sclerosis (ALS) carrying an intermediate ATXN2 polyQ number of repeats in a large population-based series of Italian patients with ALS. METHODS The study population includes 1330 patients with ALS identified through the Piemonte and Valle d'Aosta Register for ALS, diagnosed between 2007 and 2019 and not carrying C9orf72, SOD1, TARDBP and FUS mutations. Controls were 1274 age, sex and geographically matched Italian subjects, identified through patients' general practitioners. RESULTS We found 42 cases and 4 controls with≥31 polyQ repeats, corresponding to an estimated OR of 10.4 (95% CI 3.3 to 29.0). Patients with≥31 polyQ repeats (ATXN2+) compared with those without repeat expansion (ATXN2-) had more frequently a spinal onset (p=0.05), a shorter diagnostic delay (p=0.004), a faster rate of ALSFRS-R progression (p=0.004) and King's progression (p=0.004), and comorbid frontotemporal dementia (7 (28.0%) vs 121 (13.4%), p=0.037). ATXN2+ patients had a 1-year shorter survival (ATXN2+ patients 1.82 years, 95% CI 1.08 to 2.51; ATXN2- 2.84 years, 95% CI 1.67 to 5.58, p=0.0001). ATXN2 polyQ intermediate repeats was independently related to a worse outcome in Cox multivariable analysis (p=0.006). CONCLUSIONS In our population-based cohort, ATXN2+ patients with ALS have a distinctive phenotype, characterised by a more rapid disease course and a shorter survival. In addition, ATXN2+ patients have a more severe impairment of cognitive functions. These findings have relevant implications on clinical practice, including the possibility of refining the individual prognostic prediction and improving the design of ALS clinical trials, in particular as regards as those targeted explicitly to ATXN2.
Collapse
Affiliation(s)
- Adriano Chio
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
- Neurology 1, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
| | - Cristina Moglia
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
- Neurology 1, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
| | - Antonio Canosa
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
- Neurology 1, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
| | - Umberto Manera
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
- Neurology 1, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
| | - Maurizio Grassano
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
| | - Rosario Vasta
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
| | - Francesca Palumbo
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
| | - Salvatore Gallone
- Neurology 1, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
| | - Maura Brunetti
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
| | - Marco Barberis
- Genetics, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
| | - Fabiola De Marchi
- Neurology, Azienda Ospedaliero-Universitaria Maggiore della Carità, Novara, Italy
| | - Clifton Dalgard
- Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Ruth Chia
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, NIH, Porter Neuroscience Research Center, Bethesda, Maryland, USA
| | - Gabriele Mora
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
| | - Barbara Iazzolino
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
| | - Laura Peotta
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
| | - Bryan Traynor
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, USA
- Department of Neurology, Johns Hopkins, Baltimore, Maryland, USA
| | - Lucia Corrado
- Department of Health Sciences Interdisciplinary Research Center of Autoimmune Diseases, University of Eastern Piedmont Amedeo Avogadro School of Medicine, Novara, Italy
| | - Sandra D'Alfonso
- Department of Health Sciences Interdisciplinary Research Center of Autoimmune Diseases, University of Eastern Piedmont Amedeo Avogadro School of Medicine, Novara, Italy
| | - Letizia Mazzini
- Neurology, Azienda Ospedaliero-Universitaria Maggiore della Carità, Novara, Italy
| | - Andrea Calvo
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Torino, Italy
- Neurology 1, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
| |
Collapse
|
50
|
Genetic variations in evolutionary accelerated regions disrupt cognition in schizophrenia. Psychiatry Res 2022; 314:114586. [PMID: 35623238 PMCID: PMC10150587 DOI: 10.1016/j.psychres.2022.114586] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 04/03/2022] [Accepted: 04/30/2022] [Indexed: 02/03/2023]
Abstract
Cognition is believed to be a product of human evolution, while schizophrenia is ascribed as the by-product with cognitive impairment as it's genetically mediated endophenotype. Genomic loci associated with these traits are enriched with recent evolutionary markers such as Human accelerated regions (HARs). HARs are markedly different in humans since their divergence with chimpanzees and mostly regulate gene expression by binding to transcription factors and/or modulating chromatin interactions. We hypothesize that variants within HARs may alter such functions and thus contribute to disease pathogenesis. 49 systematically prioritized variants from 2737 genome-wide HARs were genotyped in a north-Indian schizophrenia cohort (331 cases, 235 controls). Six variants were significantly associated with cognitive impairment in schizophrenia, thirteen with general cognition in healthy individuals. These variants were mapped to 122 genes; predicted to alter 79 transcription factors binding sites and overlapped with promoters, enhancers and/or repressors. These genes and TFs are implicated in neurocognitive phenotypes, autism, schizophrenia and bipolar disorders; a few are targets of common or repurposable antipsychotics suggesting their draggability; and enriched for immune response and brain developmental pathways. Immune response has been more strongly targeted by natural selection during human evolution and has a prominent role in neurodevelopment. Thus, its disruption may have deleterious consequences for neuronal and cognitive functions. Importantly, among the 15 associated SNPs, 12 showed association in several independent GWASs of different neurocognitive functions. Further analysis of HARs may be valuable to understand their role in cognition biology and identify improved therapeutics for schizophrenia.
Collapse
|