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Jiang Z, Sullivan PF, Li T, Zhao B, Wang X, Luo T, Huang S, Guan PY, Chen J, Yang Y, Stein JL, Li Y, Liu D, Sun L, Zhu H. The pivotal role of the X-chromosome in the genetic architecture of the human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.30.23294848. [PMID: 37693466 PMCID: PMC10491353 DOI: 10.1101/2023.08.30.23294848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.
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Genius P, Calle ML, Rodríguez-Fernández B, Minguillon C, Cacciaglia R, Garrido-Martin D, Esteller M, Navarro A, Gispert JD, Vilor-Tejedor N. Compositional structural brain signatures capture Alzheimer's genetic risk on brain structure along the disease continuum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.08.24307046. [PMID: 38766190 PMCID: PMC11100942 DOI: 10.1101/2024.05.08.24307046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Traditional brain imaging genetics studies have primarily focused on how genetic factors influence the volume of specific brain regions, often neglecting the overall complexity of brain architecture and its genetic underpinnings. METHODS This study analyzed data from participants across the Alzheimer's disease (AD) continuum from the ALFA and ADNI studies. We exploited compositional data analysis to examine relative brain volumetric variations that (i) differentiate cognitively unimpaired (CU) individuals, defined as amyloid-negative (A-) based on CSF profiling, from those at different AD stages, and (ii) associated with increased genetic susceptibility to AD, assessed using polygenic risk scores. RESULTS Distinct brain signatures differentiated CU A-individuals from amyloid-positive MCI and AD. Moreover, disease stage-specific signatures were associated with higher genetic risk of AD. DISCUSSION The findings underscore the complex interplay between genetics and disease stages in shaping brain structure, which could inform targeted preventive strategies and interventions in preclinical AD.
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Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. Data leakage inflates prediction performance in connectome-based machine learning models. Nat Commun 2024; 15:1829. [PMID: 38418819 PMCID: PMC10901797 DOI: 10.1038/s41467-024-46150-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage-involving feature selection, covariate correction, and dependence between subjects-on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, USA
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Coffman C, Feczko E, Larsen B, Tervo-Clemmens B, Conan G, Lundquist JT, Houghton A, Moore LA, Weldon K, McCollum R, Perrone AJ, Fayzullobekova B, Madison TJ, Earl E, Dominguez OM, Fair DA, Basu S. Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575231. [PMID: 38260520 PMCID: PMC10802572 DOI: 10.1101/2024.01.11.575231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (> 50%).
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Affiliation(s)
- Christian Coffman
- Division of Biostatistics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Bart Larsen
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Brenden Tervo-Clemmens
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Gregory Conan
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Jacob T. Lundquist
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Audrey Houghton
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Lucille A. Moore
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Kimberly Weldon
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Rae McCollum
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Anders J. Perrone
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Begim Fayzullobekova
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Thomas J. Madison
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Eric Earl
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Oscar Miranda Dominguez
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Damien A. Fair
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
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Lahey BB, Durham EL, Brislin SJ, Barr PB, Dick DM, Moore TM, Pierce BL, Tong L, Reimann GE, Jeong HJ, Dupont RM, Kaczkurkin AN. Mapping potential pathways from polygenic liability through brain structure to psychological problems across the transition to adolescence. J Child Psychol Psychiatry 2024. [PMID: 38185921 DOI: 10.1111/jcpp.13944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/08/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition. METHODS Three annual assessments of child conduct problems, attention-deficit/hyperactivity problems, and internalizing problems were conducted across across 9-13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®). RESULTS The extPGS predicted conduct problems in each wave (R2 = 2.0%-2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems (R2 = 1.7%-2.1%), but also variance that conduct problems shared with other measured problems (R2 = .8%-1.4%). Longitudinally, extPGS predicted levels of specific conduct problems (R2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R2 = .4%) and lower TGMV predicted both specific conduct problems (R2 = 1.7%-2.1%) and the variance common to all problems in each wave (R2 = 1.6%-3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV. CONCLUSIONS Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis.
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Affiliation(s)
| | | | | | - Peter B Barr
- SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | | | | | | | - Lin Tong
- University of Chicago, Chicago, IL, USA
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Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. The effects of data leakage on connectome-based machine learning models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.09.544383. [PMID: 38234740 PMCID: PMC10793416 DOI: 10.1101/2023.06.09.544383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging modalities, functional and structural connectomes. Using over 400 different pipelines spanning four large datasets and three phenotypes, we evaluated five forms of leakage fitting into three broad categories: feature selection, covariate correction, and lack of independence between subjects. As expected, leakage via feature selection and repeated subjects drastically inflated prediction performance. Notably, other forms of leakage had only minor effects (e.g., leaky site correction) or even decreased prediction performance (e.g., leaky covariate regression). In some cases, leakage affected not only prediction performance, but also model coefficients, and thus neurobiological interpretations. Finally, we found that predictive models using small datasets were more sensitive to leakage. Overall, our results illustrate the variable effects of leakage on prediction pipelines and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.
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Affiliation(s)
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Department of Bioengineering, Northeastern University, Boston, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Statistics & Data Science, Yale University, New Haven, CT
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Li T, Zhu H, Li T, Zhu H. Asynchronous functional linear regression models for longitudinal data in reproducing kernel Hilbert space. Biometrics 2023; 79:1880-1895. [PMID: 36205584 DOI: 10.1111/biom.13767] [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: 06/30/2021] [Accepted: 09/21/2022] [Indexed: 11/02/2022]
Abstract
Motivated by the analysis of longitudinal neuroimaging studies, we study the longitudinal functional linear regression model under asynchronous data setting for modeling the association between clinical outcomes and functional (or imaging) covariates. In the asynchronous data setting, both covariates and responses may be measured at irregular and mismatched time points, posing methodological challenges to existing statistical methods. We develop a kernel weighted loss function with roughness penalty to obtain the functional estimator and derive its representer theorem. The rate of convergence, a Bahadur representation, and the asymptotic pointwise distribution of the functional estimator are obtained under the reproducing kernel Hilbert space framework. We propose a penalized likelihood ratio test to test the nullity of the functional coefficient, derive its asymptotic distribution under the null hypothesis, and investigate the separation rate under the alternative hypotheses. Simulation studies are conducted to examine the finite-sample performance of the proposed procedure. We apply the proposed methods to the analysis of multitype data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, which reveals significant association between 21 regional brain volume density curves and the cognitive function. Data used in preparation of this paper were obtained from the ADNI database (adni.loni.usc.edu).
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Affiliation(s)
- Ting Li
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
- Shanghai Institute of International Finance and Economics, Shanghai University of Finance and Economics, Shanghai, China
| | - Huichen Zhu
- Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [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] [Indexed: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Fürtjes AE, Arathimos R, Coleman JRI, Cole JH, Cox SR, Deary IJ, de la Fuente J, Madole JW, Tucker‐Drob EM, Ritchie SJ. General dimensions of human brain morphometry inferred from genome-wide association data. Hum Brain Mapp 2023; 44:3311-3323. [PMID: 36987996 PMCID: PMC10171533 DOI: 10.1002/hbm.26283] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2 = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.
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Affiliation(s)
- Anna E. Fürtjes
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
| | - Ryan Arathimos
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - James H. Cole
- Department of NeuroimageingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonWC1V 6LJUK
- Dementia Research CentreInstitute of Neurology, University College LondonLondonWC1N 3BGUK
| | - Simon R. Cox
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Ian J. Deary
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Javier de la Fuente
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - James W. Madole
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Stuart J. Ritchie
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
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Lee Y, Park JY, Lee JJ, Gim J, Do AR, Jo J, Park J, Kim K, Park K, Jin H, Choi KY, Kang S, Kim H, Kim S, Moon SH, Farrer LA, Lee KH, Won S. Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians. Cereb Cortex 2023; 33:6051-6062. [PMID: 36642501 PMCID: PMC10183741 DOI: 10.1093/cercor/bhac483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 01/17/2023] Open
Abstract
This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.
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Affiliation(s)
- Younghwa Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Jun Young Park
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Jang Jae Lee
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
| | - Jungsoo Gim
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Biomedical Science, Chosun University, Gwangju, Korea
| | - Ah Ra Do
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Juhong Park
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Kangjin Kim
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Kyungtaek Park
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Heejin Jin
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
| | - Sarang Kang
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
| | - Hoowon Kim
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Neurology, Chosun University Hospital, Gwangju, Korea
| | - SangYun Kim
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Seoul, Korea
| | - Lindsay A Farrer
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Kun Ho Lee
- Gwangju Alzheimer’s Disease & Related Dementia Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Biomedical Science, Chosun University, Gwangju, Korea
- Dementia Research Group, Korea Brain Research Institute, Daegu, Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
- RexSoft Inc., Seoul, Korea
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11
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Arenella M, Mota NR, Teunissen MWA, Brunner HG, Bralten J. Autism spectrum disorder and brain volume link through a set of mTOR-related genes. J Child Psychol Psychiatry 2023. [PMID: 36922714 DOI: 10.1111/jcpp.13783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/06/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Larger than average head and brain sizes are often observed in individuals with autism spectrum disorders (ASDs). ASD and brain volume are both highly heritable, with multiple genetic variants contributing. However, it is unclear whether ASD and brain volume share any genetic mechanisms. Genes from the mammalian target of rapamycin (mTOR) pathway influence brain volume, and variants are found in rare genetic syndromes that include ASD features. Here we investigated whether variants in mTOR-related genes are also associated with ASD and if they constitute a genetic link between large brains and ASD. METHODS We extended our analyses between large heads (macrocephaly) and rare de novo mTOR-related variants in an intellectual disability cohort (N = 2,258). Subsequently using Fisher's exact tests we investigated the co-occurrence of mTOR-related de novo variants and ASD in the de-novo-db database (N = 23,098). We next selected common genetic variants within a set of 96 mTOR-related genes in genome-wide genetic association data of ASD (N = 46,350) to test gene-set association using MAGMA. Lastly, we tested genetic correlation between genome-wide genetic association data of ASD (N = 46,350) and intracranial volume (N = 25,974) globally using linkage disequilibrium score regression as well as mTOR specific by restricting the genetic correlation to the mTOR-related genes using GNOVA. RESULTS Our results show that both macrocephaly and ASD occur above chance level in individuals carrying rare de novo variants in mTOR-related genes. We found a significant mTOR gene-set association with ASD (p = .0029) and an mTOR-stratified positive genetic correlation between ASD and intracranial volume (p = .027), despite the absence of a significant genome-wide correlation (p = .81). CONCLUSIONS This work indicates that both rare and common variants in mTOR-related genes are associated with brain volume and ASD and genetically correlate them in the expected direction. We demonstrate that genes involved in mTOR signalling are potential mediators of the relationship between having a large brain and having ASD.
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Affiliation(s)
- Martina Arenella
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Nina R Mota
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Mariel W A Teunissen
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Department of Neurology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Han G Brunner
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Department of Clinical Genetics, Maastricht University Medical Centre, Maastricht, The Netherlands.,GROW School of Development and Oncology, MHENS School of Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Janita Bralten
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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12
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Cabeen RP, Toga AW, Allman JM. Mapping frontoinsular cortex from diffusion microstructure. Cereb Cortex 2023; 33:2715-2733. [PMID: 35753692 PMCID: PMC10016069 DOI: 10.1093/cercor/bhac237] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 11/13/2022] Open
Abstract
We developed a novel method for mapping the location, surface area, thickness, and volume of frontoinsular cortex (FI) using structural and diffusion magnetic resonance imaging. FI lies in the ventral part of anterior insular cortex and is characterized by its distinctive population von Economo neurons (VENs). Functional neuroimaging studies have revealed its involvement in affective processing, and histopathology has implicated VEN loss in behavioral-variant frontotemporal dementia and chronic alcoholism; however, structural neuroimaging of FI has been relatively limited. We delineated FI by jointly modeling cortical surface geometry and its coincident diffusion microstructure parameters. We found that neurite orientation dispersion in cortical gray matter can be used to map FI in specific individuals, and the derived measures reflect a range of behavioral factors in young adults from the Human Connectome Project (N=1052). FI volume was larger in the left hemisphere than the right (31%), and the percentage volume of FI was larger in women than men (15.3%). FI volume was associated with measures of decision-making (delay discounting, substance abuse), emotion (negative intrusive thinking and perception of hostility), and social behavior (theory of mind and working memory for faces). The common denominator is that larger FI size is related to greater self-control and social awareness.
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Affiliation(s)
- Ryan P Cabeen
- Laboratory of Neuro Imaging, USC Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, United States
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, United States
| | - John M Allman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States
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13
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Jamshidi J, Park HRP, Montalto A, Fullerton JM, Gatt JM. Wellbeing and brain structure: A comprehensive phenotypic and genetic study of image-derived phenotypes in the UK Biobank. Hum Brain Mapp 2022; 43:5180-5193. [PMID: 35765890 PMCID: PMC9812238 DOI: 10.1002/hbm.25993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 01/15/2023] Open
Abstract
Wellbeing, an important component of mental health, is influenced by genetic and environmental factors. Previous association studies between brain structure and wellbeing have typically focused on volumetric measures and employed small cohorts. Using the UK Biobank Resource, we explored the relationships between wellbeing and brain morphometrics (volume, thickness and surface area) at both phenotypic and genetic levels. The sample comprised 38,982 participants with neuroimaging and wellbeing phenotype data, of which 19,234 had genotypes from which wellbeing polygenic scores (PGS) were calculated. We examined the association of wellbeing phenotype and PGS with all brain regions (including cortical, subcortical, brainstem and cerebellar regions) using multiple linear models, including (1) basic neuroimaging covariates and (2) additional demographic factors that may synergistically impact wellbeing and its neural correlates. Genetic correlations between genomic variants influencing wellbeing and brain structure were also investigated. Small but significant associations between wellbeing and volumes of several cerebellar structures (β = 0.015-0.029, PFDR = 0.007-3.8 × 10-9 ), brainstem, nucleus accumbens and caudate were found. Cortical associations with wellbeing included volume of right lateral occipital, thickness of bilateral lateral occipital and cuneus, and surface area of left superior parietal, supramarginal and pre-/post-central regions. Wellbeing-PGS was associated with cerebellar volumes and supramarginal surface area. Small mediation effects of wellbeing phenotype and PGS on right VIIIb cerebellum were evident. No genetic correlation was found between wellbeing and brain morphometric measures. We provide a comprehensive overview of wellbeing-related brain morphometric variation. Notably, small effect sizes reflect the multifaceted nature of this concept.
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Affiliation(s)
- Javad Jamshidi
- Neuroscience Research Australia, Sydney, New South Wales, Australia.,School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
| | - Haeme R P Park
- Neuroscience Research Australia, Sydney, New South Wales, Australia.,School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
| | - Arthur Montalto
- Neuroscience Research Australia, Sydney, New South Wales, Australia.,School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
| | - Janice M Fullerton
- Neuroscience Research Australia, Sydney, New South Wales, Australia.,School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Justine M Gatt
- Neuroscience Research Australia, Sydney, New South Wales, Australia.,School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
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14
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Bayrak Ş, de Wael RV, Schaare HL, Hettwer MD, Caldairou B, Bernasconi A, Bernasconi N, Bernhardt BC, Valk SL. Heritability of hippocampal functional and microstructural organisation. Neuroimage 2022; 264:119656. [PMID: 36183945 DOI: 10.1016/j.neuroimage.2022.119656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/18/2022] [Accepted: 09/28/2022] [Indexed: 01/07/2023] Open
Abstract
The hippocampus is a uniquely infolded allocortical structure in the medial temporal lobe that consists of the microstructurally and functionally distinct subregions: subiculum, cornu ammonis, and dentate gyrus. The hippocampus is a remarkably plastic region that is implicated in learning and memory. At the same time it has been shown that hippocampal subregion volumes are heritable, and that genetic expression varies along a posterior to anterior axis. Here, we studied how a heritable, stable, hippocampal organisation may support its flexible function in healthy adults. Leveraging the twin set-up of the Human Connectome Project with multimodal neuroimaging, we observed that the functional connectivity between hippocampus and cortex was heritable and that microstructure of the hippocampus genetically correlated with cortical microstructure. Moreover, both functional and microstructural organisation could be consistently captured by anterior-to-posterior and medial-to-lateral axes across individuals. However, heritability of functional, relative to microstructural, organisation was found reduced, suggesting individual variation in functional organisation may be explained by experience-driven factors. Last, we demonstrate that structure and function couple along an inherited macroscale organisation, suggesting an interplay of stability and plasticity within the hippocampus. Our study provides new insights on the heritability of the hippocampal of the structure and function within the hippocampal organisation.
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Affiliation(s)
- Şeyma Bayrak
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Cognitive Neurology, University Hospital Leipzig and Faculty of Medicine, University of Leipzig, Leipzig, Germany.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Meike D Hettwer
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Max Planck School of Cognition, Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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15
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Biswas D, Kumari N, Lachén-Montes M, Dutta S, Agrawal I, Samanta D, Shenoy SV, Halder A, Fernández-Irigoyen J, Padhye AR, Santamaría E, Srivastava S. Deep Phosphoproteome Landscape of Interhemispheric Functionality of Neuroanatomical Regions of the Human Brain. J Proteome Res 2022; 22:1043-1055. [PMID: 36317652 DOI: 10.1021/acs.jproteome.2c00244] [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] [Indexed: 03/03/2023]
Abstract
Post-translational modifications (PTMs) are one of the compulsive and predominant biological processes that regulate the diverse molecular mechanism, modulate the onset of disease, and are the reason behind the functional diversity of proteins. Despite the widespread research findings in neuroproteomics, one of the key drawbacks has been the lack of proteome-level knowledge of hemispheric lateralization. We have investigated the proteome level expression in different neuroanatomical regions under the Human Brain Proteome Project (HBPP) and developed the global interhemispheric brain proteome map (Brainprot) earlier. Furthermore, this study has extended to decipher the phosphoproteome map of human brain interhemispheric regions through high-resolution mass spectrometry. The phosphoproteomics examination of 12 unique interhemispheric neurological brain regions using Orbitrap fusion liquid chromatography with tandem mass spectrometry provided comprehensive coverage of 996 phosphoproteins, 2010 phosphopeptides, and 3567 phosphosites. Moreover, interhemispheric phosphoproteome profiling has been categorized according to synaptic ontologies and interhemispheric expression to understand the functionality. Finally, we have integrated the phosphosites data under the PhosphoMap section in the Inter-Hemispheric Brain Proteome Map Portal (https://www.brainprot.org/) for the advancement and support of the ongoing neuroproteomics research worldwide. Data is available via ProteomeXchange with the identifier PXD031188.
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Affiliation(s)
- Deeptarup Biswas
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai400076, India
| | - Neha Kumari
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai400076, India
| | - Mercedes Lachén-Montes
- Clinical Neuroproteomics Unit, Proteomics Platform, Proteored-ISCIII, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Navarra Institute for Health Research (IdiSNA), 31008Pamplona, Spain
| | - Sampurna Dutta
- School of Biological Sciences, Indian Association for the Cultivation of Science, 2B, Raja S. C. Mullick Road, Jadavpur, Kolkata700032, India
| | - Ishita Agrawal
- Department of Molecular and Human Genetics, Banaras Hindu University, Varanasi221005, India
| | - Debabrata Samanta
- Department of Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal721302, India
| | - Sanjyot Vinayak Shenoy
- Department of Mathematics, Indian Institute of Technology Bombay, Powai, Mumbai400076, India
| | - Ankit Halder
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai400076, India
| | - Joaquín Fernández-Irigoyen
- Clinical Neuroproteomics Unit, Proteomics Platform, Proteored-ISCIII, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Navarra Institute for Health Research (IdiSNA), 31008Pamplona, Spain
| | - Advait Rahul Padhye
- Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai400076, India
| | - Enrique Santamaría
- Clinical Neuroproteomics Unit, Proteomics Platform, Proteored-ISCIII, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Navarra Institute for Health Research (IdiSNA), 31008Pamplona, Spain
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai400076, India
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16
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Mai H, Bao J, Thompson PM, Kim D, Shen L. Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data. BMC Bioinformatics 2022; 23:398. [PMID: 36171548 PMCID: PMC9520794 DOI: 10.1186/s12859-022-04947-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms and complex traits like brain volume. However, it is unclear how these genetic variations influence regional gene expression levels, which may subsequently lead to phenotypic changes. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics initiatives, the UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis, to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV). RESULTS As a result of the analysis, we identified 10 genes that are highly associated with both TBV and ICV. Nine out of 10 genes were found to be associated with TBV in another study using a different gene-based association analysis. Moreover, most of our discovered genes were also found to be correlated with multiple cognitive and behavioral traits. Further analyses revealed the protein-protein interactions, associated molecular pathways and biological functions that offer insight into how these genes function and interact with others. CONCLUSIONS These results confirm that S-PrediXcan can identify genes with tissue-specific transcriptomic effects on complex traits. The analysis also suggested novel genes whose expression levels are related to brain volumetric traits. This provides important insights into the genetic mechanisms of the human brain.
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Affiliation(s)
- Hung Mai
- Perelman School of Medicine, University of Pennsylvania, B306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, USA
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Perelman School of Medicine, University of Pennsylvania, B306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, USA
- School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dokyoon Kim
- Perelman School of Medicine, University of Pennsylvania, B306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, USA
| | - Li Shen
- Perelman School of Medicine, University of Pennsylvania, B306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, USA.
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17
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Janahi M, Aksman L, Schott JM, Mokrab Y, Altmann A. Nomograms of human hippocampal volume shifted by polygenic scores. eLife 2022; 11:e78232. [PMID: 35938915 PMCID: PMC9391046 DOI: 10.7554/elife.78232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/06/2022] [Indexed: 11/25/2022] Open
Abstract
Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients. While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity.
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Affiliation(s)
- Mohammed Janahi
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
- Medical and Population Genomics Lab, Human Genetics Department, Research Branch, Sidra MedicineDohaQatar
| | - Leon Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Jonathan M Schott
- Dementia Research Centre (DRC), Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Younes Mokrab
- Medical and Population Genomics Lab, Human Genetics Department, Research Branch, Sidra MedicineDohaQatar
- Department of Genetic Medicine, Weill Cornell Medicine-QatarDohaQatar
| | - Andre Altmann
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
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18
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A cognitive neurogenetic approach to uncovering the structure of executive functions. Nat Commun 2022; 13:4588. [PMID: 35933428 PMCID: PMC9357028 DOI: 10.1038/s41467-022-32383-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 07/27/2022] [Indexed: 11/08/2022] Open
Abstract
One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.
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19
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Probing individual-level structural atrophy in frontal glioma patients. Neurosurg Rev 2022; 45:2845-2855. [PMID: 35508819 DOI: 10.1007/s10143-022-01800-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 10/18/2022]
Abstract
Although every glioma patient varies in tumor size, location, histological grade and molecular biomarkers, non-tumoral morphological abnormalities are commonly detected by a statistical comparison among patient groups, missing the information of individual morphological alterations. In this study, we introduced an individual-level structural abnormality detection method for glioma patients and proposed several abnormality indexes to depict individual atrophy patterns. Forty-five patients with a glioma in the frontal lobe and fifty-one age-matched healthy controls participated in the study. Individual structural abnormality maps (SAM) were generated using patients' preoperative T1 images, by calculating the degree of deviation of voxel volume in each patient with the normative model built from healthy controls. Based on SAM, a series of individual abnormality indexes were computed, and their relationship with glioma characteristics was explored. The results demonstrated that glioma patients showed unique non-tumoral atrophy patterns with overlapping atrophy regions mainly located at hippocampus, parahippocampus, amygdala, insula, middle temporal gyrus and inferior temporal gyrus, which are closely related to the human cognitive functions. The abnormality indexes were associated with several molecular biomarkers including isocitrate dehydrogenase (IDH) mutation, 1p/19q co-deletion and telomerase reverse transcriptase (TERT) promoter mutation. Our study provides an effective way to access the individual-level non-tumoral structural abnormalities in glioma patients, which has the potential to significantly improve individualized precision medicine.
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20
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Kang J, Jia T, Jiao Z, Shen C, Xie C, Cheng W, Sahakian BJ, Waxman D, Feng J. Increased brain volume from higher cereal and lower coffee intake: shared genetic determinants and impacts on cognition and metabolism. Cereb Cortex 2022; 32:5163-5174. [PMID: 35136970 PMCID: PMC9383440 DOI: 10.1093/cercor/bhac005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 12/27/2022] Open
Abstract
It is unclear how different diets may affect human brain development and if genetic and environmental factors play a part. We investigated diet effects in the UK Biobank data from 18,879 healthy adults and discovered anticorrelated brain-wide gray matter volume (GMV)-association patterns between coffee and cereal intake, coincidence with their anticorrelated genetic constructs. The Mendelian randomization approach further indicated a causal effect of higher coffee intake on reduced total GMV, which is likely through regulating the expression of genes responsible for synaptic development in the brain. The identified genetic factors may further affect people's lifestyle habits and body/blood fat levels through the mediation of cereal/coffee intake, and the brain-wide expression pattern of gene CPLX3, a dedicated marker of subplate neurons that regulate cortical development and plasticity, may underlie the shared GMV-association patterns among the coffee/cereal intake and cognitive functions. All the main findings were successfully replicated. Our findings thus revealed that high-cereal and low-coffee diets shared similar brain and genetic constructs, leading to long-term beneficial associations regarding cognitive, body mass index (BMI), and other metabolic measures. This study has important implications for public health, especially during the pandemic, given the poorer outcomes of COVID-19 patients with greater BMIs.
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Affiliation(s)
| | - Tianye Jia
- Corresponding author: Jianfeng Feng, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China, ; Tianye Jia, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China. ; Barbara J. Sahakian, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, United Kingdom.
| | - Zeyu Jiao
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Fudan, Shanghai 200433, China
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Fudan, Shanghai 200433, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Fudan, Shanghai 200433, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Fudan, Shanghai 200433, China
| | - Barbara J Sahakian
- Corresponding author: Jianfeng Feng, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China, ; Tianye Jia, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China. ; Barbara J. Sahakian, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, United Kingdom.
| | - David Waxman
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Fudan, Shanghai 200433, China
| | - Jianfeng Feng
- Corresponding author: Jianfeng Feng, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China, ; Tianye Jia, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China. ; Barbara J. Sahakian, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, United Kingdom.
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21
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Hernandez LM, Kim M, Hernandez C, Thompson W, Fan CC, Galván A, Dapretto M, Bookheimer SY, Fuligni A, Gandal MJ. Decoupling Sleep and Brain Size in Childhood: An Investigation of Genetic Covariation in the Adolescent Brain Cognitive Development Study. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 3:139-148. [PMID: 36712562 PMCID: PMC9874135 DOI: 10.1016/j.bpsgos.2021.12.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/17/2021] [Accepted: 12/22/2021] [Indexed: 02/01/2023] Open
Abstract
Background Childhood sleep problems are common and among the most frequent and impairing comorbidities of childhood psychiatric disorders. In adults, sleep disturbances are heritable and show strong genetic associations with brain morphology; however, little is known about the genetic architecture of childhood sleep and potential etiological links between sleep, brain development, and pediatric-onset psychiatric symptoms. Methods Using data from the Adolescent Brain Cognitive Development Study (n Phenotype = 4428 for discovery/replication, n Genetics = 4728; age 9-10 years), we assessed phenotypic relationships, heritability, and genetic correlations between childhood sleep disturbances (insomnia, arousal, breathing, somnolence, hyperhidrosis, sleep-wake transitions), brain size (surface area, cortical thickness, volume), and dimensional psychopathology. Results Sleep disturbances showed widespread positive associations with multiple domains of childhood psychopathology; however, only insomnia showed replicable associations with smaller brain surface area. Among the sleep disturbances assessed, only insomnia showed significant heritability (h 2 SNP = 0.15, p < .05) and showed substantial genetic correlations with externalizing and attention-deficit/hyperactivity disorder symptomatology (r G s > 0.80, ps < .05). We found no evidence of genetic correlation between childhood insomnia and brain size. Furthermore, polygenic risk scores calculated from genome-wide association studies of adult insomnia and adult brain size did not predict childhood insomnia; instead, polygenic risk scores trained using attention-deficit/hyperactivity disorder genome-wide association studies predicted decreased surface area at baseline as well as insomnia and externalizing symptoms longitudinally. Conclusions Findings demonstrate a distinct genetic architecture underlying childhood insomnia and brain size and suggest genetic overlap between childhood insomnia and attention-deficit/hyperactivity disorder symptomatology. Additional research is needed to examine how genetic risk manifests in altered developmental trajectories and comorbid sleep/psychiatric symptoms across adolescence.
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Affiliation(s)
- Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Leanna Hernandez, Ph.D.
| | - Minsoo Kim
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Cristian Hernandez
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Wesley Thompson
- Department of Family Medicine and Public Health, University of California San Diego, San Diego, California
| | - Chun Chieh Fan
- Center for Human Development, University of California San Diego, San Diego, California
| | - Adriana Galván
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, California
| | - Susan Y. Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Staglin IMHRO Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, California
| | - Andrew Fuligni
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Department of Psychology, University of California, Los Angeles, Los Angeles, California
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California,Address correspondence to Michael J. Gandal, M.D., Ph.D.
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22
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Friedman NP, Banich MT, Keller MC. Twin studies to GWAS: there and back again. Trends Cogn Sci 2021; 25:855-869. [PMID: 34312064 PMCID: PMC8446317 DOI: 10.1016/j.tics.2021.06.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 01/01/2023]
Abstract
The field of human behavioral genetics has come full circle. It began by using twin/family studies to estimate the relative importance of genetic and environmental influences. As large-scale genotyping became cost-effective, genome-wide association studies (GWASs) yielded insights about the nature of genetic influences and new methods that use GWAS data to estimate heritability and genetic correlations invigorated the field. Yet these newer GWAS methods have not replaced twin/family studies. In this review, we discuss the strengths and weaknesses of the two approaches with respect to characterizing genetic and environmental influences, measurement of behavioral phenotypes, and evaluation of causal models, with a particular focus on cognitive neuroscience. This discussion highlights how twin/family studies and GWAS complement and mutually reinforce one another.
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Affiliation(s)
- Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA.
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Matthew C Keller
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
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23
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Zhao Y, Zhao X, Kim M, Bao J, Shen L. A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12905:678-687. [PMID: 35299630 PMCID: PMC8922551 DOI: 10.1007/978-3-030-87240-3_65] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Heritability analysis is an important research topic in brain imaging genetics. Its primary motivation is to identify highly heritable imaging quantitative traits (QTs) for subsequent in-depth imaging genetic analyses. Most existing studies perform heritability analyses on regional imaging QTs using predefined brain parcellation schemes such as the AAL atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion is largely deteriorate with inner partition noise and signal dilution. To bridge the gap, we propose a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs. Our method leverages the aggregate of genetic signals to imaging QT construction by developing a new brain parcellation driven by voxel-level heritability. To ensure biological plausibility and clinical interpretability of the resulting brain heritability parcellations, hierarchical sparsity and smoothness, coupled with structural connectivity of the brain, are properly imposed on genetic effects to induce spatial contiguity of heritable imaging QTs. Using the ADNI imaging genetic data, we demonstrate the strength of our proposed method, in comparison with the standard GCTA method, in identifying highly heritable and biologically meaningful new imaging QTs.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Xiwen Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Mansu Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
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24
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Biton A, Traut N, Poline JB, Aribisala BS, Bastin ME, Bülow R, Cox SR, Deary IJ, Fukunaga M, Grabe HJ, Hagenaars S, Hashimoto R, Kikuchi M, Muñoz Maniega S, Nauck M, Royle NA, Teumer A, Valdés Hernández M, Völker U, Wardlaw JM, Wittfeld K, Yamamori H, Bourgeron T, Toro R. Polygenic Architecture of Human Neuroanatomical Diversity. Cereb Cortex 2021; 30:2307-2320. [PMID: 32109272 DOI: 10.1093/cercor/bhz241] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 01/15/2023] Open
Abstract
We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.
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Affiliation(s)
- Anne Biton
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France.,Hub de Bioinformatique et Biostatistique-Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris 75015, France
| | - Nicolas Traut
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France
| | - Jean-Baptiste Poline
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Benjamin S Aribisala
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Department of Computer Science, Lagos State University, Lagos, 102101, Nigeria
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Robin Bülow
- The Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, 17489, Germany
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan.,Department of Physiological Sciences, School of Life Sciences, The Graduate University for Advanced Studies (SOKENDAI), Hayama, 240-0193, Japan
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, 17485, Germany.,German Centre of Neurodegenerative Diseases (DZNE) Site Greifswald/Rostock, Greifswald, 17489, Germany
| | - Saskia Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,The Social Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, 187-0031, Japan
| | - Masataka Kikuchi
- Department of Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, 17475, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, 17475, Germany
| | - Natalie A Royle
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
| | - Maria Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Uwe Völker
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, 17475, Germany.,Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Greifswald, Greifswald, 17475, Germany
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK.,Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, 17485, Germany.,German Centre of Neurodegenerative Diseases (DZNE) Site Greifswald/Rostock, Greifswald, 17489, Germany
| | - Hidenaga Yamamori
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan
| | | | - Thomas Bourgeron
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, UMR 3571, CNRS, Université Paris Diderot, Paris 75015, France.,Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, 75004, France
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25
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Zhao B, Li T, Yang Y, Wang X, Luo T, Shan Y, Zhu Z, Xiong D, Hauberg ME, Bendl J, Fullard JF, Roussos P, Li Y, Stein JL, Zhu H. Common genetic variation influencing human white matter microstructure. Science 2021; 372:372/6548/eabf3736. [PMID: 34140357 DOI: 10.1126/science.abf3736] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 04/23/2021] [Indexed: 12/11/2022]
Abstract
Brain regions communicate with each other through tracts of myelinated axons, commonly referred to as white matter. We identified common genetic variants influencing white matter microstructure using diffusion magnetic resonance imaging of 43,802 individuals. Genome-wide association analysis identified 109 associated loci, 30 of which were detected by tract-specific functional principal components analysis. A number of loci colocalized with brain diseases, such as glioma and stroke. Genetic correlations were observed between white matter microstructure and 57 complex traits and diseases. Common variants associated with white matter microstructure altered the function of regulatory elements in glial cells, particularly oligodendrocytes. This large-scale tract-specific study advances the understanding of the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mads E Hauberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8210 Aarhus, Denmark.,Centre for Integrative Sequencing (iSEQ), Aarhus University, 8000 Aarhus, Denmark
| | - Jaroslav Bendl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F Fullard
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panagiotis Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. .,Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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26
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Zhao B, Shan Y, Yang Y, Yu Z, Li T, Wang X, Luo T, Zhu Z, Sullivan P, Zhao H, Li Y, Zhu H. Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits. Nat Commun 2021; 12:2878. [PMID: 34001886 PMCID: PMC8128893 DOI: 10.1038/s41467-021-23130-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/16/2021] [Indexed: 02/03/2023] Open
Abstract
Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10-8. The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10-31) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhaolong Yu
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Patrick Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongyu Zhao
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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27
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Szalontai L, Jokkel Z, Horvath T, Piroska M, Forgo B, Olah C, Kostyal L, Tarnoki DL, Tarnoki AD. Are the Morphological Indices of the Vertebrobasilar System Heritable? A Twin Study Based on 3D Reconstructed Models. MEDICINA (KAUNAS, LITHUANIA) 2021; 57:medicina57020127. [PMID: 33535526 PMCID: PMC7912720 DOI: 10.3390/medicina57020127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/21/2021] [Accepted: 01/23/2021] [Indexed: 11/28/2022]
Abstract
Background and Objectives: The asymmetrical vertebral artery (VA) flow and diameter are common findings, which can result in an asymmetrical blood flow in the basilar artery (BA), leading to bending of the artery over time. This study investigated whether the variation of the different vertebrobasilar morphological indices that influence flow characteristics might be inherited. Materials and Methods: We analyzed 200 cerebral magnetic resonance imaging (MRI) scans of healthy Caucasian twins (100 pairs) who underwent time-of-flight MRI. From the scans, we reconstructed the 3D mesh of the posterior circulation from the start of the V4 segment to the basilar tip and subsequently analyzed the morphology of the vertebrobasilar system. The phenotypic covariances of the different morphological parameters were decomposed into heritability (A), shared (C), and unshared (E) environmental effects. Results: 39% of the twins had left dominant VA, while 32.5% had right dominant. In addition, 28.5% were classified as equal. The vertebral artery V4 segment diameter, curvature, and tortuosity were mainly influenced by shared (C) and unshared (E) environmental factors. A moderate heritability was found for the BA length (A: 63%; 95% CI: 45.7–75.2%; E: 37%; 95% CI: 24.8–54.3%) and volume (A: 60.1%; 95% CI: 42.4–73.2%; E: 39.9%; 95% CI: 26.8–57.6%), while the torsion of both arteries showed no heritability and were only influenced by the unshared environment. Conclusions: The length and volume of the BA show a moderate genetical influence. However, most of the measured morphological indices were influenced by shared and unshared factors, which highlight the role of the ever-changing hemodynamic influences shaping the geometry of the vertebrobasilar system.
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Affiliation(s)
- Laszlo Szalontai
- Medical Imaging Centre, Semmelweis University, 78/A Üllői Street, 1082 Budapest, Hungary; (Z.J.); (M.P.); (D.L.T.); (A.D.T.)
- Oncologic Imaging Diagnostic Center, Department of Radiology, National Institute of Oncology, Ráth György Street 7-9, 1122 Budapest, Hungary
- Correspondence:
| | - Zsofia Jokkel
- Medical Imaging Centre, Semmelweis University, 78/A Üllői Street, 1082 Budapest, Hungary; (Z.J.); (M.P.); (D.L.T.); (A.D.T.)
| | - Tamas Horvath
- HeartBit, Hűvösvölgyi Road 42, 1021 Budapest, Hungary;
| | - Marton Piroska
- Medical Imaging Centre, Semmelweis University, 78/A Üllői Street, 1082 Budapest, Hungary; (Z.J.); (M.P.); (D.L.T.); (A.D.T.)
| | - Bianka Forgo
- Department of Radiology, Faculty of Medicine and Health, Örebro University, Södra Grev Rosengatan, 701 85 Örebro, Sweden;
| | - Csaba Olah
- Department of Neurosurgery, University of Debrecen, Nagyerdei Boulevard 98, 4032 Debrecen, Hungary;
| | - Laszlo Kostyal
- Department of Radiology, Borsod County University Teaching Hospital, 72-76 Szentpéteri Gate, 3526 Miskolc, Hungary;
| | - David L. Tarnoki
- Medical Imaging Centre, Semmelweis University, 78/A Üllői Street, 1082 Budapest, Hungary; (Z.J.); (M.P.); (D.L.T.); (A.D.T.)
- Oncologic Imaging Diagnostic Center, Department of Radiology, National Institute of Oncology, Ráth György Street 7-9, 1122 Budapest, Hungary
| | - Adam D. Tarnoki
- Medical Imaging Centre, Semmelweis University, 78/A Üllői Street, 1082 Budapest, Hungary; (Z.J.); (M.P.); (D.L.T.); (A.D.T.)
- Oncologic Imaging Diagnostic Center, Department of Radiology, National Institute of Oncology, Ráth György Street 7-9, 1122 Budapest, Hungary
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28
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Zhong S, Wei L, Zhao C, Yang L, Di Z, Francks C, Gong G. Interhemispheric Relationship of Genetic Influence on Human Brain Connectivity. Cereb Cortex 2020; 31:77-88. [DOI: 10.1093/cercor/bhaa207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/03/2020] [Accepted: 07/07/2020] [Indexed: 12/25/2022] Open
Abstract
Abstract
To understand the origins of interhemispheric differences and commonalities/coupling in human brain wiring, it is crucial to determine how homologous interregional connectivities of the left and right hemispheres are genetically determined and related. To address this, in the present study, we analyzed human twin and pedigree samples with high-quality diffusion magnetic resonance imaging tractography and estimated the heritability and genetic correlation of homologous left and right white matter (WM) connections. The results showed that the heritability of WM connectivity was similar and coupled between the 2 hemispheres and that the degree of overlap in genetic factors underlying homologous WM connectivity (i.e., interhemispheric genetic correlation) varied substantially across the human brain: from complete overlap to complete nonoverlap. Particularly, the heritability was significantly stronger and the chance of interhemispheric complete overlap in genetic factors was higher in subcortical WM connections than in cortical WM connections. In addition, the heritability and interhemispheric genetic correlations were stronger for long-range connections than for short-range connections. These findings highlight the determinants of the genetics underlying WM connectivity and its interhemispheric relationships, and provide insight into genetic basis of WM connectivity asymmetries in both healthy and disease states.
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Affiliation(s)
- Suyu Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Long Wei
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, China
| | - Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zengru Di
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
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Zhao B, Luo T, Li T, Li Y, Zhang J, Shan Y, Wang X, Yang L, Zhou F, Zhu Z, Zhu H. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat Genet 2019; 51:1637-1644. [PMID: 31676860 PMCID: PMC6858580 DOI: 10.1038/s41588-019-0516-6] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 09/23/2019] [Indexed: 12/19/2022]
Abstract
Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 × 10-10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P = 3.13 × 10-24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingwen Zhang
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Liuqing Yang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fan Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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