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      Serotonin transporter (5-HTT) gene network moderates the impact of prenatal maternal adversity on orbitofrontal cortical thickness in middle childhood

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          Abstract

          In utero, the developing brain is highly susceptible to the environment. For example, adverse maternal experiences during the prenatal period are associated with outcomes such as altered neurodevelopment and emotion dysregulation. Yet, the underlying biological mechanisms remain unclear. Here, we investigate whether the function of a network of genes co-expressed with the serotonin transporter in the amygdala moderates the impact of prenatal maternal adversity on the structure of the orbitofrontal cortex (OFC) in middle childhood and/or the degree of temperamental inhibition exhibited in toddlerhood. T1-weighted structural MRI scans were acquired from children aged 6–12 years. A cumulative maternal adversity score was used to conceptualize prenatal adversity and a co-expression based polygenic risk score (ePRS) was generated. Behavioural inhibition at 18 months was assessed using the Early Childhood Behaviour Questionnaire (ECBQ). Our results indicate that in the presence of a low functioning serotonin transporter gene network in the amygdala, higher levels of prenatal adversity are associated with greater right OFC thickness at 6–12 years old. The interaction also predicts temperamental inhibition at 18 months. Ultimately, we identified important biological processes and structural modifications that may underlie the link between early adversity and future deviations in cognitive, behavioural, and emotional development.

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            The CES-D Scale: A Self-Report Depression Scale for Research in the General Population

            L Radloff (1977)
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              Second-generation PLINK: rising to the challenge of larger and richer datasets

              PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Writing – original draft
                Role: Data curationRole: Formal analysis
                Role: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                15 June 2023
                2023
                : 18
                : 6
                : e0287289
                Affiliations
                [1 ] Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
                [2 ] Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
                [3 ] Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
                [4 ] Translational Neuroscience Program, Singapore Institute for Clinical Sciences and Brain–Body Initiative, Agency for Science, Technology and Research (A*STAR), Singapore Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
                [5 ] Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
                Western University, CANADA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-6021-7509
                https://orcid.org/0000-0002-6078-0163
                https://orcid.org/0000-0001-8862-0930
                Article
                PONE-D-22-28230
                10.1371/journal.pone.0287289
                10270637
                70ad5749-e894-4e5d-888b-2454952b6615
                © 2023 Sunderji et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 October 2022
                : 3 June 2023
                Page count
                Figures: 2, Tables: 7, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award ID: RGPIN-2019-06860
                Award Recipient :
                This research was made possible by a grant from the Natural Sciences and Engineering Research Council of Canada, RGPIN-2019-06860, awarded to Geoffrey B. Hall. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Neurochemistry
                Neurotransmitters
                Biogenic Amines
                Serotonin
                Biology and Life Sciences
                Neuroscience
                Neurochemistry
                Neurotransmitters
                Biogenic Amines
                Serotonin
                Biology and Life Sciences
                Computational Biology
                Gene Regulatory Networks
                Biology and Life Sciences
                Genetics
                Gene Regulatory Networks
                Biology and Life Sciences
                Anatomy
                Brain
                Amygdala
                Medicine and Health Sciences
                Anatomy
                Brain
                Amygdala
                Biology and Life Sciences
                Psychology
                Emotions
                Social Sciences
                Psychology
                Emotions
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Biology and Life Sciences
                Genetics
                Single Nucleotide Polymorphisms
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Biology and Life Sciences
                Psychology
                Behavior
                Social Sciences
                Psychology
                Behavior
                Custom metadata
                The identification of study group membership is based on clinical data (SES, Mood Disorders, Anxiety) collected from the mothers of participants as well as assessment, imaging and developmental data collected at predefined intervals on the children. This is sensitive information and the study group size and the longitudinal collection of data weigh on participant confidentiality and respect. Inquiries to arrange data sharing can be directed to the corresponding author or to the Hamilton Integrated Research Ethics Board ( https://hireb.ca/) at telephone # 905-521-2100 ext 42680 or by mail; 293 Wellington Street North, Suite 120, Hamilton ON L8L 8E7.

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