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      Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank


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          Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (r g = 0.24, p = 1.8×10 −7 versus r g = −0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3×10 −4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

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          PRSice-2: Polygenic Risk Score software for biobank-scale data

          Abstract Background Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required. Results Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power. Conclusion PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set–based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.
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            Identification of 15 genetic loci associated with risk of major depression in individuals of European descent

            Despite strong evidence supporting the heritability of Major Depressive Disorder, previous genome-wide studies were unable to identify risk loci among individuals of European descent. We used self-reported data from 75,607 individuals reporting clinical diagnosis of depression and 231,747 reporting no history of depression through 23andMe, and meta-analyzed these results with published MDD GWAS results. We identified five independent variants from four regions associated with self-report of clinical diagnosis or treatment for depression. Loci with pval<1.0×10−5 in the meta-analysis were further analyzed in a replication dataset (45,773 cases and 106,354 controls) from 23andMe. A total of 17 independent SNPs from 15 regions reached genome-wide significance after joint-analysis over all three datasets. Some of these loci were also implicated in GWAS of related psychiatric traits. These studies provide evidence for large-scale consumer genomic data as a powerful and efficient complement to traditional means of ascertainment for neuropsychiatric disease genomics.
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              Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways

              Neuroticism is an important risk factor for psychiatric traits, including depression1, anxiety2,3, and schizophrenia4-6. At the time of analysis, previous genome-wide association studies7-12 (GWAS) reported 16 genomic loci associated to neuroticism10-12. Here we conducted a large GWAS meta-analysis (n = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts (P = 3.49 × 10-8), medium spiny neurons (P = 4.23 × 10-8), and serotonergic neurons (P = 1.37 × 10-7). Gene set analyses implicated three specific pathways: neurogenesis (P = 4.43 × 10-9), behavioral response to cocaine processes (P = 1.84 × 10-7), and axon part (P = 5.26 × 10-8). We show that neuroticism's genetic signal partly originates in two genetically distinguishable subclusters13 ('depressed affect' and 'worry'), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments.

                Author and article information

                Mol Psychiatry
                Mol. Psychiatry
                Molecular psychiatry
                5 September 2019
                23 January 2020
                July 2020
                23 July 2020
                : 25
                : 7
                : 1430-1446
                [1 ]Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
                [2 ]NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
                [3 ]Amsterdam UMC, Vrije Universiteit Medical Center, Department of Psychiatry, Amsterdam, The Netherlands
                [4 ]Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
                [5 ]Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
                [6 ]Division of Psychiatry, University of Edinburgh, Edinburgh, UK
                [7 ]Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
                [8 ]Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
                [9 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [10 ]Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
                [11 ]Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
                [12 ]Consortium members listed in Supplementary Materials
                [13 ]Department of Psychiatry, Harvard Medical School, Boston, MA, USA
                [14 ]Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
                [15 ]National and Specialist CAMHS Trauma and Anxiety Clinic, South London and Maudsley NHS Foundation Trust, London, UK
                [16 ]Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
                [17 ]Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
                Author notes
                [* ]Address correspondence to Dr Gerome Breen ( gerome.breen@ 123456kcl.ac.uk , +442078480409) or Prof. Thalia Eley ( thalia.eley@ 123456kcl.ac.uk , +442078480863), Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK.

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                Molecular medicine
                Molecular medicine


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