A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

John P. Rice | P. O’Reilly | P. Visscher | N. Wray | A. Uitterlinden | I. Deary | E. Mihailov | J. Marchini | H. Stefánsson | S. Cichon | S. Steinberg | E. Sigurdsson | T. Thorgeirsson | M. Rietschel | T. Werge | M. Nöthen | K. Stefánsson | J. Potash | T. Schulze | M. Gill | N. Craddock | M. Owen | P. Sullivan | J. Rice | K. Tansey | Jianxin Shi | Z. Kutalik | I. Hickie | A. Beekman | M. Weissman | G. Breen | L. Jones | P. McGuffin | C. Lewis | I. Kohane | H. Völzke | Yunpeng Wang | W. Thompson | S. Mostafavi | W. Maier | H. Whalley | J. Smoller | N. Martin | G. Crawford | A. McIntosh | M. Preisig | B. Penninx | V. Arolt | G. Willemsen | A. Metspalu | T. Esko | G. Montgomery | L. Milani | J. Knowles | D. Mehta | J. Wellmann | U. Dannlowski | B. Baune | K. Kendler | D. Posthuma | D. Boomsma | E. D. de Geus | R. Perlis | P. McGrath | D. Porteous | D. Levinson | S. Paciga | D. Nyholt | J. Hottenga | P. Magnusson | N. Pedersen | J. Smit | G. Lewis | K. Domschke | H. Gaspar | S. Bacanu | A. Heath | O. Mors | R. Uher | E. Derks | M. O’Donovan | P. Mortensen | A. Børglum | M. Nordentoft | D. Hougaard | M. Mattheisen | H. Grabe | G. Homuth | A. Teumer | S. Medland | B. Müller-Myhsok | J. Bryois | S. Ripke | Qingqin S. Li | H. Xi | A. Abdellaoui | D. Umbricht | B. Riley | S. Hamilton | G. Davies | Jian Yang | H. Tiemeier | C. Hayward | P. Lind | W. Peyrot | K. Berger | P. Madden | Danny J. Smith | B. Webb | Y. Milaneschi | T. Andlauer | J. Grove | Jingqing Yang | C. Schaefer | E. Domenici | E. Binder | F. Goes | C. Dolan | R. Schoevers | H. Finucane | B. Couvy-Duchesne | M. Nauck | P. Hoffmann | S. Gordon | Yang Wu | H. Mbarek | R. Jansen | C. Middeldorp | R. Maier | E. Agerbo | J. Bybjerg-Grauholm | M. Bækvad-Hansen | C. Hansen | C. Pedersen | M. Pedersen | V. Escott-Price | A. V. van Hemert | W. Hill | L. Hall | E. Byrne | T. Eley | J. Painter | L. Colodro-Conde | S. Witt | F. Degenhardt | A. Forstner | S. Herms | Futao Zhang | J. Coleman | I. Jones | E. Jorgenson | M. Adams | D. Macintyre | N. Mullins | G. Pistis | P. Thomson | H. Teismann | D. MacKinnon | F. Mondimore | J. R. DePaulo | T. Bigdeli | T. Clarke | M. Nivard | L. Shen | J. Grove | J. Christensen | P. Qvist | F. Streit | J. Treutlein | M. Trzaskowski | J. Strohmaier | S. Lucae | H. Oskarsson | J. Frank | T. Hansen | M. Ising | Stanley I. Shyn | N. Direk | B. Ng | E. Dunn | G. Sinnamon | R. Peterson | S. Kloiber | S. Mirza | X. Shen | David Mark Howard | J. Depaulo | T. Air | H. Buttenschøn | S. V. D. Auwera | J. Foo | A. Viktorin | N. Cai | E. Castelao | Farnush Farhadi Hassan Kiadeh | Carsten Horn | J. Kraft | Yihan Li | E. Pettersson | J. Quiroz | M. Rivera | E. Schulte | M. Traylor | V. Trubetskoy | S. Weinsheimer | Wesley Thompson | Patrick J. McGrath | K. Berger | L. Jones | W. Kretzschmar | Mark J. Toni-Kim Andrew M. Ian J. Naomi R. Stephan Manu Adams Clarke McIntosh Deary Wray Ripke Matth | C. Lewis | Myrna M. Weissman | N. Martin | H. Grabe | Xueyi Shen | I. Kohane | M. Nöthen | M. Gill | D. Porteous | G. Lewis | A. Uitterlinden | S. Auwera | Farnush Hassan Farhadi Kiadeh | P. O’Reilly | Per Hoffmann | W. Maier | Gregory E. Crawford | Mark J. Toni-Kim Andrew M. Ian J. Naomi R. Stephan Manu Adams Clarke McIntosh Deary Wray Ripke Matth

[1]  P. McCullagh,et al.  Generalized Linear Models , 1972, Predictive Analytics.

[2]  H. Whalley,et al.  White Matter Microstructure and Its Relation to Longitudinal Measures of Depressive Symptoms in Mid- and Late Life , 2019, Biological Psychiatry.

[3]  D. Geschwind,et al.  Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders , 2019, Cell.

[4]  Dan J Stein,et al.  Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group , 2019, bioRxiv.

[5]  Mark E Bastin,et al.  Associations between vascular risk factors and brain MRI indices in UK Biobank , 2019, bioRxiv.

[6]  R. Marioni,et al.  Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions , 2018, Nature Neuroscience.

[7]  Ahmad R. Hariri,et al.  General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks , 2018, NeuroImage.

[8]  R. Marioni,et al.  Genome-wide meta-analysis of depression in 807,553 individuals identifies 102 independent variants with replication in a further 1,507,153 individuals , 2018, bioRxiv.

[9]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[10]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[11]  Stuart J. Ritchie,et al.  Resting-State Connectivity and Its Association With Cognitive Performance, Educational Attainment, and Household Income in the UK Biobank , 2018, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[12]  N. Allen,et al.  Corrigendum: Mental health in UK Biobank: Development, implementation and results from an online questionnaire completed by 157 366 participants (BJPsych Open (2018) 4:3 (83-90) DOI: 10.1192/bjo.2018.12) , 2018 .

[13]  J. L. de la Pompa,et al.  A novel source of arterial valve cells linked to bicuspid aortic valve without raphe in mice , 2018, eLife.

[14]  H. Völzke,et al.  43. Accelerated Aging in Depression: From Physiological Aging to Brain Aging , 2018, Biological Psychiatry.

[15]  B. Neale,et al.  Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[16]  R. Marioni,et al.  Edinburgh Research Explorer Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways , 2022 .

[17]  J. Ioannidis,et al.  Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis , 2018, The Lancet.

[18]  Ann John,et al.  Mental health in UK Biobank: development, implementation and results from an online questionnaire completed by 157 366 participants , 2018, BJPsych Open.

[19]  J. Gallacher,et al.  Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. , 2018, The Lancet. Planetary health.

[20]  Valeriia Haberland,et al.  The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.

[21]  Warren W. Kretzschmar,et al.  Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression , 2017, Nature Genetics.

[22]  D. Hasselquist,et al.  No evidence that carotenoid pigments boost either immune or antioxidant defenses in a songbird , 2018, Nature Communications.

[23]  Thomas E. Nichols,et al.  Statistical Challenges in “Big Data” Human Neuroimaging , 2018, Neuron.

[24]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[25]  John P. Rice,et al.  Does Childhood Trauma Moderate Polygenic Risk for Depression? A Meta-analysis of 5765 Subjects From the Psychiatric Genomics Consortium , 2017, Biological Psychiatry.

[26]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[27]  M. Zerial,et al.  Rab5 and Alsin regulate stress-activated cytoprotective signaling on mitochondria , 2017, bioRxiv.

[28]  Mark W. Woolrich,et al.  Investigations into within- and between-subject resting-state amplitude variations , 2017, NeuroImage.

[29]  J. Marchini,et al.  Genome-wide association studies of brain structure and function 1 in the UK Biobank 2 , 2018 .

[30]  P. Donnelly,et al.  Genome-wide genetic data on ~500,000 UK Biobank participants , 2017, bioRxiv.

[31]  Stuart J. Ritchie,et al.  Resting-state connectivity and its association with cognitive performance, educational attainment, and household income in UK Biobank (N = 3,950) , 2017, bioRxiv.

[32]  Blair H. Smith,et al.  Genome-wide Association for Major Depression Through Age at Onset Strati fi cation: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium , 2016 .

[33]  A. Pollard,et al.  Limb proportions show developmental plasticity in response to embryo movement , 2017, Scientific Reports.

[34]  Stephen Burgess,et al.  Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants , 2016, Epidemiology.

[35]  I. Deary,et al.  Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank imaging data , 2016, bioRxiv.

[36]  Mark E Bastin,et al.  Ageing and brain white matter structure in 3,513 UK Biobank participants , 2016, Nature Communications.

[37]  M. Munafo,et al.  Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample Mendelian randomization study , 2016, Psychological Medicine.

[38]  M. Cecchini,et al.  Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease , 2016, Scientific Reports.

[39]  H. Whalley,et al.  Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank , 2016, Scientific Reports.

[40]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[41]  I. Deary,et al.  Psychological distress, neuroticism, and cause-specific mortality: early prospective evidence from UK Biobank , 2016, Journal of Epidemiology & Community Health.

[42]  J. Bressler,et al.  Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions , 2016, Translational Psychiatry.

[43]  D. Hinds,et al.  Identification of 15 genetic loci associated with risk of major depression in individuals of European descent , 2016, Nature Genetics.

[44]  D. Melzer,et al.  Events in Early Life are Associated with Female Reproductive Ageing: A UK Biobank Study , 2016, Scientific Reports.

[45]  R. Shelton The Course of Illness After Initial Diagnosis of Major Depression. , 2016, JAMA psychiatry.

[46]  A. Dehghan,et al.  Polygenic dissection of major depression clinical heterogeneity , 2016, Molecular Psychiatry.

[47]  R. Atun,et al.  Estimating the true global burden of mental illness. , 2016, The lancet. Psychiatry.

[48]  Anbupalam Thalamuthu,et al.  Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof-of-concept and roadmap for future studies , 2016, Nature Neuroscience.

[49]  Stuart J. Ritchie,et al.  Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia , 2015, Molecular Psychiatry.

[50]  Lachlan T. Strike,et al.  Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group , 2015, Molecular Psychiatry.

[51]  Xinghuan Wang,et al.  The impact of surgical treatments for lower urinary tract symptoms/benign prostatic hyperplasia on male erectile function , 2016, Medicine.

[52]  C. Büchel,et al.  The neural bases of emotion regulation , 2015, Nature Reviews Neuroscience.

[53]  D. Vancampfort,et al.  Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta‐analysis , 2015, World psychiatry : official journal of the World Psychiatric Association.

[54]  R. Yirmiya,et al.  Depression as a Microglial Disease , 2015, Trends in Neurosciences.

[55]  P. Visscher,et al.  Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores , 2015, bioRxiv.

[56]  D. Mozaffarian,et al.  Changes in Intake of Fruits and Vegetables and Weight Change in United States Men and Women Followed for Up to 24 Years: Analysis from Three Prospective Cohort Studies , 2015, PLoS medicine.

[57]  J. Andrews-Hanna,et al.  Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. , 2015, JAMA psychiatry.

[58]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[59]  Ruth C. Brown,et al.  Genetic Determinants of Depression: Recent Findings and Future Directions , 2015, Harvard review of psychiatry.

[60]  Jack Euesden,et al.  PRSice: Polygenic Risk Score software , 2014, Bioinform..

[61]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[62]  L. Uddin Salience processing and insular cortical function and dysfunction , 2014, Nature Reviews Neuroscience.

[63]  N. Wray,et al.  Research review: Polygenic methods and their application to psychiatric traits. , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[64]  P. Sullivan,et al.  Effect of polygenic risk scores on depression in childhood trauma , 2014, British Journal of Psychiatry.

[65]  Daniel L. Oberski,et al.  lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models , 2014 .

[66]  John Blangero,et al.  Arguments for the sake of endophenotypes: Examining common misconceptions about the use of endophenotypes in psychiatric genetics , 2014, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[67]  J. Flint,et al.  The Genetics of Major Depression , 2014, Neuron.

[68]  Matthew C. Keller,et al.  Gene × Environment Interaction Studies Have Not Properly Controlled for Potential Confounders: The Problem and the (Simple) Solution , 2014, Biological Psychiatry.

[69]  Jonathan J. Evans,et al.  Prevalence and Characteristics of Probable Major Depression and Bipolar Disorder within UK Biobank: Cross-Sectional Study of 172,751 Participants , 2013, PloS one.

[70]  P. Fox,et al.  High Dimensional Endophenotype Ranking in the Search for Major Depression Risk Genes , 2012, Biological Psychiatry.

[71]  J. Leek,et al.  Temporal dynamics and genetic control of transcription in the human prefrontal cortex , 2011, Nature.

[72]  Matthew C Keller,et al.  A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. , 2011, The American journal of psychiatry.

[73]  G. Tononi,et al.  Sleep and wake modulate spine turnover in the adolescent mouse cortex , 2011, Nature Neuroscience.

[74]  G. Šimić,et al.  Extraordinary neoteny of synaptic spines in the human prefrontal cortex , 2011, Proceedings of the National Academy of Sciences.

[75]  Giulio Tononi,et al.  Sleep and Synaptic Homeostasis: Structural Evidence in Drosophila , 2011, Science.

[76]  Gerd Wagner,et al.  Structural brain alterations in patients with major depressive disorder and high risk for suicide: Evidence for a distinct neurobiological entity? , 2011, NeuroImage.

[77]  Emily L. Dennis,et al.  Neural correlates of rumination in depression , 2010, Cognitive, affective & behavioral neuroscience.

[78]  I. Deary,et al.  The neuroscience of human intelligence differences , 2010, Nature Reviews Neuroscience.

[79]  C. Pariante Risk Factors for Development of Depression and Psychosis , 2009, Annals of the New York Academy of Sciences.

[80]  R. DeRubeis,et al.  Cognitive therapy versus medication for depression: treatment outcomes and neural mechanisms , 2008, Nature Reviews Neuroscience.

[81]  Stafford L. Lightman,et al.  The HPA axis in major depression: classical theories and new developments , 2008, Trends in Neurosciences.

[82]  Nicholas D. Walsh,et al.  Neural basis of the emotional Stroop interference effect in major depression , 2007, Psychological Medicine.

[83]  B. Blaine a review and meta-analysis , 2006 .

[84]  S. Ebrahim,et al.  Mendelian randomization: prospects, potentials, and limitations. , 2004, International journal of epidemiology.

[85]  P. Sullivan,et al.  Genetic epidemiology of major depression: review and meta-analysis. , 2000, The American journal of psychiatry.

[86]  E. Miller,et al.  The prefontral cortex and cognitive control , 2000, Nature Reviews Neuroscience.

[87]  Y. Benjamini,et al.  On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics , 2000 .

[88]  E. Miller,et al.  THE PREFRONTAL CORTEX AND COGNITIVE CONTROL , 2000 .

[89]  Douglas M. Bates,et al.  LINEAR AND NONLINEAR MIXED-EFFECTS MODELS , 1998 .