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Author

Willem A. Nolen

Bio: Willem A. Nolen is an academic researcher from University Medical Center Groningen. The author has contributed to research in topics: Bipolar disorder & Mania. The author has an hindex of 93, co-authored 413 publications receiving 29608 citations. Previous affiliations of Willem A. Nolen include Duke University & University of Groningen.
Topics: Bipolar disorder, Mania, Mood, Population, Anxiety


Papers
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Journal ArticleDOI
S. Hong Lee1, Stephan Ripke2, Stephan Ripke3, Benjamin M. Neale2  +402 moreInstitutions (124)
TL;DR: Empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
Abstract: Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

2,058 citations

Journal ArticleDOI
TL;DR: The Canadian Network for Mood and Anxiety Treatments published guidelines for the management of bipolar disorder in 2005, with updates in 2007 and 2009, and this third update, in conjunction with the International Society for Bipolar Disorders, reviews new evidence and is designed to be used in conjunctionWith the previous publications.
Abstract: The Canadian Network for Mood and Anxiety Treatments published guidelines for the management of bipolar disorder in 2005, with updates in 2007 and 2009. This third update, in conjunction with the International Society for Bipolar Disorders, reviews new evidence and is designed to be used in conjunction with the previous publications.The recommendations for the management of acute mania remain largely unchanged. Lithium, valproate, and several atypical antipsychotic agents continue to be first-line treatments for acute mania. Monotherapy with asenapine, paliperidone extended release (ER), and divalproex ER, as well as adjunctive asenapine, have been added as first-line options.For the management of bipolar depression, lithium, lamotrigine, and quetiapine monotherapy, as well as olanzapine plus selective serotonin reuptake inhibitor (SSRI), and lithium or divalproex plus SSRI/bupropion remain first-line options. Lurasidone monotherapy and the combination of lurasidone or lamotrigine plus lithium or divalproex have been added as a second-line options. Ziprasidone alone or as adjunctive therapy, and adjunctive levetiracetam have been added as not-recommended options for the treatment of bipolar depression. Lithium, lamotrigine, valproate, olanzapine, quetiapine, aripiprazole, risperidone long-acting injection, and adjunctive ziprasidone continue to be first-line options for maintenance treatment of bipolar disorder. Asenapine alone or as adjunctive therapy have been added as third-line options.

1,369 citations

Journal ArticleDOI
Stephan Ripke1, Naomi R. Wray2, Cathryn M. Lewis3, Steven P. Hamilton4, Myrna M. Weissman5, Gerome Breen3, Enda M. Byrne2, Douglas Blackwood6, Dorret I. Boomsma7, Sven Cichon8, Andrew C. Heath9, Florian Holsboer, Susanne Lucae4, Pamela A. F. Madden9, Nicholas G. Martin2, Peter McGuffin3, Pierandrea Muglia8, Markus M. Noethen10, Brenda P Penninx7, Michele L. Pergadia9, James B. Potash11, Marcella Rietschel10, Danyu Lin12, Bertram Müller-Myhsok8, Jianxin Shi13, Stacy Steinberg8, Hans J. Grabe, Paul Lichtenstein14, Patrik K. E. Magnusson14, Roy H. Perlis7, Martin Preisig15, Jordan W. Smoller16, Kari Stefansson, Rudolf Uher3, Zoltán Kutalik17, Katherine E. Tansey3, Alexander Teumer, Alexander Viktorin14, Michael R. Barnes11, Thomas Bettecken18, Elisabeth B. Binder19, René Breuer10, Victor M. Castro20, Susanne Churchill13, William Coryell11, Nicholas John Craddock, Ian W. Craig3, Darina Czamara6, Eco J. C. de Geus7, Franziska Degenhardt8, Anne Farmer3, Maurizio Fava16, Josef Frank10, Vivian S. Gainer, Patience J. Gallagher16, Scott D. Gordon2, Sergey Goryachev, Magdalena Gross8, Michel Guipponi21, Anjali K. Henders2, Stefan Herms8, Ian B. Hickie22, Susanne Hoefels8, Witte J.G. Hoogendijk3, Jouke-Jan Hottenga7, Dan V. Iosifescu16, Marcus Ising9, Ian Jones2, Lisa Jones22, Tzeng Jung-Ying15, James A. Knowles18, Isaac S. Kohane16, Martin A. Kohli2, Ania Korszun9, Mikael Landén5, William Lawson19, Glyn Lewis23, Donald J. MacIntyre6, Wolfgang Maier8, Manuel Mattheisen8, Patrick J. McGrath5, Andrew M. McIntosh6, Alan W. McLean6, Christel M. Middeldorp7, Lefkos T. Middleton23, G. M. Montgomery2, Shawn N. Murphy16, Matthias Nauck, Willem A. Nolen, Dale R. Nyholt2, Michael Conlon O'Donovan24, Hogni Oskarsson, Nancy L. Pedersen14, William A. Scheftner20, Andrea Schulz, Thomas G Schulze16, Stanley I. Shyn9, Engilbert Sigurdsson, Susan L. Slager25, Johannes H. Smit7, Hreinn Stefansson17, Michael Steffens8, Thorgeir E. Thorgeirsson, Federica Tozzi, Jens Treutlein10, Manfred Uhr, Edwin J. C. G. van den Oord26, Gerard van Grootheest7, Henry Völzke14, Jeffrey B. Weilburg16, Gonneke Willemsen7, Frans G. Zitman27, Benjamin M. Neale, Mark J. Daly1, Douglas F. Levinson28, Patrick F. Sullivan12 
TL;DR: This article conducted a genome-wide association studies (GWAS) mega-analysis for major depressive disorder (MDD) using more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18,759 independent and unrelated subjects of recent European ancestry.
Abstract: Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

989 citations

Journal ArticleDOI
TL;DR: The modified CGI-BP is anticipated to be more useful than the original CGI in studies of bipolar disorder and interrater reliability of the scale was demonstrated in preliminary analyses.
Abstract: The Clinical Global Impressions Scale (CGI) was modified specifically for use in assessing global illness severity and change in patients with bipolar disorder. Criticisms of the original CGI were addressed by correcting inconsistencies in scaling, identifying time frames for comparison, clarifying definitions of illness severity and change, and separating out assessment of treatment side effects from illness improvement during treatment. A Detailed User's Guide was developed to train clinicians in the use of the new CGI-Bipolar Version (CGI-BP) for rating severity of manic and depressive episodes and the degree of change from the immediately preceding phase and from the worst phase of illness. The revised scale and manual provide a focused set of instructions to facilitate the reliability of these ratings of mania, depression, and overall bipolar illness during treatment of an acute episode or in longer-term illness prophylaxis. Interrater reliability of the scale was demonstrated in preliminary analyses. Thus, the modified CGI-BP is anticipated to be more useful than the original CGI in studies of bipolar disorder.

918 citations

Journal ArticleDOI
TL;DR: The Netherlands Study of Depression and Anxiety is a multi‐site naturalistic cohort study to describe the long‐term course and consequences of depressive and anxiety disorders and to integrate biological and psychosocial research paradigms within an epidemiological approach.
Abstract: The Netherlands Study of Depression and Anxiety (NESDA) is a multi-site naturalistic cohort study to: (1) describe the long-term course and consequences of depressive and anxiety disorders, and (2) to integrate biological and psychosocial research paradigms within an epidemiological approach in order to examine (interaction between) predictors of the long-term course and consequences. Its design is an eight-year longitudinal cohort study among 2981 participants aged 18 through 65 years. The sample consists of 1701 persons with a current (six-month recency) diagnosis of depression and/or anxiety disorder, 907 persons with life-time diagnoses or at risk because of a family history or subthreshold depressive or anxiety symptoms, and 373 healthy controls. Recruitment took place in the general population, in general practices (through a three-stage screening procedure), and in mental health organizations in order to recruit persons reflecting various settings and developmental stages of psychopathology. During a four-hour baseline assessment including written questionnaires, interviews, a medical examination, a cognitive computer task and collection of blood and saliva samples, extensive information was gathered about key (mental) health outcomes and demographic, psychosocial, clinical, biological and genetic determinants. Detailed assessments will be repeated after one, two, four and eight years of follow-up. The findings of NESDA are expected to provide more detailed insight into (predictors of) the long-term course of depressive and anxiety disorders in adults. Besides its scientific relevance, this may contribute to more effective prevention and treatment of depressive and anxiety disorders.

882 citations


Cited by
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Journal ArticleDOI
TL;DR: It is found that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size, and the LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control.
Abstract: Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

3,708 citations

Journal ArticleDOI
TL;DR: This work introduces a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap, and uses this method to estimate 276 genetic correlations among 24 traits.
Abstract: Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

2,993 citations

Journal ArticleDOI
TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
Abstract: Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

2,669 citations