Robin M. Murray
Other affiliations: University of Cambridge, National Institutes of Health, University of Pennsylvania ...read more
Bio: Robin M. Murray is an academic researcher from King's College London. The author has contributed to research in topic(s): Schizophrenia & Psychosis. The author has an hindex of 171, co-authored 1539 publication(s) receiving 116362 citation(s). Previous affiliations of Robin M. Murray include University of Cambridge & National Institutes of Health.
Papers published on a yearly basis
Stephan Ripke1, Stephan Ripke2, Benjamin M. Neale2, Benjamin M. Neale1 +351 more•Institutions (102)
TL;DR: Associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses.
Abstract: Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.
deCODE genetics1, Ludwig Maximilian University of Munich2, University of Bonn3, Utrecht University4, University of Copenhagen5, Copenhagen University Hospital6, GlaxoSmithKline7, Hammersmith Hospital8, Duke University9, Royal Cornhill Hospital10, King's College London11, University of Verona12, Sichuan University13, University of Oslo14, Glostrup Hospital15, Radboud University Nijmegen Medical Centre16, University of California, Los Angeles17, Heidelberg University18, Broad Institute19, Wellcome Trust Sanger Institute20, University of Iceland21
TL;DR: In a genome-wide search for CNVs associating with schizophrenia, a population-based sample was used to identify de novo CNVs by analysing 9,878 transmissions from parents to offspring and three deletions significantly associate with schizophrenia and related psychoses in the combined sample.
Abstract: Reduced fecundity, associated with severe mental disorders, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism, schizophrenia and mental retardation. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation and autism. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novo CNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.
TL;DR: In this article, the authors conducted a systematic search for structural magnetic resonance imaging (MRI) studies of patients with schizophrenia that reported volume measurements of selected cortical, subcortical, and ventricular regions in relation to comparison groups.
Abstract: Objective: The authors’ goal was to determine whether patients with schizophrenia differ from comparison subjects in regional brain volumes and whether these differences are similar in male and female subjects. Method: They conducted a systematic search for structural magnetic resonance imaging (MRI) studies of patients with schizophrenia that reported volume measurements of selected cortical, subcortical, and ventricular regions in relation to comparison groups. They carried out a meta-analysis of the volumes of these regions in the patients with schizophrenia and the comparison subjects using a random effects model; they also used random effects regression analysis to examine the influence of gender on effect sizes. Results: Fifty-eight studies were identified as suitable for analysis; these studies included 1,588 independent patients with schizophrenia. Assuming a volume of 100% in the comparison group, they found that the mean cerebral volume of the subjects with schizophrenia was smaller (98%), but the mean total ventricular volume of the subjects with schizophrenia was greater (126%). Relative to the cerebral volume differences, the regional volumes of the subjects with schizophrenia were 94% in the left and right amygdala, 94% in the left and 95% in the right hippocampus/amygdala, and 93% in the left and 95% in the right parahippocampus. Relative to the global ventricular system differences, the largest differences in ventricular subdivisions were in the right and left body of the lateral ventricle, where the volumes of schizophrenic subjects were 116% and 116%, respectively. For most regions, effect size was not significantly related to gender. Conclusions: Regional structural differences in patients with schizophrenia include bilaterally reduced volume of medial temporal lobe structures. There is a need for greater integration of results from structural MRI studies to avoid redundant research activity. (Am J Psychiatry 2000; 157:16‐25)
Maastricht University Medical Centre1, deCODE genetics2, University of California, Los Angeles3, Utrecht University4, University of Oslo5, University of Bonn6, Ludwig Maximilian University of Munich7, Copenhagen University Hospital8, Wellcome Trust Sanger Institute9, Aarhus University Hospital10, Aarhus University11, University of Iceland12, University of Helsinki13, Bispebjerg Hospital14, Glostrup Hospital15, Heidelberg University16, Semmelweis University17, University of Verona18, Radboud University Nijmegen Medical Centre19, Russian Academy20, University of Valencia21, King's College London22, Royal Cornhill Hospital23, Duke University24, University of Santiago de Compostela25, Hospital General Universitario Gregorio Marañón26, Karolinska Institutet27, Hammersmith Hospital28, GlaxoSmithKline29, Sichuan University30
TL;DR: Findings implicating the MHC region are consistent with an immune component to schizophrenia risk, whereas the association with NRGN and TCF4 points to perturbation of pathways involved in brain development, memory and cognition.
Abstract: Schizophrenia is a complex disorder, caused by both genetic and environmental factors and their interactions. Research on pathogenesis has traditionally focused on neurotransmitter systems in the brain, particularly those involving dopamine. Schizophrenia has been considered a separate disease for over a century, but in the absence of clear biological markers, diagnosis has historically been based on signs and symptoms. A fundamental message emerging from genome-wide association studies of copy number variations (CNVs) associated with the disease is that its genetic basis does not necessarily conform to classical nosological disease boundaries. Certain CNVs confer not only high relative risk of schizophrenia but also of other psychiatric disorders. The structural variations associated with schizophrenia can involve several genes and the phenotypic syndromes, or the 'genomic disorders', have not yet been characterized. Single nucleotide polymorphism (SNP)-based genome-wide association studies with the potential to implicate individual genes in complex diseases may reveal underlying biological pathways. Here we combined SNP data from several large genome-wide scans and followed up the most significant association signals. We found significant association with several markers spanning the major histocompatibility complex (MHC) region on chromosome 6p21.3-22.1, a marker located upstream of the neurogranin gene (NRGN) on 11q24.2 and a marker in intron four of transcription factor 4 (TCF4) on 18q21.2. Our findings implicating the MHC region are consistent with an immune component to schizophrenia risk, whereas the association with NRGN and TCF4 points to perturbation of pathways involved in brain development, memory and cognition.
19 Nov 1994-The Lancet
TL;DR: Differences between children destined to develop schizophrenia as adults and the general population were found across a range of developmental domains, and the origins of schizophrenia may be found in early life.
Abstract: Schizophrenia has been linked with childhood psychological abnormalities since it was first described, but studies of associations have not used population samples and so may be subject to bias. We have studied associations between adult-onset schizophrenia and childhood sociodemographic, neurodevelopmental, cognitive, and behavioural factors within a cohort of 5362 people born in the week March 3-9, 1946. Childhood data were gathered prospectively and case ascertainment was independent of routine follow-up of this cohort. 30 cases of schizophrenia arose between ages 16 and 43 years (cumulative risk 0.63% [95% CI 0.41-0.86%]). Milestones of motor development were reached later in cases than in controls, particularly walking (difference in means 1.2 months [0.1-2.3], p = 0.005), and up to age 15, cases had more speech problems than had controls (odds ratio 2.8 [0.9-7.8], p = 0.04). Low educational test scores at ages 8, 11, and 15 years were a risk factor, with significant linear trends across population distributions; risk was not confined to very low scores. Solitary play preference at ages 4 and 6 years predicted schizophrenia (odds ratios 2.1, 2.5, p = 0.05). At 13 years cases rated themselves as less socially confident (p for trend, 0.04). At 15 years, teachers rated cases as being more anxious in social situations (p for trend 0.003), independent of intelligence quotient. A health visitor's rating of the mother as having below average mothering skills and understanding of her child at age 4 years was a predictor of schizophrenia in that child (odds ratio 5.8 [0.8-31.8], p = 0.02). Differences between children destined to develop schizophrenia as adults and the general population were found across a range of developmental domains. As with some other adult illnesses, the origins of schizophrenia may be found in early life.
01 Dec 1996-ACM Computing Surveys
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
TL;DR: The AUDIT provides a simple method of early detection of hazardous and harmful alcohol use in primary health care settings and is the first instrument of its type to be derived on the basis of a cross-national study.
Abstract: The Alcohol Use Disorders Identification Test (AUDIT) has been developed from a six-country WHO collaborative project as a screening instrument for hazardous and harmful alcohol consumption. It is a 10-item questionnaire which covers the domains of alcohol consumption, drinking behaviour, and alcohol-related problems. Questions were selected from a 150-item assessment schedule (which was administered to 1888 persons attending representative primary health care facilities) on the basis of their representativeness for these conceptual domains and their perceived usefulness for intervention. Responses to each question are scored from 0 to 4, giving a maximum possible score of 40. Among those diagnosed as having hazardous or harmful alcohol use, 92% had an AUDIT score of 8 or more, and 94% of those with non-hazardous consumption had a score of less than 8. AUDIT provides a simple method of early detection of hazardous and harmful alcohol use in primary health care settings and is the first instrument of its type to be derived on the basis of a cross-national study.
01 Mar 1990-Clinical Radiology
01 Jul 1960-The Eugenics Review
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to
01 Jan 2014
TL;DR: These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care.
Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.