Author
Stuart MacGregor
Other affiliations: Queensland University of Technology, University of Wales, Australian National University ...read more
Bio: Stuart MacGregor is an academic researcher from QIMR Berghofer Medical Research Institute. The author has contributed to research in topics: Genome-wide association study & Medicine. The author has an hindex of 81, co-authored 484 publications receiving 28690 citations. Previous affiliations of Stuart MacGregor include Queensland University of Technology & University of Wales.
Papers published on a yearly basis
Papers
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Broad Institute1, Harvard University2, QIMR Berghofer Medical Research Institute3, Cardiff University4, North Carolina State University5, Trinity College, Dublin6, University of Edinburgh7, Uppsala University8, Karolinska Institutet9, University of Southern California10, University of North Carolina at Chapel Hill11, University College London12, National Health Service13, University of Oxford14, University of Aberdeen15, Strathclyde Institute of Pharmacy and Biomedical Sciences16, State University of New York Upstate Medical University17, University of Coimbra18
TL;DR: The extent to which common genetic variation underlies the risk of schizophrenia is shown, using two analytic approaches, and the major histocompatibility complex is implicate, which is shown to involve thousands of common alleles of very small effect.
Abstract: Schizophrenia is a severe mental disorder with a lifetime risk of about 1%, characterized by hallucinations, delusions and cognitive deficits, with heritability estimated at up to 80%(1,2). We performed a genome-wide association study of 3,322 European individuals with schizophrenia and 3,587 controls. Here we show, using two analytic approaches, the extent to which common genetic variation underlies the risk of schizophrenia. First, we implicate the major histocompatibility complex. Second, we provide molecular genetic evidence for a substantial polygenic component to the risk of schizophrenia involving thousands of common alleles of very small effect. We show that this component also contributes to the risk of bipolar disorder, but not to several non-psychiatric diseases.
4,573 citations
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Harvard University1, Massachusetts Institute of Technology2, Broad Institute3, Cardiff University4, University College London5, University of Edinburgh6, Trinity College, Dublin7, Karolinska Institutet8, Uppsala University9, University of Southern California10, University of Aberdeen11, University of North Carolina at Chapel Hill12, QIMR Berghofer Medical Research Institute13, Royal College of Surgeons in Ireland14, National Health Service15, University of Oxford16, Queen Mary University of London17, State University of New York System18, University of Coimbra19
TL;DR: A genome-wide survey of rare CNVs in 3,391 patients with schizophrenia and 3,181 ancestrally matched controls provides strong support for a model of schizophrenia pathogenesis that includes the effects of multiple rare structural variants, both genome- wide and at specific loci.
Abstract: Schizophrenia is a severe mental disorder marked by hallucinations, delusions, cognitive deficits and apathy, with a heritability estimated at 73 - 90% ( ref. 1). Inheritance patterns are complex, and the number and type of genetic variants involved are not understood. Copy number variants ( CNVs) have been identified in individual patients with schizophrenia(2-7) and also in neurodevelopmental disorders(8-11), but large- scale genome- wide surveys have not been performed. Here we report a genome- wide survey of rare CNVs in 3,391 patients with schizophrenia and 3,181 ancestrally matched controls, using high- density microarrays. For CNVs that were observed in less than 1% of the sample and were more than 100 kilobases in length, the total burden is increased 1.15- fold in patients with schizophrenia in comparison with controls. This effect was more pronounced for rarer, single- occurrence CNVs and for those that involved genes as opposed to those that did not. As expected, deletions were found within the region critical for velo- cardio- facial syndrome, which includes psychotic symptoms in 30% of patients(12). Associations with schizophrenia were also found for large deletions on chromosome 15q13.3 and 1q21.1. These associations have not previously been reported, and they remained significant after genome- wide correction. Our results provide strong support for a model of schizophrenia pathogenesis that includes the effects of multiple rare structural variants, both genome- wide and at specific loci.
1,465 citations
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TL;DR: This work derived a versatile gene-based test for genome-wide association studies (GWAS) that has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics.
Abstract: We have derived a versatile gene-based test for genome-wide association studies (GWAS). Our approach, called VEGAS (versatile gene-based association study), is applicable to all GWAS designs, including family-based GWAS, meta-analyses of GWAS on the basis of summary data, and DNA-pooling-based GWAS, where existing approaches based on permutation are not possible, as well as singleton data, where they are. The test incorporates information from a full set of markers (or a defined subset) within a gene and accounts for linkage disequilibrium between markers by using simulations from the multivariate normal distribution. We show that for an association study using singletons, our approach produces results equivalent to those obtained via permutation in a fraction of the computation time. We demonstrate proof-of-principle by using the gene-based test to replicate several genes known to be associated on the basis of results from a family-based GWAS for height in 11,536 individuals and a DNA-pooling-based GWAS for melanoma in ∼1300 cases and controls. Our method has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics. We have implemented the approach in both an easy-to-use web interface, which only requires the uploading of markers with their association p-values, and a separate downloadable application.
755 citations
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TL;DR: The rate-limiting step is the electrophilic attack of the palladium on an ortho arene C-H bond to form an agostic complex rather than a Wheland intermediate, and the cyclometalated product is formed by intramolecular deprotonation by acetate via a six-membered transition state.
Abstract: Various mechanisms for the cyclometalation of dimethylbenzylamine by palladium acetate have been studied by DFT calculations. Contrary to previous suggestions, the rate-limiting step is the electrophilic attack of the palladium on an ortho arene C−H bond to form an agostic complex rather than a Wheland intermediate. The cyclometalated product is then formed by intramolecular deprotonation by acetate via a six-membered transition state; this step has almost no activation barrier.
622 citations
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Trinity College, Dublin1, National Institutes of Health2, University of Pennsylvania3, University College London4, Indiana University – Purdue University Indianapolis5, University of Birmingham6, Johns Hopkins University School of Medicine7, Icahn School of Medicine at Mount Sinai8, University of Chicago9, University of Helsinki10, University of Antwerp11, University of California, San Diego12, Garvan Institute of Medical Research13, University of New South Wales14, Laval University15, University of Utah16, University of Edinburgh17, Washington University in St. Louis18, University of California, Los Angeles19, University of Bonn20, Dalhousie University21, University of Ottawa22, McGill University23, Université libre de Bruxelles24, Umeå University25, University of California, Irvine26, Macquarie University27, University of Texas Health Science Center at San Antonio28, University of California, San Francisco29, University of Costa Rica30, Aarhus University Hospital31, Odense University Hospital32, University of Geneva33, Hacettepe University34
TL;DR: The present results for the very narrow model are promising but suggest that more and larger data sets are needed to support linkage, as well as suggest that linkage might be detected in certain populations or subsets of pedigrees.
Abstract: Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for "very narrow" (i.e., BP-I and schizoaffective disorder-BP) and "narrow" (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A "broad" model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.
585 citations
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TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
9,244 citations
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National Institutes of Health1, University of Chicago2, Duke University3, Harvard University4, University of Oxford5, GlaxoSmithKline6, Johns Hopkins University7, Yale University8, deCODE genetics9, Howard Hughes Medical Institute10, Princeton University11, Washington University in St. Louis12, University of California, Berkeley13, Stanford University14, University of Michigan15, Cornell University16, University of Washington17, University of Queensland18, Vanderbilt University19, North Carolina State University20, QIMR Berghofer Medical Research Institute21
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
7,797 citations
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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.
6,809 citations
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TL;DR: The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets and focuses on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation.
Abstract: For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the “missing heritability” problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
5,867 citations