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      Genome-wide association studies establish that human intelligence is highly heritable and polygenic

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      , PhD 1 , , PhD 2 , 3 , , PhD 4 , , PhD 5 , , PhD 6 , 7 , , BSc 1 , 7 , , PhD 8 , , PhD 9 , , PhD 9 , , PhD 1 , 7 , , PhD 1 , , PhD 1 , 7 , , PhD 1 , 7 , , BSc 1 , , BSc 1 , , PhD 10 , , PhD 11 , , PhD 10 , , PhD 10 , , PhD 12 , , PhD 13 , , PhD 14 , , PhD 13 , , PhD 15 , , PhD 9 , 16 , , PhD 4 , , PhD 6 , 7 , , PhD 17 , , PhD 7 , 18 ,   , PhD 17 , , PhD 5 , 7 , , , PhD 1 , 7 ,
      Molecular psychiatry
      Intelligence, genetics, GWAS, quantitative trait

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          Abstract

          General intelligence is an important human quantitative trait that accounts for much of the variation in diverse cognitive abilities. Individual differences in intelligence are strongly associated with many important life outcomes, including educational and occupational attainments, income, health and lifespan 1, 2 . Data from twin and family studies are consistent with a high heritability of intelligence 3 , but this inference has been controversial. We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 SNPs and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits. We partitioned genetic variation on individual chromosomes and found that, on average, longer chromosomes explain more variation. Finally, using just SNP data we predicted approximately 1% of the variance of crystallized and fluid cognitive phenotypes in an independent sample ( P = 0.009 and 0.028, respectively). Our results unequivocally confirm that a substantial proportion of individual differences in human intelligence is due to genetic variation, and are consistent with many genes of small effects underlying the additive genetic influences on intelligence.

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          Most cited references70

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            GCTA: a tool for genome-wide complex trait analysis.

            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.
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              Finding the missing heritability of complex diseases.

              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.
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                Author and article information

                Journal
                9607835
                20545
                Mol Psychiatry
                Mol. Psychiatry
                Molecular psychiatry
                1359-4184
                1476-5578
                22 June 2011
                09 August 2011
                October 2011
                01 April 2012
                : 16
                : 10
                : 996-1005
                Affiliations
                [1 ]Department of Psychology, The University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK
                [2 ]Colon Cancer Genetics Group, MRC Human Genetics Unit, Western General Hospital, The University of Edinburgh, Edinburgh, UK
                [3 ]The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Roslin, UK
                [4 ]Centre for Integrated Genomic Medical Research, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT
                [5 ]Queensland Institute of Medical Research, 300 Herston Road, Brisbane, Queensland 4006, Australia
                [6 ]Medical Genetics Section, The University of Edinburgh Molecular Medicine Centre, Institute of Genetics and Molecular Medicine, Western General Hospital Edinburgh, UK
                [7 ]Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK
                [8 ]Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH
                [9 ]Dr Einar Martens Research Group for Biological Psychiatry, Department of Clinical Medicine, University of Bergen, Bergen, Norway
                [10 ]Institute of Applied Health Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD
                [11 ]Nutrition and Epigenetics Group, Rowett Institute of Nutrition and Health, University of Aberdeen, Greenburn Road, Bucksburn, Aberdeen AB21 9SB
                [12 ]Department of Food and Agricultural Systems, University of Melbourne, Parkville 3011, Australia and Victorian Department of Primary Industries, Bundoora, Australia
                [13 ]Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Box 1094, Blindern, N-0317 Oslo, Norway
                [14 ]Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
                [15 ]School of Epidemiology and Health Science, Department of Medicine, University of Manchester, Manchester M13 9PT, UK
                [16 ]Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway.
                [17 ]School of Community-Based Medicine, Neurodegeneration Research Group, University of Manchester, Clinical sciences Building, Salford Royal NHS Foundation Trust, Salford M6 8HD
                [18 ]Geriatric Medicine Unit, The University of Edinburgh, Royal Victoria Hospital, Edinburgh EH4 2DN
                Author notes
                [*]

                These authors contributed equally to the work

                Ian J. Deary, University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, Scotland, UK, Telephone: +44 131 650 3452, Fax: +44 131 651 1771, Ian.Deary@ 123456ed.ac.uk

                [] Correspondence regarding phenotypes, cohorts and materials to i.deary@ 123456ed.ac.uk and regarding statistical methods to peter.visscher@ 123456qimr.edu.au .
                Article
                UKMS35731
                10.1038/mp.2011.85
                3182557
                21826061
                1a7648e6-45c5-4d6d-9de0-01a95b257318

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                History
                Funding
                Funded by: Wellcome Trust :
                Award ID: 064359 || WT
                Funded by: Medical Research Council :
                Award ID: G0700704(84698) || MRC_
                Funded by: Chief Scientist Office :
                Award ID: CZB/4/505 || CSO_
                Funded by: Biotechnology and Biological Sciences Research Council :
                Award ID: SAG09977 || BB_
                Funded by: Biotechnology and Biological Sciences Research Council :
                Award ID: BB/F019394/1 || BB_
                Categories
                Article

                Molecular medicine
                intelligence,genetics,gwas,quantitative trait
                Molecular medicine
                intelligence, genetics, gwas, quantitative trait

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