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      A genome-wide association study for extremely high intelligence

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          Abstract

          We used a case–control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (~170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population.

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          Most cited references28

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          One Hundred Years of Social Psychology Quantitatively Described.

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            Common disorders are quantitative traits.

            After drifting apart for 100 years, the two worlds of genetics - quantitative genetics and molecular genetics - are finally coming together in genome-wide association (GWA) research, which shows that the heritability of complex traits and common disorders is due to multiple genes of small effect size. We highlight a polygenic framework, supported by recent GWA research, in which qualitative disorders can be interpreted simply as being the extremes of quantitative dimensions. Research that focuses on quantitative traits - including the low and high ends of normal distributions - could have far-reaching implications for the diagnosis, treatment and prevention of the problematic extremes of these traits.
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              Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT)

              Motivation: Over the last few years, methods based on suffix arrays using the Burrows–Wheeler Transform have been widely used for DNA sequence read matching and assembly. These provide very fast search algorithms, linear in the search pattern size, on a highly compressible representation of the dataset being searched. Meanwhile, algorithmic development for genotype data has concentrated on statistical methods for phasing and imputation, based on probabilistic matching to hidden Markov model representations of the reference data, which while powerful are much less computationally efficient. Here a theory of haplotype matching using suffix array ideas is developed, which should scale too much larger datasets than those currently handled by genotype algorithms. Results: Given M sequences with N bi-allelic variable sites, an O(NM) algorithm to derive a representation of the data based on positional prefix arrays is given, which is termed the positional Burrows–Wheeler transform (PBWT). On large datasets this compresses with run-length encoding by more than a factor of a hundred smaller than using gzip on the raw data. Using this representation a method is given to find all maximal haplotype matches within the set in O(NM) time rather than O(NM 2) as expected from naive pairwise comparison, and also a fast algorithm, empirically independent of M given sufficient memory for indexes, to find maximal matches between a new sequence and the set. The discussion includes some proposals about how these approaches could be used for imputation and phasing. Availability: http://github.com/richarddurbin/pbwt Contact: richard.durbin@sanger.ac.uk
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                Author and article information

                Journal
                Mol Psychiatry
                Mol. Psychiatry
                Molecular Psychiatry
                Nature Publishing Group
                1359-4184
                1476-5578
                May 2018
                04 July 2017
                : 23
                : 5
                : 1226-1232
                Affiliations
                [1 ]King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience , London, UK
                [2 ]NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Trust , London, UK
                [3 ]Department of Medical and Molecular Genetics, Division of Genetics and Molecular Medicine, Guy’s Hospital , London, UK
                [4 ]Duke University Talent Identification Program, Duke University , Durham, NC, USA
                [5 ]Department of Psychology and Human Development, Vanderbilt University , Nashville, TN, USA
                Author notes
                [* ]King's College London, MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience , 16 De Crespigny Park, London SE5 8AF, UK. E-mail: gerome.breen@ 123456kcl.ac.uk
                Author information
                http://orcid.org/0000-0002-1843-9842
                http://orcid.org/0000-0003-2053-1792
                Article
                mp2017121
                10.1038/mp.2017.121
                5987166
                29731509
                257db8aa-132b-43b8-9ab9-1d2270f81df8
                Copyright © 2018 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                History
                : 17 November 2016
                : 20 March 2017
                : 11 April 2017
                Categories
                Original Article

                Molecular medicine
                Molecular medicine

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